From f2cc68c8d5da7356e1d42bb0486ef395dc0d22dd Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 20 Mar 2023 12:53:54 -0400 Subject: [PATCH 001/446] =?UTF-8?q?feat:=20add=20objects=20for=20handling?= =?UTF-8?q?=20controller=20state=20=E2=80=93=20snapshots=20and=20history?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- autora/controller/__init__.py | 0 autora/controller/protocol/__init__.py | 0 autora/controller/protocol/v1.py | 134 ++++++ autora/controller/state/__init__.py | 3 + autora/controller/state/history.py | 583 +++++++++++++++++++++++++ autora/controller/state/param.py | 110 +++++ autora/controller/state/snapshot.py | 124 ++++++ 7 files changed, 954 insertions(+) create mode 100644 autora/controller/__init__.py create mode 100644 autora/controller/protocol/__init__.py create mode 100644 autora/controller/protocol/v1.py create mode 100644 autora/controller/state/__init__.py create mode 100644 autora/controller/state/history.py create mode 100644 autora/controller/state/param.py create mode 100644 autora/controller/state/snapshot.py diff --git a/autora/controller/__init__.py b/autora/controller/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/autora/controller/protocol/__init__.py b/autora/controller/protocol/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/autora/controller/protocol/v1.py b/autora/controller/protocol/v1.py new file mode 100644 index 00000000..87b55b70 --- /dev/null +++ b/autora/controller/protocol/v1.py @@ -0,0 +1,134 @@ +from enum import Enum +from typing import Any, Dict, Optional, Protocol, Sequence, TypeVar, Union + +from numpy.typing import ArrayLike +from sklearn.base import BaseEstimator + +from autora.variable import VariableCollection + +State = TypeVar("State") + + +class ResultKind(str, Enum): + """ + Kinds of results which can be held in the Result object. + + Examples: + >>> ResultKind.CONDITION is ResultKind.CONDITION + True + + >>> ResultKind.CONDITION is ResultKind.METADATA + False + + >>> ResultKind.CONDITION == "CONDITION" + True + + >>> ResultKind.CONDITION == "METADATA" + False + + >>> ResultKind.CONDITION in {ResultKind.CONDITION, ResultKind.PARAMS} + True + + >>> ResultKind.METADATA in {ResultKind.CONDITION, ResultKind.PARAMS} + False + """ + + CONDITION = "CONDITION" + OBSERVATION = "OBSERVATION" + THEORY = "THEORY" + PARAMS = "PARAMS" + METADATA = "METADATA" + + def __repr__(self): + cls_name = self.__class__.__name__ + return f"{cls_name}.{self.name}" + + +class SupportsDataKind(Protocol): + """Object with attributes for `data` and `kind`""" + + data: Optional[Any] + kind: Optional[ResultKind] + + +class SupportsControllerStateFields(Protocol): + """An object which can support representing snapshots of a controller state.""" + + def __init__( + self, + metadata: Optional[VariableCollection], + params: Optional[Dict], + conditions: Optional[Sequence[ArrayLike]], + observations=Optional[Sequence[ArrayLike]], + theories=Optional[Sequence[ArrayLike]], + ) -> None: + ... + + metadata: VariableCollection + params: Dict + conditions: Sequence[ArrayLike] + observations: Sequence[ArrayLike] + theories: Sequence[BaseEstimator] + + def update(self: State, **kwargs) -> State: + ... + + +class SupportsControllerStateProperties(Protocol): + def __init__( + self, + metadata: Optional[VariableCollection], + params: Optional[Dict], + conditions: Optional[Sequence[ArrayLike]], + observations=Optional[Sequence[ArrayLike]], + theories=Optional[Sequence[ArrayLike]], + ) -> None: + ... + + def update(self: State, **kwargs) -> State: + ... + + @property + def metadata(self) -> VariableCollection: + ... + + @property + def params(self) -> Dict: + ... + + @property + def conditions(self) -> Sequence[ArrayLike]: + ... + + @property + def observations(self) -> Sequence[ArrayLike]: + ... + + @property + def theories(self) -> Sequence[BaseEstimator]: + ... + + +SupportsControllerState = Union[ + SupportsControllerStateFields, SupportsControllerStateProperties +] + + +class SupportsControllerStateHistory(SupportsControllerStateProperties, Protocol): + def __init__( + self, + metadata: Optional[VariableCollection], + params: Optional[Dict], + conditions: Optional[Sequence[ArrayLike]], + observations=Optional[Sequence[ArrayLike]], + theories=Optional[Sequence[ArrayLike]], + history=Optional[Sequence[SupportsDataKind]], + ) -> None: + ... + + def filter_by(self: State, **kwargs) -> State: + ... + + @property + def history(self) -> Sequence[SupportsDataKind]: + ... diff --git a/autora/controller/state/__init__.py b/autora/controller/state/__init__.py new file mode 100644 index 00000000..a6d644ee --- /dev/null +++ b/autora/controller/state/__init__.py @@ -0,0 +1,3 @@ +from .history import ControllerStateHistory +from .param import resolve_state_params +from .snapshot import ControllerState diff --git a/autora/controller/state/history.py b/autora/controller/state/history.py new file mode 100644 index 00000000..ed3da291 --- /dev/null +++ b/autora/controller/state/history.py @@ -0,0 +1,583 @@ +""" Classes for storing and passing a cycle's state as an immutable history. """ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Dict, Iterable, List, Optional, Sequence, Set, Union + +from numpy.typing import ArrayLike +from sklearn.base import BaseEstimator + +from autora.controller.protocol.v1 import ResultKind, SupportsDataKind +from autora.controller.state.snapshot import ControllerState +from autora.variable import VariableCollection + + +class ControllerStateHistory: + """ + An immutable object for tracking the state and history of an AER cycle. + """ + + def __init__( + self, + metadata: Optional[VariableCollection] = None, + params: Optional[Dict] = None, + conditions: Optional[List[ArrayLike]] = None, + observations: Optional[List[ArrayLike]] = None, + theories: Optional[List[BaseEstimator]] = None, + history: Optional[Sequence[Result]] = None, + ): + """ + + Args: + metadata: a single datum to be marked as "metadata" + params: a single datum to be marked as "params" + conditions: an iterable of data, each to be marked as "conditions" + observations: an iterable of data, each to be marked as "observations" + theories: an iterable of data, each to be marked as "theories" + history: an iterable of Result objects to be used as the initial history. + + Examples: + Empty input leads to an empty state: + >>> ControllerStateHistory() + ControllerStateHistory([]) + + ... or with values for any or all of the parameters: + >>> from autora.variable import VariableCollection + >>> ControllerStateHistory(metadata=VariableCollection()) # doctest: +ELLIPSIS + ControllerStateHistory([Result(data=VariableCollection(...), kind=ResultKind.METADATA)]) + + >>> ControllerStateHistory(params={"some": "params"}) + ControllerStateHistory([Result(data={'some': 'params'}, kind=ResultKind.PARAMS)]) + + >>> ControllerStateHistory(conditions=["a condition"]) + ControllerStateHistory([Result(data='a condition', kind=ResultKind.CONDITION)]) + + >>> ControllerStateHistory(observations=["an observation"]) + ControllerStateHistory([Result(data='an observation', kind=ResultKind.OBSERVATION)]) + + >>> from sklearn.linear_model import LinearRegression + >>> ControllerStateHistory(theories=[LinearRegression()]) + ControllerStateHistory([Result(data=LinearRegression(), kind=ResultKind.THEORY)]) + + Parameters passed to the constructor are included in the history in the following order: + `history`, `metadata`, `params`, `conditions`, `observations`, `theories` + >>> ControllerStateHistory(theories=['t1', 't2'], conditions=['c1', 'c2'], + ... observations=['o1', 'o2'], params={'a': 'param'}, metadata=VariableCollection(), + ... history=[Result("from history", ResultKind.METADATA)] + ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data='from history', kind=ResultKind.METADATA), + Result(data=VariableCollection(...), kind=ResultKind.METADATA), + Result(data={'a': 'param'}, kind=ResultKind.PARAMS), + Result(data='c1', kind=ResultKind.CONDITION), + Result(data='c2', kind=ResultKind.CONDITION), + Result(data='o1', kind=ResultKind.OBSERVATION), + Result(data='o2', kind=ResultKind.OBSERVATION), + Result(data='t1', kind=ResultKind.THEORY), + Result(data='t2', kind=ResultKind.THEORY)]) + """ + self._history: List + + if history is not None: + self._history = list(history) + else: + self._history = [] + + self._history += _init_result_list( + metadata=metadata, + params=params, + conditions=conditions, + observations=observations, + theories=theories, + ) + + def update( + self, + metadata=None, + params=None, + conditions=None, + observations=None, + theories=None, + history=None, + ): + """ + Create a new object with updated values. + + Examples: + The initial object is empty: + >>> s0 = ControllerStateHistory() + >>> s0 + ControllerStateHistory([]) + + We can update the metadata using the `.update` method: + >>> from autora.variable import VariableCollection + >>> s1 = s0.update(metadata=VariableCollection()) + >>> s1 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data=VariableCollection(...), kind=ResultKind.METADATA)]) + + ... the original object is unchanged: + >>> s0 + ControllerStateHistory([]) + + We can update the metadata again: + >>> s2 = s1.update(metadata=VariableCollection(["some IV"])) + >>> s2._by_kind # doctest: +ELLIPSIS + ControllerState(metadata=VariableCollection(independent_variables=['some IV'],...), ...) + + ... and we see that there is only ever one metadata object returned. + + Params is treated the same way as metadata: + >>> sp = s0.update(params={'first': 'params'}) + >>> sp + ControllerStateHistory([Result(data={'first': 'params'}, kind=ResultKind.PARAMS)]) + + ... where only the most recent "params" object is returned from the `.params` property. + >>> sp = sp.update(params={'second': 'params'}) + >>> sp.params + {'second': 'params'} + + ... however, the full history of the params objects remains available, if needed: + >>> sp # doctest: +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data={'first': 'params'}, kind=ResultKind.PARAMS), + Result(data={'second': 'params'}, kind=ResultKind.PARAMS)]) + + When we update the conditions, observations or theories, a new entry is added to the + history: + >>> s3 = s0.update(theories=["1st theory"]) + >>> s3 # doctest: +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data='1st theory', kind=ResultKind.THEORY)]) + + ... so we can see the history of all the theories, for instance. + >>> s3 = s3.update(theories=["2nd theory"]) # doctest: +NORMALIZE_WHITESPACE + >>> s3 # doctest: +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data='1st theory', kind=ResultKind.THEORY), + Result(data='2nd theory', kind=ResultKind.THEORY)]) + + ... and the full history of theories is available using the `.theories` parameter: + >>> s3.theories + ['1st theory', '2nd theory'] + + The same for the observations: + >>> s4 = s0.update(observations=["1st observation"]) + >>> s4 + ControllerStateHistory([Result(data='1st observation', kind=ResultKind.OBSERVATION)]) + + >>> s4.update(observations=["2nd observation"] + ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data='1st observation', kind=ResultKind.OBSERVATION), + Result(data='2nd observation', kind=ResultKind.OBSERVATION)]) + + + The same for the conditions: + >>> s5 = s0.update(conditions=["1st condition"]) + >>> s5 + ControllerStateHistory([Result(data='1st condition', kind=ResultKind.CONDITION)]) + + >>> s5.update(conditions=["2nd condition"]) # doctest: +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data='1st condition', kind=ResultKind.CONDITION), + Result(data='2nd condition', kind=ResultKind.CONDITION)]) + + You can also update with multiple conditions, observations and theories: + >>> s0.update(conditions=['c1', 'c2']) # doctest: +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data='c1', kind=ResultKind.CONDITION), + Result(data='c2', kind=ResultKind.CONDITION)]) + + >>> s0.update(theories=['t1', 't2'], metadata={'m': 1}) # doctest: +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data={'m': 1}, kind=ResultKind.METADATA), + Result(data='t1', kind=ResultKind.THEORY), + Result(data='t2', kind=ResultKind.THEORY)]) + + >>> s0.update(theories=['t1'], observations=['o1'], metadata={'m': 1} + ... ) # doctest: +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data={'m': 1}, kind=ResultKind.METADATA), + Result(data='o1', kind=ResultKind.OBSERVATION), + Result(data='t1', kind=ResultKind.THEORY)]) + + """ + + if history is not None: + history_extension = history + else: + history_extension = [] + + history_extension += _init_result_list( + metadata=metadata, + params=params, + conditions=conditions, + observations=observations, + theories=theories, + ) + new_full_history = self._history + history_extension + + return ControllerStateHistory(history=new_full_history) + + def __repr__(self): + return f"{type(self).__name__}({self.history})" + + @property + def _by_kind(self): + return _history_to_kind(self._history) + + @property + def metadata(self) -> VariableCollection: + """ + + Examples: + The initial object is empty: + >>> s = ControllerStateHistory() + + ... and returns an emtpy metadata object + >>> s.metadata + VariableCollection(independent_variables=[], dependent_variables=[], covariates=[]) + + We can update the metadata using the `.update` method: + >>> from autora.variable import VariableCollection + >>> s = s.update(metadata=VariableCollection(independent_variables=['some IV'])) + >>> s.metadata # doctest: +ELLIPSIS + VariableCollection(independent_variables=['some IV'], ...) + + We can update the metadata again: + >>> s = s.update(metadata=VariableCollection(["some other IV"])) + >>> s.metadata # doctest: +ELLIPSIS + VariableCollection(independent_variables=['some other IV'], ...) + + ... and we see that there is only ever one metadata object returned.""" + return self._by_kind.metadata + + @property + def params(self) -> Dict: + """ + + Returns: + + Examples: + Params is treated the same way as metadata: + >>> s = ControllerStateHistory() + >>> s = s.update(params={'first': 'params'}) + >>> s.params + {'first': 'params'} + + ... where only the most recent "params" object is returned from the `.params` property. + >>> s = s.update(params={'second': 'params'}) + >>> s.params + {'second': 'params'} + + ... however, the full history of the params objects remains available, if needed: + >>> s # doctest: +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data={'first': 'params'}, kind=ResultKind.PARAMS), + Result(data={'second': 'params'}, kind=ResultKind.PARAMS)]) + """ + return self._by_kind.params + + @property + def conditions(self) -> List[ArrayLike]: + """ + Returns: + + Examples: + View the sequence of theories with one conditions: + >>> s = ControllerStateHistory(conditions=[(1,2,3,)]) + >>> s.conditions + [(1, 2, 3)] + + ... or more conditions: + >>> s = s.update(conditions=[(4,5,6),(7,8,9)]) # doctest: +NORMALIZE_WHITESPACE + >>> s.conditions + [(1, 2, 3), (4, 5, 6), (7, 8, 9)] + + """ + return self._by_kind.conditions + + @property + def observations(self) -> List[ArrayLike]: + """ + + Returns: + + Examples: + The sequence of all observations is returned + >>> s = ControllerStateHistory(observations=["1st observation"]) + >>> s.observations + ['1st observation'] + + >>> s = s.update(observations=["2nd observation"]) + >>> s.observations # doctest: +ELLIPSIS + ['1st observation', '2nd observation'] + + """ + return self._by_kind.observations + + @property + def theories(self) -> List[BaseEstimator]: + """ + + Returns: + + Examples: + View the sequence of theories with one theory: + >>> s = ControllerStateHistory(theories=["1st theory"]) + >>> s.theories # doctest: +NORMALIZE_WHITESPACE + ['1st theory'] + + ... or more theories: + >>> s = s.update(theories=["2nd theory"]) # doctest: +NORMALIZE_WHITESPACE + >>> s.theories + ['1st theory', '2nd theory'] + + """ + return self._by_kind.theories + + @property + def history(self) -> List[Result]: + """ + + Examples: + We initialze some history: + >>> s = ControllerStateHistory(theories=['t1', 't2'], conditions=['c1', 'c2'], + ... observations=['o1', 'o2'], params={'a': 'param'}, metadata=VariableCollection(), + ... history=[Result("from history", ResultKind.METADATA)]) + + Parameters passed to the constructor are included in the history in the following order: + `history`, `metadata`, `params`, `conditions`, `observations`, `theories` + + >>> s.history # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + [Result(data='from history', kind=ResultKind.METADATA), + Result(data=VariableCollection(...), kind=ResultKind.METADATA), + Result(data={'a': 'param'}, kind=ResultKind.PARAMS), + Result(data='c1', kind=ResultKind.CONDITION), + Result(data='c2', kind=ResultKind.CONDITION), + Result(data='o1', kind=ResultKind.OBSERVATION), + Result(data='o2', kind=ResultKind.OBSERVATION), + Result(data='t1', kind=ResultKind.THEORY), + Result(data='t2', kind=ResultKind.THEORY)] + + If we add a new value, like the params object, the updated value is added to the + end of the history: + >>> s = s.update(params={'new': 'param'}) + >>> s.history # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + [..., Result(data={'new': 'param'}, kind=ResultKind.PARAMS)] + + """ + return self._history + + def filter_by(self, kind=Set[Union[str, ResultKind]]) -> ControllerStateHistory: + """ + Return a copy of the object with only data belonging to the specified kinds. + + Examples: + >>> s = ControllerStateHistory(theories=['t1', 't2'], conditions=['c1', 'c2'], + ... observations=['o1', 'o2'], params={'a': 'param'}, metadata=VariableCollection(), + ... history=[Result("from history", ResultKind.METADATA)]) + + >>> s.filter_by(kind={"THEORY"}) # doctest: +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data='t1', kind=ResultKind.THEORY), + Result(data='t2', kind=ResultKind.THEORY)]) + + >>> s.filter_by(kind={ResultKind.OBSERVATION}) # doctest: +NORMALIZE_WHITESPACE + ControllerStateHistory([Result(data='o1', kind=ResultKind.OBSERVATION), + Result(data='o2', kind=ResultKind.OBSERVATION)]) + + """ + kind_ = {ResultKind(s) for s in kind} + filtered_history = _filter_history(self._history, kind_) + new_object = ControllerStateHistory(history=filtered_history) + return new_object + + +@dataclass(frozen=True) +class Result(SupportsDataKind): + """ + Container class for data and metadata. + + Examples: + >>> Result() + Result(data=None, kind=None) + + >>> Result("a") + Result(data='a', kind=None) + + >>> Result(None, "THEORY") + Result(data=None, kind=ResultKind.THEORY) + + >>> Result(data="b") + Result(data='b', kind=None) + + >>> Result("c", "OBSERVATION") + Result(data='c', kind=ResultKind.OBSERVATION) + """ + + data: Optional[Any] = None + kind: Optional[ResultKind] = None + + def __post_init__(self): + if isinstance(self.kind, str): + object.__setattr__(self, "kind", ResultKind(self.kind)) + + +def _init_result_list( + metadata: Optional[VariableCollection] = None, + params: Optional[Dict] = None, + conditions: Optional[Iterable[ArrayLike]] = None, + observations: Optional[Iterable[ArrayLike]] = None, + theories: Optional[Iterable[BaseEstimator]] = None, +) -> List[Result]: + """ + Initialize a list of Result objects + + Returns: + + Args: + metadata: a single datum to be marked as "metadata" + params: a single datum to be marked as "params" + conditions: an iterable of data, each to be marked as "conditions" + observations: an iterable of data, each to be marked as "observations" + theories: an iterable of data, each to be marked as "theories" + + Examples: + Empty input leads to an empty state: + >>> _init_result_list() + [] + + ... or with values for any or all of the parameters: + >>> from autora.variable import VariableCollection + >>> _init_result_list(metadata=VariableCollection()) # doctest: +ELLIPSIS + [Result(data=VariableCollection(...), kind=ResultKind.METADATA)] + + >>> _init_result_list(params={"some": "params"}) + [Result(data={'some': 'params'}, kind=ResultKind.PARAMS)] + + >>> _init_result_list(conditions=["a condition"]) + [Result(data='a condition', kind=ResultKind.CONDITION)] + + >>> _init_result_list(observations=["an observation"]) + [Result(data='an observation', kind=ResultKind.OBSERVATION)] + + >>> from sklearn.linear_model import LinearRegression + >>> _init_result_list(theories=[LinearRegression()]) + [Result(data=LinearRegression(), kind=ResultKind.THEORY)] + + The input arguments are added to the data in the order `metadata`, + `params`, `conditions`, `observations`, `theories`: + >>> _init_result_list(metadata=VariableCollection(), + ... params={"some": "params"}, + ... conditions=["a condition"], + ... observations=["an observation", "another observation"], + ... theories=[LinearRegression()], + ... ) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS + [Result(data=VariableCollection(...), kind=ResultKind.METADATA), + Result(data={'some': 'params'}, kind=ResultKind.PARAMS), + Result(data='a condition', kind=ResultKind.CONDITION), + Result(data='an observation', kind=ResultKind.OBSERVATION), + Result(data='another observation', kind=ResultKind.OBSERVATION), + Result(data=LinearRegression(), kind=ResultKind.THEORY)] + + """ + data = [] + + if metadata is not None: + data.append(Result(metadata, ResultKind.METADATA)) + + if params is not None: + data.append(Result(params, ResultKind.PARAMS)) + + for seq, kind in [ + (conditions, ResultKind.CONDITION), + (observations, ResultKind.OBSERVATION), + (theories, ResultKind.THEORY), + ]: + if seq is not None: + for i in seq: + data.append(Result(i, kind=kind)) + + return data + + +def _history_to_kind(history: Sequence[Result]) -> ControllerState: + """ + Convert a sequence of results into a ControllerState instance: + + Examples: + History might be empty + >>> history_ = [] + >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS + ControllerState(metadata=VariableCollection(...), params={}, + conditions=[], observations=[], theories=[]) + + ... or with values for any or all of the parameters: + >>> history_ = _init_result_list(params={"some": "params"}) + >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS + ControllerState(..., params={'some': 'params'}, ...) + + >>> history_ += _init_result_list(conditions=["a condition"]) + >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS + ControllerState(..., params={'some': 'params'}, conditions=['a condition'], ...) + + >>> _history_to_kind(history_).params + {'some': 'params'} + + >>> history_ += _init_result_list(observations=["an observation"]) + >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS + ControllerState(..., params={'some': 'params'}, conditions=['a condition'], + observations=['an observation'], ...) + + >>> from sklearn.linear_model import LinearRegression + >>> history_ = [Result(LinearRegression(), kind=ResultKind.THEORY)] + >>> _history_to_kind(history_) # doctest: +ELLIPSIS + ControllerState(..., theories=[LinearRegression()]) + + >>> from autora.variable import VariableCollection, IV + >>> metadata = VariableCollection(independent_variables=[IV(name="example")]) + >>> history_ = [Result(metadata, kind=ResultKind.METADATA)] + >>> _history_to_kind(history_) # doctest: +ELLIPSIS + ControllerState(metadata=VariableCollection(independent_variables=[IV(name='example', ... + + >>> history_ = [Result({'some': 'params'}, kind=ResultKind.PARAMS)] + >>> _history_to_kind(history_) # doctest: +ELLIPSIS + ControllerState(..., params={'some': 'params'}, ...) + + """ + namespace = ControllerState( + metadata=_get_last_data_with_default( + history, kind={ResultKind.METADATA}, default=VariableCollection() + ), + params=_get_last_data_with_default( + history, kind={ResultKind.PARAMS}, default={} + ), + observations=_list_data( + _filter_history(history, kind={ResultKind.OBSERVATION}) + ), + theories=_list_data(_filter_history(history, kind={ResultKind.THEORY})), + conditions=_list_data(_filter_history(history, kind={ResultKind.CONDITION})), + ) + return namespace + + +def _list_data(data: Sequence[SupportsDataKind]): + """ + Extract the `.data` attribute of each item in a sequence, and return as a list. + + Examples: + >>> _list_data([]) + [] + + >>> _list_data([Result("a"), Result("b")]) + ['a', 'b'] + """ + return list(r.data for r in data) + + +def _filter_history(data: Iterable[SupportsDataKind], kind: Set[ResultKind]): + return filter(lambda r: r.kind in kind, data) + + +def _get_last(data: Sequence[SupportsDataKind], kind: Set[ResultKind]): + results_new_to_old = reversed(data) + last_of_kind = next(_filter_history(results_new_to_old, kind=kind)) + return last_of_kind + + +def _get_last_data_with_default(data: Sequence[SupportsDataKind], kind, default): + try: + result = _get_last(data, kind).data + except StopIteration: + result = default + return result diff --git a/autora/controller/state/param.py b/autora/controller/state/param.py new file mode 100644 index 00000000..a62c9c8e --- /dev/null +++ b/autora/controller/state/param.py @@ -0,0 +1,110 @@ +""" Functions for handling cycle-state-dependent parameters. """ +from __future__ import annotations + +import copy +from typing import Dict, Mapping + +import numpy as np + +from autora.controller.protocol.v1 import SupportsControllerState +from autora.utils.dictionary import LazyDict + + +def _get_state_dependent_properties(state: SupportsControllerState): + """ + Examples: + Even with an empty data object, we can initialize the dictionary, + >>> from autora.controller.state import ControllerState + >>> state_dependent_properties = _get_state_dependent_properties(ControllerState()) + + ... but it will raise an exception if a value isn't yet available when we try to use it + >>> state_dependent_properties["%theories[-1]%"] # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + IndexError: list index out of range + + Nevertheless, we can iterate through its keys no problem: + >>> [key for key in state_dependent_properties.keys()] # doctest: +NORMALIZE_WHITESPACE + ['%observations.ivs[-1]%', '%observations.dvs[-1]%', '%observations.ivs%', + '%observations.dvs%', '%theories[-1]%', '%theories%'] + + """ + + n_ivs = len(state.metadata.independent_variables) + n_dvs = len(state.metadata.dependent_variables) + state_dependent_property_dict = LazyDict( + { + "%observations.ivs[-1]%": lambda: state.observations[-1][:, 0:n_ivs], + "%observations.dvs[-1]%": lambda: state.observations[-1][:, n_ivs:], + "%observations.ivs%": lambda: np.row_stack( + [np.empty([0, n_ivs + n_dvs])] + list(state.observations) + )[:, 0:n_ivs], + "%observations.dvs%": lambda: np.row_stack(state.observations)[:, n_ivs:], + "%theories[-1]%": lambda: state.theories[-1], + "%theories%": lambda: state.theories, + } + ) + return state_dependent_property_dict + + +def _resolve_properties(params: Dict, state_dependent_properties: Mapping): + """ + Resolve state-dependent properties inside a nested dictionary. + + In this context, a state-dependent-property is a string which is meant to be replaced by its + updated, current value before the dictionary is used. A state-dependent property might be + something like "the last theorist available" or "all the experimental results until now". + + Args: + params: a (nested) dictionary of keys and values, where some values might be + "cycle property names" + state_dependent_properties: a dictionary of "property names" and their "real values" + + Returns: a (nested) dictionary where "property names" are replaced by the "real values" + + Examples: + + >>> params_0 = {"key": "%foo%"} + >>> cycle_properties_0 = {"%foo%": 180} + >>> _resolve_properties(params_0,cycle_properties_0) + {'key': 180} + + >>> params_1 = {"key": "%bar%", "nested_dict": {"inner_key": "%foobar%"}} + >>> cycle_properties_1 = {"%bar%": 1, "%foobar%": 2} + >>> _resolve_properties(params_1,cycle_properties_1) + {'key': 1, 'nested_dict': {'inner_key': 2}} + + >>> params_2 = {"key": "baz"} + >>> _resolve_properties(params_2,cycle_properties_1) + {'key': 'baz'} + + """ + params_ = copy.copy(params) + for key, value in params_.items(): + if isinstance(value, dict): + params_[key] = _resolve_properties(value, state_dependent_properties) + elif isinstance(value, str) and ( + value in state_dependent_properties + ): # value is a key in the cycle_properties dictionary + params_[key] = state_dependent_properties[value] + else: + pass # no change needed + + return params_ + + +def resolve_state_params(state: SupportsControllerState) -> Dict: + """ + Returns the `params` attribute of the input, with `cycle properties` resolved. + + Examples: + >>> from autora.controller.state import ControllerStateHistory + >>> s = ControllerStateHistory(theories=["the first theory", "the second theory"], + ... params={"experimentalist": {"source": "%theories[-1]%"}}) + >>> resolve_state_params(s) + {'experimentalist': {'source': 'the second theory'}} + + """ + state_dependent_properties = _get_state_dependent_properties(state) + resolved_params = _resolve_properties(state.params, state_dependent_properties) + return resolved_params diff --git a/autora/controller/state/snapshot.py b/autora/controller/state/snapshot.py new file mode 100644 index 00000000..8d3c8812 --- /dev/null +++ b/autora/controller/state/snapshot.py @@ -0,0 +1,124 @@ +""" Classes for storing and passing a cycle's state as an immutable snapshot. """ +from dataclasses import dataclass, field +from typing import Dict, List + +from numpy.typing import ArrayLike +from sklearn.base import BaseEstimator + +from autora.variable import VariableCollection + + +@dataclass(frozen=True) +class ControllerState: + """An object passed between and updated by processing steps in the Controller.""" + + # Single values + metadata: VariableCollection = field(default_factory=VariableCollection) + params: Dict = field(default_factory=dict) + + # Sequences + conditions: List[ArrayLike] = field(default_factory=list) + observations: List[ArrayLike] = field(default_factory=list) + theories: List[BaseEstimator] = field(default_factory=list) + + def update( + self, + metadata=None, + params=None, + conditions=None, + observations=None, + theories=None, + ): + """ + Create a new object with updated values. + + The initial object is empty: + >>> s0 = ControllerState() + >>> s0 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + ControllerState(metadata=VariableCollection(...), params={}, conditions=[], + observations=[], theories=[]) + + We can update the params using the `.update` method: + >>> s0.update(params={'first': 'params'}) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + ControllerState(..., params={'first': 'params'}, ...) + + ... but the original object is unchanged: + >>> s0 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + ControllerState(..., params={}, ...) + + For params, only one object is returned from the respective property: + >>> s0.update(params={'first': 'params'}).update(params={'second': 'params'}).params + {'second': 'params'} + + ... and the same applies to metadata: + >>> from autora.variable import VariableCollection + >>> (s0.update(metadata=VariableCollection(["1st IV"])) + ... .update(metadata=VariableCollection(["2nd IV"]))).metadata + VariableCollection(independent_variables=['2nd IV'], dependent_variables=[], covariates=[]) + + When we update the conditions, observations or theories, the respective list is extended: + >>> s3 = s0.update(theories=["1st theory"]) + >>> s3 + ControllerState(..., theories=['1st theory']) + + ... so we can see the history of all the theories, for instance. + >>> s3.update(theories=["2nd theory"]) + ControllerState(..., theories=['1st theory', '2nd theory']) + + The same applies to observations: + >>> s4 = s0.update(observations=["1st observation"]) + >>> s4 + ControllerState(..., observations=['1st observation'], ...) + + >>> s4.update(observations=["2nd observation"]) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + ControllerState(..., observations=['1st observation', '2nd observation'], ...) + + + The same applies to conditions: + >>> s5 = s0.update(conditions=["1st condition"]) + >>> s5 + ControllerState(..., conditions=['1st condition'], ...) + + >>> s5.update(conditions=["2nd condition"]) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + ControllerState(..., conditions=['1st condition', '2nd condition'], ...) + + You can also update with multiple conditions, observations and theories: + >>> s0.update(conditions=['c1', 'c2']) + ControllerState(..., conditions=['c1', 'c2'], ...) + + >>> s0.update(theories=['t1', 't2'], metadata={'m': 1}) + ControllerState(metadata={'m': 1}, ..., theories=['t1', 't2']) + + >>> s0.update(theories=['t1'], observations=['o1'], metadata={'m': 1}) + ControllerState(metadata={'m': 1}, ..., observations=['o1'], theories=['t1']) + + + Inputs to theories, observations and conditions must be Lists + which can be cast to lists: + >>> s0.update(theories='t1') # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + AssertionError: 't1' must be a list, e.g. `['t1']`?) + + """ + + def _coalesce_lists(old, new): + assert isinstance( + old, List + ), f"{repr(old)} must be a list, e.g. `[{repr(old)}]`?)" + if new is not None: + assert isinstance( + new, List + ), f"{repr(new)} must be a list, e.g. `[{repr(new)}]`?)" + return old + list(new) + else: + return old + + metadata_ = metadata or self.metadata + params_ = params or self.params + conditions_ = _coalesce_lists(self.conditions, conditions) + observations_ = _coalesce_lists(self.observations, observations) + theories_ = _coalesce_lists(self.theories, theories) + return ControllerState( + metadata_, params_, conditions_, observations_, theories_ + ) From d2261c4451da3f74dd5172abb3fdb4cfd13acf6c Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 20 Mar 2023 12:56:23 -0400 Subject: [PATCH 002/446] docs: update docstrings --- autora/controller/protocol/v1.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/autora/controller/protocol/v1.py b/autora/controller/protocol/v1.py index 87b55b70..358fbe2a 100644 --- a/autora/controller/protocol/v1.py +++ b/autora/controller/protocol/v1.py @@ -52,7 +52,7 @@ class SupportsDataKind(Protocol): class SupportsControllerStateFields(Protocol): - """An object which can support representing snapshots of a controller state.""" + """Support representing snapshots of a controller state as mutable fields.""" def __init__( self, @@ -75,6 +75,8 @@ def update(self: State, **kwargs) -> State: class SupportsControllerStateProperties(Protocol): + """Support representing snapshots of a controller state as immutable properties.""" + def __init__( self, metadata: Optional[VariableCollection], @@ -115,6 +117,8 @@ def theories(self) -> Sequence[BaseEstimator]: class SupportsControllerStateHistory(SupportsControllerStateProperties, Protocol): + """Represents controller state as a linear sequence of entries.""" + def __init__( self, metadata: Optional[VariableCollection], From 0e196c9595f3bd6f302526fbe194882a133c8545 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 20 Mar 2023 12:57:25 -0400 Subject: [PATCH 003/446] test: add doctest-modules in CI --- .github/workflows/test-pytest.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/test-pytest.yml b/.github/workflows/test-pytest.yml index 9c0b9ca9..8325785c 100644 --- a/.github/workflows/test-pytest.yml +++ b/.github/workflows/test-pytest.yml @@ -26,3 +26,5 @@ jobs: cache: "poetry" - run: poetry install - run: poetry run pytest + - run: poetry run pytest --doctest-modules autora/controller + From 444d7647e785cdf51ec28496f86b92a3cad085ad Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 20 Mar 2023 13:01:29 -0400 Subject: [PATCH 004/446] refactor: rename [ControllerState]History and Snapshot --- autora/controller/state/__init__.py | 4 +- autora/controller/state/history.py | 110 ++++++++++++++-------------- autora/controller/state/param.py | 8 +- autora/controller/state/snapshot.py | 32 ++++---- 4 files changed, 76 insertions(+), 78 deletions(-) diff --git a/autora/controller/state/__init__.py b/autora/controller/state/__init__.py index a6d644ee..c6ecd529 100644 --- a/autora/controller/state/__init__.py +++ b/autora/controller/state/__init__.py @@ -1,3 +1,3 @@ -from .history import ControllerStateHistory +from .history import History from .param import resolve_state_params -from .snapshot import ControllerState +from .snapshot import Snapshot diff --git a/autora/controller/state/history.py b/autora/controller/state/history.py index ed3da291..7d8eba90 100644 --- a/autora/controller/state/history.py +++ b/autora/controller/state/history.py @@ -8,11 +8,11 @@ from sklearn.base import BaseEstimator from autora.controller.protocol.v1 import ResultKind, SupportsDataKind -from autora.controller.state.snapshot import ControllerState +from autora.controller.state.snapshot import Snapshot from autora.variable import VariableCollection -class ControllerStateHistory: +class History: """ An immutable object for tracking the state and history of an AER cycle. """ @@ -38,34 +38,34 @@ def __init__( Examples: Empty input leads to an empty state: - >>> ControllerStateHistory() - ControllerStateHistory([]) + >>> History() + History([]) ... or with values for any or all of the parameters: >>> from autora.variable import VariableCollection - >>> ControllerStateHistory(metadata=VariableCollection()) # doctest: +ELLIPSIS - ControllerStateHistory([Result(data=VariableCollection(...), kind=ResultKind.METADATA)]) + >>> History(metadata=VariableCollection()) # doctest: +ELLIPSIS + History([Result(data=VariableCollection(...), kind=ResultKind.METADATA)]) - >>> ControllerStateHistory(params={"some": "params"}) - ControllerStateHistory([Result(data={'some': 'params'}, kind=ResultKind.PARAMS)]) + >>> History(params={"some": "params"}) + History([Result(data={'some': 'params'}, kind=ResultKind.PARAMS)]) - >>> ControllerStateHistory(conditions=["a condition"]) - ControllerStateHistory([Result(data='a condition', kind=ResultKind.CONDITION)]) + >>> History(conditions=["a condition"]) + History([Result(data='a condition', kind=ResultKind.CONDITION)]) - >>> ControllerStateHistory(observations=["an observation"]) - ControllerStateHistory([Result(data='an observation', kind=ResultKind.OBSERVATION)]) + >>> History(observations=["an observation"]) + History([Result(data='an observation', kind=ResultKind.OBSERVATION)]) >>> from sklearn.linear_model import LinearRegression - >>> ControllerStateHistory(theories=[LinearRegression()]) - ControllerStateHistory([Result(data=LinearRegression(), kind=ResultKind.THEORY)]) + >>> History(theories=[LinearRegression()]) + History([Result(data=LinearRegression(), kind=ResultKind.THEORY)]) Parameters passed to the constructor are included in the history in the following order: `history`, `metadata`, `params`, `conditions`, `observations`, `theories` - >>> ControllerStateHistory(theories=['t1', 't2'], conditions=['c1', 'c2'], + >>> History(theories=['t1', 't2'], conditions=['c1', 'c2'], ... observations=['o1', 'o2'], params={'a': 'param'}, metadata=VariableCollection(), ... history=[Result("from history", ResultKind.METADATA)] ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data='from history', kind=ResultKind.METADATA), + History([Result(data='from history', kind=ResultKind.METADATA), Result(data=VariableCollection(...), kind=ResultKind.METADATA), Result(data={'a': 'param'}, kind=ResultKind.PARAMS), Result(data='c1', kind=ResultKind.CONDITION), @@ -104,31 +104,31 @@ def update( Examples: The initial object is empty: - >>> s0 = ControllerStateHistory() + >>> s0 = History() >>> s0 - ControllerStateHistory([]) + History([]) We can update the metadata using the `.update` method: >>> from autora.variable import VariableCollection >>> s1 = s0.update(metadata=VariableCollection()) >>> s1 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data=VariableCollection(...), kind=ResultKind.METADATA)]) + History([Result(data=VariableCollection(...), kind=ResultKind.METADATA)]) ... the original object is unchanged: >>> s0 - ControllerStateHistory([]) + History([]) We can update the metadata again: >>> s2 = s1.update(metadata=VariableCollection(["some IV"])) >>> s2._by_kind # doctest: +ELLIPSIS - ControllerState(metadata=VariableCollection(independent_variables=['some IV'],...), ...) + Snapshot(metadata=VariableCollection(independent_variables=['some IV'],...), ...) ... and we see that there is only ever one metadata object returned. Params is treated the same way as metadata: >>> sp = s0.update(params={'first': 'params'}) >>> sp - ControllerStateHistory([Result(data={'first': 'params'}, kind=ResultKind.PARAMS)]) + History([Result(data={'first': 'params'}, kind=ResultKind.PARAMS)]) ... where only the most recent "params" object is returned from the `.params` property. >>> sp = sp.update(params={'second': 'params'}) @@ -137,19 +137,19 @@ def update( ... however, the full history of the params objects remains available, if needed: >>> sp # doctest: +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data={'first': 'params'}, kind=ResultKind.PARAMS), + History([Result(data={'first': 'params'}, kind=ResultKind.PARAMS), Result(data={'second': 'params'}, kind=ResultKind.PARAMS)]) When we update the conditions, observations or theories, a new entry is added to the history: >>> s3 = s0.update(theories=["1st theory"]) >>> s3 # doctest: +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data='1st theory', kind=ResultKind.THEORY)]) + History([Result(data='1st theory', kind=ResultKind.THEORY)]) ... so we can see the history of all the theories, for instance. >>> s3 = s3.update(theories=["2nd theory"]) # doctest: +NORMALIZE_WHITESPACE >>> s3 # doctest: +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data='1st theory', kind=ResultKind.THEORY), + History([Result(data='1st theory', kind=ResultKind.THEORY), Result(data='2nd theory', kind=ResultKind.THEORY)]) ... and the full history of theories is available using the `.theories` parameter: @@ -159,36 +159,36 @@ def update( The same for the observations: >>> s4 = s0.update(observations=["1st observation"]) >>> s4 - ControllerStateHistory([Result(data='1st observation', kind=ResultKind.OBSERVATION)]) + History([Result(data='1st observation', kind=ResultKind.OBSERVATION)]) >>> s4.update(observations=["2nd observation"] ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data='1st observation', kind=ResultKind.OBSERVATION), + History([Result(data='1st observation', kind=ResultKind.OBSERVATION), Result(data='2nd observation', kind=ResultKind.OBSERVATION)]) The same for the conditions: >>> s5 = s0.update(conditions=["1st condition"]) >>> s5 - ControllerStateHistory([Result(data='1st condition', kind=ResultKind.CONDITION)]) + History([Result(data='1st condition', kind=ResultKind.CONDITION)]) >>> s5.update(conditions=["2nd condition"]) # doctest: +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data='1st condition', kind=ResultKind.CONDITION), + History([Result(data='1st condition', kind=ResultKind.CONDITION), Result(data='2nd condition', kind=ResultKind.CONDITION)]) You can also update with multiple conditions, observations and theories: >>> s0.update(conditions=['c1', 'c2']) # doctest: +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data='c1', kind=ResultKind.CONDITION), + History([Result(data='c1', kind=ResultKind.CONDITION), Result(data='c2', kind=ResultKind.CONDITION)]) >>> s0.update(theories=['t1', 't2'], metadata={'m': 1}) # doctest: +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data={'m': 1}, kind=ResultKind.METADATA), + History([Result(data={'m': 1}, kind=ResultKind.METADATA), Result(data='t1', kind=ResultKind.THEORY), Result(data='t2', kind=ResultKind.THEORY)]) >>> s0.update(theories=['t1'], observations=['o1'], metadata={'m': 1} ... ) # doctest: +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data={'m': 1}, kind=ResultKind.METADATA), + History([Result(data={'m': 1}, kind=ResultKind.METADATA), Result(data='o1', kind=ResultKind.OBSERVATION), Result(data='t1', kind=ResultKind.THEORY)]) @@ -208,7 +208,7 @@ def update( ) new_full_history = self._history + history_extension - return ControllerStateHistory(history=new_full_history) + return History(history=new_full_history) def __repr__(self): return f"{type(self).__name__}({self.history})" @@ -223,7 +223,7 @@ def metadata(self) -> VariableCollection: Examples: The initial object is empty: - >>> s = ControllerStateHistory() + >>> s = History() ... and returns an emtpy metadata object >>> s.metadata @@ -251,7 +251,7 @@ def params(self) -> Dict: Examples: Params is treated the same way as metadata: - >>> s = ControllerStateHistory() + >>> s = History() >>> s = s.update(params={'first': 'params'}) >>> s.params {'first': 'params'} @@ -263,7 +263,7 @@ def params(self) -> Dict: ... however, the full history of the params objects remains available, if needed: >>> s # doctest: +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data={'first': 'params'}, kind=ResultKind.PARAMS), + History([Result(data={'first': 'params'}, kind=ResultKind.PARAMS), Result(data={'second': 'params'}, kind=ResultKind.PARAMS)]) """ return self._by_kind.params @@ -275,7 +275,7 @@ def conditions(self) -> List[ArrayLike]: Examples: View the sequence of theories with one conditions: - >>> s = ControllerStateHistory(conditions=[(1,2,3,)]) + >>> s = History(conditions=[(1,2,3,)]) >>> s.conditions [(1, 2, 3)] @@ -295,7 +295,7 @@ def observations(self) -> List[ArrayLike]: Examples: The sequence of all observations is returned - >>> s = ControllerStateHistory(observations=["1st observation"]) + >>> s = History(observations=["1st observation"]) >>> s.observations ['1st observation'] @@ -314,7 +314,7 @@ def theories(self) -> List[BaseEstimator]: Examples: View the sequence of theories with one theory: - >>> s = ControllerStateHistory(theories=["1st theory"]) + >>> s = History(theories=["1st theory"]) >>> s.theories # doctest: +NORMALIZE_WHITESPACE ['1st theory'] @@ -332,7 +332,7 @@ def history(self) -> List[Result]: Examples: We initialze some history: - >>> s = ControllerStateHistory(theories=['t1', 't2'], conditions=['c1', 'c2'], + >>> s = History(theories=['t1', 't2'], conditions=['c1', 'c2'], ... observations=['o1', 'o2'], params={'a': 'param'}, metadata=VariableCollection(), ... history=[Result("from history", ResultKind.METADATA)]) @@ -359,27 +359,27 @@ def history(self) -> List[Result]: """ return self._history - def filter_by(self, kind=Set[Union[str, ResultKind]]) -> ControllerStateHistory: + def filter_by(self, kind=Set[Union[str, ResultKind]]) -> History: """ Return a copy of the object with only data belonging to the specified kinds. Examples: - >>> s = ControllerStateHistory(theories=['t1', 't2'], conditions=['c1', 'c2'], + >>> s = History(theories=['t1', 't2'], conditions=['c1', 'c2'], ... observations=['o1', 'o2'], params={'a': 'param'}, metadata=VariableCollection(), ... history=[Result("from history", ResultKind.METADATA)]) >>> s.filter_by(kind={"THEORY"}) # doctest: +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data='t1', kind=ResultKind.THEORY), + History([Result(data='t1', kind=ResultKind.THEORY), Result(data='t2', kind=ResultKind.THEORY)]) >>> s.filter_by(kind={ResultKind.OBSERVATION}) # doctest: +NORMALIZE_WHITESPACE - ControllerStateHistory([Result(data='o1', kind=ResultKind.OBSERVATION), + History([Result(data='o1', kind=ResultKind.OBSERVATION), Result(data='o2', kind=ResultKind.OBSERVATION)]) """ kind_ = {ResultKind(s) for s in kind} filtered_history = _filter_history(self._history, kind_) - new_object = ControllerStateHistory(history=filtered_history) + new_object = History(history=filtered_history) return new_object @@ -491,51 +491,51 @@ def _init_result_list( return data -def _history_to_kind(history: Sequence[Result]) -> ControllerState: +def _history_to_kind(history: Sequence[Result]) -> Snapshot: """ - Convert a sequence of results into a ControllerState instance: + Convert a sequence of results into a Snapshot instance: Examples: History might be empty >>> history_ = [] >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS - ControllerState(metadata=VariableCollection(...), params={}, + Snapshot(metadata=VariableCollection(...), params={}, conditions=[], observations=[], theories=[]) ... or with values for any or all of the parameters: >>> history_ = _init_result_list(params={"some": "params"}) >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS - ControllerState(..., params={'some': 'params'}, ...) + Snapshot(..., params={'some': 'params'}, ...) >>> history_ += _init_result_list(conditions=["a condition"]) >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS - ControllerState(..., params={'some': 'params'}, conditions=['a condition'], ...) + Snapshot(..., params={'some': 'params'}, conditions=['a condition'], ...) >>> _history_to_kind(history_).params {'some': 'params'} >>> history_ += _init_result_list(observations=["an observation"]) >>> _history_to_kind(history_) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS - ControllerState(..., params={'some': 'params'}, conditions=['a condition'], + Snapshot(..., params={'some': 'params'}, conditions=['a condition'], observations=['an observation'], ...) >>> from sklearn.linear_model import LinearRegression >>> history_ = [Result(LinearRegression(), kind=ResultKind.THEORY)] >>> _history_to_kind(history_) # doctest: +ELLIPSIS - ControllerState(..., theories=[LinearRegression()]) + Snapshot(..., theories=[LinearRegression()]) >>> from autora.variable import VariableCollection, IV >>> metadata = VariableCollection(independent_variables=[IV(name="example")]) >>> history_ = [Result(metadata, kind=ResultKind.METADATA)] >>> _history_to_kind(history_) # doctest: +ELLIPSIS - ControllerState(metadata=VariableCollection(independent_variables=[IV(name='example', ... + Snapshot(metadata=VariableCollection(independent_variables=[IV(name='example', ... >>> history_ = [Result({'some': 'params'}, kind=ResultKind.PARAMS)] >>> _history_to_kind(history_) # doctest: +ELLIPSIS - ControllerState(..., params={'some': 'params'}, ...) + Snapshot(..., params={'some': 'params'}, ...) """ - namespace = ControllerState( + namespace = Snapshot( metadata=_get_last_data_with_default( history, kind={ResultKind.METADATA}, default=VariableCollection() ), diff --git a/autora/controller/state/param.py b/autora/controller/state/param.py index a62c9c8e..dd8f1c78 100644 --- a/autora/controller/state/param.py +++ b/autora/controller/state/param.py @@ -14,8 +14,8 @@ def _get_state_dependent_properties(state: SupportsControllerState): """ Examples: Even with an empty data object, we can initialize the dictionary, - >>> from autora.controller.state import ControllerState - >>> state_dependent_properties = _get_state_dependent_properties(ControllerState()) + >>> from autora.controller.state import Snapshot + >>> state_dependent_properties = _get_state_dependent_properties(Snapshot()) ... but it will raise an exception if a value isn't yet available when we try to use it >>> state_dependent_properties["%theories[-1]%"] # doctest: +ELLIPSIS @@ -98,8 +98,8 @@ def resolve_state_params(state: SupportsControllerState) -> Dict: Returns the `params` attribute of the input, with `cycle properties` resolved. Examples: - >>> from autora.controller.state import ControllerStateHistory - >>> s = ControllerStateHistory(theories=["the first theory", "the second theory"], + >>> from autora.controller.state import History + >>> s = History(theories=["the first theory", "the second theory"], ... params={"experimentalist": {"source": "%theories[-1]%"}}) >>> resolve_state_params(s) {'experimentalist': {'source': 'the second theory'}} diff --git a/autora/controller/state/snapshot.py b/autora/controller/state/snapshot.py index 8d3c8812..dd07165a 100644 --- a/autora/controller/state/snapshot.py +++ b/autora/controller/state/snapshot.py @@ -9,7 +9,7 @@ @dataclass(frozen=True) -class ControllerState: +class Snapshot: """An object passed between and updated by processing steps in the Controller.""" # Single values @@ -33,18 +33,18 @@ def update( Create a new object with updated values. The initial object is empty: - >>> s0 = ControllerState() + >>> s0 = Snapshot() >>> s0 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - ControllerState(metadata=VariableCollection(...), params={}, conditions=[], + Snapshot(metadata=VariableCollection(...), params={}, conditions=[], observations=[], theories=[]) We can update the params using the `.update` method: >>> s0.update(params={'first': 'params'}) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - ControllerState(..., params={'first': 'params'}, ...) + Snapshot(..., params={'first': 'params'}, ...) ... but the original object is unchanged: >>> s0 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - ControllerState(..., params={}, ...) + Snapshot(..., params={}, ...) For params, only one object is returned from the respective property: >>> s0.update(params={'first': 'params'}).update(params={'second': 'params'}).params @@ -59,38 +59,38 @@ def update( When we update the conditions, observations or theories, the respective list is extended: >>> s3 = s0.update(theories=["1st theory"]) >>> s3 - ControllerState(..., theories=['1st theory']) + Snapshot(..., theories=['1st theory']) ... so we can see the history of all the theories, for instance. >>> s3.update(theories=["2nd theory"]) - ControllerState(..., theories=['1st theory', '2nd theory']) + Snapshot(..., theories=['1st theory', '2nd theory']) The same applies to observations: >>> s4 = s0.update(observations=["1st observation"]) >>> s4 - ControllerState(..., observations=['1st observation'], ...) + Snapshot(..., observations=['1st observation'], ...) >>> s4.update(observations=["2nd observation"]) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - ControllerState(..., observations=['1st observation', '2nd observation'], ...) + Snapshot(..., observations=['1st observation', '2nd observation'], ...) The same applies to conditions: >>> s5 = s0.update(conditions=["1st condition"]) >>> s5 - ControllerState(..., conditions=['1st condition'], ...) + Snapshot(..., conditions=['1st condition'], ...) >>> s5.update(conditions=["2nd condition"]) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - ControllerState(..., conditions=['1st condition', '2nd condition'], ...) + Snapshot(..., conditions=['1st condition', '2nd condition'], ...) You can also update with multiple conditions, observations and theories: >>> s0.update(conditions=['c1', 'c2']) - ControllerState(..., conditions=['c1', 'c2'], ...) + Snapshot(..., conditions=['c1', 'c2'], ...) >>> s0.update(theories=['t1', 't2'], metadata={'m': 1}) - ControllerState(metadata={'m': 1}, ..., theories=['t1', 't2']) + Snapshot(metadata={'m': 1}, ..., theories=['t1', 't2']) >>> s0.update(theories=['t1'], observations=['o1'], metadata={'m': 1}) - ControllerState(metadata={'m': 1}, ..., observations=['o1'], theories=['t1']) + Snapshot(metadata={'m': 1}, ..., observations=['o1'], theories=['t1']) Inputs to theories, observations and conditions must be Lists @@ -119,6 +119,4 @@ def _coalesce_lists(old, new): conditions_ = _coalesce_lists(self.conditions, conditions) observations_ = _coalesce_lists(self.observations, observations) theories_ = _coalesce_lists(self.theories, theories) - return ControllerState( - metadata_, params_, conditions_, observations_, theories_ - ) + return Snapshot(metadata_, params_, conditions_, observations_, theories_) From de4048533f6439c79791170a5157a150f717d9da Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Mon, 20 Mar 2023 14:19:00 -0400 Subject: [PATCH 005/446] test: update types in test --- autora/controller/state/snapshot.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/autora/controller/state/snapshot.py b/autora/controller/state/snapshot.py index dd07165a..51e75c96 100644 --- a/autora/controller/state/snapshot.py +++ b/autora/controller/state/snapshot.py @@ -51,10 +51,10 @@ def update( {'second': 'params'} ... and the same applies to metadata: - >>> from autora.variable import VariableCollection - >>> (s0.update(metadata=VariableCollection(["1st IV"])) - ... .update(metadata=VariableCollection(["2nd IV"]))).metadata - VariableCollection(independent_variables=['2nd IV'], dependent_variables=[], covariates=[]) + >>> from autora.variable import VariableCollection, IV + >>> (s0.update(metadata=VariableCollection([IV("1st IV")])) + ... .update(metadata=VariableCollection([IV("2nd IV")]))).metadata + VariableCollection(independent_variables=[IV(name='2nd IV',...)], ...) When we update the conditions, observations or theories, the respective list is extended: >>> s3 = s0.update(theories=["1st theory"]) From 0d8196c0e5f12815570f04980bee8e3f0d5492b5 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 21 Mar 2023 09:49:53 -0400 Subject: [PATCH 006/446] feat: add basic Planner code which uses a Dict executor collection --- autora/controller/planner.py | 126 +++++++++++++++++++++++++++++++ autora/controller/protocol/v1.py | 10 +++ 2 files changed, 136 insertions(+) create mode 100644 autora/controller/planner.py diff --git a/autora/controller/planner.py b/autora/controller/planner.py new file mode 100644 index 00000000..b5bc6458 --- /dev/null +++ b/autora/controller/planner.py @@ -0,0 +1,126 @@ +""" +Functions which look at state and output the next function to execute. +""" +import random + +from autora.controller.protocol.v1 import ( + ExecutorCollection, + ResultKind, + SupportsControllerStateHistory, +) + + +def full_cycle_planner(_, executor_collection: ExecutorCollection): + """Always returns the `full_cycle` method. + + Examples: + We simulate a productive executor_collection using a simple dict + >>> executor_collection_ = dict( + ... experimentalist = "experimentalist", + ... experiment_runner = "experiment_runner", + ... theorist = "theorist", + ... full_cycle = "full_cycle", + ... ) + + The full_cycle_planner always returns the full cycle Executor + >>> full_cycle_planner([], executor_collection_) + 'full_cycle' + + """ + return executor_collection["full_cycle"] + + +def last_result_kind_planner( + state: SupportsControllerStateHistory, + executor_collection: ExecutorCollection, +): + """ + Chooses the operation based on the last result, e.g. new theory -> run experimentalist. + + Interpretation: The "traditional" AutoRA Controller – a systematic research assistant. + + Examples: + We initialize a new list to run our planner on: + >>> from autora.controller.state import History + >>> state_ = History() + + We simulate a productive executor_collection using a dict + >>> executor_collection_ = dict( + ... experimentalist = "experimentalist", + ... experiment_runner = "experiment_runner", + ... theorist = "theorist", + ... ) + + Based on the results available in the state, we can get the next kind of executor we need. + When we have no results of any kind, we get an experimentalist: + >>> last_result_kind_planner(state_, executor_collection_) + 'experimentalist' + + ... or if we had produced conditions, then we could run an experiment + >>> state_ = state_.update(conditions=["some condition"]) + >>> last_result_kind_planner(state_, executor_collection_) + 'experiment_runner' + + ... or if we last produced observations, then we could now run the theorist: + >>> state_ = state_.update(observations=["some observation"]) + >>> last_result_kind_planner(state_, executor_collection_) + 'theorist' + + ... or if we last produced a theory, then we could now run the experimentalist: + >>> state_ = state_.update(theories=["some theory"]) + >>> last_result_kind_planner(state_, executor_collection_) + 'experimentalist' + + """ + + filtered_history = state.filter_by( + kind={ResultKind.CONDITION, ResultKind.OBSERVATION, ResultKind.THEORY} + ).history + + try: + last_result_kind = filtered_history[-1].kind + except IndexError: + last_result_kind = None + + callback = { + None: executor_collection["experimentalist"], + ResultKind.THEORY: executor_collection["experimentalist"], + ResultKind.CONDITION: executor_collection["experiment_runner"], + ResultKind.OBSERVATION: executor_collection["theorist"], + }[last_result_kind] + + return callback + + +def random_operation_planner(_, executor_collection: ExecutorCollection): + """ + Chooses a random operation, ignoring any data which already exist. + + Interpretation: A mercurial PI with good technique but poor planning, who doesn't remember what + they did last. + + Examples: + We simulate a productive executor_collection using a simple dict + >>> executor_collection_ = dict( + ... experimentalist = "experimentalist", + ... experiment_runner = "experiment_runner", + ... theorist = "theorist", + ... ) + + For reproducibility, we seed the random number generator consistently: + >>> from random import seed + >>> seed(42) + + Now we can begin to see which operations are returned by the planner. The first (for this + seed) is the theorist. (The first argument is provided for compatibility with the + protocol, but is ignored.) + >>> random_operation_planner([], executor_collection_) + 'theorist' + + If we evaluate again, a random executor will be suggested each time + >>> [random_operation_planner([], executor_collection_) for i in range(5)] + ['experimentalist', 'experimentalist', 'theorist', 'experiment_runner', 'experimentalist'] + + """ + choice = random.choice(list(executor_collection.values())) + return choice diff --git a/autora/controller/protocol/v1.py b/autora/controller/protocol/v1.py index 358fbe2a..90491278 100644 --- a/autora/controller/protocol/v1.py +++ b/autora/controller/protocol/v1.py @@ -136,3 +136,13 @@ def filter_by(self: State, **kwargs) -> State: @property def history(self) -> Sequence[SupportsDataKind]: ... + + +class Executor(Protocol): + """A Callable which, given some state, returns an updated state.""" + + def __call__(self, __state: State) -> State: + ... + + +ExecutorCollection = Dict[str, Executor] From 7f2d32298b6046b43d21a10784405531a2019836 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 21 Mar 2023 09:50:31 -0400 Subject: [PATCH 007/446] refactor: make random choice code more readable --- autora/controller/planner.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/autora/controller/planner.py b/autora/controller/planner.py index b5bc6458..e144309d 100644 --- a/autora/controller/planner.py +++ b/autora/controller/planner.py @@ -122,5 +122,6 @@ def random_operation_planner(_, executor_collection: ExecutorCollection): ['experimentalist', 'experimentalist', 'theorist', 'experiment_runner', 'experimentalist'] """ - choice = random.choice(list(executor_collection.values())) + all_executors = list(executor_collection.values()) + choice = random.choice(all_executors) return choice From 7f9db39eb39acce9cc81b65bb746cc8bab823b97 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 21 Mar 2023 15:25:25 -0400 Subject: [PATCH 008/446] feat: add initial version of executor --- autora/controller/executor.py | 329 +++++++++++++++++++++++++++++++ autora/controller/protocol/v1.py | 4 +- 2 files changed, 331 insertions(+), 2 deletions(-) create mode 100644 autora/controller/executor.py diff --git a/autora/controller/executor.py b/autora/controller/executor.py new file mode 100644 index 00000000..f8d05036 --- /dev/null +++ b/autora/controller/executor.py @@ -0,0 +1,329 @@ +""" +Objects for handling input and outputs from experimentalists, experiment runners and theorists. +""" + +from __future__ import annotations + +import copy +from functools import partial +from types import MappingProxyType +from typing import Callable, Iterable, Literal, Tuple, Union + +import numpy as np +from sklearn.base import BaseEstimator + +from autora.controller.protocol.v1 import ( + Executor, + ExecutorCollection, + State, + SupportsControllerState, +) +from autora.controller.state import resolve_state_params +from autora.experimentalist.pipeline import Pipeline + + +class OnlineExecutorCollection: + """ + Runs experiment design, observation and theory generation in a single session. + + This object allows a user to specify + - an experimentalist: a Pipeline + - an experiment runner: some Callable and + - a theorist: a scikit-learn-compatible estimator with a fit method + + ... and exposes methods to call these and update a CycleState object with new data. + + Examples: + >>> from autora.experimentalist.pipeline import Pipeline + >>> from sklearn.linear_model import LinearRegression + >>> experimentalist_pipeline_ = Pipeline([('p', (1, 2))]) + >>> def experiment_runner_(x): + ... return 2 * x + 1 + >>> theorist_estimator_ = LinearRegression() + >>> c = OnlineExecutorCollection( + ... experimentalist_pipeline=experimentalist_pipeline_, + ... theorist_estimator=theorist_estimator_, + ... experiment_runner_callable=experiment_runner_ + ... ) + >>> c # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + OnlineExecutorCollection(experimentalist_pipeline=Pipeline(steps=[('p', (1, 2))], + params={}), experiment_runner_callable=, + theorist_estimator=LinearRegression()) + + We can access the collection as a mapping: + >>> c["experimentalist_pipeline"] + Pipeline(steps=[('p', (1, 2))], params={}) + + ... or using the attributes directly: + >>> c.experimentalist_pipeline + Pipeline(steps=[('p', (1, 2))], params={}) + + Updating the pipeline functions + """ + + def __init__( + self, + experimentalist_pipeline: Pipeline, + experiment_runner_callable: Callable, + theorist_estimator: BaseEstimator, + ): + self.experimentalist_pipeline = experimentalist_pipeline + self.experiment_runner_callable = experiment_runner_callable + self.theorist_estimator = theorist_estimator + + def __getitem__(self, item): + """Mapping interface.""" + return getattr(self, item) + + def __repr__(self): + return ( + f"{type(self).__name__}(" + f"experimentalist_pipeline={self.experimentalist_pipeline}, " + f"experiment_runner_callable={self.experiment_runner_callable}, " + f"theorist_estimator={self.theorist_estimator}" + f")" + ) + + def experimentalist( + self, state: SupportsControllerState + ) -> SupportsControllerState: + """Interface for running the experimentalist pipeline.""" + new_state = experimentalist_wrapper(state, self.experiment_runner_callable) + return new_state + + def experiment_runner( + self, state: SupportsControllerState + ) -> SupportsControllerState: + """Interface for running the experiment runner callable""" + new_state = experiment_runner_wrapper(state, self.experiment_runner_callable) + return new_state + + def theorist(self, state: SupportsControllerState) -> SupportsControllerState: + """Interface for running the theorist estimator.""" + new_state = theorist_wrapper(state, self.theorist_estimator) + return new_state + + def full_cycle(self, state: SupportsControllerState) -> SupportsControllerState: + """ + Executes the experimentalist, experiment runner and theorist on the given state. + + Returns: A list of new results + """ + experimentalist_result = self.experimentalist(state) + experiment_runner_result = self.experiment_runner(experimentalist_result) + theorist_result = self.theorist(experiment_runner_result) + return theorist_result + + +def experimentalist_wrapper( + state: SupportsControllerState, pipeline: Pipeline +) -> SupportsControllerState: + """Interface for running the experimentalist pipeline.""" + params = resolve_state_params(state).get("experimentalist", dict()) + new_conditions = pipeline(**params) + + assert isinstance(new_conditions, Iterable) + # If the pipeline gives us an iterable, we need to make it into a concrete array. + # We can't move this logic to the Pipeline, because the pipeline doesn't know whether + # it's within another pipeline and whether it should convert the iterable to a + # concrete array. + new_conditions_values = list(new_conditions) + new_conditions_array = np.array(new_conditions_values) + + assert isinstance(new_conditions_array, np.ndarray) # Check the object is bounded + new_state = state.update(conditions=[new_conditions_array]) + return new_state + + +def experiment_runner_wrapper( + state: SupportsControllerState, callable: Callable +) -> SupportsControllerState: + """Interface for running the experiment runner callable""" + params = resolve_state_params(state).get("experiment_runner", dict()) + x = state.conditions[-1] + y = callable(x, **params) + new_observations = np.column_stack([x, y]) + new_state = state.update(observations=[new_observations]) + return new_state + + +def theorist_wrapper(state: State, estimator: BaseEstimator) -> State: + params = resolve_state_params(state).get("theorist", dict()) + metadata = state.metadata + observations = state.observations + all_observations = np.row_stack(observations) + n_xs = len(metadata.independent_variables) + x, y = all_observations[:, :n_xs], all_observations[:, n_xs:] + if y.shape[1] == 1: + y = y.ravel() + new_theorist = copy.deepcopy(estimator) + new_theorist.fit(x, y, **params) + new_state = state.update(theories=[new_theorist]) + return new_state + + +def full_cycle_wrapper( + state: State, + experimentalist_pipeline: Pipeline, + experiment_runner_callable: Callable, + theorist_estimator: BaseEstimator, +): + experimentalist_result = experimentalist_wrapper(state, experimentalist_pipeline) + experiment_runner_result = experiment_runner_wrapper( + experimentalist_result, experiment_runner_callable + ) + theorist_result = theorist_wrapper(experiment_runner_result, theorist_estimator) + return theorist_result + + +def make_online_executor( + kind: Literal["experimentalist", "experiment_runner", "theorist"], + core: Union[Pipeline, Callable, BaseEstimator], +) -> Executor: + """ + + Args: + kind: a string specifying the kind of function (and thus the correct wrapper to use) + core: the object to wrap – "experimentalist": a Pipeline, "experiment_runner": a + Callable, "theorist": a BaseEstimator + + Returns: a curried function which will run the kind of AER step requested + + """ + if kind == "experimentalist": + assert isinstance(core, Pipeline) + curried_function = partial(experimentalist_wrapper, pipeline=core) + elif kind == "experiment_runner": + assert isinstance(core, Callable) + curried_function = partial(experiment_runner_wrapper, callable=core) + elif kind == "theorist": + assert isinstance(core, BaseEstimator) + curried_function = partial(theorist_wrapper, estimator=core) + else: + raise NotImplementedError( + f"{kind=} is not implemented for executor definitions." + ) + return curried_function + + +def make_executor_collection( + x: Iterable[ + Tuple[ + str, + Literal["experimentalist", "experiment_runner", "theorist"], + Union[Pipeline, Callable, BaseEstimator], + ] + ] +) -> ExecutorCollection: + """ + + Make an executor collection using experimentalists, experiment_runners and theorists. + + Args: + x: + + Returns: + + Examples: + >>> from sklearn.linear_model import LinearRegression + >>> make_executor_collection([("t", "theorist", LinearRegression())]) # doctest: +ELLIPSIS + {'t': functools.partial(, estimator=LinearRegression())} + + >>> make_executor_collection([("er", "experiment_runner", lambda x_: x_ + 1)] + ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + {'er': functools.partial(, callable= at 0x...>)} + """ + c: ExecutorCollection = {} + for name, kind, core in x: + c[name] = make_online_executor(kind, core) + + return c + + +def make_default_executor_collection( + experimentalist_pipeline: Pipeline, + experiment_runner_callable: Callable, + theorist_estimator: BaseEstimator, +) -> ExecutorCollection: + """ + Make the default AER executor collection. + + Args: + experimentalist_pipeline: an experimentalist Pipeline to be wrapped + experiment_runner_callable: an experiment runner function to be wrapped + theorist_estimator: a scikit learn-compatible estimator to be wrapped + + Returns: A dictionary with keys "experimentalist", "experiment_runner", "theorist" and + "full_cycle", with values which are Callables. + + + Examples: + >>> from autora.experimentalist.pipeline import Pipeline + >>> from sklearn.linear_model import LinearRegression + >>> experimentalist_pipeline_ = Pipeline([('p', (1, 2))]) + >>> def experiment_runner_(x): + ... return 2 * x + 1 + >>> theorist_estimator_ = LinearRegression() + >>> c = make_default_executor_collection( + ... experimentalist_pipeline=experimentalist_pipeline_, + ... theorist_estimator=theorist_estimator_, + ... experiment_runner_callable=experiment_runner_ + ... ) + >>> c # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + mappingproxy({'experimentalist': functools.partial(...), + 'experiment_runner': functools.partial(...), + 'theorist': functools.partial(...), + 'full_cycle': functools.partial(...)}) + + We can access the collection as a mapping: + >>> c["experimentalist"] # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + functools.partial(, + pipeline=Pipeline(steps=[('p', (1, 2))], params={})) + + ... + >>> c["experiment_runner"] # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + functools.partial(, + callable=) + + ... + >>> c["theorist"] # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + functools.partial(, + estimator=LinearRegression()) + + ... + >>> c["full_cycle"] # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + functools.partial(, + experimentalist_pipeline=Pipeline(steps=[('p', (1, 2))], params={}), + experiment_runner_callable=, + theorist_estimator=LinearRegression()) + + You cannot update the collection. To replace a value, create a new collection. + >>> other_theorist = LinearRegression(fit_intercept=False) + >>> c["theorist"] = partial(theorist_wrapper, estimator=other_theorist) + Traceback (most recent call last): + ... + TypeError: 'mappingproxy' object does not support item assignment + + (Updating the collection is restricted because the "full_cycle" depends on all the + input functions directly, and doesn't reuse the wrapped functions created for the + experimentalist, experiment_runner and theorist.) + + """ + + c = make_executor_collection( + [ + ("experimentalist", "experimentalist", experimentalist_pipeline), + ("experiment_runner", "experiment_runner", experiment_runner_callable), + ("theorist", "theorist", theorist_estimator), + ] + ) + + c["full_cycle"] = partial( + full_cycle_wrapper, + experimentalist_pipeline=experimentalist_pipeline, + experiment_runner_callable=experiment_runner_callable, + theorist_estimator=theorist_estimator, + ) + + return MappingProxyType(c) diff --git a/autora/controller/protocol/v1.py b/autora/controller/protocol/v1.py index 90491278..69dd5a93 100644 --- a/autora/controller/protocol/v1.py +++ b/autora/controller/protocol/v1.py @@ -1,5 +1,5 @@ from enum import Enum -from typing import Any, Dict, Optional, Protocol, Sequence, TypeVar, Union +from typing import Any, Dict, Mapping, Optional, Protocol, Sequence, TypeVar, Union from numpy.typing import ArrayLike from sklearn.base import BaseEstimator @@ -145,4 +145,4 @@ def __call__(self, __state: State) -> State: ... -ExecutorCollection = Dict[str, Executor] +ExecutorCollection = Mapping[str, Executor] From f68b2b1b8c69828d067bd95348d381139f122d0d Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 21 Mar 2023 18:11:13 -0400 Subject: [PATCH 009/446] refactor: simplify executor and planner interfaces to use just simple dictionaries --- autora/controller/executor.py | 131 +++++-------------------------- autora/controller/planner.py | 63 +++++---------- autora/controller/protocol/v1.py | 5 ++ 3 files changed, 43 insertions(+), 156 deletions(-) diff --git a/autora/controller/executor.py b/autora/controller/executor.py index f8d05036..73b5d71b 100644 --- a/autora/controller/executor.py +++ b/autora/controller/executor.py @@ -12,109 +12,11 @@ import numpy as np from sklearn.base import BaseEstimator -from autora.controller.protocol.v1 import ( - Executor, - ExecutorCollection, - State, - SupportsControllerState, -) +from autora.controller.protocol.v1 import SupportsControllerState from autora.controller.state import resolve_state_params from autora.experimentalist.pipeline import Pipeline -class OnlineExecutorCollection: - """ - Runs experiment design, observation and theory generation in a single session. - - This object allows a user to specify - - an experimentalist: a Pipeline - - an experiment runner: some Callable and - - a theorist: a scikit-learn-compatible estimator with a fit method - - ... and exposes methods to call these and update a CycleState object with new data. - - Examples: - >>> from autora.experimentalist.pipeline import Pipeline - >>> from sklearn.linear_model import LinearRegression - >>> experimentalist_pipeline_ = Pipeline([('p', (1, 2))]) - >>> def experiment_runner_(x): - ... return 2 * x + 1 - >>> theorist_estimator_ = LinearRegression() - >>> c = OnlineExecutorCollection( - ... experimentalist_pipeline=experimentalist_pipeline_, - ... theorist_estimator=theorist_estimator_, - ... experiment_runner_callable=experiment_runner_ - ... ) - >>> c # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - OnlineExecutorCollection(experimentalist_pipeline=Pipeline(steps=[('p', (1, 2))], - params={}), experiment_runner_callable=, - theorist_estimator=LinearRegression()) - - We can access the collection as a mapping: - >>> c["experimentalist_pipeline"] - Pipeline(steps=[('p', (1, 2))], params={}) - - ... or using the attributes directly: - >>> c.experimentalist_pipeline - Pipeline(steps=[('p', (1, 2))], params={}) - - Updating the pipeline functions - """ - - def __init__( - self, - experimentalist_pipeline: Pipeline, - experiment_runner_callable: Callable, - theorist_estimator: BaseEstimator, - ): - self.experimentalist_pipeline = experimentalist_pipeline - self.experiment_runner_callable = experiment_runner_callable - self.theorist_estimator = theorist_estimator - - def __getitem__(self, item): - """Mapping interface.""" - return getattr(self, item) - - def __repr__(self): - return ( - f"{type(self).__name__}(" - f"experimentalist_pipeline={self.experimentalist_pipeline}, " - f"experiment_runner_callable={self.experiment_runner_callable}, " - f"theorist_estimator={self.theorist_estimator}" - f")" - ) - - def experimentalist( - self, state: SupportsControllerState - ) -> SupportsControllerState: - """Interface for running the experimentalist pipeline.""" - new_state = experimentalist_wrapper(state, self.experiment_runner_callable) - return new_state - - def experiment_runner( - self, state: SupportsControllerState - ) -> SupportsControllerState: - """Interface for running the experiment runner callable""" - new_state = experiment_runner_wrapper(state, self.experiment_runner_callable) - return new_state - - def theorist(self, state: SupportsControllerState) -> SupportsControllerState: - """Interface for running the theorist estimator.""" - new_state = theorist_wrapper(state, self.theorist_estimator) - return new_state - - def full_cycle(self, state: SupportsControllerState) -> SupportsControllerState: - """ - Executes the experimentalist, experiment runner and theorist on the given state. - - Returns: A list of new results - """ - experimentalist_result = self.experimentalist(state) - experiment_runner_result = self.experiment_runner(experimentalist_result) - theorist_result = self.theorist(experiment_runner_result) - return theorist_result - - def experimentalist_wrapper( state: SupportsControllerState, pipeline: Pipeline ) -> SupportsControllerState: @@ -147,7 +49,9 @@ def experiment_runner_wrapper( return new_state -def theorist_wrapper(state: State, estimator: BaseEstimator) -> State: +def theorist_wrapper( + state: SupportsControllerState, estimator: BaseEstimator +) -> SupportsControllerState: params = resolve_state_params(state).get("theorist", dict()) metadata = state.metadata observations = state.observations @@ -163,11 +67,11 @@ def theorist_wrapper(state: State, estimator: BaseEstimator) -> State: def full_cycle_wrapper( - state: State, + state: SupportsControllerState, experimentalist_pipeline: Pipeline, experiment_runner_callable: Callable, theorist_estimator: BaseEstimator, -): +) -> SupportsControllerState: experimentalist_result = experimentalist_wrapper(state, experimentalist_pipeline) experiment_runner_result = experiment_runner_wrapper( experimentalist_result, experiment_runner_callable @@ -179,7 +83,7 @@ def full_cycle_wrapper( def make_online_executor( kind: Literal["experimentalist", "experiment_runner", "theorist"], core: Union[Pipeline, Callable, BaseEstimator], -) -> Executor: +): """ Args: @@ -194,7 +98,7 @@ def make_online_executor( assert isinstance(core, Pipeline) curried_function = partial(experimentalist_wrapper, pipeline=core) elif kind == "experiment_runner": - assert isinstance(core, Callable) + assert callable(core) curried_function = partial(experiment_runner_wrapper, callable=core) elif kind == "theorist": assert isinstance(core, BaseEstimator) @@ -206,7 +110,7 @@ def make_online_executor( return curried_function -def make_executor_collection( +def make_online_executor_collection( x: Iterable[ Tuple[ str, @@ -214,7 +118,7 @@ def make_executor_collection( Union[Pipeline, Callable, BaseEstimator], ] ] -) -> ExecutorCollection: +): """ Make an executor collection using experimentalists, experiment_runners and theorists. @@ -226,26 +130,27 @@ def make_executor_collection( Examples: >>> from sklearn.linear_model import LinearRegression - >>> make_executor_collection([("t", "theorist", LinearRegression())]) # doctest: +ELLIPSIS + >>> make_online_executor_collection([("t", "theorist", LinearRegression())] + ... ) # doctest: +ELLIPSIS {'t': functools.partial(, estimator=LinearRegression())} - >>> make_executor_collection([("er", "experiment_runner", lambda x_: x_ + 1)] + >>> make_online_executor_collection([("er", "experiment_runner", lambda x_: x_ + 1)] ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE {'er': functools.partial(, callable= at 0x...>)} """ - c: ExecutorCollection = {} + c = {} for name, kind, core in x: c[name] = make_online_executor(kind, core) return c -def make_default_executor_collection( +def make_default_online_executor_collection( experimentalist_pipeline: Pipeline, experiment_runner_callable: Callable, theorist_estimator: BaseEstimator, -) -> ExecutorCollection: +): """ Make the default AER executor collection. @@ -265,7 +170,7 @@ def make_default_executor_collection( >>> def experiment_runner_(x): ... return 2 * x + 1 >>> theorist_estimator_ = LinearRegression() - >>> c = make_default_executor_collection( + >>> c = make_default_online_executor_collection( ... experimentalist_pipeline=experimentalist_pipeline_, ... theorist_estimator=theorist_estimator_, ... experiment_runner_callable=experiment_runner_ @@ -311,7 +216,7 @@ def make_default_executor_collection( """ - c = make_executor_collection( + c = make_online_executor_collection( [ ("experimentalist", "experimentalist", experimentalist_pipeline), ("experiment_runner", "experiment_runner", experiment_runner_callable), diff --git a/autora/controller/planner.py b/autora/controller/planner.py index e144309d..7fbd2c14 100644 --- a/autora/controller/planner.py +++ b/autora/controller/planner.py @@ -1,39 +1,24 @@ """ -Functions which look at state and output the next function to execute. +Functions which look at state and output the next function name to execute. """ import random -from autora.controller.protocol.v1 import ( - ExecutorCollection, - ResultKind, - SupportsControllerStateHistory, -) +from autora.controller.protocol.v1 import ResultKind, SupportsControllerStateHistory -def full_cycle_planner(_, executor_collection: ExecutorCollection): +def full_cycle_planner(_): """Always returns the `full_cycle` method. Examples: - We simulate a productive executor_collection using a simple dict - >>> executor_collection_ = dict( - ... experimentalist = "experimentalist", - ... experiment_runner = "experiment_runner", - ... theorist = "theorist", - ... full_cycle = "full_cycle", - ... ) - The full_cycle_planner always returns the full cycle Executor - >>> full_cycle_planner([], executor_collection_) + >>> full_cycle_planner([]) 'full_cycle' """ - return executor_collection["full_cycle"] + return "full_cycle" -def last_result_kind_planner( - state: SupportsControllerStateHistory, - executor_collection: ExecutorCollection, -): +def last_result_kind_planner(state: SupportsControllerStateHistory): """ Chooses the operation based on the last result, e.g. new theory -> run experimentalist. @@ -44,31 +29,24 @@ def last_result_kind_planner( >>> from autora.controller.state import History >>> state_ = History() - We simulate a productive executor_collection using a dict - >>> executor_collection_ = dict( - ... experimentalist = "experimentalist", - ... experiment_runner = "experiment_runner", - ... theorist = "theorist", - ... ) - Based on the results available in the state, we can get the next kind of executor we need. When we have no results of any kind, we get an experimentalist: - >>> last_result_kind_planner(state_, executor_collection_) + >>> last_result_kind_planner(state_) 'experimentalist' ... or if we had produced conditions, then we could run an experiment >>> state_ = state_.update(conditions=["some condition"]) - >>> last_result_kind_planner(state_, executor_collection_) + >>> last_result_kind_planner(state_) 'experiment_runner' ... or if we last produced observations, then we could now run the theorist: >>> state_ = state_.update(observations=["some observation"]) - >>> last_result_kind_planner(state_, executor_collection_) + >>> last_result_kind_planner(state_) 'theorist' ... or if we last produced a theory, then we could now run the experimentalist: >>> state_ = state_.update(theories=["some theory"]) - >>> last_result_kind_planner(state_, executor_collection_) + >>> last_result_kind_planner(state_) 'experimentalist' """ @@ -82,17 +60,17 @@ def last_result_kind_planner( except IndexError: last_result_kind = None - callback = { - None: executor_collection["experimentalist"], - ResultKind.THEORY: executor_collection["experimentalist"], - ResultKind.CONDITION: executor_collection["experiment_runner"], - ResultKind.OBSERVATION: executor_collection["theorist"], + executor_name = { + None: "experimentalist", + ResultKind.THEORY: "experimentalist", + ResultKind.CONDITION: "experiment_runner", + ResultKind.OBSERVATION: "theorist", }[last_result_kind] - return callback + return executor_name -def random_operation_planner(_, executor_collection: ExecutorCollection): +def random_operation_planner(_): """ Chooses a random operation, ignoring any data which already exist. @@ -114,14 +92,13 @@ def random_operation_planner(_, executor_collection: ExecutorCollection): Now we can begin to see which operations are returned by the planner. The first (for this seed) is the theorist. (The first argument is provided for compatibility with the protocol, but is ignored.) - >>> random_operation_planner([], executor_collection_) + >>> random_operation_planner([]) 'theorist' If we evaluate again, a random executor will be suggested each time - >>> [random_operation_planner([], executor_collection_) for i in range(5)] + >>> [random_operation_planner([]) for i in range(5)] ['experimentalist', 'experimentalist', 'theorist', 'experiment_runner', 'experimentalist'] """ - all_executors = list(executor_collection.values()) - choice = random.choice(all_executors) + choice = random.choice(["experimentalist", "experiment_runner", "theorist"]) return choice diff --git a/autora/controller/protocol/v1.py b/autora/controller/protocol/v1.py index 69dd5a93..37ecbb38 100644 --- a/autora/controller/protocol/v1.py +++ b/autora/controller/protocol/v1.py @@ -146,3 +146,8 @@ def __call__(self, __state: State) -> State: ExecutorCollection = Mapping[str, Executor] + + +class ExperimentRunner(Protocol): + def __call__(self, __x: ArrayLike) -> ArrayLike: + ... From dcef974fed6cbec4a5095b960ed451bc73a4eda9 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 21 Mar 2023 18:11:46 -0400 Subject: [PATCH 010/446] refactor: remove redundant ExecutorCollection --- autora/controller/protocol/v1.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/autora/controller/protocol/v1.py b/autora/controller/protocol/v1.py index 37ecbb38..69dd5a93 100644 --- a/autora/controller/protocol/v1.py +++ b/autora/controller/protocol/v1.py @@ -146,8 +146,3 @@ def __call__(self, __state: State) -> State: ExecutorCollection = Mapping[str, Executor] - - -class ExperimentRunner(Protocol): - def __call__(self, __x: ArrayLike) -> ArrayLike: - ... From 24a12bef48de6a70ca17627e4bbbde958578a25a Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 21 Mar 2023 18:13:56 -0400 Subject: [PATCH 011/446] refactor: add typeVar for executorName --- autora/controller/protocol/v1.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/autora/controller/protocol/v1.py b/autora/controller/protocol/v1.py index 69dd5a93..26c1e250 100644 --- a/autora/controller/protocol/v1.py +++ b/autora/controller/protocol/v1.py @@ -145,4 +145,6 @@ def __call__(self, __state: State) -> State: ... -ExecutorCollection = Mapping[str, Executor] +ExecutorName = TypeVar("ExecutorName", bound=str) + +ExecutorCollection = Mapping[ExecutorName, Executor] From a22ff46e7e6f803c38bf5ba2100da0fe2084a4d1 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 21 Mar 2023 18:15:30 -0400 Subject: [PATCH 012/446] refactor: simplify planner to use simple dicts with names --- autora/controller/planner.py | 63 ++++++++++++------------------------ 1 file changed, 20 insertions(+), 43 deletions(-) diff --git a/autora/controller/planner.py b/autora/controller/planner.py index e144309d..7fbd2c14 100644 --- a/autora/controller/planner.py +++ b/autora/controller/planner.py @@ -1,39 +1,24 @@ """ -Functions which look at state and output the next function to execute. +Functions which look at state and output the next function name to execute. """ import random -from autora.controller.protocol.v1 import ( - ExecutorCollection, - ResultKind, - SupportsControllerStateHistory, -) +from autora.controller.protocol.v1 import ResultKind, SupportsControllerStateHistory -def full_cycle_planner(_, executor_collection: ExecutorCollection): +def full_cycle_planner(_): """Always returns the `full_cycle` method. Examples: - We simulate a productive executor_collection using a simple dict - >>> executor_collection_ = dict( - ... experimentalist = "experimentalist", - ... experiment_runner = "experiment_runner", - ... theorist = "theorist", - ... full_cycle = "full_cycle", - ... ) - The full_cycle_planner always returns the full cycle Executor - >>> full_cycle_planner([], executor_collection_) + >>> full_cycle_planner([]) 'full_cycle' """ - return executor_collection["full_cycle"] + return "full_cycle" -def last_result_kind_planner( - state: SupportsControllerStateHistory, - executor_collection: ExecutorCollection, -): +def last_result_kind_planner(state: SupportsControllerStateHistory): """ Chooses the operation based on the last result, e.g. new theory -> run experimentalist. @@ -44,31 +29,24 @@ def last_result_kind_planner( >>> from autora.controller.state import History >>> state_ = History() - We simulate a productive executor_collection using a dict - >>> executor_collection_ = dict( - ... experimentalist = "experimentalist", - ... experiment_runner = "experiment_runner", - ... theorist = "theorist", - ... ) - Based on the results available in the state, we can get the next kind of executor we need. When we have no results of any kind, we get an experimentalist: - >>> last_result_kind_planner(state_, executor_collection_) + >>> last_result_kind_planner(state_) 'experimentalist' ... or if we had produced conditions, then we could run an experiment >>> state_ = state_.update(conditions=["some condition"]) - >>> last_result_kind_planner(state_, executor_collection_) + >>> last_result_kind_planner(state_) 'experiment_runner' ... or if we last produced observations, then we could now run the theorist: >>> state_ = state_.update(observations=["some observation"]) - >>> last_result_kind_planner(state_, executor_collection_) + >>> last_result_kind_planner(state_) 'theorist' ... or if we last produced a theory, then we could now run the experimentalist: >>> state_ = state_.update(theories=["some theory"]) - >>> last_result_kind_planner(state_, executor_collection_) + >>> last_result_kind_planner(state_) 'experimentalist' """ @@ -82,17 +60,17 @@ def last_result_kind_planner( except IndexError: last_result_kind = None - callback = { - None: executor_collection["experimentalist"], - ResultKind.THEORY: executor_collection["experimentalist"], - ResultKind.CONDITION: executor_collection["experiment_runner"], - ResultKind.OBSERVATION: executor_collection["theorist"], + executor_name = { + None: "experimentalist", + ResultKind.THEORY: "experimentalist", + ResultKind.CONDITION: "experiment_runner", + ResultKind.OBSERVATION: "theorist", }[last_result_kind] - return callback + return executor_name -def random_operation_planner(_, executor_collection: ExecutorCollection): +def random_operation_planner(_): """ Chooses a random operation, ignoring any data which already exist. @@ -114,14 +92,13 @@ def random_operation_planner(_, executor_collection: ExecutorCollection): Now we can begin to see which operations are returned by the planner. The first (for this seed) is the theorist. (The first argument is provided for compatibility with the protocol, but is ignored.) - >>> random_operation_planner([], executor_collection_) + >>> random_operation_planner([]) 'theorist' If we evaluate again, a random executor will be suggested each time - >>> [random_operation_planner([], executor_collection_) for i in range(5)] + >>> [random_operation_planner([]) for i in range(5)] ['experimentalist', 'experimentalist', 'theorist', 'experiment_runner', 'experimentalist'] """ - all_executors = list(executor_collection.values()) - choice = random.choice(all_executors) + choice = random.choice(["experimentalist", "experiment_runner", "theorist"]) return choice From 5beb79c9c4e7a01f0087a5acebcb957c72d80b96 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 21 Mar 2023 18:17:14 -0400 Subject: [PATCH 013/446] fix: error on filter by type spec --- autora/controller/state/history.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autora/controller/state/history.py b/autora/controller/state/history.py index 7d8eba90..aa4e089b 100644 --- a/autora/controller/state/history.py +++ b/autora/controller/state/history.py @@ -359,7 +359,7 @@ def history(self) -> List[Result]: """ return self._history - def filter_by(self, kind=Set[Union[str, ResultKind]]) -> History: + def filter_by(self, kind: Set[Union[str, ResultKind]]) -> History: """ Return a copy of the object with only data belonging to the specified kinds. From 8ca6613978c69adb1ac685081d7514301a3fde43 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 08:36:21 -0400 Subject: [PATCH 014/446] refactor: remove __init__ method on controller state protocols --- autora/controller/protocol/v1.py | 31 ------------------------------- 1 file changed, 31 deletions(-) diff --git a/autora/controller/protocol/v1.py b/autora/controller/protocol/v1.py index 358fbe2a..5dfb1f1d 100644 --- a/autora/controller/protocol/v1.py +++ b/autora/controller/protocol/v1.py @@ -54,16 +54,6 @@ class SupportsDataKind(Protocol): class SupportsControllerStateFields(Protocol): """Support representing snapshots of a controller state as mutable fields.""" - def __init__( - self, - metadata: Optional[VariableCollection], - params: Optional[Dict], - conditions: Optional[Sequence[ArrayLike]], - observations=Optional[Sequence[ArrayLike]], - theories=Optional[Sequence[ArrayLike]], - ) -> None: - ... - metadata: VariableCollection params: Dict conditions: Sequence[ArrayLike] @@ -77,16 +67,6 @@ def update(self: State, **kwargs) -> State: class SupportsControllerStateProperties(Protocol): """Support representing snapshots of a controller state as immutable properties.""" - def __init__( - self, - metadata: Optional[VariableCollection], - params: Optional[Dict], - conditions: Optional[Sequence[ArrayLike]], - observations=Optional[Sequence[ArrayLike]], - theories=Optional[Sequence[ArrayLike]], - ) -> None: - ... - def update(self: State, **kwargs) -> State: ... @@ -119,17 +99,6 @@ def theories(self) -> Sequence[BaseEstimator]: class SupportsControllerStateHistory(SupportsControllerStateProperties, Protocol): """Represents controller state as a linear sequence of entries.""" - def __init__( - self, - metadata: Optional[VariableCollection], - params: Optional[Dict], - conditions: Optional[Sequence[ArrayLike]], - observations=Optional[Sequence[ArrayLike]], - theories=Optional[Sequence[ArrayLike]], - history=Optional[Sequence[SupportsDataKind]], - ) -> None: - ... - def filter_by(self: State, **kwargs) -> State: ... From 1fe22e26360c66e03c4635bc3882fdd88ac0e4cc Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 08:40:36 -0400 Subject: [PATCH 015/446] refactor: make protocol a single version --- autora/controller/{protocol/v1.py => protocol.py} | 0 autora/controller/protocol/__init__.py | 0 autora/controller/state/history.py | 2 +- autora/controller/state/param.py | 2 +- 4 files changed, 2 insertions(+), 2 deletions(-) rename autora/controller/{protocol/v1.py => protocol.py} (100%) delete mode 100644 autora/controller/protocol/__init__.py diff --git a/autora/controller/protocol/v1.py b/autora/controller/protocol.py similarity index 100% rename from autora/controller/protocol/v1.py rename to autora/controller/protocol.py diff --git a/autora/controller/protocol/__init__.py b/autora/controller/protocol/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/autora/controller/state/history.py b/autora/controller/state/history.py index aa4e089b..92623af5 100644 --- a/autora/controller/state/history.py +++ b/autora/controller/state/history.py @@ -7,7 +7,7 @@ from numpy.typing import ArrayLike from sklearn.base import BaseEstimator -from autora.controller.protocol.v1 import ResultKind, SupportsDataKind +from autora.controller.protocol import ResultKind, SupportsDataKind from autora.controller.state.snapshot import Snapshot from autora.variable import VariableCollection diff --git a/autora/controller/state/param.py b/autora/controller/state/param.py index dd8f1c78..fecea459 100644 --- a/autora/controller/state/param.py +++ b/autora/controller/state/param.py @@ -6,7 +6,7 @@ import numpy as np -from autora.controller.protocol.v1 import SupportsControllerState +from autora.controller.protocol import SupportsControllerState from autora.utils.dictionary import LazyDict From 9c3d811cb3fb83d1d2c43a790367b5445f82cb89 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 08:42:20 -0400 Subject: [PATCH 016/446] refactor: update protocol import for planner --- autora/controller/planner.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/autora/controller/planner.py b/autora/controller/planner.py index 7fbd2c14..a8617b44 100644 --- a/autora/controller/planner.py +++ b/autora/controller/planner.py @@ -3,7 +3,7 @@ """ import random -from autora.controller.protocol.v1 import ResultKind, SupportsControllerStateHistory +from autora.controller.protocol import ResultKind, SupportsControllerStateHistory def full_cycle_planner(_): From a548aa6ca465aa9c3d75064e9983315c0e11b753 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 08:49:21 -0400 Subject: [PATCH 017/446] test: add more doctests for executor --- autora/controller/executor.py | 27 +++++++++++++++++++++++---- 1 file changed, 23 insertions(+), 4 deletions(-) diff --git a/autora/controller/executor.py b/autora/controller/executor.py index 73b5d71b..7d57b7ce 100644 --- a/autora/controller/executor.py +++ b/autora/controller/executor.py @@ -12,7 +12,7 @@ import numpy as np from sklearn.base import BaseEstimator -from autora.controller.protocol.v1 import SupportsControllerState +from autora.controller.protocol import SupportsControllerState from autora.controller.state import resolve_state_params from autora.experimentalist.pipeline import Pipeline @@ -120,7 +120,6 @@ def make_online_executor_collection( ] ): """ - Make an executor collection using experimentalists, experiment_runners and theorists. Args: @@ -129,15 +128,35 @@ def make_online_executor_collection( Returns: Examples: + We can create an executor collection with one theorist: >>> from sklearn.linear_model import LinearRegression >>> make_online_executor_collection([("t", "theorist", LinearRegression())] ... ) # doctest: +ELLIPSIS {'t': functools.partial(, estimator=LinearRegression())} + ... or with two different theorists (e.g., if a Planner had several options) + >>> make_online_executor_collection([ + ... ("t0", "theorist", LinearRegression(fit_intercept=False)), + ... ("t1", "theorist", LinearRegression(fit_intercept=True)) + ... ]) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + {'t0': functools.partial(, + estimator=LinearRegression(fit_intercept=False)), + 't1': functools.partial(, + estimator=LinearRegression())} + + The same applies for experiment runners: >>> make_online_executor_collection([("er", "experiment_runner", lambda x_: x_ + 1)] ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE - {'er': functools.partial(, callable= at 0x...>)} + {'er': functools.partial(, + callable= at 0x...>)} + + ... and experimentalists: + >>> from autora.experimentalist.pipeline import make_pipeline + >>> make_online_executor_collection([("ex", "experimentalist", make_pipeline([(1,2,3)]))] + ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + {'ex': functools.partial(, + pipeline=Pipeline(steps=[('step', (1, 2, 3))], params={}))} + """ c = {} for name, kind, core in x: From c10cf6991d5e4dadcd842beccdc86fa4b25e5f85 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 09:02:27 -0400 Subject: [PATCH 018/446] feat: add fundamental controller code in a base class --- autora/controller/base.py | 108 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 autora/controller/base.py diff --git a/autora/controller/base.py b/autora/controller/base.py new file mode 100644 index 00000000..cd24ecfc --- /dev/null +++ b/autora/controller/base.py @@ -0,0 +1,108 @@ +""" The cycle controller for AER. """ +from __future__ import annotations + +import logging +from typing import Callable, Mapping, Optional, TypeVar, Union + +from autora.controller.protocol import ( + SupportsControllerState, + SupportsControllerStateHistory, +) + +_logger = logging.getLogger(__name__) + + +State = TypeVar( + "State", bound=Union[SupportsControllerState, SupportsControllerStateHistory] +) +ExecutorName = TypeVar("ExecutorName", bound=str) + + +class BaseController: + """ + Runs an experimentalist, theorist and experiment runner in a loop. + + Once initialized, the `controller` can be started by calling `next(controller)` or using the + `controller.run` method. + + Attributes: + state (CycleState or CycleStateHistory): an object which is updated during the cycle and + has the following properties: + + - `metadata` (VariableCollection) + - `params` (dict): a nested dictionary with parameters for the cycle parts. + `{ + "experimentalist": {}, + "theorist": {}, + "experiment_runner": {} + }` + - `conditions`: a list of ArrayLike objects representing all the IVs proposed by the + experimentalist + - `observations`: a list of ArrayLike objects representing all the IVs and DVs + returned by the experiment runner + - `theories`: a list of all the fitted theories (scikit-learn compatible estimators) + - `history`: (only when using CycleStateHistory) a sequential list of all the above. + + executor_collection (FullCycleExecutorCollection, OnlineExecutorCollection): an + object with interfaces for running the theorist, experimentalist and + experiment_runner. This must be compatible with the `state`. + + planner (Callable): a function which takes the `state` as input and returns one of the + `executor_collection` methods. This must be compatible with both the `state` and + the `executor_collection`. + + monitor (Callable): a function which takes the controller as input and is called at + the end of each step. + + """ + + def __init__( + self, + state: State, + planner: Callable[[State], ExecutorName], + executor_collection: Mapping[ExecutorName, Callable[[State], State]], + monitor: Optional[Callable[[State], None]] = None, + ): + """ + Args: + state: a fully instantiated controller state object compatible with the planner, + executor_collection and monitor + planner: a function which maps from the state to the next ExecutorName + executor_collection: a mapping from the ExecutorName to a callable which can operate + on the state and return an updated state + monitor: a function which takes the state object as input + """ + + self.state = state + self.planner = planner + self.executor_collection = executor_collection + self.monitor = monitor + + def run(self, num_steps: int = 1): + """Execute the next step in the cycle.""" + for i in range(num_steps): + next(self) + return self + + def __next__(self): + + # Plan + next_function_name = self.planner(self.state, self.executor_collection) + + # Map + next_function = self.executor_collection[next_function_name] + + # Execute + result = next_function(self.state) + + # Update + self.state = result + + # Monitor + if self.monitor is not None: + self.monitor(self) + + return self + + def __iter__(self): + return self From 76db0c74a174a4efaa9917eef65f70bceda96a03 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 09:44:08 -0400 Subject: [PATCH 019/446] =?UTF-8?q?feat:=20add=20core=20Cycle=20code=20?= =?UTF-8?q?=E2=80=93=20basic=20Controller?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- autora/controller/__init__.py | 273 ++++++++++++++++++++++++++++++++++ autora/controller/base.py | 49 ++---- autora/controller/cycle.py | 235 +++++++++++++++++++++++++++++ autora/controller/executor.py | 34 ++++- 4 files changed, 549 insertions(+), 42 deletions(-) create mode 100644 autora/controller/cycle.py diff --git a/autora/controller/__init__.py b/autora/controller/__init__.py index e69de29b..5479d73d 100644 --- a/autora/controller/__init__.py +++ b/autora/controller/__init__.py @@ -0,0 +1,273 @@ +""" + +Functions and classes for running the complete AER cycle. + +# Basic Usage + +Aim: Use the Controller to recover a simple ground truth theory from noisy data. + +Examples: + + >>> def ground_truth(x): + ... return x + 1 + + The space of allowed x values is the integers between 0 and 10 inclusive, + and we record the allowed output values as well. + >>> from autora.variable import VariableCollection, Variable + >>> metadata_0 = VariableCollection( + ... independent_variables=[Variable(name="x1", allowed_values=range(11))], + ... dependent_variables=[Variable(name="y", value_range=(-20, 20))], + ... ) + + The experimentalist is used to propose experiments. + Since the space of values is so restricted, we can just sample them all each time. + >>> from autora.experimentalist.pipeline import make_pipeline + >>> example_experimentalist = make_pipeline( + ... [metadata_0.independent_variables[0].allowed_values]) + + When we run a synthetic experiment, we get a reproducible noisy result: + >>> import numpy as np + >>> def get_example_synthetic_experiment_runner(): + ... rng = np.random.default_rng(seed=180) + ... def runner(x): + ... return ground_truth(x) + rng.normal(0, 0.1, x.shape) + ... return runner + >>> example_synthetic_experiment_runner = get_example_synthetic_experiment_runner() + >>> example_synthetic_experiment_runner(np.array([1])) + array([2.04339546]) + + The theorist "tries" to work out the best theory. + We use a trivial scikit-learn regressor. + >>> from sklearn.linear_model import LinearRegression + >>> example_theorist = LinearRegression() + + We initialize the Controller with the metadata describing the domain of the theory, + the theorist, experimentalist and experiment runner, + as well as a monitor which will let us know which cycle we're currently on. + >>> cycle = Cycle( + ... metadata=metadata_0, + ... theorist=example_theorist, + ... experimentalist=example_experimentalist, + ... experiment_runner=example_synthetic_experiment_runner, + ... monitor=lambda state: print(f"Generated {len(state.theories)} theories"), + ... ) + >>> cycle # doctest: +ELLIPSIS + <...Cycle object at 0x...> + + We can run the cycle by calling the run method: + >>> cycle.run(num_cycles=3) # doctest: +ELLIPSIS + Generated 1 theories + Generated 2 theories + Generated 3 theories + <...Cycle object at 0x...> + + We can now interrogate the results. The first set of conditions which went into the + experiment runner were: + >>> cycle.data.conditions[0] + array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) + + The observations include the conditions and the results: + >>> cycle.data.observations[0] + array([[ 0. , 0.92675345], + [ 1. , 1.89519928], + [ 2. , 3.08746571], + [ 3. , 3.93023943], + [ 4. , 4.95429102], + [ 5. , 6.04763988], + [ 6. , 7.20770574], + [ 7. , 7.85681519], + [ 8. , 9.05735823], + [ 9. , 10.18713406], + [10. , 10.88517906]]) + + In the third cycle (index = 2) the first and last values are different again: + >>> cycle.data.observations[2][[0,-1]] + array([[ 0. , 1.08559827], + [10. , 11.08179553]]) + + The best fit theory after the first cycle is: + >>> cycle.data.theories[0] + LinearRegression() + + >>> def report_linear_fit(m: LinearRegression, precision=4): + ... s = f"y = {np.round(m.coef_[0].item(), precision)} x " \\ + ... f"+ {np.round(m.intercept_.item(), 4)}" + ... return s + >>> report_linear_fit(cycle.data.theories[0]) + 'y = 1.0089 x + 0.9589' + + The best fit theory after all the cycles, including all the data, is: + >>> report_linear_fit(cycle.data.theories[-1]) + 'y = 0.9989 x + 1.0292' + + This is close to the ground truth theory of x -> (x + 1) + + We can also run the cycle with more control over the execution flow: + >>> next(cycle) # doctest: +ELLIPSIS + Generated 4 theories + <...Cycle object at 0x...> + + >>> next(cycle) # doctest: +ELLIPSIS + Generated 5 theories + <...Cycle object at 0x...> + + >>> next(cycle) # doctest: +ELLIPSIS + Generated 6 theories + <...Cycle object at 0x...> + + We can continue to run the cycle as long as we like, + with a simple arbitrary stopping condition like the number of theories generated: + >>> from itertools import takewhile + >>> _ = list(takewhile(lambda c: len(c.data.theories) < 9, cycle)) + Generated 7 theories + Generated 8 theories + Generated 9 theories + + ... or the precision (here we keep iterating while the difference between the gradients + of the second-last and last cycle is larger than 1x10^-3). + >>> _ = list( + ... takewhile( + ... lambda c: np.abs(c.data.theories[-1].coef_.item() - + ... c.data.theories[-2].coef_.item()) > 1e-3, + ... cycle + ... ) + ... ) + Generated 10 theories + Generated 11 theories + + ... or continue to run as long as we like: + >>> _ = cycle.run(num_cycles=100) # doctest: +ELLIPSIS + Generated 12 theories + ... + Generated 111 theories + +# Passing Static Parameters + +Aim: pass parameters to the cycle components, when they are needed. + +Examples: + + Here we have an experimentalist which takes a parameter: + >>> uniform_random_rng = np.random.default_rng(180) + >>> def uniform_random_sampler(n): + ... return uniform_random_rng.uniform(low=0, high=11, size=n) + >>> example_experimentalist_with_parameters = make_pipeline([uniform_random_sampler]) + + The cycle can handle that using the `params` keyword: + >>> cycle_with_parameters = Cycle( + ... metadata=metadata_0, + ... theorist=example_theorist, + ... experimentalist=example_experimentalist_with_parameters, + ... experiment_runner=example_synthetic_experiment_runner, + ... params={"experimentalist": {"uniform_random_sampler": {"n": 7}}} + ... ) + >>> _ = cycle_with_parameters.run() + >>> cycle_with_parameters.data.conditions[-1].flatten() + array([6.33661987, 7.34916618, 6.08596494, 2.28566582, 1.9553974 , + 5.80023149, 3.27007909]) + + For the next cycle, if we wish, we can change the parameter value: + >>> cycle_with_parameters.params["experimentalist"]["uniform_random_sampler"]\\ + ... ["n"] = 2 + >>> _ = cycle_with_parameters.run() + >>> cycle_with_parameters.data.conditions[-1].flatten() + array([10.5838232 , 9.45666031]) + +# Accessing "State-dependent Properties" + +Some experimentalists, experiment runners and theorists require access to the values +created during the cycle execution, e.g. experimentalists which require access +to the current best theory or the observed data. These data update each cycle, and +so cannot easily be set using simple `params`. + +For this case, it is possible to use "state-dependent properties" in the `params` +dictionary. These are the following strings, which will be replaced during execution by +their respective current values: + +- `"%observations.ivs[-1]%"`: the last observed independent variables +- `"%observations.dvs[-1]%"`: the last observed dependent variables +- `"%observations.ivs%"`: all the observed independent variables, +concatenated into a single array +- `"%observations.dvs%"`: all the observed dependent variables, +concatenated into a single array +- `"%theories[-1]%"`: the last fitted theorist +- `"%theories%"`: all the fitted theorists + +Examples: + + In the following example, we use the `"observations.ivs"` cycle property for an + experimentalist which excludes those conditions which have + already been seen. + + >>> metadata_1 = VariableCollection( + ... independent_variables=[Variable(name="x1", allowed_values=range(10))], + ... dependent_variables=[Variable(name="y")], + ... ) + >>> random_sampler_rng = np.random.default_rng(seed=180) + >>> def custom_random_sampler(conditions, n): + ... sampled_conditions = random_sampler_rng.choice(conditions, size=n, replace=False) + ... return sampled_conditions + >>> def exclude_conditions(conditions, excluded_conditions): + ... remaining_conditions = list(set(conditions) - set(excluded_conditions.flatten())) + ... return remaining_conditions + >>> unobserved_data_experimentalist = make_pipeline([ + ... metadata_1.independent_variables[0].allowed_values, + ... exclude_conditions, + ... custom_random_sampler + ... ] + ... ) + >>> cycle_with_state_dep_properties = Cycle( + ... metadata=metadata_1, + ... theorist=example_theorist, + ... experimentalist=unobserved_data_experimentalist, + ... experiment_runner=example_synthetic_experiment_runner, + ... params={ + ... "experimentalist": { + ... "exclude_conditions": {"excluded_conditions": "%observations.ivs%"}, + ... "custom_random_sampler": {"n": 1} + ... } + ... } + ... ) + + Now we can run the cycler to generate conditions and run experiments. The first time round, + we have the full set of 10 possible conditions to select from, and we select "2" at random: + >>> _ = cycle_with_state_dep_properties.run() + >>> cycle_with_state_dep_properties.data.conditions[-1] + array([2]) + + We can continue to run the cycler, each time we add more to the list of "excluded" options: + >>> _ = cycle_with_state_dep_properties.run(num_cycles=5) + >>> cycle_with_state_dep_properties.data.conditions + [array([2]), array([6]), array([5]), array([7]), array([3]), array([4])] + + By using the monitor callback, we can investigate what's going on with the + state-dependent properties: + >>> cycle_with_state_dep_properties.monitor = lambda state: print( + ... np.row_stack(state.observations)[:,0] # just the independent variable values + ... ) + + The monitor evaluates at the end of each cycle + and shows that we've added a new observed IV each step + >>> _ = cycle_with_state_dep_properties.run() + [2. 6. 5. 7. 3. 4. 9.] + >>> _ = cycle_with_state_dep_properties.run() + [2. 6. 5. 7. 3. 4. 9. 0.] + + We deactivate the monitor by making it "None" again. + >>> cycle_with_state_dep_properties.monitor = None + + We can continue until we've sampled all of the options: + >>> _ = cycle_with_state_dep_properties.run(num_cycles=2) + >>> cycle_with_state_dep_properties.data.conditions # doctest: +NORMALIZE_WHITESPACE + [array([2]), array([6]), array([5]), array([7]), array([3]), \ + array([4]), array([9]), array([0]), array([8]), array([1])] + + If we try to evaluate it again, the experimentalist fails, as there aren't any more + conditions which are available: + >>> cycle_with_state_dep_properties.run() # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + ValueError: a cannot be empty unless no samples are taken + +""" +from .cycle import Cycle diff --git a/autora/controller/base.py b/autora/controller/base.py index cd24ecfc..3c72196e 100644 --- a/autora/controller/base.py +++ b/autora/controller/base.py @@ -2,19 +2,12 @@ from __future__ import annotations import logging -from typing import Callable, Mapping, Optional, TypeVar, Union - -from autora.controller.protocol import ( - SupportsControllerState, - SupportsControllerStateHistory, -) +from typing import Callable, Mapping, Optional, TypeVar _logger = logging.getLogger(__name__) -State = TypeVar( - "State", bound=Union[SupportsControllerState, SupportsControllerStateHistory] -) +State = TypeVar("State") ExecutorName = TypeVar("ExecutorName", bound=str) @@ -27,31 +20,15 @@ class BaseController: Attributes: state (CycleState or CycleStateHistory): an object which is updated during the cycle and - has the following properties: - - - `metadata` (VariableCollection) - - `params` (dict): a nested dictionary with parameters for the cycle parts. - `{ - "experimentalist": {}, - "theorist": {}, - "experiment_runner": {} - }` - - `conditions`: a list of ArrayLike objects representing all the IVs proposed by the - experimentalist - - `observations`: a list of ArrayLike objects representing all the IVs and DVs - returned by the experiment runner - - `theories`: a list of all the fitted theories (scikit-learn compatible estimators) - - `history`: (only when using CycleStateHistory) a sequential list of all the above. - - executor_collection (FullCycleExecutorCollection, OnlineExecutorCollection): an - object with interfaces for running the theorist, experimentalist and - experiment_runner. This must be compatible with the `state`. - - planner (Callable): a function which takes the `state` as input and returns one of the - `executor_collection` methods. This must be compatible with both the `state` and - the `executor_collection`. - - monitor (Callable): a function which takes the controller as input and is called at + is compatible with the `executor_collection`, `planner` and `monitor`. + + planner: a function which takes the `state` as input and returns the name one of the + `executor_collection` names. + + executor_collection: a mapping between names and functions which take the state as + input and return a state. + + monitor (Callable): a function which takes the state as input and is called at the end of each step. """ @@ -87,7 +64,7 @@ def run(self, num_steps: int = 1): def __next__(self): # Plan - next_function_name = self.planner(self.state, self.executor_collection) + next_function_name = self.planner(self.state) # Map next_function = self.executor_collection[next_function_name] @@ -100,7 +77,7 @@ def __next__(self): # Monitor if self.monitor is not None: - self.monitor(self) + self.monitor(self.state) return self diff --git a/autora/controller/cycle.py b/autora/controller/cycle.py new file mode 100644 index 00000000..53afc04c --- /dev/null +++ b/autora/controller/cycle.py @@ -0,0 +1,235 @@ +""" The cycle controller for AER. """ +from __future__ import annotations + +import logging +from typing import Callable, Dict, Optional + +from sklearn.base import BaseEstimator + +from autora.controller.base import BaseController +from autora.controller.executor import make_default_online_executor_collection +from autora.controller.planner import full_cycle_planner +from autora.controller.state import Snapshot +from autora.experimentalist.pipeline import Pipeline +from autora.variable import VariableCollection + +_logger = logging.getLogger(__name__) + + +class Cycle(BaseController): + """ + Runs an experimentalist, theorist and experiment runner in a loop. + + Once initialized, the `cycle` can be started by calling `next(cycle)` or using the + `cycle.run` method. Each step runs the full AER cycle + + Attributes: + state (CycleState or CycleStateHistory): an object which is updated during the cycle and + has the following properties: + + - `metadata` (VariableCollection) + - `params` (dict): a nested dictionary with parameters for the cycle parts. + `{ + "experimentalist": {}, + "theorist": {}, + "experiment_runner": {} + }` + - `conditions`: a list of ArrayLike objects representing all the IVs proposed by the + experimentalist + - `observations`: a list of ArrayLike objects representing all the IVs and DVs + returned by the experiment runner + - `theories`: a list of all the fitted theories (scikit-learn compatible estimators) + - `history`: (only when using CycleStateHistory) a sequential list of all the above. + + executor_collection (FullCycleExecutorCollection, OnlineExecutorCollection): an + object with interfaces for running the theorist, experimentalist and + experiment_runner. This must be compatible with the `state`. + + planner (Callable): a function which takes the `state` as input and returns one of the + `executor_collection` methods. This must be compatible with both the `state` and + the `executor_collection`. + + monitor (Callable): a function which takes the controller as input and is called at + the end of each step. + + """ + + def __init__( + self, + metadata: VariableCollection, + theorist: Optional[BaseEstimator] = None, + experimentalist: Optional[Pipeline] = None, + experiment_runner: Optional[Callable] = None, + params: Optional[Dict] = None, + monitor: Optional[Callable[[Snapshot], None]] = None, + ): + """ + Args: + metadata: a description of the dependent and independent variables + theorist: a scikit-learn-compatible estimator + experimentalist: an autora.experimentalist.Pipeline + experiment_runner: a function to map independent variables onto observed dependent + variables + monitor: a function which gets read-only access to the `data` attribute at the end of + each cycle. + params: a nested dictionary with parameters to be passed to the parts of the cycle. + E.g. if the experimentalist had a step named "pool" which took an argument "n", + which you wanted to set to the value 30, then params would be set to this: + `{"experimentalist": {"pool": {"n": 30}}}` + """ + if params is None: + params = {} + state = Snapshot( + metadata=metadata, + conditions=[], + observations=[], + theories=[], + params=params, + ) + planner = full_cycle_planner + + self._experimentalist_pipeline = experimentalist + self._experiment_runner_callable = experiment_runner + self._theorist_estimator = theorist + + executor_collection = make_default_online_executor_collection( + experimentalist_pipeline=self._experimentalist_pipeline, + experiment_runner_callable=self._experiment_runner_callable, + theorist_estimator=self._theorist_estimator, + ) + + super().__init__( + state=state, + planner=planner, + executor_collection=executor_collection, + monitor=monitor, + ) + + def run(self, num_cycles: int = 1): + """Execute the next step in the cycle.""" + super().run(num_steps=num_cycles) + return self + + @property + def data(self): + """An alias for `.state`.""" + return self.state + + @property + def params(self): + """ + The parameters passed to the `theorist`, `experimentalist` and `experiment_runner`. + + Should be a nested dictionary like + ``` + {'experimentalist': {... params for experimentalist ...}, + 'experiment_runner': {... params for experiment_runner ...}, + 'theorist': {... params for theorist ...}} + ``` + + + Examples: + >>> from autora.controller.cycle import Cycle + >>> p = {"some": "params"} + >>> c = Cycle(metadata=None, theorist=None, experimentalist=None, + ... experiment_runner=None, params=p) + >>> c.params + {'some': 'params'} + + >>> c.params = {"new": "value"} + >>> c.params + {'new': 'value'} + """ + return self.state.params + + @params.setter + def params(self, value): + self.state = self.state.update(params=value) + + @property + def theorist(self): + """ + Generates new theories. + + Examples: + >>> from autora.controller.cycle import Cycle + >>> from sklearn.linear_model import LinearRegression, PoissonRegressor + >>> c = Cycle(metadata=None, theorist=LinearRegression(), experimentalist=None, + ... experiment_runner=None) + >>> c.theorist + LinearRegression() + + >>> c.theorist = PoissonRegressor() + >>> c.theorist + PoissonRegressor() + + """ + return self._theorist_estimator + + @theorist.setter + def theorist(self, value): + self._theorist_estimator = value + self.executor_collection = self._updated_executor_collection() + + @property + def experimentalist(self): + """ + Generates new experimental conditions. + + Examples: + >>> from autora.controller.cycle import Cycle + >>> from autora.experimentalist.pipeline import Pipeline + >>> c = Cycle(metadata=None, theorist=None, experiment_runner=None, + ... experimentalist=Pipeline([("pool", [11,12,13])])) + >>> c.experimentalist + Pipeline(steps=[('pool', [11, 12, 13])], params={}) + + >>> c.experimentalist = Pipeline([('pool', [21,22,23])]) + >>> c.experimentalist + Pipeline(steps=[('pool', [21, 22, 23])], params={}) + + """ + return self._experimentalist_pipeline + + @experimentalist.setter + def experimentalist(self, value): + self._experimentalist_pipeline = value + self.executor_collection = self._updated_executor_collection() + + @property + def experiment_runner(self): + """ + Generates new observations. + + Examples: + >>> from autora.controller.cycle import Cycle + >>> def plus_one(x): return x + 1 + >>> c = Cycle(metadata=None, theorist=None, experimentalist=None, + ... experiment_runner=plus_one) + >>> c.experiment_runner # doctest: +ELLIPSIS + + >>> c.experiment_runner(1) + 2 + + >>> def plus_two(x): return x + 2 + >>> c.experiment_runner = plus_two + >>> c.experiment_runner # doctest: +ELLIPSIS + + >>> c.experiment_runner(1) + 3 + + """ + return self._experiment_runner_callable + + @experiment_runner.setter + def experiment_runner(self, value): + self._experiment_runner_callable = value + self.executor_collection = self._updated_executor_collection() + + def _updated_executor_collection(self): + executor_collection = make_default_online_executor_collection( + experimentalist_pipeline=self._experimentalist_pipeline, + experiment_runner_callable=self._experiment_runner_callable, + theorist_estimator=self._theorist_estimator, + ) + return executor_collection diff --git a/autora/controller/executor.py b/autora/controller/executor.py index 7d57b7ce..26f1da83 100644 --- a/autora/controller/executor.py +++ b/autora/controller/executor.py @@ -5,9 +5,10 @@ from __future__ import annotations import copy +import logging from functools import partial from types import MappingProxyType -from typing import Callable, Iterable, Literal, Tuple, Union +from typing import Callable, Iterable, Literal, Optional, Tuple, Union import numpy as np from sklearn.base import BaseEstimator @@ -16,6 +17,8 @@ from autora.controller.state import resolve_state_params from autora.experimentalist.pipeline import Pipeline +_logger = logging.getLogger(__name__) + def experimentalist_wrapper( state: SupportsControllerState, pipeline: Pipeline @@ -80,9 +83,15 @@ def full_cycle_wrapper( return theorist_result +def no_op(state): + """An Executor which has no effect on the state.""" + _logger.warning("You called a `no_op` Executor. Returning the state unchanged.") + return state + + def make_online_executor( kind: Literal["experimentalist", "experiment_runner", "theorist"], - core: Union[Pipeline, Callable, BaseEstimator], + core: Optional[Union[Pipeline, Callable, BaseEstimator]] = None, ): """ @@ -94,7 +103,9 @@ def make_online_executor( Returns: a curried function which will run the kind of AER step requested """ - if kind == "experimentalist": + if core is None: + curried_function = no_op + elif kind == "experimentalist": assert isinstance(core, Pipeline) curried_function = partial(experimentalist_wrapper, pipeline=core) elif kind == "experiment_runner": @@ -166,9 +177,9 @@ def make_online_executor_collection( def make_default_online_executor_collection( - experimentalist_pipeline: Pipeline, - experiment_runner_callable: Callable, - theorist_estimator: BaseEstimator, + experimentalist_pipeline: Optional[Pipeline] = None, + experiment_runner_callable: Optional[Callable] = None, + theorist_estimator: Optional[BaseEstimator] = None, ): """ Make the default AER executor collection. @@ -183,6 +194,17 @@ def make_default_online_executor_collection( Examples: + + If we make the empty executor collection, all the executors are no-ops: + >>> make_default_online_executor_collection() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + mappingproxy({'experimentalist': , + 'experiment_runner': , + 'theorist': , + 'full_cycle': functools.partial(, + experimentalist_pipeline=None, + experiment_runner_callable=None, + theorist_estimator=None)}) + >>> from autora.experimentalist.pipeline import Pipeline >>> from sklearn.linear_model import LinearRegression >>> experimentalist_pipeline_ = Pipeline([('p', (1, 2))]) From 6d22b1d252b1690c0f259e2921052245e0dd01ed Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 09:48:18 -0400 Subject: [PATCH 020/446] docs: add cycle docstring --- autora/controller/cycle.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/autora/controller/cycle.py b/autora/controller/cycle.py index 53afc04c..16f04c82 100644 --- a/autora/controller/cycle.py +++ b/autora/controller/cycle.py @@ -18,10 +18,11 @@ class Cycle(BaseController): """ - Runs an experimentalist, theorist and experiment runner in a loop. + Runs an experimentalist, experiment runner, and theorist in a loop. Once initialized, the `cycle` can be started by calling `next(cycle)` or using the - `cycle.run` method. Each step runs the full AER cycle + `cycle.run` method. Each iteration runs the full AER cycle, starting with the + experimentalist and ending with the theorist. Attributes: state (CycleState or CycleStateHistory): an object which is updated during the cycle and From fbe516cb3f5688bf43f6410c78ffd05adbdfc906 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 10:43:07 -0400 Subject: [PATCH 021/446] feat: add ability to switch out planner on Controller class --- autora/controller/__init__.py | 125 +++++++++++++++++++++++++++++ autora/controller/controller.py | 104 ++++++++++++++++++++++++ autora/controller/cycle.py | 29 ------- autora/controller/protocol.py | 4 +- autora/controller/state/history.py | 21 +++-- 5 files changed, 245 insertions(+), 38 deletions(-) create mode 100644 autora/controller/controller.py diff --git a/autora/controller/__init__.py b/autora/controller/__init__.py index 5479d73d..750fd244 100644 --- a/autora/controller/__init__.py +++ b/autora/controller/__init__.py @@ -269,5 +269,130 @@ ... ValueError: a cannot be empty unless no samples are taken + +# Using Alternative Executors and Planners + +By switching out the `executor_collection` and/or the `planner`, we can specify a +different way of running the cycle. + +## Easier Seeding with a Smarter Planner + +Examples: + + In this example, we use the `Controller` which allows much more control over execution + order. It considers the last available result and picks the matching next step. This means + that seeding is relatively simple. + >>> from autora.controller import Controller + >>> def monitor(state): + ... print(f"MONITOR: Generated new {state.history[-1].kind}") + >>> cycle_with_last_result_planner = Controller( + ... monitor=monitor, + ... metadata=metadata_0, + ... theorist=example_theorist, + ... experimentalist=example_experimentalist, + ... experiment_runner=example_synthetic_experiment_runner, + ... ) + + When we run this cycle starting with no data, we generate an experimental condition first: + >>> _ = list(takewhile(lambda c: len(c.state.theories) < 2, cycle_with_last_result_planner)) + MONITOR: Generated new CONDITION + MONITOR: Generated new OBSERVATION + MONITOR: Generated new THEORY + MONITOR: Generated new CONDITION + MONITOR: Generated new OBSERVATION + MONITOR: Generated new THEORY + + However, if we seed the same cycle with observations, then its first Executor will be the + theorist: + >>> controller_with_seed_observation = Controller( + ... monitor=monitor, + ... metadata=metadata_0, + ... theorist=example_theorist, + ... experimentalist=example_experimentalist, + ... experiment_runner=example_synthetic_experiment_runner, + ... ) + >>> seed_observation = example_synthetic_experiment_runner(np.linspace(0,5,10)) + >>> controller_with_seed_observation.seed(observations=[seed_observation]) + + >>> _ = next(controller_with_seed_observation) + MONITOR: Generated new THEORY + +## Arbitrary Execution Order (Toy Example) + +In some cases, we need to change the order of execution of different steps completely. This might be + useful in cases when different experimentalists or theorists are needed at different times in + the cycle, e.g. for initial seeding, or if the _order_ of execution is the subject of the + experiment. + +Examples: + + In this example, we use a planner which suggests a different random operation at each + step, demonstrating arbitrary execution order. We do this by modifying the planner attribute + of an existing controller + + This might be useful in cases when different experimentalists or theorists are needed at + different times in the cycle, e.g. for initial seeding. + >>> from autora.controller.planner import random_operation_planner + >>> def monitor(state): + ... print(f"MONITOR: Generated new {state.history[-1].kind}") + >>> controller_with_random_planner = Controller( + ... planner=random_operation_planner, + ... monitor=monitor, + ... metadata=metadata_0, + ... theorist=example_theorist, + ... experimentalist=example_experimentalist, + ... experiment_runner=example_synthetic_experiment_runner, + ... ) + + The `random_operation_planner` depends on the python random number generator, so we seed + it first: + >>> from random import seed + >>> seed(42) + + We also want to watch the logging messages from the cycle: + >>> import logging + >>> import sys + >>> logging.basicConfig(format='%(levelname)s: %(message)s', stream=sys.stdout, + ... level=logging.INFO) + + Now we can evaluate the cycle and watch its behaviour: + >>> def step(controller_): + ... try: + ... _ = next(controller_) + ... except ValueError as e: + ... print(f"FAILED: with {e=}") + + The first step, the theorist is selected as the random Executor, and it fails because it + depends on there being observations to theorize against: + >>> step(controller_with_random_planner) # i = 0 + FAILED: with e=ValueError('need at least one array to concatenate') + + The second step, a new condition is generated. + >>> step(controller_with_random_planner) # i = 1 + MONITOR: Generated new CONDITION + + ... which is repeated on the third step as well: + >>> step(controller_with_random_planner) # i = 2 + MONITOR: Generated new CONDITION + + On the fourth step, we generate another error when trying to run the theorist: + >>> step(controller_with_random_planner) # i = 3 + FAILED: with e=ValueError('need at least one array to concatenate') + + On the fifth step, we generate a first real observation, so that the next time we try to run + a theorist we are successful: + >>> step(controller_with_random_planner) # i = 4 + MONITOR: Generated new OBSERVATION + + By the ninth iteration, there are observations which the theorist can use, and it succeeds. + >>> _ = list(takewhile(lambda c: len(c.state.theories) < 1, controller_with_random_planner)) + MONITOR: Generated new CONDITION + MONITOR: Generated new CONDITION + MONITOR: Generated new CONDITION + MONITOR: Generated new THEORY + + + """ +from .controller import Controller from .cycle import Cycle diff --git a/autora/controller/controller.py b/autora/controller/controller.py new file mode 100644 index 00000000..5ba2d842 --- /dev/null +++ b/autora/controller/controller.py @@ -0,0 +1,104 @@ +""" The cycle controller for AER. """ +from __future__ import annotations + +import logging +from typing import Callable, Dict, Optional + +from sklearn.base import BaseEstimator + +from autora.controller.base import BaseController, ExecutorName +from autora.controller.executor import make_online_executor_collection +from autora.controller.planner import last_result_kind_planner +from autora.controller.state import History +from autora.experimentalist.pipeline import Pipeline +from autora.variable import VariableCollection + +_logger = logging.getLogger(__name__) + + +class Controller(BaseController): + """ + Runs an experimentalist, experiment runner, and theorist in order. + + Once initialized, the `controller` can be started by calling `next(controller)` or using the + `controller.run` method. Each iteration runs the next logical step based on the last + result: + – if the last result doesn't exist or is a theory, run the experimentalist and add an + experimental condition as a new result, + - if the last result is an experimental condition, run the experiment runner and add an + observation as a new result, + - if the last result is an observation, run the theorist and add a theory as a new result. + + """ + + def __init__( + self, + metadata: Optional[VariableCollection], + theorist: Optional[BaseEstimator] = None, + experimentalist: Optional[Pipeline] = None, + experiment_runner: Optional[Callable] = None, + params: Optional[Dict] = None, + monitor: Optional[Callable[[History], None]] = None, + planner: Callable[[History], ExecutorName] = last_result_kind_planner, + ): + """ + Args: + metadata: a description of the dependent and independent variables + theorist: a scikit-learn-compatible estimator + experimentalist: an autora.experimentalist.Pipeline + experiment_runner: a function to map independent variables onto observed dependent + variables + monitor: a function which gets read-only access to the `data` attribute at the end of + each cycle. + params: a nested dictionary with parameters to be passed to the parts of the cycle. + E.g. if the experimentalist had a step named "pool" which took an argument "n", + which you wanted to set to the value 30, then params would be set to this: + `{"experimentalist": {"pool": {"n": 30}}}` + planner: a function which maps from the state to the next ExecutorName. The default + is to map from the last result in the state's history to the next logical step. + """ + + if params is None: + params = {} + state = History( + metadata=metadata, + conditions=[], + observations=[], + theories=[], + params=params, + ) + + self._experimentalist_pipeline = experimentalist + self._experiment_runner_callable = experiment_runner + self._theorist_estimator = theorist + + executor_collection = make_online_executor_collection( + [ + ( + "experimentalist", + "experimentalist", + self._experimentalist_pipeline, + ), + ( + "experiment_runner", + "experiment_runner", + self._experiment_runner_callable, + ), + ( + "theorist", + "theorist", + self._theorist_estimator, + ), + ] + ) + + super().__init__( + state=state, + planner=planner, + executor_collection=executor_collection, + monitor=monitor, + ) + + def seed(self, **kwargs): + for key, value in kwargs.items(): + self.state = self.state.update(**{key: value}) diff --git a/autora/controller/cycle.py b/autora/controller/cycle.py index 16f04c82..9805c475 100644 --- a/autora/controller/cycle.py +++ b/autora/controller/cycle.py @@ -24,35 +24,6 @@ class Cycle(BaseController): `cycle.run` method. Each iteration runs the full AER cycle, starting with the experimentalist and ending with the theorist. - Attributes: - state (CycleState or CycleStateHistory): an object which is updated during the cycle and - has the following properties: - - - `metadata` (VariableCollection) - - `params` (dict): a nested dictionary with parameters for the cycle parts. - `{ - "experimentalist": {}, - "theorist": {}, - "experiment_runner": {} - }` - - `conditions`: a list of ArrayLike objects representing all the IVs proposed by the - experimentalist - - `observations`: a list of ArrayLike objects representing all the IVs and DVs - returned by the experiment runner - - `theories`: a list of all the fitted theories (scikit-learn compatible estimators) - - `history`: (only when using CycleStateHistory) a sequential list of all the above. - - executor_collection (FullCycleExecutorCollection, OnlineExecutorCollection): an - object with interfaces for running the theorist, experimentalist and - experiment_runner. This must be compatible with the `state`. - - planner (Callable): a function which takes the `state` as input and returns one of the - `executor_collection` methods. This must be compatible with both the `state` and - the `executor_collection`. - - monitor (Callable): a function which takes the controller as input and is called at - the end of each step. - """ def __init__( diff --git a/autora/controller/protocol.py b/autora/controller/protocol.py index 80673854..a086a0b4 100644 --- a/autora/controller/protocol.py +++ b/autora/controller/protocol.py @@ -1,5 +1,5 @@ from enum import Enum -from typing import Any, Dict, Mapping, Optional, Protocol, Sequence, TypeVar, Union +from typing import Any, Dict, Mapping, Optional, Protocol, Sequence, Set, TypeVar, Union from numpy.typing import ArrayLike from sklearn.base import BaseEstimator @@ -99,7 +99,7 @@ def theories(self) -> Sequence[BaseEstimator]: class SupportsControllerStateHistory(SupportsControllerStateProperties, Protocol): """Represents controller state as a linear sequence of entries.""" - def filter_by(self: State, **kwargs) -> State: + def filter_by(self: State, kind: Optional[Set[Union[str, ResultKind]]]) -> State: ... @property diff --git a/autora/controller/state/history.py b/autora/controller/state/history.py index 92623af5..8386e874 100644 --- a/autora/controller/state/history.py +++ b/autora/controller/state/history.py @@ -7,12 +7,16 @@ from numpy.typing import ArrayLike from sklearn.base import BaseEstimator -from autora.controller.protocol import ResultKind, SupportsDataKind +from autora.controller.protocol import ( + ResultKind, + SupportsControllerStateHistory, + SupportsDataKind, +) from autora.controller.state.snapshot import Snapshot from autora.variable import VariableCollection -class History: +class History(SupportsControllerStateHistory): """ An immutable object for tracking the state and history of an AER cycle. """ @@ -359,7 +363,7 @@ def history(self) -> List[Result]: """ return self._history - def filter_by(self, kind: Set[Union[str, ResultKind]]) -> History: + def filter_by(self, kind: Optional[Set[Union[str, ResultKind]]]) -> History: """ Return a copy of the object with only data belonging to the specified kinds. @@ -377,10 +381,13 @@ def filter_by(self, kind: Set[Union[str, ResultKind]]) -> History: Result(data='o2', kind=ResultKind.OBSERVATION)]) """ - kind_ = {ResultKind(s) for s in kind} - filtered_history = _filter_history(self._history, kind_) - new_object = History(history=filtered_history) - return new_object + if kind is None: + return self + else: + kind_ = {ResultKind(s) for s in kind} + filtered_history = _filter_history(self._history, kind_) + new_object = History(history=filtered_history) + return new_object @dataclass(frozen=True) From 0e69aa16602b7016b27df02d75f7e0ecd414d313 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 10:44:33 -0400 Subject: [PATCH 022/446] feat: add protocol inheritance on Snapshot --- autora/controller/state/snapshot.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/autora/controller/state/snapshot.py b/autora/controller/state/snapshot.py index 51e75c96..366a41b8 100644 --- a/autora/controller/state/snapshot.py +++ b/autora/controller/state/snapshot.py @@ -5,11 +5,12 @@ from numpy.typing import ArrayLike from sklearn.base import BaseEstimator +from autora.controller.protocol import SupportsControllerStateFields from autora.variable import VariableCollection @dataclass(frozen=True) -class Snapshot: +class Snapshot(SupportsControllerStateFields): """An object passed between and updated by processing steps in the Controller.""" # Single values From 3ae3a00eed461998d9178f515bafdebaf97b869d Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 10:52:25 -0400 Subject: [PATCH 023/446] feat: fix type errors on params function --- autora/controller/state/param.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/autora/controller/state/param.py b/autora/controller/state/param.py index fecea459..874857cf 100644 --- a/autora/controller/state/param.py +++ b/autora/controller/state/param.py @@ -34,8 +34,12 @@ def _get_state_dependent_properties(state: SupportsControllerState): n_dvs = len(state.metadata.dependent_variables) state_dependent_property_dict = LazyDict( { - "%observations.ivs[-1]%": lambda: state.observations[-1][:, 0:n_ivs], - "%observations.dvs[-1]%": lambda: state.observations[-1][:, n_ivs:], + "%observations.ivs[-1]%": lambda: np.array(state.observations[-1])[ + :, 0:n_ivs + ], + "%observations.dvs[-1]%": lambda: np.array(state.observations[-1])[ + :, n_ivs: + ], "%observations.ivs%": lambda: np.row_stack( [np.empty([0, n_ivs + n_dvs])] + list(state.observations) )[:, 0:n_ivs], From c9b43a01ad742c589133fe74df855b98c28d0bd0 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 13:07:39 -0400 Subject: [PATCH 024/446] docs: update example of using base Controller for full control over the cycle --- autora/controller/__init__.py | 159 ++++++++++++++++++++++++++++++++++ 1 file changed, 159 insertions(+) diff --git a/autora/controller/__init__.py b/autora/controller/__init__.py index 750fd244..07b2688b 100644 --- a/autora/controller/__init__.py +++ b/autora/controller/__init__.py @@ -391,7 +391,166 @@ MONITOR: Generated new CONDITION MONITOR: Generated new THEORY +## Arbitrary Executors and Planners +In some cases, we need to go beyond adding different orders of planning the three +`experimentalist`, `experiment_runner` and `theorist` and build more complex cycles with +different Executors for different states. + +For instance, there might be a situation where in the +first iteration, the controller needs to gather observations over a uniform sample of the domain, +but in subsequent samples we use a different active experimentalist. + +In these cases, we need full control over (and have full responsibility for) the planners and +executors. + +Examples: + The theory we'll try to discover is: + >>> def ground_truth(x, m=3.5, c=1): + ... return m * x + c + >>> rng = np.random.default_rng(seed=180) + >>> def experiment_runner(x): + ... return ground_truth(x) + rng.normal(0, 0.1) + >>> metadata_2 = VariableCollection( + ... independent_variables=[Variable(name="x1", value_range=(-10, 10))], + ... dependent_variables=[Variable(name="y", value_range=(-100, 100))], + ... ) + + We now define a planner which chooses a different experimentalist when supplied with no data + versus some data. + >>> from autora.controller.protocol import ResultKind + >>> def seeding_planner(state): + ... # First, we have to filter the history by the kinds of objects we care about. + ... # If other objects were added later – parameters, or metadata updates – we don't want + ... # them to affect the order. + ... filtered_history = state.filter_by( + ... kind={ResultKind.CONDITION, ResultKind.OBSERVATION, ResultKind.THEORY} + ... ).history + ... + ... # In case there aren't any results, we need to have a fallback type – None + ... try: + ... last_result_kind = filtered_history[-1].kind + ... except IndexError: + ... last_result_kind = None + ... + ... # We map the result kind (or None) to the next step we care about + ... executor_name = { + ... None: "seed_experimentalist", # specify a special seeding experimentalist + ... ResultKind.THEORY: "main_experimentalist", # the usual experimentalist + ... ResultKind.CONDITION: "experiment_runner", + ... ResultKind.OBSERVATION: "theorist", + ... }[last_result_kind] + ... + ... return executor_name + + Now we can see what would happen with a particular state. If there are no results, then we get + the seed experimentalist: + >>> from autora.controller.state import History + >>> seeding_planner(History()) + 'seed_experimentalist' + + ... whereas if we have a theory to work on, we get the main experimentalist: + >>> seeding_planner(History(theories=['a theory'])) + 'main_experimentalist' + + If we had a condition last, we choose the experiment runner next: + >>> seeding_planner(History(conditions=['a condition'])) + 'experiment_runner' + + If we had an observation last, we choose the theorist next: + >>> seeding_planner(History(observations=['an observation'])) + 'theorist' + + Now we need to define an executor collection to handle the actual execution steps. + >>> from autora.experimentalist.pipeline import make_pipeline + >>> from autora.experimentalist.sampler.random import random_sampler + >>> from functools import partial + + Wen can run the seed pipeline with no data: + >>> experimentalist_which_needs_no_data = make_pipeline([ + ... np.linspace(*metadata_2.independent_variables[0].value_range, 1_000), + ... partial(random_sampler, n=10)] + ... ) + >>> np.array(experimentalist_which_needs_no_data()) + array([ 6.71671672, -0.73073073, -5.05505506, 6.13613614, 0.03003003, + 4.59459459, 2.79279279, 5.43543544, -1.65165165, 8.0980981 ]) + + + ... whereas we need some model for this sampler: + >>> from autora.experimentalist.sampler.model_disagreement import model_disagreement_sampler + >>> experimentalist_which_needs_a_theory = make_pipeline([ + ... np.linspace(*metadata_2.independent_variables[0].value_range, 1_000), + ... partial(model_disagreement_sampler, num_samples=10)]) + >>> experimentalist_which_needs_a_theory() + Traceback (most recent call last): + ... + TypeError: model_disagreement_sampler() missing 1 required positional argument: 'models' + + We'll have to provide the models during the cycle run. + + We need a reasonable theorist for this situation. For this problem, a linear regressor will + suffice. + >>> t = LinearRegression() + + Let's test the theorist for the ideal case – lots of data: + >>> X = np.linspace(*metadata_2.independent_variables[0].value_range, 1_000).reshape(-1, 1) + >>> tfitted = t.fit(X, experiment_runner(X)) + >>> f"m = {tfitted.coef_[0][0]:.2f}, c = {tfitted.intercept_[0]:.2f}" + 'm = 3.50, c = 1.04' + + This seems to work fine. + + Now we can define the executor component. We'll use a factory method to generate the + collection: + >>> from autora.controller.executor import make_online_executor_collection + >>> executor_collection = make_online_executor_collection([ + ... ("seed_experimentalist", "experimentalist", experimentalist_which_needs_no_data), + ... ("main_experimentalist", "experimentalist", experimentalist_which_needs_a_theory), + ... ("theorist", "theorist", LinearRegression()), + ... ("experiment_runner", "experiment_runner", experiment_runner), + ... ]) + + We need some special parameters to handle the main experimentalist, so we specify those: + >>> params = {"main_experimentalist": {"models": "%theories%"}} + + We now instantiate the controller: + >>> from autora.controller.base import BaseController + >>> from autora.controller.state import History + >>> c = BaseController( + ... state=History(metadata=metadata_2, params=params), + ... planner=seeding_planner, + ... executor_collection=executor_collection + ... ) + >>> c # doctest: +ELLIPSIS + <...BaseController object at 0x...> + + On the first step, we generate a condition (as we expected): + >>> next(c).state.history[-1] # doctest: +NORMALIZE_WHITESPACE + Result(data=array([ 9.4994995 , -8.17817818, -1.19119119, 8.6986987 , 7.45745746, + -6.93693694, 8.05805806, -1.45145145, -5.97597598, 1.57157157]), + kind=ResultKind.CONDITION) + + On the second step, we generate some new observations: + >>> next(c).state.history[-1] + Result(data=array([[ 9.4994995 , 34.1750017 ], + [ -8.17817818, -27.69687017], + [ -1.19119119, -3.24241572], + [ 8.6986987 , 31.3721989 ], + [ 7.45745746, 27.02785455], + [ -6.93693694, -23.35252583], + [ 8.05805806, 29.12995666], + [ -1.45145145, -4.15332663], + [ -5.97597598, -19.98916246], + [ 1.57157157, 6.42725395]]), kind=ResultKind.OBSERVATION) + + + On the third step, we generate a new theory: + >>> next(c).state.history[-1] + Result(data=LinearRegression(), kind=ResultKind.THEORY) + + On the fourth step, we switch to using the main experimentalist and generate some new + experimental data that way + >>> next(c).state.history[-1] """ from .controller import Controller From 6b499502641c99c93e81dee69e6284a7ad9d57ee Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 16:24:19 -0400 Subject: [PATCH 025/446] docs: update example of using base Controller for full control over the cycle --- autora/controller/__init__.py | 116 +++++++++++++++++----------------- 1 file changed, 59 insertions(+), 57 deletions(-) diff --git a/autora/controller/__init__.py b/autora/controller/__init__.py index 07b2688b..53645bc5 100644 --- a/autora/controller/__init__.py +++ b/autora/controller/__init__.py @@ -397,9 +397,11 @@ `experimentalist`, `experiment_runner` and `theorist` and build more complex cycles with different Executors for different states. -For instance, there might be a situation where in the -first iteration, the controller needs to gather observations over a uniform sample of the domain, -but in subsequent samples we use a different active experimentalist. +For instance, there might be a situation where at the start, the main "active" experimentalist +can't be run as it needs one or more theories as input. +Once there are at least two theories, then the active experimentalist _can_ be run. +One method to handle this is to run a "seed" experimentalist until the main experimentalist can +be used. In these cases, we need full control over (and have full responsibility for) the planners and executors. @@ -419,38 +421,31 @@ We now define a planner which chooses a different experimentalist when supplied with no data versus some data. >>> from autora.controller.protocol import ResultKind + >>> from autora.controller.planner import last_result_kind_planner >>> def seeding_planner(state): - ... # First, we have to filter the history by the kinds of objects we care about. - ... # If other objects were added later – parameters, or metadata updates – we don't want - ... # them to affect the order. - ... filtered_history = state.filter_by( - ... kind={ResultKind.CONDITION, ResultKind.OBSERVATION, ResultKind.THEORY} - ... ).history - ... - ... # In case there aren't any results, we need to have a fallback type – None - ... try: - ... last_result_kind = filtered_history[-1].kind - ... except IndexError: - ... last_result_kind = None - ... - ... # We map the result kind (or None) to the next step we care about - ... executor_name = { - ... None: "seed_experimentalist", # specify a special seeding experimentalist - ... ResultKind.THEORY: "main_experimentalist", # the usual experimentalist - ... ResultKind.CONDITION: "experiment_runner", - ... ResultKind.OBSERVATION: "theorist", - ... }[last_result_kind] - ... - ... return executor_name - - Now we can see what would happen with a particular state. If there are no results, then we get - the seed experimentalist: + ... # We're going to reuse the "last_available_result" planner, and modify its output. + ... next_function = last_result_kind_planner(state) + ... if next_function == "experimentalist": + ... if len(state.theories) >= 2: + ... return "main_experimentalist" + ... else: + ... return "seed_experimentalist" + ... else: + ... return next_function + + Now we can see what would happen with a particular state. If there are no results, + then we get the seed experimentalist: >>> from autora.controller.state import History >>> seeding_planner(History()) 'seed_experimentalist' - ... whereas if we have a theory to work on, we get the main experimentalist: - >>> seeding_planner(History(theories=['a theory'])) + ... and we also get the seed experimentalist if the last result was a theory and there are less + than two theories: + >>> seeding_planner(History(theories=['a single theory'])) + 'seed_experimentalist' + + ... whereas if we have at least two theories to work on, we get the main experimentalist: + >>> seeding_planner(History(theories=['a theory', 'another theory'])) 'main_experimentalist' If we had a condition last, we choose the experiment runner next: @@ -462,7 +457,7 @@ 'theorist' Now we need to define an executor collection to handle the actual execution steps. - >>> from autora.experimentalist.pipeline import make_pipeline + >>> from autora.experimentalist.pipeline import make_pipeline, Pipeline >>> from autora.experimentalist.sampler.random import random_sampler >>> from functools import partial @@ -478,9 +473,9 @@ ... whereas we need some model for this sampler: >>> from autora.experimentalist.sampler.model_disagreement import model_disagreement_sampler - >>> experimentalist_which_needs_a_theory = make_pipeline([ - ... np.linspace(*metadata_2.independent_variables[0].value_range, 1_000), - ... partial(model_disagreement_sampler, num_samples=10)]) + >>> experimentalist_which_needs_a_theory = Pipeline([ + ... ('pool', np.linspace(*metadata_2.independent_variables[0].value_range, 1_000)), + ... ('sampler', partial(model_disagreement_sampler, num_samples=5)),]) >>> experimentalist_which_needs_a_theory() Traceback (most recent call last): ... @@ -511,7 +506,11 @@ ... ]) We need some special parameters to handle the main experimentalist, so we specify those: - >>> params = {"main_experimentalist": {"models": "%theories%"}} + >>> params = {"experimentalist": {"sampler": {"models": "%theories%"}}} + + Warning: the dictionary `{"sampler": {"models": "%theories%"}}` above is shared by + both the seed and main experimentalists. This behavior may change in future to allow separate + parameter dictionaries for each executor in the collection. We now instantiate the controller: >>> from autora.controller.base import BaseController @@ -524,33 +523,36 @@ >>> c # doctest: +ELLIPSIS <...BaseController object at 0x...> - On the first step, we generate a condition (as we expected): + >>> class PrintHandler(logging.Handler): + ... def emit(self, record): + ... print(self.format(record)) + + On the first step, we generate a condition sampled randomly across the whole domain (as we + expected): >>> next(c).state.history[-1] # doctest: +NORMALIZE_WHITESPACE Result(data=array([ 9.4994995 , -8.17817818, -1.19119119, 8.6986987 , 7.45745746, -6.93693694, 8.05805806, -1.45145145, -5.97597598, 1.57157157]), kind=ResultKind.CONDITION) - On the second step, we generate some new observations: - >>> next(c).state.history[-1] - Result(data=array([[ 9.4994995 , 34.1750017 ], - [ -8.17817818, -27.69687017], - [ -1.19119119, -3.24241572], - [ 8.6986987 , 31.3721989 ], - [ 7.45745746, 27.02785455], - [ -6.93693694, -23.35252583], - [ 8.05805806, 29.12995666], - [ -1.45145145, -4.15332663], - [ -5.97597598, -19.98916246], - [ 1.57157157, 6.42725395]]), kind=ResultKind.OBSERVATION) - - - On the third step, we generate a new theory: - >>> next(c).state.history[-1] - Result(data=LinearRegression(), kind=ResultKind.THEORY) - - On the fourth step, we switch to using the main experimentalist and generate some new - experimental data that way - >>> next(c).state.history[-1] + After three more steps, we generate a new condition, which again is sampled across the whole + domain. Here we iterate the controller until we've got two sets of conditions: + >>> _ = list(takewhile(lambda c: len(c.state.conditions) < 2, c)) + >>> c.state.history[-1] # doctest: +NORMALIZE_WHITESPACE + Result(data=array([ 1.57157157, -3.93393393, -0.47047047, -4.47447447, 8.43843844, + 6.17617618, -3.49349349, -8.998999 , 4.93493493, 2.25225225]), + kind=ResultKind.CONDITION) + + Once we have two theories: + >>> _ = list(takewhile(lambda c: len(c.state.theories) < 2, c)) + >>> c.state.theories + [LinearRegression(), LinearRegression()] + + ... when we run the next step, we'll get the main experimentalist, which samples five points + from the extreme parts of the problem domain where the disagreement between the two theories + is the greatest: + >>> next(c).state.history[-1] # doctest: +NORMALIZE_WHITESPACE + Result(data=array([-10. , -9.97997998, -9.95995996, -9.93993994, -9.91991992]), + kind=ResultKind.CONDITION) """ from .controller import Controller From 2292df3db68e7ec2f96e7b60acf0a78d35eea4e6 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 16:48:45 -0400 Subject: [PATCH 026/446] revert: remove unnecessary Protocol definition --- autora/controller/protocol.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/autora/controller/protocol.py b/autora/controller/protocol.py index eaee1d4c..5dfb1f1d 100644 --- a/autora/controller/protocol.py +++ b/autora/controller/protocol.py @@ -105,13 +105,3 @@ def filter_by(self: State, **kwargs) -> State: @property def history(self) -> Sequence[SupportsDataKind]: ... - - -class Executor(Protocol): - """A Callable which, given some state, returns an updated state.""" - - def __call__(self, __state: State) -> State: - ... - - -ExecutorCollection = Dict[str, Executor] From ed9a4e88c9d0f7203d2178a9f179552a01b8d425 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 17:01:05 -0400 Subject: [PATCH 027/446] docs: add missing docstrings. --- autora/controller/executor.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/autora/controller/executor.py b/autora/controller/executor.py index 7d57b7ce..e9f7a9e0 100644 --- a/autora/controller/executor.py +++ b/autora/controller/executor.py @@ -40,7 +40,7 @@ def experimentalist_wrapper( def experiment_runner_wrapper( state: SupportsControllerState, callable: Callable ) -> SupportsControllerState: - """Interface for running the experiment runner callable""" + """Interface for running the experiment runner callable.""" params = resolve_state_params(state).get("experiment_runner", dict()) x = state.conditions[-1] y = callable(x, **params) @@ -52,6 +52,7 @@ def experiment_runner_wrapper( def theorist_wrapper( state: SupportsControllerState, estimator: BaseEstimator ) -> SupportsControllerState: + """Interface for running the theorist estimator given some State.""" params = resolve_state_params(state).get("theorist", dict()) metadata = state.metadata observations = state.observations @@ -72,6 +73,7 @@ def full_cycle_wrapper( experiment_runner_callable: Callable, theorist_estimator: BaseEstimator, ) -> SupportsControllerState: + """Interface for running the full AER cycle.""" experimentalist_result = experimentalist_wrapper(state, experimentalist_pipeline) experiment_runner_result = experiment_runner_wrapper( experimentalist_result, experiment_runner_callable From f2e63aa76a80c6e81d3e2fe9ffbff0f9c6ce1868 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 17:13:59 -0400 Subject: [PATCH 028/446] feat: merge upstream changes to Executor --- autora/controller/executor.py | 34 ++++++++++++++++++++++++++++------ 1 file changed, 28 insertions(+), 6 deletions(-) diff --git a/autora/controller/executor.py b/autora/controller/executor.py index e9f7a9e0..4889f849 100644 --- a/autora/controller/executor.py +++ b/autora/controller/executor.py @@ -5,9 +5,10 @@ from __future__ import annotations import copy +import logging from functools import partial from types import MappingProxyType -from typing import Callable, Iterable, Literal, Tuple, Union +from typing import Callable, Iterable, Literal, Optional, Tuple, Union import numpy as np from sklearn.base import BaseEstimator @@ -16,6 +17,8 @@ from autora.controller.state import resolve_state_params from autora.experimentalist.pipeline import Pipeline +_logger = logging.getLogger(__name__) + def experimentalist_wrapper( state: SupportsControllerState, pipeline: Pipeline @@ -82,9 +85,15 @@ def full_cycle_wrapper( return theorist_result +def no_op(state): + """An Executor which has no effect on the state.""" + _logger.warning("You called a `no_op` Executor. Returning the state unchanged.") + return state + + def make_online_executor( kind: Literal["experimentalist", "experiment_runner", "theorist"], - core: Union[Pipeline, Callable, BaseEstimator], + core: Optional[Union[Pipeline, Callable, BaseEstimator]] = None, ): """ @@ -96,7 +105,9 @@ def make_online_executor( Returns: a curried function which will run the kind of AER step requested """ - if kind == "experimentalist": + if core is None: + curried_function = no_op + elif kind == "experimentalist": assert isinstance(core, Pipeline) curried_function = partial(experimentalist_wrapper, pipeline=core) elif kind == "experiment_runner": @@ -168,9 +179,9 @@ def make_online_executor_collection( def make_default_online_executor_collection( - experimentalist_pipeline: Pipeline, - experiment_runner_callable: Callable, - theorist_estimator: BaseEstimator, + experimentalist_pipeline: Optional[Pipeline] = None, + experiment_runner_callable: Optional[Callable] = None, + theorist_estimator: Optional[BaseEstimator] = None, ): """ Make the default AER executor collection. @@ -185,6 +196,17 @@ def make_default_online_executor_collection( Examples: + + If we make the empty executor collection, all the executors are no-ops: + >>> make_default_online_executor_collection() # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + mappingproxy({'experimentalist': , + 'experiment_runner': , + 'theorist': , + 'full_cycle': functools.partial(, + experimentalist_pipeline=None, + experiment_runner_callable=None, + theorist_estimator=None)}) + >>> from autora.experimentalist.pipeline import Pipeline >>> from sklearn.linear_model import LinearRegression >>> experimentalist_pipeline_ = Pipeline([('p', (1, 2))]) From 51437641725eb59b522acd888f7b0d65cafe81b9 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 17:22:13 -0400 Subject: [PATCH 029/446] docs: update type hints and inheritance of History and Snapshot --- autora/controller/protocol.py | 4 ++-- autora/controller/state/history.py | 21 ++++++++++++++------- autora/controller/state/snapshot.py | 3 ++- 3 files changed, 18 insertions(+), 10 deletions(-) diff --git a/autora/controller/protocol.py b/autora/controller/protocol.py index 5dfb1f1d..aeac873d 100644 --- a/autora/controller/protocol.py +++ b/autora/controller/protocol.py @@ -1,5 +1,5 @@ from enum import Enum -from typing import Any, Dict, Optional, Protocol, Sequence, TypeVar, Union +from typing import Any, Dict, Optional, Protocol, Sequence, Set, TypeVar, Union from numpy.typing import ArrayLike from sklearn.base import BaseEstimator @@ -99,7 +99,7 @@ def theories(self) -> Sequence[BaseEstimator]: class SupportsControllerStateHistory(SupportsControllerStateProperties, Protocol): """Represents controller state as a linear sequence of entries.""" - def filter_by(self: State, **kwargs) -> State: + def filter_by(self: State, kind: Optional[Set[Union[str, ResultKind]]]) -> State: ... @property diff --git a/autora/controller/state/history.py b/autora/controller/state/history.py index 92623af5..8386e874 100644 --- a/autora/controller/state/history.py +++ b/autora/controller/state/history.py @@ -7,12 +7,16 @@ from numpy.typing import ArrayLike from sklearn.base import BaseEstimator -from autora.controller.protocol import ResultKind, SupportsDataKind +from autora.controller.protocol import ( + ResultKind, + SupportsControllerStateHistory, + SupportsDataKind, +) from autora.controller.state.snapshot import Snapshot from autora.variable import VariableCollection -class History: +class History(SupportsControllerStateHistory): """ An immutable object for tracking the state and history of an AER cycle. """ @@ -359,7 +363,7 @@ def history(self) -> List[Result]: """ return self._history - def filter_by(self, kind: Set[Union[str, ResultKind]]) -> History: + def filter_by(self, kind: Optional[Set[Union[str, ResultKind]]]) -> History: """ Return a copy of the object with only data belonging to the specified kinds. @@ -377,10 +381,13 @@ def filter_by(self, kind: Set[Union[str, ResultKind]]) -> History: Result(data='o2', kind=ResultKind.OBSERVATION)]) """ - kind_ = {ResultKind(s) for s in kind} - filtered_history = _filter_history(self._history, kind_) - new_object = History(history=filtered_history) - return new_object + if kind is None: + return self + else: + kind_ = {ResultKind(s) for s in kind} + filtered_history = _filter_history(self._history, kind_) + new_object = History(history=filtered_history) + return new_object @dataclass(frozen=True) diff --git a/autora/controller/state/snapshot.py b/autora/controller/state/snapshot.py index 51e75c96..366a41b8 100644 --- a/autora/controller/state/snapshot.py +++ b/autora/controller/state/snapshot.py @@ -5,11 +5,12 @@ from numpy.typing import ArrayLike from sklearn.base import BaseEstimator +from autora.controller.protocol import SupportsControllerStateFields from autora.variable import VariableCollection @dataclass(frozen=True) -class Snapshot: +class Snapshot(SupportsControllerStateFields): """An object passed between and updated by processing steps in the Controller.""" # Single values From 2d8a81368e036213f23dc9b5bae684acca48cb7b Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 17:27:27 -0400 Subject: [PATCH 030/446] merge: upgrades from state --- autora/controller/state/history.py | 21 ++++++++++++++------- autora/controller/state/snapshot.py | 3 ++- 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/autora/controller/state/history.py b/autora/controller/state/history.py index 92623af5..8386e874 100644 --- a/autora/controller/state/history.py +++ b/autora/controller/state/history.py @@ -7,12 +7,16 @@ from numpy.typing import ArrayLike from sklearn.base import BaseEstimator -from autora.controller.protocol import ResultKind, SupportsDataKind +from autora.controller.protocol import ( + ResultKind, + SupportsControllerStateHistory, + SupportsDataKind, +) from autora.controller.state.snapshot import Snapshot from autora.variable import VariableCollection -class History: +class History(SupportsControllerStateHistory): """ An immutable object for tracking the state and history of an AER cycle. """ @@ -359,7 +363,7 @@ def history(self) -> List[Result]: """ return self._history - def filter_by(self, kind: Set[Union[str, ResultKind]]) -> History: + def filter_by(self, kind: Optional[Set[Union[str, ResultKind]]]) -> History: """ Return a copy of the object with only data belonging to the specified kinds. @@ -377,10 +381,13 @@ def filter_by(self, kind: Set[Union[str, ResultKind]]) -> History: Result(data='o2', kind=ResultKind.OBSERVATION)]) """ - kind_ = {ResultKind(s) for s in kind} - filtered_history = _filter_history(self._history, kind_) - new_object = History(history=filtered_history) - return new_object + if kind is None: + return self + else: + kind_ = {ResultKind(s) for s in kind} + filtered_history = _filter_history(self._history, kind_) + new_object = History(history=filtered_history) + return new_object @dataclass(frozen=True) diff --git a/autora/controller/state/snapshot.py b/autora/controller/state/snapshot.py index 51e75c96..366a41b8 100644 --- a/autora/controller/state/snapshot.py +++ b/autora/controller/state/snapshot.py @@ -5,11 +5,12 @@ from numpy.typing import ArrayLike from sklearn.base import BaseEstimator +from autora.controller.protocol import SupportsControllerStateFields from autora.variable import VariableCollection @dataclass(frozen=True) -class Snapshot: +class Snapshot(SupportsControllerStateFields): """An object passed between and updated by processing steps in the Controller.""" # Single values From 926448a5c1677651a8acff7e26e27ef8dac51d76 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 17:34:44 -0400 Subject: [PATCH 031/446] docs: remove extra space in docstring --- autora/controller/cycle.py | 1 - 1 file changed, 1 deletion(-) diff --git a/autora/controller/cycle.py b/autora/controller/cycle.py index 9805c475..90221069 100644 --- a/autora/controller/cycle.py +++ b/autora/controller/cycle.py @@ -99,7 +99,6 @@ def params(self): 'theorist': {... params for theorist ...}} ``` - Examples: >>> from autora.controller.cycle import Cycle >>> p = {"some": "params"} From 506e73cd2aee1a2c2aa2da1a67707927847d435e Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 17:45:00 -0400 Subject: [PATCH 032/446] docs: add some extra tests to get coverage to 100% --- autora/controller/executor.py | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/autora/controller/executor.py b/autora/controller/executor.py index 4889f849..29812cc7 100644 --- a/autora/controller/executor.py +++ b/autora/controller/executor.py @@ -86,7 +86,15 @@ def full_cycle_wrapper( def no_op(state): - """An Executor which has no effect on the state.""" + """ + An Executor which has no effect on the state. + + Examples: + >>> from autora.controller.state import Snapshot + >>> s = Snapshot() + >>> s_returned = no_op(s) + >>> assert s_returned is s + """ _logger.warning("You called a `no_op` Executor. Returning the state unchanged.") return state @@ -104,6 +112,18 @@ def make_online_executor( Returns: a curried function which will run the kind of AER step requested + Examples: + Initializing executors which are understood: + >>> make_online_executor("experiment_runner", lambda x: x + 1 + ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + functools.partial(, + callable= at 0x...>) + + >>> make_online_executor("not_allowed_kind", lambda x: x + 1) + Traceback (most recent call last): + ... + NotImplementedError: kind='not_allowed_kind' is not implemented for executor definitions. + """ if core is None: curried_function = no_op From b75f8a4ad1ec1be673756962a035eb96ee2aa502 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 17:47:41 -0400 Subject: [PATCH 033/446] test: update logging in param file to ensure 100% coverage --- autora/controller/state/param.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/autora/controller/state/param.py b/autora/controller/state/param.py index 874857cf..b490bf25 100644 --- a/autora/controller/state/param.py +++ b/autora/controller/state/param.py @@ -2,6 +2,7 @@ from __future__ import annotations import copy +import logging from typing import Dict, Mapping import numpy as np @@ -9,6 +10,8 @@ from autora.controller.protocol import SupportsControllerState from autora.utils.dictionary import LazyDict +_logger = logging.getLogger(__name__) + def _get_state_dependent_properties(state: SupportsControllerState): """ @@ -92,7 +95,7 @@ def _resolve_properties(params: Dict, state_dependent_properties: Mapping): ): # value is a key in the cycle_properties dictionary params_[key] = state_dependent_properties[value] else: - pass # no change needed + _logger.debug(f"leaving {params=} unchanged") return params_ From c3422e9ac58ef01d77fa4c49add62c5aa40b2c43 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 17:57:15 -0400 Subject: [PATCH 034/446] test: update tests in History.filter_by to get full coverage --- autora/controller/state/history.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/autora/controller/state/history.py b/autora/controller/state/history.py index 8386e874..73bc0322 100644 --- a/autora/controller/state/history.py +++ b/autora/controller/state/history.py @@ -363,7 +363,7 @@ def history(self) -> List[Result]: """ return self._history - def filter_by(self, kind: Optional[Set[Union[str, ResultKind]]]) -> History: + def filter_by(self, kind: Optional[Set[Union[str, ResultKind]]] = None) -> History: """ Return a copy of the object with only data belonging to the specified kinds. @@ -380,6 +380,18 @@ def filter_by(self, kind: Optional[Set[Union[str, ResultKind]]]) -> History: History([Result(data='o1', kind=ResultKind.OBSERVATION), Result(data='o2', kind=ResultKind.OBSERVATION)]) + If we don't specify any filter criteria, we get the full history back: + >>> s.filter_by() # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS + History([Result(data='from history', kind=ResultKind.METADATA), + Result(data=VariableCollection(...), kind=ResultKind.METADATA), + Result(data={'a': 'param'}, kind=ResultKind.PARAMS), + Result(data='c1', kind=ResultKind.CONDITION), + Result(data='c2', kind=ResultKind.CONDITION), + Result(data='o1', kind=ResultKind.OBSERVATION), + Result(data='o2', kind=ResultKind.OBSERVATION), + Result(data='t1', kind=ResultKind.THEORY), + Result(data='t2', kind=ResultKind.THEORY)]) + """ if kind is None: return self From c94b03c0334123ba1a4bbe6aa190003e5b992ace Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Wed, 22 Mar 2023 17:57:25 -0400 Subject: [PATCH 035/446] test: update tests in History.update to get full coverage --- autora/controller/state/history.py | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/autora/controller/state/history.py b/autora/controller/state/history.py index 73bc0322..00f844dc 100644 --- a/autora/controller/state/history.py +++ b/autora/controller/state/history.py @@ -193,8 +193,19 @@ def update( >>> s0.update(theories=['t1'], observations=['o1'], metadata={'m': 1} ... ) # doctest: +NORMALIZE_WHITESPACE History([Result(data={'m': 1}, kind=ResultKind.METADATA), - Result(data='o1', kind=ResultKind.OBSERVATION), - Result(data='t1', kind=ResultKind.THEORY)]) + Result(data='o1', kind=ResultKind.OBSERVATION), + Result(data='t1', kind=ResultKind.THEORY)]) + + We can also update with a complete history: + >>> History().update(history=[Result(data={'m': 2}, kind=ResultKind.METADATA), + ... Result(data='o1', kind=ResultKind.OBSERVATION), + ... Result(data='t1', kind=ResultKind.THEORY)], + ... conditions=['c1'] + ... ) # doctest: +NORMALIZE_WHITESPACE + History([Result(data={'m': 2}, kind=ResultKind.METADATA), + Result(data='o1', kind=ResultKind.OBSERVATION), + Result(data='t1', kind=ResultKind.THEORY), + Result(data='c1', kind=ResultKind.CONDITION)]) """ From f4c4268361dc7888405d0dd8726b42ffd0fdcd06 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 23 Mar 2023 07:34:16 -0400 Subject: [PATCH 036/446] test: update s to h in history tests --- autora/controller/state/history.py | 52 +++++++++++++++--------------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/autora/controller/state/history.py b/autora/controller/state/history.py index 00f844dc..72d34576 100644 --- a/autora/controller/state/history.py +++ b/autora/controller/state/history.py @@ -108,89 +108,89 @@ def update( Examples: The initial object is empty: - >>> s0 = History() - >>> s0 + >>> h0 = History() + >>> h0 History([]) We can update the metadata using the `.update` method: >>> from autora.variable import VariableCollection - >>> s1 = s0.update(metadata=VariableCollection()) - >>> s1 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + >>> h1 = h0.update(metadata=VariableCollection()) + >>> h1 # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE History([Result(data=VariableCollection(...), kind=ResultKind.METADATA)]) ... the original object is unchanged: - >>> s0 + >>> h0 History([]) We can update the metadata again: - >>> s2 = s1.update(metadata=VariableCollection(["some IV"])) - >>> s2._by_kind # doctest: +ELLIPSIS + >>> h2 = h1.update(metadata=VariableCollection(["some IV"])) + >>> h2._by_kind # doctest: +ELLIPSIS Snapshot(metadata=VariableCollection(independent_variables=['some IV'],...), ...) ... and we see that there is only ever one metadata object returned. Params is treated the same way as metadata: - >>> sp = s0.update(params={'first': 'params'}) - >>> sp + >>> hp = h0.update(params={'first': 'params'}) + >>> hp History([Result(data={'first': 'params'}, kind=ResultKind.PARAMS)]) ... where only the most recent "params" object is returned from the `.params` property. - >>> sp = sp.update(params={'second': 'params'}) - >>> sp.params + >>> hp = hp.update(params={'second': 'params'}) + >>> hp.params {'second': 'params'} ... however, the full history of the params objects remains available, if needed: - >>> sp # doctest: +NORMALIZE_WHITESPACE + >>> hp # doctest: +NORMALIZE_WHITESPACE History([Result(data={'first': 'params'}, kind=ResultKind.PARAMS), Result(data={'second': 'params'}, kind=ResultKind.PARAMS)]) When we update the conditions, observations or theories, a new entry is added to the history: - >>> s3 = s0.update(theories=["1st theory"]) - >>> s3 # doctest: +NORMALIZE_WHITESPACE + >>> h3 = h0.update(theories=["1st theory"]) + >>> h3 # doctest: +NORMALIZE_WHITESPACE History([Result(data='1st theory', kind=ResultKind.THEORY)]) ... so we can see the history of all the theories, for instance. - >>> s3 = s3.update(theories=["2nd theory"]) # doctest: +NORMALIZE_WHITESPACE - >>> s3 # doctest: +NORMALIZE_WHITESPACE + >>> h3 = h3.update(theories=["2nd theory"]) # doctest: +NORMALIZE_WHITESPACE + >>> h3 # doctest: +NORMALIZE_WHITESPACE History([Result(data='1st theory', kind=ResultKind.THEORY), Result(data='2nd theory', kind=ResultKind.THEORY)]) ... and the full history of theories is available using the `.theories` parameter: - >>> s3.theories + >>> h3.theories ['1st theory', '2nd theory'] The same for the observations: - >>> s4 = s0.update(observations=["1st observation"]) - >>> s4 + >>> h4 = h0.update(observations=["1st observation"]) + >>> h4 History([Result(data='1st observation', kind=ResultKind.OBSERVATION)]) - >>> s4.update(observations=["2nd observation"] + >>> h4.update(observations=["2nd observation"] ... ) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE History([Result(data='1st observation', kind=ResultKind.OBSERVATION), Result(data='2nd observation', kind=ResultKind.OBSERVATION)]) The same for the conditions: - >>> s5 = s0.update(conditions=["1st condition"]) - >>> s5 + >>> h5 = h0.update(conditions=["1st condition"]) + >>> h5 History([Result(data='1st condition', kind=ResultKind.CONDITION)]) - >>> s5.update(conditions=["2nd condition"]) # doctest: +NORMALIZE_WHITESPACE + >>> h5.update(conditions=["2nd condition"]) # doctest: +NORMALIZE_WHITESPACE History([Result(data='1st condition', kind=ResultKind.CONDITION), Result(data='2nd condition', kind=ResultKind.CONDITION)]) You can also update with multiple conditions, observations and theories: - >>> s0.update(conditions=['c1', 'c2']) # doctest: +NORMALIZE_WHITESPACE + >>> h0.update(conditions=['c1', 'c2']) # doctest: +NORMALIZE_WHITESPACE History([Result(data='c1', kind=ResultKind.CONDITION), Result(data='c2', kind=ResultKind.CONDITION)]) - >>> s0.update(theories=['t1', 't2'], metadata={'m': 1}) # doctest: +NORMALIZE_WHITESPACE + >>> h0.update(theories=['t1', 't2'], metadata={'m': 1}) # doctest: +NORMALIZE_WHITESPACE History([Result(data={'m': 1}, kind=ResultKind.METADATA), Result(data='t1', kind=ResultKind.THEORY), Result(data='t2', kind=ResultKind.THEORY)]) - >>> s0.update(theories=['t1'], observations=['o1'], metadata={'m': 1} + >>> h0.update(theories=['t1'], observations=['o1'], metadata={'m': 1} ... ) # doctest: +NORMALIZE_WHITESPACE History([Result(data={'m': 1}, kind=ResultKind.METADATA), Result(data='o1', kind=ResultKind.OBSERVATION), From cda613f753d31a9df3650f7dc3f3dcfab66a27f5 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 23 Mar 2023 07:36:19 -0400 Subject: [PATCH 037/446] test: update s to h in history tests --- autora/controller/state/history.py | 56 +++++++++++++++--------------- 1 file changed, 28 insertions(+), 28 deletions(-) diff --git a/autora/controller/state/history.py b/autora/controller/state/history.py index 72d34576..511e3e7e 100644 --- a/autora/controller/state/history.py +++ b/autora/controller/state/history.py @@ -238,21 +238,21 @@ def metadata(self) -> VariableCollection: Examples: The initial object is empty: - >>> s = History() + >>> h = History() ... and returns an emtpy metadata object - >>> s.metadata + >>> h.metadata VariableCollection(independent_variables=[], dependent_variables=[], covariates=[]) We can update the metadata using the `.update` method: >>> from autora.variable import VariableCollection - >>> s = s.update(metadata=VariableCollection(independent_variables=['some IV'])) - >>> s.metadata # doctest: +ELLIPSIS + >>> h = h.update(metadata=VariableCollection(independent_variables=['some IV'])) + >>> h.metadata # doctest: +ELLIPSIS VariableCollection(independent_variables=['some IV'], ...) We can update the metadata again: - >>> s = s.update(metadata=VariableCollection(["some other IV"])) - >>> s.metadata # doctest: +ELLIPSIS + >>> h = h.update(metadata=VariableCollection(["some other IV"])) + >>> h.metadata # doctest: +ELLIPSIS VariableCollection(independent_variables=['some other IV'], ...) ... and we see that there is only ever one metadata object returned.""" @@ -266,18 +266,18 @@ def params(self) -> Dict: Examples: Params is treated the same way as metadata: - >>> s = History() - >>> s = s.update(params={'first': 'params'}) - >>> s.params + >>> h = History() + >>> h = h.update(params={'first': 'params'}) + >>> h.params {'first': 'params'} ... where only the most recent "params" object is returned from the `.params` property. - >>> s = s.update(params={'second': 'params'}) - >>> s.params + >>> h = h.update(params={'second': 'params'}) + >>> h.params {'second': 'params'} ... however, the full history of the params objects remains available, if needed: - >>> s # doctest: +NORMALIZE_WHITESPACE + >>> h # doctest: +NORMALIZE_WHITESPACE History([Result(data={'first': 'params'}, kind=ResultKind.PARAMS), Result(data={'second': 'params'}, kind=ResultKind.PARAMS)]) """ @@ -290,13 +290,13 @@ def conditions(self) -> List[ArrayLike]: Examples: View the sequence of theories with one conditions: - >>> s = History(conditions=[(1,2,3,)]) - >>> s.conditions + >>> h = History(conditions=[(1,2,3,)]) + >>> h.conditions [(1, 2, 3)] ... or more conditions: - >>> s = s.update(conditions=[(4,5,6),(7,8,9)]) # doctest: +NORMALIZE_WHITESPACE - >>> s.conditions + >>> h = h.update(conditions=[(4,5,6),(7,8,9)]) # doctest: +NORMALIZE_WHITESPACE + >>> h.conditions [(1, 2, 3), (4, 5, 6), (7, 8, 9)] """ @@ -310,12 +310,12 @@ def observations(self) -> List[ArrayLike]: Examples: The sequence of all observations is returned - >>> s = History(observations=["1st observation"]) - >>> s.observations + >>> h = History(observations=["1st observation"]) + >>> h.observations ['1st observation'] - >>> s = s.update(observations=["2nd observation"]) - >>> s.observations # doctest: +ELLIPSIS + >>> h = h.update(observations=["2nd observation"]) + >>> h.observations # doctest: +ELLIPSIS ['1st observation', '2nd observation'] """ @@ -347,14 +347,14 @@ def history(self) -> List[Result]: Examples: We initialze some history: - >>> s = History(theories=['t1', 't2'], conditions=['c1', 'c2'], + >>> h = History(theories=['t1', 't2'], conditions=['c1', 'c2'], ... observations=['o1', 'o2'], params={'a': 'param'}, metadata=VariableCollection(), ... history=[Result("from history", ResultKind.METADATA)]) Parameters passed to the constructor are included in the history in the following order: `history`, `metadata`, `params`, `conditions`, `observations`, `theories` - >>> s.history # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + >>> h.history # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE [Result(data='from history', kind=ResultKind.METADATA), Result(data=VariableCollection(...), kind=ResultKind.METADATA), Result(data={'a': 'param'}, kind=ResultKind.PARAMS), @@ -367,8 +367,8 @@ def history(self) -> List[Result]: If we add a new value, like the params object, the updated value is added to the end of the history: - >>> s = s.update(params={'new': 'param'}) - >>> s.history # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE + >>> h = h.update(params={'new': 'param'}) + >>> h.history # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE [..., Result(data={'new': 'param'}, kind=ResultKind.PARAMS)] """ @@ -379,20 +379,20 @@ def filter_by(self, kind: Optional[Set[Union[str, ResultKind]]] = None) -> Histo Return a copy of the object with only data belonging to the specified kinds. Examples: - >>> s = History(theories=['t1', 't2'], conditions=['c1', 'c2'], + >>> h = History(theories=['t1', 't2'], conditions=['c1', 'c2'], ... observations=['o1', 'o2'], params={'a': 'param'}, metadata=VariableCollection(), ... history=[Result("from history", ResultKind.METADATA)]) - >>> s.filter_by(kind={"THEORY"}) # doctest: +NORMALIZE_WHITESPACE + >>> h.filter_by(kind={"THEORY"}) # doctest: +NORMALIZE_WHITESPACE History([Result(data='t1', kind=ResultKind.THEORY), Result(data='t2', kind=ResultKind.THEORY)]) - >>> s.filter_by(kind={ResultKind.OBSERVATION}) # doctest: +NORMALIZE_WHITESPACE + >>> h.filter_by(kind={ResultKind.OBSERVATION}) # doctest: +NORMALIZE_WHITESPACE History([Result(data='o1', kind=ResultKind.OBSERVATION), Result(data='o2', kind=ResultKind.OBSERVATION)]) If we don't specify any filter criteria, we get the full history back: - >>> s.filter_by() # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS + >>> h.filter_by() # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS History([Result(data='from history', kind=ResultKind.METADATA), Result(data=VariableCollection(...), kind=ResultKind.METADATA), Result(data={'a': 'param'}, kind=ResultKind.PARAMS), From 824722a511e7edb1aaeb89a2da05d56048b892c2 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 23 Mar 2023 17:24:51 -0400 Subject: [PATCH 038/446] refactor: make resolve_state_params take params as an explicit argument --- autora/controller/state/param.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/autora/controller/state/param.py b/autora/controller/state/param.py index b490bf25..18f0e85c 100644 --- a/autora/controller/state/param.py +++ b/autora/controller/state/param.py @@ -100,18 +100,18 @@ def _resolve_properties(params: Dict, state_dependent_properties: Mapping): return params_ -def resolve_state_params(state: SupportsControllerState) -> Dict: +def resolve_state_params(params: Dict, state: SupportsControllerState) -> Dict: """ Returns the `params` attribute of the input, with `cycle properties` resolved. Examples: >>> from autora.controller.state import History - >>> s = History(theories=["the first theory", "the second theory"], - ... params={"experimentalist": {"source": "%theories[-1]%"}}) - >>> resolve_state_params(s) + >>> params = {"experimentalist": {"source": "%theories[-1]%"}} + >>> s = History(theories=["the first theory", "the second theory"]) + >>> resolve_state_params(params, s) {'experimentalist': {'source': 'the second theory'}} """ state_dependent_properties = _get_state_dependent_properties(state) - resolved_params = _resolve_properties(state.params, state_dependent_properties) + resolved_params = _resolve_properties(params, state_dependent_properties) return resolved_params From 2a2b973345e8d29b10766420a3c429c3e77f2e90 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 23 Mar 2023 17:30:07 -0400 Subject: [PATCH 039/446] refactor: use new params in Executors --- autora/controller/executor.py | 38 +++++++++++++++++++++-------------- autora/controller/protocol.py | 4 ++-- 2 files changed, 25 insertions(+), 17 deletions(-) diff --git a/autora/controller/executor.py b/autora/controller/executor.py index 29812cc7..27f0c761 100644 --- a/autora/controller/executor.py +++ b/autora/controller/executor.py @@ -8,7 +8,7 @@ import logging from functools import partial from types import MappingProxyType -from typing import Callable, Iterable, Literal, Optional, Tuple, Union +from typing import Callable, Dict, Iterable, Literal, Optional, Tuple, Union import numpy as np from sklearn.base import BaseEstimator @@ -21,11 +21,11 @@ def experimentalist_wrapper( - state: SupportsControllerState, pipeline: Pipeline + state: SupportsControllerState, pipeline: Pipeline, params: Dict ) -> SupportsControllerState: """Interface for running the experimentalist pipeline.""" - params = resolve_state_params(state).get("experimentalist", dict()) - new_conditions = pipeline(**params) + params_ = resolve_state_params(params, state) + new_conditions = pipeline(**params_) assert isinstance(new_conditions, Iterable) # If the pipeline gives us an iterable, we need to make it into a concrete array. @@ -41,22 +41,22 @@ def experimentalist_wrapper( def experiment_runner_wrapper( - state: SupportsControllerState, callable: Callable + state: SupportsControllerState, callable: Callable, params: Dict ) -> SupportsControllerState: """Interface for running the experiment runner callable.""" - params = resolve_state_params(state).get("experiment_runner", dict()) + params_ = resolve_state_params(params, state) x = state.conditions[-1] - y = callable(x, **params) + y = callable(x, **params_) new_observations = np.column_stack([x, y]) new_state = state.update(observations=[new_observations]) return new_state def theorist_wrapper( - state: SupportsControllerState, estimator: BaseEstimator + state: SupportsControllerState, estimator: BaseEstimator, params: Dict ) -> SupportsControllerState: """Interface for running the theorist estimator given some State.""" - params = resolve_state_params(state).get("theorist", dict()) + params_ = resolve_state_params(params, state) metadata = state.metadata observations = state.observations all_observations = np.row_stack(observations) @@ -65,7 +65,7 @@ def theorist_wrapper( if y.shape[1] == 1: y = y.ravel() new_theorist = copy.deepcopy(estimator) - new_theorist.fit(x, y, **params) + new_theorist.fit(x, y, **params_) new_state = state.update(theories=[new_theorist]) return new_state @@ -75,24 +75,32 @@ def full_cycle_wrapper( experimentalist_pipeline: Pipeline, experiment_runner_callable: Callable, theorist_estimator: BaseEstimator, + params: Dict, ) -> SupportsControllerState: """Interface for running the full AER cycle.""" - experimentalist_result = experimentalist_wrapper(state, experimentalist_pipeline) + experimentalist_params = params.get("experimentalist", {}) + experimentalist_result = experimentalist_wrapper( + state, experimentalist_pipeline, experimentalist_params + ) + experiment_runner_params = params.get("experiment_runner", {}) experiment_runner_result = experiment_runner_wrapper( - experimentalist_result, experiment_runner_callable + experimentalist_result, experiment_runner_callable, experiment_runner_params + ) + theorist_params = params.get("theorist", {}) + theorist_result = theorist_wrapper( + experiment_runner_result, theorist_estimator, theorist_params ) - theorist_result = theorist_wrapper(experiment_runner_result, theorist_estimator) return theorist_result -def no_op(state): +def no_op(state, params): """ An Executor which has no effect on the state. Examples: >>> from autora.controller.state import Snapshot >>> s = Snapshot() - >>> s_returned = no_op(s) + >>> s_returned = no_op(s, {}) >>> assert s_returned is s """ _logger.warning("You called a `no_op` Executor. Returning the state unchanged.") diff --git a/autora/controller/protocol.py b/autora/controller/protocol.py index a086a0b4..9cdacfe3 100644 --- a/autora/controller/protocol.py +++ b/autora/controller/protocol.py @@ -108,9 +108,9 @@ def history(self) -> Sequence[SupportsDataKind]: class Executor(Protocol): - """A Callable which, given some state, returns an updated state.""" + """A Callable which, given some state, and some parameters, returns an updated state.""" - def __call__(self, __state: State) -> State: + def __call__(self, __state: State, __params: Dict) -> State: ... From 50ca132e4c5bd3b04087dfe7861e30cc9658fab4 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 23 Mar 2023 17:30:52 -0400 Subject: [PATCH 040/446] refactor: use new params in Controllers --- autora/controller/__init__.py | 6 +----- autora/controller/base.py | 3 ++- autora/controller/cycle.py | 6 +++--- 3 files changed, 6 insertions(+), 9 deletions(-) diff --git a/autora/controller/__init__.py b/autora/controller/__init__.py index 53645bc5..75ba0584 100644 --- a/autora/controller/__init__.py +++ b/autora/controller/__init__.py @@ -506,11 +506,7 @@ ... ]) We need some special parameters to handle the main experimentalist, so we specify those: - >>> params = {"experimentalist": {"sampler": {"models": "%theories%"}}} - - Warning: the dictionary `{"sampler": {"models": "%theories%"}}` above is shared by - both the seed and main experimentalists. This behavior may change in future to allow separate - parameter dictionaries for each executor in the collection. + >>> params = {"main_experimentalist": {"sampler": {"models": "%theories%"}}} We now instantiate the controller: >>> from autora.controller.base import BaseController diff --git a/autora/controller/base.py b/autora/controller/base.py index 3c72196e..fcb66169 100644 --- a/autora/controller/base.py +++ b/autora/controller/base.py @@ -68,9 +68,10 @@ def __next__(self): # Map next_function = self.executor_collection[next_function_name] + next_params = self.state.params.get(next_function_name, {}) # Execute - result = next_function(self.state) + result = next_function(self.state, params=next_params) # Update self.state = result diff --git a/autora/controller/cycle.py b/autora/controller/cycle.py index 90221069..7b82a91f 100644 --- a/autora/controller/cycle.py +++ b/autora/controller/cycle.py @@ -56,7 +56,7 @@ def __init__( conditions=[], observations=[], theories=[], - params=params, + params={"full_cycle": params}, ) planner = full_cycle_planner @@ -111,11 +111,11 @@ def params(self): >>> c.params {'new': 'value'} """ - return self.state.params + return self.state.params["full_cycle"] @params.setter def params(self, value): - self.state = self.state.update(params=value) + self.state = self.state.update(params={"full_cycle": value}) @property def theorist(self): From ccc9889b30a6fc3b02b2cd09f28c80ff05644ecc Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 24 Mar 2023 08:38:43 -0400 Subject: [PATCH 041/446] refactor: move plotting module from cycle to controller --- .../plot_utils.py => controller/plotting.py} | 103 ++++++++++-------- 1 file changed, 55 insertions(+), 48 deletions(-) rename autora/{cycle/plot_utils.py => controller/plotting.py} (86%) diff --git a/autora/cycle/plot_utils.py b/autora/controller/plotting.py similarity index 86% rename from autora/cycle/plot_utils.py rename to autora/controller/plotting.py index 59c7de07..20e63769 100644 --- a/autora/cycle/plot_utils.py +++ b/autora/controller/plotting.py @@ -9,7 +9,7 @@ from matplotlib.patches import Patch from matplotlib.ticker import MaxNLocator -from .simple import SimpleCycle as Cycle +from .protocol import SupportsControllerState # Change default plot styles rcParams["axes.spines.top"] = False @@ -18,41 +18,40 @@ def _get_variable_index( - cycle: Cycle, + state: SupportsControllerState, ) -> Tuple[List[Tuple[int, str, str]], List[Tuple[int, str, str]]]: """ Extracts information about independent and dependent variables from the cycle object. Returns a list of tuples of (index, name, units). The index is in reference to the column number in the observed value arrays. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run Returns: Tuple of 2 lists of tuples """ l_iv = [ - (i, s.name, s.units) - for i, s in enumerate(cycle.data.metadata.independent_variables) + (i, s.name, s.units) for i, s in enumerate(state.metadata.independent_variables) ] n_iv = len(l_iv) l_dv = [ (i + n_iv, s.name, s.units) - for i, s in enumerate(cycle.data.metadata.dependent_variables) + for i, s in enumerate(state.metadata.dependent_variables) ] return l_iv, l_dv -def _observed_to_df(cycle: Cycle) -> pd.DataFrame: +def _observed_to_df(state: SupportsControllerState) -> pd.DataFrame: """ Concatenates observation data of cycles into a single dataframe with a field "cycle" with the cycle index. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run Returns: Dataframe """ - l_observations = cycle.data.observations + l_observations = state.observations l_agg = [] for i, data in enumerate(l_observations): @@ -63,18 +62,18 @@ def _observed_to_df(cycle: Cycle) -> pd.DataFrame: return df_return -def _min_max_observations(cycle: Cycle) -> List[Tuple[float, float]]: +def _min_max_observations(state: SupportsControllerState) -> List[Tuple[float, float]]: """ Returns minimum and maximum of observed values for each independent variable. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run Returns: List of tuples """ l_return = [] - iv_index = range(len(cycle.data.metadata.independent_variables)) - l_observations = cycle.data.observations + iv_index = range(len(state.metadata.independent_variables)) + l_observations = state.observations # Get min and max of observation data # Min and max by cycle - All IVs l_mins = [np.min(s, axis=0) for s in l_observations] # Arrays by columns @@ -88,17 +87,19 @@ def _min_max_observations(cycle: Cycle) -> List[Tuple[float, float]]: return l_return -def _generate_condition_space(cycle: Cycle, steps: int = 50) -> np.array: +def _generate_condition_space( + state: SupportsControllerState, steps: int = 50 +) -> np.array: """ Generates condition space based on the minimum and maximum of all observed data in AER Cycle. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run steps: Number of steps to define the condition space Returns: np.array """ - l_min_max = _min_max_observations(cycle) + l_min_max = _min_max_observations(state) l_space = [] for min_max in l_min_max: @@ -110,17 +111,17 @@ def _generate_condition_space(cycle: Cycle, steps: int = 50) -> np.array: return l_space[0].reshape(-1, 1) -def _generate_mesh_grid(cycle: Cycle, steps: int = 50) -> np.ndarray: +def _generate_mesh_grid(state: SupportsControllerState, steps: int = 50) -> np.ndarray: """ Generates a mesh grid based on the minimum and maximum of all observed data in AER Cycle. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run steps: Number of steps to define the condition space Returns: np.ndarray """ - l_min_max = _min_max_observations(cycle) + l_min_max = _min_max_observations(state) l_space = [] for min_max in l_min_max: @@ -130,12 +131,12 @@ def _generate_mesh_grid(cycle: Cycle, steps: int = 50) -> np.ndarray: def _theory_predict( - cycle: Cycle, conditions: Sequence, predict_proba: bool = False + state: SupportsControllerState, conditions: Sequence, predict_proba: bool = False ) -> list: """ Gets theory predictions over conditions space and saves results of each cycle to a list. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run conditions: Condition space. Should be an array of grouped conditions. predict_proba: Use estimator.predict_proba method instead of estimator.predict. @@ -143,7 +144,7 @@ def _theory_predict( """ l_predictions = [] - for i, theory in enumerate(cycle.data.theories): + for i, theory in enumerate(state.theories): if not predict_proba: l_predictions.append(theory.predict(conditions)) else: @@ -181,7 +182,7 @@ def _check_replace_default_kw(default: dict, user: dict) -> dict: def plot_results_panel_2d( - cycle: Cycle, + state: SupportsControllerState, iv_name: Optional[str] = None, dv_name: Optional[str] = None, steps: int = 50, @@ -200,7 +201,7 @@ def plot_results_panel_2d( range of the observed data. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run iv_name: Independent variable name. Name should match the name instantiated in the cycle object. Default will select the first. dv_name: Single dependent variable name. Name should match the names instantiated in the @@ -260,7 +261,7 @@ def plot_results_panel_2d( d_kw[key] = _check_replace_default_kw(d1, d2) # ---Extract IVs and DV metadata and indexes--- - ivs, dvs = _get_variable_index(cycle) + ivs, dvs = _get_variable_index(state) if iv_name: iv = [s for s in ivs if s[1] == iv_name][0] else: @@ -273,16 +274,16 @@ def plot_results_panel_2d( dv_label = f"{dv[1]} {dv[2]}" # Create a dataframe of observed data from cycle - df_observed = _observed_to_df(cycle) + df_observed = _observed_to_df(state) # Generate IV space - condition_space = _generate_condition_space(cycle, steps=steps) + condition_space = _generate_condition_space(state, steps=steps) # Get theory predictions over space - l_predictions = _theory_predict(cycle, condition_space) + l_predictions = _theory_predict(state, condition_space) # Cycle Indexing - cycle_idx = list(range(len(cycle.data.theories))) + cycle_idx = list(range(len(state.theories))) if query: if isinstance(query, list): cycle_idx = [cycle_idx[s] for s in query] @@ -348,7 +349,7 @@ def plot_results_panel_2d( def plot_results_panel_3d( - cycle: Cycle, + state: SupportsControllerState, iv_names: Optional[List[str]] = None, dv_name: Optional[str] = None, steps: int = 50, @@ -368,7 +369,7 @@ def plot_results_panel_3d( Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run iv_names: List of up to 2 independent variable names. Names should match the names instantiated in the cycle object. Default will select up to the first two. dv_name: Single DV name. Name should match the names instantiated in the cycle object. @@ -388,7 +389,7 @@ def plot_results_panel_3d( Returns: matplotlib figure """ - n_cycles = len(cycle.data.theories) + n_cycles = len(state.theories) # ---Figure and plot params--- # Set defaults, check and add user supplied keywords @@ -416,7 +417,7 @@ def plot_results_panel_3d( d_kw[key] = _check_replace_default_kw(d1, d2) # ---Extract IVs and DV metadata and indexes--- - ivs, dvs = _get_variable_index(cycle) + ivs, dvs = _get_variable_index(state) if iv_names: iv = [s for s in ivs if s[1] == iv_names] else: @@ -429,13 +430,13 @@ def plot_results_panel_3d( dv_label = f"{dv[1]} {dv[2]}" # Create a dataframe of observed data from cycle - df_observed = _observed_to_df(cycle) + df_observed = _observed_to_df(state) # Generate IV Mesh Grid - x1, x2 = _generate_mesh_grid(cycle, steps=steps) + x1, x2 = _generate_mesh_grid(state, steps=steps) # Get theory predictions over space - l_predictions = _theory_predict(cycle, np.column_stack((x1.ravel(), x2.ravel()))) + l_predictions = _theory_predict(state, np.column_stack((x1.ravel(), x2.ravel()))) # Subplot configurations if n_cycles < wrap: @@ -501,29 +502,35 @@ def plot_results_panel_3d( return fig -def cycle_default_score(cycle: Cycle, x_vals: np.ndarray, y_true: np.ndarray): +def cycle_default_score( + state: SupportsControllerState, x_vals: np.ndarray, y_true: np.ndarray +): """ Calculates score for each cycle using the estimator's default scorer. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run x_vals: Test dataset independent values y_true: Test dataset dependent values Returns: List of scores by cycle """ - l_scores = [s.score(x_vals, y_true) for s in cycle.data.theories] + l_scores = [s.score(x_vals, y_true) for s in state.theories] return l_scores def cycle_specified_score( - scorer: Callable, cycle: Cycle, x_vals: np.ndarray, y_true: np.ndarray, **kwargs + scorer: Callable, + state: SupportsControllerState, + x_vals: np.ndarray, + y_true: np.ndarray, + **kwargs, ): """ Calculates score for each cycle using specified sklearn scoring function. Args: scorer: sklearn scoring function - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run x_vals: Test dataset independent values y_true: Test dataset dependent values **kwargs: Keyword arguments to send to scoring function @@ -533,9 +540,9 @@ def cycle_specified_score( """ # Get predictions if "y_pred" in inspect.signature(scorer).parameters.keys(): - l_y_pred = _theory_predict(cycle, x_vals, predict_proba=False) + l_y_pred = _theory_predict(state, x_vals, predict_proba=False) elif "y_score" in inspect.signature(scorer).parameters.keys(): - l_y_pred = _theory_predict(cycle, x_vals, predict_proba=True) + l_y_pred = _theory_predict(state, x_vals, predict_proba=True) # Score each cycle l_scores = [] @@ -546,7 +553,7 @@ def cycle_specified_score( def plot_cycle_score( - cycle: Cycle, + state: SupportsControllerState, X: np.ndarray, y_true: np.ndarray, scorer: Optional[Callable] = None, @@ -561,7 +568,7 @@ def plot_cycle_score( """ Plots scoring metrics of cycle's theories given test data. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run X: Test dataset independent values y_true: Test dataset dependent values scorer: sklearn scoring function (optional) @@ -579,13 +586,13 @@ def plot_cycle_score( # Use estimator's default scoring method if specific scorer is not supplied if scorer is None: - l_scores = cycle_default_score(cycle, X, y_true) + l_scores = cycle_default_score(state, X, y_true) else: - l_scores = cycle_specified_score(scorer, cycle, X, y_true, **scorer_kw) + l_scores = cycle_specified_score(scorer, state, X, y_true, **scorer_kw) # Plotting fig, ax = plt.subplots(figsize=figsize) - ax.plot(np.arange(len(cycle.data.theories)), l_scores, **plot_kw) + ax.plot(np.arange(len(state.theories)), l_scores, **plot_kw) # Adjusting axis limits if ylim: From dc9d118f6a80554ab27da0a37c371c76b5ca6465 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 24 Mar 2023 08:39:23 -0400 Subject: [PATCH 042/446] refactor: change imports in autora/cycle to import from controller module, and include deprecation warning --- autora/cycle/__init__.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/autora/cycle/__init__.py b/autora/cycle/__init__.py index f7682c7e..a7746d51 100644 --- a/autora/cycle/__init__.py +++ b/autora/cycle/__init__.py @@ -1,8 +1,17 @@ -from .plot_utils import ( +from warnings import warn + +from ..controller.cycle import Cycle +from ..controller.plotting import ( cycle_default_score, cycle_specified_score, plot_cycle_score, plot_results_panel_2d, plot_results_panel_3d, ) -from .simple import SimpleCycle as Cycle + +warn( + "The `autora.cycle` module is deprecated. " + "Use the new `autora.controller` module", + DeprecationWarning, + stacklevel=2, +) From 38782f2812453c7250b980e65f31bec9db4ed643 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 24 Mar 2023 08:39:35 -0400 Subject: [PATCH 043/446] chore: remove unused SimpleCycle --- autora/cycle/simple.py | 527 ----------------------------------------- 1 file changed, 527 deletions(-) delete mode 100644 autora/cycle/simple.py diff --git a/autora/cycle/simple.py b/autora/cycle/simple.py deleted file mode 100644 index fb311b2e..00000000 --- a/autora/cycle/simple.py +++ /dev/null @@ -1,527 +0,0 @@ -import copy -from collections.abc import Mapping -from dataclasses import dataclass, replace -from typing import Callable, Dict, Iterable, List, Optional - -import numpy as np -from sklearn.base import BaseEstimator - -from autora.experimentalist.pipeline import Pipeline -from autora.utils.dictionary import LazyDict -from autora.variable import VariableCollection - - -@dataclass(frozen=True) -class SimpleCycleData: - """An object passed between and updated by processing steps in the SimpleCycle.""" - - # Static - metadata: VariableCollection - - # Aggregates each cycle from the: - # ... Experimentalist - conditions: List[np.ndarray] - # ... Experiment Runner - observations: List[np.ndarray] - # ... Theorist - theories: List[BaseEstimator] - - -def _get_cycle_properties(data: SimpleCycleData): - """ - Examples: - Even with an empty data object, we can initialize the dictionary, - >>> cycle_properties = _get_cycle_properties(SimpleCycleData(metadata=VariableCollection(), - ... conditions=[], observations=[], theories=[])) - - ... but it will raise an exception if a value isn't yet available when we try to use it - >>> cycle_properties["%theories[-1]%"] # doctest: +ELLIPSIS - Traceback (most recent call last): - ... - IndexError: list index out of range - - Nevertheless, we can iterate through its keys no problem: - >>> [key for key in cycle_properties.keys()] # doctest: +NORMALIZE_WHITESPACE - ['%observations.ivs[-1]%', '%observations.dvs[-1]%', '%observations.ivs%', - '%observations.dvs%', '%theories[-1]%', '%theories%'] - - """ - - n_ivs = len(data.metadata.independent_variables) - n_dvs = len(data.metadata.dependent_variables) - cycle_property_dict = LazyDict( - { - "%observations.ivs[-1]%": lambda: data.observations[-1][:, 0:n_ivs], - "%observations.dvs[-1]%": lambda: data.observations[-1][:, n_ivs:], - "%observations.ivs%": lambda: np.row_stack( - [np.empty([0, n_ivs + n_dvs])] + data.observations - )[:, 0:n_ivs], - "%observations.dvs%": lambda: np.row_stack(data.observations)[:, n_ivs:], - "%theories[-1]%": lambda: data.theories[-1], - "%theories%": lambda: data.theories, - } - ) - return cycle_property_dict - - -class SimpleCycle: - """ - Runs an experimentalist, theorist and experiment runner in a loop. - - Once initialized, the `cycle` can be started using the `cycle.run` method - or by calling `next(cycle)`. - - The `.data` attribute is updated with the results. - - Attributes: - data (dataclass): an object which is updated during the cycle and has the following - properties: - - - `metadata` - - `conditions`: a list of np.ndarrays representing all of the IVs proposed by the - experimentalist - - `observations`: a list of np.ndarrays representing all of the IVs and DVs returned by - the experiment runner - - `theories`: a list of all the fitted theories (scikit-learn compatible estimators) - - params (dict): a nested dictionary with parameters for the cycle parts. - - `{ - "experimentalist": {}, - "theorist": {}, - "experiment_runner": {} - }` - - - Examples: - - ### Basic Usage - - Aim: Use the SimpleCycle to recover a simple ground truth theory from noisy data. - - >>> def ground_truth(x): - ... return x + 1 - - The space of allowed x values is the integers between 0 and 10 inclusive, - and we record the allowed output values as well. - >>> from autora.variable import VariableCollection, Variable - >>> metadata_0 = VariableCollection( - ... independent_variables=[Variable(name="x1", allowed_values=range(11))], - ... dependent_variables=[Variable(name="y", value_range=(-20, 20))], - ... ) - - The experimentalist is used to propose experiments. - Since the space of values is so restricted, we can just sample them all each time. - >>> from autora.experimentalist.pipeline import make_pipeline - >>> example_experimentalist = make_pipeline( - ... [metadata_0.independent_variables[0].allowed_values]) - - When we run a synthetic experiment, we get a reproducible noisy result: - >>> import numpy as np - >>> def get_example_synthetic_experiment_runner(): - ... rng = np.random.default_rng(seed=180) - ... def runner(x): - ... return ground_truth(x) + rng.normal(0, 0.1, x.shape) - ... return runner - >>> example_synthetic_experiment_runner = get_example_synthetic_experiment_runner() - >>> example_synthetic_experiment_runner(np.ndarray([1])) - array([2.04339546]) - - The theorist "tries" to work out the best theory. - We use a trivial scikit-learn regressor. - >>> from sklearn.linear_model import LinearRegression - >>> example_theorist = LinearRegression() - - We initialize the SimpleCycle with the metadata describing the domain of the theory, - the theorist, experimentalist and experiment runner, - as well as a monitor which will let us know which cycle we're currently on. - >>> cycle = SimpleCycle( - ... metadata=metadata_0, - ... theorist=example_theorist, - ... experimentalist=example_experimentalist, - ... experiment_runner=example_synthetic_experiment_runner, - ... monitor=lambda data: print(f"Generated {len(data.theories)} theories"), - ... ) - >>> cycle # doctest: +ELLIPSIS - - - We can run the cycle by calling the run method: - >>> cycle.run(num_cycles=3) # doctest: +ELLIPSIS - Generated 1 theories - Generated 2 theories - Generated 3 theories - - - We can now interrogate the results. The first set of conditions which went into the - experiment runner were: - >>> cycle.data.conditions[0] - array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) - - The observations include the conditions and the results: - >>> cycle.data.observations[0] - array([[ 0. , 0.92675345], - [ 1. , 1.89519928], - [ 2. , 3.08746571], - [ 3. , 3.93023943], - [ 4. , 4.95429102], - [ 5. , 6.04763988], - [ 6. , 7.20770574], - [ 7. , 7.85681519], - [ 8. , 9.05735823], - [ 9. , 10.18713406], - [10. , 10.88517906]]) - - In the third cycle (index = 2) the first and last values are different again: - >>> cycle.data.observations[2][[0,-1]] - array([[ 0. , 1.08559827], - [10. , 11.08179553]]) - - The best fit theory after the first cycle is: - >>> cycle.data.theories[0] - LinearRegression() - - >>> def report_linear_fit(m: LinearRegression, precision=4): - ... s = f"y = {np.round(m.coef_[0].item(), precision)} x " \\ - ... f"+ {np.round(m.intercept_.item(), 4)}" - ... return s - >>> report_linear_fit(cycle.data.theories[0]) - 'y = 1.0089 x + 0.9589' - - The best fit theory after all the cycles, including all the data, is: - >>> report_linear_fit(cycle.data.theories[-1]) - 'y = 0.9989 x + 1.0292' - - This is close to the ground truth theory of x -> (x + 1) - - We can also run the cycle with more control over the execution flow: - >>> next(cycle) # doctest: +ELLIPSIS - Generated 4 theories - - - >>> next(cycle) # doctest: +ELLIPSIS - Generated 5 theories - - - >>> next(cycle) # doctest: +ELLIPSIS - Generated 6 theories - - - We can continue to run the cycle as long as we like, - with a simple arbitrary stopping condition like the number of theories generated: - >>> from itertools import takewhile - >>> _ = list(takewhile(lambda c: len(c.data.theories) < 9, cycle)) - Generated 7 theories - Generated 8 theories - Generated 9 theories - - ... or the precision (here we keep iterating while the difference between the gradients - of the second-last and last cycle is larger than 1x10^-3). - >>> _ = list( - ... takewhile( - ... lambda c: np.abs(c.data.theories[-1].coef_.item() - - ... c.data.theories[-2].coef_.item()) > 1e-3, - ... cycle - ... ) - ... ) - Generated 10 theories - Generated 11 theories - - ... or continue to run as long as we like: - >>> _ = cycle.run(num_cycles=100) # doctest: +ELLIPSIS - Generated 12 theories - ... - Generated 111 theories - - ### Passing Static Parameters - - It's easy to pass parameters to the cycle components, if there are any needed. - Here we have an experimentalist which takes a parameter: - >>> uniform_random_rng = np.random.default_rng(180) - >>> def uniform_random_sampler(n): - ... return uniform_random_rng.uniform(low=0, high=11, size=n) - >>> example_experimentalist_with_parameters = make_pipeline([uniform_random_sampler]) - - The cycle can handle that using the `params` keyword: - >>> cycle_with_parameters = SimpleCycle( - ... metadata=metadata_0, - ... theorist=example_theorist, - ... experimentalist=example_experimentalist_with_parameters, - ... experiment_runner=example_synthetic_experiment_runner, - ... params={"experimentalist": {"uniform_random_sampler": {"n": 7}}} - ... ) - >>> _ = cycle_with_parameters.run() - >>> cycle_with_parameters.data.conditions[-1].flatten() - array([6.33661987, 7.34916618, 6.08596494, 2.28566582, 1.9553974 , - 5.80023149, 3.27007909]) - - For the next cycle, if we wish, we can change the parameter value: - >>> cycle_with_parameters.params["experimentalist"]["uniform_random_sampler"]\\ - ... ["n"] = 2 - >>> _ = cycle_with_parameters.run() - >>> cycle_with_parameters.data.conditions[-1].flatten() - array([10.5838232 , 9.45666031]) - - ### Accessing "Cycle Properties" - - Some experimentalists, experiment runners and theorists require access to the values - created during the cycle execution, e.g. experimentalists which require access - to the current best theory or the observed data. These data update each cycle, and - so cannot easily be set using simple `params`. - - For this case, it is possible to use "cycle properties" in the `params` dictionary. These - are the following strings, which will be replaced during execution by their respective - current values: - - - `"%observations.ivs[-1]%"`: the last observed independent variables - - `"%observations.dvs[-1]%"`: the last observed dependent variables - - `"%observations.ivs%"`: all the observed independent variables, - concatenated into a single array - - `"%observations.dvs%"`: all the observed dependent variables, - concatenated into a single array - - `"%theories[-1]%"`: the last fitted theorist - - `"%theories%"`: all the fitted theorists - - In the following example, we use the `"observations.ivs"` cycle property for an - experimentalist which excludes those conditions which have - already been seen. - - >>> metadata_1 = VariableCollection( - ... independent_variables=[Variable(name="x1", allowed_values=range(10))], - ... dependent_variables=[Variable(name="y")], - ... ) - >>> random_sampler_rng = np.random.default_rng(seed=180) - >>> def custom_random_sampler(conditions, n): - ... sampled_conditions = random_sampler_rng.choice(conditions, size=n, replace=False) - ... return sampled_conditions - >>> def exclude_conditions(conditions, excluded_conditions): - ... remaining_conditions = list(set(conditions) - set(excluded_conditions.flatten())) - ... return remaining_conditions - >>> unobserved_data_experimentalist = make_pipeline([ - ... metadata_1.independent_variables[0].allowed_values, - ... exclude_conditions, - ... custom_random_sampler - ... ] - ... ) - >>> cycle_with_cycle_properties = SimpleCycle( - ... metadata=metadata_1, - ... theorist=example_theorist, - ... experimentalist=unobserved_data_experimentalist, - ... experiment_runner=example_synthetic_experiment_runner, - ... params={ - ... "experimentalist": { - ... "exclude_conditions": {"excluded_conditions": "%observations.ivs%"}, - ... "custom_random_sampler": {"n": 1} - ... } - ... } - ... ) - - Now we can run the cycler to generate conditions and run experiments. The first time round, - we have the full set of 10 possible conditions to select from, and we select "2" at random: - >>> _ = cycle_with_cycle_properties.run() - >>> cycle_with_cycle_properties.data.conditions[-1] - array([2]) - - We can continue to run the cycler, each time we add more to the list of "excluded" options: - >>> _ = cycle_with_cycle_properties.run(num_cycles=5) - >>> cycle_with_cycle_properties.data.conditions - [array([2]), array([6]), array([5]), array([7]), array([3]), array([4])] - - By using the monitor callback, we can investigate what's going on with the cycle properties: - >>> cycle_with_cycle_properties.monitor = lambda data: print( - ... _get_cycle_properties(data)["%observations.ivs%"].flatten() - ... ) - - The monitor evaluates at the end of each cycle - and shows that we've added a new observed IV each step - >>> _ = cycle_with_cycle_properties.run() - [2. 6. 5. 7. 3. 4. 9.] - >>> _ = cycle_with_cycle_properties.run() - [2. 6. 5. 7. 3. 4. 9. 0.] - - We deactivate the monitor by making it "None" again. - >>> cycle_with_cycle_properties.monitor = None - - We can continue until we've sampled all of the options: - >>> _ = cycle_with_cycle_properties.run(num_cycles=2) - >>> cycle_with_cycle_properties.data.conditions # doctest: +NORMALIZE_WHITESPACE - [array([2]), array([6]), array([5]), array([7]), array([3]), \ - array([4]), array([9]), array([0]), array([8]), array([1])] - - If we try to evaluate it again, the experimentalist fails, as there aren't any more - conditions which are available: - >>> cycle_with_cycle_properties.run() # doctest: +ELLIPSIS - Traceback (most recent call last): - ... - ValueError: a cannot be empty unless no samples are taken - - """ - - def __init__( - self, - metadata: VariableCollection, - theorist, - experimentalist, - experiment_runner, - monitor: Optional[Callable[[SimpleCycleData], None]] = None, - params: Optional[Dict] = None, - ): - """ - Args: - metadata: a description of the dependent and independent variables - theorist: a scikit-learn-compatible estimator - experimentalist: an autora.experimentalist.Pipeline - experiment_runner: a function to map independent variables onto observed dependent - variables - monitor: a function which gets read-only access to the `data` attribute at the end of - each cycle. - params: a nested dictionary with parameters to be passed to the parts of the cycle. - E.g. if the experimentalist had a step named "pool" which took an argument "n", - which you wanted to set to the value 30, then params would be set to this: - `{"experimentalist": {"pool": {"n": 30}}}` - """ - - self.theorist = theorist - self.experimentalist = experimentalist - self.experiment_runner = experiment_runner - self.monitor = monitor - if params is None: - params = dict() - self.params = params - - self.data = SimpleCycleData( - metadata=metadata, - conditions=[], - observations=[], - theories=[], - ) - - def run(self, num_cycles: int = 1): - for i in range(num_cycles): - next(self) - return self - - def __next__(self): - assert ( - "experiment_runner" not in self.params - ), "experiment_runner cannot yet accept cycle properties" - assert ( - "theorist" not in self.params - ), "theorist cannot yet accept cycle properties" - - data = self.data - params_with_cycle_properties = _resolve_cycle_properties( - self.params, _get_cycle_properties(self.data) - ) - - data = self._experimentalist_callback( - self.experimentalist, - data, - params_with_cycle_properties.get("experimentalist", dict()), - ) - data = self._experiment_runner_callback(self.experiment_runner, data) - data = self._theorist_callback(self.theorist, data) - self._monitor_callback(data) - self.data = data - - return self - - def __iter__(self): - return self - - @staticmethod - def _experimentalist_callback( - experimentalist: Pipeline, data_in: SimpleCycleData, params: dict - ): - new_conditions = experimentalist(**params) - if isinstance(new_conditions, Iterable): - # If the pipeline gives us an iterable, we need to make it into a concrete array. - # We can't move this logic to the Pipeline, because the pipeline doesn't know whether - # it's within another pipeline and whether it should convert the iterable to a - # concrete array. - new_conditions_values = list(new_conditions) - new_conditions_array = np.array(new_conditions_values) - else: - raise NotImplementedError(f"Object {new_conditions} can't be handled yet.") - - assert isinstance( - new_conditions_array, np.ndarray - ) # Check the object is bounded - data_out = replace( - data_in, - conditions=data_in.conditions + [new_conditions_array], - ) - return data_out - - @staticmethod - def _experiment_runner_callback( - experiment_runner: Callable, data_in: SimpleCycleData - ): - x = data_in.conditions[-1] - y = experiment_runner(x) - new_observations = np.column_stack([x, y]) - data_out = replace( - data_in, observations=data_in.observations + [new_observations] - ) - return data_out - - @staticmethod - def _theorist_callback(theorist, data_in: SimpleCycleData): - all_observations = np.row_stack(data_in.observations) - n_xs = len( - data_in.metadata.independent_variables - ) # The number of independent variables - x, y = all_observations[:, :n_xs], all_observations[:, n_xs:] - if y.shape[1] == 1: - y = y.ravel() - new_theorist = copy.deepcopy(theorist) - new_theorist.fit(x, y) - data_out = replace( - data_in, - theories=data_in.theories + [new_theorist], - ) - return data_out - - def _monitor_callback(self, data: SimpleCycleData): - if self.monitor is not None: - self.monitor(data) - - -def _resolve_cycle_properties(params: Dict, cycle_properties: Mapping): - """ - Resolve "cycle properties" inside a nested dictionary. - - In this context, a "cycle property" is a string which is meant to be replaced by a - different value before the dictionary is used. - - Args: - params: a (nested) dictionary of keys and values, where some values might be - "cycle property names" - cycle_properties: a dictionary of "cycle property names" and their "real values" - - Returns: a (nested) dictionary where "cycle property names" are replaced by the "real values" - - Examples: - - >>> params_0 = {"key": "%foo%"} - >>> cycle_properties_0 = {"%foo%": 180} - >>> _resolve_cycle_properties(params_0, cycle_properties_0) - {'key': 180} - - >>> params_1 = {"key": "%bar%", "nested_dict": {"inner_key": "%foobar%"}} - >>> cycle_properties_1 = {"%bar%": 1, "%foobar%": 2} - >>> _resolve_cycle_properties(params_1, cycle_properties_1) - {'key': 1, 'nested_dict': {'inner_key': 2}} - - """ - params_ = copy.copy(params) - for key, value in params_.items(): - if isinstance(value, dict): - params_[key] = _resolve_cycle_properties(value, cycle_properties) - elif ( - isinstance(value, str) and value in cycle_properties - ): # value is a key in the cycle_properties dictionary - params_[key] = cycle_properties[value] - else: - pass # no change needed - - return params_ From 659ccd0e377ed06bcd47369e131d51256b95a386 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 24 Mar 2023 08:42:12 -0400 Subject: [PATCH 044/446] test: update cycle_results_plots notebook to use new interface --- example/cycle/cycle_results_plots.ipynb | 316 +++++------------------- 1 file changed, 57 insertions(+), 259 deletions(-) diff --git a/example/cycle/cycle_results_plots.ipynb b/example/cycle/cycle_results_plots.ipynb index 6e99c8c5..1ba31d67 100644 --- a/example/cycle/cycle_results_plots.ipynb +++ b/example/cycle/cycle_results_plots.ipynb @@ -14,17 +14,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { - "collapsed": true, - "pycharm": { - "name": "#%%\n" - } + "collapsed": true }, "outputs": [], "source": [ "from autora.variable import VariableCollection, Variable\n", - "from autora.cycle import Cycle, plot_results_panel_2d, plot_results_panel_3d\n", + "from autora.controller import Cycle\n", + "from autora.controller.plotting import plot_results_panel_2d, plot_results_panel_3d\n", "from autora.experimentalist.pipeline import Pipeline\n", "from autora.experimentalist.pooler.general_pool import grid_pool\n", "from autora.experimentalist.sampler import random_sampler\n", @@ -36,17 +34,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Simple linear regression cycle\n", "random.seed(1)\n", @@ -123,26 +112,14 @@ }, { "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Plot cycle results with each cycle as one panel\n", - "plot_results_panel_2d(cycle); # Add semicolon to supress creating two figures in jupyter notebook" + "plot_results_panel_2d(cycle.state); # Add semicolon to supress creating two figures in jupyter notebook" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { @@ -159,29 +136,17 @@ }, { "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change wrap to 3 and Adjust dimensions\n", - "plot_results_panel_2d(cycle,\n", + "plot_results_panel_2d(cycle.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(9,4.5))\n", " );" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { @@ -202,20 +167,11 @@ }, { "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change wrap to 3, Adjust dimensions, adjust scatter plot and line colors, shapes, and sizes\n", - "fig = plot_results_panel_2d(cycle,\n", + "fig = plot_results_panel_2d(cycle.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(10,5)), # Panel configurations\n", " scatter_previous_kw=dict(color='rebeccapurple', marker='o', s=10), # Previous data point\n", @@ -224,10 +180,7 @@ " );" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { @@ -244,18 +197,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Loop by the axes to draw annotations\n", "for i,ax in enumerate(fig.axes[:-1]):\n", @@ -288,36 +231,11 @@ }, { "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 0.98, 'Last Cycle')" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Querying using indexing\n", - "fig = plot_results_panel_2d(cycle,\n", + "fig = plot_results_panel_2d(cycle.state,\n", " query=[0, 2, 4],\n", " subplot_kw=dict(figsize=(8,3), gridspec_kw={\"bottom\": 0.25})\n", " );\n", @@ -325,7 +243,7 @@ "fig.suptitle('Cycles 0, 2, 4')\n", "\n", "# Last Cycle\n", - "fig = plot_results_panel_2d(cycle,\n", + "fig = plot_results_panel_2d(cycle.state,\n", " query=[-1],\n", " subplot_kw=dict(figsize=(4,4), gridspec_kw={\"bottom\": 0.25})\n", " );\n", @@ -334,80 +252,32 @@ "fig.suptitle('Last Cycle')" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 0.1, 'x1')" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Querying using slicing with the slice() function\n", - "fig = plot_results_panel_2d(cycle,\n", + "fig = plot_results_panel_2d(cycle.state,\n", " query=slice(0,5,2), # (Start, Stop, Step)\n", " subplot_kw=dict(figsize=(8,3), gridspec_kw={\"bottom\": 0.25})\n", " );\n", "fig.supxlabel('x1', y=0.1)" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 0.98, 'Last 2 Cycles')" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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2b96MLVu2YPTo0crn3NzcYGBgUObtodq0aYNbt27B2dkZrq6uhb6KmoNYHLlcjry8PMjl8jK/hoioKmlSHr1w4QJCQ0OVX8uWLYOJiQlCQ0Px5ptvlq/jRLWVXA6c+QL4Y7LiTxXUGCyoNdCGDRsgk8nQoUMH/P7774iKikJ4eDi+/vpr+Pj4FGo7btw4bN26FeHh4Rg5cqTyuL6+PmbPno1Zs2Zh27ZtiImJwcWLF5Ur1182adIkPHv2DEOHDsVff/2FmJgYHDlyBKNHj4ZMJivyNTt27MDu3bsRHh6OO3fuYPfu3Zg7dy6GDBkCHR2dqvuGEBGVk6bkUS8vLzRr1kz51bBhQ0ilUjRr1gz16tWrum8IkSbLTAIeXgbSExR/Zpa+c05V45QPDeTi4oKrV69ixYoVmD59OuLi4mBlZYW2bdviu+++K9TW19cXDRo0QNOmTWFnZ1fouYULF0JbWxuLFi3C48eP0aBBA3z44YdFfqadnR3OnTuH2bNno3fv3sjJyYGTkxP69OkDaTGXVrS1tbF69Wrcvn0bQgg4OTlh8uTJmDp1atV8I4iIKkhT8igRlYGhJWDfTlFM27dTPK5hElGe1RlUJVJTU2FmZoaUlBSYmppW62elp6ejYcOG2Lx5M956661q/SwioprCPEpEhah4DjVHqGspuVyOpKQkfPnllzA3N8cbb7yh6pCIiDQK8yiRBpFKAWNrlX08C+pa6v79+2jUqBHs7e2xZcsWaGvzn5qIqDyYR4morJgdailnZ+dy7bVKRESFMY8SUVlxFQQRERERUSWwoCYiIiIiqgQW1ERERERElcCCmoiIiIioElhQExERERFVQq0uqAMCAtC+fXuYmJjA2toagwYNQmRkZKmv++233+Dp6Ql9fX00b94chw4dKvS8EAKLFi1CgwYNYGBgAF9fX0RFRVVXN4iIiIiokqpz155aXVCfOnUKkyZNwsWLF3Hs2DHk5eWhd+/eyMjIKPY158+fx9ChQzF27Fhcu3YNgwYNwqBBg3Dz5k1lmzVr1uDrr7/Gxo0bERISAiMjI/j5+SE7O7smukVEREREZZQvk2Pbhbt467vzyMmXVctn1KlbjycmJsLa2hqnTp1Ct27dimwzZMgQZGRk4MCBA8pjHTt2RKtWrbBx40YIIWBnZ4fp06djxowZAICUlBTY2Nhgy5Yt+M9//lNqHDV5y1wiotqIeZSIyiI48gk+OxiO6CfpAICAt5pjaAfHKv+cWj1C/bKUlBQAgIWFRbFtLly4AF9f30LH/Pz8cOHCBQBAbGws4uPjC7UxMzODt7e3ss3LcnJykJqaWuiLiIjKjnmUiMojKiENIzddwqjNfyH6STrqGepg+aBmeLetfbV8Xp25U6JcLsenn36Kzp07o1mzZsW2i4+Ph42NTaFjNjY2iI+PVz5fcKy4Ni8LCAjA0qVLKxM+EdVhV+8/R2R8WrWMqmgK5lEiKotnGblYe/w2doTch0wuoKMlwahOzpjcyw1mBjrV9rl1pqCeNGkSbt68ibNnz9b4Z8+dOxfTpk1TPk5NTYWDg0ONx0FEmiU2KQOfH4nAoRvx0NWWoqubJezrGao6LJVgHiWikuTmK+ZJfx0UhdTsfABA7yY2mNvXC40sjar98+tEQT158mQcOHAAp0+fhr19yUP9tra2SEhIKHQsISEBtra2yucLjjVo0KBQm1atWhX5nnp6etDT06tED4ioLnmanoP1J6Lxv4v3kC8XkEiAQa3soKtdp2bpFcI8SkRFEUIgKPwJVhwKR2ySYtMJT1sTLOrfBJ1cLWssjlpdUAsh8PHHH2Pfvn0IDg5Go0aNSn2Nj48PgoKC8OmnnyqPHTt2DD4+PgCARo0awdbWFkFBQcoCOjU1FSEhIZg4cWJ1dIOI6oisXBk2nYvFd8ExSM9RjLD08LDCHH9PeNpy4R0R0YvC41Lx2cEwnIt+CgCwNNbDjN7ueLedA7SkkhqNpVYX1JMmTcLOnTvxxx9/wMTERDnH2czMDAYGBgCAESNGoGHDhggICAAATJkyBd27d8eXX36Jfv36YdeuXbh8+TJ++OEHAIBEIsGnn36Kzz77DG5ubmjUqBEWLlwIOzs7DBo0SCX9JCLNJpML/H7lIb48FomE1BwAQLOGppjr74XONTjCQkSkCRLTcvDVsUj8+tcDyAWgqy3FuC6N8FFPVxjrqaa0rdUF9XfffQcA6NGjR6HjmzdvxqhRowAA9+/fh1T672XUTp06YefOnViwYAHmzZsHNzc37N+/v9BCxlmzZiEjIwMTJkxAcnIyunTpgsDAQOjr61d7n4io9hBCIPh2IlYdikBkQhoAoKG5AWb6eeCNlnaQ1vAICxFRtZDLgcwkwNASkFZ86lp2ngybz93FhpPRyqt4/Vo0wJw+nnCwUO36kjq1D7W64P6pRHTzUQpWHgrH+RjFpUpTfW183MsNw32coK+jpeLo1B/zKJGGkMuBM18ADy8D9u2ArjPKXVQLIXD4ZjwCDofjwbMsAEALezMs7N8E7Z2L3wq5JtXqEWoiInXz4FkmvjgaiT9CHwMAdLWkGNXZGR/1aAxzQ10VR0dEVMUykxTFdHqC4s/MJMDYuswvv/EwBcsPhOHS3WcAABtTPczy88SbrRuq1VU8FtRERDUgOTMXG05GY+v5e8iVyQEodu6Y3ttD5ZcqiYiqjaGlYmS6YITasGzrQhJSs/H5kUj8fvUhhAD0daSY0K0xPuzuAkNd9Stf1S8iIqJaJDtPhu0X7uGbk9FIycoDAHRqXB/z+nqhWUMzFUdHRFTNpFLFNI8yzqHOzpPhx9N38N2pGGTmygAoBh9m9fGEnblBTURcISyoiYiqgVwu8Of1x/j8SCQeJSvm/HnammCOvye6u1tBIlGfS5VERNVKKi11mocQipy5+nAEHqdkAwBaO5pjUf8maO1YryairBQW1EREVex8dBJWHg7HzUepAABbU31Me90db7e1r/G9UYmI1N3V+8+x/EAYrt1PBgDYmeljtr8n3mhppzGDDyyoiYiqSER8KlYdjkBwZCIAwFhPGxN7NMaYzo1goMudO4iIXvQ4OQtrAiOw/59F2oa6WpjYvTHGd3PRuN2OWFATEVVSXEoWvjp6G3v+WTyjLZXg/Y5O+LiXK+ob83bZREQvysjJx/enYvDDmTvIzpNDIgHeaWOPGX4esDHVzHt6sKAmIqqg1Ow8fH8qBj+fjUV2nmLnjr7NbTHTzxONLI1UHB0RkXqRywX2XnuEz49EKO8K26GRBRb1b6Lxi7RZUBMRlVNuvhw7Q+7h6xPReJaRCwBo51QP8/p5oY0GLJ4hIqppf919hmX/F4Ybj1IAAA4WBpjn74U+zWxrZp50Fd2tsTgsqImIyqjgbl1rAiNw92kmAMDF0giz/T3Ru4mNxiyeISKqKQ+eZSLgcDgO3YgHoFhb8nEvV4zq7Aw97RqaJ10Fd2ssDQtqIqr239xrg8t3n2HFoXDlKnRLY1186uuO/7R3gLYWv2dEBMjlciQmJsLKygrSOp5L07LzsOFkDDadjUWuTA6pBBjS3hHTXneHlUkNry2p5N0ay4IFNVFdVwO/uWuymMR0rAmMwJFbCQAAAx0tTOjmgvHdXGCsxxRKRApyuRwrVqxASEgIvL29MX/+/DpZVMvkAr9dfoAvjt5GUrpinnRn1/pY0K8JvBqYqiaoCt6tsTx4NiCq62rgN3d1UZ7Ro8S0HKwLuo1fLj2ATC6UoytTfd1graGr0Imo+iQmJiIkJATx8fEICQlBYmIibGxsVB1WlSspj56PScLyA+EIj1Pswd/I0gjz+nrB18tatVPiynm3xopgQU1U19XAb+7qoKyjR5m5+fjxdCy+P/3vbW99vWwwx98DrtYmNR02EWkIKysreHt7K3OMlZWVqkOqcsXl0btJGVh5KBxHwxRX8kz1tfHJa24Y4eMMXW01GaUvw90aK4MFNVFdVwO/uauD0kaP8mVy7L78EP89fhuJaYrLlC3tzTC3rxc6utRXVdhEpCGkUinmz59fq+dQv5xH7zyIw683U7Dl/F3kyQS0pBK87+2IKb7usDDSVXW4NYoFNRFV+2/u6qC40SMhBILCn2BVYASin6QDUGznNMvPE/1bNKiay5Rc9ElUJ0il0lo5zaNAQR69GHIJJm36YvDWW3iWmQcA6Olhhfn9vKrtSp66L/iUCCGEqoOoa1JTU2FmZoaUlBSYmqpogj5RHfRyQg59kIyVh8JxKfYZAMDcUAef9HLDsI6OVbedExd9VgvmUSLVCI5IwNI/byL2WTYAwM3aGPP7eaGHR/UNymjCgk+OUBNRnVEwenTvaQY+PxKJA3/HAQD0tKUY06URPuzeGGYGOlX7oXVo0ScR1V7RT9Lw2cFwBEcmAgDqGepg2uvuGNrBsdq3DtWEBZ8sqImozniWkYv1J6Lwv4v3kCcTkEiAN1s3xIzeHrAzN6ieD60jiz6JqHZ6npGLtcdv438h9yGTC2hLJRjVyRkf93KDmWEVD0AUQxMWfHLKhwrwUiVRzcrOk2HTuVh8dzIGaTn5AIBu7laY08cTTexq4P8g51BXOeZRouqVmy/H9ov3sO74baRmK/Kmr5cN5vX1hIuVcY3Ho+5zqDlCTUS1lkwusO/aI3x5NBJxKYr5fk0amGJuX090davBEY46sOiTiGoHIQRORDzBioPhuJOUAQDwtDXBwv5N0NlVdVfY1H3BJwtqIqqVTt9ORMDhCOUNBuzM9DG9twfebN0QUqkKbzBARKSmIuJT8dmBcJyNTgIAWBrrYnpvDwxu5wAt5s0SsaAmolrl1uMUrDocgTNRihOCib42JvV0xahOztDXqaKdO4iIapGk9Bx8dew2dl26D7kAdLUUC7Un9WwME/2amSet6VhQE1Gt8Cg5C18ejcS+a48gBKCjJcEIH2dM7umKenXsBgNERGWRky/DlnN38c2JaOX6kr7NbTGnjxcc6xuqODrNwoKaiDRaSlYevg2OxuZzd5GbLwcADGhph5m9PXhCICIqghACR27FY+WhCNx/lgkAaNbQFIv6N0WHRhYqjk4zsaAmIo2Uky/D9gv38M3JaCT/c6cu70YWmNfXCy0dzFUbHBGRmrr5KAXLD4Qh5J8bWlmb6GFWH0+8xfUllaJ++45UodOnT2PAgAGws7ODRCLB/v37S2w/atQoSCSSV76aNm2qbLNkyZJXnvf09KzmnhBRAblc4M/rj+H71Sl8djAcyZl5cLM2xqZR7bBrQkcW00RERXiSmo2Zv13HgG/OIiT2GfS0pfiklytOzuiBd9ras5iupFo9Qp2RkYGWLVtizJgxeOutt0ptv27dOqxatUr5OD8/Hy1btsS7775bqF3Tpk1x/Phx5WNt7Vr9bSRSGxdiniLgcDj+fpgCQDGyMvV1d7zb1r7a79RFRKSJsvNk+OnMHXwbHIPMXBkAYKDZHcxqnY+Gvn7cG7+K1OpK0N/fH/7+/mVub2ZmBjMzM+Xj/fv34/nz5xg9enShdtra2rC1ta2yOImoZLcT0rD6cASCIp4AAIx0tfBh98YY27URDHVrdRojIqoQIQT+7+84rD4cgUfJWQCAVg2NsEh/N9rIbwFPbYDMYdwjv4rwTFSCn3/+Gb6+vnBycip0PCoqCnZ2dtDX14ePjw8CAgLg6OiooiiJaq+E1Gz899ht7L78AHIBaEkleK+DI6b4usHSWE/V4RERqaXQB8lYfiAMV+49B6DYh3+2vyfeaG4LydlLwMMkwL6d4u6tVCVYUBfj8ePHOHz4MHbu3FnouLe3N7Zs2QIPDw/ExcVh6dKl6Nq1K27evAkTE5Mi3ysnJwc5OTnKx6mpqdUaO5GmS8/Jx/enYvDjmTvIzlPs3NGnqS1m9fFQyS1vSfWYR4lKF5eShTWBiu1DAcBARwsTezTG+K4uMND9Zx/+rjOAzCRFMc3pHlWGBXUxtm7dCnNzcwwaNKjQ8RenkLRo0QLe3t5wcnLC7t27MXbs2CLfKyAgAEuXLq3OcIlqhTyZHLsu3ce6oCgkpecCANo4mmN+Py+0deJWTnUZ8yhR8TJz8/H9qTv4/nSMchDi7Tb2mOnnAVsz/cKNpVJO86gGEiGEUHUQNUEikWDfvn2vFMhFEULA3d0d/fv3x3//+99S27dv3x6+vr4ICAgo8vmiRlYcHByQkpICU1PTMveBqLZS7ImagDWBEbiTlAEAaGRphNl9PODX1BYSCVef13XMo0SvkssF9oc+wprASMSnZgMA2jvXw8L+TdDC3ly1wdUxHKEuwqlTpxAdHV3siPOL0tPTERMTg+HDhxfbRk9PD3p6nO9JVJQr955j5aFw5Vy/+ka6+NTXDf/p4Agd7txB/2AeJSrs8t1nWHYgTLnrkYOFAeb6e8G/GQchVKFWF9Tp6emIjo5WPo6NjUVoaCgsLCzg6OiIuXPn4tGjR9i2bVuh1/3888/w9vZGs2bNXnnPGTNmYMCAAXBycsLjx4+xePFiaGlpYejQodXeH6La5E5iOj4/EonDN+MBAPo6Uozv6oIJ3Vxgoq+j4uiIiNTTg2eZWBUYgYN/xwEAjPW0MamnK0Z3doa+jpaKo6u7anVBffnyZfTs2VP5eNq0aQCAkSNHYsuWLYiLi8P9+/cLvSYlJQW///471q1bV+R7Pnz4EEOHDsXTp09hZWWFLl264OLFi7Cysqq+jhDVIknpOfg6KAo7Q+4jXy4glQDvtnXAtN7usDHVL/0NiIjqoPScfHx7Mho/nY1Fbr4cEgnwn/YOmPa6B6xMePVG1erMHGp1kpqaCjMzM879ozolMzcfP5+JxcZTMcj45+YCvTytMbuPJzxsi94hh6g4zKNUV8jkAnuuPMDnR24jKV2xjsDHpT4W9m+CJnb82VcXtXqEmohUr+Bk8NWx20hIVZwMmjc0w9y+nujUmHugEhEV50LMUyw/EIawOMU2kc71DTGvrxdeb2LDedJqhgU1EVULIQSCIxMRcDgctxPSAQD29Qww088DA1rYQSrlyYCIqCh3kzIQcDgcR24lAABM9LUx5TU3jPBxhq42F2urIxbURFTlbjxMwcpD4bhw5ykAwMxABx/3csVwHyfoaXPRDBFRUVKz8/DNiWhsPheLPJlQ3h126uvusDDSVXV4VAIW1ERUZR48y8TnRyLx5/XHAABdbSlGd3bGR91dYWbInTuIiIqSL5Nj11+KqXHPMhQ3termboWF/bzgZsM1JpqABTURVVpyZi6+ORGNbRfuIVemWH0+qFVDTO/tDvt6hqoOj4hIbZ2+nYjPDoYpp8a5Whtjfj8v9PTg3Qw1CQtqIqqw7DwZtp6/iw0no5GanQ8A6OJqiTn+nmjW0EzF0RERqa/oJ+lYeSgcJyKeAADMDXUw1dcd73nzplaaiAU1EZWbXC7wx/VH+OLIbTxKzgIAeNqaYG5fL3Rzs+TqcyKiYiRn5mLt8Sj87+I95MsFtKUSjOzkjE96uXFqnAZjQU1E5XI2KgkrD4Urt3GyNdXHtN7ueLuNPbS4cwcRUZHyZHL87+I9rD0ehZSsPACAr5c15vX1gouVsYqjo8piQU1EZRIel4qAwxE4fTsRAGCip42JPRtjTOdGvN0tEVExhBA4GfkEnx0Mx53EDACKK3oL+jVBFzfuxV9bsKAmohLFpWThy6O38fvVhxAC0NGS4P2OTvi4lxu3cSIiKkFkfBo+OxiGM1FJAID6RrqY3tsDQ9o78IpeLcOCmoiKlJqdh43BMfj5bCxy8uUAgH4tGmBmbw84WxqpODoiIvX1ND0HXx27jV8u3YdcALpaUozu4oxJPV1hqs950rURC2oiKiQ3X44dIfew/kS0cj/UDs4WmNvXE60d66k4OiIi9ZWTr9j5aH1QNNJyFDsf9Wlqi7l9PeFUnwMRtRkLaiICoJjnd+hGPNYcicC9p5kAgMZWRpjj7wVfL2vu3EFEVAwhBI6GJWDloXBl/mxqZ4qF/Zugo0t9FUdHNYEFNRHhUuwzrDgUjusPkgEAlsZ6mPa6Owa3s4c290MlIirWrccpWH4gDBfvPAMAWJnoYZafB95uYw8p50nXGSyoiTSAXC5HYmIirKysIJVWXYEb/SQdqwMjcCwsAQBgqKuFCd1cML6rC4z0mB6IqPao6jz6JC0bXx65jd1XHkAIQE9bivFdXTCxR2PmzzqI/+JEak4ul2PFihUICQmBt7c35s+fX+mTwZO0bKw9HoVf/3oAmVxASyrBkPYO+NTXDdYm+lUUORGReqjKPJqdJ8PPZ2Px7cloZOTKAABvtLTDbH9PNDQ3qMqwSYOwoCZSc4mJiQgJCUF8fDxCQkKQmJgIGxubCr1XRk4+fjh9Bz+euYPMf04Erzexwew+HnC1NqnKsImI1EZV5FEhBA78HYdVhyOUd4ht5WCOhf2boK0TF2zXdSyoidSclZUVvL29lSMrVlZWAMp3+TJfJseuvx5g7fEoJKXnAFCcCOb19UKHRhbV3gciIlUqLo8CZcul1x8kY/mBMFy+9xyA4g6xc/w98UZLO86TJgCARAghVB1EXZOamgozMzOkpKTA1NRU1eGQBng54Zf18qUQAsfCErA6MAIx/9yhy6m+IWb5eaJvc1vu3EEai3mUyquowrm0XBqfko01gRHYe+0RAMBARwsfdm+MCd1cYKDLO8TSvzhCTaQBpFJpocuTZbl8ee3+cwQcisClu4qV5xZGuviklyve83aCrjZ37iCiuuXlPAoUn0uzcmX4/nQMNp6KQXae4sZWb7VpiFl+nrA14zoTehULaiINVNLly3tPM7AmMBIHb8QBUKw8H9e1ET7o3ph36CIiesHLubR+fUvsu/YQawIjEZeSDQBo51QPC/s3QUsHc9UGS2qNUz5UgJcqqSq8fPnyWUYuvg6Kwo6Qe8iTCUgkwNtt7DG9tzsamFXPyvPq2s6PqDTMo1RVCvLY/UxtfHYoQrkff0NzA8zt64l+zRtU6/Q45tHagSPURBqq4PJlVq4Mm85FY2NwjPJWt93drTC3ryc8bauv0KiO7fyIiGra45RsrAp6hAN/K67qGelqYVIvV4zp3Aj6OtU7T5p5tPZgQU2koWRygd+vPsRXR28jPlVxabKpnSnm+nuhi5tltX9+VW7nR0RU09Jz8vFdcDR+PBOL3Hw5JBJgcFsHTPdzr7H9+JlHaw8W1EQaRgiBU7cTsepwBCLi0wAoLk3O8HPHwJYNa2wLp5LmcRMRqSuZXOD3Kw/x+dFIJKYpthH1camPBf290NTOrEZjYR6tPTiHWgU4948q6uajFKw6HIGz0UkAAFN9bUzu5YoRPs7VfmmyKJz7R6rCPEoVcfHOUyw/EIZbj1MBKLYRnd/XC683sVHZNqLMo7UDR6iJNMDD55n48uht7PtnL1RdLSlG+Dhhci9XmBvqqiyuorahIiJSN/eeZiDgUAQCb8UDAEz0tfFJLzeM6OQEPW3V7ifNPFo71OpfhU6fPo0BAwbAzs4OEokE+/fvL7F9cHAwJBLJK1/x8fGF2m3YsAHOzs7Q19eHt7c3Ll26VI29oLosJTMPAYfC0evLU8piemArOwRN744F/ZuotJgmIlJ3qdmKHPr6V6cReCseUgnwfkdHBM/ogfHdXFReTFPtUatHqDMyMtCyZUuMGTMGb731VplfFxkZWegSorW1tfLvv/76K6ZNm4aNGzfC29sba9euhZ+fHyIjIwu1I6qMnHwZtl+4h/UnopGSlQdAMcdvXl8vNLev2Tl+RESaJl8mx6+XH+Cro7fxNCMXANDVzRIL+jWBh62JiqOj2qhWF9T+/v7w9/cv9+usra1hbm5e5HNfffUVxo8fj9GjRwMANm7ciIMHD2LTpk2YM2dOZcIlglwu8H9/P8bnRyLx8HkWAMDdxhhz/b3Qw8OKtwonIirF2agkfHYwTLlo28XKCAv7NWEOpWpVqwvqimrVqhVycnLQrFkzLFmyBJ07dwYA5Obm4sqVK5g7d66yrVQqha+vLy5cuFDs++Xk5CAnJ0f5ODU1tfqCJ411PiYJAYcicONRCgDAxlQP0153xzttHaBVQzt3EKkr5lEqzZ3EdKw8FI7j4U8AAGYGOpjq64ZhHZ2go1WrZ7iSGmBB/YIGDRpg48aNaNeuHXJycvDTTz+hR48eCAkJQZs2bZCUlASZTPbK4gEbGxtEREQU+74BAQFYunRpdYdPGioyPg2rAyNwIkJxEjDW08bEHo0xpnMjGOhyfh8RwDxKxUvOzMW6oChsv3AP+XIBbakEw32cMOU1N64zoRpTZ7bNk0gk2LdvHwYNGlSu13Xv3h2Ojo7Yvn07Hj9+jIYNG+L8+fPw8fFRtpk1axZOnTqFkJCQIt+jqJEVBwcHbvdUx8WnZOO/x27jtysPIBeAtlSCYd6O+Pg1N1ga66k6PCK1wjxKL8uTybHj4j2sDYpCcqZirclrntaY188Lja2MVRwd1TUcoS5Fhw4dcPbsWQCApaUltLS0kJCQUKhNQkICbG1ti30PPT096OmxQCKFtOw8fH/qDn46ewfZeXIAgH8zW8z084ALTwJERWIepQJCCARHJuKzg2GIScwAAHjYmGBBfy90deONUUg1WFCXIjQ0FA0aNAAA6Orqom3btggKClKOdMvlcgQFBWHy5MkqjJI0QZ5Mjl8u3ce641HKVedtnephXl8vtHWqp+LoiIjU3+2ENCw/EIYzUYqbW1kY6WJ6b3cMaecAbc6TJhWq1QV1eno6oqOjlY9jY2MRGhoKCwsLODo6Yu7cuXj06BG2bdsGAFi7di0aNWqEpk2bIjs7Gz/99BNOnDiBo0ePKt9j2rRpGDlyJNq1a4cOHTpg7dq1yMjIUO76QfQyIQQCb8ZjzZFIxCYpRlNcLI0wq48n/Jqq7u5cRESa4ml6Dv57/DZ2htyHXAA6WhKM7twIk3u5wlRfR9XhEdXugvry5cvo2bOn8vG0adMAACNHjsSWLVsQFxeH+/fvK5/Pzc3F9OnT8ejRIxgaGqJFixY4fvx4ofcYMmQIEhMTsWjRIsTHx6NVq1YIDAzkXY6oSFfuPcOKg+G4ej8ZAGBprIspvu74T3sHrjonIipFbr4c2y7cxbqgKKRl5wMA/JraYK6/F5wtjVQcHdG/6syiRHWSmpoKMzMzLqapxe4kpmNNYKTyNrcGOloY380FE7q5wFivVv8eS1QjmEdrNyEEjoUlYOWhcNx9mgkAaNLAFAv7N4FP4/oqjo7oVTyzE1WhxLQcfB0UhZ2X7kMmF5BKgCHtHTDV1x3WpvqqDo+ISO2FPU7F8gNhuHDnKQDA0lgPM/24Jz+pNxbURFUgMzcfP52JxfenYpCRKwMA+HpZY3YfT7jZ8Da3RESlSUzLwZdHI/Hr5QcQAtDVlmJ810aY2MOVV/ZI7fEnlKgS8mVy7LnyEF8du40naYo9clvYm2GuvxcvSxIRlUF2ngybzsXi25MxSM9RzJPu36IBZvfxhIOFoYqjIyobFtREFSCEwImIJ1h1OAJRT9IBAPb1DDCrjyf6N28AKS9LEhGVSAiBQzfiEXA4HA+fZwEAWtqbYWH/JmjnbKHi6IjKhwU1UTldf5CMlYfCERL7DABgbqiDj3u54f2OjtDT5q3CiYhK8/fDZCw/EIa/7j4HANia6mO2vwcGtmzIAQnSSCyoicro/tNMfH40Ev93/TEAxfy+MZ0bYWKPxjAz4D6oRESliU/JxpojEdh79REAQF9Hig+7N8aEbi4w1GVJQpqLP71EpXiekYv1J6Kx/eJd5MkEJBLgzVYNMd3PAw3NDVQdHhGR2svKleGH03ew8VQMsvIUC7ffbN0Qs/p4oIEZ8yhpvgoV1CEhIfD29q7qWIjUSnaeDJvP3cW3wdHKGwp0dbPEHH9PNLUzU3F0RETqTy4X+PP6Y6wOjEBcSjYAoK1TPSzs3wStHMxVGxxRFapQQe3j4wNXV1cMHz4cw4YNg4uLS1XHRRpCLpcjMTERVlZWkEprx53/5HKBfdce4cujkXj8zwnAq4Ep5vp7opu7lYqjI6LapjbmUQC4cu85lh8IQ+iDZABAQ3MDzPH3RP8WDSCRcJ401S4VulPizp07sWPHDhw7dgwymQwdO3bE8OHDMXjwYFhYcGVuaWrLHb7kcjlWrFihvGIxf/58jT8ZnIlKxMpDEQiPSwUA2JnpY1pvD7zZuiFvKECkRphH1dej5CysPhyBP/9Zb2Kkq4WJPRpjXFcX6Otw4TbVTpW69XhSUhJ27dqFnTt34uLFi9DV1UWfPn3w/vvv44033oCurm5Vxlpr1JYTQUJCAsaOHYv4+HjY2tri559/ho2NjarDqpCwx6kIOByOM1FJAAATPW181NMVozs78wRApIaYR9VPRk4+Np6KwQ+n7yAnXw6JBHi3rT1m9PbgnWKp1qvUr8GWlpaYPHkyzp8/j6ioKMyfPx8REREYMmQIbG1tMWHCBJw9e7aqYiU1Y2VlBW9vb9ja2sLb2xtWVlU3HUIulyMhIQFyubxaX/M4OQvTd19Hv/VncCYqCTpaEozp3AinZvXExB6Na6yYrkjsRKT51C2PVuR1crnAb5cfoOcXwVh/Iho5+XJ0dLHA/03ugjXvtKyxYpp5lFSpUiPUL3r8+DF++eUXbN++HX///Tfq1asHbW1tJCUloU2bNti6dSuaNGlSFR+l8TR9ZOXF+X4AqnzuX0UugZb3NSlZefguOAabzsUiN1+RfPu3aICZfh5wqm9UJf0oq9p4yZeoujGPlv7+Fckr5X1dyJ2nWH4wDDcfKabJOdU3xFx/L/g1tanRedLMo6RqlfppS0tLw+bNm+Hr6wsnJyfMmzcPzs7O2LNnD+Lj4/H48WP8+uuvePLkCUaPHl1VMZMKFSStsWPHYsWKFQAAGxubKk1ciYmJCAkJQXx8PEJCQpCYmFhlr8nJl2HT2Vj0+PwkNp6KQW6+HB0aWWD/pM745r02NV5MAxXrLxFpLnXNo+V53f2nmZj4vysY8sNF3HyUChM9bczr64mjU7uhTzPbGl90yDxKqlahXT7++OMP7NixAwcOHEB2djbat2+PtWvX4j//+Q/q169fqO0777yD58+fY9KkSVUSMKlWUUmrquf7FVwCLRhpKMsl0NJeI4TAgb/jsOZIBB48U9zi1tXaGHP6eOI1L2uVrjivSH+JSHOpax4ty+vSsvPwzclobD57F7kyOaQSYGgHR0x93R2WxnpV2ofyYB4lVavQlA+pVAoHBwe8//77GDFiBDw8PEpsf+nSJXz33XfYvHlzhQOtTTT5UmVNXVaryDZSxb3m4p2nCDgUjusPUwAAViZ6mOrrjsHt7KGtVc7Y5XIgMwkwtASqsN+1ddssourCPFq2z6lIXinqdTK5wK9/PcCXRyPxNCMXgGJf/vn9vOBpW77vf3XlO+ZRUqUKFdTBwcHo0aNHNYRTN2jyiQDQnKQVlZCG1YEROB7+BIBi66YPujfGuK6NKnaLW7kcOPMF8PAyYN8O6DqjSotqIio75tGacy46CcsPhCEiPg0A4GJphAX9vdDTo/xX9zjXmWqrCk35YDFdt0ml0mrZ1qmqTjBPUrPx3+O38etfDyAXgJZUgvc6OOKT19xgZVKJS5KZSYpiOj1B8WdmEmBsXfH3I6I6S93zKADcSUzHykMROB6eAAAwM9DBlNfcMNzHCTrlvbr3j5qY7kKkChUqqImqWlWMWqTn5OOH03fw4+k7yMqTAQD8mtpgVh9PNLYyrnyQhpaKkemCEWpDy8q/JxFRFamq0d+UzDx8fSIKW8/fRb5cQEsqwfCOTpjymhvqGVXu/hKc60y1FQtqUguVGbXIk8nx618PsPZ4FJLScwAAbRzNMa+vF9o5V+GdO6VSxTSPaphDTURUWZUd/c2XybHz0n3899htPM/MAwD09LDC/H5ecLU2qZIYpVIp5s+frzHTXYjKigU1qYWKjFoIIXA0LAGrAyNwJzEDAOBc3xCz+3hW37ZNUimneRCRWqrM6O/JyCdYcTAc0U/SAQBu1sZY0L8JurtX/QhydU13IVKlKruxC5Wdpi+mqS7lmft39f5zBBwKx193nwMA6hvpYoqvG4Z2cKzw3L7yxkBEqsM8WrTy5rCohDR8djAcp24r9m2uZ6iDaa+7Y2gHx/LvglTBGIhqA45Qk9ooy6hFbFIGPj8SgUM34gEA+jpSjOvigg+6u8BEX6dSn8/V50Sk6co6+vssIxdrj9/GjpD7kMkFdLQkGNXJGZN7ucHMoOK5lHmU6ioW1KQRktJzsD4oCjtC7iNfLiCVAO+0tce01z1ga6ZfJZ/B1edEVNvl5sux7cJdrAuKQlp2PgCgdxMbzOvrBWfLyt8plnmU6ioW1KQSZb0kmJUrw89n72DjqTtIz1Ek/54eVpjt71numwmUhqvPiUiTlGdqhRACx8ISEHA4ArFJijUnXg1MsbC/Fzo1rrodi5hHqa7iHGoVqOtz/8pySVAmF/j9ykN8eSwSCamKnTuaNTTFPH8vdHKtvu3qOPePSDMwj5Z9akV4XCqWHwjD+ZinAABLYz3M9HPHO20doCWt+sXbzKNUF9Xqn/TTp09jwIABsLOzg0Qiwf79+0tsv3fvXrz++uuwsrKCqakpfHx8cOTIkUJtlixZAolEUujL09OzGntR+xR1SbCAEAInI5+g77ozmPX730hIzUFDcwOsHdIKf07qUq3FNPDv/EOeBIhInZWUR5Vt0nIwd+/f6Pf1GZyPeQpdbSkm9miMkzO6Y0h7x2oppgHmUaqbavWUj4yMDLRs2RJjxozBW2+9VWr706dP4/XXX8fKlSthbm6OzZs3Y8CAAQgJCUHr1q2V7Zo2bYrjx48rH2tr1+pvY5Ur7pLgzUcpWHkoXDmKYmagg497uWK4jxP0tLVUGTIRkVopaWpFTr4Mm8/dxTcnopVT5fo1b4A5/p5wsDBUVchEtVqdmfIhkUiwb98+DBo0qFyva9q0KYYMGYJFixYBUIxQ79+/H6GhoRWOpa5fqgQKXxJ8lJyNL49GYn/oYwCArrYUozs546MerjAzrNzOHURUOzGPvjq1QgiBwzfjEXA4HA+eZQEAWtibYWH/JmhflTe5IqJXcGi1BHK5HGlpabCwKJyIoqKiYGdnB319ffj4+CAgIACOjo4qilIzSaVS6JnUQ8DhCGw9fw+5MjkAYFArO8zw84B9PY6iEBGV5MUt8m48TMHyA2G4dPcZAMDGVA+z/DzxZuuGkFbT1A4i+hcL6hJ88cUXSE9Px+DBg5XHvL29sWXLFnh4eCAuLg5Lly5F165dcfPmTZiYFH1r1pycHOTk5Cgfp6amVnvs6iw7T4ZtFxSXI1P/2baps2t9zPX3QrOGZiqOjojUEfNo0RJSs7EmMBJ7rz2EEIq9+Sd0a4wPurnASI+neKKawv9txdi5cyeWLl2KP/74A9bW/95q2t/fX/n3Fi1awNvbG05OTti9ezfGjh1b5HsFBARg6dKl1R6zupPLBf68/hifH4nEo2TF5UgPGxPM7euJ7u5W1XOrcCKqFZhHC8vKleHHM3fwXXAMsvJkABRX+Gb18YSduYGKoyOqeziHugi7du3CmDFj8Ntvv6Ffv36ltm/fvj18fX0REBBQ5PNFjaw4ODjUqbl/56KTsPJQOG49Vowq2ZrqY9rr7ni7rX21rTQnotqDeVRBCMXAxOrDEXickg0AaONojoX9m6C1Yz0VR0dUd3GE+iW//PILxowZg127dpWpmE5PT0dMTAyGDx9ebBs9PT3o6elVZZgaIyI+FQGHInDqtmJLJ2M9bUzs0RhjOjeCgS537iCisqnLebTA1fvPsfxAGK7dTwYA2JnpY7a/J95oaccrfEQqVqsL6vT0dERHRysfx8bGIjQ0FBYWFnB0dMTcuXPx6NEjbNu2DYBimsfIkSOxbt06eHt7Iz4+HgBgYGAAMzPF3N4ZM2ZgwIABcHJywuPHj7F48WJoaWlh6NChNd9BNRaXkoWvjt7GnquKeX3aUgne7+iEj3u5or5x3T4pEhGVx+PkLKwOjMAf/+yEZKirhY96NMa4ri7Q1+HABJE6qNUF9eXLl9GzZ0/l42nTpgEARo4ciS1btiAuLg73799XPv/DDz8gPz8fkyZNwqRJk5THC9oDwMOHDzF06FA8ffoUVlZW6NKlCy5evMjbq/4jNTsP35+Kwc9nY5Gdp9i5o1/zBpjp5wFnSyMVR0dEpDkycvLx/akY/HDmDrLz5JBIgHfa2GOGnwdsTPVVHR4RvaDOzKFWJ7Vx/9TcfDl+uXQf64Ki8CwjFwDQ3rke5vb1QhvO6yOiKlYb82gBuVxg77VH+PxIBBJSFfPGOzSywKL+TbgTEpGaqtUj1FT9Cm4ksCYwAnefZgIAXKyMMKePJ15vYsN5fURE5fDX3WdY9n9huPEoBQDgYGGAef5e6NPMlvmUSI2xoKYK++vuM6w8FK5cIGNprIepr7thSDsHaGtJVRvcC16+mxgRkbp58CwTqw5H4OCNOACKBdyTe7liVCdntZgnzTxKVDIW1FRu0U/SsSYwAkfDEgAABjpamNDNBeO7ucBYzW4kIJfLsWLFCoSEhMDb2xvz58/nyYCI1EZadh6+DVasO8nNl0MqAYa0d8S0191hZaIeC7iZR4lKp17VD6m1J2nZWHc8Crv+egCZXCgT/1RfN1ir6QKZxMREhISEID4+HiEhIUhMTFTeqpeISFVkcoHfLj/AF0dvIyldMU+6s2t9LOjXBF4N1GtOOPMoUelYUFOpMnLy8eOZO/jh9B1k5iruyOXrZYM5/h5wtS76duvqwsrKCt7e3sqRFe7GQkSqdj46CcsPhiM8TnGjKxdLI8zr64XXvKzVcp408yhR6bjLhwpoyur0fJkcuy8/xH+P30ZimmIEpaWDOeb5e8Lbpb6Koys7zv0jqn00JY++KDYpAysPhePYP9PlTPW1McXXHcM7OkFXW71zE/MoUck4Qk2vEEIgKPwJVgVGIPpJOgDA0cIQs/p4oF/zBmo5glISqVTKy5NEpDIpWXlYHxSFrRfuIk8moCWV4H1vR3zq6456RrqqDq9MmEeJSsaCmgq5/iAZKw6F41LsMwBAPUMdfPKaG4Z5lz6CwhEMIqJ/5csU+/N/dew2nmfmAQB6eFhhQT+vYqfLMY8SaSYW1AQAuPc0A58ficSBvxVbNulpSzGmSyNM7NEYpvo6pb6eq8CJiP516nYiPjsQhqh/rvK5WhtjQT8v9PCwLvY1zKNEmosFdR33LCMX609E4X8X7yFPJiCRAG+1tsf03u6wMzco8/twFTgRERD9JA0rDobjZGQiAMVVvqmvu+O9Do6l7s/PPEqkuVhQ11HZeTJsOheL707GIC0nHwDQ3d0Kc/w9K7RlE1eBE1Fd9jwjF+uCorD94j3I5ALaUglGdnLGJ73cYGZY+lU+gHmUSJNxlw8VUOXqdJlcYN+1R/jyaCTiUrIBAE0amGJeXy90cbOs1HuXa+6fXA5kJgGGlgAvaRJROanLLh+5+XJsv3gPXwdFISVLMU/a18sG8/p6wsXKuNzvV548yvnWROqDI9R1yKnbiQg4FI6I+DQAgJ2ZPmb4eWBQq4aQSiu/c0eZV4HL5cCZL4CHlwH7dkDXGSyqiUijCCFwIuIJVhwMx52kDACAp60JFvVvgk6uFR+cKGse5XxrIvXCgroOuPU4BasOR+BMVBIAwERfG5N7umJkJ2fo62jVfECZSYpiOj1B8WdmEmBc/EIdIiJ1EhGfis8OhONstCKnWhrrYnpvDwxu5wCtKhicKAvOtyZSLyyoa7GHzzPx1dHb2Bf6CEIAOloSjPBxxuSerqrd+9TQUjEyXTBCbVi5qSZERDUhKT0HXx27jV2X7kMuAF0txW5Ik3o2hkkZdkOqSpxvTaReOIdaBap77l9KVh6+PRmNzefvIjdfDgB4o6UdZvp5wMHCsMo/r0I4h5qIKqEm51Dn5Muw5dxdfHMiWrmIu29zW8zp4wXH+qrLqZxDTaQ+OEJdi+Tky/C/i/ex/kQUkv+5iUBHFwvM6+uFFvbmqg3uZVIpp3kQkVoTQiDwZjwCDkfg/rNMAECzhqZY1L8pOjSyKPsbVdMAAu9eSKQ+WFDXAnK5wIEbcfj8SAQePMsCALjbGGOuvxd6eFhp3K3CiYhU7eajFCw7EKa8a6y1iR5m9fHEW63LuYibi7CJ6gQW1BruQsxTBBwOx98PUwAokv60193xTlv7Um8iQEREhT1JzcbnRyKx5+pDCKG4a+yEbi74sHtjGOlV4JTJRdhEdQILag11OyENqw9HICjiCQDASFcLH3ZvjLFdG8FQl/+sRETlkZ0nw89nY7HhZDQyc2UAFGtPZvt7omE57hr7Ci7CJqoTWHlpmITUbPz32G3svvwAcgFoSyUY2sERU3zdYGmsp+rwiIg0ihAC//d3HFYfjsCjZMWUuVYO5lg0oAnaONar/AdIpYppHlyETVSrsaDWEGnZefjh9B38eOYOsvMUO3f0aWqLWX08KnQ3LiKiui70QTKWHwjDlXvPAShudjXb3xNvtLSr2rUnXIRNVOuxoNYAJyOfYMbu63iakQsAaOtUD/P6eqKtUzlWmRMREQAgLiULawIjse/aIwCAgY4WPurRGOO6usBAVwU3uyIijceCWgM41DNEclYeGlkaYXYfT/g1teHOHURE5ZSZm4+Np+7gh9Mxyit977S1x0w/D9iY6qs4OiLSZCyoNYCrtTH+N9Yb7ZzrQYc7dxARlVvY41SM3nIJCak5AIAOzhZY2L8JmtubqTgyIqoNWFBrCJ/G9VUdAhGRxmpkaQQtiQT29Qwwr68X/JvZ8kofEVUZFtRERFTrGehqYcuYDnC0MIS+DudJE1HVqtXzB06fPo0BAwbAzk6xYnv//v2lviY4OBht2rSBnp4eXF1dsWXLllfabNiwAc7OztDX14e3tzcuXbpU9cETEVGVcrcxYTFNRNWiVhfUGRkZaNmyJTZs2FCm9rGxsejXrx969uyJ0NBQfPrppxg3bhyOHDmibPPrr79i2rRpWLx4Ma5evYqWLVvCz88PT548qa5uEBEREZEakwghhKqDqAkSiQT79u3DoEGDim0ze/ZsHDx4EDdv3lQe+89//oPk5GQEBgYCALy9vdG+fXt88803AAC5XA4HBwd8/PHHmDNnTpliSU1NhZmZGVJSUmBqalrxThER1VHMo0SkTmr1CHV5XbhwAb6+voWO+fn54cKFCwCA3NxcXLlypVAbqVQKX19fZZui5OTkIDU1tdAXERGVHfMoEakzFtQviI+Ph42NTaFjNjY2SE1NRVZWFpKSkiCTyYpsEx8fX+z7BgQEwMzMTPnl4OBQLfETEdVWzKNEpM5YUNeAuXPnIiUlRfn14MEDVYdERKRRmEeJSJ1x27wX2NraIiEhodCxhIQEmJqawsDAAFpaWtDS0iqyja2tbbHvq6enBz09vWqJmYioLmAeJSJ1xhHqF/j4+CAoKKjQsWPHjsHHxwcAoKuri7Zt2xZqI5fLERQUpGxTXeRyORISEiCXy6v1c4iIaivmUSKqLrW6oE5PT0doaChCQ0MBKLbFCw0Nxf379wEoLiGOGDFC2f7DDz/EnTt3MGvWLERERODbb7/F7t27MXXqVGWbadOm4ccff8TWrVsRHh6OiRMnIiMjA6NHj662fsjlcqxYsQJjx47FihUrSj8ZyOVA+hPFn0REVO48yuKbiMqjVk/5uHz5Mnr27Kl8PG3aNADAyJEjsWXLFsTFxSmLawBo1KgRDh48iKlTp2LdunWwt7fHTz/9BD8/P2WbIUOGIDExEYsWLUJ8fDxatWqFwMDAVxYqVqXExESEhIQgPj4eISEhSExMLP7z5HLgzBfAw8uAfTug6wxAWqt/byIiKlV58mhB8R0SEgJvb2/Mnz8fUuZRIipBrS6oe/TogZK22S7qLog9evTAtWvXSnzfyZMnY/LkyZUNr8ysrKzg7e2tTO5WVlbFN85MUhTT6QmKPzOTAGPrGouViEgdlSePlmsQg4gItbygri2kUinmz5+PxMREWFlZlTxSYmipGJkuGKE2tKy5QImI1FR58mi5BjGIiFCH7pSoTqr9Dl9yuWJk2tCS0z2IqFaq7jwql8vLNohBRASOUNdOUimneRARVYJUKuU0DyIqM/7aTURERERUCSyoiYiIiIgqgQU1EREREVElsKAmIiIiIqoELkpUgYKNVVJTU1UcCRHVdiYmJpBIJKoOo8oxjxJRTSlLHmVBrQJpaWkAAAcHBxVHQkS1XbVtz6lizKNEVFPKkke5D7UKyOVyPH78GEIIODo64sGDB7XqhJeamgoHBwf2S0OwX5qlvP2qrSPUzKOaif3SLOyXAkeo1ZRUKoW9vb3yUqWpqWmt+kEtwH5pFvZLs9TWfpUV86hmY780C/tVOi5KJCIiIiKqBBbURERERESVwIJahfT09LB48WLo6empOpQqxX5pFvZLs9TWflVUbf1+sF+ahf3SLNXRLy5KJCIiIiKqBI5QExERERFVAgtqIiIiIqJKYEFNRERERFQJLKiJiIiIiCqBBXU127BhA5ydnaGvrw9vb29cunSpxPa//fYbPD09oa+vj+bNm+PQoUM1FGn5lKdfP/74I7p27Yp69eqhXr168PX1LfX7oCrl/fcqsGvXLkgkEgwaNKh6A6yg8vYrOTkZkyZNQoMGDaCnpwd3d3e1/Fksb7/Wrl0LDw8PGBgYwMHBAVOnTkV2dnYNRVu606dPY8CAAbCzs4NEIsH+/ftLfU1wcDDatGkDPT09uLq6YsuWLdUeZ01jHmUeVQfMowrMo8UQVG127doldHV1xaZNm8StW7fE+PHjhbm5uUhISCiy/blz54SWlpZYs2aNCAsLEwsWLBA6Ojrixo0bNRx5ycrbr/fee09s2LBBXLt2TYSHh4tRo0YJMzMz8fDhwxqOvGTl7VeB2NhY0bBhQ9G1a1cxcODAmgm2HMrbr5ycHNGuXTvRt29fcfbsWREbGyuCg4NFaGhoDUdesvL2a8eOHUJPT0/s2LFDxMbGiiNHjogGDRqIqVOn1nDkxTt06JCYP3++2Lt3rwAg9u3bV2L7O3fuCENDQzFt2jQRFhYm1q9fL7S0tERgYGDNBFwDmEcVmEdVi3lUgXm0eCyoq1GHDh3EpEmTlI9lMpmws7MTAQEBRbYfPHiw6NevX6Fj3t7e4oMPPqjWOMurvP16WX5+vjAxMRFbt26trhArpCL9ys/PF506dRI//fSTGDlypFqeCMrbr++++064uLiI3NzcmgqxQsrbr0mTJolevXoVOjZt2jTRuXPnao2zospyIpg1a5Zo2rRpoWNDhgwRfn5+1RhZzWIeLRrzaM1iHlVgHi0ep3xUk9zcXFy5cgW+vr7KY1KpFL6+vrhw4UKRr7lw4UKh9gDg5+dXbHtVqEi/XpaZmYm8vDxYWFhUV5jlVtF+LVu2DNbW1hg7dmxNhFluFenXn3/+CR8fH0yaNAk2NjZo1qwZVq5cCZlMVlNhl6oi/erUqROuXLmivJx5584dHDp0CH379q2RmKuDJuSMymAeLR7zaM1hHv0X82jxtKsyKPpXUlISZDIZbGxsCh23sbFBREREka+Jj48vsn18fHy1xVleFenXy2bPng07O7tXfoBVqSL9Onv2LH7++WeEhobWQIQVU5F+3blzBydOnMCwYcNw6NAhREdH46OPPkJeXh4WL15cE2GXqiL9eu+995CUlIQuXbpACIH8/Hx8+OGHmDdvXk2EXC2KyxmpqanIysqCgYGBiiKrGsyjxWMerTnMo/9iHi0eR6ipRq1atQq7du3Cvn37oK+vr+pwKiwtLQ3Dhw/Hjz/+CEtLS1WHU6Xkcjmsra3xww8/oG3bthgyZAjmz5+PjRs3qjq0SgkODsbKlSvx7bff4urVq9i7dy8OHjyI5cuXqzo0onJhHlV/zKN1D0eoq4mlpSW0tLSQkJBQ6HhCQgJsbW2LfI2trW252qtCRfpV4IsvvsCqVatw/PhxtGjRojrDLLfy9ismJgZ3797FgAEDlMfkcjkAQFtbG5GRkWjcuHH1Bl0GFfn3atCgAXR0dKClpaU85uXlhfj4eOTm5kJXV7daYy6LivRr4cKFGD58OMaNGwcAaN68OTIyMjBhwgTMnz8fUqnmjS8UlzNMTU01fnQaYB4tCvNozWMe/RfzaPE0r+caQldXF23btkVQUJDymFwuR1BQEHx8fIp8jY+PT6H2AHDs2LFi26tCRfoFAGvWrMHy5csRGBiIdu3a1USo5VLefnl6euLGjRsIDQ1Vfr3xxhvo2bMnQkND4eDgUJPhF6si/16dO3dGdHS08sQGALdv30aDBg3U4iQAVKxfmZmZryT7gpOdYu2K5tGEnFEZzKOFMY+qBvPov5hHS1CuJYxULrt27RJ6enpiy5YtIiwsTEyYMEGYm5uL+Ph4IYQQw4cPF3PmzFG2P3funNDW1hZffPGFCA8PF4sXL1bb7Z7K069Vq1YJXV1dsWfPHhEXF6f8SktLU1UXilTefr1MXVenl7df9+/fFyYmJmLy5MkiMjJSHDhwQFhbW4vPPvtMVV0oUnn7tXjxYmFiYiJ++eUXcefOHXH06FHRuHFjMXjwYFV14RVpaWni2rVr4tq1awKA+Oqrr8S1a9fEvXv3hBBCzJkzRwwfPlzZvmC7p5kzZ4rw8HCxYcOGWrltHvMo86iqMY8qMI8WjwV1NVu/fr1wdHQUurq6okOHDuLixYvK57p37y5GjhxZqP3u3buFu7u70NXVFU2bNhUHDx6s4YjLpjz9cnJyEgBe+Vq8eHHNB16K8v57vUhdTwRClL9f58+fF97e3kJPT0+4uLiIFStWiPz8/BqOunTl6VdeXp5YsmSJaNy4sdDX1xcODg7io48+Es+fP6/5wItx8uTJIv+vFPRj5MiRonv37q+8plWrVkJXV1e4uLiIzZs313jc1Y15lHlUHTCPMo+WRCKEho7RExERERGpAc6hJiIiIiKqBBbURERERESVwIKaiIiIiKgSWFATEREREVUCC2oiIiIiokpgQU1EREREVAksqImIqERxcXGYM2cOevbsCRMTE0gkEgQHB6s6LCIitcGCmoiIShQZGYnVq1fj0aNHaN68uarDISJSOyyoiYioRG3btsXTp09x+/ZtTJs2TdXhEBGpHRbURER1VFZWFjw9PeHp6YmsrCzl8WfPnqFBgwbo1KkTZDIZTExMYGFhocJIiYjUGwtqIqI6ysDAAFu3bkV0dDTmz5+vPD5p0iSkpKRgy5Yt0NLSUmGERESaQVvVARARkep4e3tj1qxZWL16Nd58800kJCRg165dWLt2Ldzd3VUdHhGRRmBBTURUxy1ZsgQHDhzAyJEjkZ6eju7du+OTTz5RdVhERBqDUz6IiOo4XV1dbNq0CbGxsUhLS8PmzZshkUhUHRYRkcZgQU1ERDhy5AgAIDs7G1FRUSqOhohIs7CgJiKq4/7++28sW7YMo0ePRuvWrTFu3DikpKSoOiwiIo3BgpqIqA7Ly8vDqFGjYGdnh3Xr1mHLli1ISEjA1KlTVR0aEZHG4KJEIqI67LPPPkNoaCiCgoJgYmKCFi1aYNGiRViwYAHeeecd9O3bV9kOAG7dugUA2L59O86ePQsAWLBggWqCJyJSExIhhFB1EEREVPOuXr0Kb29vTJw4EV9//bXyuEwmg4+PDx49eoRbt27B3Ny8xEWKPI0QUV3HgpqIiIiIqBI45YOoCDKZDHl5eaoOg4iI1IyOjg7vIEqvYEFN9AIhBOLj45GcnKzqUIiISE2Zm5vD1taW+7WTEgtqohcUFNPW1tYwNDRksiQiIiUhBDIzM/HkyRMAQIMGDVQcEakLFtRE/5DJZMpiun79+qoOh4iI1JCBgQEA4MmTJ7C2tub0DwLAfaiJlArmTBsaGqo4EiIiUmcF5wmutaECLKiJXsJpHkREVBKeJ+hlLKiJqEK2bNkCc3NzVYdBRESkciyoiTTcqFGjIJFIIJFIoKurC1dXVyxbtgz5+fnV+rlDhgzB7du3q/UzSvNi33V0dGBjY4PXX38dmzZtglwuL9d78ReEqlHwb7Jq1apCx/fv31/jo3oFPxsSiQRGRkZwc3PDqFGjcOXKlXK/V48ePfDpp59WfZAEAAgODoZEIuEOS6SxWFAT1QJ9+vRBXFwcoqKiMH36dCxZsgSff/55kW1zc3Or5DMNDAxgbW1dJe9VGQV9v3v3Lg4fPoyePXtiypQp6N+/f7X/UkFF09fXx+rVq/H8+XNVh4LNmzcjLi4Ot27dwoYNG5Ceng5vb29s27ZN1aHVGS/+YlPU15IlS1QdIlGlsaAmqgX09PRga2sLJycnTJw4Eb6+vvjzzz8BKEYMBw0ahBUrVsDOzg4eHh4AgAcPHmDw4MEwNzeHhYUFBg4ciLt37wIAjh49Cn19/VdGi6ZMmYJevXoBKHpE97vvvkPjxo2hq6sLDw8PbN++Xfnc3bt3IZFIEBoaqjyWnJwMiUSC4OBgAMDz588xbNgwWFlZwcDAAG5ubti8eXOZ+t6wYUO0adMG8+bNwx9//IHDhw9jy5YtynZfffUVmjdvDiMjIzg4OOCjjz5Ceno6AMXo2OjRo5GSkvLKSX779u1o164dTExMYGtri/fee0+5ZRYVzdfXF7a2tggICCix3dmzZ9G1a1cYGBjAwcEBn3zyCTIyMgAA33zzDZo1a6ZsWzDCvXHjxkKfs2DBghI/o2C/YGdnZ/Tu3Rt79uzBsGHDMHnyZGXB//TpUwwdOhQNGzaEoaEhmjdvjl9++UX5HqNGjcKpU6ewbt065c/H3bt3IZPJMHbsWDRq1AgGBgbw8PDAunXryv39qu3i4uKUX2vXroWpqWmhYzNmzFBZbFU1wEDEgpqoFjIwMCh0oggKCkJkZCSOHTuGAwcOIC8vD35+fjAxMcGZM2dw7tw5GBsbo0+fPsjNzcVrr70Gc3Nz/P7778r3kMlk+PXXXzFs2LAiP3Pfvn2YMmUKpk+fjps3b+KDDz7A6NGjcfLkyTLHvXDhQoSFheHw4cMIDw/Hd999B0tLy3L3v1evXmjZsiX27t2rPCaVSvH111/j1q1b2Lp1K06cOIFZs2YBADp16vTKib7gJJ+Xl4fly5fj+vXr2L9/P+7evYtRo0aVO6a6REtLCytXrsT69evx8OHDItvExMSgT58+ePvtt/H333/j119/xdmzZzF58mQAQPfu3REWFobExEQAwKlTp2Bpaan85SsvLw8XLlxAjx49yh3f1KlTkZaWhmPHjgEAsrOz0bZtWxw8eBA3b97EhAkTMHz4cFy6dAkAsG7dOvj4+GD8+PHKnw8HBwfI5XLY29vjt99+Q1hYGBYtWoR58+Zh9+7d5Y6pNrO1tVV+mZmZQSKRFDpmbGysbHvlyhW0a9cOhoaG6NSpEyIjIwu91x9//IE2bdpAX18fLi4uWLp0aaErUffv38fAgQNhbGwMU1NTDB48GAkJCcrnlyxZglatWuGnn35Co0aNoK+vj23btqF+/frIyckp9FmDBg3C8OHDq+m7QrWOICIhhBBZWVkiLCxMZGVlqTqUchk5cqQYOHCgEEIIuVwujh07JvT09MSMGTOUz9vY2IicnBzla7Zv3y48PDyEXC5XHsvJyREGBgbiyJEjQgghpkyZInr16qV8/siRI0JPT088f/5cCCHE5s2bhZmZmfL5Tp06ifHjxxeK7d133xV9+/YVQggRGxsrAIhr164pn3/+/LkAIE6ePCmEEGLAgAFi9OjRFer7y4YMGSK8vLyKfe1vv/0m6tevr3z8cn+K89dffwkAIi0trcxx1iUv/pt07NhRjBkzRgghxL59+8SLp5yxY8eKCRMmFHrtmTNnhFQqFVlZWUIul4v69euL3377TQghRKtWrURAQICwtbUVQghx9uxZoaOjIzIyMoqNBYDYt2/fK8ezsrIEALF69epiX9uvXz8xffp05ePu3buLKVOmlNh3IYSYNGmSePvtt0ttV1XkcrnIyMlTydeL+aOsivt/dvLkSQFAeHt7i+DgYHHr1i3RtWtX0alTJ2Wb06dPC1NTU7FlyxYRExMjjh49KpydncWSJUuEEELIZDLRqlUr0aVLF3H58mVx8eJF0bZtW9G9e3fleyxevFgYGRmJPn36iKtXr4rr16+LzMxMYWZmJnbv3q1sl5CQILS1tcWJEyeK7Iemni+o+vDGLkTVQC6XIzExEVZWVpBKq/9C0IEDB2BsbIy8vDzI5XK89957heYlNm/eHLq6usrH169fR3R0NExMTAq9T3Z2NmJiYgAAw4YNQ8eOHfH48WPY2dlhx44d6NevX7EL98LDwzFhwoRCxzp37lyuS+ATJ07E22+/jatXr6J3794YNGgQOnXqVObXv0gIUWgR3PHjxxEQEICIiAikpqYiPz8f2dnZyMzMLHHv8StXrmDJkiW4fv06nj9/rlzseP/+fTRp0qRCsdU4uRzITAIMLYEa+HkssHr1avTq1avIS/rXr1/H33//jR07diiPCSEgl8sRGxsLLy8vdOvWDcHBwfD19UVYWBg++ugjrFmzBhERETh16hTat29foX3jhRAA/t36TCaTYeXKldi9ezcePXqE3Nxc5OTklOm9N2zYgE2bNuH+/fvIyspCbm4uWrVqVe6YKiorT4Ymi47U2Oe9KGyZHwx1q7aMWLFiBbp37w4AmDNnDvr164fs7Gzo6+tj6dKlmDNnDkaOHAkAcHFxwfLlyzFr1iwsXrwYQUFBuHHjBmJjY+Hg4AAA2LZtG5o2bYq//voL7du3B6CY5rFt2zZYWVkpP/e9997D5s2b8e677wIA/ve//8HR0bFCV0CobuKUD6IqJpfLsWLFCowdOxYrVqwo924TFdGzZ0+EhoYiKioKWVlZ2Lp1K4yMjJTPv/h3AEhPT0fbtm0RGhpa6Ov27dt47733AADt27dH48aNsWvXLmRlZWHfvn3FTvcoi4JfLAqKGeDVmyL4+/vj3r17mDp1Kh4/fozXXnutwvMrw8PD0ahRIwCK+dv9+/dHixYt8Pvvv+PKlSvYsGEDgJLnUGZkZMDPzw+mpqbYsWMH/vrrL+zbt6/U16kVuRw48wXwx2TFnzXw81igW7du8PPzw9y5c195Lj09HR988EGhn7/r168jKioKjRs3BqDYWSM4OBhnzpxB69atYWpqqiyyT506pSy8yis8PBwAlD8fn3/+OdatW4fZs2fj5MmTCA0NhZ+fX6n/xrt27cKMGTMwduxYHD16FKGhoRg9erTm/GyooRYtWij/XnBb74I1C9evX8eyZctgbGys/CqYhpOZmYnw8HA4ODgoi2kAaNKkCczNzZX/5gDg5ORUqJgGgPHjx+Po0aN49OgRAMUakYIda4jKgiPURFUsMTERISEhiI+PR0hICBITE2FjY1Otn2lkZARXV9cyt2/Tpg1+/fVXWFtbw9TUtNh2w4YNw44dO2Bvbw+pVIp+/foV29bLywvnzp1Tjh4BwLlz55SjuAUnsLi4OLRu3RoACi1QLGBlZYWRI0di5MiR6Nq1K2bOnIkvvviizH0DgBMnTuDGjRuYOnUqAMUos1wux5dffqks7F+e56qrqwuZTFboWEREBJ4+fYpVq1YpT9KXL18uVywql5kEPLwMpCco/sxMAoxrbneWVatWoVWrVsrFsAXatGmDsLCwEn9uu3fvjk8//RS//fabcqSwR48eOH78OM6dO4fp06dXKKaC+fK+vr4AFD+nAwcOxPvvvw9A8Uvx7du3C12BKOrn49y5c+jUqRM++ugj5bGCKzw1xUBHC2HL/Gr0M1/87Kqmo6Oj/HtBMVswKJGeno6lS5firbfeeuV1+vr6Zf6MlwcYAKB169Zo2bIltm3bht69e+PWrVs4ePBgecOnOowFNVEVs7Kygre3N0JCQuDt7f3KSIg6GDZsGD7//HMMHDgQy5Ytg729Pe7du4e9e/di1qxZsLe3V7ZbsmQJVqxYgXfeeQd6enrFvufMmTMxePBgtG7dGr6+vvi///s/7N27F8ePHwegWCjZsWNHrFq1Co0aNcKTJ09e2aFh0aJFaNu2LZo2bYqcnBwcOHAAXl5eJfYlJycH8fHxkMlkSEhIQGBgIAICAtC/f3+MGDECAODq6oq8vDysX78eAwYMwLlz5wrtFgEAzs7OSE9PR1BQEFq2bAlDQ0M4OjpCV1cX69evx4cffoibN29i+fLl5f5+q5ShJWDfTlFM27dTPK5BzZs3x7Bhw/D1118XOj579mx07NgRkydPxrhx42BkZISwsDAcO3YM33zzDQDFaGW9evWwc+dOHDhwAICioJ4xYwYkEgk6d+5c6ucnJycjPj4eOTk5uH37Nr7//nvs378f27ZtU05fcnNzw549e3D+/HnUq1cPX331FRISEgoV1M7OzggJCcHdu3dhbGwMCwsLuLm5Ydu2bThy5AgaNWqE7du346+//lKOfNcEiURS5dMu1FWbNm0QGRlZ7C9hXl5eePDgAR48eKD8BTgsLAzJycllmp41btw4rF27Fo8ePYKvr2+hkW6iUql2CjeR+qjKRSYymUzEx8cLmUxWBZGVrKSFeSU9HxcXJ0aMGCEsLS2Fnp6ecHFxEePHjxcpKSmF2nXo0EEAeGVxTlGLi7799lvh4uIidHR0hLu7u9i2bVuh58PCwoSPj48wMDAQrVq1EkePHi20KHH58uXCy8tLGBgYCAsLCzFw4EBx586dEvsGQAAQ2trawsrKSvj6+opNmza98r3/6quvRIMGDYSBgYHw8/MT27ZtEwCUiyyFEOLDDz8U9evXFwDE4sWLhRBC7Ny5Uzg7Ows9PT3h4+Mj/vzzz1cWV6o9mUyItATFn9WsqJ+32NhYoaurK14+5Vy6dEm8/vrrwtjYWBgZGYkWLVqIFStWFGozcOBAoa2trVwEKpPJRL169UTHjh1LjaXgZwOA0NfXF40bNxYjR44UV65cKdTu6dOnYuDAgcLY2FhYW1uLBQsWiBEjRhTqR2RkpOjYsaMwMDAQAERsbKzIzs4Wo0aNEmZmZsLc3FxMnDhRzJkzR7Rs2bLs37A6prRFiS/+f7x27Zryey2EEIGBgUJbW1ssWbJE3Lx5U4SFhYlffvlFzJ8/XwihWKDZqlUr0bVrV3HlyhUREhJS5KLE4v59kpOThaGhodDV1RW7du0qsR9clEgvkwjxwoRGojosOzsbsbGxyq2UiIioam3ZsgWffvrpK3vcBwcHo2fPnnj+/LnyykFoaChat26N2NhYODs7AwCOHDmCZcuW4dq1a9DR0YGnpyfGjRuH8ePHA1AsFv74448RFBQEqVSKPn36YP369cppd0uWLMH+/fuLnG4GACNGjMDBgwfx+PHjEq/I8XxBL2NBTfQPJkgiorrttddeQ9OmTV+ZovQyni/oZXVj4hURERFRMZ4/f47g4GAEBwfj22+/VXU4pIFYUBMREVGd1rp1azx//hyrV69+ZUcaorJgQU1ERER12t27d1UdAmk43tiFiIiIiKgSWFATvYTrdImIqCQ8T9DLWFAT/aPgDl2ZmZkqjoSIiNRZwXnixTs7Ut3GOdRE/9DS0oK5uTmePHkCADA0NFTe+paIiEgIgczMTDx58gTm5ubQ0qr626+TZuI+1EQvEEIgPj7+lZsOEBERFTA3N4etrS0HXUiJBTVREWQyGfLy8lQdBhERqRkdHR2OTNMrWFATEREREVUCFyUSEREREVUCC2oiIiIiokpgQU1EREREVAksqImIiIiIKoEFNRERERFRJbCgJiIiIiKqBBbURERERESV8P9h+GScY9J/swAAAABJRU5ErkJggg==\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Querying using slicing with np.s_[]\n", - "fig = plot_results_panel_2d(cycle,\n", + "fig = plot_results_panel_2d(cycle.state,\n", " query=np.s_[0:5:2], # [Start:Stop:Step]\n", " subplot_kw=dict(figsize=(8,3), gridspec_kw={\"bottom\": 0.25})\n", " );\n", @@ -415,7 +285,7 @@ "fig.suptitle('Cycles 0, 2, 4')\n", "\n", "# Last 2 Cycles\n", - "fig2 = plot_results_panel_2d(cycle,\n", + "fig2 = plot_results_panel_2d(cycle.state,\n", " query=np.s_[-2:], # You can use other list slicing conventions\n", " subplot_kw=dict(figsize=(8,3), gridspec_kw={\"bottom\": 0.25})\n", " );\n", @@ -423,10 +293,7 @@ "fig2.suptitle('Last 2 Cycles')" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { @@ -444,17 +311,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Simple multiple linear regression cycle\n", "random.seed(1)\n", @@ -516,71 +374,38 @@ }, { "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Plot cycle results with each cycle as one panel using defaults\n", - "fig = plot_results_panel_3d(cycle_mlr); # Add semicolon to supress creating two figures in jupyter notebook" + "fig = plot_results_panel_3d(cycle_mlr.state); # Add semicolon to supress creating two figures in jupyter notebook" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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Hk1DeIhbSui8kU1jsfm7GYglmYWhhBrQNBsPdwlxjiSI+xnHMlStXqFarvPTSS3ie14thpVKJUqnEpk2bUEpx9epVDh06RLPZ5MKFC2RZRl9fX6+DWalUTIJpMBjuSeb6bRQxbLk+mjf7/sUSzCiK6Ha7JsE0LAmTUN4C5lbdZ77olpNQrvTFutQE01hMGwyG2818xTaAc+fOceHCBcrlMi+88ELP+Xo+hBA9o4knn3wSpRStVouJiQkmJiY4e/YsSqkbEszi8LRYgllckxkhMBgMt5uZLtcw23hnuQ2JlZzp5o4CzBwhMDPqhsUwCeUaslDVfSa3okN5M2YmmMX9SSmJ45jvfe97PPLII5TLZZNgGgyGW8p8VfckSThw4ACTk5Ns3ryZLMtmHaAWYm6hrlKpUKlU2Lp1K0opGo1GTyJ7+vRphBC95LKvr49yubxogpkkCYcPH6ZerzM8PGwSTIPBcEuZ63I9N8Ys9/wIqz9DLjajfuHCBaIo4oEHHjAz6gaTUK4Vi9k5z+R2dCiXcp9FgtlsNnvXFMfxrOqTSTANBsNasFDVfWJign379vUkrhcvXmRqampZ9zsfQghqtRq1Wo1t27YhpaTRaDAxMcHVq1c5ceIEtm33Esz+/n7CMLwhwYyiqBfTzYy6wWC4Fcznt7Gc82Nh3jjf9681M+PjzI7lfCMEcyWyhvsbk1CuAfNV3RfiTnQob8ZCHcwoihZdU2IOUAaD4WbMt1sS4OTJk5w6dYqHH364tyZkvvi4mDnZUrEsi3q9Tr1eZ8eOHUgpmZ6eZmJigtHRUY4dO4bnebMksmEY9g5qNxshmJlgmhl1g8GwVJbajIA706FcCvPtUTcz6m88TEK5CmZW3ZVSSxpSvtMdyqU+ZvGin2kxPTfBNEtyDQbDQixUdY+iiNdff51Op8MLL7xAvV7v3eZ2Fdwsy6Kvr4++vj4eeOABsixjamqKiYkJLl++zNGjR/F9HyklnucxNDSE7/u9a1yqCdrMA5SJjwaDYSaL+W3Mx+2YoVwO83VGzYz6GxeTUK4QKSVpmi6pqjSTu61DuVIHMLMk12AwLMRCVferV6+yf/9+BgcHeeaZZ3Cc2W9Bd6rgZts2AwMDDAwMAJCmKVNTUxw9epTJyUm+9a1vEYbhLIms53m96zAmaAaDYaksxW9jPu7WDuViLDajPp/CwySY9y4moVwmMw8LRXVmOQeDu71DeTNMgmkwGBZjvqq7lJJjx45x7tw5du3axebNm1c9Yw637rDkOA6Dg4OEYcj69esZHh7uGfycPXuWgwcPUi6XewY//f39uK7b+xkWMkEzM+oGwxub5Uhc53K3dShXwnwJptmjfn9gEsplUASCQ4cO0dfXx4YNG5b9JL9bDkwzWc0LdSkJ5sjICOvWres5yZoE02C4/yiq7mfPnqXVavHII48ghKDdbrNv3z6klOzevZtKpbLgfdytBTfHcRgaGmJoaAjQBj3FipLTp09z4MABKpXKLBfZmYegxRLMiYkJPM9jYGDAJJgGw31MlmWMjY1x/PhxnnvuuVt+foQ736G8GTdTeLRaLaIoYsOGDWZG/S7HJJRLZGbVvdPpUCqVVvSEXu6B6W4PBnOZL8E8e/ZsL5k0S3INhvuPmVX3OI5pt9sIIRgZGeHAgQNs2rSJRx99dNbBYT7uxoLbfLiuy/DwMMPDwwBEUdTrYB4/fpxOp0O1Wp2VYM48BM1MMEdHR6lWq5TLZWOCZjDch8z028iyjEajccvPj8X330oWcpddDXMTzEajwfj4OIODg2ZG/S7HJJQ3YSGt+0ILt2/G3ZYk3uprKX7e4nC02JJck2AaDPcec12uLctCSsmBAwcYGRnhiSeeYMOGDUu6r5V0KG/FoWa5+L7P+vXrWb9+PQDdbpeJiQkmJyc5evQoURRRq9V6yWW9Xp91CCpGBOaaoC20RNzER4Ph3mC+3ZJrdX5M05RTp05hWRaDg4NUKpUb4sLddN5cKcX5EMyM+t2MSSgXYSGtu2VZK36R3u8dyvmYeeBbaEB7boJZHJxm6udNgDAY7h4WcrmO45jx8fHebskwDJd8n3eb5HWlyWoQBGzcuJGNGzcC0Ol0ehLZS5cukaZpL8EsDkVgZtQNhvuFhfw2itf1SphZRGs2m+zdu7eXSJ09exYhRE8VURiM3etnyLkx2Myo372YhHIBiifofA5ct6tDebteAHdSFnEzi+mZQXiuft4ECIPhzjCfyzXA+fPnOXPmDL7v88ILLyzbqW+lM0J3eywIw5AwDNm0aRNKqVkJ5vT0NNPT01y7dq13GKxWq733naUkmPMtEb/bfycGw/3K3GbEzNfxas+PABcuXODIkSPs2LGDbdu29WJmIQ+9cuUKx48fB+Ds2bN0u10GBgZ6q4/uJaSUi54fYfEZdZNg3j5MQjmHoltWVJXme2O+XR3K4nrudZZz4FtOgmmW5BoMt5eFqu5JknDw4EEmJibYvn07U1NTK3pN3o0Ft7VGCEGpVKJUKrF582Zee+21nonP5OQk586dQynVc4/t7+/vSdkWSjCllL0E08yoGwx3jpvtllzN+bFIRI8dO8YzzzzD0NDQrMeq1+vU6/Xebt3vfOc7OI7DhQsXOHz4MKVSadbqo8KZeqXcjmLecs+PMDvBXGiPukkw1x6TUM5gqbslV6uBX+ptb5ek63Y8xkp/FpNgGgx3BzMlrnD9tTk5Ocm+ffsol8vs2bOHsbExJiYmVvQY8yWUN0sy74eiWyGR3bp1a0/ONtNFVggxK8Esl8uLJpjFCIExQTMYbg9L3S1ZxLPlnouazSavvfYaAG9961sXdcsGeqqFTZs2MTAwQJIkPeOwwpl6PuOwu421OD/CjQoPM6O+9piEkuXvllzNbKPpUK6OhRLMq1evcvr0aZ555pl5q08mwTQYVs7MqvvMOfLTp09z4sQJdu7cyY4dO277jPmt5k5U4IUQVKtVqtUq27ZtQ0rZSzDHxsY4efIktm3P2oFZuI4vFB+TJOGb3/wmL7zwAp7n3XCAMiMEBsPKWc5uyZmvzaW+5i5evMihQ4fYunUrrVZrWdLVIp66rsu6detYt24dQG9l0cTExCzjsIGBAfr7+6nVanfFuelWnB/hxgTzu9/9Llu2bGFoaMjMqK+QN3xCuVDVfTEsy+oFjuWyUAV+oe+9HdzKxyl+1lv1GHOr9IWT7EwHMCGESTANhhWwUNU9iiL2799Pq9Xi+eefp6+vr3eb1Sg4ise8ld9/t3GzA5NlWdRqNWq1Gtu3b0dKyfT0NBMTE1y5coUTJ07gOM4sKVsQBLNioxCCLMt6yWPxNzUz6gbD6ljMb2M+in8vvn8xsizj0KFDXLlyhaeffpqBgQHOnDkzK+bdrPmxEL7vs2HDBjZs2HDDXPeFCxeQUlKv13sJ5nwOsjd7jLXgVhb1Zsa5Ij4WBVEzo7583tAJ5XxV96VwKzqUC71o7ofDEnDLE7hicHu+Cn3xd06SBOCGBHPmAnKDwaBZqOo+NjbG66+/Tn9/P3v27LlhDmc18XE53c375TW73AOTZVn09fXR19fXm5UqEszLly9z9OhRPM+blWAW8bA4DC3XBM0U4AyG2SzFb2M+Zr72FqNwcXVdl5deeokgCHqFupm3vdn9LCWezp3rVkrRarUYHx/vSWSLuFM4yIZheNtGpm5H/Jm59goWnlE3CebCvCETyqVq3RdiOXOQ89127otwoQPF7aj83GpudYeyYKFqX/Hin3k9c3cYzUwwZ1boDYY3KvNV3aWUHD9+nLNnz/LYY4+xZcuWBeOW6VAundVW4Av5a39/P6Ar7ZOTk0xOTnLx4kWOHDnSk8hduXKF/v7+3udmRt1gWD5L9duYj+K1s1iMLCSu27dvZ+fOnb3bFI9xq4tuQggqlQqVSqUnu5/rIOt5Ho7j4HkeURTdMgfZ25lQzp0hXUgiK6UkiiIzoz6HN1xCqZSi0Whw+fJltm3btqI//P3m8novS14LliIfKa7jZgmmZVk3HKDeiMHB8MajKLYdO3aMzZs34/s+Qgg6nQ779u0jTVNefPFFqtXqgvexmvg497ZpmnLu3DmCILjBlfB+naFcLbZtMzg4yODgIKB/h6Ojoxw9epRz585x6NChWW6PfX19eJ4H3DzBhPkdEk2CaXgjUJwXzp07R7lcplarLfu1u1hSOFfiWsw7zncdy7nm1TKfg+zU1BSnTp2i2WzyrW99a80dZAtuRwyGxdeTFMwdBZhrgrbQmpI3SoL5hkooi6p7q9Xi1KlT7NixY0X3s1qX1+IFrpTiwoULnD17dtYw9Mw393uduy2hnMvMBLO41vkSTGMxbbjfmSlxPXHiBOvXrycIAkZHRzlw4ADr169n165dN3UCXKsOZSH5Av2a7HQ6PVfCgYGBnsvhG71DeTMcx6Fer+M4Di+88MINbo+tVotKpdKTs/X19fUOg4uZ/JgZdcMbiZl+GxcuXGDTpk3U6/Vl38/MGcqZFPHOcZyexHW+2y6nKXGr4opt2wwMDDA+Pk6tVuOBBx64ZQ6ytzOhXMne5Pni4xt1Rv0NkVDO1bo7jrOqA89qZoSK60nTlEOHDnHt2jV27NhBp9Ph7NmzHDx4kEqlwsDAwKqvcynXcau52xPKmcyUNsD8S3KVUkxOTjI8PIzneSbBNNwXzIyPxRtkUS2/dOkSjz/+OBs3blzSfa2FguPy5cscOHCAbdu2sX37doQQxHHcm+k5ePBgz0jtwoULrFu3bkHTiLud23Fgmhkf57o9xnHMxMQEk5OTnDx5kna7PeswWCSjMP8Bau6M+uTkJPV6nVKpZGbUDfcFc/02bNte1dlsboy8dOkSBw8eZNu2bTz88MM3PcvcLSq3pTjIHjlyhDiOV+wgezcnlHNZyghBq9WaVeS7nxLM+z6hnM9YYrUuhKvtUCZJwl/+5V/iui4vvvhirx1eHJomJiYYHx9nZGSEOI754Q9/2HshVqvVe6r6ey8llHOZL8GM45gDBw7w0ksv9Q60poNpuFdZyOXasiz27duH67rs2bOHUqm05PtcTXwsrufgwYM89dRTrFu3rpfoRlFEkiQ89NBD7Nq1i2azyfe+9z2mp6c5f/48lmX1kqCBepnQ98ENV3QdM7kdsetOPobneaxfv57169cD868TmJtgFjFxvhGC48ePs3Pnzt6/mxl1w73KQn4bqz1DFiqOLMs4fPgwo6Oji0pcZ3IvGJetlYMs3L6xg1sxqzlfgnnx4kV838fzvPtuRv2+TiiLqvtc450iGKz0ibqaDmWj0WBiYoIdO3bw8MMPI4S24AcQrSv4WcL64Y2sX7+e8fFxDh061Pv/c+fOoZTqSb5m7h9bDbfyxXorO6wzuR2D28WbgBCiJ0suAlEURcRxzFe/+lX+9b/+13z1q1+9pddiMKyWoupevEaLg8qFCxdIkoShoSGefPLJFcmAVhIfO50OBw8eRErJj/zIj1AqlXr302q1+OIXv8jo6CiPP/4473rXu3qS1127duG6bs80Yuz8cVp/+S08S5Bt30Nl865ZowTL4XapOG5HQrlUydnMwyDov0shZzt8+DBxHFOv13sS2Xq9Pss0JMsyPM/Ddd0bZtQbjQYvvvgiR48epVar3bKf12BYLQu5XMPqZf2WZdFqtdi3bx+O47Bnzx7CcOnFr6WuDZn7vXcCIVbmIDtz1vRWxkfr4vexzn2LddMKS7x0yx4Hrj9vXNftxce5M+o/8RM/wa/92q/x7ne/+5Zey63gvkwo51bd5w7Ezuw4reSJupLqlJSSI0eOcPnyZSqVCo8++uisaxCNy9hnvoZIumRbX0QOP9677s2bN/deiEVCevXqVU6cOIHrurMSzFvltLVSej/fPdihnI8sy254Y4HrDmCTk5O0Wq1bfh0Gw0qZecifWWybKcP3fZ8tW7as6DW1kvh45coV9u/fz+DgIO12+4aOaLvdZmpqijiOGRsbu8E2f6ZphFWNsFSFbqfNlXj8hlGC1c70rDW3oxi2mi5oGIaEYcjGjRtv6DZcunSJNE2p1+u9DmaWZQt2MNvtNiMjI8s6PBsMt5ub7ZZcreRVKcXrr7/O9u3blyRxnclyvne141lLfYzlfv9SHGSLeFLMIt4Skg7e65+DiTNs79jY3b8O/qZb81g5xRkS5u9gFmqbe5H7LqGcW3WfL5mZadu8UiOX5bxIO50Oe/fuRSnFQw89xPj4+I33mXYQUQNkoj9y4wtVCDFrwXXhtDUxMcH58+c5dOgQ5XK5l2D29fX1Zl/m436pvsPtTSgXOogW+vhyuXzLr8NgWAkLVd2npqbYt28fQRCwZ88evv/976/aeGwpr30pJSdOnODs2bM8/vjj1Go1rly5csP3DQwM8Nxzz3H58mWeeOKJG5z2Zt1neT2ifwdBLWHzthfZVN00a5SgkHEWSdDAwMAdHSW43TOUq2G+bkO73e4lmOfPn++5BA8ODvbGNIqfr9VqEYbhXZPMGwwzmeu3sZA750olr4XENU1THnnkER588MEVXeftUn7djLWIXQs5yBby2EajwfT0NJ1OZ80dZLFdlN+HsBwSy0d4Sx/tWCmLnSGBnknavch9k1AuVHWfj6XsAVqM5QSTq1ev8vrrr7NhwwYee+wxLl++PG8iJ6ubyTY+g0g7ZEOP9r6+WNJXOG0NDAzw0EMPkSRJ7439+PHjdLtdarVa79C0nEHoteJ2JpSLJc9rxf0cDAz3NzONJYr4qJTizJkzHD9+nAcffJAHH3xw1TNCS5UqRVHE3r17SZKE3bt3U6lUaLVa88Y8y7J49tlnF3ysWYT9ZA+/H1Bga5nrzDnBmV228fFxzp8/3xslKGLlzFGC2zG/c68klHMRQlAulymXy2zZsoUsy/jGN75Bf38/U1NTnD17FoC+vj7CMOSHP/wh5XJ51T/vpz71Kf7wD/+QI0eOEIYhe/bs4dOf/nRP+TMfr7zyCj/90z8962u+7/cWlhve2CwmcZ3LSuLjTBfXIAhW5BBbPPZMFnst3Y4O5Voz91y7d+9egiBACLHmDrJYDvFbXya9uJdjJyd4MVjZ32Q53M9nyPsioVxOICj+HVaeUC7lRTq38r5p06bFb+v4yC1vveFxloPrugwPDzM8PAwwS5q0f/9+pJSzdOqFLfWtPMzcTTuE1upxFgsG7Xb7ng0GhvuThYwl4jhm//79NBoN3vKWt9Df39+7zWoSypnynYUYGxvj9ddf73UeZ7qILvcANO/32wtXsOfrsjWbTcbHx7l27RonT57EcRwGBgaI47g323KruJcTyvkeB2Dr1q04jjNrTOM73/kOv/ALv4CUkg996EP82I/9GD/zMz+zosf5xje+wcsvv8zzzz9PmqZ8/OMf5z3veU9PpbMQtVqNo0eP9j435kAGWNhvYyEKF+ylMtfF9Vvf+taquoxLve398PwWQlCtVtm8eTMwv4PsTMn9chsnqjxMtOVHiC69esfPkFmW0e1279kz5D2fUM5Xdb8ZhQz2VnUooyhi3759RFF0wxLw5R6YVlNdKmZfNm3atOAgNMDIyAjr1q2bd+/RarkfJa+LPY6RvBruJhYqto2Pj7Nv3z76+vrYs2fPDYY1a5FQzvfGqZTi1KlTnDp1ikcffZStW7fOig/LNWJYi9hSHFiq1eoNowRJknDkyBHOnTvXm7/s7+9fUzXE7XIxvJ0J5cwZypljGuVymV/91V/l2Wef5dixYyt+nC9+8YuzPn/llVcYHh7mBz/4AW9/+9sXvJ0Qomc2ZDDczG9jISzLWlKhaaaL61NPPdUr9q+mczj3+q5du0an02FwcHBeD427bYZyucxtFizFQbZonNzMQXahx7iVLHaGbDabACahvN0sVHVfKquVdC30Ii0OagMDAzz77LM3HDyWE0jW8gk+3yD0xMQE+/btY3R0lBMnThAEwaxD01ro1O/HhHKxDmWz2bxng4Hh/mK+qrtSihMnTnD69Ol5E7qCtZK8zqToiDabTV544YV5JV8rcfZb6wPTTMnV2NgY27Ztw7ZtxsfHOXnyJJ1Oh2q12ouVM11OV8L91KG8WWE3iiIGBwf5+Mc/vqaPOzU1BehZ28VoNpts374dKSXPPvssn/zkJ3n88cfX9FoM9wbzuVwvlaXEx1arxd69e7Es6wYX19Xu6pVSIqXk6NGjXLx4kTAMOXLkCOVyedYZ7nYUqm41i8XH1TrIFtyu+AiLnyELQ8d79Qx5TyaUy5W4zsdyJQtzbzs3mCilOH36NCdPnlz0oHY7O5SLUQxCAzz11FMIIZicnGR8fHyWTn3moWklOvV7OaFMsowkA9cWuLa+76Xo34t9bgbDnWChqnu322Xfvn3Eccxb3/rWRdc2rDY+wmxZ1tTUFK+99hq1Wo09e/YsWKxaiWPgrcZxHIaGhno74rrdbm/+snA57evrW9JOtbks1bxotdyK+KiUIs4kSkHg6ph4MwXHrSi4SSn52Mc+xksvvcQTTzyx4Pc9+uij/Lt/9+9485vfzNTUFL/xG7/Bnj17OHjwIFu2bFnTazLcvSzHb2MhbpZQXr58mQMHDrB161YeeeSReeceV1Owi+OYV199lSzLeOGFF3Bdt9ckGB8f59ixY0RR1NtpWKlUZplj3UssJz4u10F2YGAA3/fvqoQyCILb4gdyK7jnrjrLMkZHR7l69SqPPvroil8ga9mhTJKE119/nUajsWDlfeZtl/M4twMhRO/QNDQ0BFzXqY+Pj3P48GGSJJm1iHapwel2JpQAUZqRZJIoVSSpJJGKOJUkmSTJFKlURGmGVNCJ9YG5GWXYFjS6KZYFzW6GJQTdNKPiO3zo2U3k56UlzVAayavhTiGlpNVqceDAAZ5++umevL9YyzE8PDxrZnEhVltBL65FKcW5c+c4duwYO3fuZMeOHTc1kShuu9SD3q2sks9330EQsHHjxt4ajVar1YuVRUV85iqnpazIuFMJZSolUSKJU0k3LT5mpJmik2SkmaQdZ6RS0YmL2CrpJhIhoJtIPvj0Rjb16Z/xZgW3WxEfX375ZQ4cOMBf/MVfLPp9u3fvZvfu3b3P9+zZw65du/id3/kdPvGJT6zpNRnuTopmRLGyY6VJ1kLnxyzLOHLkCCMjI7MkrvPdfqVxS0rJwYMHGR4e5vHHH+/9TK7rUiqVGBgYwHEc2u02+/fvp9vtsnfvXoAbdpjfC6xGrn8zB9nDhw/3Opwzf4+3iqKYsVhCuRa75e8U90xCObPqXiQ7q/mlr3ZGqLjt1NQUe/fupVKpzDuLNJflVqbulEPXXJ16YQ0/Pj7OmTNnEELccGia7+9xs2CgEz1JnOkEMM4TvzjNSKT+9ziVJKkkVYpuIpFK0U0ylIJWnOFYgiOnOqy7OoU4mVD1HRpROuOjTaOrP+9mEt+2SKXEybuOQgkQCgEgBZ4tUEDVd3jPrnW96jssbYbyXpUrGO5dZlbd0zTtrd5QSnHkyBEuXLgwyxzsZqwmPha3T5KEY8eOMTExwXPPPXdTOSLMn1gtJQG9U8ysiG/duhUpJdPT00xMTHD58mWOHj1KEASzYuXMA0sR31f6cyiliFKZ/5cR5bGyk0jSTH/MlOLk2QZZmnGwc1EnwVGGVLrYNlNmbFmCNFN4jiBOJYFrkaSKkmeTSInvWCQSAtciU4pnN9V7ySQsTcGxlgnlz/3cz/GFL3yBb37zm8vuMrquyzPPPMOJEyfW7HoMdy8z/TbGx8fZtGnTmjYkFpO4LuX2N6Nw5O52u2zbto1du3YhhOgpUfbu3ctrr73G+vXrede73kWpVCIIAgYHB9m8eXOvUzc6OsoPfvADoiji4YcfZtOmTasacbqXXLDn24wwOTnJpUuXSJKEP//zP187B9l5mDtjPpdms3lPNyTuiYRSSkmapj0JluM4K5ZjFay2Qyml5Ny5cxw9epSHHnqIBx54YM1NJO4W/ftMa/hNmzfTjVMmpqa5OjbJoTOXmNh3DNtxCas1SpUqQakClk2cKianpjgzkjJxYJRWrBfUNrspji2Y6iSUPIdWlFL2HVpxRsVzaMYpZdeik0oqvkM3kYSuRSIlnm2RSoVjCTIJlgWJVASWRFiCvsAhzhR9gUM7yaj4Fq04JXBtWkmGY1l00gyBIJOSTIFnC6I0peS5xElK6DqkUvHWHXWGa7ONim73gclguBlzRwCKg0Gj0eDgwYOA7sYs53m5Gslrwd69eymVSuzZs2des4j5mNmhXCp3ky1+Ma/T19fHAw88QJqmTE5O9uZ5ZlreDwwMUKlUSKWiFaU0EogSnRTGme4cJpmkk2RkmaKd6E5hO87IpO4SpplCUfz8AlAIBLKXHEoCx2JksotnW6hOgudYCBSBY+HYAtuCNAPHFsSJpOTZtOOMauDQ6KZUfJfpKKHsO0x2UkquzVSUMlD2eMv2/lk//80UHGtVcFNK8fM///P80R/9EV//+td54IEHln0fWZaxf/9+3v/+96/6egx3L/P5bdi2var4Njc+Xr58uSednk/iOpflNhaSJGH//v1MT09TKpUYGhq64Xx4/PhxxsfHeyY1GzdunLX6qDDHGhwcZN++fZw+fZqJiQmeeuqpWSNOAwMDS54Lv9MzlKvFdd3eKEMcx7z5zW9eUwfZucwc05uPIj7e6ULpSrmrE8qZVffiSSWEwLbtVS92XU1CWQSoEydO8OyzzzI4OLjk296JGUo9C6iIUzWrI9iJYs41Yd/FaTIlyJQiSmTe+UsRQDPOsIWgEaW4lmC6m1LOk76q79FIhyj3DXNtqok1FjFx5iIqjXFcj3olRAqbKFZcbcbYFmRS4dqCRCrqgUsiFX2hS5RJ6qFDO8qoBw7T3ZSKrz9WfYepbkrF0weasmcz3UkJHYs41cllIkEgSJXCtkCi8s6iouw7oMCxbASQSgvbFsSpouwKmlFGxfd63czpKOWR4QqPb7pxxizLskUreaZDabidLOZy/eqrr7JlyxYeffTRZb8JriY+Xrx4ESklg4ODPP7447e0iHY73nhv6hCotIw+SuSMbqH+L8kk3USQiX7a1RpxkHB6fJr2lQmuTl4AmdFOFPs7RxBuQH+1RDvOKPkO7bzQ1o51DIzSjMC1SfIOoiPA920yeb2z6FgQpYrQFrQSXZBrRCmeLehkqlfIq/oOE/nH6XZK6Nt0YolrWyR5EqqUVmkooaiHLlIqKr6DVDq2/g+PDePMeV7drhnKl19+mc9//vP8yZ/8CdVqlZGREQDq9XqvM/ThD3+YzZs386lPfQqAX//1X+fFF19k586dTE5O8pnPfIazZ8/ysz/7s6u+HsPdyUJ+G6s9QxbxcabE9cknn1yyf8JyJK+NRoPXXnutV5z7/ve/P+9tH3vsMTqdDsPDw7PUIHO/t9vt0mw2cRyHMAx54YUXiOO4Z2Rz8ODBWXPhAwMDa7I7dqXcjrGpIm7dCgfZuY8DCyeU9/rI1F2bUM4NBEUyCay6ugQrPzAVy2mVUuzZs2fZqzbmSyjnfk1LkTJacUYjVoxMdYkzXY2OZySEqcwTQHTVWiBoximOEEx1EzzbYrKjK8uNPCFr5pXmdpzhW4pj4yAvTqMQ+LZFkkocx0JJBRZYCGIUrqWr3rXAIZGSeuDQTSW1QFezB+tVOkmJhwaHaHRiRBox3miTdhu0Y4k8fxbHLzNQK2M5Hl7ecXQsnch6tkApKPs2qdSPU3yMU0nFt4nTjNC1idIMxxZEUv/OZKalW6lE/1yOlX+vQ5JJQtcmlgrPFaQpBK4glVDyIJVQLzmkeVczlpKNNZ93PjK/PG+xCnwhDb6XA4Lh3mAhl+s0TTl8+DCgDxhbt25d0f2vJD7OtMh3HGdBY7LFuFMdyjjNiFKdGBbzhFGacWIioXGhgT+u8uRQkkhJN8nIMmgnuvAWZwrftuimWk3RSTKtvohTKq5NlCl81yKTCr9UwgnK9A8KOt0OF86fJ0ki4ulJJq8JquWQrhfSVwlpxVANZhfWqnmnsBo4THYSqp5DK8oIPYsoTzalgopvIxW6UDepqHoOUkItcImSjJJr005SbMvSM5EKpJJzJK82nUTPkkeJpORZZBLevLnOuuqNXeelzFAupwC7EL/1W78FwDvf+c5ZX//3//7f85GPfASAc+fOzTq4TUxM8NGPfpSRkRH6+/t57rnn+Pa3v82b3vSmVV+P4e5jsd2Sqz1D2rZNmqZ897vfRQjB7t27lzWTuNQOZbG/8oEHHuChhx7qnYPni3lPPvkkO3fuxPf93vN+vvg7NDTESy+9xMWLF3nyyScRQuD7/qJz4bZt91QVhZHN7eJOrVWaz0G22Wz2EsyZDrI3G/0qKM6PC32PkbzeAm62W3It5FgrOTAVL+5NmzZx/vz5Fe1tXKxDebUR8aevj2Bbgnac4QnJ0UswcfgqjW5CxXfoJCklVyd1rmMhM7BtUEp36AQKJYTu2mU68YvyZKmT6i5gM9YJ2lQ7xrWhHWWUfZdGN8V3bZr5xzTK8F2bTEps2+q9sHXfD3xHvwDLvnYYqwa6el4vecSZw7ZKhfHpBtbkBF5YI4k6nDo3hWUpPD+kUgrxgpDAcxDCwhYghKUlXBYIpQCBa+sgGrgWCHCEg7AUqQRb6M6raykUgrJn04gyqr5LI0qpBQ6T3ZSKZzPZ1t3NqY6u9EepxLUEsdLzkxngWjZ/5dEh3EUWz95sbcjMvaMGw1qzUNV9enqaffv24Xle741upRQHpqXSbrd57bXXsG2bPXv28N3vfndFBbuVzlBmudlWN82IU0WUZMSZ6klIo7T4XJJKbTSjFDSjFEvoQlShvKj4Ds1IJ2yHr2VMO20sT8fMbiLxXSvvBmr3Z0vQM+2q2Lbu4Hk2qZT4tqCTSWwh6OTz3o2uwrUt2lmKJWw6KWwb3oBUiixNaLdadNtNzo5fw7EtWqUS1VKZ2A6pBy5xpjuPnTgjcK1cyi9oRxm2gFam3wtAgQIhoJOk2I5DpiQ2Wurq2ALHcnR3UyocoRUeZU93OauhTlSL30fZ0wnt+mrAs9v75v17LGUkYK0krzfj61//+qzPf/M3f5Pf/M3fXPVjG+5ulrJbcrVnyMnJSVqtFtu3b1+RAuRmSjUpJYcPH2ZkZISnn366J8u82W3nm9ucr4Hx7LPP8uyzzy54bXPnwgsjm4sXL3L48GHK5TL9/f10Op1b7kh6t7hgC3F9X/FcB9nR0VGOHTs2r4PsTO73kam7KqFc6m7J2y15nfnifuqpp6jVapw/f35FT/SFgkEzSvnq0au9w0otcGh2IkoOdJKMaujmXUaXZpwQ5lVpz7boRrJnJCOEwEIhxfXZGDfvAoZ5clj1bFKlqAU2IwrdBZSKcmDT6qaEnpZZhZ6eawxdm3aeXLY7MaHnkMR50plJPMdCSoVjWzhC/2499ERPYAnatsX6oT6UqiOlIooTmq0WnXabq9fGcR0LLwiolcvYnk858GhFepax2Y6pBHqOsuTZxKnEdwRJBp4NqRSUPC3PqgQ2CugLHZJM0Rc6RKmk6uvkMXQtukmGlSfbWf73ixPdzWzEGe/dNUh/aWFjJWPKY7iTLLRb8uzZsxw7dqxXyf7a1762JpKupTA6Osr+/fvZvHlz73C1Ulv8+XZYzvz/752ZYHS6Sya1C+nRcyn7upewPQ/P0h3CkmfRmjEDWM3lnGXfIUp0MSlVCkvouUEL3bWTSlHLY0ctcOnEKaEDrViyrmzrrqDv9iSjU7ksv9lNqQRuLkm1yFJdbMuEwhaQ5UmdkoAASyr9ubJIU70cXSqtlnBdj7DqUuvrJ04y0iRmutnkyvg4nU5MOfTwg4B6tYLnh3iOhWtpyWsmFbbQ4wS2EESpTn6bUarfJzIYsqzr3c5OPlbQSah4No1Ydzm7iU4uswxKroUQUA+cPLa6vOuxIawF3veWcmAyBTfDrWLubsmZyraZrPQMmWVZb/ej53ns2rVrRde5mOS10+n0VHDzdT7nniFvtWlZ4Vrd39/Pgw8+SJIkvS5dYUA2NTXV615Wq9U1XcFxtySUc1nIQXZ8fHyWg2zxu+vv77/vz493TUK5nN2SRTBYzRNtqQGl3W73LJeLF3ccx8DNDQjmY76EMk4zvnL4Ku1E4lrkHUCF71hIpah4FqnUiVKc6tnDdiFZijN81yJOtWtplikQOpnLUoVrgczAtSySLMO1BXGmcB2IM7At3d0MXS1lqocucZ7Qxpmi6tt0U0k50J3Lsu/qanV+kCrktCXPYaodUwlcWt2E0LdJUgVCW0bo6rc+aNmWRa0UkCqwlKTZ6RJ1WkxPT5HEEVgu9UqJthswVC/TjHX3c6bca9Y8ZTfVJhWpouQokvzXm0lwLIElBL6jD0Z6jlIn2HaeWNZCl3ac8dTmGjuHF68OLXZgKiSv93JAMNydzKy6F/IcIQRJknDgwAEmJydnOamuhenEzeKjlJJjx45x4cIFnnjiCTZs2DDr9qtZOzLfbY+ONHj9wpR2Hs20DF8phe8IfM/WM3+2lsxXfYdunFH1nHxG2maqqxPByVztMd0pzMASQt8lTTM8x8FCAYrQs7EQVAM9e90XuMRSqzw6saQW6CJf2XeYzu+7SNImOwlV32Wym0tSY30NUaq0wVimKPsWHSQlBzzHoiQEidTzi91UUQk82pbFtmqZRpRScgRXp5pYacSZS1dxSZG2z2CthHJ9hmolOokidCwSpZ1YpYJa6DCNoh7o+cd6oJPmqu/QTfWIQCct1i4pskyiHJsoTrVBT3K9a/sjO4cYKC8sdzNrlQx3goX8NhZiJfGxOAsKIXjyySd7owUrYaH4eu3aNfbt28f69evZtWvXvK+lO73L3HVdhoeHGR4e7iVilUqFiYkJzp8/DzBr/vJmMtCbcbsSytU+xkwHWaDnIDsxMcGpU6dot9uEYUiWZYyNjc3rIGs6lGuAlJI4jpe8e6zI8LMsW3G7fSkHpmJ328aNG3nsscdu0KWv5IV6w7ykVHzp0FUuT0f4jqCdKBzHwpLavVQpSHIzhVTq2ZZMKsq+lpfWQj3/V3zuOHolRvEbzHIpbJTJ61Jax2K6k+FaiigDJaATSwJXEGf05hp9R5BJQclzyHITnVSqPOmU1ENXd08DfWirlrzeeo7i43QkcSyLyW5KzdeSspJr00mlNtVRgmq5RKkUsm6dIEky4m6b6VaHqDHOybErlAKPjl+iVikRpVrW2k2zfGYp0wdrqX/WTEGS6J+xlaSUfTuXs+WHOUcQS4lnabdY39FzTZvqAbsfvPlag8UOTFEUkaapSSgNa8pcl+siRk5MTLBv3z6q1SovvfTSrJVFq5V03Sw+FrvNsixj9+7dN7wJrnZx99zYOtaM+fqxa/iOxVQ+Fz7ZTRCWpU1nArR6wbUR6O5j4OpZlVqeSPXlcas/1AW5WujQ6Ga9IlnFd2h0E0r5mqHAtWmnel7dSjJcRyfJWb5aCLTKQ88puqRSx8Q4ldQCrfIoe7aOeblctOxpM5yyZzPRTvGEpJUKUIp2mq/lyBShbZGSz5IrrbbIJGwe6iPOJOvXC5rtiCSOmGg0SSanGB1RVEohrh9QKZWwXTfvJCptuBMonCRDeI4uSggLz7ZwbSsvvkGqwPH0zHnouvmcvJ6539wX8ubNNxqVzeR2mfIYDAWL+W0sxHJHnkZGRjhw4EBPhdFsNlelAJkbH5VSnDp1ilOnTrFr165FV+AsJ6FcbvK5ElzXZfPmzb05w0IGeuXKFY4fP47v+7PmL5e7nuRu7VCmudN2N8noJnqsopNkRImiE6ckUtKOJc/v2M4jjzxCFEWcPXuW0dHRBR1k1yKh/NSnPsUf/uEfcuTIEcIwZM+ePXz605/m0UcfXfA2r7zyCj/90z8962u+79Ptdpf12Hc0oSwkrkVVaamLrIsD/a2SdEkpOX78OOfOneOJJ55g48aNN9x2pY8/9wX+zePXuDTZIXAF3VT1DhSW0DKoWOqPzSglcC06scR3bLqJwreFTiJzuVNgW6RK28FLVQSt3FReWWQoPMcijWO8rEMkQiygE2s5ayPKCBzd8Qsdi1aaG00kel1HnEkcWyebrq27pyVPuwwWJjr1UBvh9AUOnURScQVXW7DRt5iO0t6MY8nTHcfQtZjuaqmYNtSxsYISg0EZYekOTNTt0Gq2uXx5hExKSmFAWCpTLoUEnoclwEZR9rTktZIfgKr5gVEntvqxJ7sZoWsx1U1xbYtOIvEcwTseHsK2bv7cW6xD2Wq1AMyBybAmLFR1V0px8uRJTp06xcMPP8z27dtviJtr5WI4H0upoq9lhzJOJd84fo2Sp6X11dwQrOo7XEgV613regGrm+QdQ50QRknaU3rYlo2FLtD5hYtpoDub/SWdCPaVHD0/GLi04xTf1tLRatmmFaWUfP0xcG3a3RTPtUmSDN+zkZlEOHqOHSxC10aIXE6L7hTKTFH1bBIJoWvT6iS4FnRSiQ20kwxLQJRJLARdPciAyNeB2LZOPpUQlEIfuxTQV6/pOcluRLfTptlqc3V6HIVFuRRi+yGu0KZlYVHsC5zrHdtOQsVzmOjoeNxOJa6ti4kCkFJQ8R3e9di6m74/Z1m2qGHHvV6BN9xd3MxvYyGW2qGUUnLkyBEuXbo0y8V1Lfb0FjEujmP2799Pq9XirW99K7Xa4kWb5UhebzVzk72Z60l27NhBlmVMTk4yPj7O2bNnOXjw4Ky1SfV6/aYqv9uRUEZJRidVXGlEeYKo/4tSPW+vVzvp3b2Nrl5/V4w+NKIZc/e+TSfRRpCpVDy4rsRwbl7m+z7VapVWq8XTTz99g4Psf/gP/4EDBw4wPDzM3r17efOb37wi6fA3vvENXn75ZZ5//nnSNOXjH/8473nPezh06NCisbdWq3H06NHe5yv5nd+xhHKhqvtSmNmhXCkLBYRut8u+fftIkoTdu3fPmxysxIlw5m2LYLD33CRnxlpUfIdUSqq+lrYGrk4EZQa2gDjLcO3cEMKxafSSI20204j0x1a+rzFKtWyq6GxKCZYtsJTAUjHBlR9CNIVb2YQlSnq2UkHg6KTRzR1UHQs6iU4iW7FOwJpR3hmUen4zyfI1HZnCQie2hblD4Ao6QMW1sBDUfV1tL37ekmeTZNqqPs0kloBUKqRUYAniOMN3HCy/zHClRjtOcIVkutEm6ra5OjaGa4MXlHEEpFlGxffJ5AyZcHDdNTbJJGXXRqJ/L5aAFMW7H1tPNVjaS2GxhLLZbPacwQyG1TDXWKJIJrvdLvv376fT6fDCCy9Qr9fnvf2tkLwqpThx4gRnzpxZ0yr6YrdtNpv8my++yuVGSl+tihcEVMslfFsgBJQcQEFfySVJdReym2RUA5dWXCSAeg68kysXskyrGoRSZErPfkcpuLZFnBfoGs0mlVKAEIK6b2MJ6CsVIwfa4KwSOD35/8xDRTmfSyz7Tj6TbhEleoYzyRSBZ2Gji3KZY+E7gpJrY1mQZQrLQhuN2YJuqvfktmJJOU9mi1Ugeo4zX/3R1WucpHDY1t9PJ8kgjZlutYnbDaI4QmbjXMliKuUSaRrmMVFR8pw87utOpVSA0PPmhVHRux5dRy24eWfhZiMBZobSsBYs1W9jISzLuqnp2Mxxpz179sx6X19tQll0KKempti7dy+VSoXdu3cvqXu30KaApX7v7cS2bQYHB3vOzsV6kvHxcQ4fPkySJLNcUudbw7GchFLlK5y6idJJYSp1sphIkiyjFWVkChpdPbs+2U5wLMHpixOUXEEwfrE3e18LHLrFzL3UmwgyCXbewOnP3w/6csVLxbf1+TifWx8oe7z00GxH65mFj7kOspVKhV/7tV/j0qVLvO1tb+OTn/wkP//zP7/s3/kXv/jFWZ+/8sorDA8P84Mf/IC3v/3tC95OCDFrbGUl3PaEcrla9/kQQtwSSdfY2Bj79u1jaGiI5557bkE57VpIXk9ebfK9s+N4tqArJZ6lzRT0m7r+KKSFY0Ho6etwHT2XWLgNVvLDUtnTHbierXygk82qbzPd1S597Vx2lbRalKJJkqSL27mKxVZsC+z8Zyq6dApAKhwXpKTXkbQtnSyC6ElyM5W7zAqVf9Sfu7au/KQovSvNFlgKLAtsBHb+d7QEZJmNJQSplDiOTSIVNdemnWhTnUZUGBNlDA8N0IhShjdYjE23UUmXsQzOnb8IlkW1HFIKywRhCJ5DpsBSOjl38qBQSF2f3trHtoEbndEWYjFJVzE/ea8upTXcHcysuhexDuDq1avs37+fwcFBnnnmmUXl/msdH+M4Zt++fXQ6HV588cWbJgWrOXAVMXJ0dJT/8q19jKcVNgyHXJtsohrXOH8xpVoKCRywmiNkXkrkP5jPRFt4uTS15tukPTmqlqfGqcTzbOJEYtkC7YmjD3cxEhS8fuQwFy+ep69/gOGBfpqJxPElcZYnnbKYb9fdx0xqSWoqoT90iVJJvaTnsrWs9nrSWcxZFis/PKCd6njR7GaU8znLwNbGQaV8DrKvpJO9vvxn6euNHTi0Y5mvhErxHR3zXVuQWh7Vuoes9dE6fx6vXKGbZTSujNGNYqolHz8sUa+UcLwAz7WQEq06UVrymmSKR9ZX2LVx8a5JgZmhNNxqluO3sRC2bRNF0YL/PlfiOvc9v+gwrrR7ZlkWU1NTvPrqqzz00EM88MADS76f5Upe7yY8z5u157HdbvcSzGINR5Fc1vv6UbbDVDdjZDpGdFrEqU4O4yyjFUkyKWlGGVIqprsJAkEn0eZinVgSejadfBY8leDl51jHAqlEr+FSdiFDj0d04oyyZ/XGHhrdlMCxmIozPAdUZuFakKS6qCmVbuAAVPNVTW7g8J43DePaN+7pXWgu9plnnsH3ff723/7bvPzyyyRJsia/86mpKYBZu0nno9lssn37dqSUPPvss3zyk5/k8ccfX9Zj3daEslgUev78ebZt27aiZLJgLSVdM/Xrjz32GFu2bLmpc9ZqXAwnupL/7+Aovmsx1UkpuYLJjq6ItGJtthOneuZRAnqRhjaX8XJJVcm3kfL6Xsi+XAZWz+WetcCm0dVVk0ZUSJxSqqU64+E2au41xoNtMJ3RjmVuopPvbMz0Oo3UEvqj0K6BUqn8BaRNf6TSMqxU6sNZnGpprH5B23TiDNuymE7pSQTKvkOjnWhL+ljLT6NMy3VjqXCd6wlfKqGSB4J64JDI3FBC6rmeKMkYqJaIEh97bIItW7fS7kSkcZeRa+MImYDtMVAtodyAdbWydmr0bJpRyua+kGe39i3r77fYganZbFIqle66IG64Nyiq7qdPn2ZoaKhnZjBTgv+mN72JTZs23fQ5tpbxcWJigr1799Lf33/TRLZgNTOUAGfPnuXI2Ut0y5t4cF2JOM3oq1XpJhlCZlybauBMnCDrXiMaHWMqhqA+rH9nto1raafXwLHpZhmhYxNnueRfQehZZFIQhLpAZjkWmZQoJbk6cpl2o4GUkkqpxCB6b61UCqn0ITZOwRKCjtRFNqW0o2tqaafrLNPmO0rpQ4pC5KY4OimMMm3qM9mM8R1oxRm+I2h0tUnOdJTiO0IrRCyLLC4eByyhkLb++yjLxncElgUV38lXmGipbSYL4zGFb2vJ1aZ6lUaUEtpwdaqJSiJOXRjFERnCCeirlrBcn0oY0lZ6d+bcKvti3O8uhoY7i5SSixcv4roufX19a35+lFL2XFznGo3NZObY03KNGbMs4+rVq7RaLZ577rll72W906Y8KyHJdNcwSlM6ibreNUwzWnFIGmyiUV9Hu9PmB2cadA6dZrx5mErg0uxKTqTn8cIygesgpcRzHTKZbxjQW+Z6yWIlcEgz7fatz7IWUaqwgLbUq5W6iT7TWraW9WdSq9ZAF9QQUA20z4bevauVKyBIU4nlWD1X72asXb6TVKvylIIXtvcxUL5xW8BS1s5VKhVc1132rOl8SCn52Mc+xksvvcQTTzyx4Pc9+uij/Lt/9+9485vfzNTUFL/xG7/Bnj17OHjw4KJKpLnctoSyqLp3Oh2OHDky79zPclgLSVccxz39erPZXFRCNt/tV/JCbUQZe6/BI4M64ar4Do1OTNm3mY4yakFhR28z3U4QwGQ7php4dJMM39VyUdfSLyTQ3UWJTsIkuoN5fZ5R76CMMp2MtRNJdesTTHcTSragM3ma0NPJZzXQczTaCVFfQyO3xu+mksDRXUnP1i+0Ys4mcLRktuJZZArqoZbC1kOXkaai4mk31WKvWS10mM5nGwu57mQ3d2ztaHnaVJziOYIoRc9IFjJjJbDyLWueo3dXKgm+DSXfoxb6JLLKlg2CVpyQRh0mptukzatcGR2lWgqY9gIG6xXe9ejQsp6DRXd9sRlKU303rISZVfdTp05RqVQolUq022327duHlHJBCf58rEV8LJLbEydO8Mgjj/SKgEu9/UriYxzHpGnK5SvXmChtx7W0E6mFIs5k3kWz2Dw8iHRbNNojOOU6TuAzOT3JxLUr2K5HWCoRBCEiCLVkPzfVme7q6vVUJ6PsO0y0E6qBRScld1+1eGDbFk4prYSolkKqnoXrWHoNU6ryvY0SK59R1zOPYDkWcSJRjsil/Ndn3Iv1SplUeI6Nklo24Vl6Z3DF106yeaEbW1gIS+/lFXkxz7YFnSijVBiN5VLXwrG2p1DprTJxdJHP0d3NsuuAgHrgIlFsHuonU4r1G/SoR6fdod3p0hgbZ9wCyyvxnjcNQxqj3IWXcc9ksQNTHMckSWIkr4ZlM9Nv49KlS705vJUyX3xcTOI6l5UmlMVjJEnCunXrlp1Mwp3vUGpJqTaiudxIEF2bhjdFJ0qJM0U7zkgymX/U0tNM6k5upooEL+uNWvl5E8G1BRke6wYHSQcG2YJkqtEmG7nM2PgklryGdHwG6mWwffqrJabauknRTSUlX/t5eJaFskRvrEkI1VOpxVIX/pI8HncTSeDatGJJtSR03PQd4kT7ilhCb0jIhEI42hgzCHVcrQb6drXAZbrnJJ4tquhYioJjLePjyy+/zIEDB/iLv/iLRb9v9+7d7N69u/f5nj172LVrF7/zO7/DJz7xiSU/3i1PKOdq3YusezUOrbA2kq5ut8u3v/1tarUae/bsWVZFYCUHpijJ+PrxMWR+u7Lv5IY2NlGm6A8duqnKbel1gnc+g4qfrwnxHNpRiu9atOLCOEHiCJBouaoApND3ryT5HjSFZ+kKu5ZQadOJbhxTtvXcYl+gnVfrgUMzzq7LTGccUmau7aj6DpNtPb/TTCQl16KbV8LjTOD2dmAqUkTPvKcaOMhcIpbkSW43zah4tnZfdSziLNOrUyS5VNbqDTo3ctONqULSG0l8S5FI3TWOMr16JZXguw6hW6NaqYAQxFFCu92i0+myPrnI9//y8qwltDezt54psZmPZrNJuVw2HUrDspjrcl0cdi5fvszBgwfZtGkTjz766LIOLquNj1JKut0uZ8+e5fnnn6evr29Zt19Jh3J6eprXXnsNgGveRuLY1qMAicpdSSFKdIxoRSmlykauBlNsGFpHqTrMgCOIcpfoRrPN1PgV0lRq865SGTsMKfs+aaYIPR139GFCf5zO1x9t2PEQW3fsIFYWYyMXmeqmDAeKTqx3+SZSz7KnUlENtRlZ6OpiW+hoJUXgQDdVlD1tNlbKZxGLOctKngzaQDu9brxW83XRr+RZvUNPmhVFQugvOaRKrzBJVLFrV/WkWqVcfeHZeo7HEYJWnBGnkq5UpN0Mz9FqksDVlfuKb2M5HkNDIUmm2GZBu9OlbieEyRTf/e45PM/rSdEGBgZmOQrP5GYz5mBMywzLY67fhuM4a76HvNilu2nTplmO/ovdvri2pXLlyhVef/11Nm3ahO/7NBqNFV37WnUopVJzHEolcSp7CWErSkkyve83zmcRu6lWqaWZdtQ+e7FDvSQpN8e1J4bUKrU03yqQKXp7ygEkOp55+by6LaAV6dtMJ9p4Ms5EPpIk6KtVGB2FnTu2kklF1O0w1WgRR5NcHVX0VUpM5+vlptqSauAy2Umo+U4+O2/Nmn/UyhSou9q/o5Yr+0JXr9Fb588ZH+udd23d0MnjsWvr5VLVPC7XQz2HPlz1eccjCxcJlrKnd608OH7u536OL3zhC3zzm99cVpcRtHPvM888w4kTJ5Z1u1uaUM6ndV+rhHI1ki6lFNPT04yNjfHII4+wY8eOFc1xLufxpVR88eAIV6cjpIQkzXBsC6kkjUYL2xbYVimv1mj31DjNCB09z6ifsOS7zPSBoxtLXEe7EHq2RZJKbMsiK2ZTldQegUIPNtr5deukk95HL3/hB47udFZ8GymhP3CIczltVLz48uHjVpxQ8nXyGeQOsb5r0ciDQyN3hm3FEikVnUQilMK2LZSS6ClKPYvp2RaWJfAtCxvd9bQs8pUlEGeSWujQTST1kkMzuj5XWfZsJpsRttAHMjd3oxXo6n6W5Xs6E71DU9l13vHEdp7cXOvZW1+9epXjx4/f9NBUPI8XCghmPsiwXNI0vcHl2rIszpw5w9TU1CxnweWwmvg4PT3NoUOHUEqxZ8+eBZOHxVhuwe3SpUscPHiQBx98kK/vP0NXZNQrvp6p9hzSREsVbEt36zzHpptJlF+loUoMWSJXdjh03ZANG2o0ugm+yBifbtFpt7hy9Sq+6xCEIbVyBT8s4bkWnqNlSoVTddV3iDOLiiW4mEqGXJtGJHNH6oTQc5jqJJQ83eEsezaTsVZatFNJYOv1S74jkAqqgUOmFH0lJ5+DdEgybSQ0MRVTdkXPAGc60klto5sSujbTnZTAKRQbujPpWPk8uwBp6SiqlMBzLL13t5C+KoEt9PuBb+tDUCXQLoSFOqTocpY8bSTkOTadWFLyA37y+Z0Erj1rYff58+d7boFFrOzr6+u9ly/FBduYlhmWSlFsm+m3Ydv2TQ11bkZRtFuqxHUuxXlxKUW7mWZmjz/+OJs2beLMmTNr5oI9MjLCtWvXGBgYoFytM9HNjWhSyclrEbFKOJ9dpdnVLqXNbkqmdKIIotex812bKNFz3Emq8NwiedJzhghB4Irc40OPRvm2oJso+h0rV0vYenyrcDv19Do7L19F5woLR2iXaj2DmncQUdiWlY8SQCtOcSyLTpySSogyvRfYcatU61WQWvHQaLXottucPTuG5zhM+iF91RITmU+95PU8RCY7KRXfotnNjSuTPMnMzSiVhFrJRqEbOlGqPUrasXYJb8YZrq3HKGwh6AIiN1ZTUmG5eszirzw6hLdIwphl2YLvp2tlWqaU4ud//uf5oz/6I77+9a/zwAMPLPs+sixj//79vP/971/W7W5pQlm86ObOSgohVlU9h5VX4NM05cCBA4yPj1Ov11f0y4blJ5RfO3qVc+NtPM8mkrqj2IpSroyO0um0ezM4fliiWgkJ/DK+q6sfjiAfKL6ebKaKnrS16tu6G+jZpJnCsfJdlLYgSfMZHCkhl2gJoXWiaaZIMminEkuBsAVK6e9HQSK0Nl2il4iD3u0GumuqFLnxhaLi6UNTsbKkmLnUAcPSO+IcXa2f22mseNelte1E69KjTOE7kGRaciCllqRlslgPoqvySukuwLhFz6ZfZAIn31FZLjqtgZbZPryuzJu3aFnzXHvrmx2a5pqkzKXoUBoMS6U4GBTJZKPRoN1uk2UZL730EmG4dMOomaxE8qqU4sKFCxw5coRNmzZx+fLlFSWTsPT4WBzoLl26xNNPP03sVDgyfprtoWQq0ussukmmVyTJXB6V6YKTcmxsoeeshdAS+yjN8m5dmruwwvp1AzSjlI0bLa5OtRBpxLkrY1jZZZTjM1DVpjR9tRKZFNgOOJY2RAssXWEPgnwlkeUgUZRydUmQu2q7tl63YQtBK/9YzMEXc5aZ1A6uAi3FUqlAIFH5ocay9JylQiehCihbOsYFjoWw9A5M2xLEscTLpa+F62ulZ8rmMt1NqPr6Y8W36aZ6RijJZC9GV30HRD7jqbRiRgC2ZfOux9b1Yv3chd1xHPdWARw/fpxut0utVmNgYIA0TRcszhYjASuxwje8cZlr3ug4zqKGOkvBtm2SJOG73/1ur3C2nEJHcQ64WYxbyMxsLfb0zkyGh4eHOXnqFF8/3aaDR7kUUi2XmW5EeI5Ny27j2YI0NycsYopCmy76rk2U5k2B3BysMA9rRVmumNAz6Ero9UUWAgvtWq3y2cVO7gHSiLQkfyrv7k12Eq28SyQlV+Su0jZSSVxXFz9Vfj4FhSXzdkNu8KibEgoLhURhI7Acl1qfVpgpJYm6Ee12i4mJCeI4Zsr1KJdLZHFIqRTSiXXDpR3ruNzMdHKogCjLCPPH0TJXgSUsSp7+fTuW7sAILCwLkjgj9Jw8/mp1y9sfHmSosvDKJFhah3K1Z8iXX36Zz3/+8/zJn/wJ1WqVkZERAOr1eu888eEPf5jNmzfzqU99CoBf//Vf58UXX2Tnzp1MTk7ymc98hrNnz/KzP/uzy3rsWy55nVupLipMq00oV3IfjUaDvXv34vs+Dz74IOPj4yt+/OVU4H94doKz423qoUsnSghsiKKI0csjYFls3boVhJ7p7HbajE00yJIxlKXf4EcmGwz3VWnG2oynnUh8R2gzG0dLB3pmNq6WwYa2duoLfCefe9Rdx8KsQdiCOFG9gWTb1klfsZvRtQVJbl2fJHpVSCQzSo5Nkuq9a1mmEI5OPoWlsBVYQlfPfUdXxksuJJk+KEWZpB7adBJdBWrFklo+O1pY35c97VJY8iymOzrAdaIMJze+kOjh6yzThhFpJvWONmHhF3s489UgFc9GInIHRsXGms9feXRo3r/RYoemY8eOEUVRz8F1amqKarV6w8HIGE4YlotlWb1Ycv78eY4cOYLv+2zfvn3FyWRxv8txiUvTlEOHDnHt2jWeffZZPM/j0qVLq3r8m8XHKIrYu3cvaZrq+Q3H47997zyeDe04Y6CkO3Ul12ayE1EL3bzSrA8q5VytIZXSs42u6s1ah54NCOqBq7uDoUuSSTYOVOmmFYbXDTHdjlBpxLXJFkxPc/Gyor9aAtdnXV+FVqYPT9PdFNeHbqxVDlki8V0LULiORSrAtgqnaz1iINAVeQtBjF69lGYSx7J70tR2lIEQdDJQiN4OzVYuuSoOK1GmY1qSKSo+ustZcskU9IXWbNfXwKGT6ENdK05xLUE70b+jOANLSWQuDS75Nu0Z605qgXam3bWhyraBhQ/XnucxPDzM8PAwQG+f2vj4OGma9gycimJcMQawFiMBK1naDfAHf/AH/Oqv/ipnzpzh4Ycf5tOf/vSyq++G2898Bdy1OD9OTU3RarXYtm3bkiSu83GzGDc5OcnevXup1+vs2bNnliJvtXt60zTl+9//PnEc8+KLL+I4Dn9xYpy+7gRhp0Oj2ebi1GWkkliOi+24OF5ArezTTRWhY5HkRjNZvuPW8R2yTOo9v0nWm8Uu57OB1+WfFu1IF9PyDW84loUAyr6NVIqarSWgtUBvGNCmkHm3sBih6sS5S7Wea08zPf+YSYmbnx89SxcNA88mU/mqt0ThuCJfo2fRzruo2B79AyHVusSxFM1mm6jbYWJslLErEj8ItPt/KcR1fVSmi3PdJNM7fxNFTQimO1q50Y4TKr7uVgaulY8y6OvqL7nE2fWRg0fWV3hy8839V27HDOVv/dZvAfDOd75z1tf//b//93zkIx8B4Ny5c7Oe8xMTE3z0ox9lZGSE/v5+nnvuOb797W/zpje9aVmPfUf2UK6VZGE5FZ6LFy9y6NAhduzYwc6dO7l48eKa7BG6GSeuNNl7fhLHEWQZeI5+Mo1cPE+93sf64XWkmUIpSeiFVMsB64cGtF692+HipVGak+Ncu3qNWjnA9kIG6mWmE5vKDBOdqY5+8rciqS2TU0ngCuJU5ZWp3ERnRrJX9vSLteo7pPnC7SiTlH29N63k5h3FfD1JxbNp9ioyGRWv2H+Wfwz0/E7Z03KHwNXSL0dcN/PJJISuloIV+yj7At3lrAU2We7smuUvXtR1U55OKikVBy3vug3/1UaKJQQTXS0/03Ix7RJriXy21BK845GhG2ycF2K+Q9OlS5dotVq8/vrrSCln7U8ql8u9tSG3ipUepgx3L0IIkiTh4MGDTExM8Oyzz3L+/PlVzwgt59DVbDbZu3cvruuyZ88egiCg1Wrd0vhYOMcODAzwxBNPYFkW/23/ZaQCx4ayZ+NYeuVHnGbUAzefuXZpJ2lu7JX2ZiDLFYvpTqpl+N0Uz9UjA1qupchsC5QkEdryXSqolwIy5dNfq5MqSRonTDdbRN0Wp06PE3p6bqYvi2m0Y+plL+/46f2Ptd4By9X7IANHm5v5tp7L8bRztq7uQy3UyW1/Wf8sfSWHiekuFVfH5Xru8t2To3q641jKJba+a9Pu6DmeVnxdeWIJQZY7HFpCm1HoMQJ90EszSWBrdUnou0T57rRO/ntt5rb6jW5CLXTZ/dDi9vJzCcOQMAzZuHEjV65c4YknnqDdbjM2NsbJkydxHN1l/dKXvrTsWdy5rGRp97e//W3+xt/4G3zqU5/iAx/4AJ///Of54Ac/yA9/+MNFnQ8NdyerSSiLrt6FCxdwXXfZB+alXEdRHDx69Cg7d+6cd6RqNR3KJEkYGRlhcHCQZ599FsuyeO3MNc6MtamFAZnvMdBXR0rFocNH6LSbXAXIEi5aLoO1MhOOz1C9QjM390pyN1Rb6MQwdPX6i3puQNMXzo5NlXzXrhCCZiypKUWc6MRL5g2LQtWmz3mKvsAlyrJekqnjaNYrEFYDh8lukptH6k0B3TjrGZVphRr4oZ5/rAY62Svnc+PlGfPpzSilWqth+SHDw8NMtyNIOky1OkxNjCGFTbUU4pVK1CplxpRewZRmilro9lY9FWMUk51i97sedSjUJ4nSM+hvf3hp5kqLuWAnSUIURavuUC6lUPH1r3991ue/+Zu/yW/+5m+u6nHhDiWUjuPctg5llmUcPnyY0dFRnn76adatWwesfjHtUipMl6c6/H+HRvEdi6m2XldxfvQaAugbHGbjYD/dLMMRkEjt8CfQVRPLEoRuhWv2KDu2bUMpSaPZottpc+7sBL5rMeGH9FXLTEufSuDRLHZSdmfsO5tZEco17s1YdwCj4kUhFZ6jTXQCT1ep9C4yHVBSCX2hTZLpucpuXgnvJnq+shEXjrB5spnPNja6GQJFK1X0KUUj0ge8LNadBGELUKByOZhna0FDkfQV1bMAh1Qp+l2XRCr6A5s4N/bp5onvpEIPdPfWrQhSmRG6Oji9+7F1DM5j47xUwjCkv7+fK1eu8OKLL9JsNpmYmOgdmv7sz/6M7373u5RKJS5evMjmzZtX/FgLsZLDlOHuZmpqiu9973uUy2X27NmD7/tcvnx51QW3pY4EXL58mQMHDrBt2zYefvjh3pudbdur3rM2X3ycedh6+OGHe27ff3lqjJNXm7nsHVKpzSF81yJNJUIobKElWqX8sNMXukwIqAV6BrKe74Cs5F26orhU8nVXMHBt2lFhdJP15jEFek7dth1q9TpWfx/rlaTb6ZJevsTU9BRyfIzpIKBSKtGRJephSKzU9blyz6aZzz02opTQs5nuJNqxuqMT36lOiu/q7/MdQZQIVKbIlMjN0/ShSQg9zwmz5aigFSHCEthSYFta/RG41qzDnu4kaMnrZCeh5Fq0Uv0zTncSXEvQ7T2mInRsHFvgBIL/4bEbd6ctleLvXavVGBoaYtu2bb3l7d/5znf40pe+xPHjx3nsscf4m3/zb/KP//E/XvZjrGRp92c/+1ne97738Uu/9EsAfOITn+DLX/4y/+pf/St++7d/e9nXYLizrLQhUThmK6V48sknOXjw4KquY76ksFB6jI2N8dxzzy24+2+lHcqLFy9y5coV+vr6ePrppxFCcOZak++cGidw9Q5bP3cjnRi/xpnTJ0nTlOeeG2T91h0kcZfx6RZZ8xoHr1yhvxIw5oYM1Su0E0sb6yS6+5gpEErkXh+Kkutol9YsYv+Bo1RLAcoJ6A+sXqNBxz5d1AtcPXqld/YqpMg7iegkUxfR7LyTafditp7p1vPjvg2tVJ+LW5HuZMZpfl6VUMpNd+qhq9fnhdpkp5Y3Nyq+ox1YSwHNyGFzvY9GN8VRCVONNo2pKUZHrmALGJ+cor8PMquki3/5ero41WfMONNKk0RKMgkI7fr9Y0+sx3eWZpi3FNOye9kF+5YnlPMdRtZCsrCUA1Nh0SyEYM+ePbMkZGu5p20+pjoJXzw4SuDovYyBIzh27gIiiWhnsLNeYyJ3Kp1sx5Rdi+lOQtl3aUYpJVe7EAJkUhL4Hv19LvZAP1JKOp0unXabqVwvPun5VCslWmmJWjnQL9J8JrHi27QiXVlpRllejc7wbEE7H4Ke7OS2x+2Yknv9ANaROsGLUy2PSJTeUSnRO3ek0p1N7Tzo5AvE9R6gsm/TUArf1lUrOz+5ZQocW8uuQs+mVZhDRNeHuqu+zVQ+/9NIJGH+e3QsQST1DrgsX12SoPAcQWALUgGOrTucJdcjziTPbKnx6PrVdw6LYCCEoFqtUq1We4cmx3HYv39/byXOpz71qd4BZq1YyWHKcHfjeR7bt2+ftUbpdowESCk5cuQIly5d4qmnnup14gtWs2cN5j9sFcW9K1euzDpsnRtrsf/CJH2hq91bXT3nsr5q5wcTQaubErj6DbwwFsukJM1na/xQ6Rmf3ECi5OY7H8suWZZLXvPYFGeSkufQSfIOZ6QTwU6iY143zvBcG+UEZMKhb2CYwHNpNFs0O11GxsZxLQvfD6jVKgRBiO16VANHz9vkhbKK76DQSpCZbodCCBR6NCFTiljqjmk31oloO0+C24XRT6INIqJEu8zGUvZmOOuh6El6i1VRaaZnMNNMH4SiRO9ey6SO11hWbgJ0/dA12Ul4ZmsfG+vBsv/WM/++MNu0zLIs+vv7ef/730+z2eS3fuu3+Mf/+B/3Dk+rZSlLu7/zne/wi7/4i7O+9t73vpc//uM/XpNrMNw65js/rqQhMdPF9dFHH6XT6ayJCmTmfbRaLV577bWe0sP3F56nW76p4/VZ83Xr1vX2XU+0Y750YATHtnRDoTDFCRxa3YQ0TUmlIk0SfNfB96rUqlVSqZBpzOR0mzhqcersBIEjEF7IQK1CkgYEnkMryXDyXY2WpbCxOHLsGMePHcPzfXY+9BD91S096Wc1l82XfDuXy+ozZzhDOZZkev1dsfuxkMuGrt2bHy/Oj+1ugifIx7z0z+g7FtNdiW9bOvbbOnG1LXoOslJpczWZKz+S3Clby1dt0sxiYMAnU/0MSsm58+dJpODSlWvEUUq17OOHIfVKBc/3cWwLO5+FT3OpcJRKdj/Uz/ra0mPmUkzL7uXmwB2TvK7FgWmxF2QRQDZv3syjjz56Q5t5tR3Kxaybu0nGlw+PYgFYAt+SnD17ntB1WbdtO2dPn9SLukOPVpxRD12m2jHVwKURFZLShJJn08kABI284tONUlzHRrg+fYMhtXwgudVq0e10mJi4xBUgDEuUyyXKlTIWFrV8lqjqO6RKywVa7a6e50xlL4n0XS3Xcuw8gbNFb4g5yoeYM6UQSu9jU+gB5kwpyF/INgJLCGyhq+C2rQ9TltBS19BFzzo6LlGm6Cu52sE1sGnH2sG1sPJvRoXhRaYPkUrPIvlOfijK5bYWFtORpOoL3dmwBZ00Y6Dk8cIDK99XNZOFgoFlWfzIj/wIn/vc53jb297G3//7f39Z82srZSmHKcPdTalUYseOHbO+VhhGrIbF4uPMSv1CZhSrTSjnVuA7nQ6vvfZar7gXBPpNuNFJ+Maxq7036pKv35Kqvo0F9Jc8okTLoZJMJ1Td3CE7kwLQRl1xbmcfZVoK2k5SfMfuHWY6kTYDi1JtSpPI66s46rkUtc/RSZmXrzSqBTaX0PJbadkMD/bRijM2exsYb7QRacTo+DSkV8mEw2C1jHK1lKyb6hnuKFWEnoVMi5UjilIeL0uuzUQsqLjacKTosPaVXL2vN3Bp5O8D0/ks6VQ3IXC1vNe1teGPPnQrpNQznZnUhyalJJ6j32sDWx/YHFv/nsMiAQ1cUinZWA946wOriyNLWatUr9f5iZ/4iVU9TsFSl3aPjIzc4JS8fv36nlmF4e5m7llrOefHmcY1jz/+OBs3bpx1HytVYMDsM+TIyAgHDhxgy5YtPPLII0taO7LUDmUxa54kCbt37+bcuXMopYiSjC8fGsF3Lbr5mrl2ct2PYtOG9Vza+iA2CfWh9SRSoWSGY9sgFJbjs27QR6o+Nm+CRj5zOD4xQRrFCM+jVinjeAHlUkiWKhIysFxwAyzXQQrt1p9KhW8LMnTsTvI57yjVDv3tPNltdlPKgUMrSvJOZpYry7TJTbGP0sodvX1HKzEqgQ1SG4ZJlc9+5uqSRGolTSoFSkqEpcccgtzsx3XsXP4vsBwbBHi2/runmcR2tFfJusE+lO3jW4prU01UGnHy3CV8G4TrM1gr4fglKqFHnMHmvpCntvQt6zmzWELZbrcJw3BF77d3C3dM8nqrJF1SSo4dO8b58+d58sknF7SBXgvJ63y3zzLJlw6McK0R4bs2U1PTjI5cpt7Xx/C6YSyhq9G+o5/0Zc8hTlL6Sg5pJvJKj6QeunpPmq0XyVZDn1as52mKIeYiAYwzRblSww2rDAxBs90hjSPGJqe4PHoN27GoVysEfpgnmHpliOPoIecwd/NzLF1R9/K1IY6l3VRt7T+ByHc9ZpnEdfVSWF3Zvy5zuO7cqj8qKekofQedWBJ4Ft3c7CeWCtdGG+zkK0tCTwfiiqevpeLra5PKwhH6NmEu7aiFLq04JXQF01JbWk938wQ00Y/xPzw2pF1r14BiT+BCFA5dq50TWuq1LOUwZbj3sG2bTqez6vuYLz5euXKF/fv3s3HjxkXNKFayZ20mM528x8bG2Lt3Lxs2bGDXrl0z7lvxpYMjTHXj3MlZ6c6dEjS6CWFZ0Y0SHSsSSeg5xHnMzPJu45ilZaJ+LhWVMneWtq2egiFO9ZxhO9aJ6HSU4Ns2U7GeN5/q6M5gK58l7MSqZ8LgWTLfN+b0zBhSCev7KqSyzPDQoLa2jzqMTbfI2uMcGr1CXyVg3A1YV68wlTpUAt0FLHv6Y8mzacYpSZbRTkEoSSfJ580zPX+u8n3BADVfuwyWc6O2kqcPU6kQufGOLq4VktfGjF1qnlC0Uz3v0+zo1Urt/BDmOBYCwdt2DmFbq9ufW8wHLXRAX+sZ86Uu7TbcXyxV8trpdNi7dy9SSnbv3j2r81Mc2ldaMIPrZ9CjR49y/vz5Za8dWUpsnZqa4rXXXqOvr4/nnnsOx3HyRCjjK0eu0OwkWEKfjUAXv7JcAhqnGQ/v2EQrSumrlnsjBN1UarMjMmL02rgYqFRLlMol1q0bohsnRJ02080WrekpxjJJuVLC9UvseGA75WpFd2ClopMqKpmik69oS5RuMiQZ+T5JRTmfo+wveXpkKnR7pmPNKKPiu/lspt1bP9LIJa9604EgQvZ8OIqioFUkhbb2/fC9653R1gwH1opvM9lOeuvniuKem5tX+rbejlDJzc22DvfTTSXrN8Bko0OWRFybapNEY2DZ9FdLvGvbFuI4XpYT+mLPt/thj/k9K3m1bZs4jmd9rdvtsm/fvl4lZ7E3r1vVofzq0aucm2gTeg7nL1+hMTXB4Lr19NdqRPl+xDSDbiIJhCKRGUIookQ7WyWZbuFnasaKDs/qVbALSWlRYS+GkrvJdefAerVMJw4YHOyn1U1RacTEdIvp5jU6l0aplgNsP6AWenTypdpxqiv4caYIbG3a4+ZzlXoNicJ1dQcycHUlqR5auVGGlpbqJLhw9tLmFFdTPePUjGS+LiTTi8ljLV+NUyB3aU3yKlWc6p8lznTVK87t+ZM0N/ZR2qWsMBaaaEPF1RWtsmfnTouCdz0yRC0/kK0Ft8PyeamYw9T9y1rMmM+Nb1JKTpw4wdmzZ3u70BajiNsrjZHF458+fZoTJ06wa9euG5Yr/+Wpa1rW7mkzBMexSGTR5RK9Ge8iVkzmux/1fKDDRCeBXA5VBaJUx4k0K2RNEltox2drRrx2Le3+HHrapdB1bOJU4lja+MsWel7HtgTtVJBmikaU4liCVqxwreuy/TQDx7ZwShW2lCu6Yp4ktNptuu02Fy9dxAIaYZlyKSQmJHTd3n63Tu68nSpIUolw9MEocG06cSF91cYVzXwWvpNIyp6l56VcfQ2VQK9WqocOMt91mUqdCHejiMDRow2uLfIupZbNNqOUF3YMsK66uN39UrjZ4Xwt4+NylnZv2LCB0dHRWV8bHR1d8uHfcHexlPhYFM42bNjAY489dsPzcrUKjIITJ04ghODFF19cVrFkKR3KCxcucPjw4RuMfYQQvHapw6TV0GNM3RjHAin1jlo7d/L3HN06KLn5mE6gzXc8xyLJdKEMoJvHomY3I3Qtmt2MkucgSlVq9TpppsjiSO987DSZGuviug6ebRGlEbbwSZXKVyVpWWg71psCOrEeK4gz7Z8RZQpXaKfqkmeRKUE9V4TUi13jgV47UvIdJpodXFvk5pMz5LxR1pvRdIsZT98iy7SqRc9T2kQp1AObZqxn66eLlSjFnGZXz7l3UkWmoJNm+YonncRaQjBQK6FUicGBAZSUtDodnh2CK5cvcvLYESqVyqz1cos9nxYz5bmd58dbxX0jeR0bG2Pfvn0MDQ3xlre85aZBYi0Syrm3//6Zcc6Nd6h6FmcuXCCNI7Zs2Uq1FGq5KHqtRWFCE2W6dd9JJK6AZpQROPpJHrg23URP/nZSRdml59iqHVNF/qLM90D6DqmCWj5DUw8c0qhFv6OI/Ap91QrdVGLJjGvTTWTc4eylSQJbcfrCZYbqZTqxT1/JY3KGs1U1r6zrbqA282knGYHj5GtG9JCya+v5nMDRH0ueNvWouFq+UA2cvFJl50u5LRDkq0m0jK0aOPnOyMLgR89Vlj2bqbaW/E53dWDqKP1m4Dk23TiFfO9b4Gh7/V0bK2wfXNsF2ktJKG/H2pDlHKYM9x5rPUM5VzK1lOfoUvesLYRSirGxMcbGxnj++edv6NqfvNpk34UpSoUM1RXaZMFzcG1B6AhK+eoP19JmF15e5a74+WoMz2Ykhaol8vlr7YZa9V0m29fdWCu+k+9l1DG15Oe7LR1dKPMdnYxpOb9OvLJcCudZWqHhWnopryVBWBYyycDSMcdztGTLyxeD+66DE5QZqtYodWMcIZmcbjLdmKY9eoVS4OGH2l3QsfQ+Yc+xKeVV88CxiKWiXowChDNm3yN9QJvu6k5jo6u7r1GqtEOuVXQ49c9a9m2iROJY+m8Suvmce/5+sW2gxHPb++b7Ey6b2xEfV7K0e/fu3XzlK1/hYx/7WO9rX/7yl/WqGsNdz3IkrzMVak888URP4jqX4nmaZRmuu/yi88TEBNPT09RqNZ5//vlZK0GWwmIdymK+/fLlyzzzzDMMDc1edXZyPOLMRMzWzXrmPPAcmt2klyhGhUdFqlUIcaZjWCbJC24Kx7WQSiDQcaNw/+8m+kzVyONNsUIjFi6Dg4PEaT+eDVONNknUohlFpHFE1G1TLpUJSiVs20Xk50Db0p1EAXSSfPYwUzi2QqUWrp2bkhUyVVsglTYj03HK5hpQD21akbzu4u/pawzzcYDQLcakLJIkw7W1YZlj6Z3vFc9CouczZb67PcnnLZNMIhVIpZUwnmPRTrXqrtUtVocoAg9SJdj9yEae3dYH6PVyxcqko0ePEscx9Xq9tzapWq32CgHF7tDFZihNh3IFrNXakEIDf+rUKU6dOsWuXbvYvHnzkv4ga+3yeny0wesXp5Bpwpnz5/E9ly07HsDvuaY6KAmeBa4Fni3wXAulIHRs4izFd7STlJ8fsjzHIpJ6EW0zr6gUlZWp3MZ4qispe4JWomeDogw8G5LGVYLRH5Iqm3DTm4lLw5Rcm1RZbBke0LKtpMvpMxfoL7mMj0+SxhHjnkdfpcxkHNBXKfUMcxqFg2FXzzY2ugm+Y9GIdFWsm2bY5KYPKFzH1pUtqRBKS2ylzB3IlMCxdZXfK/YWObkkIt8ZWc+7sFVf30/oWbkMVwefJNFVtFasD4bjXYFn69/Plr6Qt+5Ym7nJmdzphHIlhynD3c1CCo61io9jY2O8/vrrDA4O9iRTS2WlMbLVanHhwgUAXnrppRvMKaY6CX92aBTHEr3ds8V+ycm2tqKfjjIqCjpxkstPIbSvrwEqeTYCQcnVMzb10EXmpjRJKqkHLp18nqiVW+NPzzCg6a368N3rSWg3oRq4WiKVu2BrFajA9/KVR6HuWBYziIFr66TU1StCaoF2GawGOgmsl3ymo5RNG9bR6KZscgVXJ5vIJOLUxVEclZIoG8cbAyegVgqIFPpAlEosdLchdCxsW1DzXRAKz3a0QzYCy9KGRK5l0ckLc3rNk6U7qza0M/1e04i0RC7Sa8P5kZ2DWGt0gLlZfGw2m6t2MFzJ0u5f+IVf4B3veAf/4l/8C378x3+c/+f/+X/4/ve/z+/+7u+u6loMd4aimTB3BGWmxHXPnj2LdnuEELNk+UtFKcXZs2c5fvw45XKZTZs2LTuZhIU7lHN3886db7840ebgaBdXb1SjFrrEufFiKhUin0XPMt0p7GZgo5jupFQCm+mOLjIlmcLPnVIdS2Ap/Ttx80ZFxc9HqXJzMH32u26eWK2WaXk+pThFOJ4u2rU6TE6MgbColMvax6NSQghttuPkvz8hFGDlu3EFSknEjD+DjvEWCkUqJRYKpYoCI1QDLeste7oA5+cOrJbQu9MVWqEmZaYT2jTTeyylJHC1W7Zt6Rlz29a+HoGl7yfw9N7fWujSzJsaPV+TTsbW/pBntl7fN+l5HuvXr2f9+vUopeh0OoyPjzMxMcG5c+cQQtDf39/rXgI3TSjvZe6I5NVxnFWbThSLu3/wgx/Qbrd561vfSq1WW9btV9MFmHnYujjZ4cuHr5B225y6cJmNg3XKfYM4tpZ2ho522vMdfehwcrdTR+i1GJYtEELPsjj5hmzX0zM7gaNfhOVAy7JKnt1btKo7hbnxhKuTu9DVOyLL0RTT7S6hlTI5PUUQDNHIJWHtWOLZekckQK1/gP7+QVKZ0Wm1aLc7tKdGGL8C5VKJpBRSKZexLJuKrzXnpVwu5jl6KFulAtsWJKnUqzrifDlsIhkILaa611eYVHybyW7uMtiJtfNXnjinmf7luPkbhSUEtqWDoxTg50l4kHcX6oHD1Zai5llIJRkse/zorqFbUuVZrLqklLrleyiXcphaC86cOTNvsvqOd7zjhv1FhtUztwK/FpLXYr/lD3/4Qx577DG2bNmy7NfEShLKK1eu8Prrr1OtVnFd94ZkMs0kXzuiVymBwnMcsny/ZJTL5kczxZBr6V1igct0OyZ0RG8n2FQ76S2eziQ0uwlhWerZ7vx+Jaq3/kLL4AX1wMlXIWnJaT1wc8t6V6/+8HWlv+QVq5UcOqlACPICnsNEW89BTuTzkK1OSugJurGWk8Y9p1lFxbfyQ58uJhaPv3Gwj0wqhocVV66O0Wq3aLY6NDvjjNkWTr4OynJ8qqXr66Cm8qRbS19dGlGSryuRvU6vl5tVhK4FQlf6O52EkiPwHQvHtvRy8Ezxlh399JdWvkppLovJuUDPUK5WZrqSpd179uzh85//PP/oH/0jPv7xj/Pwww/zx3/8xyuePTfx8c5SJHAzn283k7jOx3Kd/tM05cCBA0xOTvL8889z+vTpFa3+gPlj6+TkJK+99lpvN+/cn2G6k/DfD4yQSkknkQgUUe5H0Y1VfiYChzbW2DEyLErWEK1MJ2PNXO6pd4ZrBVrFs/O1b7oQ5zsWEj0q4Dva6Kzq20ilVWY6Xjq9RsNICoOhA0GVzfV+prsJrkqYmG4xOTnBxZFRKqGHH5apVcp4vo+Xz8Dr9U0S29LFOzdX6wVuvhkhX4ekhE2jE1P2XWIp8R0bpcB1dGy2LUFmaUVJJiWWJfKEWRtNVgL3+u7M7nXVXbE3uOxZel+6pX9/XuFvElgolb9vSMVg2ePdu9Yt+D4qhKBUKlEqldiyZQtSShqNBuPj44yMjHDs2DEATp48yeDgIH19fbO6481m87Yo3G4ld6xD2e12V3UfURQxOTnJunXr2L1797JlC0WFaKUuX8UhsLBtbk6OM3JtnO2bNuAEZb1PJ9LV4qmu1n0XSVUrEyihmOrEVAKXVpToKksi8VydqGkjHJXbNesqfODaCMD2bEDh2k7PHjmTMl8gq2UBcbAOvzpIrARudR1Rpqs+3VSv7+imilRCoiDJFInShhhuqUItKNE/NEQ3ikmiDhOT01y7OoawtLmPF4TUKiWE0FVzKSHwLVIUdcfTjoklvby74pLvjHP0i9vP5z1d/dGx9NLvTCksO3f7ch0a+fcWv7Nih+ZUJyV0LNpSD2ILpUjSFN1IELzj4UFK3q15Wt9MHnOrA8JSDlNrwdatW7l8+XLv85GREd797neb1SS3idVKXuM45siRIyileOGFF6jX6ze/0TwsJ6FUSnHy5ElOnz7NE088QRzHjI2N3fB9f378KufG2lQCh26s5VqJlFiWjS1Er1gEUA89pNKup1Gc0JcbJtRLHt3c+XVEQt3TEtgi2Sos64PcubqQonqOrlb7rk2a5nKR/D3As7UjddV3dCIW5o7Ujp57r/gOUmqlSZJpy/oozbAsiDNtBCRT/XtI85GGQn7qOVoa6+eFxXLg9ubEHcemGnqsW78RR0Cj3SbqdGhMT5JEEVOeT7VcohWUqObroCp5Mu3Zlj5YCuikkkwqlCOIEqlnLyOdBE93UizLur6HOJJs7gt5YtPSC7BL4XbMmK9kaTfAhz70IT70oQ+t6rELTHy8s8yUq9q23ZO4LmU2fO79LDXONptNXnvtNYIgYM+ePXietyqV29wi4kLzkgVxmvGF1y+RZpJMWtgWetYxV1yUHT1rXnIdkomLeOPHEQiCSkrqb8YSglo+q9iXmxnWcvMuPVt4fX+tds6Pe7HOc+y8mG5p6T+5SRnXz3f1wjcjcGhGgo3rSzSjlI0OWpGRdjl5/hKBJbH9Ev3VMl4QUgp8MqUdsVMF9dxHpFB6lF2LKxLKgZcXGHWntJcQ+0Xn1O7FWL1BwOoZtyX5TLn+2fP3Cs+m1U3wc7WdAlqJxHUEKo/jjmMhM92kkQpeemhgWWdLy7Ko1+vU63UeeOABms0mr776KpZlcerUKdrtNtVqlYGBATzPY2pqatXx8VOf+hR/+Id/yJEjRwjDkD179vDpT3+aRx99dNHb/cEf/AG/+qu/ypkzZ3j44Yf59Kc/zfvf//5lP/49N0NZSA5Onz6N53m95a7LpahsrWZxdztOefXAZS5dvEA3injsoR14no9l0XvhxmlGPcidWwMtvyo50IoyhupBvsTVYbodUw08mvm+yEJi2km1c2yjk/YMa4JcXuXmT3RdiBdYQulBYhuCch1Reqm3usQDyHXi2g0RRN4VdC1dWXcdQRRreUA7yqiUQlqOx47+ARrdBFvGXJ1qQXucsxdH6a8EKDdgXV8ZKVxKvu6a+rY2yLDzik+AIpPg5wlx6OWJsWX3VolYAm1nH+gq1MzfWTvOemtNXEvPkGZS/9zdJNMOjqngqc31Ve1RuxlZlvXWHczH7ZC83g5s2+51ErrdLh/84AfZvXs3v/Zrv3ZbHv+NzmriY+EKWDwPVyMxXOqBKUkSXn/9dZrNJi+++CLVapVz587dcNujI9McvjxNLXTzmUbdZdOSem2O0IwyUgWdRNKJU1BKy+iVJEu0PCvNMr3bVkHZ1bF7IC9g9Zf0AaIWOsSpoha4vV28ek+bNqIpe3mV3bsu5Zrq5CY43ZSS79COEqTU5kAB+uAUOAI912mRKb2iROrVjmSZXqWUZhLHtYgyRTWvthfOq5XQ1QeiXHJrCZiOYUgpJqOMkh8iLY+N9QHaUUIatWl2unQnL3FZKcIwpFauEIQhgWOjsHrzUb3VK55NnF5fx1RyBVNSOy52Em0+9M5H117FsRRTnnu9Ag8mPt5u5j5Pi/nuYs94lmU3lbjOx1Lj7OXLlzlw4ADbt2/n4Ycf7l3PahPKQrZ7+PBhRkZGePbZZxkcHLzhe5VSfO3IVe0R4dp6L28C5aAo0jtMtyOqoZbt1xyPSVWm5EhaMkAqSSozHCxs0B4Xro1EqzWSTNIXOHRSqaWeUUrZ1yuLingZena+AzJf25YbNBbnTwU9VUQ9tHuqk26asXGwTiuqMDQ8zGSjA2nE6EQD0qskOHodhxcyUCvTRa8KySSEjkUzgZILAh3P4lRfq16PYs/aqx7ms+WBazMVpfiOoJvq9wypBBY6hnu2jSXyIiFQciwci56Co9hV2Yqv+3g8v72PrQOr9+SwbZtHHnkE0E2x8fFxxsfH+df/+l/zuc99jsHBQf7lv/yXfOADH2Dnzp3Lvv9vfOMbvPzyyzz//POkacrHP/5x3vOe93Do0KEFXx/f/va3+Rt/42/wqU99ig984AN8/vOf54Mf/CA//OEPl63iuGOS15XMCBWSg4mJCXbu3MmlS5dWtUMIbi7TWYhMKr5xfILz16YoBQGbtm7PZaoSJQVWvrjadfKqe955rPgOApHLCKA/dOnGKX2hSyT1C7qb5FXoRK8N6aaK/ur1tRzFAajRqybpJLRIThvthEqgdwCFrkOUL+yOpewNIXu2RZKAb+nOZcXWXcIg1Lb4faXCwVV3EPpKHlHq8GClQjvJ2KQkY1MNsqjDiTOTeLbADUKq5RJxEOq/sZR0M4mvhA5Kbr6s29cLcyueThKD/PBVJLZWvuvSyQNV4OodlpZl6/1DUjuKpUoRui7tacUD/e4sbfutYLEK/O2QvN4J/rf/7X+j0Wjw5S9/eUWvE8PNmc90YrnxUSnFuXPnOHbsGDt37mTz5s189atfvemqm8VYcCxASaxj/w3aY0xvfic/PHKWUqnEnj17eh38uTNC462IV0+PUfFtpFT0hR5SSvrzRLAv1DtpK77DFaWbh2kub0qSDKH07yhTALojaAlBJwVPKiY7CYGTL7p2LKJES15jqeevs9wBNVN6TjuROvYmMj+oZDI3wcmo5SY41dDlrITAzddwzJBKTXWT65/3TIBsWrlTYjc3d0ilInT0+qWq76BUIX3Vye61lh4fUFy3/HfyPU22bVGu1/FKFUreeiabHUQacWWqibh2jVjZDNRK4AQM1is0cpOJTqxNOKS0UEofeD1LELj69/DCjv41db8uWIrk1cRHw1oghOCHP/xhbxXRSnflLpYQztxh+dRTTzE8PHzD7Vda+Cse+9VXX+3NfC40tvKXp8Y4fa1J6Gk/CdvS694soecJk1R34tJMG3i1rc2Utvs0YoWlXBrtGLB6a5NkKnFsgUAr5azcZTp07V5cSqXKz3wZ1VxdVsrHEMq+ntEOPe2O6km0OZBjkSod9/Q+cpWPNkAlcFBKMVQvk2QhAwP9dJKUpNNlqtmm0Rrn8ugo5TDA9UPq1TLCdsiSjDjTzQal9DxlhtAqllzSL1FUAu1VEuYxVCe5Ovm1hCDOJbTNji4WFslikmpDTAUEjk2G0GNdPadsxY7BEm9ZA0+OuQU33/fZuHEjGzdu5DOf+QxCCPbu3cuXvvQlOp0Ov/Irv7Lsx/jiF7846/NXXnmF4eFhfvCDHyyooPjsZz/L+973Pn7pl34JgE984hN8+ctf5l/9q3/Fb//2by/r8e+ZDmWj0eC1114jDENeeuklGo1Gz/hhJaxmz5pSim+fbXJitMnGdf2U6wO4jt17wWZKYaHlqkViVCxs1Q+ndLUpgDiTOLbuaOo2va70ZLnD1WUhqPn6WvvKLklaHL7yNR2JNoBoxZJqkFe/Qy+vgucD1HOko7VAzzJ6QtBIdfWnGenOZCuW+LlDmGvppNK2BBItC1NogwiEzaZ1/cAAW6Sk0+3SarXpNKcZu3qVMPDwwhIO2kGrGmiXrsLJteLpayvls5+eo62mhRB6tUruvtid0cGoBdclbe1ES8miNMOz4PnNt36YebGEstPpIKW8rw5M//Sf/lO+9KUv8eqrr67aTMOwdBxHyyuXqp6YWWh7y1veQn9/fy+uZVm2ItMIWPjAJU5/A/sv/gVZt8F46c/Z+CO/zM6dO2dd68zbJpnkv+8fodHNEEKvycjiDNcRZCl5Iij1CgypCFy9c7GeF7fqtqAbJziOrnyLohsotExVKv3/iQTQsctC0ZYZthBEicKxBe1YJ5mtLMOzLLoKPAfasV79URTapIJyHnNLjgKhqPv5/GXgkkhJ1XdJUm1w0U21oVon1mtOOo0xrOYo3dIAqjxMQlEg0wccvaZEm4l1U4mDnmkMHQspdNc1yaBe0gfIvtDVM5d9JbKsxLqBAaIsI+50mW61aDcmmbh2BcfzqJbL2F7AQK2Ud2Ydxroptq33bT4wWGbXxrWVuhbcTWuVbgcmPt5+pJQcP36cLMt48MEHe92elbDYObTb7c7qfs41x4HVdSgbjYZ2XQ7DeeclC46MTPO9MxO6iNVOeqsvUFK7N9sCqSRS6RgnpaLsu6TuBuoWXLk2TsnV3xO4Np1C+p+p3HAs7zIK6CqFb9ukKH0mlXptW5bv/i2SzCS7bj4W2lrBMeBpib3eR66TzUZXO/PHxRqnfDuBY1lYFpRcB8ur0lev5A6rMa1Wi1arzeVLkzrJ9fVKkiRJ6WLpa49TPSKW6sfp7TEXeq4ykyLfqHDdayMIdbGwHrq9vZTTxTaDZgQKpvNVJJ1YK0+E0B4h73xkaE2MyxaLj0U+snv3bv7lv/yXq36sgqmpKQAGBgYW/J7vfOc7/OIv/uKsr733ve/lj//4j5f9eLcloVyO7fN8XLx4kUOHDrFjx47ewWUtTHVg+QmlUoo/+ov9nBtrMtxXZt26IRzLJskkZU8PLfsWxFIfBoXSbXaRd99sSxBLC4nebeY7Ns0oxbcF04mu8rRjSZib5ygUnVRS9/XaENe6nmxlUukXlJJU86pKPdQDxP2hQ5zpj1Gq6AttOokOBJ1Yv6AmGhGBDY38hdSK9GLaVpLpg1eSYnPdvEcPPSvtIptKgkKC61k4fsBwGJKoAWxgutkk6nRIooQsHiON2pTLJWJK1AM9Z1n39cqTsq9XjNhCYAuLWEoqeZW/1y0IHKZz86FmviOum+jn1PObPHx35buklspi3Z5WqwVw3ySU//k//2d+/dd/nf/+3/87Dz300J2+nDcUM2eEbpYMNhoN9u7d25vtKUxwigXza2U8NhOVxUTdNjLqsn5bP/7DD9/wPTNj/jeOXaEdJ4SenhO3hEAKpWX3QCefpdYO0pbe9eiq3ADH5tSFEay0g+WXGKyXwfEp+TZJCr6rDygVT1fX9TVr6WmW29VLqW2mBTqZtYWFEpClGe0kw3ZcQOaVbdH7mEpJnAk6sQQrQ2YKL99vaeUyLFtYeLalpa+2wlYZYvR72K0R0nAQ9dCPktohjg1RoqiEDu08njW6Kb4FjUgxbAumousO3oFrMdXWO9Y6UptjiFTvBfadXNpaLuEEIZs2CDpRQtLt0Gi1aU2MMj0GpVJIFpYIHUkTwUDJ4x2PDN3wt1orbqbguF8kr2Di4+2kKFR1Oh327dtHmqaUSiX6+1fXNVroDFmsoFu3bh1vetObFk0CVhJfz58/z+HDhwF48sknFzxTjEx1+cuTY/SHDt1UUc9Xt5V9m7GOjnGdJNNquEwvtNUqr6y3McCxIFYQurkqIvfZ8Fxbz64L/X3ad1UQZRJbQDvVyVk7X88UxfkWglTh5e6woWujhKAe6BGmeugSpYpqLsUtFzL/XJp6PYnTa+CqgZ5nDD09oxn6Hp7rsW6gnzSTJHHE2MQkMu5y7uxZwsDF8kr010q0pUct9K4rR7r5nsq2/tjITdWiVObxUn+U6rpSpR5qQ7iSp98PvPxMrdC+Jd0k40ffNEzZX5s0aSkKjrkrYlaDlJKPfexjvPTSS4tKV0dGRli/fv2sr61fv75n+rgc7uq1IVmWcfjwYUZHR2/Yx7Nch665FLbRy7mPJEn4L3/+A75/oUW9WkHYDq6dV0Ac0bMwVgrKjp5TxFa9BbLk7lHFXG/o2vqJ7trESYZXdAbzir3nWMRSL1dtRFm+pkPLwFodrRVvRwmeaxFJHTySTGBZkKb5oll5fQeRn0upwsJ+2XeYEGjZgJJUfO2eZeezjpbQVZok05X9KH/xt6JiJijLh6L151NRSsVzaMQp5UoVyyvR7ES6U2kJxqaadEfG/n/2/jTGsiw9z0Ofvfa895lizIyMnCtr6ip1dVePWSTFpkmp1aLpS0rmxb0wRAqCLmhc2bIsA7Slqx+GQcA/BEEGbMOSNdqCBP8gQEuUSXFosdVsq8lmdVfWPFdWjjFHnPnsca3741vnRGRVzpmVVV3MD2iczqw8Q8Q5e521vu99n5cwUPhhzHyzgR/FRIFLpR181/oorTQrDdUMYQ3QilwwDm4gU0xt4Oypecrt4QORG91swzQcDnFd96Yeyx+WeuWVV/iFX/gF/uv/+r/mqaeemi0sQRDctNP1sO5PTT9jVVXd9EB5vUbbwbof0UgfvH9RFLy022Lu0J/lxFJK+Ox/ct37TtfW1672eG9zROiJFEkOgpowFKCY40zhaOwHVPuW4Kzg3fcv4BiDn7agzHj34jqRCwQRi+0Gg0wTRUY2FNE0h1JI183AlS56IBuh0JeJXxoqxpOMl7//RwxGYz7z5OMcPXbC+h8NkS+gndSTaWXsS0e/Mdsgia9oRg20eZctS7Gew9CrI1JthBabGLLSECjJgJtCHhqhTzaARuDiKJFvGWNIQ896kkTWWzqibhkXskZPN1GDKfV2UgmMI0iYjxtUWlPmBVk2Yas7QJc5RQ1fMl1GvZBgbu6usvduVbfyUN6P2JBPQj1cHx98bW1t8dJLL3Ho0CGefPJJvve9793XrF6Qpsf58+d59913b4uMrZSiKIrbfj6tNa+99hobGxs888wzvPDCCzfkIgyykm++vmGjM2TSVhs5rGUjiUsCM4Mbal2jlEtZi5d6NB1WFKLWGNlmVW6VcKVlWuhaCNHaGBwb+6YBpQy1EQl9VokdaVzUBK6inwmJdZBpjNFklaGBqCxCV9QY6VQ2qgrqnavMBSET7wjtyLNwnf39Y29iVXQHbhuhi1YB7VaTLC94/Ngq/dGEfDJic2MLoyv6UUK7kTA2Ma0oIrOgs2EuU9hRUeEph2GhcRB2SK3lZ9Fmug4bXMBXisQCfRKlqLTmCyfmOH4ffJPTetAKjr/yV/4Kr7zyCt/5znfu22Peqj6WA+XtYPFHoxHnzp3DdV1+5Ed+5EMb9fsR/n0nG67hcMhv/d/P8/1txamTJ7i8vk2EoTuRDcXuuKQV+4xyicMY57VAdOqayPMoKuunROJCXLtJmObh+I4BxyF0RV7qB/Jht2cp0bfrKRpaYj/KWnJ0ytqggKIGxzHU9qxeIYdZmRbIZEBkB/IY1GZGWK1rwSUXte3oGAiVXGBpMJXgelRmmhXJTIrVifdx0mWtadh8oMCTKUXg+8y1msSNFrGv2O2PoMq5vLmDZyq0GzDfkslDuxGLNE05aETO5ruSLyQh3TIRzSvDmaWUxw83eGnz7n1id1I3WxDG4zFpmn4qfDTPP/884/GYX/mVX+FXfuVXZn//EIv/0dT1DoI3my4ebLR97nOfY2lp6br/7l7XyA827abAn3a7zYmf/f/d9LCrlKKb1Zx7c4vQU/Qt4GFvvO89bIUevUlxoKMt64ZyHHRdcuni+6RxwsrKYXkdjmLVGMZZxmQ8ptvt4VQZWzsFc52KqohoN1ILahBJ/RS2IwevcpZDOerucXm7B+WEty+uc/TocUa5UBLzSpQQsvnSGKxXxxxc8/xr83Ijn7yqSaOQ4aEv4TfWGYQLuH7MuBRSbGZqas1M+iqbHtsozKagIJleZqUmDWQzF9o4gIb13bcOeDEN4suH/XgUpR0aYUIWR6wsL3B5Y4d5N+fkfMT58+d59dVXaTabLCwszMK378e6dauJ+qfFQ/lwfXywdfnyZV5++eVrKK73K6t3JssvS15++WUGg8Ftk7HvZP84ldBO/ZLT6+R6B8qy1vxfL63Ry0ocZL84KauZN9u1EvrQUxRlzd7mOr3BEN/3iJOEOE6Jk5hSC0SnnxuWPSWZv6HL3vTgZqd5WaVlIFLv+75dV80UJIHdRypHhiWBpygqifsoaogcaXb5rkw0hT4tt3rnPbz+JbQXEh5JMd48qW0mdmI5ALen2b2hgHBiX1Rpoa/oD2vAYVRBlIicvzUnkViTyZjecMRkcxvf84iTmEYjJYkTlLJEWnuIdG0kSfwB2E4z9NjOKhzlsJdVJIFiklcsN0O+dLJzex+k26zbOVDer4bbf/af/Wf863/9r/n2t7/N0aNHb/pvDx8+zMbGxjV/t7GxcVcRT59Iyev6+jqvvPIKq6urPP7449f9spsuBndLaT34GLeq9fV1vvuDlzhfzvPoyUWKSjoro7xkJfQYFgJwmGbc9Ga3pUwBpxsZm582qmCh1vTHJWkstMHIdaSL7u0f+kTbLhNKX8kh1HHYN1Tb2acxDkwlZI5IvOTwaC/6SjIwx4VAe0a2u747yfE9RySlB3yW3Q9mRk4qUl/RqyT3Mq9qPBeqyk4KQcaFSl6va9HSrlJ4SgBEnuvQ8Xybw9aiqg0rh5YYZQVFNqE/GDGZ7LGzBVEU02qkqCCimYR2IuruB8xmNYuNgB89M29/fvOxHyiHw+Gnxh/0F//iX7yvMSQP687rRk238XjMCy+8gOu6NwU5wH3O2rXT0EceeYRTp07dcs0ta8ML6wWrJ2Qda8Y+dS3QsbyoaYU+w2Laqd5fN1uhR3dSQJmRtOc5fGiRYV4RKMiqktBTGOXR6czRbLXZ3FzHwaGuKnrddXY2IU1SdJrQSFNcR3yPApuwDa/QwzRaHFpoMxgFHD28JMh412FUVLjKYZCJrH5SiSRMFzXKsfYF2+3WRuMrK7V1pMPvKvCbiwSdZZTWuI7YG1xHmniiImGWJ5zag2vbZqWldgIZeyIP813FKJdokHIGepPGWhKKb79pQW3N0LObJmU98KJMaYWKLx9p8qiVJmdZNqMLXrp0CYC5uTnm5+eZn5+/60zb25G8fhrWyIfr44OtQ4cO0Wg0rmlG3I+s3un6OOVzJEnC2bNnCYLby2a93f3j3t4e586dY2Fhgaeeeuqa/e8H72+M4ZuvbzDIKgIljfyp/3pcCFxxWGqMga3emO7WGjUep04eZ5wVlMWEnZ1N9EZNlMQoRxEpg4NMN/NqSkcVkmv/wNorsSFyyBxZ8KM08L3ZYVNrS7SeegyV+C+D6bDEWgZAFHuO41PjYZwAXSlMXuG5AiEzRoCVBkPoKpt5KU2ypitquchVjB1mmcLg4CiF4/rMzc2TlRWHXMXeYIQpc65s7KD0OsoPmWsmBHFKM46oLQizNvuwnU7iyzrqOexqsRIUlfyuf+rJ5fvimzxYt0PBvh+xSv/5f/6f82u/9mt861vfum5e7gfr7NmzfPOb3+Sv/bW/Nvu73/md3+Hs2bN3/PwfK5Tng4fBg1Stp59++qYn5IOU1vsNnZiWMYa3336bt949z054lMV2Ag6EvsLBIXSFDtWKJAdyzuakdWa30mluxx6TQiA6w7wi9R3GpeZQUyA6ie/St/TAflbSCH16luA6KiUnrTsRiuA4FxDEuJTDYV4KLrmokamlNgSeSGljf5pLKZrxjqUaCllRXvcecoEV1X5GTyuU2zQQMpjAIySSpKg1GEOFdLRCT5HbaWw/E8lr78DB2hhNP9eEsWFUTgFEtslQI52ldou00SRwHYbjjDwbMxj0KfJtur5PsxEzqhJaaYrBYS72+fpnlvHukdR7p3WzBeHTsll6WJ+Mul7TbWNjg5dffvmmjbZbPcad1HTD9dprr7G2tvYh28HN6jvne+xNKlYMlKUmCCz1VRu7OTB2smahM3Z9WtvYxC0nhGmTU0eWre/bpzfJ7fpY7UdwhB5Z7dBpxARxk8WlQ3SHY3SZs77Tw2xsgfKYa6W4fsSchT/4jkMax/zo2bNkeU6zkVJrg1KgXduUE9slviPyLkeJVzOvpgAxmYKOiutLYLu2idjLbDMxK2RtnAiCf1TU+K6Na1IycYj9aQPOQynwXA/XFRmuZ+OSfMclryWbc1zUNmTcEgyLGs/6qmoNeA55WfHM4ZjI3/+ei6KII0eOcOTIEYwxs/DtjY0N3nrrLaIomk0vO53ObX+/3uxAmWUZdV1/KiSvD+vBVhAEH2pg3Q+Fmuu69Ho93nvvPU6dOsUjjzxyR8OJ29k/Xrp0iTfffJPHHnuM48ePzx5/evvBCeX3L+xxtZvNJK6+ay0B2MlgrUkCl7VuSb52BS9ucPrIIVF6NBoM84gT80sMRhN0lbO7u4eua94/f55Go0GapHhJQjOaTglFPdeORVXXOiilP7CWTXMfmzbHNg1ksOAqWSfF0qBR1rYg8lJwlk5RRw2UF1L6DUJPJLqR784OyNOIvHFeHcjo9Sh1jatkuhh7DjUOsa+sf1NR1OKDHBeGQ/NNBlnCoeUl9kYZTpWz0x+h93rkNbTTGDeI6DRTK/eX37sMbEQtGHkSpfJjZxZoRvf/aHSrfer98Jj/lb/yV/gX/+Jf8C//5b+k2WzO5PjtdnvWKPyFX/gFVldX+e//+/8egP/iv/gv+PEf/3H+zt/5O/z0T/80/8f/8X/w/PPP87/+r//rHT//x3aghGsPgwepWmfPnr3l5nz6GPfbIzStsix58cUXGQxH7LUeIS9A14ayrvHswa2oDbXR1BUiOTViaK60nsFzpqh2yf0RJPMGDu1IYZhCczSdxKOskA5+JTCaSalJfYes1My3/Vmg64zgOjUiX3M7RdnLZHT675JAJnyhqxhV4tcsao1jjEhmFRa4o8ARfbmjIFUSgWJckeRqu2jUNcS+Q6llw5dX04OzdMDGuZVo1NByFZPK4BhDrUWiG/kSxNsIDmzCsopGHFEpn5X2HOO8opiMGWUZW3vrKMAPI37yiSW8OscYbzb9flATyptBedI0ve/Zbg/r01/X+8wclHRprXnrrbe4fPnyLRttH3yMe1kfpxuiIAg4e/bsdUmH16sXL3W5sCeboulBazCpZpCGyHcZV7VkOVpVhdGatbU1qjInTFoEvkDFQk9R1fXM391JfHRtZnEjzUBJRlks+bVLnQZZGbO8OE9/XODUBVvdIWa4zdrGJvPNBPxI/p12aCaJ3TBJE296aG3Z2CbXkUzdto1RSkOhvArYATp2+tlJfOp6H4bWjuS2YQO3I0+RVUKdLWtDVYs/sqg1ruvNcPy9A77IplW1TNUtSaAYWbBbZl/bNFbJcxWBa3CsP8hVUNfwxOEmh+lyo7234zi0Wi1arRYnT56kqir29vbY3d3l7bffJssy2u32bHrZbDZvuMbd7ED5aYOWPayPt+71QKm1ptvtMh6P+fznP39D28DN6mb7x6lfcnNzky984Qsf8tZOr6GD9397Y8B3390htfCaNBQZfeS7su9ULoGr2J2McHXJ0vIhFhfmyEvJOp+UogAZZBXtRsIg85mbd9jrDVlc6LDXHzHY2qIoKhppTBInNJsJfhBijENibVbtWIis7dlhU7LAm9EBXobdsw0LQ9pw6E7K/Zz1wBWyq6coa59wbpVKG1pKzR63rM2MFNuMLHgx2U8nmD7+Vi7+x25W0wgUo0r21HmFZW7YVASrQNHGsNCIqHRIp9OmqDR5LgkE/eGAfncH40iTUfniw+9lFa5y6U1KPnu0zanFj2Yw8CA8lP/L//K/APC1r33tmr//J//kn8xUFRcvXrxmH/vcc8/xL/7Fv+Bv/a2/xd/8m3+TRx99lP/z//w/7ziDEh6g5PWaJ7WHyOmBcnt7mxdffHFmuL6dTKGDE8q7rRstCAclEMO5RxjuZYJcLmoB6FS19TjJxsBgwICjINcWUW/lq+OiJnIdxlqw8FllwBgmpSayVC1PQV5oyQgy+1CaxHcxODRC+f11ZgRXOwGNPOlWx54gkS01qxlO5a1iTA59l3EpHfGs0rgKJpUcJEvtkJe1ACnsgVLyK13Gdlo6KiW7Z9pZykuLi67FyznFQdfaEHoujiOYaceByJXDY+QrXNu1SgLZVHUi6baLhr6ebb4CTx5LuYpOp01YNlhdkRy2Uy1o6BHPP/88vu8zPz9Pnuf3tHG+nZrKqx9OKB/Wg6ippOtOG20H6142XXt7e2xubpIkCV/5ylduO+dtozfh3KU92pFLz6o2Kg2dNKCqbZ5tqWmEIn0NPYfBJGdz7SpGuRw7fpLNrW0qbSgrTQlIzJKQYOtCchrLoiJ0FeNS03CNkGJd8faErvV8JwFGB7SbTWpjqIqc/nBENhny/oUdosBjHKW0GilZ4dhGmA3Knubm1g6e49DPBHLRzwohrxZmJu9yRI8KZkqXFUy+NjJR9FSN7yk865+sjcELZN1s+A6OcphL/JlKZEpKzCtNZDv6yoFSS5PSsx6lNPBmTcbetHk4KUl8l2FW04p9vnJqnvPv7t52w83zPJaWlmYb7MlkMpPHXrx4EcdxrpHHHmQb3ErB4TjOXctpH9Yf37pVw+1OazKZ8MILL1AUBYcOHbqrwyTceP+YZRkvvPACIHLC633mp1DI6YRyo5/xrTe3rNpBGlz9adbtuJC88XFBv7vN3l6fKAxoz7VnubVVJYqy2lJMp5LOzYkh9sELIo6tpIyLChdNdzBiNJmwvr2D77mEUUyr2SCKIgLfwwHG44x+r0un0yGJQrRxbIavoZN45IUm8R0mlWa56dpm2IE4jsm+ZaphZbaJr+gOJ2ytXyFttFk5vIzW2BxNWf+m+ed5LZaAraEcIselUK/HhdgRinqf3F0f2INO197AcTCuImkkNJOE1UNiV8unFqveDm9tb9pcSs2JAM6e/uigWrdjCbhXBceNIE8H63o+75//+Z/n53/+5+/pueFjmlBOoRNVVXHx4kXef/99nnzyyVuaRw/W/YoO+eCCsL6+zssvv8zJkyfZcefYXh+Shj61lqlhrSEIPTJP4SuD74JC8sOMNmBzKKcHLV9JuLZIkQy+51AYWSBHhcin+nmNpyArhE41trdlpamNZlRolK8pp3lBen+D4dnJom916ZGnAIdGINotLxBNgqfkgyxZkhqjHZTn4DrGdsAEejMu9v2KzdifdaT6WUUayGKR+C49S50d5vVMa14bkQ5M84EmeY3WhnFpSBMYl7ZTX2p8VyBCynEseEfhOg6hqwTdbyBVErjb8QQC9NhKhz/zlGjb67qm1+uxu7tLURS8/vrrXLlyZbbRabVa93VqOf2c3WzD9LD7/rDuV7muS7fb5dVXX72jRtvBuhvKqzGGixcv8tZbb9HpdGg2m7f9vFlZ8xuvrJOXNePCUBqsxFMxzmyTqqgsAALi0KU3GLJz9QqtVoeVQ8sYHJJAoj+SUEBkGINjFEqJd1EhgIjaAAhArDYGXQn+vtIajHjMHcdgKjsFdTySVpu42WZOTzvXI9Y2NqirijRNSZIEv5HSDH0cxyHxwFcQWzS363r2+TWemlK5HbJC28iTeibJPbgp7E0EutOdSBTKXiE+yUGhiUJDf1LKxsgIedFVLgqZPpa1g6dEhusFarYm1haMVtmmXKXFmwkij/vJx5eFnKv1XVNd4zhmdXWV1dVVtNYMBgN2dna4evUqb7zxBmmaztbcqqpuquBoNBoPFRwP676U53nkeX7H95vSYg8fPszS0hJZlt31a7je+rq7u8u5c+fI85y1tTWAG3rRpvcf5RXfemOTwJ3GWoj6ohP7ZFVNO/HpjQt2N9cZ5SWHDi2zvdulqm2juypgsE7hBfiNJSHme9K0D1yHDIdmJIfMViQwsSNLIUVVc9SB3f6IqsjY2NqGusILQtI45pXXXqPX7bKwuMjZr3wZRynrnRSIo+fKtRx7sjZPJ4RTr3ozdMnsnm9KiB0WNT944QXeO3+BRhzzIz/2J1mY79jYEtCI7EIb8JXDBEPsOYSewld2L+uKaq6oReWSVfuwndnedQYf8tibVDQCl36miX0HP4xZTlKMMVRFSa+7h65HLIwv8gffXZutZ/Pz87ftp72d0lrfElr2wz6U+FgOlCAX08svv0xRFHz1q1+9q5P5vUq6Di4IxhjeeustLl26xGc/+1m26ojn39yiEbiMyprEErYiT3w0nmMjQFwl3hblUDH94ncQQaua8rEAYwmuENoLMfEtJt9T5DYcW+SyMv1UyqE2CowgmQFqB4yZgo7kMFdrCWIttYAl8qIkDj2KqiIOfIqiIgym0iuXsnYIPIcst6/FgTiQqeh+nqVLXUu3S1uaoeQYCYQiDRQaCBxlZWGi7Z+U+kCkiM/FCksYsxmXhci+ar2fpzkqJbOodyBTqBG67E1kujsyhsT3+PFHF2aHV9d1Zxf91tYWp0+fRmvN7u4uL7/8MsaY+wKamNb0c/LHAcrzsB5sfXCTbYwhyzL29vZ46qmn7qjRdrDudEJZ1zWvvvoqOzs7fPGLX2Rzc/O27z+FSVS1rEGeqzBaKNRFZfAcxcQ2ygYWO3/h6g7D7g7N+UWWl+at99pnWGgSjxnqPqtEvVEbiTyqLPlU1iGJSWqEHpWlXlf1PpTMUwK/8V2RiQauNM2S0KcyDoebDcZ5hYdmpz9kMBxxaX2bRuTjBBG1NvTyivkgEI9PaBUaluA99ak3ZsAHocDur5siiZ0e+NLAwiY82RwpxMdTGyzldd+TOaXhTqWwsSdIfN/dz0vzlENRaZJQMoETC+R56kiLlY5MD2+Wn3snpZSi3W7Tbrc5ffo0ZVnO5LFvvvkmWZbx3nvvMRqNmJ+fv+YAOV0fHx4oH9b9qDtd24wxvPvuu5w/f57PfOYzrK6ucv78+fs2kDjol3z00Uf57/67/46XXnqJlZUV/qf/6X/ixIkTH7q/4wiH4ndeX2NnmON7yj6WYyXrss5NJjm7Vy/jhRGfOXOU3mBI5AkQ0XMdyu4aTv8iqIBCBaikwygTEOOg0PbQWkt8Ur0PfowDGSQcnm9T6pbk2WYZk8mYza1dttYuMyoqjKnZ2t1jeWGeSSn7uImFmGW1IdaGSSEKjsoOU1wl8SWRr1COI4MMB3zXoxiLz71QUFUFynFEjeHLd0RslReJ3UcCDLOKhqVfR77ELyWBS1Wzv/YmHlXNzBfaiqZ+TVF0iMXAUGvwreolDgIqN+Rzqw5/5se+QLfbnakxXnvtNZrN5mwP2W6372kdrev6pgfUT8NQ4mM5UO7t7c38aAcRynda9ws6URQFL730EpPJhK9+9avsFopvvXaVJNiHP/SyitY0QDXyGJRiPN6zSPruuKQR+XTHhd0UVcShb2VLHkWpCQMHU2lcJYxWz3WotYNScvh0lYOn3NnUznEgdIU+FXrKdrBBYagMeI74emJ/2qVRklUW+5bc6l+TWdb6AM3Lc2BQCWlrGqY9zEXGKs8DjnYw2hDYbLjAVWi7KJU2aqSqp5mRhmZoZReRHDwTTzw/kc0/8XwX5cjkMVbSRWvHPlkpC8C4kE3RpNR4jiXIGvipJxZvGDCrtSYMQzqdDisrK9eAJtbX13nrrbeI43i2MMzNzd3xtGf6eb3RhujTgsR/WB9vFUXBiy++SJ7nHD9+/K4Pk3BnlNfxeMy5c+dQSnH27FmiKGJ7e5uyLG/r/j+4sMeVvYlVO0j2o3JE9aCtLNRoZxa+fXV9jXw45OjRozTSlLISlPs4r4htnMaSJ8CGJHDpjXOakb/vKbQE7UFhaCeO9Y3b/MlQmn+xL5L6xJfctZY91HUSn1rDfBpYSJncnkgiilpzDMPeYESdT9jQsLN+hb3dmPlWSlFGNJOIflbKhi0v8ZQ8n3IcMiRTc5ppKYj9KThNmm65JbEWtSHxwXWV9WIKkVbb0G1tbEawFoCRsVEBvqss6l86/w2b7ZbaqJSFNOBLp/ZD3+/XgfKD5fs+y8vLLC8vY4zh93//95mfn6fX6/H+++/jui5zc3OkacqVK1fuS8Pt29/+Nn/7b/9tvv/977O2tsav/dqv8bM/+7M3/Pff+ta3+Imf+IkP/f3a2tpdYfEf1oOve5W8Tvd34/GYr3zlK7RaLeD+UbCn4LKtrS2++MUv0mq1qKoKrfXsv9/o/t96c4vNgSaykBtPKau2ENXW7k6f7uYajc48q4eXqDQEFtQT+QLCCQKFdh2MC7XnUjuOjf8Q+X+3lsbTMBfLUm9SkYQu3bGozYalgMLyqiaJQgI/YHFuDtcxvP/ee8wvLUEx5sL7PRpJhBcmtJspVe3hK/GYd3yXoSXD9m0TbLpWj2y6QFlpIt/hmc99jkYa055b4OTqYbRxJIu81rQTn7w0tOOAcSkAy+2Jw2pg8yntbRLI6w99xSATtZxxEN4HktEJitBVKAX+bF8tlNrSkrXHpeaxhZATqbwf0z0iQJ7nM7n/K6+8gtZ6NqRYWFi44yHFzSSvRVFQluUPPbTsgXoojTFcuHCBt99+myAIOHXq1F0fJuH+BHePx2O++93v0mw2+epXv0o/1/y7t9ZFGmClRKXep2F1YjESt0LFxsjYoNaaVjQ9vAUzudOUltW3pL/euJQv/9LQhFmMyKWNbTwq3DBhoZkwLMQfk5faehvBtbj5wB7kInfaZZKDXCOcbkTkQDdnc9I6sbvftak17cidZUb2BjmRayhqjW/zhyTxw7EyKZdJIYvEMK9peobRhRdpqpxe5ynSRoOuvbh7EyvTLWRj5WDQtaHShqysiSPIKvFTFlaKIfRCK9kV4KP1WjooR/InKw1fWm2y2rnxxfvBDdPNQBNvvfUWeZ7T6XRmi8ftSLFuh9D1cEL5sO6lpnj5ubk5Dh06dE9rI9y+gmPqYV9ZWeGJJ56Yfc5vd329vDfmu+/tyJf8pLBrnuSHTTcVwyntdDCmv7NJVlY8cvIUGvnCVzZyKA1cxq5D6kv8UCv20VrWYQ2z9bgViddQlCM1HRtoLdRAue1npWygMgEBdScWu59XRJ7QCkNPMapFgp9X0lXXBpqNFNVssLPXY3V1lSLPmYzHTLJt+q5PkiaoNCGOEly7CXSVoqynUlhNM3Jt5NF+5lnf5k127cSxXxiaHgzyyvqBZLIqZG19bTZlJSHkuW0eVlp+fgM0Iw8Hh3bs85NP7hOw4cHEKk39YCsrK6RpitZ6Zkn47d/+bX75l3+ZRqPBf/Pf/Df8zM/8DD/6oz96V88zGo145pln+Et/6S/x5/7cn7vt+7355puzgwTA8vLyXT3/w/pk1O0OE6bZua1Wi7Nnz14j/b4fCreqqvje974HCNxk6in+G3/jb/B7v/d7PP7445w+ffq693+vpynrnFaaUGtNatUOOJJFvrm9TXd3l4XlwyzOtRlkouTq26ld35L/s3gFfyGgxCNIWjgGXEdRK0PuOkSeg+s6pK48/sGm1qSSCd6scWcPm3uTitOPPMKR46dIPLFtKVMxGI7JJmMuX97DdR20cUiiEm0Pg0UlYKBJIR7uKThyuv51JxVLS0vErTnbAKxsI1EOhUU1ZYloYk8xwdAI5HjYtrLdZuhS1YbQlzXHVdKoLKuayJf1Mj3w2BJHNc24lJ/ddwUoudDw+fycw3Dw4cZpGIasrKzMhhTD4ZCdnR02Nzd5++23iaLomiHFrb6vbxU7Bz/80LIHNqEsy5JXXnmFXq/Hl770JV577bV7Bqnc64SyKAref/99Tp8+zSOPPMK4qPnNV9eZWIIeQGUEK1xpg6tcC55RTFCEjpl1kSvbVa41+8ZlazCeS6YkV18koYGiqAyH2h7vXVpD5yMmXoQz2GRt3bDYSuj5McudBqMSFhzoZaIL79ouzcGMyEboinE7cBlVshmZ+jbL2tjMSgfHQG3ksIYBz5PDW+SpWcaap1x7UPWptJ7RFOcil2rjNeY3vkuhYc7UjOOzpIGSkG5332AuWH0jEoXaIQpkERQdu4SGD/N9Y7XW0snPKqG+9vJ97fuphYRnjt48ZPhWHfgPgibG4/Gs8zTtpN9KN3+rDKHhcMjCwsKtPnIP62Fdt86fP88777wzw8u/8cYb9wWLf7PHMMZw/vx53n333ZkM7GDdzoFylFf8/ltbtEKRxLdjC9+JA67ag59Ef3hs98bsba2BF/HoI8dtwLQ7i0iaRSUVGhfDwB4Is6LGdwy53WTUtcZ1XBxE2uq6ErfRiDwUDr7rAA6eDbZuKPmaSwNXfOaB2AjET24sQV5osq6NBol8OQziOBRa0e7M4SVNDvuKrd4QU+VcuLqNT4XxQpbaKSpMaCUR5ZROO6N7Qyv0MDYT0yB5a1hoWeg5RL6Lq5CpZWgjQawXc5pNGXmKYVGhbJB4rTWh75IVckgdFSVfOTXPUiO85j36qCaUB8sYc03TTSnF3Nwcc3NzPPLIIyil+Pt//++zvb3Nr/7qr971gfIb3/gG3/jGN+74fsvLy3Q6nbt6zof18dcHs8xvlUNpjOHy5cu88cYbN8zOvdf943A4JM9zlpaW+MxnPnPNNfbkk0/y5JNP3vC+720NebtrOJFq8koyHisbw1ZWms3NdSbjCY+cPE4cJ5Ra04g8ijxjrv862d6I5PhR+jk0w4BuOU8j8oSGGro2rsinqCV2rtYQuDKpVMrBtUyNpiuTzKb1PabhfsNqyvIY2HiPSaVotlt4cYPFZUV3MKK7u8leb0i32yMIQhqNlCRJSaLQfh9Y6f+M7CoHuoaNTgo9ufWUEqo2DgXynjjAONeYWsCRoS9qF99X9iApa2vi79NeSwv2yUptJ5AVaaAk4cATSKXryDDGcRx+4vEleptXb7k+Oo5Ds9mk2WzOhhTdbpednR3eeeeda2jYCwsL1x1S3A4F+3ZJ6p/UeiAHyn6/z/PPP0+SJDz33HMEQXBPlK5p3a1kYeqXHAwGrKyscObMGcpa8+svrdEbl/iuyJHkA1FbCRPiVXQdysJQGxhX8qWelfaQWRsCF7IKPCXdFulaG+t7EWiCAQJHc+HiRVyjWT1+At910DiUeUZvOCIb93h3d5tAQbffZ2l+jqJyaIUuuQXoZGVNYvN8Ql8IsnJIk4VXa4PRBtd1yGyeZG7phf28xDUwrgCcfUmvPbAO85LYEwmtpxRZbfCMonI8HKfCOLIB8pXCQX5OPQXpaOhEihpD4ooAYUqobUWyKESe3L+yC0ReTzef+5TaduTxk08s3nJ6eKcbpiRJSJKEo0ePXtNJv5lu/naQz9fzSTysh3WrGgwGXLx4kS996UuzTa/ruhRFcU+Pe7P1saoqXn75ZXq9Hl/+8pdptz/ctLnV+qq14bdeXWdnWBD6Mp0LXIM2AtJRYKl7im6vz876VRYXl1haXBQVRSIyp7kkEPXHtMMdefStxHVkD1TdcT5TgTQO5FCOSk1DKYvZ9xjk9lCal3LIshaFSWlx9pUW+X697yFKEpHtJ56iMoY49qmMYS71WUPWLIPAMvKyZmW+zbisWFxcpDfKqIqMncGYYmuXK8olTRLSNCEII0sFF+qrkFlde5D2GGYVDg6jQtN2DJNCDpd5Wc+yKRNf4SpFGspm0LdNwFqbWfMvSiRH+Nh8zLPHO9d5nz76A+WtPOYAx44d4x/9o3/0kb6OG9XnPvc58jzn6aef5r/9b/9bfuRHfuRjeR0P6/7UzQ6DByWozz777A0bvfeyf7x48SJvvvkmruvecbzC9jDnt17dwFNyWFtOnFlk0dYgo7e1Rqnh9MmTaCTGTSEWpHj7Fbj6hxyvCtTmPJ3VZymqWiLbbONuqowb5BWBp9itROE2bdgNRgVpJIq72PcskwM8C7qMfYGfTTMw/cijroWbUWmrktOaZprQ6wV0Wi3CKGIyHtMfTdjY2cNxFEkc0WikRHFC7HuAxuASenJASwORofqW46GNgMiKWjgik1KmkP0CIn+65ssQZZpz3ghcukVNEigKSwAvKontkymni+NAI3RQjjQZleNQGcNzj8wzlwTs3cX66Hkei4uLs1zmyWTCzs4Ou7u7XLhw4Rr57Pz8PGEY3laO+YOIv/so64EcKI0xrK6ucvr06dnh4FYdptupu+kwTT1KWZaxtLREkiQYY/i3b2wymuSkFkrjuc7M+6OnjTElmyhXCVXQQTrKrkJMyp7DuDQEnjMjuGaFwHbGuXRYRnlFWRsubezQakQsHjqK4ziMSsmGNF7A3FwEc/NoXXPx4kXqquTSlcs4BtJGgziOaTQaxL5nQ1lFL6pdZpNGB/FhKtu9aUayeZri6Nuxz+6gJPEQH04ofqXIdxna1z4uRQJb1Fp0/fNnGNeGRJUMWo/SQM0yMOXilqlibHX5vuuQV5DXBq/SFsIjm6DQdygrQ2ihRqGNCok88Sk2Io+vP7lE6N3c62iMuSdJ1wc76UVRzKaXr776KnVdMzc3d91w5YP1aSB0PayPp1qtFj/2Yz92zWf4fgV3X69pNxwOeeGFF4iiaNbgu17dakL5h+d3WOtOZIJmD3TjoiK2BzkNjLKSfq/LXrfL4cNHSJsNBlmF5zqMagGVTcqayBPfdGCbeb4D2ojKoa4Nc5FPjTOD3nSspL8VSJdarAjy91PS6qgQkMPggNy0EXr0p773iRw698YliZXMxoHLcGIlsVVNVcv6GAYuWsv1XxlRf7hK0Ukj3GbM0vwcCsNgJJKwQXeXuioIwphWM6U0Ce04JKv2O+dxoFgvatqRIPGNgbyGqtaz4O+pZaIZ7pNiB0Upa2xe4SsHs/Ym7tZr/MRXHkE5P/uh90lr/ZHDcG5Fwf64POYrKyv8vb/39/jiF79Inuf8w3/4D/na177GH/7hH/Lss88+8NfzsO5P3WhtG4/HvPDCC7iue40E9UaPcacquYPgsqeffppXX331ju4/Liq++doGSSCqsGYgMMV25LPdG7CzuYYKU86cWLHZtA4Dy7eY5DVBbdBagVH4tRYitAtVBZEvxMaOVcx1Io+dzJB6EhHXiqVBN817bFgpaiN06Vn71XBSSERcVpFEPmUtKgitDJ4lvCpHhgeOA4EyhB6koU8j6lB0WrhAdzimyjM2t/dw9CZ4IfOtFMcLmWuljO3zjqaHQZsvWVrYTllDM4RsKL8jg5k1HNs2Ji8JbByecshszrnGUNfSNMxyAfuM83r2XGkgVoJHFlMePyR+xfvRcIvjmKNHj86GFP1+n52dHS5fvszrr79Oo9Egy4QqvrCw8KHn+7RAyx7IgbLT6Xxos32/Nkx3siD0+31eeOEFms0mZ8+e5Y033kBrzXff3eHyG88T9C9SNo/hH/kTAqDxxPTsW5yzY6NBHOVQei6+MjNMe+jJQS4IjZU6iQ4+9t1ZNk6tNUU+ZjjOSJOY5cOrOA6UtWwmSi0EV+0YjNGCxsdlvj2HH8boMqc3HDHa7XLx6hbNNET5IYudFhpFq/8meXeN8NBjZI3jhL5g7WeHNteh1IbIk+535DkYIAkVCslhEyyzi4NIJBzHQVsPZ6UN8epnJPvSbgBbkVzcDTsxFW+khNEqRxD3oecyntFf943Vkafo29gRB3n8KZHra48tstS88ZfBtKbv//3q7ARBwOHDhzl8+PBMN7+7u8va2trMb3s93fxH7aG8UxjFw/rhqWkE0sH6qBpum5ubvPTSSxw7doxHH330ptfNzQ6UF3dGvLHepxl5FnAjeZNzSWA3Lx6eA/2dDcZZwaOnTqJ8D9dRYLT4BTGCiQcrzYKJrtEGslq83XVhUAqKosK361jgOowKUYNMpgTXSkvzq9KEroDGGoGHAVqhj3EEmQ+ihDBGZKiVMTQCwd1LfplAL7RFOzjKgtCUw6So7fRz3xs/7ZS3Qo9eXtNIU7QXcnxhid4ooy4mbPdGVNk2ynVJkoRmo0EYxwSuInRFqhX7Lr6S9bkRula6FVBpbf3vhmbo2aabi8HgOg6+nlC9+X/xJ92XWPnmeUaPfAXTXLnmvXoQE8rp5+xmsSEfR8Pt8ccf5/HHH5/9+bnnnuPdd9/l7/7dv8s/+2f/7IG/nod1d3U7ktfp2ra6usrjjz9+y8/8ne5Bp/mVU3DZzYA716u61vz2Kxv0JuWM6Ko1hK5Lt9dld32NxaVlFhfm5fpPArEBRS6Twqoc5j+DWxZs7Q04tPQUejICP5LXojW+62KsYqzWxkawCW1Vaxko1EYab6VdW/LK0E58RnltJ5v1NQyQ3qwBV818j83ItSAyGVj4ruz10kCks4fmmtSmwerhZSZ5QTGZsDsYUma7bG0q5pspQz9iqdOkn4kdqnsAthP5LuOqJq9qylp+d9rId4TWjsTMKQfluHiOQ2XEBlFqIz5zC3s8GCWS+GK56iQ+P/ro/tT6fnvMlVJ0Oh06nc41Q4o33niDS5cu8f7771+TQJAkyX1ZHz8JwLKPLTbkQUte19bWeOWVVzh9+vRsUqqU4s2tCeeH67R779MbZzTN+3SbJ2k2m3Qn5T7BdYpyj6X7rhxFWckCV1jiXqUNnieRIa7rgLYjdmVwgc29Lrs727QTn3anQeK74Bhq5VBV2mLkRfrqWjlT6MlC2ghdMjfmaJowqWp8DNu9IZQT3r1wmTkGsP1HtE2PQX+T6Nn/F70Js+zIyFf0Mi1T01IkvJNSk1dQlIZSi14+t4HZk0pCa7NSDnmi83cotJ55MwPFjDyolEPs2wxJbexFLpJX5Th0ph6ryLMQITmIK0fuM+1MZaXmM4ebPLVye7Sr6ZfMR7FhOqib9zyPzc1Njh07xu7uLu+++y6TyYR2u81rr71GVVUfqf79bmEUD+uHs+5Hw+3g+miM4Z133uH999/nT/yJP3FbXxg3OlAOspLfenUdY2BUy3RxmMtha892uvcGORjJnj1z+hQ1Qh6UvFuJ+IgCl7o2uIGNSbJ068xX1CVErqJyDAZD7YCy0i8DluMnNGrXOORWGVJYP1JRTbN87bpmZaeCkXft4dCdrXelPagVtSYMBPoQJw6uIzAIlCN+8gPT0fY0AzKWznnsOXz79/89u3s7PPPMMxw+soobBURpk9BT7A1GUGZc3dzBNSXGDfGpxP/eMKLu8ETF4rkOEyN2C9+VeIHEFx9SHCjyShqBZeZwUm3yOXVeTr/qwxPCByV5dV33hh324XD4iQFOfPnLX+Y73/nOx/0yHtY91MH10RjD22+/zYULF3j66adZWVm5xb2l7mT/uLOzw7lz5zh8+DBPPvkkSqlZhuXtXl+/9+YWl/ZGouDIa7SjGBcVe1fXGfT3OHR4lXY7pahl/1RrQ2yjOBrTHMlmysD5PLm+jLvzJsHgMkV8CL38FFpD7Vioj23qZ5WhNoZhVhG4wqkIPUuUtetK4DroWkjStT1kVtowF/vk9XQiKMODoZ0s9i3TY1RCUzn7UW9j8Sz2M03qKyZ1ReD7uJ7L8XYTrQ1FljEcjSlHPc7vbRGHIXmS0mik1LVjI6E0zNqOgKMY59Mokf2DbtNCdxqhmkWalDUESpQkcaBwbPMQB1qez5/+jOTzTutW+ZD3WtMhxVtvvcUzzzyDUord3V22t7d55513WF9f51d/9VeJooh+v39d+8nt1CcBWPZAKa8H60FNKLXWvPXWW1y+fJlnnnnmml/W+rDm5fUJR48cIt9ZpF1fZRIu0WmkDItaLiCbjXaQ3NoMPTbHE5SDlVG5cvi0WPvUEl3TSA6jSeDyzqWrmGLC/OFVsn6XcV7TwTApakIXC3IQeWnoOpSVQ+ArHOSgZmAW+C2dalhdnkObDqtHYLy7RnN3Ql0agrLP5StXaDSa5DomjSK0McS+XKAoYzcs0uUvtSYNpoGw0wxJl34usoJBJlr8YS4ZkiVmliGZlda7ZLtXU0jQXiYB5uNaJrC6rDFMMfpyeCxqmZ4WWha1Sotv6UfPzN/2Z+B+Tyhv9jzX083v7u7yP/6P/yPf+973ePHFF/nt3/5tfuVXfoVTp07d1+e/WxjFw/rhqA924O9Hw226PpZlyYsvvsh4PL6jzN/rra+1lrxJT0mmbuyKJzq29NHIc9npDtjeXCfT8MSRFUal5CT2bINub1zQDGWtbNk1shn59DK5HRUi1RiX+1S+KJBmV+S5VHa9qg00AoXGoW03QUkgNGyJ6TBEsUQ3CRRoyPqlCzTmlzi+sjyLVOpn042JpXFPStLApTuq0AZ6WUUSia/bU4qJlkgnB7FA+J746zeuXublV1+inIypypL/98+fpNCaJFTUNazMN6lNk5VDkBclk/GYza1t8mGX86M+cRKjkwZ+GBF6solLQyEwNqw3KrENwsCViCfPjXnu5/6/1O8do3zsz2LSD28GHtSE8lYU7Lm5uRv+9wdZ586du+1Dx8P6ZNZ0bZpMJrzyyitkWcbZs2fvqGlxO/vHg8kETzzxBMeOHZv9t+nn/Xaurxcvdbm4O541nxqBizGGXneXqtYcO34SPwjIS4PvwtCufdP91CCrZw13z1NQV0SjS1TZiNRUjKvT+GGKsTlrxpFb1+aWT32D4Mwmi0Up0v3MUk/HpfBD8ko8jLnWlh0i6jbDwQmnNLdSX9R7ndAjqwS4OCm1hexoXAXjsrbqEfl+c/yQuBXQnpujKEuqPGMwGjHo7Ul0UiMlSlKajYSxgiBwcZVDJwn2/aKFntkahNwt+bzjXFvVH/u0bCuPzSrDj52ZZ/FjgJZNn8fzPJIkodFocPz4ceq65sUXX8TzPC5evMjCwgK/9Eu/xP/8P//Pd/z4nwRg2cc2ofQ877Yzzm5UtzqUHsx0O3v27DUj5a1BxgvrGRgtBKwTX6LK+kRRE42iGUGtsRsVIbeKpMunrDSdyKMHluAqXeusNrQTj6wUCcE4r0l8ePv8RSIP2keOMd+IeGuvy3ysxIht/UfNSBDLqe/SyyvSwGVvmKONpjsuWQ41w0zPvD6hJ3AgX0kHR7UOM3niZ3EG69TtE8RVwGQyYXN7l9BThElCp5kSRAmxr6iNQxIqesqRrDMDndilthEjlZZpojaGRiBSWFdNgRAQ+yJTayf+rHs1LmrpSpW17arvTw+LWqRlw3y/q5QG4r0MPEVeSz7nTz6+dE336FY1/UJ4EB6hDy46cRyzurrKP/7H/5hnnnmGv/pX/yq7u7sPvZQP657rfkle8zzn3//7f0+j0fgQNv9WJXL3azdc3313m7VuRhq6FIV4Dgsj+bRObRj09tjZ2OTIymGurq3hKYck8GdQm+lkr7RewqySyKVxUc3iRWLfZWdsWHEdemOJIRkWhjTwGWT2sDcuSQMl64iv2BsVNCKPvbFMSgeZzbMt9ieU/+Zf/0suX75CI0n4T37hF+i0WjMcvYF9KWwkUSWN0MUzJc3Bu6iyCemqZAcbmWYOi2om429GPk6QkAQBQzw67RZ7k4LYd+nZzU49O4hK/nCUNqm2exxeaFPiQjFhc2cXqgI/jGmmMSZJSeNAJLyhi6sUzdCRddgYvnJ6nuTQKbLHvnbD9/FBHShvBuSZTCb3lKk6reFwyDvvvDP78/nz5zl37hzz8/McP36cv/E3/gZXrlzhf//f/3cA/of/4X/g1KlTPPXUU2RZxj/8h/+Qf/tv/y2//du/fc+v5WE9uPrg9/t0ovQHf/AHzM3N8fnPf/6Op0zT/aMx5rr7h4N+yYPAtIP3B255KL2wPeJ753dsNiT4nstkkkGV4/oBZ86cwlEuxoBxpUkVBy6lzdKdxl2IT91lZ1RjlMdeeIymvsSuf4hm0mJSyVBiGotR1xpfaVxHE3rO7PdojEB3Km0NB2pf9aGN/K/UAldztDP7/RgjKhJjDNqT4UZt4WtKIWkBysFzJQ1SnsexcXQS6+G5onqb/lyNKGToeBxtd+hnJT4VW3sDJntdzl/ZIA0cjAqIkglBGNooOTP7eWZgH6vMqI0zy5mceixbsXwfnFlOeXr1w9O/B0XBvh6Ux3Vdnn32Wb7+9a+T5zn/5J/8E3Z2dj7S1/LBup/Aso9V8jqVDNxt3UyyMPVLtlqtDy02w7zi119aJ68lG7GsDWUNftBmVGgCV1PmYkae1HIxjgqZrk1KCYsdaYOja4rK4FuyaeTt44tFilVy/v3LdJKYQ4dX8Dzp5jcDB61rWqEAJeZSG80RBxRaM5eGZGVF1t+lqDSHGzFZWeMrmEyxx7VdABDvYeA6ZMkqcfs4eVEx3/IYFS0OH1Zs94Y4Vcb7V7fxTA1+yGK7QVbLfXsTOdTVtQYMbi1St3CaeRa45JV4LvPaELkOeS3Y/aoys8iQxBeiVmLR1HWlSTzR16dWutGxHa6WPaymgYfjSKfsT55ZoJPc/oYX9heDB3GgvNmGaTr9OXv27Ef6Oh7WH4+6HwqOXq/HYDDgzJkzPPLII3d8jXxQ8vrO5oAfXNyjGQnQZnrbslPHUXeHre6AR44fx7g+Bod+VtJyPXILtinrGuO5aGNsdqN0vkMbXdSMPKqJQ+yJh7EVeeBIvqIxMJeG0uhLFFvbu9TZCL95iNCFcV7iOo69VUwqkU3lVU1Va/YGYyrHY1Ibtrt94rQhnktfpK9x4DK20t2xzQJuDN4h6V0m85skZ36SLF2hEXpUtZXvG4kC0cZw8sgSP/Mz/yHdbo8TJ44TBC7gkNhNT6WNBZVp4sBnVFTEHmS1w+JcyqSIODG3wLgoKSYTBpMx69t7BJ7CDWMWWg2MF9BJQ7pZyamFfbDEzeqTcKCcQifutZ5//vlrfD9//a//dQB+8Rd/kX/6T/8pa2trXLx4cfbfi6Lgv/qv/iuuXLlCkiR89rOf5Xd/93ev6x16WD8cNY0EATh69Chnzpy5q+//gxPGD352x+Mx586dQynFc889RxiGN73/jWp3VPBvXl3HVc4MBra23aW/vUFuXJbnO2S1IXEdKl0TuO4M+qUCF4WD5zkYO9woakMr9NjUhvjo0/SGJ2mkKf1coj9mw4lJiaoLLqztcGSpY3OAxYsZh64AEX1ReUxZIcoXAJrsqzXKdSgrmYhmZU3guTPZ6TgXZdqkNHQcx/os3dntuBB1hRCulZXqu1QaWhFUZjq4YOYRb8c+eeVy7HBEVtUsaM36+obAKa9clbilNMEPI+ZaDYoa2zwsZzm9sedSVrJnr7QRdZ9xONQM+dpjS9d9jz4JHvMp1PH48eMcP378I30t0/oogGU/9JLX6005r169yquvvnqNX3JaRaX5zZfXLa3VwXVkiua7Mnnz3X0vZF4JuXVi/TljG70xyCscRzGu5SA1yOSi6U0kv6eX11T5mItX1ji8uEDaaqM1DLNSOiy47G7vMcpymklK2mjgeS61lShUVcX61Ssox+HMqRP2IAqgqWuD40j3yLWeoyiQA+xU9jWX+OS1YS6Wi+zwQpO8bLC0tMQoy8knE7rDIdkkxxjY292h2UhQXkgaeYwLbT2jNc1I9PKSbSTG5l5Wz6JVMEL/Kuqa2PcYl/U+kdBzGFVQY+jlFZ5yKJALOLKStNiTSJInDzU4vXTnG44HQTCEW2+YRqPRJ8Yj9LB++Op6kte7XR+11rz55ptcvnyZKIo4c+bMXT3OQUlYd1TwB+/t0In8GdShNrLWTPKS/uZV8trw9KOnqR1F6Cq0cWiEIt2c0lanXvQ0EDvBVC6fHKBMTyrDYDTh7fMX6bQaeGFMM4nIy4rY98iriv7eLt3BgBPHj+P5Pr7r4JUaT4nE3nWExO0pMI5D4vn8xJ/8UX7w/B9x7PhJzhw/OpuWClxMyKutSCT/041KbCb0dEhc5/QmGX5sGGQVrpJ4KCywp7I0xKg5z+nFJclww5mBLSZWwpZZkm1Za2Ib4J34Ct91cEMPV0Hgh7iNiFp3UA6MxhnZZEh3b4+qzOkGEZ1myuc/M3fD6coHPw8PykN5o7pf6+PXvva1a66TD9Y//af/9Jo///Iv/zK//Mu/fM/P+7A+GVVVFa+++iq7u7s4jsORI0fu+vv/4ITx4Gd36pdcWVnhiSeeuOG14zjOdVUc08rKmt95bZ3QVdSWg7G+tU1vd4fWwhJRNmaQGzodz6rVPPFTRz49C6iZVEKcro3Gsdm7OA4uhksXLtBopEw8j3YcS8M+CcjKGpOPeH99ixNHDqHCxLI0RN3RG8ukc3dU0Io89ibWapDXJKGiKLXN0RUlRmWm/koZflS13BaVSF5rbViIPIZFvQ9enFK1LWwntSDG2HfpWoXdRMvecfrr00bSFVzHmSUYjOMA11V05hcoi5xef0Q+7vPezjZpFNALExZaDQYZJIE8t++KPUveHwEf/ZmnDxF4138fPwkU7I9j//hRAMs+Vsnr/fAIHZxyHvRLfu5zn5sF2e//d8PvvbnJIC8JPAdfCfQh8NQM9sCB21DSOAhC6Yr5lrKXhh6TSUWoRCiQBB6VFnlCpWtGvT02d3c5cvgwjaZ8SAothKqi0nTm5kgbDUajMbu9Ppc3NkmikDBKSOKAtY1t2mlMe2ERR3kzJP8oF3P2YFKShC7DrCLxBCsd+4pBWRO5ilFlCFyZLrqOSHflWnJIwpBmHDHX6ZBNxqytrxN7sLezjalrhnFMs5FSqoROFFIZkabVRjTr2oiUwHGgrsFzlY0jEa9S+4D0dZSXeA5UtQEjgKLJjPYqpu5eXnO4FfLV07fvm7z2PX0w+ve6rq/bpZy+ho8Li/+wPp11tx7KPM85d+4cZVny1FNP8fbbb9/1a5hOKKta829eXac3Lm2ckvx3R0F3krG+doUoSjhx/DA1Smis00xKrWkn4SzqozYwF/vSoU5sBIhVaLRjj2zY5Wj/Beb8iEnnDDuDMfXuNmv4LLYbDLyAcjxglNecOXWSrHKI3altYLpW+gysD7KXlaShSy/Peeyxx1g9+QjNwJtl946LaXNMlCcaQyOQ9aQZelxqPMKJoIsbd0iXjqOUwrfSr+m6KmRBn6zStBKPSX7A3xO4jCyhdlzIpiJHz6JBJhU4Sjr8zQM5wN1CAD1ZVeN5AWFzjsW0g0LTH455vGN4+41XeOdNZ0YLXFhYuG4EzP2mGF6vbuWhfLg+Pqx7reFwyLlz5/B9n+eee47vfOc797SHnG7u67rG932MMbz//vu88847PPnkk7cl0b4RuExrw+++tsEwq/AEkc/6+jrD4ZCTJ06QJBEXLo5ohs5sbczKmlbiM8osbTUTDsco35e8Bq6oxI4eP8VgOGSUTdjavYRSijQWb95kMmY4HHLm5CpRlKAcIVXPN0LKSjPfEDJ/O3bsYyv6k0IIqyN7sJ2UNCKPbrZ/GExCJcMTz2Vi7VYa8Fxm1qiphUAbZgDGZujaaaH4310F2jiWQguToiZmzFAHpHFE74AtqjaGooZ5pXC8kOWlmNos4Jia4XDEZDzm6tpVHGNI05Q4SfCSBM8Te0BRa86emWO5ef29m7xXD6bhdj2a+7Q+KQOJewWWPbAD5f3swE/roOS1KArOnTtHURQf8ktO69vvbPPmxnBGbkUphoXglgd2lC/SJ89STV3KSvIjq9rgT3XogGM/GMqRC8pBuiEb6xtkkwmPnTpBEkWCnHcEe4wjyGPlu5jAZ76ZUNYGx2j2+gPGwz4b63tigHYcVJWTOQ6tyBdIUCxShlZs4UBxKJ4jm//mKxgVFUqJVMEBXHvgE8KhBQZZCMWgkAspbC2wsLjM3nCCKTM2dvuU2TZe4BLHMa1mkyAMbfSJGLRzGz9S1DK9rbWYyWsjVFjlCExIsPguriua+7lYiIrtWHLdFtOArz+5hKvurkP0IA3VN5MrGGM+EQvCw/p0lOd5aK1vawI1rW63y7lz5+h0OnzhC19gNBrdcc7awZpulr7z9vasu51Z2nNeasaDIZfX1lleXKDVmaM2DhpNVWiU41BoaWrVdiM0yg2hrxiWouQY2rV2NK6IfEcgYJf/gGLjTVKl8ZMmcyeeptKafDym2+8z7O1hLLhhNBzRbjYwyGG01nJbVNKpzytNKwktlEEmpZ5yGBUVjgOTQuKajDE2sFvosJHvMskrkkDRddocOfE5+jU0nClRcL/7Psg1sacYl5IJWVQGzxUQRuy7eK6SzEpLUvSUwDHcQDZZDU+AGU0LuZDNl9wXDL6rCFxRyaT2wPy5R1b4U08uo7VmMBhck3fWbDZZWFhgYWGBZrM5m6B83JLXjys25GF9Ompzc5MXXnjhmrije/WZTyeMdV1T1zWvvPIKu7u71/VL3qhudKD8d29t8d7W0BKvJ2xvrlNpw7FjJ8CT6Z5Bsrhdq3CIfHdmB6q0oZMGIq23a5rwKSriUKA0C/MdsrLFineY/mhCNh5xaW0DjCEIQ/b6Y5IKYpvFqZUj+eRA4Iuctp16GA2+Jx7vVmihZ75DUcigIq+k6ZaVGtdxmFQaF5hoQ1EZ8srglQLDqbUMaLTWVkprCHyFMnIAnVL9NQKZrDWka3+IXnuRRhjTf+zP004aNsLEZas0NCKHYVZZcFBl99oObpgy32iSFDVKl+z1RxR7Xa6sb9KIA8Io5ZGVzi0TAz4J6+NwOLxtWN5HWfcKLPtYPZT3i/La6/V44YUXaLfbPPvss9c1Z5+7uMfrawPakceoqGnHPleGhtCFUaFpRoEd1Qv4oRH5DG2HqD8pZxKoxN76jsO4NGigNykJFLxz4TKx5zB/+Ch+ENrNhysTxUB05Y1AaFgSxSHm4qJWBA5sTXKOHTmE4/pk4yGb29vURUUURzQaKZlJaUchGmhF8uFsJz7GOLTtOL/WBgdNXUtHpLIkrlJrmqErGOhIpKmNQLFXI/CKqiaNI3QUEjVauI6hNxhjyozLaxsEjoYgZqmdMvQiWkk4k77mlXgvPVeRl/to58iFUQXgzHInR4UQv5R9bT/x+GEa0Z35Jg/Wg5xQ3kyuAHykB8pbwSge1g93ffDQeLB7fjuwiUuXLvHGG2/w6KOPcuLEiVk39F7WWKUUl/oV1caAKNinM1faMBnssb27x6PHV4nTFE+JREopkeE7OASuZNlGlmgYBdJUjHzHylHlAOc4Ig9zgJwIVykmTojGI7PrRaahOxzRaLRptlpMRmO29npcXt/E930aaUocxzTSREARjpzqXKUIfZfAdQh8aymop/lpGoVIrSJXpPnt0KMCOqlPUdYknig8WpHPpKxoBCJfDVzxaCpEfVLXYkHISi3qEQvsESmbT29c0ghEXhb5LkN7AB2XhqTUeKW2v19FWWnS0GVSGqLAldcRyOZsIQ35kzZDTSlFu92m3W5z+vTpWd7Zzs4OL730EsaYGVn1XtVAt6qbrY/GGEaj0Sdiw/SwfjjL932efvrpa+KO7tcecuqX9Dzvhn7JG9X1DpQvXe7y3vaITuLTG07YWruMF8acOLJCXgvApqg1tZHYDlFJOIyzktBTjC23IrMWq7wy+I6l8Yc+2kArtgfASNbjThLy/vYmrTSiPb9AXRTs9AZ0u3toFJ1mAz+MaTdTqhJ8T6CJvivRSqEnh1nXdcDUKNfBw+A6kruuHEnmVcoB44CSKWzgOigHkbAegAhNrQ2zeA/rt3/z1Rd558IVPvvEo5w48ziJ71BsvUNQDiiqnGC8ho7OEHtKeBwe+MqZxczVWppyRW2II5dxIdCdfuaweihmWNQsO4a9wQhdZrRGl/nOdy7PFBzz8/Mfen8/CRTs8Xh819mP0/okAMt+qHMop4vB9773PR555BFOnTp13W7++a0h5y73aARC2WraQOt25LI5krydWhs6iSeU08QGSie+DZj2KbShHUvXux37DLOC2IOyqvGpeef8ZeabCenckkwSMxsCOylnERyt6CCafj8sdtLfZXO3z+njR9HKJwpc8ELa8+J5LCYTdvojrmzu4HkucZLQSlO8MCQJfLKyJAn92XQ1Kw1xIPTVyDdklUwPi6rGcxyKsiJQMDEC2PFdobcqlxmZS2toLHYotWb1sMM4y8kmQwaDAflkm57vkqQN0jSx9C1vFpQ7qWShG04KQoVMNZTgpY0RM/io0Hzl5BzH5uJ7+gx8Ug6Uvu/f0RfRndatYBQP69NV089aVVU3PVBqrXnttdfY3Nzk2WefZWFhP7D5Xjdc3UnFKzvw5BL0xuJz3Bvl9Ha26I0zHj1xggIFjsPepKQV+fQspGeQl7jKYVxq0sQhq8WXU1YC4Cm1wfMdSut7rI2SzdnJL+OlKVrFcPhRtFEM+n0219dZOXSIRrOF7zrEYcjh5QVGeUmdT9juDRkP+1xZg4V2Cn7E8lxrJoXtTSR6o39AGpsEHsO8JPGEFhv5iqHd1A21wcOh1JJnVtcaZeTgH7oOoedSaOm619rgHZR1GejYDd90ctq0dokk8HAsFdezNOskdClrbUPF96NBfFcxyioMoI2mqDU/+cQyoXf9dWiad3b48GHx9g8GbG1tAfC9732PRqMx21S12+37um4+KA/lw/rjWfPz89dk5cH9OVA6jsOLL77IkSNHbuqXvFF98EB5eW/MH53fxXNht9tna2ON+YUllhYXqbSm5SsqIz7qwHXwHAisx9K3ajKRxwtYRnyHDt2spmHX12boMpxo0kiaW1QFFy9fod1KWVw8ZGmyKUsLc5SVJs/G9AYjRr0ddrc3aCQxfpQw12qSl8baqaoP3wYug7wi9R1RZESSwR77irKCyFdUWrN28X12ooRHz5wGx9nfR1vVRTsWxV85GfD8Cy8xGvQZ9vscO3WKiePjLTyOXs+ogxbEKxS5DFwmmUYbGFeGeceZZQiXU1WckQacMQ6tUABordDFOLASdvhTTy5zqBXy/e9/n1//9V/nscceY25ubrYOLiws0Gq1Hpgl4KNWcHwSgGUfm+T1XuUKWosmfTwe8+yzz37ILzmtjX7Gv3ltE1/BsJQw7dFE5EzDXKN1TT+zE8RcNj2TUhP6AuUJLHxmGi4dekJYjeyXepWPuHp1nSOLC3TmF/CUY8l/Et7digWo05keWuP9cOysrBjsrJPlFU88cgJHeTb3RxDMBojDgDQMabZauAq6/SFVMeHKxhY+NU4Qs9RuUJQRnTSaTVOnpu5BYbN4ao1CNjmVlpyh3jinNoa9UU4z9umPBeHft7r57qSamacd5ZI0O4RpC9dxGAzHVMWYK2vreBj8KKHdSNBxSuC7GA2eEt9k6CnZeBnBPFda8+hSyheO312A6wc/B5+EA2Waph+psftWMIqH9ekqpdQtJ4xZlvHCCy8AcPbsWeL42uaMUmqGK7/Ta6SoNL//7i6Bku54EHlkRcHO+hVc1+UzZ06iUbQ8RW4zeyX6w2eQyxo0qRyWFbMmWj8T6E3XSu67I4n+6GZ23ZlUNKOY7vznbD5kSTHYZX2ny6ljq7h+TOBKxFDiu5Ta0IoDqtBnvtOi0lAVGb3BiPGoy3u7WyRJSBE3aDUaGOPSspuchl2f08hD15BGgfjkXSzIQaOdmlKD5xjptkdyUG6GPns2U7M7KUl8l2Eu2ZAjrVHOPv3R94QgmwSKSWlohIqsEpBaUWo8pdHaIQ4lNqAReCjl0AhAWZuAZyniT660OD6f3Nb75zgOrVaLKIq4cOECzz33HN1ul93dXV555RW01td07SMri7vbeggte1gfZV3vu/VeOBxTv2RZlpw6deoaOMmd1MEDZXdc8Bsvr+EA71/eYjLs0VlcYX6hTd9CdyZFTRwI/dSzao7AddDIpE/br/jADivSYJ9RkVVCoh4W2voeS3Q54cLldVYPLeInInHfs1noo0ktsRpBwpHDQrTGlPT6Q7LRiPO728SBzyRt0G40wLjoyYB+4THfbjEpazqxzzCv6aSRVeiJci8JFLvDnPPnz/PG2+/hUtMd53zu6ScZF+B5DtTgOoZKK1wHkjgmDT3yIXQaEc0wwCiFOv5F9PKTeEFEgUvTVRaU5rJVaTqRop9VMx+p74paxnFkra6spUsUJAI+++LJDofbEf1+n1/6pV9ifX2dlZUVfuu3fouyLNnd3eXll1/GGIMxhr29PZrN5kc2FHgQDbdPArDsh1Lymuf5LKg7SZIbHiYHWclvvrKOr0TyGXryJR54inFpCAOX7UqwyaO8JrIZj3HgMpjIbT+rZrfJlFQVyAZCA+9eXOfE0RW8MKHWRjIi3elUTvDInutSavlZKy1Js0VRceXKZQLf5+jRo/i+Z03KggeavuZIyVQ18gVRv7LYodZtjq3AOMuZjEf0B33yfIuu79NqNFA6oRnHGPalAp7r4iCH1dR12NnrMenv0ZpboBVZHLSn6I9ziUspK9G6G6Fkxb47I7gO8pr5TpNBnrCwuEx3OKHMJ2zs9cnXt4lDHz+M6aQR40okHl37+xvayeyffHThvhzAHqSH8kYLwnA4JElub6P3sB7W7dbN1sjd3V3OnTvH8vIyTz755HU/mwcphnd6jfzem5usdTNqoDspUKbi/QuXWJyfozO3AI6L0QZTi0zKGAh9F+XIYQ0cUl/85q3YR9smWqUlOqioZMMwVYDklaYVe2SFyD1HeUl/Z4v+KOP0iWPUjkfoycYiChT9vCL09jcYk0LjuQrtBDTbAWlrjrquGI9GdIdj1rd3wHFpNxKCMGa+06TShsTzyLX4IIvaCFK/NrhoLl5eZ2W+ieu6ND2otSb1JPJpCihrhC4YhzhwUQ442sVXkFeG2ALKmrGFBQUug7yW2Km8kmw27ZBVNaFBNkShHKRlqiq+1b2JWDS+cmruDj9B+5EGYRh+aHq5s7PD2toab775JkmSzLr2dzO9vB3J60MP5cO6n3W3e8iqqnjllVfodrvEccz8/N0BAWH/QJmXNb/96gYehktXrlKWBUeOHqMRRwwn1jqV76vSmqHHsIJGCN1JSTq1WIU+WVER+g611riuwvcclOOQ+HJ9BZGH1oZq3GdzZ4fTx1YI49RmPIpffJzLPnWqeOhNbVelotOZY5y0WPIcdnsDymzCe5eu8P577/DSK68Seoqf+/P/MSeOHcPg0IxcgewkAdoY5hshWVEx3Nuiqmp0mWGUoi4yiqpGG1C1oqplj53nFXHgUTo+P/Mz/xEX17Z45MQRhqWmEUouZRQ3KStN4IriI/bkOyV2xR7VijxJFAglE30KhMxKTTMS8FkzlPX1+HzMs8c6gBzUhsMhdV0zGAyoqupD6+C5c+fY3d3l8uXLpGk6a7TdTxXH7cTOfRoabh/7gfJOoBPAzC/Z6XQ4efIkb7zxxnX/XVFpfuf1TUDG454RDHHgGzAQ+jCqFJGSDnAQe2ib9SPadCG3tiKPspaMn7LWpIFLUdcMdrepNZw8tkoUC0mrqEW+VdQahXSdQDogdW3wXIei1uiq4MLlK8y1mjQ783ieOyO59iclrb1XGL7/PI32PN2Tf5ZGEgl1K/TojqeTx5ooCPATl0ONNlVdMx6PmWQTNneuyM8Upcy3BbufRB5lLZ2vze1d9nZ2OHxkhUbaAAxJKBuPNHJQgGu16rWBKJTcuLlI/EXt6e8q8tAY5psJTitheXEBTM1gOCYfj9jY3CL2DJcur9FqpJROQuR7/OnPLBH5N7647qQehFwBbq6Bn3aXHkR8ycP6dNbtRisZY7hw4QJvv/02TzzxBMeOHbvhY96pD3NaL1/ucn5rSDvxeb+Gajzg8uYuJ1ZXcKOUKNyP/BgWEuUxLGzkR6kJfPFGlkY8035ZU9X78AkHQ601vn19ZW0wGGmouaDris2rl/Fcj0dPHyfwvVmothe6KCWEbgBfeeAYfFcgE5WSzn9eaSI/wPd8Di3OM8xKqAu29gYU/V3WNjdl3fJjluaadEslG69xhS4zLl5ZY3F+jrTdoTIC0qlqjedAXk1/VvEKjfKabLBLkDSZb6USDeIpqlpy0bQ2pL6LpxzS0MNXEGiF5zr4StQrrnLoxILm70SiYmmEQktMApefemIJ373zdW5KFzz4+ZpOL1utFqdOnZp17Hd3d3n11Vep65q5ubnZAfN2ppd1XV+XMAv70LKHHsqHdT/rbg6U4/GYH/zgB/i+z9mzZ/nBD35wzz7zqq755hsbbPXHbF69jHI9Tp86iass9TTxZ6q10lqr8lLyyCel5nDi24xK4XfIOlTQiHwGuRwQhRitKIqa4bDHhQuXabcSjhw5SprGdn1EGls4uLEHBlp2r9aMfGmIBWJNinxFVUOn1UI3WywsGV566WWyrCBXih+ce4mi0sRJQjNN8Twf35c9sWs0Fy9dIo0jnnvuLO1mQl0bPvf0kwS+5IrXWqwNVa1pBC6l1rRjj7Hb5JnPdBjmMnEcWnnrwEJ3ilLPAJd1bSg16ELT0npf4VEam20pP4c2hobvoh2HpUbATz6xPFvvVlZW+Jt/82/yr/7Vv+Lnfu7nOHTo0Oy9m66DSimefPJJ4jieedCn6+D9UnHcykP5aWm4PVDJ6zVPbDc4d7LZuXLlCq+99trML9ntdq+7GGht+M1X1rmyN7HmZrsBKCqiwLPkVsk/rLUht5ugWgt5q9Za8nHk7InjMAM8mLpi/fIlHCX690Yc4nqSnVPWoByD1oIs1hhcBPbgePLBLyYj1tbXObq8SLPdIXAdG+q6H+5a/+D36RS7ZFt7tA5dZuKfsoRF0ZDnlby+upbYEseR55rvtBkXDY6srLDTG0KVcXlzF1eXaDdgvt2gP86psjFLh4/gBTF5pWcZalkp+WtDu6jJVFaxNy6IPEW/LvGUmkkzosCbASPGpSYJXLJS0Ww0xF/Zyrl69Qp+4DPo91jf3OSzh2PG2xV9FmcUwnupT5Lk9WE9rPtZH5R03SmJcHpt3cmGaXOQ8fz7u4SeS1WL2mJnb48nTh8njGJcF5tHFlgYhE9V70v5W7FLUUMj9HCVQ+w7GBziwGFs88dGNudxXNSzkOzE+nZcat67eIW5RkJzbhHXdelPbF5ltg95mPp8xA8pE8NxUYmvqBB1yQxyUxuasU+tPU43ErSGqiwZjoYMh0MuvL9NEPhkaQNw2N3b5djKYdrtFtqA60hIdhp4TCrNXOQzzCpakdC2f+s3/hXnXnmd0Hf5y/+fX2Ku3SY3Av0JfZkapME+XbtrVRp745K6NvTymgQ9O1SHniKrxFM5LiqePdbhcPvuvOa3sz76vs+hQ4c4dOgQxhiGwyE7OztsbGzw1ltvEcfx7HDZ6XSu+3g3U3A8CGjZw/p0140abncied3a2uKll17iyJEjPP7447dlK7hVKaX43oUeb27ldLfX8eMmR44cYlxpQld8z4GrcMw+ZbWqJW/ROIrYFZ9gJ/YoNbQjIWnPpaGoOGIhujYiOXz197b41X/5f1GOB3zui1/hkUfOzA5nIysLzQo5pJVVTRiItD/wlf2dOThKSKu+a8RiVUsSwef+xFNsrF0ijBLOfvmLuH5INhlx8XKXyFOoMKGThlzd3GV5vkXcmqcZefyJZ78y86V7nnBD0kAaboES4JqnIC+mnnNN6isc5dCMJJvPc1wc23D0XJEEx5HLhjZ0Qo9RoYVFktUHpK8ORV1hjGMtaTU/+cQKSXDtOvSLv/iL/OIv/uIN38PpGnmjdfB+qDhuR8HxaVgfP9YJJdzegVJrzRtvvMHa2hqf//znWVxcnD3GjZDNV7oT2z2uZoTWNPQZF6VIAoqKwHPJKoPnKcnjCdzZ7Uz6mk2lAzWOLnjvwhXmWymt+UWG59+jNynpNDzJ8fEVg1I6Mr28nMlDk8BlVNZkgy7r29Lp96METzlklcZXQvLylPVpNuao9gYELlTxPKFnoTmOku6869ocNOuvMZAE/myDV2vD4bkGlW5waGmRrCgZD4ds7+xQVyKjyEd9AkcTxgmNyKOoJW9yUtU27LumGQcUlSYJZBFwwGZJSobkcCrNsnJWCfx2KGrZTDlAYRSHlxYZ5DWfnw/53KIzCw52HGfW/VlYWMD375z2+vBA+bA+rXWwAz8ej3nhhRfuiEToOM4N18jrVV7W/ObLa2Slpqor1tfWyDWcWF2hUj6q1gxzTRwohpkcBrtjWV+7dhMxzMXfmFvYTlkb2jaOY+qf7iQBtYFOYgmFNpfS0zmXrqxxdHGe1tw8niskwk7iWRiaJxNQKn7v298l9l2e/vwXJHfSSrz6E2keTqWwg0xyz/IKDAbXEZBE4Hp4cZOV9hyjrMSUGWtbuzh1Tq4dRqMR46JmqdMk1w6xnUgKFVFUGwqHZuzwyksvosuSyaRm4/IFVpeesYAeZfPYXPl5reS3ZfPZ0sDDcSQuQKjfmiSSaUQzkinwQhry5VN3L8m70/XRcRyazSbNZpOTJ09SVdWsa//6669TluU108upb/dW66Pruh8ptOxh/fGr251QGmM4f/487777Lk899RRHjhy55jHuJVrpQq/izb1tstGAhaXDLC10RG7qyXoReS5ZXRMoRV1JtAaOg9aGWkNWaOYqzbjWBC7ktSGw/vDAc2RvF0q8RqQ0z7/5JllvG+X69LfXAZl8Zjbfe1zUpKE783wPbRNuYJtYfZsv2RtPb8XXPilqnnziUY4eP0ESeBjl4rsOte5w0jH0RxMGvS7bW9tEjtjOoqzPhJRWHDKxzzvKZWgzqTSeJ+s7ylBadoenHIpKz2LsGqHLpDJCzy5kr5xXZiZ9jZQBBa3Iw2BvjSENXYl8sWC0rNJ8+eQcq507b7xdb4384Dp4ryqOH5bYkHutj+1AqZSaZQDdrKZB3VVVcfbs2Wu8atdbUM5d6vL+zphm5EmGT+Ltd9RrQycOhL6X+IzGNZErUqq2hTW0YpeqZjYxbNkMIIoxF66ssbq8SNqaI/IVtfUoToqKNFCMS5HIHgzZbtjYkWF3h93+iNPHj1M7ciAc5DWBjdJwXSEeOjiUj/159O4F/OYiuWoRGRhOD8aTauavmQVhR9Ltjn3FMNOEvmJSiwRCKSEqdgcDjOvxyMmjDCcZVTZmbXMLp64JophWM0UnCWEQYIDQkyxJx3MI3/03mI3XUUe/SHbix4l9iQhohrIYp76Dg+QkKQUah1S5DMcVDV/IsUfaET/1mUP4rmJlZQWtNf1+n52dHS5evMjrr79Oq9WaXaC3KyGdSro+ypqCTR4eKB/WR1U3k7xer7N+u3UnsrBvv7WJ1gZlStavXCKNY3TpEPkenoL33nuHSZZz4sRJmo2U0ko7s7LG92T9812HYVETuA4MN1DOhG6YELoO40JiMPpZJaTn3N6WNZNhn/WtHVYPH8KLE2qNNP1cya10XUNWglKG3/7W7/GH3/0DlONggB957jkaoYOrHFw7GfVc8BxF7WobZQKuA6WB1FEU2tDxffJaMisvru0SOjXzR47hew47e32K4YDXt7aIopAkkdDsRhJJ91xJ/FQaejz71R/lD37/92gvrfLoo4/Rz2XD1J1UeFbNYWBGcUwjX3D3kUdeg+8YsqomUA6FvdUG0sDjp+4hoxfuveHmeR7Ly8ssLy/POuk7Oztsbm7y9ttvzzxoWZbd8DGm6+ODaPw9rD8+5XkeeZ7f9N9UVcXLL79Mr9fjy1/+Mu32tSDAe2F5XNkdce7qGF1XHD16jFazIY2i0Jc8ydCnNpqm61EbyWesLXVHO0jWuGMorA0gKy3hNa8JfDWbxvWyGuqC9y9e4fTpR+gOxwz3dvns578wi2uKA5GwNkNvXzmiDXOJT1HLn/NS0058xrlA0AZ5OSNfN2Yk7NDuNY3Yq0KXQVZT5AWb3SGrh5cJophsPKLXHzHZ2Mb3fZIkod1qEkcRrqsIlIOjbAauo6iMwXccJlVNEjqM8nK2R05Dl+6kIAlkXyt7YrHCZTWo0hDXmsyut5NS0wiUTTGQYcxKO+LZ4507fg+ne7tb7SFvNL1cX1/nrbfemk0v5+fnr6viuBWUZzwefyr2kB+b5BVuTema+iXn5uZ46qmnPjTJnBqipz7MdzaH/P7b2xImO5YJ4d7Y5oGNyxnwIAk9hvl0MgiugqwUaE9us3TyWtsveE1/b4et7V1OHV0lTFN8V7rckWtBDYGLcRSt0KXGxo9oaMc+RVXR31qjMoYnz5zEdV1LEWQfmKOUBM6qqc8owj/yBEVtaHqSbSbZkSKBGBc1TQt8EDpthW+hEo7djFTGEHsu/XFOd2ud2vF45MQxyeBsNBj6Eafnl+iPM4pszE5vyHh9mzjw8MKYpbkWWvm0610GF1+hwZj+u3+Ie+iLjFUsh02w5FvF2GZu9sZCJhtWhrqsyCvJT/rxRxeu8QAppeh0OnQ6HR555BHyPGdnZ4ednR0uXLiA67qzw+X8/PwNp9gPYkI5JYHdDMrzaZArPKxPVimlWF9fZ3d390Od9Tt5jNvpwL9wcY/X1wdQTHjv0hqrywtEjTaj8ZhJWTPZ3eXFV17DlBOqquLLX/g8jrLWAAsTq12N6zqE2uBe/C4nN34XZ8NB+/8RevlpQl8OV56VJLmBvLb+7g6D4YDjR1eJoshuuiytzoZle64SyqEvsUigwI8oq0o2JdF+3tl+s628JjKkO7GblwOqE1c5XLmyRpHnHD12VLxCriIMI3zXIS9K8mxMvz+ku9Wli0MrbWDSlHYzRQP/8c/8Wf6DHztLI0lRnj977coRynVW1nKgttm/w6wg9hW9cYELjCvwPKgcyCvxOY3yiq+emmexcW9Tvfu5PjqOQ6PRoNFocOLECaqqYm9vj52dHcbjMW+99Rbb29szxcm0+Xs/1sdvf/vb/O2//bf5/ve/z9raGr/2a7/Gz/7sz970Pt/61rf463/9r/Pqq69y7Ngx/tbf+lv8xb/4F+/pdTysj6fuRvI6Go144YUXCIKA55577roe37uVvG73R/z93/wjyrqm0ZojShJ6B8jPzUh85WkgFqXQl2ZS4Ip8PnRdQtfBUZKFrhwHHEkE8D2ZXk5J1sVkyJX1TVYOLZOkTX76G0flMT3FIJdc295EDlejrCbybPPOU4xKjec55KXA04SIqjBIlJGxPktjxOtZHhiipKFHWdcMe112u3usrhwhTQU+2e7MEadtAgv20UXGu5fWCJTG8WMW2g3cIKaTBjKp9RWF9W7WGhpxgGMcOqmL0Zp27IAB5TmAxlHgK4XjGIn7qw2t2LdxTxLDF3mKoYWz/cTji3c1WJhSUW922PtgXW96OV0HX3vttWuml/Pz88RxfFMP5UPJ632qm3WHLl++zOuvv86ZM2c4efLkDRcUkC/NrVHJN9/YJAldMosPHmbSgRkeyNRJAleAEoFHb6xxlbGHzWlchpVvhS5744L+7ibdYcYjJ05QOzJ2744LMUjjcfHiZYI4pdNK8YKIwHOZaJl65rl4COMoYvHQIYzjMCnkAihq6WLnpYTYio/ToahkIShq8d+Utc0205rIU0Jc9YTYmgRym4aelcBqlKswNg9tPMnobV4mTVscXllG18jEtjaiXTfQSWNUM6ZemEc5ht5gRDEZcfXqGgpN31ecVAWVFllW5QV4YCcTmnYcMMxr2mkocmFf5GkYKMuKCofnTrZpR+5NNzdhGHLkyBGOHDmC1pper8fOzg7nz5/n1Vdfpd1uzw6YByM6HlQoLXDD5/m0ELoe1ienqqpiMBigteYrX/nKhzLYbrdupwO/1p3wvfM75IM91rb2OH3sCAQRjdBnUkvsT8+44tMhIY5C2TSF3oFNVGGhYfLnYXeHhjEMTIRef4++e5SlTpOshmboMi40sQcXL1/F0SXHjh0njcWXmQSSy9iOxZ+ZxIrSGDqewC2+/hM/SmAq/MDjK1/6Mo3E5p0lHmW1n3vWjDzyqbe7qAk8h7ysZ57IsqpZ21ynrOH0yRPkFYTKmSlMJMbEJ69TVo60GGYVps7Z643Y3dzmwuU1oiik2ZDQcOO6M1/n0Hb9x0Utvv3aEAVWxuULEdZDPEahC7HvUOMQJz51bTgxn/D5u+i4f7A+SmiZ53ksLS2xtLREt9vl2LFj1HXN9vY277zzDlEU4Xke/+7f/bu7/vxOazQa8cwzz/CX/tJf4s/9uT93y39//vx5fvqnf5r/9D/9T/nn//yf881vfpO//Jf/MisrK3z961+/p9fysD4ZdbO1bWtrixdffJGjR4/y2GOP3fAauBvJ685el7//G88T+hG+lxL7At6a9Pd4b2uH1cPLDGmJ39v6pnsTG5lkCa/dcYFyXTZ2hxTVFYIoYbHTwiiPwJEDn+fC9vY2g36PMydWSZMUHAdjBGgmOYzy3JFlgPiuZOe6Cgo7DS0qua20wRgbi2SMKDdsM1DX+xwR1xFOhufA+tYm+WTMmZPHieNIFCw2ezfyBDK0utRhUmhWVw6xN5xQFxM2d3tQbXLVDZhrpXhhQiOJJboEOUBXWqa2eWlmXvpGKAOS0FXkVYWqK/kOcAXS0wwl16kVydHFdxVfe3yRJLi7o8z0vb8XlZvv+9dVcRz0oDuOQ5qm192vZllGXdcPJa/3WtdbEG7kl7zR/QH2hhm/9+YOoSemZy+0hKvElw5M5KHZ12G3YyFedWKP9dl/l85MVYvHJctL9jauoHF48sxJMVArOfDF1vB85tRxxqMxw/GYjY1NykrTSGNCm8V4dX2DpbkOabtDHEz17RYmEXoHQrfL2QZm6keaEgdDX6BCrgKDg9HSaSpqmaRO7GMOrJ+xP5bbtZ0e3e0NkvYcnfk5RlmNg7E4Z00cTDdbkvkTeSIdaDdTqrTBwqIhywsGgwGXjv5HqN4l6s5JvF6fuVaKISINZFFLA0E5T0Ec2hjGwwGbezucfXSVR5fTa97nqRn+Rou8Uoq5uTnm5uY4c+YMk8lk5uM5f/48vu/PDpdVVT2wA+XNJK8PY0Me1r3UwS+04XA4y5dcXV29p834rQ6Uk6Lid19fY+3qFaqy4LHTJ4gimYoZDA1fNjCnjx0i9r5IWeQcXlklCgPKWjMX+yIfTXyKUg5zWalpHvss+fAKTaW5PPcU5ajHa5tbzDUiukHCfDPmtQsbtOOA5tJRolBIh2kkh9I0nN5KbEZyYLJovIQ/89P/IXmlcT2H/qSe+SQdbdBGgsJdbXCUgCg84+xPCZTDOC/obl4hDiLOrKxgHIc4QMiHsUdlpPmmtf1+MBKH4uCRxgmuchhnBWU2Zrc/pN7bZsPxmW+ljMcx7WbK0EaD5JWm0obIE0J4I/LZ6Y/oba2j4ibKddmblJJjmckB8KefPsRs93cP9SBjldI0pdPpcPz4ceq6Zm9vj9///d/nH/2jf8TW1hZ/+k//af7CX/gL/MIv/MIdP/43vvENvvGNb9z2v/97f+/vcerUKf7O3/k7ADz55JN85zvf4e/+3b/78ED5Q1q3k2VujOG9997jvffeuy1Vx51KXtfW1vhn//ZFVDLPkeVFLl9ZExVFmfP95/+Ind0dNg8d5qd+UsLhO9ZK1bHrYjv2GecScdHTCU+cOclWd0CZjXn17R1aSYATJCy2G1zdEqBiZ/kIYZwwsPEieaWJfQ+tZShRIGq7WskgY0rE1kayGmv757qWW/EwKiaFROWN8/19acPKUBNf8cb5ywSOprO8ShiG9OzecmCzzielJvAkIsl3EZ94GkMjZnF+gcrGNg1HQ/a2rtLFIYpFGuuEsVXZ6X17WCjDnyTwGWQFO5sbVLgoPySrNGVVWu+lphn5DPOKL5yYu+1s3uvV9ED5Uao4dnd3effdd9nZ2eHb3/72hzzo4/EYuDdo2SdFwfGxSl4/KFm4mV/yeiX+QPj1l9aZ1OBagtT0ohJpqaWzIn82GpTaX5gqoKhq2/mR6d4wy7h06SLNZoul5WU8TxYdx3FwHfEmOp6LpxR+s8F8p0mtl6mqkl5/wKC/R7+QbovnKWIlXR3RtcPcATLiFEohWGWfSmuasYeuJeNMG1C+wsWhMhrfl+lmK/TILKhielFOSk0aeqxv77G7vc3i0hKpJanWtXTqp3LZ6QU8zdccWNnspDQ24Fuhlcfy8hKj9hzxqafZ3OtTFBNef6+L70Icp8RxQquZUmksgbFE5yPev7rJU6dW+X989QkcG52itZ5JlKfvu+M4Mz/tjS7qOI5ZXV1ldXWVuq5n08t3332X8XhMFEVEUTSTWd1vT+VUrnCjxx2NRjfMQn1YD+tOan19nZdffnn2ZXSzoOLbqZtJuowx/OsXLvLS6++RhD5Lh4+i/IBhUeFbb05pZOPh5pr2/JJQsB3oT0pc1yErBPJVlEK4LgtwjWGSHsH7yl+mMA6HRc3EkapkOBoz6PW43NshVA5BkBJQgfFm0qt91P0061Fuk0DWh9BXOMh6L/9PyICTQpOE0w2SANYa0T6QojeuaEQu690he5vr+EmDxUPLDIpaMiy1mYF7SqsIycr9x2xGHoNc1C+TUpNEAbnvc6Ldoa41k8mYyXhIf3eT/o4mSlI6jZQwSWlY737o+YwnE7qbV2m3OywtScM08IR2WNWaZ4+3aYaKqqpua328WT1IaNnB53Fdl8XFRX7u536O8XjMP/gH/4Cf+ZmfmW2ePur67ne/y0/91E9d83df//rX+Wt/7a89kOd/WB99ffAwOPVL9vv921Z13K7k1RjDu+++y2/+4F3qxiEOLXToTUocZWWXsaHERTs+RS1Nes/VaC0RGKWNjatrTeTL4a5jcx1PrixSasMxo+kORkyGfS5evozngIobBE7NcFLQSgKGmagmplm13XFJM/LtUMKnP7utaMaiBEl8ta9804YkFFhOKxaA49Rv2bGS1zRweP/iJRJfsbC8amNLxF41zGsCT24912FS7Mv7JQrKIbdxSZPSkDSaqDBl5bBLdzimyodsbu9QFwVBHNNsJKi0QTMKZAgU+VR1RXdzjdDzOH5sldAV8m0jcBlZ+NAwLznUDPn80cZsqHA369z0+/WjVHEsLy+zsbFBu91mfn7+Gg+667r8b//b/8ahQ4fuad/6SVFwfGImlN1ulxdeeIH5+Xmefvrp29I0awMv7jg0/YI0jiwkQjrCgZ24BZ5LYQNTi1rIWYWlSOWVwQHBzEcBk6Imz0ZcurLOyvIicbOFchSDrCT2XbqjYnZRNexFPDUzNyOPSS2eoEGuObm6wjCryPMJr23ukgQuKohZaKe4QUzoCd3KdaRrbowBVyI8fE9Z5LxHVWki1yGrpx1uuViLWhN5roTHegKnCF2Hvd0dBnu7nD5x1GL+ZfFKfJfK7Ps7hawo01gch2bk2NRMUDjUGCL/2gPv6tIcle5w+JAhyzKGwxGT/h4725u00ohxmKDQbO12efTEKj//1TMWKOHM3m+QTc40g3T6/6d1q+ml67rMz88zPz/Po48+yssvv0xd1+zu7vLee+8RBMGs+zM3N3dH2vgbfs5uYageDoecPn36np/nYf3xLWMMb775JpcuXeKzn/0shw4d4q233qIoint63JtJuv7tS+/zwmtvszTXZnFxGc/6eyI/oKjqGV06cB2UAk+5NmtXNmHKcaiMg2ejllDSnAtchVW9Wwy8eHc8VyA0o6yg2VkgjUO6/QF7V9aEMt1I8aOY+VaDQguUZlxOyX/aRidpIk/W8jR0KStDM5JGXNtOFluRi8ahFfsYu8YZ5KA6HA7pbl5lcWGRufl5HGNwfcHoVxp8V56rFXhMypp25DG0BMNBVhH6FqLmSIac1gblK/La0Gg2UEHMoUMr7AxGUOZc2elh1jbAC2g1GihXsb29w/KSeFS1YXbwHWYVR+cSPnd8AZBD2p2ujx+sBwEtg5tTDMfjMYuLi/zVv/pXP/LXMa319fVrMucADh06RL/fZzKZzOi0D+uHtw7uH0ejET/4wQ+IooizZ8/eMBP1eo9xqzV2elB97coe/sJxjsbRDOY16DpELnh+yJe+8HnWNndZXVlCO3J95vX+gEMbYzN4DUHRI8snhO0lMnzrrXSIAp+tccZ8u0Gz1SEbj4TOXxb0I2neF6rBXBKQ25g5IWD7dlggWZZCdN0fGky9nfvKD7lNQo/BuCT2FBNtQJdcuXyVOI44fPgwjpK9ZRK4M084yHcCyPeWq/YzIYtaYETjsqIVywE39WF46UVaccyoeZpjc4uMs5J8MqI3HHNlY4fQ9/DDmLlGwpWtHRYaEe3FZXxP1nzfdamMKOoMsBB4/KnPLOO57h0PKA7WdFD0Ua+R0/Xxg9PL9957j9FoxN7eHktLS/zcz/0c//yf//M7fvxPioLjYz1QTiULU7/ko48+yokTJ277zf2/39lmXDsc8kTz3YgkeLUVCV1LwlsNceRayZFkQUahQ20g9DwUkAZi/s2Ge2zv7PHoyVWiJMV3xEjcsljmVuRZeVcgf7bxGq1IOjjD3Q3644LHTx2jdjwOpS5Z2WRpWdEbjsjGY65ubFPXNWmSEMUJnVaDCsHST6mBU+RzfwqXyPalsEL9qvCVQ15VGLBB3hWDvV32+kPOnDhOiUvkQM8uHnuTkshXDHON6ygcRzZ7EtC9L3VoRgIuin3F2OZdloBGE7qu9XQqdBixkibk9SJUJf3hkO7eLnWt8T2Xx5sVxahHHXz4UHdwQzSdWB48YB6UIUwv9pvJY6fm6KnMamdnZ7YZ73Q6swPm3cpSbxVKO5lMPhWErof18VVRFHS7Xb761a/OpC+e5zGZTO7pca8n6TLG8IevvMOvf+9djq8cwotTcLD+R5fBpCQJXStpcigrTceXxlwaCCiiYaMvoliogqGnKCtDFIvNIFVQaogDya2MfVjf3GbU22P1yAqdVoOiMsy3m2SlxlQ5u70h+bDHm1tWGusnLM83GeaGVuTbjZFkkU0tAXHg0puUhL5ifGDSCMzgZ54r9NR8MuTK+hYnVw+jggQwDPJpBNL+VDPyFFkhB2HJkZMpQxK4eK5sqnylqIxgGgFAywABAABJREFU8GstgeK19XFqDUvtFGNSFhfmqaqK0XhMv9ulP5kQew5FXpCGY3KV0Ahd8lJiqv6DJ5Zm6+UHG3B3o+64VTPsftTtULAfeswf1r3W9SSvVVWxubnJSy+9xLFjx3j00UfvKwV7Mpnwgx/8gEGl6CdHCW0OuDTNZN0TGbtLdGiZ1cPL4kNU1iPoK6pKBga1MTg4qHwAa+dwqwlVcQqWniSrDNlkxNW1dRYX5mm2OviewrghR+cWGE4KymzEdndAsbGF5/m0mg0ajZQkTqw3W0YCbWvxaseexBZZ5VvLxsMJbMceAKsaT0k8STbJ2NhYI2m0WFpeJCsNoe8wLq2KrZjm/8q+dKoEmZSW91EZAhdqI9FRxkhqgvvavyJe+yMMDt5n/p/ow5/FSxxUElLNz+E6hr3+iHw84NLaBoELg7zGHw7Ii5g49JnUNdqIR7SoND/92RUWWrKfu9GA4uDe8WZAnI8rds7zPB577DH+y//yv+T73/8+3/zmN3n33Xc/8tcCH52C42M9UCqluHr1KqPRiGeffZaFhYXbvu8fvb/Hucs9cBz2xgVLQcieBUXMbseiVReAhE8/r2mEisGBYG2lHAaTkkl/nd4455ETxzFeYLNtBOGeFTW+J4G0oSdyqNCTyV/kK4qiYvPqZZRyeezUcdSBD04aeDjKYXmuhTPXpDaGuijpDQZMRn0u7W0ThyF5I2Wu1cDBE4+nkWgT8XT6aCNUrhrrWUSM1a5FzQ93NpnkJZ955ASVo2h5skmZkroi3xU5l3JmF+UUWNQIpTs+zZIU2YIYuJWlFE717dOOV+QLJVGiSTz64xyDYv7wCk8filiJcl5//XWKomB+fp6lpSUWFxc/lNczvZhvtHk6aJq+3ubp4IIwlVktLi5ijGE8HrOzszODRNxOQPf16nYyhB4eKB/WvVQURXzlK1+55u/uBWk/rQ9SXrXWfP/FV/j1F9d55PhRvDDEcxTjXK7pcSEbjaH1K44rh4ZjpNNsm1pTafsUwpMEil62L7Vq2LV3KjNNAsW7l9dwqozO8hGajUQAaZ7IVCNPUTghK4ciar2AriqGozHj0ZD339+VLnWjSbOR4pDQioWkOgUzuJFkQvqugwK0cVBKpqJ+IIqVfNhla7fL4yePghcS+fuZw9P4pnGhwTEUtRHJqxLJaxru57n1Z8RYOXR3xxWBp6hymT4oVw7gsc1Va0Yek0rWqb1RzvEjK1QosvGIq5s7ZNkarUZMGCf8qadXaUYfzuL9YAPu4P9uNb18EBPK6efrkxSrdPjwYTY2Nq75u42NDVqt1sPp5KeklFIURcGLL77I008/zcrKyl09xo3W2N3dXc6dO0drYZnX+wkKY5tsSqxCoceoNHgY2WtOORiRDAcSz8IWXchLTegLxdVTJZgJ4+EuG3sT5prHKMuK7e1tjq+ukKYNDLJmBK5Q+6PAJw7naLbmMEYzGo8Yj8bsXL4KQJKkNNKUOIlxXZdut8eg3+XI6jFC37XARslu9JSDYxw8eyj2lEO332d3c43Dy8sszM3ZHF1pqE1zyVuxJ2qKcBr3YfeCgTTkfFfWUAcHx5FJrO8p6kGfhICxCWlMthkXQtvOK1n7yxriwGd7c8yRxTlanTaj4ZDRaMhoa4sw9EnSBq1GA9eL+MKJOU4tpte8h9cbUBxcJ6f/7oMDik9Cjvk0MuQzn/kMTz311Ef+WuCjU3B8bB7KPM/Z29sD4LnnnrujH+DtjSE/uLBHO/a4YpRkQZY1rVDkSVPKXisUvLAcJqfTNwHiTG+zGna21iiNy5nTJ8kqSJRD1+Y6TqeCg6yeBWYHnktmOzvjUcb6+hpJnLCwtERpHCq7QRHMvctkUu2bnkOPiVbMLywwTNsc8hy29vpkkzGvbe4SeBDFDZpN8SfKAiBdsDjwKEtNEnhkFsU8zAq2165SOy4nTxxDoyT7TGuUEtoXRtnFQy7gWkMayuLWDKWL1bSbs4NgHc/mts15AZUReYU+IJP1lHTur169ilfnHD1xjGMLTb7+meVZN3E4HLK9vc3a2hpvvPEGaZqyuLjI0tISrVbrQxfzzRaH622ebjQ9nFK10jTl+PHj1yDupwHdU7z9FO18o7rVgXI0Gn0qCF0P6+OtD3bg78eB8uBj5HnOD154gX9/acLq0eOkSUhZS9fXdSXHMXBFveDZ20aoUIi3xSDQHa2hbWWmzdClrGs6cUCppUlVVpo0dK38Fd69cAnXGBYPrxIFAZNSi2S0krincSlf+LU2tknmEiZN0maLdllRlTl7vQHd/jpZqZlvpagwZqnTIqvMbF1vhB79A+qO1E4zezubDIYTjh89ihuEYGVnoefguwongMBVlK78LqrakLoutZ4Sts1+t99OIJvW7zmLfrKTy6LSJJFPVstEYFJqslGfze1tjh45QpzEFLVhrpUyziUHeXNvQMst6F54je+svcPCwgJLS0vMz8/flrrjZt70W6kr7kfdioL9cUwoz549y2/8xm9c83e/8zu/w9mzZx/o63hYH01VVcVbb72F1pof+ZEfuevv3xtZAi5dusQbb7zBI2ce46Wui6dKPEeI17VVaBSVlqi0YcGRUFIFBJBYz2CLaaDojiuasb/fjHI61N4K3379Xd7fuED45oif+vEf5dCRo3Ra6cyiVWtZl7U2uI4jE07HQRtFGrapO23AMB5PGI9H7O7uoLdK+sOc3/2tf035/2fvvePjqM/88ffMbN/VrnovluXeJEuyjcFgEww2bjIkJpdyBnIh1eRLIEcCuSQXkhAucIQESCDJL5i0o9lgg+luFBtsq3fZ6m2rtL3OzOf3x+yMV31lNQP7foWXImm1mpV3n/08z/MuhMHqlctw7fVbhesKnz9FvaU9LNnqNFrhcw4gLjENCQZD2BhHFl6ohBMHZBR4XmBpAECcWg4Kgg4TBJApaVAg4HkaNB1OJwgbPnLpq2FtMkNBE9gSChEno+H0CfIBp59DwO+Fsd8IQ0IiDEmJ8Ic4GBISIdPokaGgYR5wggv50Nbdizg5UKJPQV9fAMnJySOozZNdUMyGqaN4HePFzkUmF3ySMScbSlEvKbp1TqaZ7Lf7cKrNBpU8TF+VCUJnvYYBgWBJT8hFp754jWBwkxDWDiaoZYIblUYGt9sLFU2gUCixMCcLHKFgkANs2J6Y5fghDRdPBC0iAcDIabjdbpiNRqQkJcIQnwB5+MUnTl/0Khl8kmHORWpsnEoGd0BwLvQHOSQnGMDyeiSlChQLp8sNi8WKQJBDgl4DWqlBakJcWJB9cTtgcfowYO4HLVchMz0VQQ5hS2kaAZaDRila31/8GYdP2EQEgpxEfQ3xFx1jI7cPg35WyDZiBbdCmqIQksx9wpuHjl7IKB6JKZmQy+S4ZuHFPKDIvJ78/HyEQiFpY1hVVQUASEpKQnJyMpKSkiZdHEKhEEKhkPT18ahfkRb3o1k7i8G0SUlJMBgMI6b8EzWUsQ1lDNONiXJ6o4HYUDqdTlRUVKAroIYsPhNqtTyiJggHIZEe7w0I20lfkANHKDgDLLQcH442ohEIMzaCIUEvGeJ5AEJtoGQXXQZDoSAsRiPUSgVyMjPAhbU3LEfCBmrhj4RARgkyBIYGWB6QKYSPGrkCIV6B5Pg4BFkCNhSA3elCwONAi9UCvVYFj0qIbfKFEHYtZMMB4Sz6jUaA55GRnQW5UgF/iIMm7Lg9RFbgC0GnEjaOGsXFzaOb40FRGGHUo1XJ4AlTcN1hxovTx0Ipo+AOcqAogiABrDYL/G4XsrNzkBCnFpy0FRRCPEGcShjq5WUk4abVWYIGfnAQVqsVzc3NCAQCSEhIkAZww98nx6qPkdQvUR82FeOKiTBRQ+l2u8d1a48GbrcbFy5ckD5vb29HVVUVEhMTkZubi/vuuw+9vb3429/+BgD41re+hSeeeAL33nsvvva1r+HYsWN44YUXcOTIkSldRwxzB/FcIbpgi+eFqbz3Dt9QRqYMlJSU4ExvAL2DLmgU8rCunAkP/SjIGIEJoWCEa9Or5WGKqRDNplcJMoF4jTysdxSzxBVoRBIajH4EOCX0jkEkpGXDoFOHnVQj/Dm8Ec2fmBAQjiNSK2gEWUCv00Cj0SA9NRW+QBDlZz+Gz+sFFwqguakJG65cBz+Jg0GjQkiSbAnn0e5+M3xuJxJSM5AQp4UrzFRxBwSmmjfEgQKFEAXwYYp/iCVQymmhlipl8AbDy5kQJ2W4q2RC9JNKRuPpN0+h7YIJBMBuTaMwAAgzMfweJwZM/chMT0NSgkGoj2q5kNmrlMETZJGUoEeIjUNiCoUbFxsQdNvR3d2N+vp66PV6qT7GhQ0oh//7jregCAQCoChqRusjMP5SYi4GbjPF4Jj1hlKc/CxcuBCBQGBSBya7N4jXao3gCAm7t1II8GHKZ4ATMnjCE/dQkESYQVBggzxk4SwbigacThcsJjNAU9Dp9QhwBDzPgaOFSBBZ+GCjpGmwHAeaDhvgyAUqqddlh9FsQ05WOlQaXfh3AWo5MyQ7UiOnwQHQyIWGVKsQnFt1ChoUTUOrFFbwinC+j0quRUq8DixPwLEhOJxueD1OtA9aoVYqENRqoY+Lg88nxJoYDAakJKcIDrYEQsYaIVDL5eHiITh3xYU1TzqlDIQIBj+yMM9fLZchGHad9YWEqVswzLEnRNBoCk0qL5lUqGUULrR3Q8ZQSE3PAEUz2LI8TZpgjQa5XI709HSkp6eDEAKHwwGr1YrOzk6pOIjUWJ1ON25x4DgO58+fh9/vR0JCwqSMK4ZbO0cG09bX14PjOGl7mZSUNG4xEKm1MY1QDNON6aK8ulwudHV1QZmcDd6rRCIlxGokaMLRH+JHrRxB9qLRg14lxwAoaOWUVDs8QRYaBRVuohh4Ajy0Sjk8gQgWhoqBadAFp9UEhdaA7FQ9PB3liKMDsCesgC4uXjIyEz/axem9N6wX9wq0KHtYL2n3clDJKbCQIzEhCVx8EpLYEHw+L9weNzoHraAYGeLj4qDSaKBUKtFv7INWxiAtIwdKuZCZppaJ5mRhh0OVHBwEQx+OAHq10OTplGF6GENBTgnOhVqFDD6Wh17NwBcUBof+EBc2gOME67Gw26FWwaCj1wiEfEhIy0ScRgVHuEl1hXgQCE7aQZbHNYtTpBy14ZR9i8UCs9ksDb3E749G2R9+eDIajejr68OyZcsmrU2fDMT6ONaEfTrq47lz53DttddKn999990AgFtvvRX79+9Hf38/urq6pO/n5+fjyJEj+P73v4/f/e53yM7Oxl/+8pdYZMgnHKJeMjc3F/n5+Th69OiUtvCRG8pgMIiqqioEg0GsX78edSYfWsxuxMEH9+AAtPGpcAVCUCsEBpxSzsAbIgiwBL4gB4CAUAQUoUCFt4ki9V5GiQMyYcNZkJOBJYuXYNBmxTWbNiFFrxbiRTQXa7CfFRz/JYfpcLat2GyKMUsOHxsxGJSjYFkhsusbYXN6cMUVa2D3hhAa6AJPKOh1Omh0OujUavSaLAj4vMjLy4VCoQRFAXoVBVCAnJEJG8fwcJDlBeZIkOMRp6aHxn2IUSMKUQ5Fw+kPQUYLxpiEUYCWK0FoGQgYSRbRZxmA125FQmo6EuMN8LM8ZBQQ5AS3WFCCZEzGUFAwBBsWJCMnVQekJaGgoACBQABWq1U6Q0ZKnpKSkiCTDW1vhg/gnE4nzp8/j8zMzCkZ+0SD8Z6jnyYGx6w1lDzPo66uDmazWdJLtra2IhAIRPXz/hCHN+pMoCnBmZUIXE4oZHR4Kye4UQkfCRha8BalKADhCBEQCjwIHLYBWAcHkZuViT6TCTRFIxjioZSFtUQyGr4QB41CyLqRNDQqGRzeILyOAVgcbizMzUGIkoGm6SEaI42CkTaBXlawV+Z5AlCCg6oo5vYHQtK2T9oMiocrpeAaG5+QAIVWjyQQuNweOLweGG2DgrZSowJhlOB5An+YZuYKCi9oV5CDPOy+RXiENaA8lDIGQV4w1vGHBNpXkOOF5pzjQYd9XmWUcOAJ8QTqsBmPNmw3LaN4dHf3QKtSICczEzworM4xIMOgGudfcCgoikJ8fDzi4+OxYMEC+P1+qTi0t7dDJpMNKQ6RDR0hBC0tLbBarVizZo2ky7xU44rhwbRutxs2mw39/f1obm6GXC6HTCaD3W4flaYbM52IYTow3ZRXcWhjt9uxYNlKHG33gRAeIY4TXK9ZYUDmCYTp/QEOWgUNd4CDSsbAx3KQh/U/yfIw7V4ubCL1KpnA6FALzankHq2RwTYwCJfVjNTUdCQmGMCbmpE0WI0QT5AoUyAQtwYGjRzBEA+9WqDI6lUyBFlh0xdkubC2JjwcDNcnnlDgwQMUjRDLQqNUgKcYJCYkwOUPguaCsAy64HQaBemDUgai1YPjCQa9QoaZh2Uhoyn4iJCzKaMFYwq1XGCRiMY8kdFKjrDLtzPAQkYJdROUsKVgGDpszCHY7Yd4YTPR3dsHmg0iMycHCrkcFCWwZxiKBicjYCght21Rmg4FKSNrRyRlf968eWBZVmJ31NbWgud5SZuelJQEpVI55OeNRiOamppQWFiI5OTkSWvTJ4PZYHBs2rRp3Aid/fv3j/ozYp5rDJ98tLW1oaWlBStXrpSG0oDwvi+Xj9QeRwOxxrpcLlRWViIuLg7FxcXoGPChoc+JRM4CruUdJHJ+BLKvRHzGSgQ4Pkx5FTTYHo9w/vSFCORh2rt45qOJ4HYd9kSFnwDBgA+9ff1Ys2494hOTwtpDYYnhD+Gi/puhBIOzsLOpaLajV8tAwk79oXD8B8vx0CllCPE8kg063Hr71wAImbzCoIvA5/PD5/XAaLKA51hwhEJaciLcAQ56GS9lmnvCZ11JohUSGGxBnpcov5qwweVFA6CLme8E4UgnShhE/tvNZXj72AkkxGlRVFwCrZJBt8kKr9OOxLCm3hNkAQKQsL+HRiF4dojRKCuyDChIHVonlUqlFCfH8zzsdjusVitaW1tRW1srsTuSk5NH1B+RsZObm4v58+dPWps+WUy0oZxqjvnlwuCY1YaS4zisX79eWqkOz6EcCxxP8G6jGd5ASIjU4AhkMhosKzzBKUKgUtDgOICmw1bGlGADr5ILlCqGAkIshwGzEX6fH8sW5EMmk8NhM4OhgbiwE6wQYg0Y5Az4iIyeBI0c/hALt82EQIjDigV5IDQDXXhzJ76o41Qy8JGbQLmgQeIoCA0eT4Qw2vAhyh/e+nlDF6mxWqWg0VTKhIaQYWjIaEAbp4OMJnB5vMhKTYLLH4J70AqjyYTEOA3sCjUS9HEIhAAQgKdI+MU5Mn/S4WOhkgmGFBQFyAiGvJB1KhkcfhZahdAkyxkaPEvgDwZhNfWDUaiRmZkOZ4BDQYoOhTnxU3p+qFQqZGdnIzs7GzzPY3BwEBaLBS0tLUOoX0lJSejo6MDg4CBKS0uHrOcv1bgiEpE03Xnz5iEUCqGpqQkulwu1tbUghAzZXsrl8hlvKJ988kk8/PDDMBqNKCwsxOOPP461a9fO2O+L4fJAtPVxNLAsi5qaGrjdbiQkJqLKzAFhEy8BFGRMuBmghUZIRgN+VjBx8bOCYVmApyCjeLgDrBCxxAtNmJvlIZcBvqCgH/RyPBgaMFus8LidSE3LglKjhsvPgYECASoOoEIgUIMNcZBxBCzHgQcDNkxXZ3kCGSMMARVyYXunlAumDfKwBlyjkAkZvhoh19GgkYPnCZI0CrBEATlDo7vHjcwkAyhGBo/HifYBC9RqFXS6OKg1GihlSvBEdH8VTCf8oYt1WKcUhnxKGQ1vkAVNC3RcnieQKaihtTS8KRCHiAxF0NZvBMdxyM3ORoCjIJMBnrBcwe4XzHxcfoHStX5+dEZ0MpkMaWlpSEtLAyEETqcTVqt1BPUrOTlZmrwXFRUhMTERwOS16eL/jwYTbYhiA7cYpgOJiYm44oorJL1kpA7uUiHGhnz88cfIy8vDggULYHUH8U69SchZHLRB43fAy9NQefrhZ5dDIRMaPpVcBi9FQc0IOYyinjxOJQMHCEMjDgAFadM36HDAbDIhKSkZifHxCIWzb33+ADweNwyGeMhkwtmTpijp53giMNhEZh4vGt4QYQHAE6EuyygaMgagKQYMLUTr0RQFnuehjdchoFYi5PeAUqphiNPC7vLAOWiDlVEgUa9FMKBBol4Lt58Na9AF+YPTLzhpu/wsZLRgfCYN5DgunDspsEkElhsNf1Dw/dDoDbjl8zeB4wloAGarFUGPA/l5uZArFKCZi/nwLE+gUTASYy7A8sgwqLFhwfh1kqZpKU5u0aJF8Hq90oLi/PnzUKlUEjWWoihUV1dj/vz5yMvLk35+Mtr0yQzgxPuYScrr5cLgmLWGUi6Xo6ioaITtczQT+KNNZrRZ3NCp5IK1vUoGuycInUoOdwhQqgnsnpCQDRn+fmT2jk4pg9Xlg93SD+VAExaQHjiZEujmlcDD0UjgOAx6gtAqGLhDnBQQq1XQcPoJ1DIaNrcPFnM/aEaB7OxsBHgKNCFC/hoB2LDRAx22YFaGN35quZCjo5QJ4bJKmXCIUcmFCbxSRiNEhK/zBFAyNBiKhlImmDywnKCJDPIEQdcABu12LJmfDYVCjRRaKFShQEAIDHe60Dtog1wlhyEuDmqNFgaVSrKQFkPDSVgTSkEw35CFM9iEw5pwwOJ4wV2WhGm6NE3B6/Nj0NgDQ1w8MtJTEOII0vUqfG7x1PQxw0HTtNSwidQvq9UKs9mM5uZmUBSFzMxM+Hw+KJXKqI19CCGXtL3UaDRQKBRYtGgRXC4XrFarFHXz4IMPIjk5GefPn8eSJUum3aL/+eefx913342nnnoK69atw2OPPYYtW7agubkZqamp0/q7Yri8EG19HA6v14uKigoolUrMmzcP77eYEdAEBO10iJe2fyq5oHNRh5s1hqbBh2nzXPggo5HTAAFUMlrKqRUONBCoUTTCGkMOxv5+BANBzM/LBSOTS46n8uR5CNIMlBQLryYLatnFiCRhGygPN2eMxAQRDSwEOpcQDSK6qqoUDNzekGBVHxAOIpIBmMUMQ2IiEhITBAOfpCQ4PQFQbABmuxOwWREiMiQZBGOfBJ0WngAnMTSEY5LggKiU0aBpSBtIjUIu5Kyp5OAhuG4DwuaRpmmoZSz6+/qglDHIm5crGKSFDSrEbUKcSpA8aJUyfG5pKpTyydcLiqJgMBhgMBgk6pfNZoPFYkFHRwd4nkdycjJCoRBYlp2Q+jWVWBJgYtOymCQghulAQkLCiObxUmskICwe+vv7EQqFUFRUhPT0dHgCLI7U9glDNo5AnpgLz+A8qHgPHPol0FAQqKbh9ACaouAM8MiEwILQq+VhdgQDX4iEz3nCBtNossDjtCM7MxMJhjghyo6m4PP58d7Rt2EbHMSCgvm46qqrQTF0uJkUTXFowSlWJmwzNWGtu0bJSHIDp/9iLvrQj4JO3GL3wG7ug0ytQ15mGoIcYEhIAsuyCPg8cLo98Dr6MGChEKfVAjoddFodaJqGTCUwAmU0QFMIu8MKQz+9Ui54g6hlcIUNKF1SEoDACPGGYz8GrRY43F4U5OUiQGjIKcDpZSW9qEYR/hmGho+nwFAUPrc0BTJmcttBjUaD3Nxc5ObmguO4IeyOUCgEnU4HhmHg9/ujTh64lNx08ecj72843G73lOvj5cLgmNPYkGgoXR+3D6BrwCtwy7mwwDksdPaHeOiVAh0zJcw5N2jk8AZ5xIenzQaVDDanBzZjL1QMQW6wBe4QEG86BXvKYmjkFMyDLqQly+ANUeENHSdQwUKCFfKgywOT0Yg4fRySkpLBQzxw0QiEOMEIIihMqaUDkT/C7CG8EdRGmOI4/SHImDDHHIJOUaCUMfD4QxFuXDIMeoNw2a2wu3zIz81BCAwIy4FAoBQoZArINTLkJCTC6RUPTy4Q6wCChEZ8nBYKtQYatRpymbAdFQuSKPjWKmSwe4NC4WIFOpgyfFudSgar3YUBcz/0CUlITU4Str40jU2Lky/pUBQtROqXWq2G3W6HVqtFXl4e7Hb7EOqXOJ0fTv2ajuIQqRHS6/XQ6/WYP38+vF4vdu7ciUceeQS33347tFotOjo6RhzgpoJHH30Ud9xxB26//XYAQiDtkSNH8Ne//hU/+tGPpu33xDD3GK5BE/U9JKzHiQY2mw1VVVXIzMzE4sWL8WFtK5qsASzKpy+a8PhZ6MO1SR9+/YtGEGI90IeHcwAFq8sDlcYDnlHAoFZIukcxt3bQ7Yfd2g8WDObPy0WQo6CSUfAFBX12gOOgSspBiAfiGNGKngHLCwMuLjzoEtxjhc9FV1W9WgYQQK+WgxCBxUFAoFMxoIhg3kPTFKy2AThtVmRkZCAxPg7+4MWw78Q4NTwBOebr9fAFQwj4/XC7PfA4zLCZCGQqNZIMcaDlKhi0Ssn8TDo4eoWa7QgK5mRBipcyfP0hHjoVA1t4YMnScszLyITTz0POEPgIwBEiHQh1KoFSuyLLgNzEqdGcRCiVSmRmZkqN5ZIlS+Dz+Ualfmk0mkkbVwy/3Vj1cSzETMtimClcqiyA4zjU1dVhYGAANE0jPT0dLMfjaKMJhEfY9BGgFHooVu4E4Qn0YY8OvVpweDWoZLD4ADkJwWQdRHKCPnz+k0tnN2ExweB8Ry8oLoD41CzExWkw6L1ouuNzOmCxu+H3eNDVZ8IGigj+HWFjG7VccObXK2URbvsI68CBeI1wO/FsLMZ8GNSCCaROJYPF7sKA2QS13oD0lCS4g4L22xdkQVMAo9LCoNQiMTkVbq8fQb8X3UYrKL4ftEKNFIMOjFINg1aJAAupUVbJxa2iMHgU0wLiVDJJg04DYHkepn4jgsEQli3IAwcG2rC0TKtg4A1w4W0nAQdATglxddcvS0OCRjHuv2U0z5HU1FQwDIP+/n7Mnz8fYmRhU1MTdDqdVB8NBsOk6mM02vSJTMu8Xi8MBsOUHuPlgjlvKMejKxidfnQNeJESpwDLAQky4QWdFNb2JetosC45VAoaSTplmLcOMAwFwhMwDAWbbQD8YA+W52ciLSEOzKnXwXjMgCEHocx4uAxKDAxY4RzsAcMw0MTHIzM+HvF6HUDRGBy0ocPcj2tX5CI5JQUMJWg1GUrQYzIUQEBBZJIJuk2BPy58TqQnmfg8pcO3gfC/i7cNfy7eBwDwHIvGxkawiTxWXlMClUIBUFTE74F0P5T0dYACBZ7wcIR55TabFaGgCcn6JCQnC7b0KqVS+t00PfaB1Ww2o67uPBZfuwRZWVmX9o89BXAch5qaGgQCAaxZswZyuRxZWVkghMDlcsFisaCnpwcNDQ1DqF96vX5aigPHcaM2iRqNBnv27MFPfvITuN1uSfs5XQgGgygvL8d999035Po3b96M06dPT9vvieHyhHhIH+v5FwlCCLq6utDS0oKlS5ciOzsbg54gavq90DDCVFk6iKjDB5EwEyEhbNwVr5EjxF6kGulVDCg+Dkk8h75+ExiKx6BSg8T4OLiggk6pgNXhgd1qBK3QID8zDd4QD42ChtMn0J+c/hBUCjF2SbCJV8goOP2CY2BYWg5QAOGFTFmWF7LeAiFR28iGB2DDNI4BYXvZ3mME63MjIS0TBq0WHn/Y5TXcALIcBzlNgaEAlVwGg9qABEMcFDQFl8eHgM8Dh92OUNAPp0oNXZwOAUoLvUqotXqVDFTYqIKhAY4HGIVwuIuXM/D4ArCbe6FSa5GWlgqAgkpOQU4LuiMdQ8PHCgdRb5BDolaBK+cnTtvzhBCCtrY2dHd3Y82aNRIlcDj168KFC1AqlRL1KyEhYVR2h/jcE2vhRNvLiUzLYrFKMUwHRhuqXUpD6ff7UVFRAZqmsXr1anz00UcghOBYkxkdVo/AnPBxAt0+xEOpYAR2GSVQTTmKklgcBq0aXDAeXrcdbQMWaDVqhDQ6xBviBCaCgkJXdxeUFJCclQOdWgFvcGimI6c1oCAvCz1mNVYtWwR3gA9vFoNDTHcGfWIsSfijV1gECNnBgiu3jBEWEwomLPmSMXA5nRg0G5GRng6DQQ+AgoIR44540GFqrriVTTZo4deqkZmegkGXHwj5YLa7gZAFvZQcieHmMk6jQZAVtmJCnb3o/ipoMcNsQm8QDms//CyQn5sDllBgaIIQR0DTwtlTLRei7bhwbBTHE6xMM2Bx+vTUDYvFgtraWixbtkzKK83Pz0cwGByRPBDp3TFcm3spuekTJRB4vd45OVfPBGa1oRxuOjERXSFdr8Kekuxx77NRJmRZLl06NNRWNG6h/L3YfH3JRdvy+feDMtUjlLYKRGkINw3zJd2e2WyGxdIN7yALlUoFhc+HmzesHBECOhvw+/2orKxDik6FVatKLolOqVenIicjVXpjt1gssJiMaD3fgri4OMlVdTTLZUDImGxsbMSKFSvm5G/AcRyqqqrAcRxKSkqGvMAjN4YFBQUIBoPS4amrqws0TUvFITEx8ZKLQygUgkqlGjUE1+PxQKlUQq1WY8WKFdP62K1WKziOGzWAtqmpaVp/VwyXH8Tn5Wi0xUjwPI+GhgaYzWaUlpYK1DCOxxt1/XAHeQQ4XqJ1CpoawRFaAAEJCYMpf0jQuARZgCIEIUIglysQn5yKhESCYCgIt9sFq3UAgWAAKqUCwUBQ2IClJIOmaOhowdkwTs2ABg29ShikKVQygCKQ00JTpmDCmWphrRAHgAHAEgINLUcoTL0PsDwMks09Ax8rahw5qGQU2rp7wQaDSMvIhlwuQ5AnAh2XgRBxFG5EdcqwNjwiJsXuY6FSKcEzcqTrExAMsXC7PXC4hOm8TimTIps4SiFsb8OUW1dQoHL5AwH09vYiOTEeGn08KJqC2y9qLAUjIHcwTKkN61OvXZICxTSxOgghOH/+PPr7+1FaWjqCOjWc+jUwMACLxYL6+nqwLDuE3TEW9WsibXowGAz/W44eEh7TUMYwU5iszlyMrEtJScGyZcsQCoUAAOUdA2i3emBQy8N0fCFzXKsSY5SEj0o5A28wJHh0cDxomoLOkIi4+ESEQiG43R4MOpzoN1mgkMvAciw0ajXSMzOFa+UIFAwl0d4JIUjUKrFp4yYQwoOi6XBjFzYsC1Psg5zQpAVYLixd4CBnGPjDWb4BNnxmAcBzwjIlyLLwuhwwWweQm5MFSqYEywPeQChcQ8NO1UE+HEHCQ8EILthqGQ2eBxJ0KgAqJCYkguc5eDweuD0eOCz9sIOCRqOFIU4HmVqDOBUjxKNo5EKzrJLB6Q3AajKCp2hkZWWAJRQ4qfG8SI8dHl+VqFPgyoLo9OUTwWQyoa6ubtQzrEKhQEZGBjIyMsDzPJxOJywWC9rb21FXVweDwSDVx4mSB8Zid/j9/iEDuuE1Usyh/DRgzjeU0xHcLRYFEZGGFFdccYX0ZkYIAdGkgM+7RnjzC2+ggKG6vYULF6K6uhpOpxMqlQq1tbXo6emRcgynktMSLUTXseTkZCxZsmTK9sWRURniZMZqtUq6GzGnUWy+GIZBZ2cnWltbsXr1asncYTbBsiwqKytBURSKi4sn3NIoFApkZmYiMzNzVNev+Ph4aTofLfVrYGAAg4ODSEtLk964Im8n0rk+DaG0Mcwtxno+jlcjA4GANHCJNDx7/7wF3mBYW0MEh2bR5CYo5kmGOMhl4egLhoafJZAxNPxBNuwKDSgZCoGQIAHgGTkSk5Kh0XNwOwZhtg1Cp1Ki3zoIl8cLmVKD5IQ4cJBBq5LBFQhFuAYycPs4KSIkDh64HDZok7LgZBmBHua/qAVSySm4/ARKhobHz4KhKQQ5weGbJwBNePT39YHiCRbNzwNP0VCEKWlapWCQFq9WgOWFjSs3xJFQqCP6cJMroxjQDA2lnEGCTokQlwAZBdhdHvg9LvT29YMmBA61BlqdDkpGA5VchlDAh/6+XmSnJkEZZ4BaJuS3KeU0PAEWNCXQ5jgegsNsgEVxXgIyDNPz/kEIQXNzszRImOhQwjDMkCxet9sNq9WK/v5+NDU1QavVSu8B0VK/vF4v+vv7kZKSMur2kqKomIYyhhnDZDSUvb29aGhowKJFi5CbmwuKosAwDMxegu6uQajlDDhe0ESz/EXjr3iNQnDVV8kQ4nho5YzgSK0QHPMZSqB/qpUK0IwMSUnxGHS4YLWYwTByeHx+nG/vgF6ng1qjgUatEbz06YsDNdF8h4RYwZSHEBAieGQQCN9jaKGOMBQgpxnQEaY7HM+DpoWmhQn7c9gdNtidbiyenwvCKKCSUxGadUH+IMR9CFEeKgUDVyAsxeIjNo88CTNGeGi0OhC5GqlpaRhwekGCHvSarQAXgiJs9MPrdFDK5WBDIdiMvdCqhdvLGOFxyZTC9lQpF95jDGphgKhTCfmdWqUMW5alQT5J3eRo6O/vR2NjI1atWoWUlJRxb0vTtJQ8sHDhQil5wGKxoK2tDQqFYsiCYviCZ7QFRSgUQnd3N7Ra7Zja9E9TffxUNJR+v1/63OfzoaKiAnK5HFdccYUUfhvp2gRcpDQORyAQQHV1NSiKwlVXXQWFQgGfzyds9sKuo+Ibb0pKyqi0yqliYGAA1dXVyMvLQ35+/ow0K8ObL9FVtampCcFgECqVCoFAACtXrpyTZjIUCqGyshIymQyFhYWT3s4Od/3y+XxScWhtbZWoX8nJyUhISBi1ONjtdtTV1WHRokVIT08flfplt9ul59h0Izk5GQzDjBpAm56ePiO/M4bLC+PVSNH6PD4+HitXrpSeww19DtT3OaFT0PCzPGjCwc/yUMsYBFiBHhXkeCGiQzooEeiUDEIsjzgVAy5sVx/ihUxbMa82xBH4HTZ43R4sK5gHuVIJEA4Olwd+jxu93T2QMzQ8Wh0SDHHgeVrQ9oQjQgIsjzjKB67+VcQHB+G1FUC9+EZ4gwIVNhDiwvQpABA2lyzPQ84IDtgaJQOHxw+HxQiWkqMgLzNMEaNg97Jh4x7BkdATDIGhaaH2EwIFI+RFahSyi06t4SbXGTaEsPsFbZGX4yFTqKBXaaAycKAJC5vDBad9AD39RuhUcnj8IaQnJ0Gl04OhabCEh0JGQSGjQIEOu3QTqMObhgyDCuvyp6eWEkLQ2NiIgYEBrFmzZtJDzkg367GoX0lJSVKNHI3dEQgEUFNTI0U/RTI7xOcsz/MxDWUMM4ZozpDi4KW3txerV6++yFYDMOgNocoGLIznEGAJ1AoGdh8nGTrqw7FBOjkDT4iHkhG0fWqF4N2hkgn0TK2SCbv8M7ANDMJpsyAnIx0GgwEcx8Hn88HlcsFuNcPK8YjTaSFXaRCv1yHEAwpGjKqTSX4covmOSxy0ReZODjfdCW9U4xQ03AEWdosRngCLgnm5oBghy1HIEmbAERKOHhHo/ACFODUFEECukAGUkKzAiDnlCmHoeJGmy8Ad4JCg18IbUGJeQjJ8gSB8Hi8GnR70GK1QK2XwBTkk6rXQxCeDpi9q+IUIJxp+PycYQtI0OI6HSimYnV2Rn4AknXLsf9Ao0dvbi+bmZhQWFiIpafLbzsjkAY7jMDg4CKvViubmZil5QBzAjVV/xfP06tWrpS3lcG261WodwQ75pGLWKa9Dfnl4ujQZ04nhoGkackc76IoKOHQFONsTQlpaGpYuXSpNDCJdPsWfGQ0ulwtVVVWIj4/HsmXLpAOaWq2WaEOhUEhqTCoqKoZMfRMTE6e8Sezv70dDQwOWLl2KzMzMKd1XtIjczi5atAi1tbUYGBiARqNBdXU14uLipM3eWNTY6UQwGJRcKletWjUtzqlqtRo5OTnIycmRqF9WqxWNjY0IBoNSpptI/XI4HKisrMSCBQuQnS3QrodTv1iWxe9//3s4HI4pPYfHgkKhQElJCY4ePYrdu3dLv/vo0aPYt2/ftP6uGC5PjEXpMhqNqK2txfz58zF//nzpuWdzB3Cy2QIFQ8MV4KGUyeAJATJaCJvWhDeA2jDFSKMUcr60SgZ2j/DR4eegU8ox6BVuNxj+vtUdhMNqhjfIYX5uLvxhp2tvgECr04FWaJCQQsHu8iDo96K1qx/gWWh0Omh1Gug0OjAUhZDPCTroBuFY0F4LZIKfAxQyYTMgp8VNI4UQB2gUNEI8YJDJ4fX5YDf1QqvTIyM9RQjrVoVz2iQHa8FASx7OQuOBcIwUD7VcgSAvmLeJ0U1BTjg08YRALhM2AYRQQo5wkINeJYc7SCEnPQWeQCJULgfMVhuUSiUsAwMwDzqRZNCBUqiQGh8HV1g/6vCxkDNU2OQM2LkoJSK25dIhUpwdDgdKS0un5TASSf0S80utVis6OjpQX18/gvol6rsNBgOWL18ubXvE6xPfc//6178CEHRCMcQwFYyloRyP8hoKhVBdXQ2fz4crrrhiyGDDF+TwbpMF8rB+W6MStJJasZkLO46KrAnhNR2CPtzMxUkOqkL6gFbBoLXbCNbvRnxKJvRxOsGkR8lAplQjQ6dFMMSDcCEMOl3wOe0YsJgRp9VArdUiXh8HigYS1AqECA+DRgaOE7J9g5xgxhMMG0/6gvwQHaY7yEGtoDHoDWLAYkSIp5CbnQ0/T0FGEYFuDwpBioAjQo0NcHw47kMYxHnD2b2+IA+1QqDRqsLRdUo5BY4HNHIGoAC9Sg5QBHEqGWgKUDIqJOjU4EkiAj4fOrt7BOqr14eAvxMOlQb6uDj4aBXkMoG5AQAK+UVzSJc/hEVpcVieOXWDmu7u7hHRSVMBwzBS/RPlY1arFSaTCc3NzdBoNNL34+PjAQC1tbXwer0oKSkZsnSI1KaXl5ejrq4OHR0dU77GywFzvqEEojOdGAsysMhrfBoI9APQY/GW3yNr0XLp+5Gc5rG2kgAkS+GJtoJyuXwI51rc7DU0NIBlWanxGm2qOx4IIejo6EB7e7sURj3b4HkedXV18Hg8WL9+PVQq1RBqbGdn56jU2OlEIBBARUUFNBoNVq5cOeUGfTRMRP1SqVTw+/3SdGo4xGu699570draisbGxhm5TkDIE7r11ltRWlqKtWvX4rHHHoPH45FcX2P49GC0mjOc0kUIwYULF9DZ2YnCwsIh0TFBlsfxJjNUckGHo2BoBCkOSoaHnKYgD4dO61VCXboYfyFMTkVKaLxacTFTlxDoVDL4/QHYjL2QyxVYmJ8LQgFqIlC2lGFHRLmMAgEFnVYLWZwWhoQkUDwLm90Jl92Bvn4zEuM0oOUqZCUvgstlgzZ7JZxhk53IqBCtUiZsHBUM7F7BIMPqdMNiNiIhIRE6Qzy8QYIQz0Elo+EP8eEYEuE+XOEDnzOcp+bwC1RUUf8UZHlwHAEvo4RDpJKBl+WhC+eoqeTCNlcpF7a0Cpnw+va5HXDbBzAvJwsJeh0CIQ4hvxd2lxueAROcVhO0Wi2ITgu1RgulQsglXpltQErc1KfuYo12u90oLS0d4Wo9HaAoSqJ+LViwQKJ+Wa1WtLW1QS6Xg+M46HQ6LFmyZFRqLCEE//d//4cHHngA7777LpYvXz7Gb4shhkvHeBtKj8cjnSWuuOKKIecxnid4p8EIpzcIhqEAIuTxKhmAZihoaBoUhGxJAgKdQojxSFDLwRLAoJEjxIo6bw5xShqtnT1gCIvUjGzotSpJGyhG2DnD5jpulkFqcjLccfFIZwjMA064XG509Vug1yhBKzXQyCl09ZqQmZqEhOQUKBihHgl56sKmlBBALReGZzolDTbEYtDcC7VShfz0dFC0MGgTZAIENCiwhEAlxX3I4A2NQoFViiZqF2M/giwFAj78/3moFYIJkC6iuXb6QgAbQEdPHzJSkqDTG0BTFELBAJwuFwYHbPAHgtBr1VBpdTDE6cJmccL7TIZBhU2Lx6elRgPxHF1cXCw1d9OJSPmYmFUuLijE5AHxXFxcXDyCwSaeFWtqavD5z38eDz74IP7zP/9z2q9zLkCR8cJLphksyw558fM8j7fffhvXXnvtJb8x9rafh+H1b0LtN4JJyAb2/A2IE+iA0TaTXV1duHDhApYtW3bJVMJIx1GLxQK32434+HikpqZOqLskhKCpqQlmsxmrV6+GXq+/pGuYCliWRXV1NViWxerVq0elcUY20BaLBcFgUKJFpaSkTPlw4/f7pan3smXLZqxJGw8DAwOorKyETqeDz+cDMJL6xfM87r33Xrz22ms4ceIE5s+fP6PX9MQTT+Dhhx+G0WhEUVERfv/732PdunUz+jtjmH2ImotInD59Gvn5+YKtfYQ2vLi4eITu4s26frSYXJJuUauUweHxob+nC7n5BVArZPAFWajlQgi1sIFjBZ1kmArrD0+l/Ww4QzfEgYQCaO/uQVpSAuIMCdAoBFt6tUI45AhZu0JjF+CETN0QK5hPBDki6THBhWB3uuH1uuHx+qBSKKCN00Gv1UGuVEqmQRQVdg0KB1/yBHA6HTCaTMjMSIdWp4eMhtDo0eLvCGtDZRT8LA8FTSPAcWBoGhzPg5CLLoYquZDlpgsfnHQqGdx+FmoFDV9QyIzjiXDoFLSmgnlFV78ZrNeF+JQMJOo18AQ4IVycE3ROcoaC0+MDQn5Y7W7QfBBEpsS8VAO+tL4AcaOYOkz2+VFTUwO/3z/qQWU24Pf7cfbsWelxiNQv8T1ArVaDEIIXX3wR+/btw0svvYStW7fO+nXG8OkDIQTBYHDI15qamkAIwdKlS4d83WKxoLq6Gjk5OVi0aNGI192xRhPqeh3QqWSoa7qA+blZAKMQqJgsgSKcF04DYZ0jAAJQDA0KRHLFpyFsQfv6+yBnGGRkZkLGCNpsigA8BBM0jidgGBosRyAT83kZGgGWh4wBAiGhWRxwuOD3efDOu8dgtZihN8Rj+7btgukZTV9sDsMus3JaGIgRLoiO7l4kx+uhNSQIlPwAizglA2+Ih1bBIMDxUMmEjwrpWnDRaRtCOoDouE1RwvUzAFhAMiISr1slo+AN8eH4EQ4+rwd9RpPgIG3QC/F0YQqvWGMVNIFl0Ak24IXD7YNKIYdWq4VGq8VXrixAqn5qbIu2tjZ0dXWhuLh4Ts7Roomky+WCSqWC2+2GXq9HUlLSEIZfXV0dbrzxRtx99924//77PzUeHLPaUHIcN4Ke8NZbb2HDhg2XpLFgWRZnzpyB3FiBYoMdsvwrQeZ/DsBQR7qxmkme59HS0iId1KdzmhGpuxwcHBxTd8lxnLQaX7169awY/gxHMBgcoleMZls8xDXWYoHT6ZwSNdbn86G8vByJiYlYunTpnLzAnE4nysvLMX/+fOTl5Q2hflmtVrjdbvzpT3+S9J0ffPABFi1aNOvXGcOnE6M1lGfOnEFmZiYSExMlGnhhYeGIZqK2x45zHQOQMeEgbZpGkOdBcSwaz1/AimVLEOIARbh5VDAUvEEOShkFf0hoCj1BDhoFDW9QiP5w+TnwAQ86+kzIy0gFo9INyZ8c7aOorxHjPUQtkEohNGYKGYUQCwC8kAXp8cDv84JQFAxxOihUGiTodeGNo2ByE/Q40WcZQEFuFnhaITm2ivetCNOyaFyMHVHJBY2TWi5sL0UdkFJOI8QCMgbgOOGgSMLHKSZskiFnqLCuiUKI58FQFPr6jfB6PcjOzoFSGdbl8wQULURVyRiA5YU8YZYTDpyBQBB+vxeF8SGEPA4olUqJ3TFaZMd44DgO1dXVCIVCKC4unhT7Zbog0ly1Wi1WrFghGe5YLBZYrVbY7XYcOXIE/f39ePvtt/Hcc89JVP0YYpgOBAKBIZ+fP38egUBAclgnhKCzsxPnz5/H8uXLR5UN1XTbUd45CBkDBDmgs+0CklMzoI/TIig2eiEODA34QiyUMgYBVsiSDbK8MCBjBSdVj9cHk8kIjUaL1JQUyWyH8EJ8GyHCR4QdXEGExpInBHKKBgceMpoGzxPIZIKWkAKw/9m/wWQyQqlUYcOGKxFviBfyuLVa6OPiQCgKMkoYkAX8HnT29CMjNRlKrT6stwxBp5LDHZY3eAKstFVUymkEWQIZDfCEkq5JzEH3BbkhbA93gEWcUtCdq+U0ghwPGUNLTuEMRWHQbseA1YK0jAwY9HHgpEGioMXkeCEihOMAigZ4Xqhpfp8XdqcbOUof8vS0dH5MSkqaFGuREILW1lb09vaiuLh4TmKKCCGoq6uDy+VCaWkpFAoFAoGAdH602WyorKzEhx9+iKqqKtx222349a9//alpJoHLoKE8evQo1qxZM+lpgtfrRUVFhRRFsmHDBgAjzXdEp7nhEKf9fr9/xhu5SN2lzWaTKJcJCQno7OwETdMoKiqak0OCmMkkHhIulcIaSY212WyQyWRScZiIGitSU1JSUrB48eI5eYG5XC6Ul5dj3rx5mDdv3qi38Xq9+M53voP33nsPLpcLSUlJkilFDDFMFaM1lOXl5VCr1ejv70dmZiYWL148ohExOf04UN4DBUOFN4fCJlKjlMHlDaC3ox05+QXQqgRTHHEjKaeFxkklF7Z8irCJjJwGWI7APmCFze5ATlYmVGoNmPBBQHQllByyKQKeUBCTdwEiUK2kDWPYwZCIDZho+CA0YsEQj1DAj0GnCwGPCwGOID5OA7lKCzbgg93lRV6OsEGQM4KlPiiAIpRghBOemKvDAdniNFxscjVK4eti40lRCDszijmXF39GalaVYtg20Gc0IRgMIDc7GzzooVmY/ouNrXDIIuB4PvxvwOGaRclYlR0/JLLDYrGA5/khzIfxto0sy6KqqgqEEKxevXpas26jRSgUkp6LY0kRQqEQHnnkEfzzn//E4OAgCCE4dOgQNm7cOOvXG8OnE8Mbyra2NjidThQVFYHnedTX18NqtaK4uHjUsPhOmwdHavqhCjMP1HIGLa1tyEpPhVKjhZKhEOKF+CSWE17TPIFgusXxYMLmNgwAu9MJk8mIxKRkJCYkgiU8ZFS44aIpBMObQJEN4gtdbNhGM9/xhvWangALS38PquqbkJuRgkXLVkEODha7E5zfDbefRbxODaVGBzl4GK025GZmQK2LE2o0ETanQg0WqvKQYz4BeEp0zBY042KciTfEQy2n4QkIH90Bwf01EBJzg4XaLlFf5Qz6LDYE3HYYktMFDXlYbyoO+zhe/BkaQVaotb6g4JLrZ3nkJmqwZXkaHA6HVB+9Xu8Q0xuNRjPmc0KMCDSZTCgpKZkTAzBCCOrr6+F0OlFSUjIqW4/neRw6dAgPP/wwuru74XK58NBDD+Huu++e9eudKcx5Q3nixAkUFhYiISEh6vsZHBxERUUFMjIykJqaioaGBlxzzTVRm+/4fD5UVlZCpVJh5cqVs9rIibTRvr4+yb0zJSUFqampk9ZdThViI5eYmIhly5ZNWyM3GWqs2+1GeXk5MjIysHDhwjltJkX97GgghOChhx7CU089hePHj2PhwoX4+OOPcc0118zy1cbwacVwShchBKdPn4bL5cLy5ctH1fMGQhxeONcNX4gDz1+M1aAooZEDgJaW81hQMB9U2GqeZUUqaHhazpMIC3vBVdViMsMfDCArIwNyhQIknF8pBE8LhyoZLTRoChktGTj4pYaWk5oyKQNyWKOnVTHw+LlhE3QW4Fg4XC64nXYQQqBSqZAYb4Bao4VeoxLyJUW6rYKRDjYhMeMNAENdPEzJw42ecLgBlDIKHAHkYR2SEB+FcKMpDCCFTSOHtgvnwfM8li5ZDLlcDoYSKGF0ONxc+P/hz2nhfsQ4KpoCkrSKETWNEAKn0ykN4NxuNwwGg3R4iowhEtkQDMOgqKho2jXr0UBsJlUqFVatWjXm++qbb76Jf//3f8df//pX7NmzBxUVFViwYEFs4BbDtCEYDA5pjrq6umCxWLBixQpUVlZKQ5fRjKoGvUG8cLYbFIAgJ7AYfEEO5r5uaPQJSEsyCCwLBQ2nXzCK8YxSt3QqGXpNVvgcA0hKy0BSfByCrFBf2HA9lAZzhJKYD8IQTWjMOF4YxLEEYdoqD7mMliKafEFBQuAJCtICt/9isymneFjtLvhcDniDQkamWheHOJ1OGkzRNCVtCQOhi5pHbbgW61UyyXhI2L4CIQ6QMeFrDTM3KOpifrz4HiGjhaZbTgG9Jgs8LgcysnKg1agEmjAtRCURngh6dV6g2grvAcLWVGw49Wo5vliaDeWwXF6v1yvVx8HBQWg0Gqk+xsfHD2mUm5qaYLVaUVJSMm7jOVMghKChoQF2u31cXXtbWxu2bt2KW265BY888gja29sBAAUFBbN5uTOKWW0oR5vAv//++1iyZMmEGTEienp60NjYiMWLFyM3NxeDg4Oorq7Gxo0bJTve8fSSdrsd1dXVSEtLw6JFi+ZEp+dwOFBVVYW0tDRkZGQMOVhEq7ucKsTIgaysLCxYsGDGGrnxqLFarRaNjY3Izc0d4lQ5m3C73Th37px0DaOBEIJHH30Ujz32GI4ePYqioqLZvcgYPhOIbChFJ8++vj6kp6dj1apVo/7MqVYrOq2ecGMl6ANZXqBnCR8plFdUo3DFMlAMAwUjHGYUMhoElNRUycIfeY7FhQsXIGcoFCxYALVCAQJIzZLYRDHhDkxGAaAoqYETG6mLzZnQYNER/5+CcOChwx+lz6mLcRRVVVVgGAaLFi2C3W4fIR1ITU2dUcfpS5EBXCoi884GBgYkamxCQgLa2tqm1e16sgiFQkMct8d6vzx27Bj+7d/+DU899RS+8pWvfKpoXDFcPhjeUPb29qKzs1Nyal++fPmor5NAiMOr1f1wB0PC9o5QIBBYFu3tHUhKiIdaFweaIuHBlKALV8rEzSINT4CDSk6jo6cfCPlhSElHkl4ztNmUhmdC4xTZhLojPmoVTJiKT4cbOkFnLpMBPAfQjHCNNCXa6lxkfBACGE398Hp9yMjMRCgYgsvlhMfrAU0xiIuLg0qjhUGnQYCDlD+pUTBDrjFyi8hyRKrPF3MnLw4FJeMeUW8up9HVZ0LI70NmVhbUSoUwsCOATsEgJEWpEGjkQlSJjKHBUADDCJnBAIWFaTqkTmBWxrIsbDabRK0HIDE7LBYLHA4HSkpK5kQuFtlMlpSUjOm43dnZia1bt2Lnzp34/e9/Pyd9x2xgzhvKU6dOYf78+ROa4URmCRUVFUm5Mk6nE2fPnpUayvGaSaPRiIaGBixYsAC5ubnT86AmCYvFgtraWhQUFCAvL2/I96LVXU4VYs5lfn7+mPTOmYJIje3r68Pg4CAYhkF6enpU1NjphthM5uTkjDklIoTg8ccfx29+8xu89dZbWLNmzaxdXwyfLYgNpdhQcRyHuLg4yGSyEaYTk8E777yDtWvXQq1Wj1sfXS4XKisrJcbCXLzpeTweVFZWSnEUkdcQKR2wWq2S4/R0RTaJEM3B4uLisGLFiln9O4jUWJPJBKPRCADSY5yIGjvdEJtJhUKBwsLCMf8O7733Hvbs2YPf/e53uP3222PNZAwzhuENZWNjIzo7O7F48WLMmzdvDK8MgsPVfegZ8AqmOxwPRXhbKGcYdHV1Qa/XI04XB5oJD7goQe9I0ULjI1DZOfT19gIgyMzMgkIhD0sABNMdGUNJGstgSNwOih85ycxGTtOCMU7YvEzGCMZhgpRAaBwphNkgovkOI5iOyRmgs6cXMoogISUd8VolfEEeBrXACqG5AAYcTrA+N0IcQaJehzh9HBLiDVDIZGBoSvpPwQgDQLn0kQZNAXKaBh2+jYwWKLEMTUMWjmGiKR6NDfUI+P0oLZ5d3w/R18JsNqO3txcsyyI+Ph5paWkTUmNn4lrELODx4pt6e3uxZcsWXH/99fjjH//4qW0mgTmODQFG2uKPBtGB1Ov1jsgSomkaLMuira0NqampI5wPAeEfvr29HR0dHVi5cmXU29DpRk9PD5qbm7FixQqkpaWN+P7wvEubzQaz2TyteZdmsxl1dXVYvHgxsrKypvqQJg2FQgGNRgOXy4UFCxZAr9fDYrFIAbDT6Ro7HkSq7UTN5NNPP42HHnoIb7zxRqyZjGFGQVGUxByIj4/HypUr0dbWNkI3NBkQQsAwDDo6OpCZmTmmtEAcdOXn50On06G3txc5OTmX/HsvBXa7HVVVVWOyJkaLbDKbzVOObIqEKANISkqaE3MwhhE2DOfPn0dKSgry8vJgs9nQ1dWFhoYGKQ8yJSVlCDV2usGyLCorKyGXy8dtJk+dOoVbbrkFDz/8cKyZjGHGIdIvCSE4f/48enp6oFQqx5SqAMAH5y0wOvxDqPUiVdQX4MAwMtgGHaBpIS9SGXatltE0OJYDTQkGW/3GfigVCqSlpSFEaLAB4dwqYyipGVTIaMgZGkqZ0LBBDSgZIXpEKaMBUFDIBLdWMYpIwdCgKEAuo0GL+bnhZo6mBWq9jKHABgNoqK/H8gUaFBWuhEqhGDPXVkwdMJvNsFgs8Fj6JE3iVNhvgp67FuB5XLF2zaz7flAUBb1ej87OTqhUKixbtkzSXra0tIxJjZ1uiFTbiZpJo9GI7du3Y+PGjfjDH/7wqW4mgVneUI5m+1xeXo6UlJQxN4ai+Y5KpUJhYaH0BBaLCsdxMBqNMJvNsNlsUKlUEmXUYDBIK+nBwUEUFRXNmftTa2sruru7UVRUNCm9KDBSkxgKhS7p8NTX14fGxkasXLlySH7dbGJgYABVVVVYuHDhkAPrdLvGjgePx4Nz584hKysLBQUFo94vIQTPPPMM7r//frz22msxrWQMM45QKIR33nkH8+bNkyjgbW1tcLlcKCwsnPT9ieZkg4OD6O/vh8ViAXBRsy0Oprq6utDa2orly5djYGAA3//+9+H1enHXXXfh5ptvnu6HOSrEQdfwuhANIiObzGYzPB7PJR2eHA4HKisrkZ2dPWZdmGmIbtcJCQkjdO1jUWMvxTV2PLAsi4qKConuOxZr5OzZs9i1axd+8Ytf4M4774w1kzHMOEKhEILBoBSftGDBArS0tGDTpk2j3t7o8ONcxwCUMho8AJVMyGZUygSXUhlN4Pf74bDZMGgfBBcKIDEhAanJSUhJToJSIYfLYUdTYwNysrOwoGA+ZIywrZOFN3mzAbfbLbFHli5dOunX+nD2m06nk+pjtGerYDA4hLEwFxR8juNQU1ODQCAwIjppLGqsuISZruY3UrdZWlo65vuL2WzGjTfeiOLiYjz77LNzYqY225jzhrKqqgoGg2HUCZOYCTjc3XC4+Y6o3eE4TnrDtVgs0u1lMhmKi4vnhGMtaqEGBwexevXqUTeok8Gl5l12dnaira0NhYWFSExMnNI1XCqsVitqamqwZMmSUe28IzEV19jxIDaTmZmZY2pHCSH4xz/+gR/84Ac4fPgwrr322kv6XTHEMFk4HI4h087Ozk7YbDYUFxdHfR+RwzbgYn3keV7SI5rNZoRCISgUCgSDQRQVFSExMRH//Oc/8eCDD4JlWWzfvh2PPvrotD/G4eju7sb58+exYsWKaRl0jSUdGE93KcoAxMiguYDX60V5eTmSk5OxZMmScQ95U3GNHQ/iZlJ0Hh+rzlZWVmLHjh348Y9/jHvuuSfWTMYwK3A4HDh37hyUSiWKiorg8/lw9uxZXHfddZO6n9EyysWhttlshtlshtvthlqths/nw8KFC+esLgwODqKqqmravCbGkg6kpqaOOZjy+XyoqKiYExmACDHjkeM4rF69etwGUaTGXopr7HgQpXcWi2XcZtJqtWL79u1YsmQJ/vWvf81JgsNcYM4byrq6OiiVSixcuHDI10XznSVLlozYZEVjvuN0OiV3PLFwJCcnS26qszFdEam6wWBwTOexqWIi3SUAXLhwQcrnmYuwV0CY1tTW1mL58uUT6mWHYzKusePB6/Xi3LlzSE9PH9NRlhCCF154AXfeeScOHDiALVu2TOpaY4hhKhiuEerp6UFfXx/Wrl0b1c9H1kexkRzteS6azvh8Psjlcvh8PiQmJiIQCOA3v/kN3G43fvjDH2Lz5s3T9thGu1axNk13DrCIsSKbIqUDJpMJ9fX1UQ26Zgoejwfl5eWSWdxkDo1jucZOlhrLcRwqKiombCZra2uxbds23HPPPbjvvvtizWQMs4aPP/4YSqVSWjB4PB588MEHUb9Pi8M2nueFGKMx6qOoj+vv74dGo4HH40FcXBxSU1NhNBpRV1eHa665BosXL57uhzgEYm1atGjRqC7fUwXP80MGUxzHISkpaQj7ze12S7FuEw26ZgrioIuiKBQVFU162xeta+x4EONJzGbzuM3k4OAgduzYgdzcXLz44ouzqnufa8x5Q9nY2AiKorBkyRLpNk1NTejr6xtiviN+b/hUaTTYbDbU1NQM0cc5nU5p8uT3+6UXTUpKyoz8gwcCgSEalNlYd0fqLsXDk0wmQzAYRElJyZzQfQGhKNbV1U0L1fZSqbHRNJMAcPDgQXzzm9/E888/jx07dkzpWmOIYbIY3lAajUa0t7dj/fr1E/5sZH2kKGrMKbLX60VVVZWUKSiTyeD1eqX6aLPZoNFokJeXh9TU1BkxOhAz4xwOB1avXj0r2WGRgymz2QyWZaHVauFyubBs2bI5ayZFPfd4rInJ4FKosRzHobKyEgCwevXqMZvJhoYG3Hjjjfjud7+Ln/3sZ7FmMoZZxfD66Pf7ceLECdxwww0Tbs2izSjnOA61tbXweDwoKiqCVqtFMBiExWJBc3MzfvCDH2BgYAD5+fl47rnnkJqaOiOvg87OTrS2ts6a78cI3WW4iXa73cjKypqzjHAxOmkiCn60GM81NikpadRtoqjZNRqNKC0tHfM90eFwYNeuXUhJScHLL788oz4glyNmtaEERgbTtrS0IBQKYfny5QiFQqiurobP5xuRKSMWgomaSdH4ZunSpWMeENxut3SocLlc0x7VIfLdRR3MXNADxImO2+0GwzBgWRZJSUmznnfZ19eHpqamGSuK0VBjfT4fzp07h9TU1HGn/6+++iq+9rWv4R//+Aduuummab/WGGKYCKFQSDrwAJAOMRs2bBj356IdtonGN+np6WPGJgUCAak+DgwMQKvVIjU1VTI9mw7KVXV1NTiOQ1FR0Zy86YrUpZ6eHqhUKvj9/mkxrZgsRBOm3Nxc5OfnT/uBLRpqrEgl43kexcXFYx7YmpubceONN+L222/Hgw8+GGsmY5h1sCw7xMSRZVm8++67uO666yakQEaTUe73+1FVVSU1L8Pvs7e3F3v27EF/fz9SU1Pxs5/9DDqdTjo/ToeWWdyEGY1GFBUVwWAwTOn+LhV9fX1oaGiQaL/i+8B0e1qMB1G3OVPRSdFQY0UmTX9//7jNpMvlwu7du6HT6fDqq6/OCCPxcsesN5TDJ0ytra1wu91YuHAhysvLoVarxzTfAcaeKokvwv7+fhQWFkZtfOP3+6WJjChWFg9Pl+KiJ/Ldxe3oXNEDqqurwbKsxDV3u93S4xR1lyJvfqYOTz09PWhpaUFhYeGQTfNMYTRqbHx8PJxOJ1JTU0eYXERCDOV+5plncMstt8z4tcYQw2gY3lAODAygpqZmTNMJIPpmsr+/Hw0NDVi0aFHUxjfD9TZyuVyqj5fiouf3+1FZWQmVSjVn2YritLm/vx+rV6+GXq+ftcimSDgcDlRUVMxafNNo1Fi9Xo9gMAiZTIaSkpIxD+Wtra3YunUr/u3f/g0PP/zwp96tMIbLExzHgWVZ6XNCCN566y1s2rRpzAN8tPXR6XSiqqpKcnge6zn+2muv4cMPP8TWrVtx9dVXS27TZrMZPM9L56qkpKRJ1zeO41BXVwe3243Vq1fPagxGJERW2bJly5CRkXFJusupIhAIoKKiAhqNBitXrpyVmjMaNZZhGHi9XqxZs2ZMDxSPx4PPf/7zoGkaR44cmRXGzeWIOW8oOzo6YDQa4fF4RqzVxzLfGQ6WZVFXVwePxzOlF2EoFJIm86JjrPiiMRgMEx4qxBfh4sWLZ4TvHg2iCeT2+XywWq0wm80zdngSnSMvxdV2OkAIgc1mQ21tLWiaRigUGpMae/ToUXzpS1/C008/jS9/+cuxyXsMc4bhDaVoQjGa6cRkhm1tbW3o6urCypUrkZycfEnXJm67xMEUMNQxdqLDk5hzKZrOzEVTwvO8lB1WXFw86ht/NLrLqUIcPBYUFMxZJrJIfQ4EAuB5fkxqbEdHB2688cZPfSh3DJc/hjeUAPD222/jyiuvHDMyLppmUnSZFk25LuUMELntipRWiVu9iVhhoVAIVVVVIISgqKhozrR34iJgLFZZZGSTxWKZtsimSIhZwAaDYc5YfqFQCA0NDbBardJzZzRqrM/nw549exAMBvHGG2/MmbTscsCcN5Q1NTXo6+vDihUrhjRh0eqBRIqCXC7HqlWrpo3KyXGcpEcUHWPFyfxoE5nZ5ruPBr/fj4qKCmi12qgnOqLuUpw8TcfhqaOjA+3t7SguLp4zuobf78e5c+eQlJSEJUuWjDgkymQyfPTRRwCARx55BI8//jhuu+22WDMZw5xiOKXL4/Hgww8/xA033DDkdpPRA0VqFafqMh35++12uzSZnyjKSHRRzcvLmxFqZzQQp/8ejwfFxcVRUZKmK7IpEjabDdXV1TNmtBENeJ5HVVUVWJZFcXExKIoaQY1tb2+HzWbD/v37ccMNN3zqQ7ljuPwxWkN57NgxlJSUDDlrRJrvjNdMEkIkB/zpcpkW73e4Y6xIpUxNTR1Re3w+HyorK6Vt3FwxNzo6OtDR0RH1ImC6IpsiIUYniREpc3Uma21tRU9PD0pKSqDVakdQY8XrrKiogNvtxltvvTVn593LBbPeUIoTeJ7nh2hYrr76auk2k6EoVFZWSu5TM/VmN9zMgeM4yTE2MTERbW1tEn1qrp5Q0xHIPdrhaTK6S3ET0t3dPaeOsmIzOVZBEh/nD3/4Q7z22mvw+/3YunUrfvvb30omTjHEMBcY3lCOZjoRrR4oEAiguroaAGZ04k0IkSj14qEiMTFRmswPDAygoaFhXF37TEOUAURjOT8WLjWyKRIWiwW1tbVYunQpMjIyLuWhTBk8z0vu48XFxSP+FuLj/NOf/oQ//elP6Ovrw/r16/Hf//3fuP766+fkmmOIARCeu6FQaMjXTp48iZUrV0pxaNEO20S2gtVqlajvMwWRUm82m2G32yXH2JSUFPA8j8rKSqSlpc2Z8U2kbrO4uPiSt2yjSQcmo7sUo5NSUlLm7G8BQGL0lJaWjjqE9Xq9OHbsGH7605+ipaUFCxcuxL59+3DnnXfOwdVePpiTpE3RlMHv92PJkiXo7OyUvhdtMynaKU+FohAtaJpGUlISkpKSsHjxYskx9sKFC/B6vaBpGvPnz5+TnEvgorFDVlbWlFwChz9O8ZDY2dmJ+vr6cXWXonC5r69vzBfhbECkSiQkJIzZWNM0jdbWVhw5cgS/+tWvsGnTJrz22mtzls8ZQwxjQZxUizFJ0dZH0RgsPj4ey5Ytm9GJN0VRiIuLQ1xcHAoKCiTH2L6+PjQ2NgIAMjMzZyQWJBqIMgC5XI6SkpJL/ltQFAW9Xg+9Xo+CgoIhh6eWlpYJpQNidNKKFSuQlpY2HQ9t0piomQSEx+nz+fCvf/0LmzZtwoMPPog333xzzv79YohhPIimg0D0wzbxDMqyLNatWzfjBipqtRq5ubnIzc2VjATNZjNaW1tBCEFCQsKk49SmC2JWut1ux5o1a6ak24x8nJGssM7Ozgl1l6LbdUZGxrhO/DON9vb2cZtJAJDL5fjXv/4FpVKJtrY2nDt3bpav8vLErG8o7XY7zp07B41Gg8LCQjidTtTW1uKaa66JWg80ExSFyULku7Msi5SUFNhsthlxjJ0IIpVspo0d/H6/NGEbrruMi4vD+fPnYTKZJHrAXCAQCODcuXPSIXqsglRRUYGdO3fiJz/5Cb7//e/HaK4xXDYYTunieR5vv/02Nm3aBKVSGVUzabVaUVtbO21B2JcC0UXVaDQiOzsbTqdTcowVDxWz4RQ4W4HcE+kuTSYTGhsb5/Q9i+d51NTUwO/3j2vAY7VasW3bNixbtgz/+te/ZiXyKoYYosFoG8rTp09j3rx5SE9Pj6o+er1eVFZWQqvVYsWKFXP2/BZdVLOzs8GyLCwWi1Q3ZtLsJhJiRIrP55uxrHRgYt2luAjIzs6eMzNLQGgmOzs7x43ZY1kW//Ef/4HGxkYcO3Zszur55YhZbyhPnToFrVYrrbMdDgfKy8uxcePGqCkKNpsNRUVFc0apHIvvPrzpmqpj7EQQheSLFy9GVlbWtN73eBiuuxR1rosXL0ZGRsac6GwCgQDKy8uh1+uxfPnyMf/WNTU12LZtG+6991788Ic/jDWTMVxWGMt0Yv369VCr1RMelrq7u9HS0iK584kghMDr9UKj0cz4c148pHi9XqxevVoarLEsK03mIx1jRbv96b6uuQrkHs1tmhAiRYPMVmTT8GsSD47jNZMDAwPYsWMH8vPz8fzzz3+mQrljuPwxWpb52bNnkZ6ejoyMjAnr4+DgIKqrq5GRkTEiQszr9UKpVM64fpEQIjUuhYWFEjMqsumKdIwVm67pvi5xS8vz/CXLAC4Fo+kuAUh+F3PF9BP1o+M1kxzH4dvf/jbKy8tx/PjxOdsqX66Y8xxKl8uF06dP48orr4RSqYyKolBUVDRnGS+ibjM1NXXcQ4roGCs2XUqlUmouo3GMnQi9vb1SvuNcTUgIIairq8PAwACSk5MxMDAg6S7FQjgbRSoYDOLcuXPSFmKsv60Yyr1v3z789Kc/jTWTMVx2GM0W/9ixY1i2bBmSk5PHZW6IG8GioqIh9ERCCJ544gkcP34c69evxz333DNjQ59gMIiqqipQFIWioqIxX/+R+YhmsxnA5BxjJ4LD4UBlZeWcT7y7urpw/vx5pKWlwe12X5LucqrgeV4yIyopKRmzSbTb7di5cyfS09Nx8ODBz1wodwyXP0ZrKMvLy6HVapGfnw+GYcZ8rYsU/NFc+F9//XU89dRTyMrKwq9+9asZk7/wPI+mpiZJtzlW4yJG/IjNZaRjrJgfOxWI+Y4KhQKFhYVzYgIECDWnoqICiYmJ4DhuCPtttlgsACTWY0lJyZiLKo7j8L3vfQ8ffPABTpw4MatLnE8KZr2hjDSdEItDVVUV7HY7DAYD0tLSRrhgeTweVFVVzTlFQXTnE+ml0T7RIx1jrVYrKIoacnia7OFOdFGNnG7NNsRDitvtRklJCZRK5RBzjtnKuwwGg9IbyniUNjGU+2tf+xp+9atfxZrJGC5LRFK6RAlAc3Mzent7oVarkZqairS0NOh0Ouk5zLKstH0qKioaoYGx2Wz4j//4D/T39yMlJQVPP/30jLwZilQynU6HFStWRH1IiXSMFTd6kzEDGw6xThcUFCAvL+9SHsq0QDykrF69WmrwZzvvMtpm0ul0Yvfu3dDr9Th8+PBnMpQ7hssfkQ2laL7T29uL8+fPg6KoUZ34CSFobW1Fd3c3Vq1aNWom9je+8Q2cO3cOCoUCv/rVr0aNaZoqxDrt9/snRS8VHWPF4dtUpVWiDEBkc82Vc/PAwACqqqqwcOFCKRd5NiKbhkOMuBuvmeR5HnfffTfeeecdnDhxYk7fVy5nzJkHuHhYoigKJSUluPrqq5Geng6LxYIPPvgAH3/8MTo6OtDf34+zZ88iJSVlzFzF2UBfXx+qqqqwdOnSSdveMwyD1NRUrFixAtdcc40U6dHQ0ICTJ0+itrYWJpNpBNVtOMRAbnEtP5fNZE1NDTweD0pLS6VJtmjOUVBQgCuuuAIbNmxAWloabDYbPvzwQ5w+fRoXLlyAw+HAdMwxom0mL1y4gB07duCrX/0qfvnLX07roe29997Dzp07kZmZCYqi8Morr0z4MydOnEBxcTGUSiUWLFiA/fv3j7jNk08+iXnz5kGlUmHdunU4c+bMtF1zDJc/Is13Fi9ejI0bN2L+/Pnwer04e/YsPvzwQ7S0tMBsNuPMmTPgeX5MQ4WEhATJBXHZsmUzEmvkcDhw9uxZJCcnY9WqVZOaeFMUhYSEBCxevBhXXXWVFCDd0dGBkydPoqKiAt3d3SPYLaPBZDKhuroaS5YsmdM3/fb2dik6KXJbLJpWlJSUYOPGjcjPz4fX60VFRQXef/99yXUyMov0UkEIQX19vTT0G6uZ9Hg82LNnD9RqNV555ZVpbyZjNTKG6UZkfczIyMA111yDFStWAADq6urw3nvvob6+XqoHRqMRa9asGbWZBID169cjKSkJ8+bNw5IlS6b9ekVJDsdxKC0tndRrjKIo6HQ65OfnY926ddiwYQNSU1NhNpvx4Ycf4qOPPkJbWxvcbveE5yq3242zZ88iKSlpRjXlE8FqtaKqqgpLliyRmklAMLvJyMjAqlWrsHHjRkm+1NDQgBMnTqCmpgb9/f0jdLSXCrGZHC+VgOd5/OhHP8Kbb76Jd999d9rfVz5N9XFONpSR0SGj8d2DwSAsFgu6urrgdruhVCqRnZ2N1NTUWXcPjeS7jzXdmsp9O51OafLk8/mG2O1HHgAIIZJ+dKxA7tkAx3Gorq5GKBQa0yVwNIyWdxkZvTLZwhYKhVBeXg61Wj1u5mZHRwe2bt2KsrIy/O53v5v2AvrGG2/gww8/RElJCW6++Wa8/PLL2L1795i3b29vx4oVK/Ctb30LX//613H06FHcddddOHLkCLZs2QIAeP7557F371489dRTWLduHR577DG8+OKLaG5ujgnAP+XgeR7BYFByKhyN4ioyHnp6eqQpbkZGBtLT0xEfHz/qwCQYDKKrqwvZ2dnT3jCIURgzsRH0er1SfXQ4HNDr9VJ9HF4DJwrkng2I2xAxvyxa+/3pzrsUm0mn0ykxSEaD1+vFnj17wLIs3njjjRl5f43VyBimE36/HzzPS87Xw+udyHjo7+9HX18fCCFITU1FRkYGkpKSRh12iee8hISEqPIXJwOPx4PKykoYDIZp3whGOsbabDaoVCqpPg6XVokygJycnDkzawMuul0vX748ag3iaJFNU8277O7uxoULF8bNS+d5Hj/96U/x/PPP4/jx41i0aNGkf89E+DTVx1lvKP/2t79hxYoVWLhw4bhhsxcuXEBPTw+WLVsGnudhMplgs9kk2tds8Kuj5btPF8QgXIvFAqfTCYPBING+Lly4MKlA7pkAx3GoqqoCz/Pj6qMmwlh5l9HqLqNtJru7u7FlyxZs3boVf/jDH2Z8GkdR1ITF4Ic//CGOHDmCuro66Wv/9m//BrvdjjfffBMAsG7dOqxZswZPPPEEAOHvlZOTgzvvvBM/+tGPZvQxxDC3eOutt8AwDEpLSyGXy6OKTdJqtVLTJdLp09LSZsUlsKenB83NzbMShREIBKSaYbPZoNFopPcCm802qUDumYDIIOnv70dJScklN2dTlQ6IzaTD4RjCIBkOv9+PL37xi3C73XjzzTdnJUM5ViNjmArq6+tRX1+PTZs2jWt06HK5UFVVhfj4eOTk5Ej10e/3Izk5GWlpaUhOTp5xxpvdbkdVVdWUI92iAcdxEl10uGOsaMq1YMEC5Obmztg1TASTyYS6urope3+MJR2Iti8Qh4/DGSSRIITgl7/8JZ555hnJx2Cm8Umvj7PKHyWE4OWXX8Ydd9yBhQsXoqysDDfddNOQvECO41BXVweXy4W1a9dKU+iMjAywLAubzQaTySTx3afT6CYSHMehpqYGPp8Pa9eunZUmThSW5+fnS46xJpMJLS0toGkaOTk5CIVCUCqVsz5dYlkWlZWVoCgKq1evnlIhHivvsqurCw0NDdLhKSUlZQSFT2wmVSrVuM1kf38/tm/fjs997nN48skn54zaMRynT5/G5s2bh3xty5YtuOuuuwBcpPHed9990vdpmsbmzZtx+vTp2bzUGOYAp0+fxu9+9ztoNBrs3LkTu3fvxpVXXim93gghkoY6chMnOpmKWsS6ujoQQqQ32aSkpGl9DRBCpPDn4uLiWWniRKaKaLMvTubPnDkDQgjS09MlXdVsv95FUySLxTLlLLfhuZ4+n096rOfPnx9Xd0kIQUNDAxwOx7ibyUAggK9+9asYHBzEO++8MyvNZLSI1cgYxkJLSwvuvfdeDAwMSMyjG264YQhbQWRMzJs3T5InxcfHY8GCBdJZo729HfX19UhMTERaWtqMGAiKdThSIziTYBgGaWlpSEtLG+IYW1NTA5ZlYTAYpOipuTDh6e/vR2NjI1atWjVlBslYeZfl5eUT6i7FZjJS2z4chBD85je/wV/+8pdZayajxeVcH2e1oaQoCgcPHoTD4cDhw4dx8OBBPProo8jNzUVZWRnWr1+PF154AXfccQfWrl07QvMhk8mkF4zoEmgymVBZWSnpFEVB9lQarkAggKqqKjAMgzVr1syJzbtKpUJaWhr6+voQHx+PjIwM2Gw2nDlzZtodYydCKBRCRUUF5HL5tDuCDT88iY20xWIZcXhSq9WorKyEUqnEqlWrxjw0mkwmbN++HevXr8ef//znOXMwGw1Go3HEJictLQ1OpxM+nw+Dg4PgOG7U2zQ1Nc3mpcYwB/jv//5v3H///Xj33Xdx4MABfPWrXwXDMNixYwe2b9+Ol19+Gdu3b8e11147gjFB0zQSExORmJiIxYsXw+FwwGQyoampCaFQSGoup2pBL8Y3DQwMSHrH2YZMJpM2kwqFAgUFBXA4HKitrZUa6ZSUlDEpbtMJsYkbHBxEaWnptJuPqdVq5OTkSANFUTpQUVEx5PCUkJCA5uZm6TrGGoKGQiHcdttt6Ovrw9GjR+dsozsWYjUyhrFw0003oaysDOfOncNLL72E//7v/8Y3vvEN3HDDDdi1axeampqQkpKCm2++eQSdcvhZQ2SEiYPshIQEqbmcqsNxd3c3zp8/P2e5s+LQ3uv1gud5LFy4EKFQCBcuXEBdXd2Y0qqZgtjEFRYWTqtsDLiouxRjY0T2W2Nj4wjpgNlslprJseoeIQSPPfYYHn/8cbz77rtYuXLltF7vVHE518dZd7gRp0V79+7F3r174XQ6ceTIEfz1r3/FY489hkWLFuHQoUNgGAYlJSVjNg2Rb6Tik8hkMqGmpgYApIZrsvo8ke+u1+vnVLTs9/tRUVEBrVYrbeKys7Ml/ZTFYpHs+afiGDsRRHtplUo1bhM3XVCpVGMenjiOg1KpRH5+/pjic4vFgp07d6KwsBDPPPPMZdVMxhBDNFAoFNi2bRu2bduGp556CidPnsTf//53fOUrX5HeBBUKBa699toxDz5inY2Pj8eiRYvgcrlgMpmkA4WoX05JSZkU24BlWdTU1CAQCGDNmjVzSr8Xsy5FBklWVpakn7JYLGhpaZmyY+xE4Hle0ipO1mzjUiCXy5Geno709PQRh6dAIACKorBw4cIx654Yyt3a2orjx49P++EuhhhmGjRNY+3atVi7di0eeughVFdX44UXXsA999yDQCCAz33uczAYDNi+ffu4A/dIRpjP54PZbEZfXx+amprGTByYCKJcq7e3d1w65Uwj0vsjkkGycOFCuN1uWCwW9PT0oLGxccZjjETjm/GauOnCcPabqLvs7OyUKKLj+QgQQvDkk0/ikUcewVtvvYXi4uIZvd5PG+bGMjUCer0eZWVluOeee3D//fdjxYoVOHjwIHbt2oX4+Hjs2rULZWVlWLdu3ZhvkpFPoqVLl0qr/oaGBnAcN4T2NV6DMZt89/Hg8XhQUVEhPZ7I64jcxPI8L1HcGhsbwbLskMPTVPUBojOZGAMw2821eHhKTk5GeXk5AOH50tzcPKrucmBgADt37sTChQvxj3/8Y84cgcdDeno6TCbTkK+ZTCbo9Xqo1WowDAOGYUa9TSxE97MHuVyOzZs347e//S22bduGO+64A0eOHMH3vvc9uN1ubNu2Dbt378Z111035mGAoijo9Xro9XosWLAAHo8HJpMJHR0dqK+vl2rGRNPqQCCAyspKyOVyrFmzZs5eXyzLSlru4QwS0TE2ISFBOjyZzWbpsSYkJEiPdarNn6hL8nq942oVZwri+15iYiJ4nofVapVYLS0tLSN0lxzH4Vvf+hbq6+tx/PjxOTMumgixGhlDtKBpGqtXr8abb76JrKwsPProozh16hSeeOIJ7Nu3D5s2bcLu3buxY8cOJCYmjnmmU6vVyMvLQ15e3gi5kWgElpqaOi6VXRwuORwOrFmzZs6MEwkhaGlpgdFoRGlp6Qgmi06nk1xj/X6/pNVuaWmBTqeTHut4GtVoIcozxjO+mSlEvu9pNBo0NDQgKysLHo8HH3744QjpAAD8+c9/xq9+9Su88cYbWLt27axeb7S4nOvjrJvyjAWj0Tjkwfp8Przzzjs4cOAAXn31VahUKuzcuRM33XTTEE3ReCCEwOFwSOGwwWBQEmQnJSUNuQ+R7z7XomXRiWuyTa3ogiU+Vq/XKzVcqampk6Y1+P1+lJeXS85kc9Vci9pNmqZRVFQEhmEk0wpRaN/Z2Ynf/e534DgOycnJeOutt2aFxjEc0QqqX3/9ddTW1kpf+/KXv4yBgYEhguq1a9fi8ccfByC8UeXm5mLfvn0xw4nPKMxmM5KTk6WhDsdx+Oijj3DgwAG8/PLLsFqtkqZoy5YtUR9mRNqXmG8mNlypqalDGiS3243KykokJCRg2bJlc8bcmEogt7iFiHSMFevjZA9/osY+EAiguLh4TuoNMFS7GUm3FQ/FZrMZAwMD+PWvfw25XI6Ojg6cOnVqVjRdoyFWI2OYCXi9XrAsKzUGokHWSy+9hIMHD6K6uhpXX301du/ejZ07dyI1NTWqM00wGJRqxsDAwJCGK5LqHwqFUF1dDY7jUFRUNOvDJRE8z6OhoQF2ux3FxcWT0nKHQiGpZoiOsWJ9nKy0StyQihr7sSI5ZgOidjOSbjs873L//v0YGBjA6dOncfjw4REaxdnCJ70+XjYN5XgIBoM4duwYXnrpJRw6dAgURWHHjh246aabcPXVV0f1Zj684fL5fEhKSkJaWhoCgQDa2trmjO8uYmBgANXV1cjPz8e8efOmdF+RQbiRjrHRuAT6fD6cO3du1A3pbILjOFRUVAxpJkfDhQsX8N3vfhcXLlyAzWbDhg0bcOzYsVm5RrfbjQsXLgAAVq9ejUcffRTXXnstEhMTkZubi/vuuw+9vb3429/+BuCi5fN3v/tdfO1rX8OxY8fwve99b4Tl86233oqnn34aa9euxWOPPYYXXngBTU1NM+6kGcMnDzzPo7y8HC+99BJefvll9Pb24vrrr0dZWRluvPHGqN/MhzdcYs1QKpVobGxETk4OCgoK5qweiIHccXFxU2ZMiNFU4uEp0jF2IpdA0e2a4zisXr16TjT2wMVNhNlsHle76ff7cccdd+Cjjz6Cy+VCXFwcqqqqZm1DGauRMcwlxObmwIEDOHjwIM6dO4f169ejrKwMu3btkvL/JsLwhktMHIiPj0dLSwvUavWkM3inE+KQy+/3SxmFU7kvm80mbS9pmpaYHRNJq0Tab19f35TcrqcDRqMRDQ0N42o3OY7Dgw8+iOeeew5erxderxcvv/zyrDWVn6b6+IloKCPBsixOnjyJF198EYcOHUIgEMCOHTtQVlaGz33uc1G/iNxuN0wmE7q7uxEKhWAwGJCVlTVrIuXhEDekS5YsQWZm5rTed+S0WrRYjpyyRRZTj8eD8vJypKamYvHixXPaTFZWVgIQXmRjFWm3242bb74ZCoUCR44cQSgUQnNzM9asWTMr13nixAlce+21I75+6623Yv/+/bjtttvQ0dGBEydODPmZ73//+2hoaEB2djZ+8pOf4Lbbbhvy80888QQefvhhGI1GFBUV4fe//z3WrVs3w48mhk86eJ5HTU2NNJlva2vDddddh7KyMmzfvn3MnMrhCAQCMJvN6OnpgdvthkqlQnZ2NtLS0qbkYHqpcLvdqKiokNxsp7MuRTrGWq1WyOVyaTIfHx8/5PAk0m0JIVN2u54KxGbSZDKhtLR0zH8Tnufxwx/+EK+++ipOnDiB7OxsfPTRR7j66qtnrbbHamQMlwsIIeju7sbBgwdx8OBBnD59GqWlpdi1axd2796N3NzcqF4XYs3o6+uTsoCzsrKQnp4+wnl5NhAKhVBVVQUAU4p0Gw2RWm2z2SwxwUQZWWQNjKxLJSUlc0b7BS5GlBQWFiI5OXnU2xBC8NJLL+G73/0uXnrpJWzZsgVVVVXIy8tDYmLirFznp6k+fuIaykhwHIcPPvhAon25XC7ceOONKCsrw+bNm6Pmuy9dulQyrXC5XIiPj5fcvmbDdKK3t1fKcpvpDam46hcPT0qlUjo8yWQyVFRUIDMzc041pOIGQDy0jdVMer1efOELXwDP83j99dfndBIWQwyXG0T3UXFz2dDQMERTlJSUNO5rvLOzE62trVi6dCl4npcm8+JAKi0tbVp0NhNhNgO5eZ7HwMCANJknhEiHJ71ej5qaGjAMMy5jYqYh0vlEjdR4zeR//dd/4cUXX8SJEyewcOHCWb7SGGK4fEEIQX9/P15++WUcPHgQ7733HlatWoXdu3ejrKxsQjaGyCjLycmBXq+XasZ0Jg5EA1HbfikygMmCEAKn0yk9Vp/PJznGJicno7W1FTabDSUlJXMyeBQhNpMTRZS88soruOOOO/Dcc89h586ds3iFn058ohvKSPA8P0RTZLFYsGXLFklTNJzvXlNTg1AohNWrVw/ZaooiZZPJJOlsRLevmXDAEkXLhYWFszYRERHpGGs2m8GyrGSpPd25dZO5JtFwY7wNgBjK7fF48Oabb84pRz+GGC53RGqKXn75ZVRVVWHDhg0S7SstLU06+IhT5v7+fqxevXqImcLwgZQYbxRtoPRkYbPZUF1dPSfa9kgNvslkgt/vh0KhwIIFC5CamjonVNdIOllpaemYGwBCCB544AE8++yzOH78OJYuXTrLVxpDDJ8cEEJgtVql5vLYsWNYsmSJ1FwOZ0X09/ejoaEBS5cuHcIoixxImc1myYU/LS0NCQkJ036m8vl8Q7wuZvvMFqnBdzqdoGka8+bNQ2Zm5oycl6OB2WxGbW3thM3ka6+9httvvx1///vfcfPNN8/iFX568alpKCPB8zwqKiok2ldPT4+kKVq+fDkOHDiAXbt2YdWqVeNSlgKBgOT2NTg4CJ1OJx2eprrKH24vPZcNkcPhQHl5OdLT00HTNCwWi5TfM12OsdGA4zhJ2D5eMxkIBPCVr3wFFosF77zzzpxZc8cQwycRhBB0dHRImqIzZ85g/fr12LVrF2644Qb89a9/xdatW7FmzZpxp8wcx8FqtcJkMklUUbE+Tkc+rslkQn19PZYuXYqMjIwp3ddUILpdK5VKGAwGWK1WuN3uaXWMjQaEELS2tqK3t3fCZvKhhx7CU089hWPHjl12OWoxxHA5gxCCwcFBHDp0CAcPHsQ777yD+fPno6ysDGVlZXjjjTewaNEiXH311WNSKQEMceEXqaJic5mYmDjlTaIoA5hreRLP86irq4PT6URWVhYGBgak8/J0OsZGA4vFgpqaGqxcuXJctt9bb72Fr371q/jrX/+KL37xizN+XZ8VfCobykiI1u4vvfQS/u///g+9vb1YuXIlvva1r2HHjh1RUxJEQbbJZBpC+xpNhxjNNYnB4MXFxXPKMx8cHERVVRUKCgqkDcB4jrHTEfo7GnieR1VVFViWRXFx8ZjNZDAYxN69e9Hd3Y133313RnLUnnzySYl7XlhYiMcff3xMC+lNmzbh5MmTI76+bds2HDlyBABw22234dlnnx3y/S1btkiOXDHEMFcghKCnpwcHDx7E888/j6qqKqSmpuL222/Hnj17kJeXF1Vt4zgOAwMDMJlMI2hfw3WI0UAMwl65cuWcxluIecBxcXFDNgDT6RgbLVpbW9HT0zOu0QUhBL/97W/x29/+FkePHkVRUdG0X0esPsbwWYLD4cCrr76KAwcO4M0334RCocCXvvQlfPWrX0VRUVFUtW0420Ec2KelpSE5OXnSzaXdbkdlZSVyc3NnXAYwHiKjk0pKSiT/EfG8bLFYJGmV+H4wHcPG0SA2kytWrBjXeOb48eP44he/iD/+8Y/46le/OiPX8lmtkZ/6hlJEX18fli9fjq985StISUnBK6+8gvr6emzcuFHSFCUnJ0ctyBZpoiLtS3yxTCTI5jgOdXV18Hg8KC4unrNgcEDQAFRVVWHRokXIzs4e83ZjOcampKRMC0+e53lUV1cjGAyiuLh4TCpZKBTCf/zHf6C5uRnHjh2bkYPm888/j7179+Kpp57CunXr8Nhjj+HFF19Ec3PzqBOvgYEBBINB6XObzYbCwkL85S9/kUTSt912G0wmE5555hnpdkqlcsZDfmOIIVrwPI+1a9ciKSkJN954I1599VVJUyRO5qPVVYsmDmJzSQiR6mM0DoEdHR3o6OhAUVHRnL5GRDqZGJUy1mOPdIwdGBiQ3B/FfLPpOLC0traiu7sbpaWl4zaTTzzxBB566CG8/fbbM2JMFquPMXxWcdddd+GNN97AnXfeiffffx+vv/46kpOTJUOfNWvWRN1cigN7kUofyQabiEpvtVpRU1Mz5xF3IqMsFAqNe26LdIy1Wq0SDTia94NoYbVaUV1dPWEz+f777+MLX/gCfve73+H222+fkWbys1wjPzMNJQCcO3cOpaWlAC5STkVNUWVlJa666ipJU5Senh71ZF7UFFksFsjlcsmwYvgkhmXZIVlFc5VdBlwsSpN1lRXdHy0WCwYGBqa0qQWibyZZlsU3v/lNVFdX49ixYzMW0Lpu3TqsWbMGTzzxhHR9OTk5uPPOO6PK73nsscfw05/+FP39/dKm4rbbboPdbscrr7wyI9ccQwzTgcrKSsn2XtQUHTp0CC+99BKOHz+OxYsXS81ltHFCIn1sOO1LdAiMnMyLOs/+/n4UFxePCOSeTXi9XpSXlyM5OXlSrrIsyw45PE11UwsAbW1t6OrqmrCZ/NOf/oSf//zneOONN7B+/fpJ/55oEKuPMXxW0dbWhvj4eMnrwuv14q233sKBAwdw5MgR6HQ67Ny5E7t378b69euj2joSQuDxeGAymWA2m+HxeJCUlCQNpIafEY1GI+rr67Fs2bI5lQFcanTSaDTgsRxjo4V4ll22bNm458LTp0/jpptuwv/8z//gW9/61oxtdT/LNfIz1VCOBUIIOjs7JU3Rxx9/jCuuuAK7du1CWVkZsrOzo57MR2b3UBQ1xA2xqqoKMpkMhYWFc2Y3D1yMKJnoBTgRIg06bDYbFArFELv9if5mYsSB3+9HSUnJuBOuffv24fTp0zhx4sS0x6qICAaD0Gg0eOmll4YEy956662w2+04dOjQhPexcuVKrF+/Hn/605+kr91222145ZVXoFAokJCQgM997nP45S9/OSN03RhimG6ITeHhw4clTdG8efNQVlaG3bt3R50JKToEioenYDAoHSYSExNx/vx5DAwMzLlDoBidlJaWhkWLFl3ywWM8x9jhzfRYaG9vR2dnJ0pKSsZssAkh2L9/P+677z689tpruOaaay7peidCrD7GEMPo8Pv9ePfdd3Hw4EEcOnQIcrkcO3fuxE033YSrrroq6obL6/VK9dHlckk67dTUVJjNZpw/fx6rVq0aV7s502BZFpWVlaAoCkVFRZd8lhXfD0R2R6RjbLTxfaJp20Q6+7Nnz6KsrAwPPPAA7rzzzhlrJj/rNTLWUA4DIQS9vb04ePAgDhw4gFOnTqG4uFiazM+bN29StK9IzrxKpcLixYuRnJw8Jw6qwMUJ10Si5clC1FBFNtOi5nI0x1iRe+/z+cZtJnmex1133YXjx4/j+PHjM0rx6OvrQ1ZWFk6dOjVkwn/vvffi5MmT+Pjjj8f9+TNnzmDdunX4+OOPh/Dln3vuOWg0GuTn56O1tRX3338/dDodTp8+PWfRAzHEcKlwOBx47bXXcPDgQbz55pvIyMjArl27cNNNN2H16tVRN5diFrDJZILX6wXDMCgoKEBmZuacOKgCgMvlQkVFBbKysiaMDZgMIjVUZrMZgUAAycnJUo0c7fGK1N+Jmsl//OMf+MEPfoDDhw+Pmmc2XYjVxxhimBihUAjHjx/HgQMH8Morr4DjOOzYsQO7d+/Gpk2bomam+Xw+ybfDbrcDAHJycpCXlzdnDqqhUAgVFRWQy+XTHlEiOsZaLJYh0qqxEhZEydZEzWRlZSV27NiBH//4x7jnnntmVG/6Wa+RsYZyHBBCYDKZ8PLLL+PAgQN47733sGLFCqm5XLhw4YRPTnHaHRcXB7VaDYvFApZlx6R9zST6+vrQ1NQ04xMukdYgTp6GO8bSNC3pSCOF3KPdz7333osjR47gxIkTyM/Pn7FrBqZeDL75zW/i9OnTqKmpGfd2bW1tKCgowLvvvovrrrtuWq49hhjmAm63G2+88QYOHDiA119/HYmJidJkfs2aNRPWNpZlUVVVJdUIm80Gt9stTapTU1NnTRrgdDpRUVEhGV3MFESam9hcjuYY29nZiba2NpSUlIzpAE4IwYsvvoh9+/bhwIED2LJly4xdMxCrjzHEMFmwLIsPPvgAL774Il555RV4vV5s374du3btwubNmyf00IiMcMrOzobD4cDg4CDi4uIk9ttssTmCwSAqKiqgUqmwatWqGV2K+P1+6fwoOsaKZ2adTieZSU4k2aqtrcW2bdtwzz334L777ptx86LPeo2cO97lJwAURSE9PR3f/va38a1vfQs2mw2HDh3CgQMH8OCDD2LRokUS7Ws0TZEYyJ2dnS1NuxcvXiwFw7a0tAyhfc1kPIfomlhUVDTjeZc0TSMxMRGJiYlYtGiRJEBvb29HXV0d5HI5KIpCcXHxuM3kj3/8Yxw+fBjHjx+f8WYSgOS2ZjKZhnzdZDJNSA32eDx47rnn8MADD0z4e+bPn4/k5GRcuHDhsioGMcQwWeh0OuzZswd79uyB1+vF22+/jQMHDuDzn/88NBqNZFixfv36EbVNPKAoFAqsXbsWDMNg4cKF8Hq9MJvN0gAsPj4eaWlpMxrPIbomzp8/H3l5eTPyO0RQFAWdTgedTof58+cP2UQ0NzdDqVQiGAxixYoV48ZJvfLKK9i3bx+ee+65GW8mgVh9jCGGyUImk2HTpk3YtGkTfv/73+PUqVM4cOAA/vM//xN2ux1bt25FWVkZbrjhhhGNIc/zaGhogN1ux9q1a6XvR5qAtba2Sj4WorRqJpomMTpJp9NFLXGYClQqFXJycpCTkzNEWtXR0QG5XI5gMIi8vLxxN5ONjY3YuXMn7rzzzllpJoFYjYxtKC8BhBDY7XbJSvrtt99GXl6e1FyuXLkSZ8+ehdvtxoIFC8Y8oIi0L5EWK3LIxcPTdNG+urq60NraitWrV89pZiMhBFVVVXA6nVCpVHC5XDAYDNLkSSyYhBD8/Oc/x9///nccP34cS5YsmbVrXLduHdauXYvHH38cgFDUc3NzsW/fvnEF1fv378e3vvUt9Pb2Tshr7+npQW5uLl555RXs2rVrWq8/hhguB/j9fhw9elTSFDEMI20uN2zYgM7OTrS3tyM9PX3cQG6/3y/VRzGeQ8y6nC7a1+DgICorK7Fw4ULk5ORMy31eKtra2tDe3g69Xg+n0wm1Wi3Vx0jHWDGU+x//+AduuummWbu+WH2MIYapg+d5nD17VjKFNBqNuP7667F7925s3boVFEXh5MmTSExMRHFx8ZhRbZHNVmTiQFpaGuLi4qalifL7/SgvL4fBYMCyZcvmTK4FCJrJqqoqxMXFwev1jukY29LSghtvvBG33XYbHnzwwVmNVfks18hYQzkNcDqdOHLkiJRTlJycDKPRiP/93//FrbfeGvULUKRBmUymaaN9tbe3o6OjA8XFxTAYDJd0H9MBQgjq6+vhdDpRUlICpVKJQCAwxG5fqVTi8OHDEpXr+PHjWLFixaxe5/PPP49bb70VTz/9NNauXYvHHnsML7zwApqampCWloa9e/ciKysLv/71r4f83NVXX42srCw899xzQ77udrvx85//HJ///OeRnp6O1tZW3HvvvXC5XKitrZ2RTM8YYricEAqFcOLECUlT5Pf7QdM0vvCFL+Chhx6Keuso1guTySTRoMTm8lKzH0VTh8WLFyMrK+uS7mO60N3djQsXLki1ejTH2BMnTkClUuE3v/kN9u/fj1tuuWVWrzFWH2OIYXohZnCLzWV7ezsyMjKQlZWFf/3rX0hKSpr2xIFoIUYnJSYmRu3sPVOw2+2oqKiQYu4iHWNFKVl9fT2CwSD++Mc/4otf/CIeeeSRWW+AP8s1MtZQTjP++Mc/4vvf/z42bNiAM2fOICEhQaJ9ibSuaODz+SS3L6fTifj4eKm5jOYARghBW1sburu7xzV1mA2M1kwORygUQnNzM/bt24eKigqkpaXhlltuwcMPPzzrjrhPPPGEFEpbVFSE3//+91i3bh0AIYR23rx52L9/v3T75uZmLFmyBG+//Tauv/76Iffl8/mwe/duVFZWwm63IzMzEzfccAN+8YtfjJuXFEMMn0acPn0aW7duxbJly9DV1QWPx4Pt27ejrKwM1113XdRbRzE422QywWazXVJ8kRiEPdcW/MBFSUJxcfGoLBLR5O273/0u3n33XdA0jZtvvhm//OUvZz2LLlYfY4hhZmA0GrFp0yYQQqDRaNDQ0IBrr70Wu3fvxvbt25GYmBh1cymaJJrN5kuKLxL9P1JTU7F48eI5byYrKyuxYMGCUVkkYrbno48+imeffRY2mw1btmzBfffdhw0bNsz69X5Wa2SsoZxm/OMf/0BOTg42btwIn88naYpee+01qNVqKafoyiuvjLpREmlfZrMZdrsder1emjyNdgATMzb7+vpQUlIyZnbZbIAQIukASktLx5ymEELw+OOP4ze/+Q0OHz4Mu92OM2fORMUnjyGGGD4ZqKqqwqlTp/Cd73wHHMfh9OnTOHDgAF5++WUMDAxgy5Yt2L17N2644Yaot44sy8JqtcJkMg2hfQ2niUbCZDKhrq5uwiDs2YDYTK5evXrcoOr33nsPe/bswWOPPYalS5filVdewY9+9KMZ18THEEMMs4OBgQE8+uij+NnPfgaZTIaWlhYcOHAABw4cQE1NDa655hqUlZVh586dSE1NnVTigMlkkuKLxPoYSRONhNvtRnl5OTIzM7FgwYI5bSYdDgcqKirGbCZF9Pb24oYbbsANN9yA73//+zh8+DCuvvrqGcvkjWEkYg3lLCEYDOLdd9/FgQMHcPjwYdA0jR07duCmm27C1VdfHbVeMhgMSs3lwMAAdDrdEEE2IQTNzc2wWCwoLi6+ZCrYdIAQgsbGRgwMDKC0tHTMzSohBE8//TQeeOCBGQ3ljiGGGC5P8DyPc+fOSbSvvr6+IZqi8cxpIsFxHGw2m9RcymSyIZN5iqLQ39+PxsZGrFy5EikpKTP8yMZHb28vmpubJ2wmT506hZtvvhm/+c1v8M1vfnNOD3gxxBDD7EJknIlZ6eXl5Vi/fj3Kysqwa9cuZGZmRlUTRP8P8Qw5WuKAy+VCeXk5cnJyMH/+/MuimSwoKBiXiWE0GrFlyxZs2LABf/nLXy6rKI3PEmIN5RwgFArh5MmTeOmll/DKK68gFAphx44dKCsrw7XXXhs1J1qkfZnNZthsNqjVatA0jWAwiNLS0jkNB59MM/nMM8/g/vvvx5EjR3D11VfP8pXGEEMMlxN4nkd1dbXUXLa1tWHz5s0oKyvD9u3bo9YD8TwvaRDFbFytVguHw4HCwsI5DQcHLsY4TeS8LYZy/+IXv8C+fftizWQMMXyGQQhBV1cXDh48iIMHD+L06dNYs2aNZAqZk5MTdXMpJg6YTCYEg0EYDAbY7XbMmzcPBQUFs/BoxobT6UR5efmEzttmsxk33ngjiouL8be//S3WTM4hYg3lHIPjOHzwwQdSc+lyubBt2zaUlZVh8+bNUWuKgsEgqqur4XK5QAiBUqmUDCvGon3NFAghaGpqgs1mm7CZnK1Q7hhiiOGTB5Ey/9JLL+HgwYNobGwcoimK1rCC53m0tLSgp6cHDMMMcQdMSkqadeOGaJtJMZT7v/7rv3D33XfHmskYYohBAiEEfX19ePnll3Hw4EG8//77KCwsxO7du1FWVhb1hlG8n8bGRsjlcrAsi6SkJCkbd7oSB6KFuCWdN28e5s2bN+btrFYrtm/fjqVLl+Jf//rXrPttxDAUc+f/GwMAgGEYbNy4EY8//jg6Ozvx+uuvIz09HT/60Y8wb9487N27FwcPHoTH4xnzPnieR1NTE0KhEK666ips2rQJixYtQiAQQEVFBd5//300NTVhcHAQMz0/ECm3VqsVJSUl4zaTL7zwAu655x4cOHBgxprJJ598EvPmzYNKpcK6detw5syZMW+7f/9+UBQ15L/h108IwU9/+lNkZGRArVZj8+bNOH/+/IxcewwxfNZBURSWL1+On/3sZ6iqqkJdXR02btyI/+//+/9QUFCAnTt34s9//jNMJtO4ta2rqwv9/f0oLS3Fpk2bUFhYCJlMhqamJpw8eRK1tbUwm83gOG7GH1N/fz+amppQWFg4bjNZW1uLXbt24d57752xZjJWH2OI4ZMLiqKQlZWFffv24ejRo+jt7cUdd9yB999/HyUlJbjyyivxP//zP2hqahq3Pg4ODkrGMBs3bsQVV1wBvV6Prq4unDx5EhUVFejp6UEwGJzxxyQ2k3l5eeM2k4ODgygrK0NBQQH+8Y9/zFgzGauR0SO2obxMwfM8ysvLJdpXb28vNm/ejN27d+PGG2+UNEU8z6OmpgZ+vx/FxcUj4kV4nh/i9iVO5tPS0pCQkDCtk3lCCFpaWmA2m1FaWjrudvXgwYP41re+heeffx7bt2+ftmuIxPPPP4+9e/fiqaeewrp16/DYY4/hxRdfRHNzM1JTU0fcfv/+/fh//+//obm5WfoaRVFDTDv+53/+B7/+9a/x7LPPIj8/Hz/5yU9QW1uLhoaGGQtdjyGGGIaCEIL29nZJU3T27FlceeWV2LVrF8rKyoZoitra2tDV1YXi4uIRWsxI2pfZbIbf70dycjLS0tKQnJw87YcUo9GI+vp6FBUVjZs11tDQgBtvvBH79u3DT3/60xlpJmP1MYYYPp0ghGBwcBCHDh3CgQMH8O6776KgoECixUbmSVqtVtTU1GDJkiXIzMwccV9er1eqj5eSODAZuN1unDt3Dnl5ecjPzx/zdg6HAzt37kRaWhoOHjw4Y9EZsRo5SZBLwBNPPEHy8vKIUqkka9euJR9//PG4t3/hhRfI4sWLiVKpJCtWrCBHjhwZ8n2e58lPfvITkp6eTlQqFbnuuutIS0vLpVzapxIcx5HKykry4x//mCxbtowolUqybds28vjjj5PNmzeT/fv3E7vdTjwez7j/uVwu0t3dTcrLy8nrr79Ojhw5Qs6cOUM6OjqIy+Wa8OfH+8/tdpOKigry5ptvEovFMu5tn3vuOaLRaMjLL788o3+3tWvXku9+97tD/o6ZmZnk17/+9ai3f+aZZ4jBYBjz/nieJ+np6eThhx+Wvma324lSqST/93//N23XHcMnG7H6OLvgeZ50dnaS3/72t+Tqq68mMpmMrFu3jvzqV78ie/fuJT/+8Y+J0WiMqoYZjUZSU1ND3n33XXL48GHywQcfkPPnz0dVXyf6r62tjRw+fJh0dnaOe7uKigqSlpZG7rvvPsLz/Iz93WL1MYa5QKw+zj7sdjv5+9//Tnbv3k3UajVZuHAh+cEPfkB+/vOfkz179pDW1taoapjNZiONjY3k5MmT5NChQ+T48eOkvr6eWK3WKddHk8lEjhw5Qmpra8e9ndFoJOvWrSPXX3898fl8M/p3i9XIyWHSDeVzzz1HFAoF+etf/0rq6+vJHXfcQeLj44nJZBr19h9++CFhGIb85je/IQ0NDeS//uu/iFwuJ7W1tdJtHnroIWIwGMgrr7xCqqurya5du0h+fv6MP1k+ieB5ntTX15P777+fxMXFkfT0dHL99deTJ598knR2dhK32x11A9jb2ys1ga+++ir56KOPSFtbG3E6nZNuJisrK8kbb7wxYTP50ksvEa1WS1544YUZ/TsFAgHCMMyIpnXv3r1k165do/7MM888QxiGIbm5uSQ7O5vs2rWL1NXVSd9vbW0lAEhlZeWQn7vmmmvI9773vel+CDF8AhGrj3MLnudJb28vefzxx0lOTg5Rq9XkyiuvJA888ACprq6Ouj56PB5iNptJXV0dOXr0KDl06BB5//33SXNzMxkcHLzkZrKjo2Pc21VXV5PMzExyzz33EI7jZuzvFKuPMcwFYvVx7uF0Oslzzz1HrrjiCsIwDCktLSXf+973yPHjxye1WBgcHCTNzc3k/fffJ4cOHSJHjx4ldXV1xGw2X1Iz+frrr5OampoJa/KGDRvIpk2biNvtntG/U6xGTh6Tbign27HfcsstZPv27UO+tm7dOvLNb36TEPLp79hnCvv27SPXXXcdqaqqIg8++CApLS0lMpmMbNq0ifz2t78lra2tk2ou+/v7SXV1NXn77bfJ4cOHyalTp8iFCxeIw+GY8GerqqrIG2+8MWEhOXz4MNFqteSf//znjP99ent7CQBy6tSpIV//z//8T7J27dpRf+bUqVPk2WefJZWVleTEiRNkx44dRK/Xk+7ubkKI8OYGgPT19Q35uT179pBbbrllZh5IDJ8oxOrj5YHnn3+e5Obmko8//pg8/fTTZMuWLUQul5OVK1eSn/zkJ+TcuXOTai6tVitpaGggx48fJ4cOHSInT54kjY2NxGazTfiz7e3tUTWT9fX1JCcnh+zbt29Gm0lCYvUxhrlBrD5eHmhvbydxcXHk5ZdfJgcOHCBf/vKXicFgINnZ2eQ73/kOefvttye1WLDb7eT8+fPkww8/JIcPHybvvPMOqampIUajccI6azabyeuvvz7hwM9qtZJNmzaRDRs2EJfLNeN/o1iNnDwmJaALBoMoLy/H5s2bpa/RNI3Nmzfj9OnTo/7M6dOnh9weALZs2SLdvr29HUajcchtDAYD1q1bN+Z9xgD88pe/xKuvvorCwkLcd999OHPmDFpaWrB9+3a88MILWLx4MbZu3Yonn3wSPT094wqyKYqCXq/HggULcOWVV2LdunXQ6XTo6OjAiRMnUFlZid7e3hGCbEIIWltb0dfXh5KSknEzL9977z18+ctfxhNPPIEvfelL0/Z3mE6sX78ee/fuRVFRETZu3IiDBw8iJSUFTz/99FxfWgyfAMTq4+WDL3zhCzh79izWrl2Lb3zjG3jjjTdgNBpx9913o7KyEldddRXWrFmDX/ziF6itrQXP8+Pen1qtRl5eHtauXYsNGzYgLS0NZrMZH3zwAc6cOYOOjg54vd4RP2c2m1FbWzth5mVPTw+2b9+Obdu24Xe/+92su85Gg1h9jGEqiNXHywfz5s3D+fPnsXv3btx888345z//CaPRiD/84Q/wer344he/iEWLFuGuu+7CyZMnwbLsuPcnl8uRmZkp1Yb58+fD4/Hg7Nmz+PDDD3H+/Hk4HI4R51CPx4Nz584hMzMTBQUFY2rF/X4/vvSlL8Hv9+O1116DTqebtr/FdOKzXiMn5ThgtVrBcdwQgSkApKWloampadSfMRqNo97eaDRK3xe/NtZtYhgJg8Ew5HOKopCfn48f/OAHuOeee9DT0yPlFN1///0oKSlBWVkZysrKkJeXN+YLl6Io6HQ66HQ6FBQUwOPxwGw2o6enB42NjUhISJAE2T09Pejt7UVpaem4zeSHH36IW265Bf/7v/+LW2+9dVas75OTk8EwDEwm05Cvm0wmpKenR3Ufcrkcq1evxoULFwBA+jmTyYSMjIwh91lUVDQ9Fx7DJxax+nj5gKbpIaYJFEUhMTERt912G2677TY4HA689tprOHDgAD73uc8hMzMTu3btwk033YSioqJxGzqVSoWcnBzk5OQgGAxKhhUXLlyATqeT6qPP50NtbS1WrFgxqoGDiP7+fmzbtg3XXnstnnzyyVlpJmP1MYbZRqw+Xl4Y/jdTqVTYuXMndu7ciWAwiOPHj+PAgQO49dZbQQjBjh07sHv3bmzcuHGE+WMkZDIZ0tPTkZ6eDo7jpCzgiooKyGQyqT7K5XJUVFQgMzMTCxYsGPNcGAgE8O///u8YGBjAO++8M+LsO1OI1cjJ4/Ibg8YwZVAUhZycHPy///f/cOLECXR1dWHv3r04evQoCgsLcc011+CRRx7B+fPnJ4wR0Wq1yM/Px7p163DVVVchOTkZRqMR7733Htrb25GZmTnuAejMmTP4whe+gAcffBB33HHHrOWoKRQKlJSU4OjRo9LXeJ7H0aNHsX79+qjug+M41NbWSi/8/Px8pKenD7lPp9OJjz/+OOr7jCGGGOYeBoMBX/nKV3Dw4EGYTCb86le/Qk9PD7Zt24aVK1fiRz/6ET7++OMJN5cKhQLZ2dkoLi7Gxo0bkZubC6fTiY8++ghVVVVITk6GRqMZs86aTCZs374dV1xxBf785z/PWih3rD7GEEMMY0GhUGDLli3405/+hL6+PrzwwgtQq9X49re/jfz8fHzzm9/E66+/Dr/fP+79MAyD1NRUrFixAhs3bsTSpUvBsiyqqqpw+vRpyOVyJCYmjlkfQ6EQbrvtNvT29uKtt95CQkLCTDzcURGrkZPHpBrKS+nY09PTx719ZMce7X3GED0oikJGRga+853v4J133kFfXx++/e1v4/Tp01i7di3Wr1+PX//612hoaJiwuRRpX0lJSZDJZJg3bx6cTic+/PBDfPzxx2hvbx9C+6qoqMBNN92En/3sZ/jud78766Hcd999N/785z/j2WefRWNjI7797W/D4/Hg9ttvBwDs3bsX9913n3T7Bx54AG+//Tba2tpQUVGBr371q+js7MTXv/51AMLf8q677sIvf/lLHD58GLW1tdi7dy8yMzOxe/fuWX1sMVx+iNXHTyZ0Oh1uueUWPP/88zAajXj00UcxMDCAm2++GUuWLMEPfvADfPDBBxNmVIq0r+zsbABATk4OKIqSaF8tLS1DaF9WqxU7d+5EYWEh9u/fP2vNpIhYfYxhNhGrj59MyGQyXHvttfjDH/6A7u5uHD58GImJibj77ruRn5+P22+/HYcOHRqV8h8JmqaRnJyM/Px8qdGMj49HXV0d3nvvPdTX18NisUhDPJZl8fWvfx2tra14++23x41ZminEauQkMVnR5dq1a8m+ffukzzmOI1lZWeOKqnfs2DHka+vXrx8hqn7kkUek7zscjpioeobB8zwZGBgg+/fvJzt37iQqlYosWbKE/PCHPySnT58e0+2rrq6OHDlyZIgN/+DgIGlpaSEffPABOXToELnnnnvI3r17SUJCAnnwwQdn1Pp+Ijz++OMkNzeXKBQKsnbtWvLRRx9J39u4cSO59dZbpc/vuusu6bZpaWlk27ZtpKKiYsj9iRblaWlpRKlUkuuuu440NzfP1sOJ4TJHrD5+euDz+cirr75Kbr/9dpKYmEjS0tLI17/+dfLaa6+NGSPS1dVFXn31VXLhwgXpa06nk7S3t5OPP/6YvPbaa+TRRx8le/bsIStXriRlZWUkGAzO2WOM1ccYZhOx+vjpAcdx5PTp0+See+4h8+fPJ1qtltx0003k2WefHTOmyWq1kjfffJOUl5dLBjzDEwdeeOEFsnXrVnLjjTeSRYsWkf7+/jl9nLEaGT0uKTZEqVSS/fv3k4aGBvKNb3yDxMfHE6PRSAgh5N///d/Jj370I+n2H374IZHJZOSRRx4hjY2N5Gc/+9mots/x8fHk0KFDpKamhpSVlZGkpCSSm5sbVVbRn/70J7JhwwYSHx9P4uPjyXXXXTfi9rfeeisBMOS/LVu2TPbhf2rhcDjIP//5T3LzzTf//+y9d5icV3n3/3n69NmqVe/FsuRuy5YMGF6DDYTX9puE0I0JOARMCOENBPgFSIDYGJM3lAQIBGwSagwYEkIgxNgGRzbYWL1asqRV29Vq6/Snnd8fZ57x7mp700o6n+vaS9qZp5zZnb3n3O17i0QiIVasWCH+7M/+TPzyl7+sOZe//vWvz3Amh1L7+tSnPiVWrlwpTNMUa9asmfYRIQrFbEHZx/MT13XFz372M/FHf/RHYs6cOaKxsVG8+c1vFg899FBtjMiuXbvOcCYHf+VyOfGjH/1IXHHFFcJxHDFnzhzx0Y9+9Gy/PIViRlD28fwkCALx9NNPiw984ANi9erVIhaLiVe96lXin/7pn8SJEydEPp8Xhw4dOsOZHGpqwI4dO8SLX/xikUwmRTweF7//+79/VoNuirEzbodSiPF57ELIwbSrV68Wtm2LdevWDTuYNvLY169fLyzLGvOsote//vXiH/7hH8SWLVvEnj17xB133CGy2aw4duxY7Zg3v/nN4uUvf7k4efJk7aurq2siL/+8J5fLiX/9138Vr3nNa0Q6nRZLliwRr3jFK8TSpUvF0aNHR5SAjoZyf+hDHxK5XE48+OCDA94fCsX5jrKP5zee54lHHnlEvPOd7xTz588XdXV14pZbbhGZTEY89thjI9rHkydPimuvvVbcdNNNIpfLiV/84hfi+9///tl+SQrFjKHs4/lNGIZi+/bt4iMf+YhYv369sG1b3HjjjaKlpUXcd999I866zOVy4m1ve5tYtmyZOHTokNi+fbv4whe+cLZfkmKMTMihnG7GO6toML7vi3Q6Lb7+9a/XHnvzm98sbr311qle6nlPsVgUf/iHfyhs2xYrV64U8+fPF3/8x38sfvrTn54xozIayv3nf/7n0z5HTaG4UFH2cfbg+7743Oc+JyzLEmvXrhWZTEa8+tWvFt/85jdFR0fHAPvY3t4urr/+evGSl7xEFAqFs710heK8RNnH2UMYhuKRRx4RDQ0NYunSpcKyLHHjjTeKz33uc+LQoUMDMpW5XE68853vFIsWLRIHDx4820tXTIBZp/I6kVlFgykWi3ieR0NDw4DHH330UebMmcOaNWt4xzveQWdn55Su/XxE13UOHDjA448/zo4dO/jyl7+M67q84Q1vYNWqVbz73e/mkUce4cCBA7zqVa/i937v97j33ntn5Rw1heJcR9nH2YVhGOzYsYMvfelL7Ny5k//6r/9iyZIlfOQjH2Hp0qW84Q1v4MEHH6S9vZ0/+IM/QNd1/u3f/o1EInG2l65QnHco+zi70DSNw4cP87u/+7scOHCA3bt387KXvYxvfOMbrFq1ile+8pV86Utf4vjx43z4wx/mhz/8IQ8//DDLly8/20tXTABNiFGkPWeYEydOsGDBAjZv3jxARvf9738/jz32GL/+9a9HvcY73/lOfvazn7Fr1y5isRgA3/nOd0gkEixbtoyDBw/yoQ99iFQqxRNPPDHj6nrnGkKIMxRaPc/j0Ucf5fvf/z4PPfQQp0+f5tWvfjXf+ta3lDOpUEwTyj7OPoayj2EYsnXrVr73ve/x0EMPsX//flasWMHTTz9NJpM5SytVKM5vlH2cnQy2kUIIWltb+f73v88PfvADNm/eTDwe5+mnn2bt2rVncaWKSXE206NDcfz4cQGIzZs3D3j8fe97n9iwYcOo599zzz2ivr5ebNu2bcTjDh48KADx3//935Nar0L2FP393/+9KJfL03qfv//7vxdLlixRjfaKCxZlH889wjAU3//+98Xu3bun9T7KPioudJR9PPeIei5/9KMfTet9lH2cfmZdKmkis4oiPv3pT/PJT36S//qv/+LSSy8d8djly5fT1NTEgQMHJr3mCx3TNLnrrrtwHGfa7vHd736X9773vXz0ox/lmWee4bLLLuPmm2/m1KlTQx7/6KOP8rrXvY5HHnmEJ554gkWLFnHTTTdx/PjxAce9/OUv5+TJk7Wvb3/72xNe4z//8z/T2NhIpVIZ8Phtt93Gm970pglfV6GIUPbx3EPTNH73d393WiPvyj4qFMo+notomsYll1zCLbfcMm33UPZxhjjbHu1QjHdWkRBC3HvvvSKTyYgnnnhiTPc4evSo0DRt2qMiiqnhXGi0LxaLIpvNDhiT0t7eLkzTFL/4xS+m7D6KCxtlHxWDUfZRoZAo+6gYjLKPM8Osy1ACvPe97+UrX/kKX//619mzZw/veMc7KBQKvOUtbwHg9ttv54Mf/GDt+HvvvZcPf/jDfO1rX2Pp0qW0tbXR1tZGPp8HIJ/P8773vY8nn3ySw4cP8/DDD3PrrbeycuVKbr755rPyGhVj51xptI/H47z+9a/n/vvvrz32jW98g8WLF/PiF794wtdVKPqj7KOiP8o+KhTPo+yjoj/KPs4gZ9ujHY7xzCpasmTJGbXMQG1gdLFYFDfddJNobm4WlmWJJUuWiDvvvFPcfffdY66pvv/++8+4vuM4A46J5iHNnTtXxGIxceONN4r9+/dP6c/lQmSyfRFCCPGOd7xDLF++XJRKpdpj3/72t2vDkB966CGxdu1acc011wjf9ye81meeeUYYhlGbYXXJJZeIj33sYxO+nkIxFMo+KiKUfVQoBjIT9rGtrW1cfXnKRp4dlH2cOWatQzndfOc73xG2bY95+O39998vMpnMgMG2bW1tA4755Cc/KbLZrPjhD38otm3bJm655RaxbNmyAW9Cxfg51xrtr7zySnH33XeLp59+Wui6LlpbWyd1PYViplH28dxB2UeFYuZRNvLcQNnHmeOCdSjHW1N9//33i2w2O+z1wjAUc+fOFffdd1/tsZ6eHuE4jvj2t789Zeu+EKlUKsIwDPHQQw8NePz2228Xt9xyy4jn3nfffSKbzYqnnnpqTPdqamoSX/rSlya6VCGEEF/4whfE6tWrxV133SVuuummSV1LoTgbKPt47qDso0Ix8ygbeW6g7OPMMSt7KKebidZU5/N5lixZwqJFi7j11lvZtWtX7blDhw7R1tY24JrZbJZrr712zHXaiqGxbZurrrqKhx9+uPZYGIY8/PDDA2ZNDeZTn/oUH//4x/npT3/K1VdfPep9jh07RmdnJ/PmzZvUel//+tdz7NgxvvKVr/CHf/iHk7qWQjHTKPt4bqHso0Ixsygbee6g7OPMcUE6lKdPnyYIAlpaWgY83tLSQltb25DnrFmzhq997Wv86Ec/4hvf+AZhGLJp0yaOHTsGUDtvPNdUjJ1zqdE+m83ye7/3e6RSKW677bZJXUuhmGmUfTz3UPZRoZg5lI08t1D2cWYwz/YCzhU2btw4IJqxadMm1q5dyz/+4z/y8Y9//Cyu7MLgNa95DR0dHXzkIx+hra2Nyy+/nJ/+9Kc149va2oquPx8f+eIXv4jruvz+7//+gOt89KMf5a/+6q8wDIPt27fz9a9/nZ6eHubPn89NN93Exz/+8SmZp3n8+HHe8IY3TOtsToVitqDs49lF2UeFYnajbOTZQ9nHmeGCdCgnM/w2wrIsrrjiitpg2+i89vb2ASnv9vZ2Lr/88qlZ+AXOu971Lt71rncN+dyjjz464PvDhw+PeK14PM7PfvazKVrZ83R3d/Poo4/y6KOP8oUvfGHKr69QTDfKPp6bKPuoUMwMykaeeyj7OP1ckCWvE62p7k8QBOzYsaP2h79s2TLmzp074Jp9fX38+te/HvM1Fec+V1xxBXfccQf33nsva9asOdvLUSjGjbKPiulC2UfF+YCykYrp4Jy3j2dbFehs8Z3vfEc4jiMeeOABsXv3bvFHf/RHoq6uribj/KY3vUl84AMfqB3/13/91+JnP/uZOHjwoPjtb38rXvva14pYLCZ27dpVO+aTn/ykqKurq82mufXWW5Xks0KhOOdQ9lGhUCiGR9lIhWIgF6xDKcT4ht++5z3vqR3b0tIiXvnKV4pnnnlmwPWiobQtLS3CcRxx4403in379o1r+O0NN9ww5JDdV77ylbVj3vzmN5/x/M033zx1PxiFQnHBM1P2UQihbKRCoTjnUHtIheJ5NCGEmKFk6AXJd7/7XW6//Xa+9KUvce211/KZz3yGBx98kH379jFnzpwzju/q6sJ13dr3nZ2dXHbZZfzTP/0Td9xxBwB33HEH7e3t3H///bXjHMehvr5+2l+PQqFQTCXKRioUCsXQKPuoOFe4IHsoZ5L/9//+H3feeSdvectbuPjii/nSl75EIpHga1/72pDHNzQ0MHfu3NrXz3/+cxKJBK9+9asHHOc4zoDjzhVD8A//8A8sXbqUWCzGtddey29+85sRj3/wwQe56KKLiMViXHLJJfzkJz8Z8LwQgo985CPMmzePeDzOS1/6Up599tnpfAkKhWIKUTZyIMpGKhSKCGUfB6Ls4+xFOZTTyESH3/bnq1/9Kq997WtJJpMDHn/00UeZM2cOa9as4R3veAednZ1Tuvbp4Lvf/S7vfe97+ehHP8ozzzzDZZddxs0338ypU6eGPH7z5s287nWv461vfStbtmzhtttu47bbbmPnzp21Yz71qU/xuc99ji996Uv8+te/JplMcvPNN1Mul2fqZSkUigmibORAlI1UKBQRyj4ORNnHWc5ZLbg9zzl+/LgAxObNmwc8/r73vU9s2LBh1PN//etfC+CMevlvf/vbtabthx56SKxdu1Zcc801wvf9KV3/VLNhwwZx11131b4PgkDMnz9f3HPPPUMe/wd/8Afid37ndwY8du2114q3v/3tQgjZbzB37lxx33331Z7v6ekRjuOIb3/729PwChQKxVSibORAlI1UKBQRyj4ORNnH2Y3KUM5ivvrVr3LJJZewYcOGAY+/9rWv5ZZbbuGSSy7htttu48c//jFPPfXUGbN0ZhMTibQ98cQTA44HuPnmm2vHHzp0iLa2tgHHZLNZrr322jFH7xQKxbmLspHKRioUiqFR9lHZx5lEOZTTyGSG3xYKBb7zne/w1re+ddT7LF++nKamptqA3NnI6dOnCYKAlpaWAY+3tLTQ1tY25DltbW0jHh/9O55rKhSK2YOykc+jbKRCoeiPso/Po+zj7Ec5lNPIZIbfPvjgg1QqFd74xjeOep9jx47R2dlZG5CrUCgU5wLKRioUCsXQKPuoOJdQDuU08973vpevfOUrfP3rX2fPnj284x3voFAo8Ja3vAWA22+/nQ9+8INnnPfVr36V2267jcbGxgGP5/N53ve+9/Hkk09y+PBhHn74YW699VZWrlzJzTffPCOvaSJMJNI2d+7cEY+P/p1I9E6hUMwOlI2UKBupUCgGo+yjRNnH2Y9yKKeZ17zmNXz605/mIx/5CJdffjlbt27lpz/9aS3F3traysmTJwecs2/fPh5//PEhSxUMw2D79u3ccsstrF69mre+9a1cddVV/OpXv+LXv/41//t//2/mz5+Ppmn88Ic/HHV9jz76KFdeeSWO47By5UoeeOCBM44Zr0zzUEwk0rZx48YBxwP8/Oc/rx2/bNky5s6dO+CYvr4+fv3rX48avVMoFLODmbKRn/rUp/j93//9WWkfQdlIhUJxJso+SpR9PAc426pAiqnjJz/5ifj//r//T/zgBz8QgHjooYdGPP65554TiURCvPe97xW7d+8Wn//854VhGOKnP/1p7ZjvfOc7wrZt8bWvfU3s2rVL3HnnnaKurk60t7ePe33f+c53hOM44oEHHhC7d+8Wf/RHfyTq6upEW1ubEEKIN73pTeIDH/hA7fj/+Z//EaZpik9/+tNiz5494qMf/aiwLEvs2LGjdswnP/lJUVdXV1Msu/XWW8WyZctEqVQa9/oUCsX5y2y3j9H1lI1UKBQzjbKPyj5OFuVQnqeMxSC8//3vF+vWrRvw2Gte8xpx8803174fr0zzaHz+858XixcvFrZtiw0bNognn3yy9twNN9wg3vzmNw84/l//9V/F6tWrhW3bYt26deI//uM/BjwfhqH48Ic/LFpaWoTjOOLGG28U+/btm9DaFArFhcFstY9CKBupUCjOLso+Kvs4ETQhhDibGVLF9KBpGg899BC33XbbsMe86EUv4sorr+Qzn/lM7bH777+f97znPfT29uK6LolEgu9973sDrvPmN7+Znp4efvSjH03fC1AoFIppQtlHhUKhGBplHxUT4YLooezo6GDu3Lncfffdtcc2b96Mbdtn1FdfSAwnqdzX10epVJqQTLNCoTi3UPZxaJR9VCgUyj4OjbKPisGYZ3sBM0FzczNf+9rXuO2227jppptYs2YNb3rTm3jXu97FjTfeeLaXp1AoFGcNZR8VCoViaJR9VCjGxgXhUAK88pWv5M477+QNb3gDV199NclkknvuuedsL+usMpykciaTIR6PYxjGhIfqKhSKcwdlH89E2UeFQgHKPg6Fso+KwVwQJa8Rn/70p/F9nwcffJBvfvObOI5ztpd0VhlNUnkyQ3UVCsW5hbKPA1H2UaFQRCj7OBBlHxWDuaAcyoMHD3LixAnCMOTw4cNnezlTTj6fZ+vWrWzduhWAQ4cOsXXrVlpbWwH44Ac/yO233147/o//+I957rnneP/738/evXv5whe+wL/+67/yZ3/2Z7VjRhuqq1Aozg+UfVT2UaFQDI2yj8o+KkbhbMvMzhSVSkVcdtll4s1vfrO4++67xZw5cyY8C2e28sgjjwjgjK9IRvnNb36zuOGGG8445/LLLxe2bYvly5eL+++//4zrjiTTrFAozn2UfVT2UaFQDI2yj8o+Kkbnghkb8r73vY/vfe97bNu2jVQqxQ033EA2m+XHP/7x2V6aQqFQnFWUfVQoFIqhUfZRoRidC6Lk9dFHH+Uzn/kM//Iv/0Imk0HXdf7lX/6FX/3qV3zxi18828ubNpYuXcodd9xxtpehUChmMco+KhQKxdBcqPZRoRgvF4RD+eIXvxjP83jBC15Qe2zp0qX09vbyjne8Y0bWcPDgQd7+9rezfPlyYrEYmUyG66+/ns9+9rOUSqUZWcNk+cpXvsINN9xAS0sLjuOwbNky3vKWt5yX/QQKxYWCso9Tj+d5XHzxxWiaxqc//emzvRyFQjFBZoN9VCjOBS6YsSFnk//4j//g1a9+NY7jcPvtt7N+/Xpc1+Xxxx/nfe97H7t27eLLX/7y2V7mqGzZsoVly5Zxyy23UF9fz6FDh/jKV77Cj3/8Y7Zt28b8+fPP9hIVCsU5xvliH/vz+c9/viZmoVAoFArF+Y5yKKeZQ4cO8drXvpYlS5bwi1/8gnnz5tWeu+uuuzhw4AD/8R//cRZXOHa+8IUvnPHYbbfdxtVXX80///M/84EPfOAsrEqhUJyrnE/2MeLUqVN87GMf4y/+4i/4yEc+craXo1AoFArFtHNBlLyeTT71qU+Rz+f56le/OmCzFLFy5Ur+9E//FIAbbriByy67bMjrrFmzhptvvrn2fRiGfPazn+WSSy4hFovR3NzMy1/+cp5++ukR19PT08N73vMeFi1ahOM4rFy5knvvvZcwDCf0+pYuXVq7rkKhUIyH89E+fuADH2DNmjW88Y1vHPM5CoVCoVCcy6gM5TTz7//+7yxfvpxNmzaNeuyb3vQm7rzzTnbu3Mn69etrjz/11FPs37+fv/zLv6w99ta3vpUHHniAV7ziFbztbW/D931+9atf8eSTT3L11VcPef1iscgNN9zA8ePHefvb387ixYvZvHkzH/zgBzl58iSf+cxnxvSaOjs7CYKA1tZWPvaxjwFw4403julchUKhiDjf7ONvfvMbvv71r/P444+jadroPwCFQqFQKM4HzvbckvOZ3t5eAYhbb711TMf39PSIWCwm/uIv/mLA4+9+97tFMpkU+XxeCCHEL37xCwGId7/73WdcIwzD2v+XLFlSmyEkhBAf//jHRTKZFPv37x9wzgc+8AFhGIZobW0d0zodx6nNKGpsbBSf+9znxnSeQqFQRJxv9jEMQ7Fhwwbxute9TgghxKFDhwQg7rvvvjG9PoVCoVAozlVUyes00tfXB0A6nR7T8dlslltvvZVvf/vbiOp40CAI+O53v8ttt91GMpkE4Pvf/z6apvHRj370jGuMFBV/8MEHeeELX0h9fT2nT5+ufb30pS8lCAJ++ctfjmmd//mf/8lPfvIT/vZv/5bFixdTKBTGdJ5CoVBEnG/28YEHHmDHjh3ce++9Y3o9CoVCoVCcL6iS12kkk8kAkMvlxnzO7bffzne/+11+9atf8aIXvYj//u//pr29nTe96U21Yw4ePMj8+fNpaGgY13qeffZZtm/fTnNz85DPnzp1akzXeclLXgLAK17xCm699VbWr19PKpXiXe9617jWo1AoLlzOJ/vY19fHBz/4Qd73vvexaNGicd1XoVAoFIpzHeVQTiOZTIb58+ezc+fOMZ9z880309LSwje+8Q1e9KIX8Y1vfIO5c+fy0pe+dNLrCcOQl73sZbz//e8f8vnVq1eP+5orVqzgiiuu4Jvf/KZyKBUKxZg5n+zjpz/9aVzX5TWveU1tLu+xY8cA6O7u5vDhw8yfPx/btie9ToVCoVAoZhvKoZxmXvWqV/HlL3+ZJ554go0bN456vGEYvP71r+eBBx7g3nvv5Yc//CF33nknhmHUjlmxYgU/+9nP6OrqGlcUfsWKFeTz+SnZfPWnVCpRqVSm9JoKheL853yxj62trXR3d7Nu3boznrv77ru5++672bJlC5dffvm4r61QKBQKxWxH9VBOM+9///tJJpO87W1vo729/YznDx48yGc/+9kBj73pTW+iu7ubt7/97eTz+TPk53/v934PIQR//dd/fcb1ot6iofiDP/gDnnjiCX72s5+d8VxPTw++7w97ru/7dHd3n/H4b37zG3bs2DGscqJCoVAMx/liH9/97nfz0EMPDfj6x3/8RwDuuOMOHnroIZYtWzbs+QqFQqFQnMtoYqRPWMWU8G//9m+85jWvIR6Pc/vtt7N+/Xpc12Xz5s08+OCD3HHHHbXNR8Qll1zCzp07Wbt2Lbt37z7jmrfffjv/8i//wite8Qpe/vKXE4Yhv/rVr3jJS15SKz1dunQpL37xi3nggQcAKYv/whe+kO3bt3PHHXdw1VVXUSgU2LFjB9/73vc4fPgwTU1NQ76Gnp4eFi5cyGte8xrWrVtHMplkx44d3H///cRiMZ588klWrVo1tT84hUJx3nM+2MehOHz4MMuWLeO+++7jz//8zyf+A1IoFAqFYrZz9gRmLyz2798v7rzzTrF06VJh27ZIp9Pi+uuvF5///OdFuVw+4/hPfepTAhB33333kNfzfV/cd9994qKLLhK2bYvm5mbxile8Qvz2t7+tHTNYFl8IIXK5nPjgBz8oVq5cKWzbFk1NTWLTpk3i05/+tHBdd9j1VyoV8ad/+qfi0ksvFZlMRliWJZYsWSLe+ta3ikOHDk3oZ6JQKBRCnPv2cSjU2BCFQqFQXCioDOUs5bOf/Sx/9md/xuHDh1m8ePHZXo5CoVDMGpR9VCgUCoVi9qAcylmIEILLLruMxsZGHnnkkbO9HIVCoZg1KPuoUCgUCsXsQqm8ziIKhQL/9m//xiOPPMKOHTv40Y9+dLaXpFAoFLMCZR8VCoVCoZidqAzlLCIScairq+Od73wnf/M3f3O2l6RQKBSzAmUfFQqFQqGYnSiHUqFQKBQKhUKhUCgUE0LNoVQoFAqFQqFQKBQKxYRQDqVCoVAoFAqFQqFQKCaEcigVCoVCoVAoFAqFQjEhlEOpUCgUCoVCoVAoFIoJoRxKhUKhUCgUCoVCoVBMCOVQKhQKhUKhUCgUCoViQiiHUqFQKBQKhUKhUCgUE0I5lAqFQqFQKBQKhUKhmBDKoVQoFAqFQqFQKBQKxYRQDqVCoVAoFAqFQqFQKCaEcigVCoVCoVAoFAqFQjEhlEOpUCgUCoVCoVAoFIoJoRxKhUKhUCgUCoVCoVBMCOVQKhQKhUKhUCgUCoViQiiHUqFQKBQKhUKhUCgUE0I5lAqFQqFQKBQKhUKhmBDKoVQoFAqFQqFQKBQKxYRQDqVCoVAoFAqFQqFQKCaEcijPMmEY4nkeYRgihDjby1EoFIpZgxACz/MIgkDZR4VCoVAoZinm2V7AhYoQgiAIcF2XUqmEYRgYhoFpmpimiWEYaJqGpmlne6kKhUIx4wRBgOd5FItFNE07wz7quq7so0KhUCgUswBNqLDvjDM46u66Lpqm1bKUkSOp6zqWZQ3YQCkUCsX5jBAC3/fxfX+AfRRC1L4iZ9I0TSzLUg6mQqFQKBRnEZWhnGGiqHsYhjUHMXIeo++jTdPhw4fJ5/OsXbu2tnmKnEvlYCoUivON/i0A8Lxt7O8oRvaxp6eH3bt3c91119WOUxlMhUKhUChmHuVQzhBR1H337t00NTXR2NhYi7oPJspQRmWvhmHUspqu6wKcsXlSDqZCoThXEUIQhiGHDx/G932WLFkyoGoDqDmH/e0jULOPQRAQBAGVSmVAhUdUKqtaCBQKhUKhmB6UQzkD9I+69/T0kE6nx7yxGVwCGz0WXdPzPACVwVQoFOck/VsACoUCnueNyT4OdjAHV3gEQYDv+wMcUNWjrlAoFArF1KMcymmkv+MXlbiOx8kbbrPTPzo/+D7DOZhRhF6hUChmC5HdCoJg3PYRGLHCY7CD6ft+zVmNbGiUwVQBOIVCoVAoJo5yKKeJ/lF3oNbPM1yZ60jXGY2RHMxI0ELX9SEj9AqFQjHT9M8gRsG28drHsdov5WAqFAqFQjG9KIdyGhgcde+/8ZmODdNQ543FwRxcIqscTIVCMd0MF2yD8dnH6FrjZTQHM1qTaiFQKBQKhWJsKIdyChku6t6fmdgwDaa/gxldLwxDXNflqaeeYvXq1SSTSeVgKhSKaWWwyvVo9nEkGzRV9mk4B9PzPPbs2UM2m2XOnDnKwVQoFAqFYhiUQzlFjBR1789MZCjHcs3Iwczn87U1ua47QCFROZgKhWIq6D9bEqbGPkbXnWr6O5iVSqVm01WPukKhUCgUQ6McyilgtKh7f2bDhmkww2UwK5XKiGNK1AZKoVCMxlCzJUcSHBts80Y6drqJVLZHayHo72CqHnWFQqFQXGgoh3IS9I+6CyHGNEj7bGcox3rPyGmMyr+EEGc4mP1nvKkh4gqFoj9DqVxPpX2M7jHTjEcErb/Ij7KPCoVCoThfUQ7lBAnDEN/3Ry1xHcxs2zCNdv3+2YTBDma5XK4dEzmYUXReOZgKxYXLWFsABjPbA25DoUTQFAqFQnGhoxzKcdJ/sxCVQ41nY3Aubpj6oxxMhUIxEiOpXI/GbAy4jddujSSCpnrUFQqFQnE+ohzKcRBF3Xfv3k1dXR1z586d0GZjNm2YYHKO61gczLa2Npqbm2tKssrBVCjOPyKV6yNHjlAoFFi9evW02sdzwX4MFkEbysHs7u7Gtm0aGhqUg6lQKBSKcxLlUI6R/lH3UqlEIpGY0Af+eDdMZ6NHaDIM5WAeOXKk5kyWy2V0XT8jQq8cTIXi3KV/iavruhSLxWm3j9F9zyWGcjDb29tJp9Mkk0klgqZQKBSKcxLlUI7CcLMlI8XC8TJ4w9TV1UVbWxt1dXXU1dVh2/ZULX1MTPeGLHq90eYoyl4GQUAQBMOWgCkHU6E4Nxiscq3r+oTtymD7WCqVeO6550gmkzQ0NBCPxwccCxMrS50tROuOWgQGi6D1t49KBE2hUCgUsxXlUI7AcMISU7FhEkLw3HPPcfDgQZqbmzl06BCFQoFUKkV9fT0NDQ2EYXjOReCHov+Gb7gh4oMdzGjj1H/Gm9pAKRSzh+FUrqfKoWxvb2fHjh3U19eTy+XYv38/juNQX19PfX09yWRyKl/OkMyEsxr97ED1qCsUCoXi3EQ5lMMQ9bgMJXc/2Qyl7/s8/fTTlEolNmzYQCwWQ9d1XNelu7ubrq4u9u3bR7lcxjAMnnvuOerr68lms7WNx1QyExumkWbJDeVg+r6P53m15yPHsv+MN7WBUijODiOpXE/WPoZhyJ49ezh+/DgXX3wxjY2NgMyE9vb20tXVRWtrK/l8HoD9+/fT0NBAfX09pnnufaSNxT7CyA7mYPuoHEyFQqFQzCTn3qfvNBNlyyIV16E+mCcTga9UKnR0dNDc3MwVV1xRcyQBbNumpaWFlpYWAI4dO8aRI0colUocP36cIAioq6urRejT6fQ5sWkYT5R/PA5m/xlv0+FoKxSKgYxF5Xoy9tHzPAqFAkIINm7cSCKRqNlH0zRpbGysOZiFQoFf//rXCCE4ePAgpVKJdDpds4/ZbHbAOI/ZykTsIwx0MMMwrDmYqkddoVAoFDONcij7MdbZkrqujzsCH5W4tre3U1dXx2WXXTZqJN9xHGzbZt26dQghKBQKdHd3093dzeHDh9E0rbZ5qq+vn5BQ0EyU1E6mbEw5mArF7KB/iSswbJXARDOUp06dYu/evei6znXXXVdzmIYT6rEsC4BVq1ZhGAblcrlmH3fv3o3v+2Sz2QEBuNloF6bCPsJABzNqIVAiaAqFQqGYCZRDyfhnS45XidB1XbZv306hUGD+/Pnj+jCP7qNpGqlUilQqxaJFiwjDkFwuR3d3Nx0dHRw4cADTNGulX/X19cRisTGvcTqZyj6k4RzMjo4ODh06VMv6DlZInI0bSYXiXKG/ynX/v7+hGG+GMgxD9u/fz9GjR1m8eDEdHR1jyiwOtimxWIx58+Yxb948hBAUi8Wag9na2ooQYkAALplMjmqXZqqHcrrto+d5/PKXv2TDhg3Ytn2GyI9qIVAoFArFZLjgHcqxRt37o+t6LYs5Gl1dXWzbto26ujo2bdrE4cOHqVQqA44ZqX9mpDVks1my2SxLly6t9Rd1d3dz/Phx9u7dSywWG+BgRhH98dxnsvR3iKeD6PcVXT9SkvU8D9d1a88pB1OhGD/DqVyPxHgqOEqlElu3biUMQzZt2kSxWOTUqVPjXuNgNE0jmUySTCZZuHAhQohaAK6zs5ODBw9iGEZNAK2+vn6AguxMMp1Oa3/7GARBzXmMfqeqR12hUCgUU8EF7VCOJ+ren7FkKIUQHDp0iIMHD7J69WoWL15c+5Ae6tzhNhVjjfQbhkFDQwMNDQ0A+L5PT08PXV1dHDp0iJ07dw5QkM1mszMiYBGtf7oduDAMB2ye+kfoo9+z53kAZziY0SZLoVA8z3Aq16Mx1gqOU6dOsWPHDlpaWli7dm2tbHU8c3rHiqZpZDIZMpkMS5YsIQzDWgDu5MmT7Nu3b4CCbENDw4yNcJqpLChQC6aNVwRNBeAUCoVCMRIXpEM5kah7f0brEepf4rphwway2eyAcwdvmIbbUExmk2GaJk1NTTQ1NdXW1NXVRXd3N/v27aNSqZDJZADo7e2tlUFNNdOdoYyIfo+DiTZH/dcTOZhDZTD7R+gViguVkVSuR2M0+9i/xHXdunXMnz9/wPPj7eueSB+4rus15xFkAK6/guzu3btJJpN4nkcul6OpqWnaAnAz4VBGv4/B91E96gqFQqGYCi44hzIqfTp58iSLFy+ekDjBSD1C3d3dbN26tVbiOrjMdLz9l1MlmmPbNnPnzmXu3LmALDXr6uqit7eX/fv3s2fPnmlRkD3bDuVgxuJg6rp+xgZKOZiKC4Eo2LZ//34WLFiA4zhTah9LpRLbtm0jCAI2btxIKpUa87mDmcq/ycEKsp7n1YJv7e3tHDt2bNoUZGfKoRzLZ91oDiagetQVCoVCcQYXlEMZRd0LhQLPPfccS5cundB1huoRGq7EdTDjcSinc5MRj8eZP38++/bt45prrsH3/SlXkIXZ51AOpr+DGa11KAdz8AZKOZiK843+Ja4HDhygpaVlQsJew2UohypxHW4d4133VGNZFnPmzKG1tZVFixaRzWanTUF2Jh3K8TKSyI/qUVcoFApFxAXhUA6eLWma5oQHb8OZTqHruuzYsYN8Pn9Gietw6xnP2qeLmVCQne0OZX/6y+/DQAfTdV0qlQpCCHp6epgzZw62bSsHU3Fe0N8+Rg7ERG3P4HPDMOTZZ5+ltbV1yBLX/syWgNtgpkNBNmI2O5SDGUuPek9PD9lslkQioXrUFQqF4gLhvHcohxKWmMgcyf70P7+7u5tt27aRzWaHLHEdzGzdMPVnKhVkzyWHcjBDOZiu67Jz506uv/76mjKwymAqzlWGU7k2DGPCNrK/fYxKXH3fH7LEdTDjbQmIXsN0MlTf4VQqyEaCYtPJdN1jqBaCZ599lpUrV9aeVz3qCoVCcf5zXjuUUdR9cP9ItOGZaGQ4Kul67rnnOHjwIKtWrWLJkiUTUkAcyxy06Wa0NQylIBtF50dTkJ2M4z4W8vk83/zmNzl27Bgve9nLpvVe0e9d07SaAmRUAlapVHBdl1/84hf8wz/8A7/4xS+mdS0KxWSJMkvR32j/gMxkgm6Rjevo6GD79u2jlrgOde5Y7zPdjGUtk1WQnc4MZRiG/OAHP2D79u2sXr2a66+/flruExGNJLFtG8uyzuhRz+VyXHfddezbt68mCqdQKBSKc5/z0qEcHHUfLEbQP+M0kQ/yMAzp6+ujWCxyzTXXUFdXN+Zzh9owDbeGiUTrZwLTNGlubqa5uRkYXkE26r2E6dv8PfbYY/zgBz8gl8thmiYvetGLpuU+EUEQDHg/9c9iRuWwhUJhWtegUEyG/pv84cRaJlvFEQQBW7duHbXEdTDjHasUPTebGKuCbHTMdFRXROzdu5dvfOMbdHR0sHfvXt74xjdOy336E4Zh7TN2cAazWCzS1tZ21mZ+KhQKhWJ6OO8cysFR96EGNEcf3hP5IO/u7ubAgQMAYypxHcxsisBPp4Jsd3c3XV1dHDt2DICtW7dOuYIsQCaTIZFI4LruuBz7iRIEwbCZFk3TKBQKJJPJaV+HQjERxjpbcqIOZblcZvv27QBcd911pNPpcZ0/Efs42xzKwQxWkHVdl56entpnied57Nmzh6ampilXkE2n0ySTSbq7u2fEPsLINrJQKBCPx6fs9SkUCoVidnDeOJRjibpH9Hcox3P9w4cPc+DAAebOnUsulxu3Mwlnb2zITBKPx2sqsrlcjt/+9rc0NjZOuYIswAte8AIsy2L//v1cc801U/xKzmSkzRLIDdNofWIKxdkgso+Ds+xDMRGHMipxbWxspK+vb0KBlfHax5koe53qe9i2zZw5c5gzZw4gqyzmzJlDqVSacgXZRYsW8eEPf5gtW7bUAn7TSdRKMtx68/n8uASLFAqFQnFucF44lGONukeM16GMVFxzuRzXXHMNlUqFXC43obXOpgzlTN1H13UWLVo0rIKsZVkDHMzxjCrQNI3rrruObDaL4zjT+Cok/cu5hqJYLCqHUjGriFSufd8f8zzC8TiU/VVcL774Ypqbm2lra5tQMGy2ifLMVEBvzpw5JJPJKVeQBbjooovIZDK0t7dP4yuQRJ/BI2UoVQWHQqFQnH+c8w7leKLuEVEZ7Fg2TD09PWzdupVMJsP111+PZVl0dHRMWrAiwvM8jh07RiqVIpvNnhHZPRczlP0Z3Ps0lQqy/ZnOPqT+RO+z4VAbJsVsYrzBtoixOpTlcplt27bheV5NxTXqXR8t+DIU/ctYI1t54sSJ2tiiSOxr8PHnMv1t5FgUZE3THOBgjqUfcaSs4VQSvWdGcyjPh9+bQqFQKJ7nnHUoJxJ1789oG6b+Ja6DVVwnI5bT/9y+vj62bNmCYRhUKhXCMKSurm6Aoup0MhPO6mjCR5NRkO3PTDqUI22S8/m8ylAqZgXDqVyPhbE4lFGJ65w5c1i7dm3t73IyvY39z/U8j23btlEoFNA0jV27dpHJZGr2IurPPN+Cbv2ZrIJsxEwH3IZ7PSrgplAoFOcn56RDOdGoe390Xa+dP5jBJa6DxQymQlL/+PHj7N69m2XLlrFw4UJAftgOnmXm+z5tbW3DbhRmO+NV0h2PgmwkYBH9PmaDQ1koFGhpaZn2dSgUwzGayvVYGMk+hmHIgQMHOHLkCBdffDELFiw449zouPESrbOnp4ft27eTzWbZsGEDuq5TqVQGiH2FYUgYhrS1tTFv3jzi8fi0zVqcLqKxQ2O9x3gUZBsaGqirq8M0zVlTwaECbgqFQnF+cs45lEEQ0N7eTkdHB2vWrJnwh/1wTmFU4ppOp9m0adOQTtxkMpRCCEqlEnv37uWKK66gsbER13UBiMViNDY2snjxYoIgoK2tjf3793P06FF27949IFNXV1c3ZUp5071hmsz1R1KQPX78OEEQUFdXR6lUIp1OT+tMNxhbD6WKwCvOFmEYUigU2LlzJ5dffvmQKtdjQdf1IW3cUCWug4nuN5mxI08//TSrV69myZIltSyr4zik02nmzZsHUBP86u7u5ujRo1iWVcte1tfXT0kAbqaynxO1WSMpyD777LOUy+WaqrZhGKMGxCbLaNdX9lGhUCjOT84Zh7J/1D2KVE/GcRjsUAohOHLkCM8++ywrV65k6dKlI6rETmSzVCwWefbZZwmCgBe+8IXE4/HahiWfz/Pwww/T19fHddddx+rVq2vZt2uuuQbXdWuO1O7duzl27Bj19fVcdNFFtfKv2diXMtW9O/0VZIUQtazuoUOHaG1trf1cpkJBdijG0kOpIvCKmaa/yrXv+5w6dQqYuKMylI07ffo027dvp6mpiauuumrI0vP+54/XGfN9nz179gBwxRVX1KoUNE0jCAIee+wxDh8+zEUXXcT1119PJpPBMAwuuugi4vF4zZE6cuQIjz32GLZts3btWpqbm6d0FMdUEv2MpspGDVaQLZfLNXGfXC7Hr371qylTkB2KsVRwKIdSoVAozj/OCYcyDEN836+VYJmmOWw51ljpv2HyPI8dO3bQ19fH1VdfXSsnGo6JZCijfqO6ujo0TTtDSKGjo4MjR47g+z4HDx5k9erVAzYZtm3T0tJCS0sLzzzzDIcPH+a5554bcK0oMt/Q0DBmoYbpZjozhpqmkUqlSKVStLe3s2jRImKx2JQpyA6F2jApZhuDWwAiEavRgh8j0b/ktX+J69q1a2sl+iMxVtGziEKhwJYtW2pOajabHfB8Lpfj0KFDtdmNV199de1vWQiBYRi1TF1raytPPPEEnZ2deJ7H/Pnz8TyPbDZby2CmUqlZEYCbaodyMLFYjHnz5pHP5wnDkIULF06pguxgRqvgUAE3hUKhOD+Z1Q5l/6h75JhEpTuTKaeC5x3KsZS4DnfuWF/DwYMHOXToEBdffDGO49Si8P2ZM2cOy5cvp7e3l5UrVw44fzD5fB7f9zFNk0WLFnHRRReRy+Xo6uqqlclORCl1OpjuEtSIaCMzmoJsPB4fsIEa788lCIIRz1EbJsVMMpLK9WRsZGTjohJX13W57rrrakI4Yzl/rAGrtrY2du7cyaJFi1i+fDkPP/zwGcdkMhlWrFjBkSNHWLNmTc2ZHMq2FAoF8vl8TfBr06ZNFIvFWi/24cOHa72IkYM5UqBpulsCpvse8Lx9nA4F2f6oHkqFQqG4MJm1DuXgqHv/XqCoF2QyaJpGe3s77e3to5a4DnXuWDZrruuyfft2isVibTPW2dl5xkYrkop/xStege/7I26WANavX0+xWCQWi7F8+fIBoziWLVuG7/v09PTQ1dXFoUOH2LVrF+l0eoBSav8P/dncQzlWhhKdGKuCbOR4D6cgO/g+w0XgoxlyKkOpmG5GUrmO/j8ZG6nrOoVCgc2bN4+pxHUwY6niiOZXHj16lPXr1zN37tyaXR1sX3Vd5yUveQmlUukMJ2fwfZYtW8YLXvACCoVCrY80cqSiWbh9fX10dXXVlFKjQFNkC6LXOt1VHDPpUA4OhI1HQXasfalj6aGM+j0VCoVCcf4wKx3K0WZLjqRAOBY8z6NUKlEqlcZU4jqYsUTfe3t72bJlC5lMho0bN9Y+zEfaaJmmecambahjGxoaePnLXz7svU3TpKmpiaamJgAqlQpdXV10dXWxa9eumpBNVFY23YPBz5ZDOZiJKsj2ZyxjQ8aaxVEoJsJYVK4nYyOFEPT29tLT08O6detYsGDBhFRiRwq6VSoVtm7dWhP3iYIwQ92n/7imRCIx5HP9icVi3HDDDSOura6urqbeHQWaurq6OHjwIKVSqWYHgiCYVvsY/YxmwqEczT6OpCB75MgRdu3aNaSCbH/G0hKgMpQKhUJx/jGrHMqxzpacTMlrb28vW7duBWD58uXjdibhzOHb/RFCcOzYMfbu3Ttk5nM8/ZdTtclwHId58+Yxb968mpBNV1cXnZ2dADz55JMD+i8dx5mS+4LcyMwWh3IwY1WQjX4uqVRKifIozipjnS05URtZLpfZvn07+XyepqamMfVLDsVIVRzd3d1s3bqVhoaGMzKfQ82wHM1eTtbhGxxoKpfLtQBcpVJh586dA+zjVAp9zWSGcrz2cawKsv0DcGNxKFXATaFQKM4/Zo1DOZ7ZktFmaTzZr8Eqrl1dXZMSrIAzyx+DIGDXrl2cPn2aK6+8csjSnvEK+kx1dLy/kM3cuXN5/PHHueiii+jt7eXYsWPs2bOHZDJZKxWd7HiSmcxQjvc+oRCUvYCiG1D2QopugCuSFJIxsOfS3ZfnVL7IGq2Hw4cP165v2zZ1dXVnbCyjklflUCqmmv4q15Fy8kjv94m0BfRXcW1sbCSXy014vUNVcfS3wWvWrGHRokXDZiRnOujWn1gsxvz585k/fz5PPPEEixYtIggCTp8+zcGDB2vjSSIHczLjSfprA0wnU6G2PZyCbHd3N7t378b3fWzbxrZtent7h1SQVS0BCoVCcX4yKxzKMAxxXXfUqHtE9CEVBMGY+no8z2Pnzp309vbWSlx7e3snnOUcKopeKBTYunUrhmGwadOmYUUeJrJZmm6nrKGhgaamJlasWIHnebUsXVQG2l8dcbzjSWbaoSx5ASU3oOwFlKrOYsUPKVQC3CCQ//rSefSCkFAACDS0ap+RThgIHEsHNF55+QqaUg5hGJLL5dixYwe5XI6nnnrqDAXZaNM/WYfynnvu4Qc/+EFNRGjTpk3ce++9rFmzZthzHnjgAd7ylrcMeMxxHMrl8qTWojj7DFa5HquNHKtDKYTgwIEDHD58mLVr17JgwQJaW1snJeozOEPp+z47duygt7eXa665plZuOty5ZzPoNphEIkFDQwNLliypCX11dXXR2tpamw8cOZjjDcDNlH2cjOLvcEQKslHlS7FYrH1mbNu2bUgF2akQ5VH2UaFQKGYfZ9WhjEpcIxXXsWyUgNoH9lg2PFGJayqVGqDiOtFZktG5/e9/6tQptm/fzoIFC1izZs2IH9xnO/oOQKUPrXAa4nPOeMqyrAFR6GKxWHMwW1tbAQaINAzuaRrMZDdMrh9Q8kKKrl/LIpYjp7HqGLp+wJZWn32cJETHtgxcL8C2DHw/IG6b+CLE1nUCITB0DV2DmGXgBwG6blB2A+KOSb7ik3JMchWfl6xppikly38j4SPTNFm1ahXZbHaAguwzzzzDBz7wAebOncvDDz/MK1/5ypoY0Hh57LHHuOuuu7jmmmvwfZ8PfehD3HTTTezevXvE6H4mk2Hfvn2172fDWATFxBlO5XosjLXkNSpxrVQqA1RcJ2Mfo/P7z9jdsmULsVhsTEra47WRU+1QVioVWltbaWlpOeO5wUJf/ecD7927tzaeJLKRowXgZsqhnOp5wIOJhI/i8Th1dXUsW7bsDAXZv/mbv6FYLPI///M/bNiwgaVLl07oXso+KhQKxezjrDmUE4m6R/TPUA6HEILW1lb279/PihUrWLZs2YDrT2bDFF3H930OHz7MkSNHWL9+PfPmzRvTuePdAE3ppsPNYz/zVbS+44g5lwELRjw8kUiQSCRYsGBBTWa+q6uL9vZ29u/fj+M4tQ3WUGM4Bq/dC6SDWHJlBrHiBRTcEC8IyZV9glDQW/LQNegsuMQtk1zFI1118tKOScUPiVs6fgiWqeH7IaYGlqHj2BZlPyQZM8mXA5LReTGT3rI8v+SGxE0dPwyxLZ0ghFTMJAgFdXGLih9y8bw0F8/LnPHziHqEBm8sC4UCd911F5/4xCe49957ueuuu+js7ByXMmbET3/60wHfP/DAA8yZM4ff/va3vOhFLxr2PE3Tav2ginOb/iWuwLicSRhbyWtnZyfbtm2jsbGRK6+8csB7dbIOZWTnTp48yc6dO1myZAmrVq0a02uYDidxPHzrW9/it7/9LUuWLOHqq68e8dj+84GFEJRKpVr/5ZEjRwaMJxlqDMdsEi2bCiL7OJSCrOu6vO997+ORRx7hC1/4Ar/5zW+44oorxn0PZR8VCoVi9jHjDuVkou4RmqaNWNIVlbj29PQMq+I6FQ7l9u3bayqFYy3jOdsZSq3UjZZvR3PzGD1HQIzuBPdfT7RJiOY8dnZ109bRyZY9B+jJFTHjSWLJDHY8heHEOdHeS1dvmV2Vo5S9AD8QaBoEQmBqGqEAywChaZiaLD41NI1ACFKOiReEZOMWRTeoZQ5TjnQOU45Jb9EnYYMXgqHr+KEgZmoEIWTjBl4AdbXzDXIVn6Rt0lfxidsGvUWfuKXjCemQBqGgPmHxgpVNQ/4MhisdSyaTvOhFL0IIwbZt28jlchNyJoeit7cXYNSMZz6fr23errzySu6++27WrVs3JWtQzBz9Va4jWzdeRrKP/WfjXnTRRSxcuHBIldjJOpRHjx6lt7eXyy67rFbxMNZzz1aG0vM8nnvuOXK5HCdOnKCvr29c46SiANzChQtrZfL9x5NE84Gj/vTzzaEcbqySruu87GUvo7u7mwcffJA1a9aMe8blcCj7qFAoFGefGXUoowju0aNHWbx48aTECIYr6YpKXJPJJNdff/2w5VWT2TD19PQAUgXv6quvnvL5bIOZyg2TSM8nWLQR/fR+3IWb4LnKGcIyJU9mEZ8XrAmqpaUh+YpPIAR9JR8NQXfRJ+nEyIsW4nVwujuPnsvTmz+FQYBuWBi6RrxUImY7GJpGiEATgBAIAX6oEQqBDyBCTFNufDRAr/68krZBEAqycQvXD0k6JiU3wDF18mUPISDvBjgmuICpaQhkaWsoBDHLQAhBJmbhByFx28APZPmrF0IYCkIheyp/59JmLGPozddIcyjz+XxNqCcayTJZwjDkPe95D9dffz3r168f9rg1a9bwta99jUsvvZTe3l4+/elPs2nTJnbt2jVhpU7FzBK1ABw6dIimpibi8fiU28eov61cLg8ocR3MZOxjuVwmn89jmiabNm0atSx+MGcz6GZZFq94xSt4+OGHWbdu3YTL1uH5Mvmh5gNH40kSiQRBENDT00Mmk5k2p2+m1LbHqoI9VcJlyj4qFArF7GDGHMoo6l4qldi7dy9LliyZ1Afc4JKu0UpcB6PrOq7rjuue/e+haRpr1qwZdxZquM3SUJHqqdoARAI1ssw0pJJ9CYXYiyiWymzp2E1lVxu9JR9N0+jMu6RiBrmSTzJmUqp4JBzphNmmLA81dbk2PxRk4iauH5KJyf7DlqY68hWfOXNMOvNFKrlucsUKx4+2IjSTdDJOLB4nnUyBoWMbsvTU0jTKXoBt6pS9EMfSKbgBtqERhlIkJxCgI++v6xoJu9pLa2hoGtIR1TT8IEQzoFSprjmQfZReGBIzDQxdk19aiK7J0lnDhLIfcsPqRhqTQwchouz6cA5loVCYcgXDu+66i507d/L444+PeNzGjRvZuHFj7ftNmzaxdu1a/vEf/5GPf/zjU7omxdTTX+X6ueeeI5VKjdsR689QJa8jlbgOZqIOZXQPwzBYtmzZhF7DYBs5mh2c6vLYTZs2sWnTJgA2b948ZdcdPB+4XC5z/Phxjh49yo4dOwjDkLq6uloGcyrHk8x0yetQuK6L53lTOjZE2UeFQqGYHUy7Qzl4tmTUYzdWhdbh6F/S1b/E9aqrrhpTVHm8Gybf99m1axddXV1cffXVPPPMMxNa91RkKF0/rGYRg37OYkjFC2oZxN6SjwZ0FVwSTtVBdKSDGHMsgjBECwN6KoKOnIuuaYQipC5uUvZDsnGTfCUg6VjkyvLcfNknZhkU3ZCYZRCGsnzV0uWmJ+1Y+KEsUS25AQ2pBMeLRbIpg0RdI5bwOd2bx+3p4UTbKVKOjR1PkE4ncZwY6ZiFF4Sk4wYVLyRpG7US16hUtbco+yHLbkDMrvZR6hqmrpGyDHwBjmngBoKUo5OvPF8qm3ZMekoeGcesPq5T8QWOoREIjdUtKS6ae2bfZET/ft+hyOfzJJPJKdsEvutd7+LHP/4xv/zlL8cdRbcsiyuuuIIDBw5MyVoU08dgleuJjPwYTH/7OJYS16HOH499FEJw6NAhDh48yNq1a2lvb5/w2s92W8BMEYvFaGxspL29nY0bN5LP5weI2JimWXMuJzueZDY4lPl8HmDKspPKPioUCsXsYVodyqFmS06VQxmVdI21xHUw49kw5fN5tm7dim3bbNq0CcdxRhzcPRKDR4G4rsuzzz6LZVk0NTWRD6TTU/FD8iWX7Z0hpd2nKPmCMBT0FD0cS6+pkRbKLqmYjReE2KZBKAS6BjqyDzETs6gEAZm4SaHik4hZFCs+jmVQ9EN8QVXpVEfXNLxQyKyggHTMxAsE2YR0EJMxs+aY5ivSuSyXfWKOSeDL++uAAOKWTiggaWsUy5CJWxQqGvNbmii5AS2GRldfntAvc/TkKbTQw3Hi1QxmklQihi9k/6PrSye1UPHJxGT/Y9ox6S3Jf7vLHpomqAQhliHvG7M0ggCycRMvFGRj0lFO2Sa5SkDc0ukt+cRMg95yQH3CZNOKM+eG9id6Hw+3YZqqGWtCCP7kT/6Ehx56iEcffZRly5aN+xpBELBjxw5e+cpXTno9iunD9/0zVK6nwqGM7GOlUmH79u2USiWuvfZaMpnhAyb9Gc/YEc/zauN0NmzYQDabpaOjY8KZw/4OZRiGHDp0CNd1aWxspK6u7gzHaDoFfKa7x7G/jkA6nSadTrN48eLaeJLu7m6OHj1aUzCdyHxgIQQVX6piU/LwgxA/FARBiBcK/EDghaF8PBD4Qj7mByFCyMoNTYOSG2DqGvmKj2nIVoOF9QlectHz/bEjOZSFQgFgUpn36PUo+6hQKBSzi2l1KKMP4sG9kpqmTXrDpGka7e3ttLW1sXz5cpYvXz6uD/6xOpRtbW3s2LGDxYsXs2rVqtpmZiocynw+zzPPPFPrlfrNniNs6QhpzCQwnTh16SQdJWjIV9B0o9pLaOIGwfMZxJhNvuITt02KFR/T0NAQ/RzEENvQCATVDKCQGT4vxDF0fAEhuiwTRQMNDDR0Q0OEAtMAISBhGfhCOpcVPyQdM8mVfRLV+8Ztk3zZw7EM/KqzGYRyQ2IZGjqQdkypqFpVam2sy1DxUzQ1aeRLLoFbpjtfwO/qQqCRTkgZ+lQyQaDbJB2DIKSmxJp2TAquj21oVEIDgUauHOAYGmVP1ER2NEDoGk61NzNdVXSNW9IBd0ydm9fNHbZvMmI0kZQoQzlZ7rrrLr71rW/xox/9iHQ6TVtbGwDZbLYmZHH77bezYMEC7rnnHgA+9rGPcd1117Fy5Up6enq47777OHLkCG9729smvR7F9BE5Q/1Vrk3TnBKHslAosHnzZurr67niiivGFcAbq33s6+urBfQ2btxYC+hN1D5G5wohcF231u+ZTqfZvXs3vu/XVFMn0984W/D8ADeEXMnDD6VjFwRC/j+wEek5JOPNWJ5LZ3cfJ072cXrPSVzPxY4lSadSmLEk8ViMih8iNEHFk73gXiCvJYCDh1z2Be1oRhe2oeH6sq2g7AYkHIOKL0jYsjIkZmt4AfK4QGBX7aiuQxiCoWmU3IB03OL6QeJlY2kJmGymVNlHhUKhmH1Me8lr/3lkwJRE4H3fp1wuUywWx1ziOtS6RtrwhGHI/v37OXbsGJdeeukZM8kGv66xEm0aT548ye7du1m6dCmLFy+mI1fmqb42ljhlOnsLVPI5jrd1EDfheHsH9Zk0jhMDU/YKhkKQdmQGMRMzKVX7DkuezNIFgRSZsQytWhaq44XV54QgbuuUyj5xXfYlGrqOG4RSYTUM8ULZZ1jxQtnnqMnexTCkOnJDCuRE9y96UignV5a9l/mqimq+7OOYmiwtNXXQpBBOzNRBA8uQpbOpuEPg2CRTGQSCSqVCqVSiuy/HyY4OLEMnmUySjMdJp9NYujw/5ViUih6OIdAQ2AYITa4zFALXFzimRrHskXBs/CAgZpsIITOqQQjXLq+nYZi+ycHviZGyApHgxGT54he/CMCLX/ziAY/ff//93HHHHQC0trYO2Jh1d3dz55130tbWRn19PVdddRWbN2/m4osvnvR6FNOHrutnbLAnax+FELXs1sUXXzymEteh1jWaQ3j8+HF2797NsmXLWLFixRljmSaToSwUCuzcuZNMJsOGDRtqmbxCoUBXVxenT5/m4MGDCCE4duwYYRjS0NAwZcrKYSidurIf0lf2CfIV6aCFovavGwiCQOAJ6bjVnhMCzxdV+yO/9wOBH4Z4gaw08UL5b75QoLurwl6/tdp2IGrtB5oGuiZrPgxNR2gahpnFaMgQC3zyhSLdnXkKhXY0TSMej8tZkIkEtmVhaBqGBUEosAwpcJawDTlWKZq5G7PIRZUf5ep4pWoVSm/JJ2Zp5MsBlqUjPEAIAkPHNDT+15pmbHPge3ckUZ6paglQ9lGhUChmH2dlDqVhGLX5auMliogLIVi+fPmEo9QjbZjK5TLbtm3D9302btw4ZNZpohH4aJO1e/duLrvsMpqbm+ktlHh032litoFhxFmaTFD0QhpKRZ7ZuYd5mkZfroAmQuLxGKlkklQqiWVZmIaGCKsZxDAkactRG7oOhBp+AIauUXQDYtVS2bhl4IYCU9cQQMySDmLCkEqq6FIoRyCze5VAYOpQ8GS02g1C6aAGIaYuy2NTjlRNzSasqkiPRb7ik7AN8m6AaWj0lj3ZF1nySTgmfhDimHKj5BjSkdU0jSAUxKwEiXgMvbGeYsWHwKO7L09XTy9HTpwim3CwYjEaMik8P8TUdUxDRzfkpixuQcUXZGI6eTcg6djk3eqokZJX3Th5XDQ3zZqWsYlEjKZgWCwWp8ShHMtG/NFHHx3w/d/93d/xd3/3d5O+t2JmGWpzPRmHMipxzeVyNDY2smjRogldZyT7GAQBe/fupa2tjSuuuKImMtOfyWQofd9n7969rFixguXLl9cE3TRNI5lM4sQTNM2dT75Q4t/+/d9J9hQ51pWnWHZJJJIkM1mSqQxOLE4owK06gEHVwRNCOooIqFTLOYsVaaPyFR/b1MmXfVIxk92HPQ4Gp/G0HtKOSb7ikXJMim5AMmZRcWXgzPWlAJgXiJpN1DWtZkOj70MhfyaWJsXELEP2f1u6RnWeEroGQXUMklct4y/7AbahUwxkgMwLNbLZOirJNHNbdHLFIn6lQk9vHx2nTqGbFulkAiceJ1UtMY3b8uM+aRt4oeyXrwRC9s170maXvYCkrVN2/drrkUJngiCUWcuyF/CqtfPJJgYG4SK9hJloCRgNZR8VCoViZjkrDuVESrqEEBw9epR9+/axfPnycc0HG4rhNkxdXV1s3bqVpqYm1q1bN+yH40Qi8FEJF8A111xDXV0dnh/w8z2nKFR8TFOvbUaSts5TOw5w7NB+CJZx1ZVX4AuolMv05Qu0dXQSswyceIK6VAInnsCxTcIgxLE0wlCT8x0F+GFYU0+1TZ39h1qpFPrINDSDgKIbELcNvPD5MtGYpRMEoBky0yc3OOALWRpb8gMsXa8ps3qBwDKkM2gbcjRIOm6Q7xVkbL2WycxVN2q56hzJnpJPplq6mqg6w7Yh50gmLLmm+qRDJbCoSycpuiGLdcGpnhy6X+HgsTZsLaTsQ3dXF4YdJ5uM4QUaTnUeZToqlY1ZVAJZKltyAxqSNi9cNfS8yaEYabMEU1fyqriwMU1zQgG3zs5Otm/fTn19PUuXLq3N55sIhiFL7Af3EJZKJbZs2YKmaWzatGnYWYITsY9RVUilUmHFihWsWLECoLYO1w/50dbjdBddNDSOHDnMM1vbyaZzrL+smbmNLbTm8oRdXfTmj2NrAiueIJtK4sQSxGM2QRiiVwNpmtBAk5lFQ5dl8nHT4ET7KU6faiNV10DClCrZ9ZlY1XZJkbK0Y9JX8qpCXzKz11dVxi66AXHHoOz6xC0TNwywDenEWbqGL2SQDxFiaFKx2jJl5YhpyDJ8ebwcl1TxQxK2QckNZICuug7ZR2+QqwSkkknypsOihnpyJQ8jcOnOFyl2dnLsxEksXdB+uotsKoUTd2rjkkxdtkPITOPz45UStvwZSTVs8AJBzJaO7fUrm1jUcGYfZPT7HstYJYVCoVCcX0y7QzkVEXjf99m5cyfd3d21Etft27dPqixs8Jw2IQSHDx/mwIEDrFmzhkWLFo06dmQ8Efi+vj62bNlSy2AlEgmEEPz3nnZO9JSxdPAqAaapY+iyxyVwKwgBwq+ACMgmUwTxGPV1dQgRkC+UqVSKHDvViSHaEaZNcyaJFUuQTsarpU46Gs+Xl3Z29/Dsnp3kCiXmNJ2med5CLFOnp+SRcgzyZdlT4/qi2mMpI+S6LtCQzq4QAj0EgcyE+qF0MoteiKWDKzQMHYJAkyVSaNJBDQWZuBxBEinBpqsiOQnHoLfkkbLlBi1pGxTckJgpo/S2rhEKjWQ1E7qwuY6KH9Iyt4W2ji60vh46cyXCSjfH0Egn41h2jFQqgWPZaBqgg2lo6GikYxY3r2sZtW+yP6M5lIVCYcrmTyouXMZrH4UQPPfcczz33HM123Xs2LFJ2ccoE9+/zLujo4Pt27czd+5c1q5dO2K2frwZStd12bp1K67rkkqlzhAP8sOQn+/ukBnE6t+s53qIwKXix7A0QSXUaG6oJ19J09Ji0NlXQPMrtHX2QHAKNJP6bArTjpHNJBECjOjvX8iAmRf67NqxnY7Tp6mrS7Nw0VLmO9EMXNn7XRezKPkB2ZhJwQ2eD5Q5z//bV5KBs56yR8o2alURhapTWPFDmWkMBAYyg2npsj3B1mXFiFN1MhO2gR+KASJplapQWdmT9y9UAlKOQaESkI7bFCo681uSFN2AJuFz8PBRHN/n0PET6GFAPCn7LtOpJIZpYVsGQRBKxzasBgcFOLqGLwTxahXLRXMzXLqwbsjf4WiiZVPVEqBQKBSK2cdZK3kd62YnKnGNx+M1hdXoGhMtqYKBDqHv++zYsYPe3t5a5nA0xiNtf+LECXbt2sXy5ctZunQpP//5zxFC8JtDXRztKpKwdYpugFUtOfWqpaCLVqwiny+wdMUqMukUfiCqKqYCMGmsS+H6SebO0egrVhBumc6+AvT0ctgXNGYS6FaM5ro0QjOwTR3HNLFNA1MLcWyLuCnV/NLVnpqkU+2lcSx6yh6ZmCUj7tbzvZghYJrSuUSTPTrV/yCQGU0RVoU1AjlapOIHSPdcR0M+F7d1hIBMzMAPZdlsxZeluWUvxDQ0Cm4gezn9EF3TMPoJPRm6vFYqZuGVLBYuWkAYhhRLZQrFIn25Pnq6OkE3qcsk0UyHhmwSN4D/dVEz9YnxyfCP5lAWi0UWLFgwrmsqLmyGC7h5njem84dTcZ0K+wjPj5uIxo5cfPHFY3qPjydD2dvby5YtW8hms1x55ZU89dRTA84NQ8Fj+0/TXXQxdZlJE8DKpYvp6jpNc2M99Q2NxG2TkicdvXwloCmbJFeJsbKxkb5iBS106erNE+ROc+zkSTLJBLYTJ5tJYegmpimrMkzbRtM1DMOWGcNQlvwHITimbAeIHKyUI6sqslWhsGxV8CwbM8m7AelqBjFds6sGvSXZs9hb8jE0KASgE1KoQMLRKHkhMavaWlAVxrGqVRs1R8+Un19RP3sq9rzT6QeCVMzEq2U2QwxN0Nw8h4ZGgQh9enMFyuUyp053EbcNNDtOYyaJMGzqU7HqqCgZ1HNMjXIQ0pB0zhDh6c9YxyopFAqF4vzjrJW8jlbSNbjEdbCK63hk7YcicihzuRxbtmypOaxTOXakv7DPZZddxpw5c2obpWdP5dhzMkfcNqi4gqRjIISMVCNkaVFTQz2NCxYxf95ceoo+mbhFyfWJO3LTYOkatiGvV5+M4cVtGuqzFN0ALfBo78mhFfPs7DhNNm6DadNUl2H9ZZfjlsvU1dVxuuMUjmkgqPY/eiHZmEXRq5Z1lWXPUG/Vyeytfu/6chalHDMio9q2Lfs4nWoJrF4V+Ik7Nq4XYlsygu6YGr4X4thy3Ihj6WgaGJpO3NIB2aMp3VTpkAaeQLdk/07MMmqiP8WKT1jtj5LJBp36dIJ0MsmCliYqfohbLtLdV8Tv62JfxylWz0lgFzV6e0PS6fSYVQdHm+UWqRgqFJPBMAxKpdKox3V1dbFt27YhVVwnK+wTvc8rlQrbtm2jWCxy3XXXjXko/ViVvKNg24oVK1i2bFlNEby/Q/k/Bzo42lUgZsm+azMSFkskWL5kEdm6ehKODEolbAM/kErObnWebsULqUs6lFyTpQvS5CseBiHdfXlct8yhw11YlokTi5NMxLl43Xrmzl9AfbaOnt5uKbITgq7JAJmpa/ihqAmYmaZGEETjlmSgzA8j0bSwNvqoLmZR9mVGsehKJ7Mt7xM3dfoqYS2zmag6nQnboLfokbANChUpjuYFsjy32tpI9FMKBejVuJ5pyM9J25CVKbYuHVHH1KttmgbpRAzXD1lsaHTnigi3RHtnN/gV2nSb+owsoc2mkxRdQdw2uOniFlmqOwxRj/lwlT1T1WOuUCgUitnHrCx57V/ieuWVV9LYeOZ8QMMwcF13wuvSdR3P83jyySdZunQpK1euHFdvx2gZyv4lXP2FfTRNo6ci2L+ng3TSoezJDYLvh1imzFBqusA0LOmMGVByfTKJGH1l2bPTW/RIOkZtbIfsO5SlsgKNhGMQhgbLFsSpeCELCenoLSDcEodPtGNrIZoVo+y6VHwIRFjLfhq6jP8nbJMwFGRjFm4gN2b5sixFzVXnQPaUPOlklnzSMYOyFxKrZjIdU5d9kKaGpmukYiZuIGRZVrVfMpql2VvtSYqu6/rgWHLTFrOiiLyBF0AmZlbHlljV8jKLE30yuyvnUlr0ljwSjkHJA8fQ0ONJFiVThEDcgOvmmfT2dNfUIfuPIYhGuAzFWEpelUOpmCyj9ZgPVeI6+D073pL8wUTXe+qpp8hms2zcuLE2Q3gsjJahDMOQffv2ceLECS6//HKam5sH3Ds6d/OBDrYd7SFh6eTKnhxTVBWMqQQhAtm3HYYamiYIQg2jKvBlmTphdTyQH8qgneuHpB0LPwyZ39xQDXxBX75IpVyiq6sb3/ewHAe/OsLDC8AMpGqroYHnS5vtUXXuPIGmCUKhoWvgh1EwTWBUnUzb0AgQOJZOWB2dJEXUdLqL0q65fkiqqsIatwxK1eBZpXo/NwjRkRUhgqrITxhg6Dq+FxC3TEquT8KqfiZYOn4oVbzRdCxD9k06Jvgh1TYEaMomCcIETc3NuJ5HuVQiXyhS6Omku6ONeDzBK9a34FeKCDs9rH0ciwq2so8KhUJxfjLrSl6HK3Ed6hoT3TCFYcjhw4fxPI+rrrpqwGZmrIzUIzS4hKt/5iBX9njmNCxdKuXYU45Jb7FC0tYpVKSj5QuBqcuNjq5B0pFCDXVxi5IXyp6danlqlKkruAGWqUEoMAxdbjwCsE2NMDSY31iHG2SYOxdyxQpepcip7hymAXuePUR9OolhxahLJwk1rbbx0QypQCgEpONmrf+x4lXnQFZkBD1XlsqtPaVqD1HV+XQDSFgygh6r9udE40bqErZ0lmMmuXJA0jHoLXs1Z1VeR/YGlT2pbhgIcCyZzc3G5cYp5Wj0VDRSjknB9bAMvVrmBYUQNMBFqibecul86hI2ixYuqM0C7erqoqOjgwMHDmBZVs25rK+vH5CxHotDqSLwiskykn10XZft27dTLBbZsGHDsD27k8lQRqM4AObNm8eaNWvGLaQykn2Msp5RsG3woPvIodxypItnDneRjln0Fiuk4hb5SkDcksGrqBQ+HoRUwhBNgKELfF/OSvSrKtR+KKsX/FCKzwQCbMPAF2HVzgkasinCTIq5c5pwvYBisUC+WABC2k+eIB6PE0skSMQTWKZJIGRPuedLZWo57kMG46QDJ+cBa4GGpiNn/CIQmlbtRRfV0n9qfdy2KVsA4tbzM4eFEFUBHwirTQNBtQRWtgcYFKLPArdaVVJ5fvxHOmbSWfLQREiu7FePF1gm+IGGrglEoBECmhAYukE2myGVTmNoUHY9LqrXscMiW7duBagF4Orr6wf87pR9VCgUiguXWTM2JNrE7N27d8i5ZkNdYyIbplKpxNatW/F9H8MwJuRMwvAR+P79koPLdF0/5OfbW8lUTmJ0CxLNyyj5Gpm4SU/RJRO36atG4fMln5ht4IVUy6mozY/0A+lcVgLZM1N2Azmk2pMKqRVflolCiK6BqelUwgDLkH2P2aRDEHfIpJIcPNTK8oXz6OrLU8j10HbqFNlkDM2OUZ9Oopk2MUv2EVmmjgjlnEfb1BBC9i+GQtRKzJK2QdH1cUydgusjEBR8QToUlNyg2oMkMA2NIAifL7eNm7XZlhVPOpFF18c2DPKVAEvXyFfkJjLavJmGjkDOc7MM2V+ZdEw0wKxmCVxf9mIW3YCXXNREXb++SU3TSKfTpNNplixZQhAE9Pb20tXVxZEjR9i1axepVKrmYHqepzZMiilluAqOoVoC+pe4jpYxnKh9DIKA3bt309HRga7rLFiwYEKqnMPZxyjYVldXd0awLbr/rl27+MUz+7AWrKW5oYGy55ONywqHKMtoGXKUhV4tjQ+rf+9lT2YBI+XqaHRR0ZUl+mVPZu7cIMSuzt61TZ2g6nwGoUbMMbGtLA11GQ4cfI7m5mY8z6NQyNPTeRrbsmrOpROPo2uA0GrjQYSm1dYTCoEu5LgQXdPwAx+7qqBtmzoVL0Bo8rPBrgqXSVEeKRIUamCiEQjZ4hBUxXpqIj0h1CVsOaqpWt6bjVsUXdmvWajOAXaFgWHIOcWGBhUPqTKr63hBgGMZ9JVc0o5FvuyTsOWxFy+oZ9MK2TcphCCXy9HV1UV7ezv79+/HcZyafRRCjKqCPdaSaYVCoVCcW5yVklfTNAeITvi+z65du+js7By2xHUwE+mhPH36NNu2baOlpYUlS5awefPmcZ0/+P79I/BRCdfx48fPKOEC+WH8i73tdJ9uJ+F2YuYDtGSaZHYhrhfURlpkYiZ7Dhyi/eRx5sxbAJpG2ReEeoihaXhQmwVpVwUako6JF4akY3Kz5ZjSkdKrzpcrQnSe7z90fdn36PkCQxPEEnEWJeMEIYSBT2++SKVU4PiJNkxdgBWjMZMCyyGTcMiVXRK2hef7MluIjLZLNViNVPW+CcukS1TVYgMZyfcCWa7lBSEgJelBivyIUKBrOpZZFduxNYQGhm5UxxcAaHI2m2VQrAQkHKkSa2paTVTIq/Zl+qGQvVUBXLowzao5Izt7hmHUNkcgs0FdXV10d3ezZ88eXNfFtm2OHDlCQ0MDqVSq9v4WQpw3PUKHDx9m2bJlZzx+ww03nDHfTTF5BpfPDy557V/iunr1ahYvXjyqkzcR+1gsFtmyZQuGYbBp0yY2b9484SqQoTKUx48fZ/fu3axcuZKlS5cO+Rra2tp4eu9hTumNLAmP0Vhfj2PqVFz5Nx2EUOjtYs/e/WSzGdKZDLoubaIfCOksVvu7S57MZuYrQa1FIFUd75GOqiFiJoVyQNyWATlpO6XNkn2TYFkWddkMDY2NMtNXKFIqFOk83UEQ+MTjcRKJBLF4AsdxENWZkzJwptcc4IoX4lgGFS8kZhsU3QBd1yhV2x76ip4URSt5NUGcRFVoKBIqc/qNaPJDMDUZaJTBPkHMlM5swpbtE+mYRa5QIW7JgJtuVstyA9liUa4K9xTcgHTMJld5vsJkSWOCjcuf/yzWNI1MJkMmk2Hp0qUEQUBPTw9dXV0cOnSIQqFQE3BqaGggm80O6DkvFovMnTt3Qu+n2YSyjwqFQnEmY5+ZMIX0j57ncjk2b96M67pcf/31Y3Imo2uMdbMjhODgwYNs2bKFNWvWsH79ekzTrM03mwj9N4Gu6/L000/T2dnJxo0bh8x6bj54mv1tObAcesMEgeHgYeOH0lHyhSBm6hRLZfZs38LBAwfZs30LbsWVTp2QziNAwQ0QAvKVAKpDumX0GmKmgaFpJGwTS9eJmQamXhW9MTS86s+s6AWEQDnQCANByZWDvtENmuqzzJ03j3VrlrNgwQKa03F6czk6Tx7l4KEjlPq66ejpwzQ0cmUf0OgteQhkv6euyVIuQwdDEziGRswyiNs6tqkRszR0kJu3IMQwdArlADSNvpJPGMrZmOhS7MLS5Ty0mG1gGRrZhIWua2TjFkEo5Ky4QApy9JU9dB16Sh6hEPQUPRxL57rl9eP+Hdu2XRuRsGnTJpqbm0kkEvT29vLMM8/w+OOPs3PnTk6cOEFbWxv5fH7SDuU999zDNddcQzqdZs6cOdx2223s27dv1PMefPBBLrroImKxGJdccgk/+clPJryGRYsWcfLkydrXli1baGxs5EUvetGEr6kYO/3to+u6/Pa3v+XYsWNs2LCBJUuWjCljON6WgFOnTrF582YaGhrYsGEDsVhsUn2Y/TOUYRiye/du9u7dyxVXXFET3xmKXGDQFZuPrssZu5omHTxDF7LPW9PYum0Hzz67Typzd3djanJMUczSMTRI2rJKQfYpPj9uIxUza6OK8pWg5jjFLJ2+so9l6PSWA0xDjlHSNensaZpOoRKgIxVXM6kU9U1NrFyxjIWLFpNOpSgWS7SdOMbhQ4foPH2KXL4Pq9q2kHRMdE0jHTPRNFmaL4QU7fFDQaLqzEaK2lGQLF61Z5ap01v2MQ2N3rLMXPeWfKlo7Yb4fojrB/hhiB9q+CKsjncCNPn5F/2MnGrQL26boGmkHAM0aUtDIWcF+2FIS9rhZWtbRq0SamxsZNWqVVx77bWsWrWKWCxGpVJh165d/PKXv2Tr1q20trZy/PjxKVF5VfZRoVAoZidnteT16NGjYy5xHeoaY4nAe57H9u3byefzA2T1o8jp4MHdYyXabI1WwgWw50Qve070kYlbFCoZ/PQCCtm5JOKNBL7A9X0sHUoh2KZJLJnE7OsllkgQt01MXcO2ZGlXEArsqiNmGTr5SkjC1mu9i/lKQMLW8cKwqr4a4hgagdCwoaokK2RkvCJ7i8qBVFUteLIkta8slQd9IYjHYjixGPWNjfh+QKVcIpcvUOk7zYHTp8gkYuTtOA2ZJPmyRjpuSbGemBTb0aqbMF3XquJB0vFN2AYBUNdvplrJC8jEI3n9foPDa9/7JC2Dihdg6lLp0TF1+oCMI0utMo6FFwQkLENmMg2dl69rqY4bmDiR+mQ0ND4MQ/r6+uju7ubEiRP8n//zf9A0jc9//vOcPn2al7/85cP2/47EY489xl133cU111yD7/t86EMf4qabbmL37t3DbsY2b97M6173Ou655x5e9apX8a1vfYvbbruNZ555hvXr1497DYZh1DIJ5XKZ2267jY0bN/JXf/VX476WYvxEtq27u5utW7dSV1fHpk2bxiWKYxgyqz+aMnEYhhw4cIAjR46wfv165s2bV3tuMg5llKGsVCq1FoOh+iX705Er89hzOZYtW0qAwbJli+ktuqRiBrmirMDIuz6pVLUUP+Zg2I5UOxXIXkUh+xhNUwa1HFPa9sieJaszHTNxWaJfV52Hm43JMtFMdZZkOi5Fv2xTVj40Zp1af3dvWfYmSlEyG6GbLKrLUnJDRFChN1egt6eHk22nSMUc7ESCbDIJTqyWYXSqvZxxHQoIMnoZL7DIxm3cQFBXnXmZqY4hSdqR6JlO2QuwDB23GmH0BYSBzIZ6nqwayZU8UjGTXMlDA8qhhqZByfexq5lJywA3oNpfKp1fgIRl8NKLW3Cs4ctXh0LXdeLxOBdffHGtYqOrq4uuri7e//73s2vXLk6cOMG8efP4nd/5HZqahh9BMhzKPioUCsXsRBMTTdGNg0qlMuD7I0eOcPDgQQAuu+yyMWcl+3Pq1Cn279/PC17wgmGP6evrY8uWLaRSKS699NIBGzLP83j44Ye58cYbx7VRi9ixYwee59HZ2TlA8n4wx7tL/PeeNinXHspI8e59+1i8eDGG6QACPwjwg1B+0HshwitxvL2DhS1NHG9rZ8WSxeiWhVnN/FX1HaR4j6ZTDmSvYaGfUE+6Omw72pDELA0/iEQpqv1G5TJHjx5l+YqVhFXlQD8Ma+Valq5Vy6t03CAgbugEyFmUgZDzzAr5AsVigUKxjGFoxOIJ0qkkmhkj4VgcaD1Gc10aI5YkZUvhiJRt1gZ7h4BZzWYYmlYb/+GFUlij4oeYOpQiJzKUSovRNlcDTnV04FgGDQ1NtTlxco1w/YoGVoxS6jpWtm/fTn19PYsWLTrjuc7OTi6//HJuueUWnnnmGTZv3nzGcPaJ0NHRwZw5c3jssceGjYC/5jWvoVAo8OMf/7j22HXXXcfll1/Ol770pUnd//Wvfz3btm3jySefVP1P04TneQMct6hqQ9f1MZe4DnXNhx9+mJe+9KVDBrngeXGcSqXCFVdccUZ2/fHHH2f16tXMmTNn3K/p2LFjHD16lEqlQn19PevWrRt2HQA9RZcfbjlGGAoOtx4lk04QS2RJOCb5skfchLwbko6ZdPUV6evqQLNjGKGH6cRpbmiQ83stHd8PMU3pUOvVShJd02ql+f1FdEIhqx/86qxJPxK8qf6799mDLF20EHRLCtq4fk2ELNVPmXrAv1XbGzfhdE8evBI9+SImIVYsQTadwHESJGIOHZ2dmH2tNNgemh0naFiNZlYDUZGt1zSECNHRCYV8nwhRLSjRqNloaVNl/2ikxp1yTNq6enELvdTPmU+iWgrcf7QTmsD3Q2K2FF17+fq5LG4cfyaxtbWV3t5eLrnkkjOeK5fL3HrrrTQ3N3Pq1Ck+8YlPcOONN477HoNR9lGhUChmBzOeoczlcjz33HOEYcgLX/jCCWVxYPSSrmPHjrFnz54hxXFg4ODu8RJlJovFIldeeeWwkdbeksdPdpyoOpNyFIYbSCcQIaTYjZBS8JYunamkY1DU4qxesYxixcfUZblTgyXFEpIxU/b4WDqGkBuKuGbWypW8QArcFCphv2Halhw5EpPZw0y19EvOUQNDk2quugaWkFFpKxSEgKVV5eA1HTeUcyFLoRT7CUKNeCpDIpOlWQjcSoViUaqmVioujuNgiwDX9WhKy77PuKVT8gIMXaPkh1WnUQ74FtWeyhDZT4lhYBsahq7J+WYIguoavOo8umJV6KfiUy0Jk6NMekoe6xdkpsyZhJFVDGOxGL29vdx33320tLRM2T17e3sBan2dQ/HEE0/w3ve+d8BjN998Mz/84Q8nde9PfOIT/OxnP+M3v/mN2izNEK7rsmfPHoQQXHPNNdTV1U3oOtH7NAiCIR25KPtZX18/bGXFZDKUPT099Pb2smbNmmH7JSMKFZ//2nkSjao9s3SEkKXtkchMvuySjZsU3ZDGTBzHXkDCMTh45DiNSaPWH150A2xDZvEMXWZJNU1DI6yuofq9DkEQ9RPKCgqpBit7Ey1dIwzB0mSgTfYkylmRfnWMkhuE1MflbMlszKwpcBfdgJQtFaebG7KU3BRNc3T6CmUCr0xHd57AO43QTBzLIF4qUglC8ELCeBEjYVWFxoxqoFH2jv9/WIUAAPFkSURBVBq6XIupaYRVBXA/hLilEXQ8Szp/DLd+Jem6RfhhpIItSNoa5YJGypblrDFTrzmjpgFlNyQRMylUAjauaDzDmdR6j2I++5+IzHz8Vb8DExirFIvFKBaLvPGNb+TVr371hN5TQ6Hso0KhUMwOZsShjMqfjh8/zp49e2hubiafz0/YmYThRSeCIGDPnj20t7dzxRVXDOvsTdShjEq4KpUK8+fPH/b6FS/gx9tPEAoIqtnHXEmKQhQDQMh+w4Rt1OaZRYOppWMoe2qOB4KUbVDo1/OTqpaDxm2DkucTs025cdKrsygFJB059qM2VDtuSeGFmnNp0tnnY1T7cjIxk7IbErOlWI9tSpELy9Sr0XzZzwhSUEevKiJSVVK1DB1h2mTqG0lk6jE1yOcL9HR30tfXQ76vl1g8TiIp1RFt0yYMBWiyXMvQq6VctkGhHEhxirJHyjbIl4PqCBLpSPuBIG5L6f66mEW+WzrTQSjIxCwKrk9z2uH6lePPfI/ESOWDhUIBYEpFecIw5D3veQ/XX3/9iKVZbW1tZzixLS0ttLW1Tfje3//+9/nYxz7Gf/7nf7JixYoJX0cxdrq7u9m2bVttczqZ91I0YH6wjRRC0Nrayv79+1m1atWIPZkTcSjDMGTv3r2cPHmSZDI5pHhJfyqez892niRXcTF1Q47HEKAj8ENRVUSVgagghKSt10pWI2cpqM6Y9EJBwjKq7QBypIdhRFUNMniGJsdjCNmyXZstGfjSUXM1ganJMlK9OmoEIZ4XFQul0nQQCkxdthbIeZcQt6MZk1VBnLhFEMheziAU1KcSeGGM+ro6/CCkVCrR1dVFQSQRhU4C28Ep+RiiRDYZp+BKxW9p841q6b9BX7WtoeiGJCydYr6XRNsWvEoRs9iDm5yLbpoEoVZVoJXVJqahoWk61XgmMV2OlsomZHvAuvkZrlx8Zq+5/eRnMY9uRjhpwuRcwgVXD/m7HMmhFEJMuQq2so8KhUIxe5gRhzJScT19+jRXXnklADt37pzUNYfKUBaLclaWpmls2rSJeDw+7PlRT9x4Nkz9+yVTqdSwzkUYCn6x9xS+L6PLelWZNBUzKZR94qZGX8VnTiJBd9HDMQT5ikc67uD5svcxKtGyTapjNWzcQEbCi56MlOeqGctCdRZl3g2wq4O0o9lmhNRGbSSryn91MSnBn46ZnArol8mszi6rZvjStsG+g0dwCznmLVxIU30doaCqnCpwqhlRxzBxQ9nnU3B94pZJwfWpq8tyuqePlqYsPgZ64NLdk6ezowPNsMikEjjxBJlkElEVhpDzKav9lNXSsaRt0Fst5+0pybmXPUVXKtYKCMKQQFBVjBVkYxY3XTxn0n2Tgxlpw5TP5zEMg1gsNmX3u+uuu9i5cyePP/74lF1zLOzcuZPbb7+dv/iLv2DdunW1jZdt2yNmAhQTI1JxPXjwIKtXr2bhwoX8/Oc/x/f9EctER2OwQxjZ4a6uLq6++mrq60cWqhqvQ9m/X3L16tUcP358xOO9IOTft53gVG8RyzAo+wG6DgFyhqQQ4Hkhui4ouR4J25bKptXSd9vQAQ1b+qHVObdyjmMQgmnLYJily1JRA1kFgdAIEXJmpCb/rwMB0tn0Qvn54PmCQEDZF2AIQkIMAZ4mK1F1XedUxyk62ttpbGxi4YL5hEJAKIN6uiyyqM6glBnB6HPH0HXidhqvUkZLxMhk11KplOnLF3H7TtKJTjaVoGjFaMymKFaCWkltypEBxrhtkKsEOIZNQUthaS4VI0MQgC4ErucRr44B0TWdousTt3QqHtiWhuvJXlMvCKlP2Lxg5TA9jZr8OUtvfPhMcxiGMzo2RNlHhUKhmD1Mu0MphODJJ5/Esiw2bdpUKw2c6NDtiMGiPB0dHWzfvp158+Zx0UUXjShEETGeDVNUQhtJ3u/fv3/Y1/A/B07T2pUnbpmIMARNxzINhIBM3OIkUkTG8wOM0GXvoWPYuqDNcqhPpzBth3jMQROy/CoU4IfVEi5BTViiLvG8Yygl8p+fv1byZNYwCIOq2IKGqcudkEAq/vmeIFYto6qLmVRCUctkphyTY+2d7N+9k1zFp7cvxyVXXUPaNugrBSQcnYoncCxZKibl6iEbt3D96pxMPyRpCdwA5tQn6as4LK2vp6/kYgQep/vy5AodtB4/SSYeI5ZIkEkl0JwYjim3f9mYhS9k5tEPpMS9H8jsQChk/1AQCjlaRUC+7PPydS0D5k1OFSM5lMVikWQyOab33Vh417vexY9//GN++ctfsnDhwhGPnTt3Lu3t7QMea29vn7BE/9NPP02xWOQTn/gEn/jEJ2qPK1n86WHXrl10dHSwYcMGstkswJDZxfHS30bm83m2bt2Kbdts2rRpTNUh41GK7enpYcuWLTQ0NLB+/Xo6OztHVNAOQ8FPd5ygvbeMY0mBHKOqVB2E8m+55AZoIuDQkaMQVNBMh4ZsCtOOU59OVBVYpQNoVWdKWoZWG6sROZ2+CLG0atlo1YHUhAzYASA0BDLoJqn2WwoNDanCXY1VgS7Hchi6RrFcYceOnXR2dVNf104ikyWdiFPwfOK2SaniEbdNXF+W41b8kJhpUAmqPYxR32aokYjFsB2H+nqZvXQrZfL5IqVijuc6O3Aci2IiSTqZJAwd0jETPxAkbJMw1NGXbEQrdxLE52BbBmU3JB2XY0Bilk5XGeaaBr3VAF2u5OOYshokVhXhMY2hbZd73Z8SNq1GpBcSzrty2N/pcOXVEVM5VknZR4VCoZhdzMgcyksuuYR0Ol0rrZro0O3+RJudMAw5ePAghw8fZt26dcyfP3/c1xiJ/iVc/UtodV0fMEszYvuxHrYc7ZYqpyW5oSh5PjFT3svQpWx7EAgq+V7a2ztYOn8OhmlTLpfozRWodJ4m0AwyqSR+KMtnTVPghwG2pVNxpUS+F4hqf40gZVv4IiQbe34AeKU6t6zkhjiWVlX203DDEA1w/QBPyI0XerU3JxTVHiY5MNuwLKxyCd2ySVazoPFqGWrcMugt+cQtWYYVs3QqgZwJ5wUhdnWDmLR1QqhmIEPqEg5lz2BhIk7FDxFhQD5fpFAq0dHVg6ZBIhEnFo9Tn06h6yaGFfX86Bi6LGGTkvsa3ZrMsnphyKULsqxonp5ZkKNlKCcriQ8yAPMnf/InPPTQQzz66KOjlgwCbNy4kYcffpj3vOc9tcd+/vOfs3Hjxgmt4Y477uCOO+6Y0LmK8bN06VJWrVo1QBxs8CzKiRC1BbS1tbFjxw4WL17MqlWrxhz0GGvALQq29S+hHan6QwjBI3vbOJUrk6yWh8aqfZNCSGcwFALfLXHk2HEa67LEEnMhcOnsyyN6ezl2EhrTCfpKPk1WSHe1N7yvHJCOGRTdkLhlUHR9YraBW50B6QUhlg4+sk/SF6LmyFq6hhDagJFQhq4RNw1MQ8fWBGEos3t+KMvuTdPC1ASGYZGwTSpeQDJmUSh7pGKWFEjrVwHSU1WK7SlFIj4BCUsnX/GIOXK2rm1qGGaM+gaHOuoJg4ByqUy+WKCtrQ3fD0gmk8STcZKJJJbbS3zvDwiCAOui/42vZcgmdDwfsjGTU/mQtK3hV9snvFB+LoDANDRevr6FpDP8VkBkFuBd+bZR3wdjKXmdrI1U9lGhUChmJzNS8lpXVzdgcxE5lBMd2QHP90A+/fTTlMtlrrvuunGX04y2YRpJ8n7wMHKA1s4Cvz3cJaXoPalImC9Xo9W+j12Nors+dJw+TanisnTxQizLkXPUYg6NDfV4foBbLtFbKGAQcurkCexYjHg8QTwh1QELlQDH0ukteiQcnUJVUt4NwTalgETS0fFDjUxcOnhJ28D1QxxTbrQMXcevqgUWXelsRmqzQghS6TRXXn455XKJljktmLpG2pG9i6mqAxerbtJMXcMNQNMEJa/qsGohZS/ADQVmEKKhExVPxSwDDZlZAINEzEYTdVT8gMBz6eorUM7n2HvqNHUJB2HFaM6m8HWr1icZs6Uwhi5ChKYxNxNn04qp7Zvsz0glXVOxWQJZxvWtb32LH/3oR6TT6Vo5VTabrZVw33777SxYsIB77rkHgD/90z/lhhtu4G//9m/5nd/5Hb7zne/w9NNP8+Uvf3nS61FMP+l0+gzncSqCbrquc+TIETo7O7n00kvHLRY1mn0Mw5A9e/bQ1tbGlVdeOUCtu/8cysE8cfA0rZ1FLEOX4jOm/FerOnSGpuF7FU4e72bxvBZS6QyaCPFCh/pshpIXQODR1ZvHEkVOdnSRyefptuM016XoKVqkYxa9ZY+UI4NeqdqYD1M+HrPoK3nS+avI2Y9lTwbJKr4U9vFDgY4gFCGmUVW+NnQCIW1oKHQ2Xn0Fp0530thQj+M4JKvjkbJxGy8MqYtLle1s3KTkhmQck4IXVEc8+TLg50OdaVAsB9imTskVGJrAj0YDawa6k6ApkaTs+pi6oCdXoJgvcrL9NIsLO6DQQ0xUqBx7Bm3dIlz/eRVbjRA0DcsAXdNkr70mA5HXLm9gbnb41pDxMJJDWS6XCYJg0iWvyj4qFArF7OSszaGE0UtkRiKfz9eutWnTpgldZ6QN0+ASrsEflIPP7c5XeHR/h1Q/FVIcwg8F2YSN5wckLVNuEAKPIPAoIViydAmhJgdL95ZckjGDigtx20DEk8xPpSjm87TMmYPr+RQLBU71dqPrOolEEjsWJ5VKUHblXLNcOSBhG/Ja1eHdstRKPu8HIXHbIBAyW1go+cRMQIiqyENA3DJlr40tNzyNzc2U3ADTkmNJErYp+4MMsNCrWUPpHIZCoCFnX2qaTtnzsQ0d1xekdU32Q0YbOlvOQkvYBq4vM65+IHtDA8usZmcFmgjoyRVxSwVaT7Shi4BTToJsKkHZjuE4NqVAEBdww+omDH1iAYqxEATBiKI8yWRywgGSiC9+8YsAvPjFLx7w+P3331+Lire2tg5Yx6ZNm/jWt77FX/7lX/KhD32IVatW8cMf/nBCM9YUM89Q75loVu9EKZfLVCoVwjBk48aNEwp2jGQfy+UyW7duJQzDIfvVhzv36cOdbD3aTdI2KFV84o5Zc+A8P5DKzaUSvuexaPFCUskUYRgSBNR6JRO2gR8aLJgX43jg0eDYaKaFVy5y7MRJTAQ9sQT16SRF4mQcm0IlkDMmKz6pmJwxmY5ZNcGb3pJHyunvdMrxH0VfvpZ8xSdu6xS9oDoLUmb5nGSK5em0VIjVqDqdUrE7ckqdasVHwtEJqj3rfijVawu9soLD0Kp2X4BtIvvuQ9m2UPEC4qZB0QtIxW3yFZ95TQ3kKj5z5+n0tJZJFZ6jO8zSV7HpO9RKKpkgHotjOw5eICi7IQKNYtVpLrkBa+enWTN38qONIkZyKKdKtEzZR4VCoZidzJjK64CbVp2/iTiUQgiOHj3Kvn37AFi7du2EndLhNj1DlXANpn+GsuwF/Pv2k5Q8Hz+Q5VuF0Cduyoygret4QiACl+NHW7F0jblzm3FsC13TKLlSVKdYkaWq/Tc3GjLz19hQTzqTxdShUCxRKhTI93bRfbqdWCyOE4+TSaVwfSlKUfR8Svkcp72A+XOa6K2K2UinTqfiCznOUmjELKOmkOqHgmy1/zEdSeDXxCBM+io+xd4uTvfkWblkAR4mmZhB2Zelsl41w+mHgnTcpEODbEyW8mUTFkU3qGVuk5Fqrd1PbKcko/ahD6YuVQmzmTR6Nk1Li8B1XfKFIoVCgZ7O0ximQRDA5c0GiWl8N4dhKGdlTnOGcixjYYfq03n1q189pXL8irPLZEpeu7q62Lp1K4ZhsHLlygm/L4ezj9HIkcbGRtatWzfk38RQFRw7j/ew+cBpMrGq+FfMpKfgVrOFPrYBBw4dgdDHSaSIxRJUXF+O9PBDDFNmPUOkTkwYaOg6hJpGUzZLmMkwZy4Ui2XKxQI9fX245XYsO0Y6mYBEgkwyjh9IYbLO3hy+W8LSG6piZ1IIrFAVwMmXfWKmRq4SUJ+ya7MnI6Xt3rJXs2GJqt2OOwanOnrp7uqgsWkOjXUZXA/QQK+K9YCQr0NQq9LRAEOXIkJRCW7ClMG/dMwkqPanR1lP16+OLvEF9UvW49Y1YeORjrWgF4pUKiWOd/XgmBqe0EnHbXoKZepTMfrKPosaEmxcPowIzwQZrYJD07QRhfLGgrKPCoVCMTs5KxnK4STtRyMIgppa7FVXXcXTTz894Tlp0Tr6nx+VcB0/fpzOzk7a29vJZDJDKrdF5wah4Kc7T+IGgSzhNDQZvbZ0OfbCMekue+CWOHL0OPPnNtPT24eh6Zi6hq5p5LpO09vXx9yWFkI7SSZmUXID0o7B0QBaLIOeqpPZV/ZJJRJg2jJ7WHHx3BL5XIHjPT1oukYykSSXy7F77140wLv0cpYsWkDRlRnLXDkkbuvkKwFCyHmZScug6Mt1u4FUmg2FkNF0IcePeEGIl+/ll7/6H0r5PjpPr+WFmzbQW91g9fRzhJO2QaEsCANZ8hof0J8pFV2lE1sVl7Ck2I6hawRCCnNoGrieLKvNuwFJx8DHpKGhgXSmDsuAXKGIkWvHKnfz+OOPk06naWxspKGhgUwmM+mMYUT0Xh1pwzSVkviKC5uJlLwKITh8+DAHDhxgzZo1kxqLAEM7lK2trezbt49iscihQ4dIJBKsXLly1HMPtOd48uBp6hOy9DMblzYuE5OK0CY+zx48SiaVwBcOtilnS1qGTkdXN6c7OshkMzQ2NqMBgSfLUCs+aIag7AWYBnge2I6D5TjUNdTj+YJKqUiumKen7SQnQkE6kSQQATt2bKdYrLBmzUouvvgSkrYc75GKmQShdNhOhLIPMQjlfN+KJ+f7lv2AlG3I7KGlU67a1kLR5cknN3P6dBfNzU38r5f8LwzTRNc0KmF1RmZF2tli2UWIkJIf0qBrFFy/Wikig5KVqOe9OmPXC6L5v9TGQ1lGVTm2fi4GOgaCdCKGHwoWa1Aoluk8fQrXdamcPEq3adFcl+aqlgxChMDwqqzjZbQKjlQqNWX2WKFQKBSzi7PiUML4N0yFQoEtW7YMUIsdjwrhUPTf9PQv4QL4p3/6JwoFKYLw53/+52ecG0XgHz/QQVfBxdZ1hC43dbYph2WnYxbFik8l18PxU10sX7wQrBh6Lkdf2SOehNO9Pezeu4fe3j56erq56qqrAUHcNgiFIGVK56ouaVJwQzJxq+aoyqyhjS905s7LUiy7aMKnN5ejrb2NU+3tGLpGZ0cbC+Y2k445hKEgHZN9jzFLr5VmlfwQ09ApVOSmJ+f5OIZG2ZczzHxkxrBccfHKJYIwxC/l0ZAZSDcISTmy/8gxDcqeVG2shFL0p+CFgEATIdrhx4jnjxMsvBarZS2aJsUhjBDZ2yNkr48XVgeFeyGZuNkvQ+CRtOXrn1OfZV6ih7UXXUQikaCrq4uuri6OHj0KyIHX0ddkRnpE74vpFuVRXHgMV/I6Hvvo+z47duygt7eXa665hrq6Ok6fPj2pPsz+s37DMGT37t2cOnWKefPm8aEPfYgTJ07wy1/+kq9+9atn/F30z1Ae7Sqw+WAHjilVq2PV8UXJah82XpnDx44xr6mJuoYGOjo6CEJpAz0/4MD+fZzqOE02kyZ5ZZJ4LC7VWoWGVi0zRROUq1UNZU86YpVAYBtatf8wgRcIRODRly/QfrydE20dhEFA69ETLF6yglgshmlUS/aRjptOSFh13ISQgmNCk1UgQkRlqoKEY8p5j1qAVy4jfBevVAKknfX8kIRlUglkYCxfCUg5Fqc8QV3KGJD93LdzG09t3cGaFcvYsPF6bEM6l3pVbjYMI0fTJ+GYVNyQhKPXxN8qflCdkSmIJxxMyyYeN6lvqKdSKnF5s8bhg8/y7N7dZLPZWgBusiX7YxEtUw6lQqFQnJ+cNYfSNM0x9wi1t7ezY8cOFi5cyOrVq2tR0MkKV0QbpsElXI8++mhNNGi4Neq6zt6OCl09vWQcQ36YWyaBkJsaw4AwCOk5fZJCqcK6VUvQDBvL1GkLNept2ZMYsy0CLDTdINCkYxaGQvYlaoIATQ7eDp8fF1Ift3ADQX3cpugFZONV5zJuk6vozGtpwbTjaIZJvuSRzdZx8FArMdvAiSfIplPEYnEsXcM2NGxLx6pWEpm6Xr2/zBQKAV4oHWU/hExjExdfehm5vhwXrVmJH8jB3rr2vCKipkFY7au0tOosTk2KBRVPHaHu1BbyoUnq0M/pyq6szb/MOCZ5N5DDyf0Qu7qGhCVHhGRiZvVfOUIkE5fzJnc8cxhd14nFYsyfP5/58+cjhKCvr4+uri5OnDjBvn37SCQSNeeyrq5uxJlpg4mi78NtiKZSEl+hGE8PZS6XY8uWLcTjcTZt2oRty5E5/R3CiRApWZfLZbZs2QJIxcxTp05V+xqDmp0cTKTy2t5X4ue72qoOmsA0dFw/xNJlz2RvdxftpztZvHABiWRKqmCjoWmyFNQx5cglU5PzJOOWiW3pEEI0CkmKzejEqsE3x5TCOZah44UysOWHQpaUajYN9Q6pVBpfCDo7u1kwfx7HTraBCHFicZLJBPF4HMs08ULZ0oBhICK7GIRYpoFf7XEMhFSIDYQgnUxy9VVXceL4URYuWkI2GSesOp7ShpvV3noTP4CUrVUrNkw8X2Dj8/P//m+KxQJdp9pYu/Yimpqa0TQwDSh7IQlHzh6Wwj5BzX4mHYO+shwTUnB9TE3DAzm32NIpeoJXXrGMpU1JhBAUi8VaAO65557DNGX1R2NjI/X19bX30VgZy1glhUKhUJyfnJUeShibMxiGIc8++yytra1ccsklZ8yNGu/g7cHouk5nZ+cZ/ZI33HADXV1d9PT0cMsttwx57tGeCrs7XFYuM+mteFWBBzkmJFfx0IKAY8ePYZsWS5csqZY9gQgFcVMDIahPWri+yZWXrqWzp4+W5ia5qbB0vCBA12REv+SH6H4IWnXemi/QDfDCkLipEQiNdEyWkNbFTcq+oKUhS/qqDcQsKSiRtA06ewvglzna1okeegjdwCQkVyiRTcbxqoISoaZh63JDqGtSWELT5AYwaVusXL2GhC03Nbqu0Vf2SdpyU5NyTMpuQNI2qARSqELTNFKOjhdCNpNFM0zqRYFybOnzcy+rGceYpZOrBFiGRt4NqyMIBL4vBYX8UBCTdV5cv6KRTNyS6xxUaqVpGtlslmw2y7Jly/A8j+7ubrq6uti7dy+e540rOj9SORdMXQ+lQgFj76E8ceIEu3btYunSpaxcuXLAe3gqKjhKpRJPPPEETU1NXHzxxRiGwZIlS/i///f/smfPHm644YYhe9h1XSdXCfm3LccRCNxqj3Wu2nfYU6yQ6+6gJ19kxeJFCNNG16RojKZptQoHz9e47JL1HG9upqmuHttxMDWNQAOj+qVrstUAqIlyGULOsbUMQ86UJFKd1QkJsQybKy67DCFA06Qj6rkuuUKRYj7PqVOnicdsAgGeWwHdIG6btTm/+aqt7yvJ1yP73qWi7PJlS2lZsIhEdS6wY8m5wLYZlbHK0lxNl5UstiMDdqahoWGRTMYpF/I4lkFdJoVl6sSqY5KycR0/hPqEiRtQ7aeUrQNedU5vWJ2/qWvgBiExE/wQrl3WwNImaaM0TSOZTJJMJlm0aBFhGNLT00NXVxdHjhxh165dpNPpmoOZyWRGHTczUg9lPp8nkUioDKVCoVCcp8zaktdKpcK2bdtwXZeNGzcOmf2ZTIYyDEPy+Tye53HVVVcN6JM0TXPEBv7TuTLPHM9jmwIQ1CUsqWQatyhVfPBcDrYeoyGbob6pmQANEQi8IOD0qTa2bNvBmhUrSGezmIZGXX09dXV1ALVIvmUZeFXlwNr8bSGqvZpyjqWl65QCuVERgZTf90OImTKjl3Jk9Fz2Pwpa6tOU/QRzmpvozlfwSzlOnu7h1MkTtIYajekEWFJ6v+RJNUU3ELVStYRj4IdyEyP/tXDDkGxMiu1kqqI9qapKYsoxyfswB+guSmexYtZjrfsDtHwHNK1Cq/ZVUhWfEAKSDoDAD2S2t+QJktWZblFZ2OUL61hW3RwN5VAOxrIs5syZw5w5c0aNzjc0NAyYCRjdY6SMZj6fHzA2QaEYKxMJuPWfj3v55ZfT3Nw87muMhBCC3t5eurq6WLt2LYsWLRqwzpe85CW85CUvGfb8ghvw9KmAlVnpzCSq/YZJx6SvUKLrVDuVEC5asZyiL8iYBr1ln7BS4Mkt26lLONQ1NpOOW7hYLF60mGLFAwG5skfCtii6PmgabiANpOtLWyV7wI2q4ir4ocxk+nLcLhpSVTUQGjqyNNc2wDHjpJNxgrABDUEun6e9rY3O0x1oGtixBJlkgrKIk447tR7QKEuYq/ik48/bvr5qIC8fZQ3LPrapU6gIDB1EAJVAYAWQK8kxSGUv5PY3vYm9+59j5dJFGHYMHaj4MsjmBtJplmNBpHiPXnWiTV06kaEme/NDIX+uXUKwqjnOVUvP1AKI0HW9Vr0B8vM3so87duwgDEPq6+tr9nGwuI4QYkw9lAqFQqE4PzmrDuVwJV1RCWpDQwNXXnnlsCquEy3pikq4fN9n8eLFQ4ruDEfR9fmP7ScpewLPlxsZ3xc4Val4r5jjRFs7i+e2kM7WSQdRCEQo8D2Pn/z053S0n6T12AkWLVmMMGwSpk6h4pOs9iI6pl4b9m3oELd1HEN+UIeaQISghXJMh6GBF8iZY0VX9s6U3KAqXQ+OoeEiMKsbjLglI9hN6Tg5PSST72PBoqX4XoXO3jx+oYfdHR1kEw6aHaepLkXJNatZBZ+EZVAIquI9ocCMxqTYBmHVefUjsZ1Q4OhCDg23ZaZThOAl5uE5c+VIkYpPzJICHOmYnCsZs3T+f/b+O1rS7KzvxT/7zW/lk2Of07knd5igmRFKSAgNSAbrgsERWYYlLtdcG8OVL75r/a6v77r+wwEDNssCE2QZLWMM2GDJoCxLghlJMx1mOkx3T+fTJ8fKb9h7//7Yb1V3z3Tunu6xVN+1RqUTquo951Q/tZ/n+YZEkrnPQjEw9+089lR/jqe3Xvqb3UxDeTleP52XUnYPz5dP5y8397kenQvMgWl6evqmr6GHHq4H27aJ4/iqX2u1Whw8eBCt9RvycS/H7dZHKSVHjx5lZWWFUqnE1NTULd2/Fad8+dUllDJNT+gag6+871BrNFiZmyXI59k2PkqioJIz0Rjl0OE/ffYrnDz6Cm6uyNTUJuzRCUqhy3ojohh4pmnzXWrthEJgdOWFwM6aTKOvNrcxOe+Si+xGKyHvuyb2w7OziCSbKDX1J8qM1BJptnuJUvSVy8zPL7BtyzSpVLSaTer1OvHKIkuOT7GQR4YhhVyIQneNy8qBQ6I0Zd8lltLQXLXCd+1sEwkCQaI1jtAkSjHou9Tj7GejyNNPPU6tneLYHRqrTa1tTIqiVCG0ofDGUhG6No0kpeCboVveNxEjvmM+H9iCt02Vb+lv6Ps+Y2NjjI2NobWmVquxurrKwsICJ06cIAiC7gCuUql0hw09jXkPPfTQw3cn7hvl9WqULq01586d4+TJk+zcuZOpqanrUmRuZwJ/uV4yl8vdko4ulYrPHJqjEUu0MA2doTRZrDcTGusrrFZrbJnahBfk8BxBIjW+ZaFsjUwFJG20kuikTZRIKqGZYPsZHaybhRY4VJsJlhC0E0khb1z+XEsgAS8LAzc7UvO7s7VAZxNrmdnTR1Kb6BGtsom2whEWwlYorZFaYNsWjh0yGQQgLGSaUKs3aLUaXJiZwbYEjpejkM+hwxDPcWglyhyKhKYrodLguRZSGh2T0IaS5juWoatKheNZXUOgKDWOio1IUggus+Nvmcn+Risxk/3IRK+kNriWzbt3DnWpbVobC/5baShfD9u2bzidz+fzSClptVpXtb7vaYR6uJu4Vm1bXl7m0KFDjIyM8OCDD163ft1OluXleslt27axsrJyS/ePU8mfvTLHWjNGCEikBGGucXVtnfn5OYZHRuivVNBa4FoCJTWBY4x6SFooYSHjJlIZnbSJyMgijbKhWzlnPl/ws6GT79JKTR1pRoq859CIJcXwsiY0YzjU2mk3a9LUW/Nx53Yto69WWwlCK5NhmffBcqhUKrTjlCRuU6s32dhY4KKU5HI58vkc+VwOy/OxhYDMbMyyBEJZJuJEmXgQqSBwYVWYOBAtjFwhkSa2KUkVJd80pnnPRmpN4FggzGbTc0yOr9FRXsrWzHum8fQdm2ZsGtEnx1xc5/broxCCUqlEqVRi8+bNpGnalQ+cPHmSdrtNqWTyLJvNJq7rvuF9u6cx76GHHnr4zsZbhvKapimHDx9mbW2NJ554gr6+vpt6jJvVCF2eX9lpVo8cOXJLGqOvnVyiGSXkPAsZWQhM49SKEpYX5mklkm1bNhMpQWDBWtNoK+ux0Ri6rs9zP/AcL+3fz7atWynlfdIkppzzieKEUujSjlVGozIOfhdS6LetbuB259DTmUTH2WRdSrqbTV8IlM7osxoEmkSZsGyJER3FqSJVhnKVSEUiM/2kTPA9m1yhSF+lzLDUpHFErV6nWVtnZXmRMPAJcnmK+TzCcfEdm3pktESNLLeyGpnrbCRmGl9tpxR9s4n0HHOg8h1jfV/0zXazHJoIkYLvdJtSpU3TLLI4lvc+OkAxuPSy7fz97qShfD2uNp0/d+4cjUaDF154gSAIutvLSqWC4zh3RUP5ta99jX/+z/85L730EnNzc/yX//Jf+OEf/uFrfv9Xv/rVq9IO5+bm3qA37uGti5sZuGmtOX36NKdPn+bBBx9kcnLyho97qwO3Tn7l8PAwDz30EHNzc7dUH6XS/PeXZ7m43sK1LNqp+VwapSyvLFNdX2d8YpJCIUc71Ti2RsosEiNROI7Fu977fnLF5/H9HNNTm1iv1ikVckSJac6UNrRWqczwSgO+Y2pEx/CmGBhqfjk0zWcluzX5upJK6NLKNqKt2FD2G7GpT43Y5OI2IpMt2VIWnmNRb2exIJHEdWxsL8fAYB6pBkmSmFazyepGnfnFZYTtUC7kcLyAgXLBZPl6Dq0sWqSd6SnbqUajSSTkMRpJK4tXsi1ACBwbbCEQ2UBQaQgc44Bd9G1SDaXQyfTzLlKZJlNrjWPZfP9kzMx8clfro+M4DA0NdWnWzWaThYUFNjY2OHTo0BX02YGBATzP69XHHnrooYfvcLwlKK/1ep0DBw7g+z7PPvssvu/f1GPcLKWrQ+FaWlq6Qi95K6Y+L55d5bWFOoErMKZ/FkJrlExYnL2A5wc8OLXJaBZ9Q2Et+g61tmkqN7Lt48DoJE886VBvtvnvf/o54lTy9JNPMjA0iCU75hKCom8cTfOOMd/p78RnZJu8om9oXB2Hv2K22Sz55uASekaD6VkWqYbQNYcw37XN1tSxqSaa0DaxHqFrIkPyWTN7uQ4yEg79g4Pkkn48odhoNElaDWYubuAIbZrLYh6lcvTlXKLUHOKasSSwNc1UUQjNdZvmM8WxzIFKIBCYsPLOlsJ3LLQNjjCxJjlXkCjN49MVNg9ceSjpOEzezQPT5ehM5/v7+5FS8sgjj3Sn8ydOnCCKIv7kT/6EZrPJ8vLyLdNvL0ej0WD37t189KMf5cMf/vBN3+/48ePdDQHA8PDwbT1/D28dXN4MJknCyy+/TL1e521ve9sVf+vr4Wbro9aa8+fPc+LECXbt2tXVS95KfdRa84UjcyxU2+Q8iyhROBbEccLy8iJxnDK9eQuu62ZxHEbz6NiCdiwzbWHKwEA/ux9/mqhZ48++9DXq6yts3fkgj+7aQS1S5DyHujQRHK3IxHtEKVhoYqlxLMMKcSyR1T9h3FgtCylNpq2UJpJEZiY2qVLku5tOB6mzLaFU5GyNADPkUqob5+Q5FpA50boBrucxNNhHvZ1gyYTljTq0VplfXGKgmGPN8RnqK1Ft62yL2KH6a3JaGzMisrikjMHRjmNDE05Scp5NM1aEnqARKzwn+7ktSFLQKCxMw20ou/A9Z3+VHS/8PhPBCIub/vCm/o63g1wux/DwMOfPn+cd73hHVz4wMzPDsWPH2L9/P9/61rfI5/PEcXzL7rEd9OpjDz300MNbF/fd5XVubo7Dhw8zPT3Njh07bskF7mY2lJdTuDr5lR3c7IHp5EKN50+tUAic7qawHklSKTlx6jQjgwNU+gcRwpg9KA0530Ep6MsMeyqh29UJzqWa2voqC6sbkEacmZmlb3CQRjvFyh7AcUzshkLgWNljZvEZxhTHBG3HiaGB1eNLRhCdZrAYOKx3aLRNM4FvxqZ5TJTGt82EvzPRr+RckmzSbZpCh0akLhlO+A61SNFXLlEPcgwO26zVGsi4xeziKiqdx/N8ivkc+XwePwhAmMOYYwkKvnm52cI21K3UbEWbySXqlqGiJaZhbicUPOOgOFIKeHLzG7fWb8aG8mromPJcbTp/9OhRvvSlL/FP/+k/5dd+7df4sz/7M/bs2XPLz/Hcc8/x3HPP3fL9hoeHu6ZOPfzPictzG+HSwK1arXLgwAEKhQLPPvvsG8yiroebqY+dYdvy8vIbmCG3wgD5xskl5tabhK5hH+Q8G1vA0vxFfD9getsktmU0hFgCrTSesEiVIvQuOZRGqSJwLGbXqywvzNFOUi6eO822bVsJXCtz0rbZaJlmq5EoQsemGpkhWq2dEnpGdxh6NhuRJHAFLSnxHWOMYzIcyeJETFOqtdkWmlbXbFaNBayREFhCgG0iTRTZtjCTFyiFod5qGMj5JNqjv1QkkgolEzZqDaJWkzNnV/Fdl3oYUiwUUJaPk7nUWpmDdzsxOZVGBuBeVndNU1lrSzxH0IpBoLD1JR1lhxWy1kp4bDRkz/O/D0CuvUC4egzGN9/0a+dW0THksSyLvr4++vr62LZtG3Ec02g0+NM//VNeeOEF+vv7+Tf/5t/wkY985Jafo1cfe+ihhx7eurivG8qlpSVmZmbYvXv3bU0Nb0Tp6lC4hoaGupb3r7//jTRGC9U2f/7aMuXQ6W7eau0U2W7QjBVbpyZw/BwaaCYSSxiKqRAic+MzTqWpMoY4UmuKvoU1MMhoZY5Y5tkyMYoQFmFg04xSPNscrByhaaeKdqyw3RRhGT2OrQWWNi6Fjg0KTaFDGw0yrVHoEmUNbCuW5DzbZDxmh62cZ7PRlmiEMdvxLOJsQxpLjWuJrjujeVyTsVkOzOS+4DnESlHK55C5kFLFbPAazSbNVouF1VlcC9oJbFSreEGeUs7PHBjN5rHg26RKUAkNVbccuCSZbirK8ihbqbHdf//DI+ZQ9zp0Drxvth39tRwMc7kcP/MzP8Nv/MZv8Ed/9Efk83l27Njxpl7L67Fnzx6iKOKRRx7hH//jf8zb3/72e/r8Pdx9OI5Du93mm9/8Jlu3bmXr1q23/Bq/UX1stVocOHAAy7J45plnrhi2waUsyRvhW2dWeHVuA8+xiVJJ4FisbdSwhCCXzzM5Pk6qFLZtWBK2ECjLbOOEMNmutrDRQM4TtCzBUKXI2vAgG+vrbN68mb5CQCo1vqOJUknBt4lTSWAZnWboCpLUGO3EqZEBRIlpIjub0CjV2JkrrAUkSoM2WketzXVFKs2yMs19W6mJetpoRhQCl0ZipAitrIFtJ6Y+RYkxU4syumpbKhOZ5Lj0V/qw+vpQStFut6jV66wsLZKkqXmPaLco5XPYlk2poxXtDA0DB6lMBq+UmrxvgwZLaBzLmAgVA5so0ZQDY8Yz1R/y9l1jJMe/D/fUF2j6wyTDj97Sa+dWcS3TMs/z+NCHPsRnPvMZPvCBD/AjP/IjN71hv1vo1cceeuihhzcf96WhbLfbzM3NkaYpzz777DVdCm+Ea1G6rkXhutr9r3dgqrUSvnB0Hq01qQbPEaRSUVtZoNZoUvQsCoUCjiOIY2Us61OFsEzDJFONbQlaiTTT9ZYk9C0iCeVykfe+//3YAlw/6G42+/LG7t7XipnZOVyh8TwPS0CUSDxbECuTr6Yg09WYzDatTUOotTF/8GwLjSDnmTf6ku1mjZzZCISuxapSuNlhy4IuDTURKstoM4dKzzFUL2ELcwC0wbesS5ofIUi1TSn0iNIy045gZaPOysIci2tVbLnMRdtnsJxHOwF9pRzrLfN7aSVg22BnlC0s8GyB7Vg4tsU7dwx2t5uvR4diei8ayhu5vFYqFZ555pk39Toux9jYGJ/4xCd44okniKKI3/zN3+Td73433/zmN9m3b989u44e7i6klJw/f54oinjiiScYHBy8rce5HuV1ZWWFQ4cOdfWSVxuW3AyD49D5NV44tUwpcDIKvs2Z2SWa1TVaKUxV+qi2InK+a7IbXYdmkprmM9NTx6nEzfTfjm02tY7j8K53v5s0jsnncoDAtkw9CrPNabu6QaNRZ3R0FIGpGVqb3EVUSrh+HGV5uIM7L6uNpr6BqbeWgFSC7Zjs347jtKGYpvgZg6KcDzIGhZ1JDi5JAmqtS1tR37GIElP34w4dv0tjtVG2x9DQCM04xROSU+cv4kRtTpw+S+DaeKHRpoe50Jj3AAplWB2Wwhbmc55t9OWB23EEN/rK/rzP9z00imVZtH/ot4jWzvKNV87waPDmNnE3qo/1ep1CocBjjz32pl7H5ejVxx566KGHe4d7TnntHGSCIKBUKt12MwlmAp8kyRWfu1wveSNzn+sdmBKp+Owrc1TbCRaG8lRrRizPz6KwmBwf59zMDL5rkUoIfKP/y3mOaYoQaEAqM7lOEoXrWDQjhevYzC6tUWlF2H7IUMWmpQS5wOSQOUJzbuYirqXZsnka1zbmC6GjiKSxjk+kwhGCKIsZiRJJkBk/+K5NPTLus+3YbB+TVBN45ho9x8LRFsoWeLYgdM01CyDVxhgiTk2j2UokOc+hkRkL1SJzu9HqZEKaQ2QzMVS1Tm6l1NBXzLM8D7u2bEIqTbvVYKPWpL2+zsqSJgzy5PI5XD8gb3s04yynrkvTTdg3VWF64NqvEaXUPQnLvpmG8l67GO7atYtdu3Z1P3722Wc5deoU/+pf/Sv+w3/4D/f0Wnq4M3Qor81mk4MHD6KUwnXd224m4eqU1cudtB944AE2bdp0S/e/HK/OVfnm6eWuuU3RtzhzYRYZNRkanWB5/iKNWNJfCozJjWdo7aFnmBiBZ9OIMypqbJgZjXaKtmzWak1iOY8f5tDCxrJtXMfoGx0BcwsLRM0mw2MT5HIhqTSRSYmSeFogLnwLZ+FFUhyETkkGHsTpbEizuu9aFkorfM9ICXKOjZRQ8l0SNEXfRQhjegOXTMMqHefZLOYo7zuk2YBOaXCzWJCO1rIVp+S9LBYkc2M1FH8TjeQX+xgZz7FRaxJFLeYWl0hkSiEMcf2AgXKRppYUQ7fbxG60EvKeMRFyLJM9aQvBD+4aInCzOiUsdP9WUi7cM0nAtdCrjz300EMP39m4ZxtKrTVnzpzh1KlTPPDAAwDMz8/f0WPatk273e5+3KFwCSHeoJe8Gq7VUGqt+fKxBRqRMZFRSlNvNJmfmyXIFdg0PkKtESHInFx9m2pbUvQc2qk0GsWMPmphZ/SkLIPME7gDA1RKJdZrdaJGjaOLS5TyPrafo5wLuDC/RF8xpNA3iGXZ1LJYjUgJAt8nTSU5x8RwFByjAyp4Ro+U92xiqSn4VqZNNFTXQqbD6RxGTDNotheNWJL3LCKpjYOghLxvk0pNOfRIlaKv65jo0Eo0xcChGZlmc7XeAhkTB3kCx6aWme7EaUqsMLlpCAqFIkGuiGcLGs0W7VaTer3KxvIi675PKZ+nqXKUcgGRVExWQt52Fd3k5bgTE5xbgZTymmZRSqm3jC3+U089xTe+8Y37fRk93AaWlpZ4+eWXGRsbY3JykhdeeOGOHu/1lFcpJUeOHGFlZeWmnLSvN3A7s1Tnm6eXTRyQ0riW5uy5C9gCNm3ZQuC6LC8ISkGnGTNNWDEwzVc+sEklWb1S5DLaqGcLdBCyfes0axt14qjF6ZUVQs/FDUIqhTyLq2vYQjM4NkE+DIx7qmc2nznXpRElFGXERuqRs1I2mjFBn2K1FlMMXBqRNA7UcUrOtbL727SizAxHmhzfRGrQilRqXEshpciilzSWZeKaHFtgIRCOhQXdCCOlBb4w28OiZ7O6sU5/pYzSJh4klVAKbJa1MemxLZv+ShEhikip0FKyXq+TtpucOb9O6Nqs+znKxTwNPHzHSDAQGEZMqvi+R0YYKLyxRt1prNLN4FqSgA569bGHHnro4Tsb96ShlFJy8OBBNjY2eOqppyiXy8zNzd1W6PbluJzSdTMUrqvd/2oHpm+dWWVmrYlr20ilaNRrLM7PMTI0zEB/H6nSlAKHi9kBqdbqOLmmlAL3UsRH04Rv19sJoecQZ1Qv27bxPZcwCBECpJLU6nWqG1Vm59bwbaO5JI2otjTF0KPaSs32rpWQD5xsym0m/HnXpp1KPAtiKXEtkFJQcCwkgnJmL98x2ylngeB532I5NcHjl2ey5X2HtWZistzaCaFr0UqUsfeXGs8WoKHgOVRrVf7gP/5HqvU6zz7zNE89/QweNpaliRONa0MsTQNai9Ku420xDEktl4m+fhrtBBm1qTUbROtrzCMoF/O8a3KcJEmu6/p7rxrK6z1Ps9lEa/2WODAdPHiQsbGx+30ZPdwiTp48yenTp3n44YcZHx+n3W6jlDIUzdvcwF++YbyRXvJquFZ9nF1r8uVj8wgBsYI0bnPuwgyVYoHB4RFcxyJWGscyTI/QNsMuw+bQ+K6dxQaB1KaZNDpIi1Qap2mlHSaG/Yw5oanVzfBpfn4OgcbJ5RFJRGxbFAKPKM0GelJRDF3ao49TQFIjpDDxINVEUQw96m2TMbnWjDKHbFOjTX3NzHzczPzGFkTSuGTLxDA4EAqlNI6dUWQdi3piHrORmPzLzva1GStcC37/D/+IC+fPsmXLVv7yD/8wacfYx7SgpikVGte2SBV4rk1q24wO9qN0P+NK02g2aTUbrK0skyQxuTBHmMsZyYVls2/rAFuH3lh/tNb3pEbeDIPjrZDT26uPPfTQQw9vDu5JQ2lZFuVymYcffrhrGX6rGWlXQ+cxzp49e1MUrqtd1+sPTMfmqnzzzAqlwGWtGdGqrrK4vMa26Sm8IAQhEEKDEHhmJE1fziORmr7wysiMUuiaBtBzqGWuhPWM+lVtJgSeocL6rkOSSFqtFmNjIya3q15ndWWZNInZCHIU8zna5CkEPnEiDd2pQxtLlNEmZTln9SghcAUbUYJvCdraaG6iDt1LQ963qCeQd427YSlwSaVxdI0v0xLZFkSpBjSr6xs8/8ILuLbNE297GwPlEifOzLC8soJEcOTwYZ56+hkC1yJVmoJnYwF9odttaDtUsVZinAlbicRxbFyngBPm8RxBrdHmqVGbjeUF/uLMa+Tz+W7uY7lcvuJwdC83lNc6MDUaDYA7bijr9TqvvfZa9+MzZ85w8OBB+vv7mZqa4hd/8Re5ePEin/rUpwD45V/+ZbZs2cLDDz9Mu93mN3/zN/nyl7/M5z//+Tu6jh7uPcIw5Omnn6ZYLAJ0X2tSShzn9sp0Z+C2srLCwYMHGR0d5cEHH7zpfy9Xq4/LtYj/duhipunWxM06F+bmmRgZwsuVsC2LaiulEDg0UgFas96MKQbZMCyLUsp5Ds1YETjCaCpt0TW4STLjrk7sUaI0nutQbzQZ7K+QL5aImi02qhvES/M4bkCxkEflCxRyOcPYKPeRFj9AwQYpNQOBIJWavoJNkkr6chZRarIc41SaaKJEmoxhpcm5hgHiWBpbGO8exzJRJ75j6lb+MmfqWpTiC8V//9I3aFXXeGzfk0xPjjG3vMbMzAxRojl34SKrG1VyuQK+a1NtxgTRMvVayECYY61lhoRrLdNcx5HCdgAtsDyfvlxIIVU4KFarDVrtJgvLq4wWHML+NktLKX19fVe8Xt7sWKUOrlcftdY0Go3ua/t20auPPfTQQw9vXdwzDeW2bduuaot/p49brVap1Wo3ReF6PV5vWjG73uL515aNk2srpro8z0YzYdf2rUTKwrIEtVaKn23sEgVxopC2wBLGNdC2jSFOzjd5Z+XQIU4l5ZxHIo2DaSuW5Hw7C892OD87T9ys0z8yTqGYJ0oU5T6fQnkAgaRWq9NoNllYWsb3XPL5AoVCnkIYgrAo+OYQZFxTNTnfJZXamAhp0wy2U3MwNImPRifZilPWqw3WXjnK5PgIlb5BLEuYDaMA1za3UmksLF46/ipnThzDcgNGB/vJP7qH7Vsm+PboJM31ZbY/vAdLCNazJnqjlSI11GOJYwmUEig0GmF0nJbAsQSWhYkasIx5xffsGuHJzSYrtNVq8elPf5pqtcqePXuwbbsbmD0wMPCWaShd173p/NRr4cUXX7wiiPsf/IN/AMBP/MRP8MlPfpK5uTnOnz/f/Xocx/z8z/88Fy9eJJfL8dhjj/HFL37xqmHePby1sWnTpitqUee1lqbpHTWUaZqyf/9+HnzwQSYnJ2/5/lrr7pZ0oxXzp6/MGs12Kmmsr7C8utZ1ug46ekHfodFOyTmCepQy3J8zRjaBQ23xPMX2RWruEIXRray3zHZvo5VQzAZwxY7BTxbTpOImZy8uMD0+jO3nCUMHJVym+irUoxQVN1mtNllbnyFVUC7m8YIclVIBqSxsG+LUGPAoDY5tHGU9xwIBLiAAR5ioEAuNJcz9VBpz/NVXGegrMzE5hR+YjWXZcZFKU8l1dJUOR48d58Thg5m5kMX05AcZGaiwaXKcCzMXmZ4cp1QsGlptoumb+zre4ivk1x2q3o8RlgZpRhI3y9LUwtBp26lpXhux7DavQwN91KMi05Mu4fzL/PEfv8DevXvxPI9KpdKtkZ2a9J2goezVxx566KGHty7umYby9TlrjuPc0Yay1Wpx+vRppJS84x3vuCkK1+vROTABbDRjvvLqIpYFrXbEwsUZXM/jkZ1bkAhydnZYymI4fMciBTSaRCqEMEYMUitD84wVnp1RvjyzsfMck9GW9x2k0pQCmwszs5DGTG7aRD70aSWS0LO6zWYzUvT399GMioyMWqxW66RRizMXZnGEwvVD+sslgjCHYwnW1tYYHhrEtWyEWRCAoJu3FmfRJs04RSYx3z74Mq3qOpWhUT78oedoJm43x7KU5W3mPZtEKSqlPH4QYqEplEqUAxcVlPmpv/Xj1JotKqVyN08ukdKYRWBcYLEtmlkjXe3SaxNKmUti3je/15FSwOPTlwYDn/70p/n//r//D4Cf+7mf4yMf+QgrKyvMzc1x/PhxPM9Da83a2tobtpd3EzdqKPP5/B2bA7373e++4t/I6/HJT37yio8//vGP8/GPf/yOnrOHtyY6mX63WyOllBw/fhzgtoZtnWsA0yxEUvP5w8bxWivF0vwscZSwc9sWHM/rulQXAtNolUKXBSEo+UaDXgldkiSmr3WeuFWn4rdptkYo+TlipankPVqxpHwZu6MRpbRr6yysrLN9agJle11X1XzWWOV9lwZ5Nk2UaUYpQiasVGs019e4OL9AIQzwwxzFQh7LdlhfW6NYLOJ6fpZDKXFtmzjbjtbbiYlYilJcoXjl6AmW5i+SK5T5gQ+8j/6h0a4Jmm0L4kQjLOMUWygWCXM52u02fX19WJbAFTY/+iM/QqNWJV8s4Tpm0Bi6AlGboaBqiNSnFC8irUHcwAFtoqDsLG6q7Lqk0jA8Eqkohw6J1PTnPMbTOX7q//h5ms0m73nPe/iN3/gNVlZWWFlZ4cyZM93c0pWVFQYGBm57OHEj3AvTsl597KGHHnp46+K+5lDe7mGpQ+Eql8vYtn1bzSRc2lDGqeQzL52itnCGVgKzdShW+hkfHSKWELhZY+gaLWIhsEkSTWiZ7DLHUqjGEsqrIJyAJJUIoJUoPNum3tb4LiY2xLXZiFI8JOcuzhG6NuMTmwh8Bykh7znZ5Nszh4mcS5KaXMlWohgdKFNr5xkdHWGt1kInLeZXN0hbF/na89+ivrrE1PYH+Cs//EGiRGf5koamlSpNITROr0nc4uLyEnbSQimJam7Qakf0lTyaUUo+C8ruOLuGns3U9gf5/nwZiWB60yS1yDjgOo5NLlfAbD7BsSxswPEsfEtQCh1SBZWcQyw1lcChlaquUZBnW123x/c+MHRF3uTi4iJpmqK1Znl5mVKpRKlUYsuWLSRJwunTp1lcXOTIkSNIKenv7+9O52/3dXE1XG8CX6/X78ituIcerobbrZHNZpMDBw50X6+3SzXsNJSNdsxnDs2y/8iraCnBsijm84xvmsJyjM5cY3TVyjITrFSarWaiFIEQzM7NkwsD7LAfR7ZJvTKe66EwGm6pNKFnms+cb5Omio2VBVqtiF1bN2F7Pk5miFPOuaTtBmVHkGBTCh0SCaXAJZEWU/kQqTRSptRqDRqNBjMzq+zff4CZ8xeoDAzw137sR8EPCRwTD5L3TQNbyfvU2imupTl1bhYLiXICkrjNSq1N34Bio5XFg8RmkymUYXEMj4zyvg/8IGnUYnB0AgvBeuZYjV8ABBuNhMA1Lt3uyBPoWhUdDuKWtxDFipxv004UgWcRZc7gcWriQhJp+B1KGXOgd+0a4tDzR4jjGKUUc3NzhGHI5OQkk5OTSClZWlri6NGjnDlzhqNHj1Iul7vsjrsxBOvgZiivbwUNZQ899NBDD28O7ntDeSumE5db3j/44IOEYciRI0du+xosy5hAfP7IAs2ZI+iFI6QxjE0/TX54kHZijCQ2WpK8bxuzncCh3jZam1SDZWn0qa8SVE+TBkPoB34A23EQCAx5yri9pspkhjXjmJnz56k1WkyMDFPqH8B2bDaaxhRivZmSzwKzA9cmSnV2f7oHrnIWfj1SyRHLkJGhQU6fu0BtZYFICs6dPMbBozsZHSiz5gYMV4pstBT5wBgFxc0qswvLTE9N4QYh506fYmpqir5KCa0h55psy4IrUNpsKFWWX5mbmkJmfzOlwXctmokk7zndzeNGO6Or1VM0mlpbdmletm2Rao2XUV2LvoMQ4CN4985B8q/Lm/zoRz/K+fPnkVLyUz/1U1d8zXVdSqUSzWaTPXv2UK/XWVlZYX5+nhMnTpDL5bray0qlckfby+u5GHam7/civqSH70xc7bVzOw3l8vIyhw4dYmxsjJ07d/LFL37xtnWYVpan+6evzHHg1dfY/9JL1BttHn30YbZt34mwstxdIYCMGqtNbq1tCWIFidS88OIBDh14CccPee5976E8MILtBkhp4aNppArXEUSJxhVw7sIFlhaXGRgcYHLTFJZtIZRGCsMIUY11rNVTIDRWZSvkB7CFRghwbCNNEIDneHiuy9BAH0ma8rnPfoY4arI81+LQoUNs2zJNmCtQKhYQItsAKo2nY87PzDIx1M/Qu9/D0SOvUCiW2bV1GoUmJ8zvxQZsoU0z7Bp37a2bxk38kWfcubtad8fK9OJGhiA1OMMPcXzDY/v0JuraoejZWRyTuQ1dw1SxhCAVKosmsanFKe/aOcR4JWTwve/lJ37iJzh+/Dh/7+/9vTe8fkqlEkIInn76aVqtFisrK6yurnL27Fkcx+kO3/r7++9oeyml7PojvB4d07I71VD20EMPPfTw1sU9pbxe8cTZm9fNHnaklBw+fJjV1VWefPJJKpUKa2trd0SbtSyLY8spntWkXV/HTtoMFStQLoJj4TvCRHP4TlfHUmt33FZTLAEbjTaDzQXWY5sc69Q21shXhomUCbJOU6OnEUJhCcEXvvolDr9ymFKpzLYf+RGKgduliUapsa1vJQov0x/5jkW9bZrLass4EDazbV4zazbjVDExOsK2rdu5cOE8jz72GA9vn2aj1qDdrHJyZZF8EFIPQ1Qas1ZrMrVpAscLmJ6aZmpqGq0hVSLTJAri7BoSKfFtTaI0gWMBgtCxUAJCt6Mj6mxRHeLUGPy0EnOIirJpepRqlNa4KOJUEXo2tSzLst5OeXK6j039b9zyjY6O8olPfOKaf8OOhlIIQbFYpFgssnnzZpIkYW1tjZWVFY4ePYqUkr6+vu7hKQzDW3qt3AzltYce7iYcx7lpnbnWmrNnz/Laa6919ZIdeuAt1ch2FVQCuQG0hpeXFBW3TdKs02w2CX2X/r4yvmuhNdiWQGuTuquFQCuwss85lkAqxeLiPI1WGzuKWFxdp29wBzK7byQNtTNOFZaweOnQK3z96/8D23H5vve9z1DmpSJ7BkPjb7VwopbJlMy1iO0Uz7VotLLBVTsh57s0Wkm2eTQDwT1PvI0DL36TkfFJHt+3m1ozplGvMbewSM73CHI5LEuwurrG6OgolXIJpTXf+73f2/25UqWxhZE5OJnm27YEWmkCWxiX2izXsuibQVwxcNAaXNtCCMwW1jPb1rwjQIgunbUUuiSpiXvSGnzHxrYglZowMFEhuycrPDJRBsDzPP6f/+f/ueaf83KN+eXbS6UU6+vrXWrskSNHKJfL3QbzVgdk12Nw3C3Tsh566KGHHt66uK8bSri5hrJD4XIch2effbZrNHCj4O0b4dWFBifWUirRBVrWBLu2jdO2Q4LiqDFWsARWtknLuaaZ6mwHKzmX8xrKoU9z8FFKK6+wFk6TLw9Sj6RxNWwazeVGKybn2ZyfX+T8+Rmk5dBuR6xubDA4OIBjCWxL4GS/E882B0Q301qGmfOgZwtiqbAExNJoItupsbPXWPzgD/1l2lEb3/OwbJtKn487OEA7imm3miwvr4A2VvftRg1LK8JCHqUzUyGp8D2bVqIohNlBLPCptRNDgW2bLepqMyLv2VQTk1spE2FcGVMwZxBN4NjEKiF0MoOi7PeZSE0Ymg1sKXBoRJKJSsiTN8ibvBauZcrjui7Dw8MMDw+jte5uLxcWFjhx4gRhGHapXzezvew1lD3ca9zshjJNUw4fPsz6+no3lgnMEO+WauTaGZxv/TrImPSxv8bX65tYi4CVJYIg5N1vfzuW67Bt6xZsBMLOtpG2ZeI9hEBbIIRGa4FnCwJHsPvhB6mtrZAvFNg2vQkv2yIqDZ5jhN4am3q9zvmzp4jbTRzXY2N9FdvaihCmQbOF2Xh6hT6SeBDXgrZbJnA7tFWHRpRSCLJ4kMA4yhYDY/bz7ne+nUd276G/mKMZSwYHCjSjEkOjgrVqnfrGOs0owhKwXq3RThTlYp4UQc418VCFwOT3FgPX1MXOBtIWme5SECWGeePYVsbisDMtvUuUKHwnG9i5FrExDCfNImLQRjZgW6bmB7Yww7zMOXusHPDMtoGbfg1dqz5altWVB4DxJFhdXWVlZYVz585h23a3Pvb19XW1mNfCjeqjbdt3bFrWQw899NDDWxf3raHsbJVudGC6nML1wAMPXPHmeCc6zAurTf7itSUCSyEtl507p6hnLnrVVtrNejSOrhLXstAobCGMCUOWsxYrRTDxCNHog5Q6zn8hxEpTCh2ixGRVnr4wh4pbPPHMOzh6aD+VwRGmJiapthJyvkO9Zaz020lKzndJlMK37YzGZWOhTOSH0girY3AkkFpjIUz+pGOhtI/n2rRiE09Sj1ICx2FhrUrgeZQHhnGFZHm9RnN5ifMXZ6kUc/hhnkqxmLnGOkhtmmapNH15nyRV9BfM9RV8lzg1kSKJVEgNaJ1tWh2qsaTgO1RbEiEsqpmTYzOShEsHkPUV3MknUXY/lZzL+x8avm26qFLqhvd9/fYyTdPu4enYsWOkqbHb70znX7+97GS59RrKHt4s3C7l9fJh2zPPPPOGQ/ut1Ehr5RSsnwUl+fPDr3GAENCs1iN2bttGW2qKgUszSggdTbOdEvoOzSghcB3ascT3jGGN7zqkyrASxic38eM//ldRWuO5xpTGsgRKmi1mqqC+vsb80hJP7NtLksQ4lsUDO3fiOw5Kq65Dq+dYpDIgP/4gqYKSwNSq0CPVinJWsyp5w5qo5FwSaWQCsdSM9BWIU0UpMHruQuCQSoVO2iiZsGVqk2Ge1Oq0GxssLS1SzPms+zmGKkUaEUb2EBnznkYs8VybRGkcxwU0KjUGP83oUnZwwbdZa0TGwbYtCR2LWislVZpYatLI3OflV1/j9PEjbH/gER7etY21lnmejZZxyf3eB0awrVvbHN4M1T8MQyYmJpiYmEApxcbGxhXby1Kp1G0wr7a9vJEkIJ/P3xM37h566KGHHu4P7hvlFa5P6boahev16OSk3Wr491oj4r996zWWFufQGnZMTSC1pi/0iKWiknNpp4p8YNOOJYFj04olnmsRSYWjjXNprCyiWBrjHscmjlNDQVVm4yiVxhGa8+dncFGMTk8Teg57H3kQxzY5bmXHohmnWWZlmk2+zeGh2jZ5bhtNMxnvHCqakSTnG7MG3zGT65xrmsBS0NmgmpiSnANnz5+nFHj0DY6Yhq+dMj2Ro96WuEKytF6jUW9wcX6ZvO/iBCGVYgHPD7BtgS0sFAobG9e2cWwL13GwMv2QyJxufcdM4gueRRSn5FzBkoTANaY77toZkuNfRCOwNy7QeOxv894Hhsl5t/8yvJ3YEMdxrtheNhoNVlZWWFxc5OTJk93tZUd72XEovp4pT4/O1cPdxo2ilTrDtvHxcXbt2nXNTdTNbijV8IOIoQd4cdnjhdowq/UZImWxa3qclqS76SuEbratc6m3jRt1LcvXrbWznN1WgmXbVFsJYU7TSBQ538SB5NxLA7S1RkyjusbKWpUtU5Moy+HH/sqP0YwlBd+mFiUUPBOZ0WngQtfKMnctWqnGdyzaqezS/13LuK9attlomg2gxrZMpJMtAGGaWaE1C/NzyCRh8/Q0YeghpaaUz5nIJamo1us0G3VmZmawBNTzBYrFArg5SoEL2Ua2Q2ctBMaFdcBziFOV5RQryp2MX8do1F1b4KCxLYtC6LCyVuX3/v1voR2PF7/9bX7u//iHlAqFLALK4gOPjBJ613ZSverf9Dbqo2VZ9PX10dfXx/bt22m3213tZWd7ebn20nXd624oe/Wxhx566OE7H/dtQwnXnp5fi8J1tfvDjTOwLkcrTvntLx5ibmmVwaERluYXaMQSWyhOnDmHYws2TU0Rep4JyA6cTNtiGjbfsUlSkzfpuTZzsxcJcwXyhTy5MCRSmGYxVmiZcu7CRYo5n8rgGLZld91MEwmebWJGcp6D0ibTTCqo5FxiqSiHLu1UUs4ZI6BiYBsNZ2BTbcksry2lEBjDoHzgUGvFFHxDJdUy4fyFGfpKBQaGzGRbaSiHDrJ767BpxEcqzZiS1OtNavU68wtzRhdZzGN7IYN9RWqJzhrbS9Svgm/TTBQ5xxyWHDvb3tqCejMmsMFGk3MtHNHGFjGWlsSywNNb+5jsuzUt4+txpzmUQggKhQKFQoHp6WnSNO1qL1999VWSJKFSqQAQRdFVjSeazWbvwNTDXce16qPWmjNnznDq1CkeeughJiYmbvkxrorSBK/s+Lt8o3mWtcVFKv2DWOurKK3pCx1On7tA1GoyPrmJcjHfdZ+OpaIUuERSUgxdothoFleERWtjjYtRm/5ykaYOKIYezVhR8Bzq7YSNlQXq7YRd26ZpS5E1qUl3oFYMXKoZfbWaNbHVTv1pXdoAmqgPU1trkWkuZaq7jtEKjS2M+6znWrRbKb4Np89fJHQtysPjeJ7LejOh6DlUM2frttQUCyW8XJ4BAc1Wm0ajzsLCMu04opjP4QU5+stFtOVkRmoKzxYk0pgEacAWFgJj5NPRXiqZIiyBZ+nMGVyiEaioCbaDZwk8R6C04O3bBhkq3rprtdb6jjeDQRBcdXt57tw5jh49SqlUot1uE0XRVYe7zWazx+DooYceevgOx33loFztsNNsNvnmN79JFEU888wz12wmO/eHmzediOOE3/jsCyytbbB5eppioYDKNH/HT53hwIEDfOvAy5w/f571VgwINprGZa+ZSCwhTDNpGa3Lrq2bmRwfJfQE6yuLzJw7xerSHI1albjd4PyFc/SV8gwMj+A5Nu3UmEs0Y4VSUIsM5anaTkikotZOkUrTThQWhk7q2gKpoODbgKAUOKCNllNmtNpEGqv9zja1GStazSanzl6gXK5Q6BsiThXtRNJMUlqJotlOSKQmTiVk7omB61CplNg6PcGu7Tt4YOs0ucBHRzXOnD7N+uJFFhaX8Uhpxyk5zzyXbVm0UkWqQWqbWAkazRYzCytU+vuptiVKKtYLO0kmnqbZ/xCVx/8yezdVbuNVcyXutKF8PRzHYWhoiAceeIBnn32WJ554outO+OKLL/L8889z4sQJVlZWuq+7RqNxx7EhX/va1/jQhz7E+Pg4Qgj+63/9rze8z1e/+lX27duH7/ts3779DTlsPfzPg2sxOF5f29I05dChQ5w/f56nnnrqus0k3FpDeWqhyp98+zXmFhYZGhlneGgQhYUlNBfnF9n/4rf49kv7OX7sKFJqfNu4nZqmxzidKqUJXQutFFMTo2zaNEkpF7C6tsb8zBnOX7hA3Nig2WyysnARATy0fTOO61LJGcpoX85DAZXQywZtbjYIc69wuS6FrjG9Cc3Xc66NEOY6bGFMcBzbOL76tjHIyXk2Sarwbc1rZ89TDl36hkap5DwzrMtikjzHbEEtSxApidIAFrbnMzI8wtD4JNu3bkV4OeKoxdFT55g5f45TF+ao1xtsNBMSKWklikYkUVpTj8wWrx4rlFK8du4ipWKJRoKREHh5PvSX/xd2PvwYH/rwjxHmclRbCQ+MFtk2fHsDq7tdHzvby+3bt/PUU0/x7LPPMjY2hpSSU6dO8Y1vfIOjR48yPz9PkiTApfp4Jy7YvfrYQw899PDWxn2lvL6e0nU9veTV0Pn6zRyYms0mn/rcN1lvCXZt24LI9Ii+ZUxwfEujtMSWkkTqbMtn9I21ttENbjSTLNojoRS4VCNJIZ9HuAFDQyPUG02TV7ayipYJvuPguzaWTvEdv5vVhm0OYIFjDmAdAwYhrK7ZTqowZjsyu8X8j20J0wDqSzpKxxI4loXjGdpVs7HB8sI8m8ZGKZfLpMpoP6NUdV1ic9kWsxiYoPDuttN3WG8YzU9bW1T6+0llP8NI6o0mzUad8xfWQAhKhQJhPk+ukIfs8JZITVprMD+/wNZNYzh+3jjWthN832Vj07sJHZvv2TtGkiQIIbp62ts5+NzKdvpW0dleCiG4cOEC3/M938Pa2hqrq6scP36cOI555ZVXOHPmDOPj43f0XI1Gg927d/PRj36UD3/4wzf8/jNnzvCDP/iD/PRP/zSf/vSn+dKXvsRP/uRPMjY2xvd///ff0bX08NbA6+tjo9HgwIEDeJ7Hs88+e82YhsvRydq9Ec4uVfmdL+xHpzEDY5soFkOqrQTbtqi2UtCaWHigY5qpadLWWjFF32Gjaaj5682EvGuxHqUUvIy5EARgu/QPDNBoxyRRk+W1KjJaQAuH/lyOjXqTYj4kSgWuLYhSiS0EicqGeNIY1aRaZywLY/Cltdk6WoCTUfOlFibKQ1s4wiJREs83dbbgGJMzV6ecuzDDUKXE0NAQthBITaYZh1LoGlfWrNYqDbYFUhln6zgzZYtTzfTYAFGqmRSwUW/QbjZYXFxASkmhkMMP8vSXi938zGYscUXK6bMzDFUqlPr7zHuj1kgteWLfHh5+5FHynkWtnbB1MM++TeXbbgzvdkP5evi+z/j4OKdOneLRRx8FTE70+fPnOXbsGMvLy3zmM5/B9/07upZefeyhhx56eGvjLUF5vRUK1+XoNCE30gitrKzwR/9jP7NJns2bRqjHioIvaMYJCDMd3rJ1q+n1LIvNm6exhMhoriafLE4VxcAymzkHTp45T6UYAv0UOk1nLuTceg2tJIWBEUJHsLRWY2V1lVRb9JcL+EGevnIBhcgor4IgO7QIAdosMbMG02iAbAGRNHrJVtJpCi9plYw2KSbnOVxcWCaqrzEwMkFfJU8kFYFtkShN3rdIlTBUVwWVnNN1rI1T2TURynu2MSKyLeJUs7Awz4ULF9ixcxcjo2O04hRLpcbYZ2WFMxfm6C8G2H4eG8nq2jqbJibI5fMmjVMISqGLJQSB6/CeXUOUcl7X7ObyA69lWd3/bgZKqRs6EN4pOk1rZ3s5NDSE1ppms8mpU6d47bXX+NznPsdXvvIVfu3Xfu22DizPPfcczz333E1//yc+8Qm2bNnCv/yX/xKABx98kG984xv8q3/1r3oHpu8Q2LZNHMcALC0tcejQISYmJq6pl7zWY9yoPp5bWOM3/+wlPNdhYHIT+dCjlRlrXZQw4AicQj9P7X2UjXqL7VumqEdZ5E9mPFPLaKfVzPn0xPmL5FxB2j9sNJftlLzvUa03UXFMrjJIORewvFFjvTrPuVRTKeZx/JC+Up62tnAdizSVWQyJcbMGcSV91bFoRJLAs6nHMXnfYaOjPc9yg40bq9k8yqjFuZlZRocHKZQqSKlIspGdUiYWxHMsklTju5ahrzqWYYtYFrHUODaZ27bRrzfqNY4cPsLU9CY2bdrE0JBCy4T1ap2oVefUyjKh7xLmCriuzdLSMqNDgwwM9GeUVIFSkPMdlILAdZFaMZnz+d4HhlBKdf+Gt1Mf70U+rpQS13XJ5/NUKhW2bdtGFEV8/etf5+LFixw4cICRkRH+4T/8h/zCL/zCLT9+rz720EMPPby1cV8bSsdxSJKEQ4cO3VAveS1cj9KltWbp23/EyYN/TiP/LrZt2U69nVL0bapRQsF3aSYgEESpYNu2HUSpQmIRxwmOZQMmUButSVOjm9z/0iGOHz9GLlfgPe96JwT9lEObczOziDhibGITpXxArZ2yY3PZGFSomKW1GvXGIudm5xgo5RFeyHBfiXpK12wnH1zKo0xSoz1MNZRci1RBJbRJs2l3nBraV5S5Fp65OE/abtA3MkFfKd8NyV5vGXrqelMSehaNyByS0kThCAtpyez3BY5j4QgILQdbwOraGr/xa79qGutigf/jF/8RlbxHK3bYMpGjEUtGtWRlo0attkEUJzi2zVq1TiOR9BXzRKnO9J0pT23pZ2rgkp6mY6rUGSy8/vAkhLju9vLNnsDD1R0MhRDk83n+zt/5O3zxi1/kIx/5CI8++ig7d+58U6+lg+eff573ve99V3zu+7//+/n7f//v35Pn7+Hu4lqU187Q4vTp0zz88MO3vAm/EeX1S19/nn/5e19kYHiE97znPdiZ5i/nOWits7gkKAYe+enNWBYmQ1KA1IJyziWVmnJgE2WGZidPneHAt7+JxuLpp54gP72Fcugwv7hMc2OdkdERyqUSiVRsKxaIpSKNY6q1Go36BmvLC+TCkDCfp5DPY9seSohLTaRtE6emiWxlcSH1yDSR9czQrNZOjd6ynXTzg+NmnYsLS0yMjRLm8mgNCSClifBoZMyNjilapyGtZvr0ajshcG1qbYVjAWhSpfn13/hNVqs1RBrz0z/795keG6YWWwz091OPSvRbsFGts16rErXbaATNdpv20hojfUVqkSbv2dRbpi6b67F5/6MThs6bDd06tbKztb4ZdsebyeDo4Fou2L7v8773vY/Dhw8zPj7OL/zCL9wzp9defeyhhx56uLe4rw2l1prTp09TKBRumsL1elyL0qWU4vjBb5J//t9xprWZgebzpOPbKQY5pIa+zE6+4IFUJnexQwltxcbCvZ2auJB2KjHGgCbAut5s05YWq3MLHDr6Kk898Tizs3M4tsXEpim8zHG1mGl+KnkXqVxKRWNZj4xZWa8Rt+q8urxMMfRY8/MM95dYbxrDisMnzjLSX8YNixmdzDSH1XZKzrVpJxrfNuYODpoLM7OINGF6ejqbcOvssGdiS1KpyHl2l/6qtXE8FA60Ytl1aMx3jX7M7fpGFe2GEDWotWK0kkSJwMuoZznPRmCxoRM8FFObp1AdZ8S1ZVYX5ykVcjT9HNvHB96QN9k5YFxusHT54anTXF7r8HQ3TCduhOs5GIJxMezv7+eDH/zgm3odl2N+fp6RkZErPjcyMkK1WqXVar0h+qSH/zmxtrbG+vo6b3vb2yiVSrd8/+sxOI6dPMP/79/9F5ZW1ri4sMzU5i1Mb96MpUEjEEKTAqlMu1R8KUFoTYxAWBqZAlqZKBDbRIDEUUQUJ1Q3Njh87FXGJiZZXFih1WoyMbkJP/BBgJvZrXq2hZfLEYShYWMkMe1mk41anbm1VSzbplgokMsXKORzaAQra0tsKJicGCVVgkrOJVVQznkopSiHHaMz47AaNTZYW1tj++ZJgsD82zCMEI3l2KZeZtEi3ducYaZ0hnY530Zr41ptIq8UOVewtrGBajcRXkjS2KCVDJDzHdqJxHMs0EajLpOIwZERyqHP4lqVtL7O4cVFSvmADT9HIZ/HsTykhvc/NEo5dLt/w06N6wzcOv/diN1xLzaUndfX9WKVisUib3/729/U67gcvfrYQw899HBvcd80lEtLS6ysrFAqlXjiiSduuym4GqUriiIOHjxIu9HgrHiKmDpS5LG1MNlpnkUz1viOQGoLzyajGtlorSnmTANWcG0SpQhdo82xBKRS8eBDD3Ly1GusrK7yF8+/QJyk7NyxnYHBYZQQ1COJYwuaUYprG+2N5wjSLHdN2R7DQwPAIONKUqvVaTXqXDh/Hse2+LOX9nP08CsE+SI/8bf+Ju7AkNksxuaAYm4FjVijleTi7By2JRgZnSRRFiqWWWabud7AdRDCHNyE0LiuOUDlPNuEZ2fB2+XQIU415dChmUjyno0eHmNqYpzZhQX27t6DxEalGm1DlEpC1+bszBy2iimPTBCGIa1YMjaaJ04VKo2pNxokrQb+ygovvDDL4OAgQ0NDlEqlN/zdX3946mwvr3V4ul7+2d3CjRrKzoGphx7uBJ14GjCvqXPnzqGU4h3veMdtDdvg6htKpRQHXznCfz04S6FviNVqA8v1sB0PqTSqo9rWGq0tYmm2eEqDJcyG0hIamRoaqlTGmEunpnkZnZyicOI1zl64yMbLR4hiyaO7H2N8fBPKtolT04A6jjE581yLKE5MvFAiCX0HvDwT4xXqUYJII5bWq1Rrc7RTxdLcLF//ixfQacRzH/ohntizx2gTLYu2NPrLlKyJU4rZuXniqM34xCT5XEicSnzHJs6MhWKp8WxBLDVuljFsWZpUgp29b7m2iYJKpcKxLKQGzzNskWee3Me3vvVtRkaG2LJ1K45tIRDYno0lBMurqzQ3lpmanKBYyJMq2JILUVoj05R6o0GtXmdpbhXLdnliSx++bCCl/4a6c7X62Gkur7a9vFf1sXNtV0Oj0ei5YPfQQw89fIfjnm8oO1vJ06dP09/fT7FYvKM3vNcfmKrVKvv376dYKrNc2km6I4+1cR5Km0iEi2tZtGKF71rU2oYuud5MGSwHJg7Dd6g3U/KuoJGY7LNEanzXmEOEro070EdfIcdsGhMlEZ4tmBwbRQijhRG2CfS2BCiAzFgCzGHl9WY7fpgnyBfo05qo3WZmZpY4TUmq6xw9cpgnnngSt1Cg4DsIAV5GS1NJxMXZi4S+z9joGLFUeI6gFUvymelOIaNqFQKH9eznW8+oXOvNhLxn0YrNgSrKstpSJfBtC8sSVFcWOXv6JEIIDr74TX7oQz+A65mGu+jbnLlwEQ9F3+g4oWe0Uh2HRLTGdjyCgsOPv+MhhvMOKysrLC8vc/DgQQAGBgYYHBxkYGDgDYfmG20vkyQhSZLu52/X2OdGuBFtrBPcfS8xOjrKwsLCFZ9bWFigVCr1pu//k2NxcZGXX36Z/v5+2u32bTeT8Mb6GMcxL+4/wDfONpicmmJwaJijx47RV+lj89REtrYDs6O0cG2Ba2nTJHUMarQ2zaVlmjbHsk0xy0zH8rmQkZFBjh0VJHGbOIrYvGkSqY3pTpwoPMcmSlOCzCTM1Ku0GxtyKT7EpdaGLZNjVNspjk555cgx2s06wg04fuwYY+OTlIt5pOOaaxRZkwucuTiLIzT9w+OEYWAiR/xL0UfrWR7mess0tPVIY1mGjSKV6kaM5H2TB1wM3Ev3baZ4tuDFQ0dJtMXFhWXOzswxNTFOOzEa09MX50lbNSrD4/hBSLWV4tiAFiaGyrVxcwU2VSrU2wnjBcHOkuT48eNEUURfX193APf6f9fXqo+Xa9M7Gtw0TW9Je3kruFFDWa/XGRwcvOvPez306mMPPfTQw73FPW0o0zTllVdeYWNjg7e97W1XWIvfLi6ndM3Pz/PKK6+wdetWzsV5NpYb2IUhZmqSAa9EznMAje8YM5pi6KCBvCdoJylF3xg45Dyb9XZCKfCyLLQO5dRhPdMl7nh4NxvVKv3Dozzz9NuQGnzbtIieZez0bWEiP4Qwk36y4GsyupRlmSBux7aIEkXg2SRewLve/U6+8OWvUs7n2LR1J6vr65y5OE85H+AGOYYqRVpxysrCLPlSH2MjQyilKXouUmoqOYtUQjnnIGWWa5kqKqFDK5HGgTE2pjvt1DS6idJIBa4jiJKE0HOot1JAYIdFVLuBFeSx0LSy3M7zMzPYlsXE5CSWZWNnbrOWRXebkSrNk5sHmKiYN/HR0VFGR0fRWrOxscHy8jLnzp3jyJEjlErGdXFwcLDrrvr6v/Xlzr4nT56k3W7T19d3R8Y+N8L1NpQdc557PYF/5pln+O///b9f8bkvfOELPPPMM/f0Onq4e9Bad/WSjzzyCI7jcPz48Tt6zMvrY61W48WXXuLVDZfKyCSB52LbLtu2bMZyXBzLIlXZBk4ZR1XLMi6qlhDYtiBNFcICpMJxLJS2zOAsc0JNlcazBLt27ODsayfBtnnu/e/Fc2yzOczqrtTgOYaab/J3ReayqjP6qu7SWCuZTrMSuqTK4V1PP87Fs6fQSrFvzx500mZ2ZgXbcSjmC+QKeTzX5eLsHHnXZmh0jNDtZPq6RImhscaJkQN0YpdUVv86dSvnGz17KXRpxZJi4NLKGsV2N09YmumgShGOj28L2okxNDozM4eKW/SPTJAPfaK0Q9+3iFLZNTUqZtmaY5WQD+2dxM5MiJrNJktLSywuLnLixAlyuRyDg4MMDg5SqVRuyO6Yn59ndnaWhx566Ja16beCTn28FrW2Vx976KGHHr7zcc8ayjiOeeGFF66wvF9aWqLdbt/R43as9U+ePMnZs2d57LHHON9yOTy7TDFw+OM//RJnThyl0D/Ej/3lH8L1fALPRmmFo63Mfr5jQgHFwOgMKznPNGOhkx0qjPNpMbA5fWGevGfxof/lxxgqF4yJg2tdit3IYjg6t83Y6G/i1Di1pkoTZHQp83zgO1Y3a23vnr08vmcPidK4jkU7VkxZioXVGjpp8urp8wSWRrsB+VzIejOmGHisN0wzXGtLAtc4wrqORZoYClSqwLYFlhCErmn89GUHQgtBqjV+ziZOJOXQwR0Z48MffI4TZ87zticep5kCOuHsxVlcx2VsbNTEA3gW9ezn75gB1SLJZH+OPVfJmxRCUKlUqFQqbN++nXa7zfLyMsvLy5w5cwbHcbqHp4GBgSsaOq01J06cYHl5mSeffJIgMIHft2tccSPciDZ2Nyhd9Xqd1157rfvxmTNnOHjwIP39/UxNTfGLv/iLXLx4kU996lMA/PRP/zT/5t/8Gz7+8Y/z0Y9+lC9/+cv8/u//Pp/97Gfv6Dp6uD/QWnPw4MHusK1UKrG6unrTGZLXgm3bRFHEwsIChw4dYlYMkuRyeI7NRivm5LHDfP35b+HZ8N73P8fW6Uk2WgmhZ9NoJYCgFqWUNNQbCb5rqPwdh+kwM7HJ+y71ZkLed5mZX6a+vsJ7n/sQYwN9NGKJEFDPsmsbkTSbyVQSOBbN2NTFZmxMaVqJxLMt2onCtiBKTUObKo3WmrHxCf73v/e/YwtTM11HECeSJGqzXq2zOD9nsiY9h1yhgiMgUcq4akuNY5uGzck0nI4Ftm0hMcO9VCkCJ3PG9kxzHXoWWkPomjoU+jZWpv/86N/667zwzW+xc/t2JibGsYELFy9iy4Sp6anMhdpsdW0hUNnjmPcZ0ywPlwKee3TMxEJxyfQrn8+zefNm0jTtsjteeeUVlFL09/czNDTEwMAAvu9f8Xefn5/n1VdfZffu3QwODt6yNv1WcC8YHL362EMPPfTw1sY9ayg9z2Pz5s2Mj49fQdW5PGftdmBZFufOnSNNU55++mkWWvDyhSXKOZdWJFk4f5p2qhGrS8wurbB98ybqWcNVi1MUFo0owXJTtDYHFtsSmXsoJFLgOgKlBL4FFy7M4KiUsalpAt8zAduBzfLqGqHnkDh5SuFlGY9t02RVL8t67NBNi4HDWpbhVs2asGZkrq3TZKYKQs9GacHkcIWVNcjX61QGBlBSsbCwgJKStTBHsZDHIodjm1gQjYkhiaQxG6pnuZrVdkohsKm2ZNfNsOA7NBLjnBgnEscyNF3fETz55JM88eQT2MKiHbWZmZmhnM8zPjJCitnwthJFOefSiFJzGI0lec/h+x4cuSlTiCAImJycZHJyEqUUa2trLC0tceLEiSuoXwMDA5w9e5a1tTWeeOKJK+hLt2tccSPcjIbyThvKF198kfe85z3dj//BP/gHAPzET/wEn/zkJ5mbm+P8+fPdr2/ZsoXPfvaz/NzP/Ry/8iu/wuTkJL/5m7/Zs8T/nxRCCMbHx3nooYe6FNe7VR/X19eZm5ujUZwiimyKtmUomYHDhdkFGhurtLyA2uoS0fhYlsFrmr9FBTlP0IoSQtdkIxZD7zJ6akoh8Lo01bMXF5AZxXO4UuzS7edWqngoklyBUhbl0Ylb6hiChVmz6TuCZmziQtLUZO0KYfSQrm3Taps61cgcXk1tdWmnPpWKxfJ6laH+MrEUrK2tc2FugUIY4oc58vkcge+TKo3vmN+DobPGWV2OKQQua82sqY4Ujm2kDBrz/FFiKLq1yNTwgdFN/JUf3UwzlkRxyoXZWbSUbNo0STvV2I42tF7Pph5JQteiHqU4liDOmuV3PzCcMWiuDsdxGBkZYWRkBK011WqV5eVlLly40GV3dAZw1WqVkydPsmfPHvr7+7uvg1vRpnf+/83gXgzcevWxhx566OGtjXtqyrNp06au6QSYN8k7mcA3m03W19dxXZdnnnmG9bbiGyfnjKV9akxnnnjySb79rW8ysWkTO6fG0ZDRnBQFzzVug5bRyziWIFHZpk5qLI0xppCQpgkXL84R+C4DwxMgjDmOKwTHXzvJi99+AdvxeM+738PI6Aj5zBGwHJpssb7LHAOTVFEKHeOu6ts0ojTLUZP4rkU9MuY71bYx32knhj66tLxCrbbB2NgkYRDgWhaVvgG0SqnW6jTrNZYWF8mHPmG+SDmfx3F90+gqqOQ82qnKTHdM1EgtSgld0wC6tkUrNn8PqTVpajQ+7cQ0oisbddaX5nDCAmOjwzQTRehYtFKFa0MqNYFrYQmBYzl830MjhN6tW9ZblsXAwAADAwNd6tfy8jKLi4scP368e/hutVr4vn/Txj6dgcGtbi+vN4GXUtJqte74wPTud7/7in8br8cnP/nJq97nwIEDd/S8Pbx1MDo6eoXB2J3WRykl8/PzNJtNvPEHWF9PyHkWUinyvoPS8PijD7KyMEvge2zbvp1SzjV0+dAl1ZqCbxs6feiQpoq+vJ/RVF2kgnJGUy36NhdmLiKSmKmpacLAI1GaSugxv7TIV7/wBaI05Zknn6Sw6wHKoXHZLocuidIUA+PKms+er7MR1F0joEyqIBWF0CZJzX2jy1xYRdrm3MU5pseGCQpFs22UCiUl9UaDer1ObX0FLIe+coHIz9FfytNKJKXA7TaorTjFd4xRmS3AAlKlCD2HZnwplqQTRxK4Ns1EAYoLF+fQCDZPTxGl+pImNPte3zVMF41GYyQP739kjOFicNN/VyEE5XKZcrnczXtcWVlhaWmJs2fPopRicHCQJElI0xTHufJt/kba9FutjzcauN0NymuvPvbQQw89vLVxX2NDbpSRdj2srq5y4MABPM9jfHycWFt85uUZEqm6gdfNlmTP3r3sePARCoFDO9WENsbdLwurDmxBo15j0HNwXUOHlapziNAIAY1mk9nZWYqFEgODg8adTxvaVCJgbnGZjXqEZcXML61QGRjsagjRgBDYCvOBzMwstDH6sRAUffNncHwHNLi+QGuB43ZCvDWLiws0mi02T21CYuPaVmZmYdOIbAYHB6i3y4y5sLhaI4paHFtcJecKnLBAfymP6wfZQQk8S5jm2ndAiMtMNzSWEIbm5RqNUDl0Wdmos7Y4R1jqY2Swn3pkGvBmolBag2MRJYbyVm2nPLNtgPHKnZsfdKhfYRiyvr5OPp9nenqa9fX1K6hfnen866lfN2Nccfn3Xm17eb0DU6PRAOi5GPZwx3j9Jr/jYK21vuXoh1arxYEDB5BSsqJzrC62yWcbxpxr04gVoWfRPzbB3/7IT5ja6Fk02llWbUZDjVNIoibLa1X6SwVaiRl6tRKF7wiiRIHWnLtwAc9xGJvYhO8bmYBjCSIpWVleZq1aQ6UpF2bn2L5zF6kyPj5Sa0wlsgBTewBsS5CkhgKbKI1vWSTaOFKn0gy60iyvUUlFY2OdpeVlNk2MUSgYx2XLEqYhdG0C32VksI9UKqJWk41qneryAqtLmkI+R5ovmMxLS5D3jB5QaSMPUNqwRKTS9HXiSUIXpaEUZm+hWnPxwiw532PTxDhSQ+gZDX0pcFCYWy3AtW1syzT2uzdV2DlyZw7Rvu8zPj7ebSwfeOABWq0Wp06d4pVXXumyOwYHB8nlctfVpt/M9vJW6iPcH9OyHnrooYce7i3ue0N5O5SuCxcu8Oqrr7Jr1y5qtRpxKvn8kXkE4FhWd1vWyZQMPYd6pMh7dneybBxObdxCH7Jd59UzM+Q8CyfIM1wpYnkBOc9meXWd1aUFhoeGGejv6+pgpFIIS6CVYNe2LawszuJ5PpsmJ4yToTSmPJFS+LYgyvSTzcRY1jczemk9SQldsxnMe8Y0J+87xFISug7tOGF5fhYlUx4Y9tCuheN6pErRl3OIpdl+RtnmsZ0qxof6aMUlhoYFG/U67WaTi/OLpKmkkM/hBzn6ygVSLchlU/dC4FDL6Lj1jIYWpSmuZbG6UWNxfpbRkRHK5Qoiy5CzhOiGnKcaQtciSjXbhwvsm6rctdeJlJKXX36ZKIp48skncV2XiYkJtNbUajWWlpaYmZnh6NGjV1C/SqXSLR2ermVcIaV8w5S/g15D2cObhc4h/Xqvv6thbW2NAwcOMDw8zJr0OXjmAru2mdpnXJ/Nv/NqOzMii8xt5+ONrhY6pr+vzOxiyuryEjNzCwyUclhejqG+ItWWxtYpp89fpL9coFgZwHaMhtt3LBqRxHUs+kfGmZjcRKvVZsu2HcQqo69aFklimsZGmpBzjS7zkqNqRxrgGofqwDV1O6OoGofYmPXVFarVGo7rkSiot1MCz6LVzqipmVvsRtM8Rmr5DA3nacYDKBlTqzWYW1olnZ3H8Xz6y0UsN6CvGBp9fKaHzzkWjchEighMQ+i7NvVmxOrCLJYfMj42apgmtkU9VljC5OQmqYlDaceSnGfRjFI29ed4cvPAHb9OOs7pFy5c4Mknn+xGGO3cubPL7lheXua1117D9/2ua2xfX99V2R2d116nFt5oe3kj07JerFIPPfTQw3c+7mlDeXnOGtw6pUspxauvvsrc3ByPP/44/f39HD16lBfO1UgDh8CxMht7Cwtj/+7YDlpoypmzazmXufv5Nu1EMtpfohHnGR0TLK3X0XGL0zPz+JYiwcHRKcWBESp9FXPQCi7FbjQiScG3GRwZ4Uc+/OErdI8FX1xhvuO7FjL7eiINhSxKFEXfoRlLCr6bOf453UPMSq3FxvI8sbZ4YPUrNE6vUsjnWH/0o4SBTyPT4zTjjsOiae4AQs/CEhZepYTVV0IqTZrEbNTqtBo1zq4tUwg9GmGBvlKBVgw5z8poWRaNOMUWgvWNdeYXlpgcH8PyQ5SGRqYL3ejoQ9uGNttMFKHv8K6dQ3ctTFtKycGDB5FS8vjjj2cGF5deT6VSiVKpxLZt24jjuHt4On/+PJZldZvL/v7+K+4LN6Z+dQ5USZIQBEGmq73yANZoNPB9/w2P3UMPd4rO6/JqtMVrYWZmhmPHjrFr1y5U2MeXXzxFztagoZLRSw39vuOaqq64TZRxg44SSSkwNXPH9ARxKlFJzOpGjXZjnZMri+R8j1YUMzbQR6lvANc2RjP5bJsXeoa+OlAp8gMf+H6U1ti2hVLge4aOmvdsYqUo+Q5RqimFLu1EZreZw2piHFaNe6pNO1XkfIc4TVleWiCOYk6+dpJv/sWfE+QLfOwn/w5WpZ9i6NCMzM9Rj8wgsRmZeJA4lTi2he0E2I7H2PAg1WYEacTSeg2SFebnbfrLBVqtkFIhR5S9VTmWee/IBw6rGw3Wl+Zxc0UmR4eoR0Y6EEtlXLM9MywsZIO6jl6+L+/xvoduTl9+PWitOXnyJHNzczzxxBNvGGzlcjmmpqaYmppCSsnq6ipLS0scOXKENE2vYHd0zM06eL2W8lra9DiOs8isN9ZH6OVQ9tBDDz18N+C+byhvtqGM45iDBw8SxzHPPPMMuVwOgCNLMWdWI7ZuEqx3ptutmNBzaEcpnmUOLq5jo5VCKYHKtHSeI9Dort5xrL+M1CVGh4c4PzODFUVo26G+ssCZeo3+coGqDCnng67ZTqfJvHyS3ZmsF/xL5jv1tiTnm5iOjoNg4Fqk2tBOpdaUM2pUOefSbLZYX7yIH+bZ3Beizs5TICFqJPjJOrEzjCWEifuQgBBEiSR0bENryybzHZ1PMXCItM3gQD/tch9jSDZqDdrNBmfPreLYFoVigXyugOuFeL7N2vo66yvL7JyexA1DXEvQzvSfzVgSejbNOMWyzHWkSvOhB4dvSzd5NaRpyoEDBxBCsG/fvhseqjv05/HxcZRSrK+vs7y83KV+VSqV7nT+Zqlfq6urrK2tMTIy0p3OX/59HTrX3Wqge/juxbVejzdTI5VSHD9+nNnZWfbt20di5/izw3OmRkgFQhNLjciiijqf79zawrA6bAuiRCKERmlhqKfZ9wnPZ2Q4QDPI0uIi6+vr+J7Lysoa1VqdYqlEPp/HDQMQYAsLKZR5DDShJUiUMQi7dAuBYyG1yCiihlmitKmPWkPo2CAMJVcgCF2NVprFuVlsYMvUFH/0+7+HlCn1tWVOnz7N258dQSlNIdOKFgIbEOR9B0sIXNsM36Q2jq1SagZLIVKFDPT1kUpJu9WkWqtTW1mkuqwI83nKxSLCyVEOXeqNJtXluUzv3Y/SUAwtky+sNbYnMr2ph1RG8ymVpr/g8wOPjuE5d1YntdYcP36cxcVFnnjiiRvSSm3bZmhoiKGhIbTW1Ot1lpeXmZub49VXXyWfz3djm8rl8k3Vx2azydzcHENDQ1fdXgoh7ktsSA899NBDD/cW/1M0lLVajQMHDlAsFq9oLF6dq3J2LaHgmoOBMZ2RWVSHJOcYs5nQdWgnEteGVpxiW1YWzG1cHzRkukEFWjE3O4dtweT0FjzHJk5SWo0atXqddmORVd+jVCjS0nnKudA0g6FDLBXl7kQ9u4Zs65fzbGot04RVM3OGatuYPzTTFNe2jBGQZfRPc3MXGewfoK+/DywL0b8Fe/UEfmkYysNILGzLaHQsj+wg5mbX4BGlklJgqLWFrLkMMpdBxxLE2iLMFSgUSxRTiU4iVjfq1OuL1KOUUuBSbSdsmRxDOT5Ca6LUaEqVAt+1cQTYwug5U6V5bLKPsfLdCY1OkoQDBw7gOA67d+++rkbnarAsi/7+fvr7+9m5cyetVovl5WWWlpY4depUl/o1ODhIX1/fGx6/4455+PBhdu7cyejo6FWpX+vr63cUPN9DD9fDzdTIJEk4ePAgURTxzDPP0FY2f/LSBdCaVmoGPa1I4TqCJKOhtpPOrfl8OzHNZBKZ5lIBtmW2bEbzTbdRWlxcotVqMjI2SS4MiJOUOGqxXq2zvLKG1IK+cgHHDxkoF7PNXIe26rDWjClmTqod52sjQzBOr6ZWm+bSpGgIpM4cVtMUV2hOn5uhlPMp9g/huDaPP/NOvvqFP6MwNMaOnTupthI8x5ir0TXWMc1rK+kwTS5lDOdci3pbZY7eoIXGDXLkLY/hkRHWag1I2swsrkAyi7ZdpIzp7x/Ez5eIU5W5v7o0kpSca2KTfFuY9xyhEVm+5/c+OEIld2c1Q2vNsWPHWF1d5cknn7zC7fpmIISgWCxSLBbZsmULcRx3Y0kOHjwIwMDAQLdGXo3dEUURL7/8cjf66XJmR+c1q5TqaSh76KGHHr4LcM8pr1c8eUZ5vZ7pxOLiIi+//DLT09Ns3769+31z6y3+x4lFhAWtROJ2p98ZpdR3SDVUchZJqsj7FlIq8p6ZWAsBUoGwNKnUCCCKYhYW5vH9gOGRYTRmIyeEhV8oExQrJGlK1GyxUa+xsLKCZdkUCwUKhQKFMIcURjMpMBRSIQSl0AKdmTPo7NqUIufZKG1osOb3A416jdn5RSZHR3HDHLawqEUp+cf+KtXaBoVimXpbUgwE681MC9U0kSONKMVzBLGUmemDJsiCuouBiyXAtayua6KFRaqVaURdh75SgUYsWV2cZ63WJB94nJ2ZI/A98vk8fpijnA+pZ5rLTgTKWjNh8+DV8yZvB3Ecs3//fnzf57HHHrvlZvJqCMOQTZs2sWnTpi71a3l5mWPHjhHHcTfTrUP92tjY4MCBA2zfvp3JyUngjdSvNE351V/9VTY2Nm7LOKWHHm6EG+nM6/U6+/fvp1Ao8PTTT9NMNJ95eQZHGEOvnOewoowBj8wo+EppQtdBakXg2khlojqkMkY7GnCEyee1bNNIKgFaSWZnZ0ErJicnCQOToZgLPFzHpq9cpp2kqDhidaNGa3WJxYUFBkp5WkGOwUqJVqwoBkYrXghcmpmzajsxcUmJ1Lgd3r4A27KIU3OdrUTh6JTXzl5kuFIkLPcb9kckec87386ePY+SC0KEZYEAjUBlP3Mrq1n1tokYuZy94bs2UaqwbAFYpEqS8x2aUUohNNEo/aU89bbPtko/C8urrK2uEAYhy8urtOo1LD/HcKYrzfsutXaK61hEUqOU7lJf375jiKmBO2uulFIcPXqUjY0NnnjiiTdQVW8HnucxNjbG2NgYWms2NjZYXl7m7NmzHDlyhHK53G0uC4UCcRzz0ksvUS6XefjhhxFCXCEd6GjTf/u3fxswTq899NBDDz185+K+byjh6qYTWmvOnDnDqVOnePTRRxkdHe1+rdpK+OwrcwgEkRSgodpOyXt217RhrWWMGNYaMQXPRHEUAhPdETgWkVT4rkUqIefZVOs1VhfmGOzrY3BgAI1AoFHKNMJSGVtC7Vj4pSK5Qh5bCGqNJu1Wk5m5eSyt8YIc5VKepZVVZBwzMTVNIQxM9IZndJOhnzWStqGTea6FlJpGdZ215RV2Tk/gh3kcR5BKRV/OI5KKcrkvs8l3MhMfm3o7wXNN5IhjmU2DRmcUNnVF7EetLbsbgULg0IgScp45qLmOIEolK4vzJEnMzm1bCHyHOE5pNRvUag02Fi+yIWzKxQI6yVMq5kikoj/v8b0PDN+VhiqKIvbv308ul+PRRx+97bDt6+FG1K8gCGi3291czNejc00f//jHOXXqFMeOHXtTrrOH7y5c7d/P9XTmS0tLHDp0iKmpKXbs2EErkXz+8BxSmuxEqQWubSMyXbkQhrqKsBBCYykbk40ksABhCWzbNJ525m5qCY1UQBpzfmaG0PcZGZ3M9JKanGshNXh+Jz7EI/XMcCpWmiSK2KjVaFbXeW15kVyYIyoUiNotTq2ssHl6GtsvkfMcY/YF2GQDL0t0m99EaRwVcWHmIpuGByj19XevseDbCGCoUsbKnKptYeimOc9BSrq000rONVr60EFpQSkw0VGe3XF01eQ88z2V0EWSRaNIo+9cXV2lXVtl+/QkQS6HjaZab9Jq1pmbnUUA9VyOQqGA7+XBcXAyau0jE0X2TvXd0WtEKcXhw4ep1+s88cQTb3C1vhsQQlCpVLqbx3a73dWmnz59Gtd1kVJSKBR44IEHrkqN1VrzH//jf+Sf/JN/whe/+EUefvjhu36dPfTQQw89vHUg9PXCne4y0jS94nCklOLzn/8873nPe654Y5RScvjwYdbW1ti3bx+lUqn7tTiVfOblOWqtBI2mulFlZW2NLZunzXTbEbRiReDY1C8L4y4FHvXo8im103U9vLiwQrO6SrFviNHBCu1EkvPM1DpwzITcd22SVHbpnY4tSKUxw+lQsxrNNq1mg+OvneKFv/hzJBaPPvQAb3vHu+nLdJeXm+7Uum6LCc2NVZbWa2yfniTFyQwkUnK+TZwqXNsyByXLHP8EHQ6awLIMDdW2BKlW2AhSDU7HXdYWtBLzszRik3HZiiW+a34GALRmZnYWR2j6h0Yp5X0a7Ssz1+rtBJW0qdYbNBs1UIpcLs8P7p7g4S3jd3y4abfb3an3Qw89dF+atE4cTaFQoNVqAW+kfiml+PjHP85nPvMZvvrVr7J169Z7fp09fOdBKUWSJFd87vnnn2fLli1XDNS01pw9e5bXXnuNRx55hLGxMeJU8kcvXWC5EeHZWa1yLJqtmJnzZ9m8fTteZhbj2aameI6gHUu8rE6EnnEhDT3jRp3zTA0SMub0hVlG+suEpb6uE3QnY7Gz8bu8ptazmtGIOjRWhVCSjVqd1ZVlvvyVr9BsNhkdGeaDH/ohSoWQKMsONiZlzhUmZbLd4PzsApNjw3i5Ao4FSptcJjtzUQ08s+nMd+7rm+vKeXZW7yySjLZv9OeSwDERJzk/e1/wzXAucAVRorFtQBsn09rGOsvrG2yf3oQUjrnWJMW1TWalQJMmCevVGipqUW1FVHIBVpBneqTCj71tK+4d6CaVUrz88su022327dt3X6j27Xabb3/7290mMoqibizJ0NAQYRiiteY//+f/zN/9u3+XP/iDP+ADH/jAPb/OHnrooYce7i3uK+W1I9xP07TbjLTbbfbv349lWTzzzDNXNClaaz53ZJ6Lqw1yvksiJUpjtoeITBMJYeb2WvCMVrKSc1EKyqFxLazkMkdB3+bC3DztRp2B4TEGyvlLTV7rSlpn12TnMt1N5/vyvtnyFajixQv05z2EZSPbTUMduniOdc+nXMjTokg5DJDKXFc7SamvLtJoRezaOoXEIrQsWnGKYwvaSeY0KjVS6WyTmEWNREaz04n76LjP1uI0O0SZZjLOaGQSky1pWQLHd7AskLYGKTk/c5GCZzE0OkHg2ETSbELbiSLnGc2VYwtsN4/tBYyPjrBRb7GrAml1ia9//RTFYrF7sCgWi7e0sWy1Wrz00kv09/fz4IMP3hf6aLVa5dChQ2zfvp3p6ekrqF/nzp3jyJEj/MZv/EZX3/mNb3yj10z28Kbi9ZRXKSVHjhxhZWWFp556inK5TCoVnz00y0rNRGmYiKLs1rWNAZhttImhZ9NKJIFjoisC16KVGIpnK0ovUT19h0YskVGDc7OLbB4fxvLzlzWP5rajzy5k9NGcZ9OIpKGoZsOrJDP7abTaXJi5QCmfx3IchFY02jFL8xeZFw6DlSJNL2S4UugO/2rtlGZ9ncXldaYmJ/CCADBmL0qqjM4qyQfuFQMwc9+EIIticmyjN1eAb1uZY6x72ZAxY3FEqdHNp1l8kLBop5LG+ior1QY7tkwTK0HomuGcbVkoZWitOd9BKpgaG6bWThmzNMsbprnsr5/jmy8sdGn1V4vsuB6klBw6dIgkSd7gdn2vEMcxBw4coFwu88gjj3QNd5aWllhcXOTEiRN89rOfZW5ujs9//vP83u/9Xq+Z7KGHHnr4LsE93VBKKd+gB/rSl77Ek08+SalUYn19nQMHDjA0NHTVDdXXTyxy8MIG+UwvmPMcltY3qK2tMjY5lR1eFBamyRQYGhfabO8ABAIhQGvJ3Ow8qUyYnJjA8zy0AtsWpEphC4tUKZMpmZqGrJ0YmmwjMhvMenTpAJVXNaIDv0dBbrAaTHOgPUG10eZtj+8lKJRJY2NaEbcaaGFRLuYJwhwbq6tYtsXY+Di+66GUoaupzAxDad0N17aEiSJxLIilxsuuKXANpTfIri1wrW6wuNJ0o1Ti1GRyti7bAAS25uTZGco5n76hYQq+OajYtsB3LCwhCF0zVQ9dG43RfNqWIPRsHp0oA3QjO5aWllhZWcFxnG5z2d/ff8Pg6/379zM0NMSuXbvuSzNZq9V46aWX2Lx5M5s3b77q9zSbTX7mZ36Gr33ta9RqNQYGBrqmFD30cKe42obypZdeYnBwkOnpadrtNgcOHABg3759+L6PUpovHZtnZq2ZmXTpK26TJOW1kyfYvmMnruNkdFadObeCQuBk5l6GbdGJINKsriyzurrOpk0T+EEON8ub7Xzdti6rU4qMNSEysYDGqBgtlDaa43/7iU+wMD9PsVTme97+LEtLyzz2yMNMbd7SNfVpNWokqaJUzBPmCrRaDRqNJhOTk4SB2X5Zgks/p778ujVKCSzLSBRsYfSgGFYvSmnzs2bvB+YxzH0todGIzH3IUG1tyyKVksWFOdpRzNSmSSzbpVOetMpouRqcy343UtH9HgF838NjDBXcbmTH0tISSqkrmA/X2zamacrBgwfRWrN3795byiS9W0iShJdeeokwDK8pRUiShH/xL/4Fn/70p1lbW0NrzR//8R/zrne9655fbw899NBDD/cW91VDCZdcDC9evMjRo0fZuXMnU1NTb2gqjs5WeW2pQSXnkGbbRqmM0U1NKwq+oR35liCWhiKaKEP/TLRCaHNIEAKSNGF2dhbXdRgenUAJkylpWwKZaBxL09YqayKNHqez5VPamO2A0e6AoBw6sLZOoNZRSjKYzvPO7/kxs31VRo0pPZswV0AITb3eotWsMTM7j2cLXDtHs9Gk5SpKOa+rcexsRzdaqdFNZu6xRt/joIFyaJq7oZIgcMzGt3Pbafxc29x6ttFIOdnnkqjFK4cO8uS+IR575OE7auReH9mxtrbG0tISr776KnEcdw9PQ0NDV2yd6/U6L730EmNjY+zYseO+NpPT09PXbCa11vzKr/wKX/nKV/jKV77Cjh07+OY3v9lrJnu4a7iehnJjY4P9+/czODjIww8/3D3Qv3B6mXMrDfKeQyO+NOAq+MbROfRcEiVMLUuN7rrWukRHzfvmfnnPOGLnfYd6K2ZteYFqM2L7ls2kWNgWNGOz8WwlZvMYJSYSRKad6wcplRnKpSZ7txWnhL5DtdGmurYGjkfUrDM+tYV3fM/bqbdTHNsidgJGRvJEyQAyTahWa8zOL4DWuL5Ps9killDJB5eus93RgqeGzh9lQ7TUtLOubdHOvrcRpRR9h2pkDMxqHYftWGFZmBqfyuz312GhRGwsL9BMJFOTk0RSoGVmtpOobgZvwXdYb6f4jkUjNoNMN9uCft/Do4yWjWnO5brtarXazcs9evQo5XK5u728PIaow4awbZu9e/feFYOyW0WnmQyC4Lq69i996Uv80i/9Er/927/Nj/7oj7J//362b99+j6+2hx566KGH+4F7uqG82gT+a1/7GsVikdXVVXbv3s3g4OAb7jez1uSPD1wkzN7sC75DI0kpeg6rtQZrC7Ns2rId1+IKvaFjd6bQJpJDWJpmo83s7EUKxSJDQ4OZ+Y7ZSgphTHA6DqmdKXbHrKFza1vm+x3Lyg5QFkpKCue+iK5eJNz6NOnIbnKeybc0t5rAtREC0qjFqddOMTzYx8jwEPWNDTY21oiaDfoqZUaGBhkdHqKYz+HaJgvOse+unrBarbJ//34mJiaucM+929Ba02g0upP5arXapcbm83mOHTvG1NQUW7duvS/NZL1e58UXX+xew9WgteaXfumX+OVf/mW+9KUvsWfPnnt7kT18V0BrTRzHV3zu8OHD3UiHHTt2MD09fUv/TrTWfO5zn+Od73ynYWFk5b6TEfh6RFHEwYMHsSyL3bt331Wd3r//9/+eT37yk3zf930f/+f/+X9eszHpXINt2+zcuZP19XWWlpZYW1vrZiUODw/fMq3+VtChd3Zii97MrWDH9GZpaYnV1VV832doaIi+vj5Onz59V92ubxVJklzhuH2tv9mXv/xlfvzHf5xPfOIT/PW//td7jtc99NBDD99luK8NZZIkfPWrX8VxHJ566qmrZlWtN2P+y/4ZNNBOJJ5jNDM536YZpThacuLMeR7ZtZ1qO6YUemZy/TpTh3LOZXl1g9rKAn2DQ0yNDSEV5Hxjo59zbbTANH1A4Jjmz3M62z0ra+zMds+5YvNndIk3g6WlJV555RW2bdvG9PT0FV9rtVrdxuvyw9PQ0BClUumuvUmvrq5y6NAhtmzZcs2N3JuFDjV2dnaWtbU1bNtmdHT0pqixdxudZnLTpk1s27btqt+jteZf/+t/zT/7Z/+Mz33uczz55JP37Pp6+O7C6xtKrTXPP/889XqdvXv3MjQ0dFuP+4UvfIGnnnqKMAy7uvWroZP329/ff99MsRqNRlend/kmFsz7RafxWl5exnGcbn3s7++/a9fbMQcrFos88sgj9/T30Ik0WlhYYH5+Hri02bwRNfZuo9NMep7H7t27r/l7+NrXvsaP/uiP8iu/8iv87b/9t3vNZA899NDDdyHuW0PZyU9LkoQdO3YwNTX1hu/XWvON15aptxP8rMHzHRvbAse2cSxI44iXDx5gemoTI0MDlIsFPMfGsS1cW+BYFo4FZ8+e5ezZszz66KO3fTC7U8zMzHD8+HEeeeQRRkZGrvu9SZKwsrLC4uIiKysrV0Rd3MnhaXFxkcOHD7Nr1y4mJiZu6zHuFB2t7ObNmymVSt0m+nrU2LuNDtV2cnLyus3kr//6r/NP/sk/4U//9E955pln3rTr6aEHMNs5MLq5Q4cOsb6+Tn9/P3v37r2tx9Na85WvfIWBgQHGx8fp6+u76oG/M+jqDJnuR1Owvr7OwYMHb4o10aHVLy4usrS0RJqm3brRcWO+HXT03AMDA/fNHKxjTJfP55menmZlZYWlpSXq9Xo3D3JoaOgKauzdRpqm7N+/H8dx2LNnzzXfb/7iL/6CD3/4w/yzf/bP+NjHPtZrJnvooYcevktxTxvKzgS+k5+2adMmarUaw8PDV20ob/RYnfDk+fn5buMVBAHDw8MMDQ1RLpfRWnP06FHW1tbYs2cPxWLxTfrprn+tp06d4sKFC+zZs4e+vlvLIrtck7i0tESSJLd1eJqdneXYsWM8+uijDA8P386PcsdYXV3l4MGD7Nixg02bNnU/fz1q7O24xl4PjUaDF198kYmJCbZt23bVx9Va8zu/8zv8o3/0j/jMZz7DO9/5zrvy3D30cD3EcdwdtoVhSLlcptlssnv37lt+LKVUt3bMzc2xtLQE0KWMdgZT58+f59SpUzz88MM3HHS9WegMul5fF24GWmtqtVrXbbTRaNDX19cdwIVheFOPs7GxwYEDB7pDpvvRHHXcrvv6+njooYeuuIZrUWNvxzX2eri8mdy9e/c1WSPf/va3+Ut/6S/x//6//y8/+7M/22sme+ihhx6+i3HPN5QnTpzgtdde4+GHH2Z8fJyDBw9SLpfZsmXLTT9Op5F8vR5IStl9w11aWuq+wTqOw759+276YHE3oZTqNrR79+6lUCjc0eNdfnjqTK0rlUq3ib7Wz3ju3DlOnz7N7t276e/vv6NruF0sLy/z8ssv88ADDzA+Pn7d770T19jrodNMjo+PX3MLorXmd3/3d/mFX/gF/uRP/oT3vOc9t/VcPfRwq5idne1u6Xbt2sX58+dZWVlh3759N/0Ylw/b4FJ9VEp19YiLi4skSYLnecRxzJ49e+5bXbhw4QInT57kkUceuSuDrmtJB66nu+zIALZu3foGKcK9QrPZ7Lr6PvDAA9dt0DrU2Ntxjb0e0jTlwIEDWJbFnj17rllnDxw4wAc/+EH+r//r/+Lnf/7ne81kDz300MN3Oe5pQ1mtVnn++efZu3cv5bKJmzh8+DC+77Njx46begytNUoppJTX1QNVq9WuO56UEqUUg4ODlEolvvGNb+B5Hj/0Qz/0pmpSOrS1OI7Zu3cvQRDc9ee4ke4S4LXXXuPixYvs27ev+7l7jcXFRV555RUefvjhK0Labwav39DeLjW22Wzy4osvMjo6ek1HWa01v//7v8/P/uzP8od/+Id8//d//y1daw893C6klHzlK19h69atXTr6zMwMs7OzPPXUUzf1GJfXx04jebXXecd0ptVq4bourVaL/v5+hoaGOHToEMvLy3zwgx+8qkna3YLWulub9uzZ86Y4Jl+uu7yWdGBhYYEjR47c1KDrzUKj0eCll15iZGSEnTt33rLxUsc19k6osVLKbgb09ZrJV155hR/4gR/g53/+5/nFX/zFXjPZQw899NDDvae8ttvtK6g5x44dQwjBAw88cFP37zSH12smV1ZWePnll68wW6lWqywuLvJbv/Vb/OEf/iGO4/AzP/MzfOxjH3tTmsooijhw4ACu677pLoEdXE136TgOcRzz+OOPX5/umzQRKyfRQw+CfXd/HwsLCxw+fPiuUG1vlxp7M80kwB/90R/xsY99jP/0n/4TH/zgB+/oWnvo4VbRbreveG3Oz89z5syZm9LvXl4fhRDXpEA2m00OHjzYzRR0HIdms8ni4iJf/OIX+aVf+iWiKOK9730vv/zLv0wul7trP18HSimOHDnCxsYGe/fuvaoh25vxnJ3B1OLiImmaks/nqdVqPPTQQ9dtJpVSvPLKK0xNTd2yZOFG6Oi5r8eauBXcDjVWStnNOL1ePMnRo0d57rnn+N/+t/+N//v//r97zWQPPfTQQw/APc6hvNohx7btN0SJXA0dPdCNmsmO8c2DDz54xQGhXC5TLpcZGxvDtm2UUszNzfG1r33tpiijt4J6vc6BAwe6Oph75RLoui6jo6OMjo52qUv1eh3btvn2t7/NwMAAw8PDb9RdJk2C33kf1voZ5Ohuor/xWbDujtvq7Owsr776Ko899thdMUMSQlAoFCgUCmzZsuUKauy5c+euSo3t6JJGRkau20z+t//23/jYxz7G7/7u7/aayR7uCzq16fKPO9TV6+Fmh20d45vR0VF27tzZrU25XI7NmzczNTWF67pEUcT6+jp/8Rd/QT6fZ3h4mOHhYQqFwh03EUmScOjQIaSUPPnkk2+q+dblsCyLgYEBBgYG2LVrF8ePH2dmZoYgCDh69Chzc3PX1F1+9KMf5Q/+4A/o6+vjhRdeuGuGZp34pqmpKbZs2XJXGrQgCJicnGRycvIKauzhw4evSo2VUnLw4EG01uzbt++azeTx48f54Ac/yE/+5E/2mskeeuihhx6uwD3dUIKhWl3+lKdOnaJer1/TdOJaeqCrfd+JEyeYm5tj9+7d15wir6ys8O/+3b/Dtm1+6qd+iiAIuk6Ba2trFAqF7uHpdlz01tbWOHjwYHc7ej/edDtU2zRN2bt3L67rUq/Xuz9nR3fZ0RXlNk4SfuoStbP1vx5AF2+Nlno1zMzMcOLECXbv3s3AwMAdP96NcDVqbKVSoVqtMjw8/AaTi8vxZ3/2Z/zNv/k3+Z3f+R3+yl/5K2/6tfbQw9WQJMkVDeXq6iovv/wy7373u695n5ttJufm5jh69Cg7d+68pvFNmqb81m/9FrOzs/yNv/E32Lx58xVRHa7rdutjpVK55frWbrc5cOAAQRDct2xFrTUnT55kbm6OvXv3UiqVbigdGBwc7G6PP/WpT/HhD3/4jq9jY2OD/fv337P4pqtRY0ulEnEc4zgOjz/++DUN3k6dOsUHPvABfvzHf5x//s//+X2JlOmhhx566OGti/veUJ49e5bV1dWrmk5cy3zn9UjTlMOHD9NoNNi7d+9tU7SSJOnSoTqOsZ2mq1wu3/Dw1KF27tq1i8nJydu6hjvFzQRyt1otlpeXWVxcNE106PH00X9Mbu0Y6Zb3EP/Ip+EOG+GOc+TtuNreDWitWVlZ4ZVXXsGyLJIkuSY19ktf+hJ/9a/+VX7913+dv/bX/lpv8t7DfcPrG8qNjQ1efPFF3vve977he29l2Hb69GnOnz/Po48+etu6yM62qzOYgisdY2/UHHZyLjumM/ejKVFKcezYse57ztWotlfTXX7qU5/i937v95iamuJrX/vaHWtLO4PHbdu23bLD+d1Ch/ocRRFKqWtSY8+ePctzzz3Hhz70IX71V3+110z20EMPPfTwBtz3hnJmZoa5ubk3BMbfrB6o3W5z8OBBXNflscceu+38sddDStnVI3YcYzuT+avpUM6dO8epU6fua87l5flljz766E298Xd0l0uLC9TmTpEGAwxl9N/bzbs8e/YsZ86cYd++fV3zpXuNdrvNiy++yMDAAA888MAbDomO4/DCCy8A8C/+xb/gX//rf81HPvKRXjPZw31FmqZXUFwbjQZ//ud/zvvf//4rvq9jvtNpPq/VTEopr9Aq3qnL9OXPv76+zuLiYtcx9npRRh0X1enp6btG7bxVSCm7g8d9+/bdlEna5ayHEydOEAQBo6Ojd5R3ubKywqFDh9i5c+d9GzwqpTh48CBpmrJv3z6EEG9wjT1z5gwrKyt88pOf5P3vfz//9t/+214z2UMPPfTQw1VxzxvK10/g5+b+/+3deVxU1f8/8Newg+y7KMqiIiqyg7ikJokrg6UtWoCZlUWllqaWWmlq5ac0tUxL0azcADEtd8FUFGUXEERAZJlh3/eZ8/vD39yvoywDMgzo+/l48Cju3Llz7jicue97znm/C5CdnS2VdELWKVySTK4mJiZyveP9aDIHkUgEY2Nj7s58ZmYmN31KUQFUVxTkbqneZavrLlsgGQm5f/++QjPKSoJJQ0PDFt8LyXl++umnOHHiBOrr6zFlyhT88MMPXBInQhTh0YCyvr4eERERmDx5Mte/PTpzo7V+r6GhAQkJCQAAJycnuWW0ZoxxU+oldSANDQ25demlpaVISUl5bF17d5IsAxCJRNwygI7qbMmmhxUVFSEpKQn29vbo27dvZ07liYnFYi77uIuLy2PvheQ8d+3ahV27diE/Px9eXl744osv8MILLyikzYQQQnq2bk3K05JHk07IGkxKUr1L6obJ8473o8kcJBljMzIyUFtbCyUlJdjY2CikziXwf4kd+vXr90RZAh89T8lF4r1795CcnCy17vLRc5WUAMjPz4ebm1uXjYR0VH19PVcYvLXAWklJCXfv3sXJkyfx9ddfY8KECThx4oTC6vAR0hrJNFJJmSRZ+0dJYjB9fX0MGzZMrmsVeTwedHR0oKOjA1tbWy5jbH5+PlJTUwEAFhYWcikLIgvJMgBVVVW4urp2+r3g8XjQ1dWFrq4ubG1tpdZdpqenP7bu8tF/G0nppBEjRsDMzKwrTq3D2gsmgQfnWVdXhz///BMTJkzAhg0bcOrUKYX9+xFCCOn5FD5CWVpaiqSkJDz33HMyrwe6d+8eMjMzu6wQdmc0NTVxU4ZMTExQUlKCqqqqLs8Y2x7JVDJ5J3aor6/nRmgfTVqho6ODO3fuQCgUwtXVtVtKALSkoaEBN2/e5C6iW7vYjo2NxcyZM7F69WosWbKEprmSHkMkEqG5uZn7XSwW48yZM5gwYQLU1dVlCiaLi4u5Ehc2NjYK+XwzxpCWlgaBQID+/fujsrISpaWlXL9hamraaomfrlRXV4fY2Fjo6OhgxIgRcpvF0l69S6FQiNTUVIV+Z4nFYiQmJqK+vr7NBDzFxcWYNm0ahg0bhj///LNbSl4RQgjp3RT+TSEZoZRlPZAkoUJJSQnc3NykplSKxWLU19fLpWbao+rq6hAXFwctLS0uzfqgQYOkgq709PQnzhjbnsLCQi4JUFelsW+NhoYGLC0tYWlp+X/rLouKEBsby61ztbOzU9gobUNDA2JiYqCnp9dmMJmYmAhfX198+umnFEySHk9JSYlLKqWiotJuMHn//n2kp6dj2LBhUlMqJfVb5dEPPUokEiEpKQm1tbXw9PTk+oTm5mYuGdjNmze5jLEmJiYwMDDo8nZVV1cjNjaWWxIhz/NWVVVF37590bdvX6mlA6mpqVzeAHnUsJSVpI5me8FkaWkpfH19MXjwYBw4cICCSUIIITLp9hHKR9cIVVVVISoqCqNHj4a6unqrd5Altcuam5vh5OQklVChqqoKa9euxd27d+Hv74+XXnpJbu2XrNs0NTVt8yJFkjFWkm5fXV2dCy5lyRjbnry8PNy+fRsODg4Ku+PNGMOtW7dQWloKY2NjlJaWcusuJXfnuypJUlsaGxtx8+ZNbhSitfdWUpQ7KCgIa9asoWCS9DiPjlAyxnDhwgUMGzYMxsbGbc7ckIwIOjk5SU1PZIzhhx9+wIULFzB69Gh8+umncpsC29jYiPj4ePB4PDg5ObX69/9wfcTCwkIAHcsY256KigrExcWhf//+CivfBDzIdn3nzh2YmZmhurq6U+sun5RYLOaSEbm6ura6lra8vBwzZ86Eubk5QkNDu60+KCGEkN5PoQElY4y7ACkvL4eenh7MzMxgamoqFTDW1NQgPj4effr0wYgRIx67a3rt2jUsW7YMtbW1sLe3x4EDB+TSdkl2Psn0UlkvUh7OGFtcXAwejyd18dTRaViSLKqOjo4KW/cnuUiprq6Gq6sr1NXVpZJztFTvUh4XT42NjYiJieE+G629l2lpaZg6dSrefPNNfP311xRMkh5JLBajqakJwP+tJ09LS0NeXh40NTVhamoKMzMzaGtrc5/h5uZmJCUloa6uDk5OTo/N0iguLoa/vz8EAgGMjY2xZ88euWQXra2tRVxcHLS1tTFixAiZg8KHM8ZK6sd2JBnYoyT9tK2tLQYOHNiZU+kSkqUZzs7OXIDfXr3Lru6XZA0mKysr4efnB11dXRw/flymDLiEEEKIhMJygEsulng8HlxdXTFu3DiYm5ujqKgIly9fxvXr15GdnY2CggLcuHEDJiYmrdZVtLW1hY2NDYyMjDBmzBi5tDc/Px/x8fGwt7fvcNp7ZWVlmJqaYsSIEXjuuee4kh4pKSmIjIxEUlIShEKh1MhESyQFubOzs+Hq6qrQYDIxMRE1NTVwc3Pj7mRLknPY2tpi1KhRGDt2LMzMzFBSUoIrV64gKioKGRkZqKioQFfcx5A1mMzIyMCMGTPw+uuvY/369V160Xbp0iXMnDkTFhYW4PF4OHbsWLvPiYiIgIuLC9TV1TFo0CAEBwc/ts+OHTtgZWUFDQ0NeHp6Ijo6usvaTHq+h5Pv2NnZYfz48bCxsUFtbS1u3LiBK1euID09HYWFhYiOjoZYLIa7u3uLU/4NDAzg4OAAAwMDDBs2TC4zGioqKnDjxg0YGxtj5MiRHRph5PF4MDAwgJ2dHcaMGQN3d3doa2sjOzsbkZGRiI2Nxf3799HQ0NDusYRCIRISEjB06FCFBpNZWVlc6aSHR4s1NTUxYMAAuLq6Yvz48bC2tkZtbS1iY2Px33//ITU1FcXFxVJ5BjqLMYbk5GTupl9rwWRNTQ3mzJkDTU1NHDt2rMuDSeojCSHk6aeQEUpJYp7W1gM1NjaiqKgIOTk5qK6uhrq6Ovr37w9TU9NWs4dWVlaiqKgI1tbWXZp4gTGGrKws3Lt3DyNHjoSRkVGXHlvS7sLCQtTV1Uml23/4AoAxxq0fba0gd3cQiURISEhAU1NTq1kCW/Lwusvi4mIoKytLlV7p6L9ZU1MTYmJioKmp2WbNzezsbEyZMgV8Ph9bt27t8qQc//77L65cuQJXV1e8+OKLCAsLg5+fX6v7Z2VlYcSIEXj33Xfx1ltv4fz581i8eDFOnjwJHx8fAMChQ4fg7++PnTt3wtPTE1u2bMGRI0eQlpamsOnNpHuIxWI0NjZyZUFamuIqmfGQm5vLJYDp27cvzM3Noa+v3+INk4aGBuTk5MDS0rLLAwZJKQx5jAjW1tZy/WNFRQV0dXW5/vHRPjA3Nxfp6ekKrQXMGMPdu3eRm5sLV1dX6OjoyPS8lko2tVXXU5Z2JCcno7KykptB0pLa2lrMmTMHzc3N+Pfff+WSnZv6SEIIefp1e0C5f/9+jBgxAoMHD241uYSkBEVubi6GDRsGsVgMoVCIkpISbtpXd2QJFIvFuH37NoqLi+Hs7CzzxUFn1dTUcNO+Kisroaenx037ysjI6FBBbnkQiUSIj4+HWCxuc31Ue1qrdynruktZg8n79+/Dx8cHU6ZMwU8//ST3otw8Hq/di6VPP/0UJ0+exK1bt7htr776KsrLy3Hq1CkAgKenJ9zd3bF9+3YAD94vS0tLfPDBB1ixYoVcz4Eo1unTp6GsrAw3NzeoqqrKVDapT58+XNAlmU5vZmYGAwMDuX/mc3NzkZaW1i2lMBoaGrg+o6SkBFpaWtx3QUlJCbKzs+Hk5KSwxDeSGSQFBQVwdXXtdHD2pEsHJMFkRUWF1AySR9XX1+OVV15BdXU1Tp061S01lKmPJISQp1O3pnBjjCEsLAwLFy7E4MGDwefzMWvWLKl6gSKRCLdu3UJVVRU8PDy4u9B9+/ZFc3MzSkpKIBQKcfPmTaipqXVpopuHiUQiJCYmoq6uDh4eHt0SxPXp0wfW1tawtrbmMsYKhUKkp6dDSUmJy7Cqrq7e7WsAm5ubERcXBx6PB2dn5yfK/tdavcucnBykpKRwF08mJiaPTeGTBJMaGhptBpMFBQWYPn06nn/+eezYsUPuF9ayioqKgre3t9Q2Hx8fLF68GMD/TeNduXIl97iSkhK8vb0RFRXVnU0lChAVFYWtW7dCS0sLM2fOhJ+fH0aPHs39vTHGuDXUD4/ESTKZStYi3rp1C4wxLggxMjLq8pkbmZmZyMnJgYuLS7cEcZKZKv3795fKGBsdHQ3GGMzNzcEY42a+dCdJUqSioqJWpx7L6tG6nnV1ddy53rlzp811l4wxpKSkoKKios2RyYaGBrz++usoKyvD2bNnuyWYlBX1kYQQ0vt0a0DJ4/EQGhqKiooKHD9+HKGhofj+++8xYMAA8Pl8eHl54fDhw1i4cCE8PDweW/OhoqICMzMzmJmZcVkChUIh4uLiuHWKpqamT5yCvqGhAfHx8VBWVoa7u3u3ZCp9lIaGBszMzJCfnw99fX307dsXJSUliI6O7vKMse1pampCbGwsVFVV4ejo2KUZIh+9eJIE0kVFRY9dPGlqaiIuLg7q6uoYOXJkqxeNQqEQ06dPh5eXF3bv3i3Xou4dJRAIHhvJMTMzQ2VlJerq6lBWVgaRSNTiPrdv3+7OphIF+OKLL7Bq1SqcO3cOISEheP3116GsrIwZM2Zg+vTpCAsLw/Tp0zFx4sTHZkwoKSnB0NAQhoaGsLOzQ0VFBYRCIW7fvo2mpiYuuDQ2Nn6ivwlJ+abS0lJuvWN3U1FR4UYm1dTUYGtri4qKCiQlJXGBtImJCYyMjOT+9y8J4srKyuDm5tblycc0NTVbLdn0cL1LAwMDpKWlce1o7SZoU1MTAgMDkZ+fj/PnzytsRLc11EcSQkjv0+1Fpng8HvT19eHv7w9/f39UVlbi5MmT2LNnD7Zs2YIhQ4YgPDwcysrKcHV1bTVoePiLVDKFUigUIjExEQC4gKuj6/NqamoQFxcHXV1duRbCbk99fT1iY2PRp08fbiSuf//+3PqpoqIiLj3/k2SMbU9jYyNiY2OhoaHRZhDXVdqqdykSiaCurg5ra+tWk/oUFRVh5syZcHR0xN69e3tUMEmILNTU1DBt2jRMmzYNO3fuRGRkJH7//XfMmzePu/hXU1PDxIkTWx2BkvSz+vr6GDJkCKqqqiAUCpGRkYFbt25x65dNTEw6NNugubkZiYmJaGhogLu7u0Kn30tqXUpmkPTr14/LGFtUVIT09PQnzhjbHrFYzK1VbCuI6yqqqqowNzeHubn5Y/UuGxoawOPxMHjw4Fb7vebmZixYsAB3797FxYsXuzQnACGEkGeXwqsW6+rqgs/n4+OPP8aqVaswYsQIhIaGwtfXF/r6+vD19QWfz4enp2erX5IPT6G0t7dHWVkZCgsLkZKSApFIJDXtq60Ao7y8HPHx8ejXrx8GDRqksNISNTU1iI2N5c7n4XY8PBIrFou5KW6pqalobm6Wunh60qLUDQ0NiImJ4coAdHdwLbl4MjY2RkxMDIAHn5e0tLQW112WlpZi5syZPboot7m5OYRCodQ2oVAIXV1daGpqQllZGcrKyi3uY25u3p1NJT2AqqoqvL298cMPP2DatGlYuHAhTp48iQ8//BDV1dWYNm0a/Pz8MGnSpFZHxng8HnR1daGrq4tBgwahpqYGQqEQ2dnZSE5O5vqMRxOBPaqhoQFxcXFQVVWFu7u7wv6+mpububXcj84gkWSMNTAwwODBg7np9JJzNTAw4M71SYM/sVjMBbVtrVWUF8n3nqGhIcRiMYqLi7lZLenp6Y+tuxSJRHj33XeRnJyMixcvKixxUXuojySEkN6n25PytEYgEEh9GdTV1eHs2bMICQnB33//DQ0NDcycOROzZs2SWlPUFsYYKioqUFhYiMLCQjQ2NsLY2BhmZmYwMjKSOoZk3dGgQYMwYMAAuZyjLCQFuTsa1DLGUFVVxZ1rbW0tF3CZmpq2eaHYkvr6esTExEBPTw/Dhw9XWHAtWbuppKQEJycnKCsrc0krJIlI7t27h61bt0IkEsHY2BinT5/u8Pl2BVkTTvzzzz9ISkrits2dOxelpaVSCSc8PDywbds2AA8uXAcMGICgoCBKOPGMKiwshLGxMXdTRyQS4dq1awgJCUFYWBiKi4u5bMY+Pj4yZ4GWJAIrLCxEVVUVF3CZmppKBUjV1dWIi4vjSo8oauaGZMaEmppah6ff19XVcecqyRgr6R87mjVbssa+oaEBLi4uCulvAOm1mw9Pt5UsHSgsLERpaSk2btwIVVVVZGdn4+rVq7C0tFRIe6mPJISQp1OPCSjb0tjYiAsXLuDo0aMIDw8Hj8fDjBkzMGvWLIwbN06mL/NHA666ujoYGRnBzMwMDQ0NyMzMxIgRIxSacry0tBQJCQmwtraGlZXVEx2rpqaGu6B4OGOsLFkC6+rqcPPmzRZHSLuTSCRCbGysVDDZkoyMDLz//vvIyMhASUkJxo4diwsXLnRLG6urq5GRkQEAcHZ2xvfff4+JEyfC0NAQAwYMwMqVK5GXl4f9+/cD+L+U+O+//z7efPNNXLhwAR9++OFjKfEDAgLwyy+/wMPDA1u2bMHhw4dx+/ZtuWfSJL2PWCxGTEwMjh49irCwMOTl5eGFF14An8/H1KlToaurK9NxHg24JH2Guro6UlNTYWlpCVtbW4X1B3V1dYiNjYWOjs4Tz5iQlKYqLCx8LGNse9nDJdmuRSIRnJ2dFbLGHnjwnSapRdrW2s36+nosXLgQ165dQ1VVFXR0dBAfH99tI5TURxJCyNOvVwSUD2tubkZkZCSOHDmC8PBwNDQ0YMaMGeDz+Xj++edlnnZUXV0NoVCI+/fvo6mpCXp6eujXr1+7077kRTJCOnToUFhYWHTpsR++W11WVoY+ffpwF0/a2tpSF081NTWIiYmBqakp7OzsFBpMxsXFAXhwEdJaMFldXY0XX3wRampqOHnyJJqampCWlgZ3d/duaWdERAQmTpz42PaAgAAEBwcjMDAQ2dnZiIiIkHrOkiVLkJKSgv79+2P16tUIDAyUev727dvx3XffQSAQwMnJCT/++CM8PT3lfDaktxOLxUhMTMTRo0cRGhqKzMxMTJo0CXw+H9OnT2+1TuWjGhoaUFhYiNzcXFRXV0NDQwP9+/eHmZnZE2Uw7azq6mrExsZy2Wy7sl96OGNscXExVFVVuZFLfX19qcBVMt2WMfbE2a6fhCSYFAqFcHNza/XfRCwW49NPP8Xff/+NiIgI9O/fH9euXcO4ceO6rW+nPpIQQp5+vS6gfJhIJMLly5e5aV9VVVWYOnUq+Hw+vL2927zwkSRTqKiogL29PZe0oqqqCvr6+jAzM+uSdTayyMvL42q5yXuEtKmpSeriSV1dnbt4UlFRQWxsLCwsLBS6hlQyAiC5aGstmKytrcXs2bMhFovxzz//KCTbJCE9lST7qGTkMiUlBRMmTICfnx9mzJgBIyOjNv/G7927h7t378Le3h5isZgbzZPckDIzM0OfPn3k3k9IlgFYWlrCxsZG7rWHS0tLuRqQjDEugZGuri4SExOhrKzc5owJeZPUuxQIBO0Gk59//jmOHDmCiIgIDB48uJtbSggh5FnRqwPKh4nFYqk1RUVFRfDx8eHWFD0cbDQ1NSExMRFNTU1wdnaWGtWsr69HYWEhhEIht87GzMxMpqminSGpKefo6AhDQ8MuP35bHs4YW1hYiObmZq58R1fXretImyQJN9oaAZAU5a6pqcGpU6dkntZHyLNIEoRIgsv4+HiMHTsWfD4fvr6+MDMz4wI1yehXQUEBnJ2dpWoUPnpDSlLeSJapop1RUlKChIQEhaxtf3gNvlAoRH19PdTU1DBo0CCYmpoqZKorYwwZGRnIz8+Hm5tbq2s/GWP46quvsG/fPly8eBH29vbd3FJCCCHPkqcmoHyYWCxGbGwsN+0rNzeXW1M0fPhwhISEwNfXFyNHjmxzylJDQwOKioogFApRVlYGbW1t7uKpo0kcHiW5MMjLy4OLi4tCA6KKigrExMTA3NwcSkpKKCoqQlNTE3dnvisyxspCJBIhISGBW5vU2ms2NDRg3rx5KCoqwtmzZ6Gvry/3thHytGCMITs7GyEhIQgNDUV0dDS8vLzg6+uLyZMnY8+ePZgyZQrc3d3bnOUhEolQXFwMoVDITRWV9I9dUR9XKBQiOTkZ9vb26Nu37xMd60lIsl2rq6tDT08PxcXFqK6u7tKMsbJgjOHu3bvIy8trN5jctGkTdu7ciQsXLsDBwUHubSOEEPJseyoDyodJUrsfPXoUf/31F/Ly8uDg4IA333wTM2bMgIGBgUwXPk1NTVxw+fC0r5bWIcrSJklhcBcXlycOTp9EWVkZ4uPjYWtry40AtJUx1sTERC7p8cViMeLj49Hc3AwXF5dWg8nGxkb4+/vj/v37OHfunFzqqO3YsYNbm+Po6Iht27bBw8OjxX0nTJiAyMjIx7ZPmzYNJ0+eBAAEBgZi3759Uo/7+PhwGQsJURTGGHJzcxEaGopDhw4hPj4epqammD9/PubMmYOBAwfK1LeJRCKUlpZCKBSiqKhIqrzRo+sQZZGbm4v09HQ4ODgotLyFpB6wjo4Ohg8fzp1HV2aMldXdu3eRm5sLV1fXVqf3M8bwww8/4IcffsD58+fh5OTU5e2g/pEQQsijnvqAUiI/Px/Dhw/HvHnzYGJigmPHjiE5ORnjx4/n1hQZGxvLdPHU3NzMTROVTPuSXDzp6uq2myHw1q1bqKmpgYuLi8IKgwMPssrGx8djyJAh6N+/f6v7tZYx1sTEpEsSdIjFYiQkJKCxsREuLi6tTiVramrCggULkJaWhgsXLsjlQvPQoUPw9/fHzp074enpiS1btuDIkSNIS0trcX1raWkpGhsbud9LSkrg6OiIX3/9lUsiERgYCKFQiL1793L7qaurc0XqCVE0sVgMDw8PGBkZYerUqfj7779x6dIljBw5Enw+H3w+X+Z11WKxGGVlZVxwyRjj+kdDQ8M2g0vJ6Gl2djacnJwU+jdSV1eHmJgYrlRKa+f+cMbY0tJSaGpqcv1je98Hsrp79y7u378PNze3NoPJ7du3Y9OmTThz5oxcEpNR/0gIIaQlz0xACQA3b96Em5sbgP+bcipZUxQXF4cxY8Zwa4rMzc1lvjMvWVNUVFQEVVVVLmHFo9O+mpubuSmdTk5OCqtdBgDFxcVITEzscFZZSfbHoqIilJaWPtFILSB7MNnc3Ix33nkHCQkJuHDhgtwKWHt6esLd3R3bt2/n2mdpaYkPPvhApvpmW7ZswZo1a1BQUMCNVAQGBqK8vBzHjh2TS5sJ6QpxcXEYOXIkV+u1uLgY4eHhOHr0KC5evAg7OzsuuJS1nBBjDGVlZdxonkgk4kbyjIyMpBLbSNZ5FhQUwMXFBTo6OvI83TbV1tYiJiYGxsbGHcoq29zcjJKSEu5m45OO1AJAZmYmcnJy2g0md+3ahS+//BL//vsvvLy8Ovw6sqD+kRBCSEueqYCyNYwx3Lt3j1tTdP36dYwaNQq+vr7g8/no37+/zHfmJRcTRUVF4PF4UtkQ4+PjoaKiAkdHR4Wlmwf+r0TJsGHDnigwezhBR0lJCdTU1KTS7bf3nklKHNTX18PV1bXVYFIkEiEoKAhRUVGIiIjo8rIqEo2NjdDS0sLRo0elCm8HBASgvLwc4eHh7R7DwcEBXl5e2LVrF7ctMDAQx44dg5qaGgwMDPD8889j/fr1cpmuS0hXkwSFx48fR2hoKM6ePQsrKyvw+Xz4+fnJXBOSMYbKykoIhUIUFhaisbGRW6dtaGiIO3fuoLS0FK6urgopTSIhKZ1kZmaGIUOGdHqEsa2MsY8G063JysrCvXv34Orq2mqAzRhDcHAwVq5ciRMnTuC5557rVHvbQ/0jIYSQ1lBA+QjGGPLy8hAaGoqQkBBcvXoVLi4u3J15KyurDk37kmQIbGpqgoaGBuzs7GBsbKyQDKoAIBAIkJycDAcHhy4tUSJZQ/VwMC1Zc9lSxljJ2ta6uro2g0mxWIzFixfj4sWLuHjxolwzPebn56Nfv364evWq1B3+5cuXIzIyEtevX2/z+dHR0fD09MT169el1hQdPHgQWlpasLa2xt27d7Fq1Spoa2sjKipKYaUHCOmsiooKnDhxAqGhoTh16hT69u0LX19fzJo1C87OzjIHl5JawEKhELW1tVBWVoatrS0sLCwUkkEVAKqqqhAbG4t+/frB1ta2y7LWPpwxtrCwEA0NDTA2Nub6yJbOVzL1t71g8sCBA/jkk09w/PjxFus9dhXqHwkhhLSGAso2MMYgFAoRFhaGkJAQXLp0CSNGjOCCy8GDB7d7wSG5262jowNNTU0UFRWhubm51Wlf8pSfn4/bt29j5MiRMDY2ltvriMVilJeXc+uKHs0Yq6SkxK0jdXV1bXXqr1gsxvLly3Hy5ElERETA2tpabm0GnvyC6Z133kFUVBQSExPb3C8zMxO2trY4d+4cJk2a1CVtJ0QRqqur8e+//yIkJAT//PMPDA0NMXPmTMyaNQvu7u7t9m3Nzc2Ij4/n+oiSkhJUV1fD0NCQmyraXUsDKisrERsbiwEDBsDGxkZur8MYQ01NDRdctpQx9t69e8jMzISrq2urGcAZYzhy5AiCgoIQEhICHx8fubUZoP6REEJI6xQ377IX4PF4MDc3x6JFi/Duu++ipKQE4eHhCAkJwYYNGzBkyBBu2ldLa4okBbn79+/P3e22s7NDZWUlCgsLkZ6eLjXtS57lOSRZE52cnORe71JJSQmGhoYwNDTEkCFDuIyxWVlZuHXrFlRVVcHj8eDi4tJmMPnZZ5/h+PHjuHjxotyDSQAwNjaGsrIyhEKh1HahUNju1OCamhocPHgQX331VbuvY2NjA2NjY2RkZNAFE+nVtLW1MWfOHMyZMwe1tbU4c+YMQkJC8NJLL0FLSwu+vr7w8/ODl5fXY31bY2MjYmNjoaamBg8PDygrK2Pw4MGora1FYWEhdwNMX18fZmZmci3PUV5ejri4ONjY2GDgwIFyeQ0JHo8HbW1taGtrw8bGBnV1dVwG8bS0NKirq6OxsREjRoxos5zUsWPHEBQUhIMHD8o9mASofySEENI6GqHsBMYYysvL8ffffyMkJARnzpzBwIEDueDSwcEBN27cQHV1NQYNGtTqBYpk2pdkWmxdXR0MDQ25i6eumvaVk5ODu3fvwtnZWaE1GxljiI+PR2VlJTQ0NFBVVQU9PT1utFaybooxhi+//BK///47Ll68iKFDh3ZbGz09PeHh4YFt27YBeBDYDhgwAEFBQW0mnQgODsa7776LvLy8dtf+5ObmYsCAATh27Bh8fX27tP2E9AT19fU4f/48QkNDER4eDmVlZW7kcuzYsbh37x6ysrJgbm4uVY6jpeNI+kdJeQ5JrUtNTc0uaWtZWRni4uIwePBgWFpadskxOyszMxNZWVnQ1dVFZWUlNDU1uf7x4YyxJ06cwPz583HgwAHMmjWr29pH/SMhhJCWUEDZBSorK3Hy5EmEhITg1KlTMDY2hkAgwP/+9z8EBATIvF5SMg1KKBR22bSvrKwsZGdnw8XFBXp6ep06RldgjCE5ORmVlZVwdXWFuro6GhoapNLtq6ur4/jx49xUrosXL2LEiBHd2s5Dhw4hICAAv/zyCzw8PLBlyxYcPnwYt2/fhpmZGfz9/dGvXz9s3LhR6nnjxo1Dv379cPDgQant1dXV+PLLL/HSSy/B3Nwcd+/exfLly1FVVYWkpCS51PQkpCdpampCREQEQkJCcOzYMdTX10NJSQmzZ8/Gpk2bZB51lPQXQqEQZWVl0NbW5oLLztZ+LCkpQUJCAuzs7NCvX79OHaOr3L9/HxkZGVxf3VLG2IiICGhoaODbb79FcHAwXn755W5tI/WPhBBCWkIBZRf7+eefsWTJEowdOxbR0dEwMDDgpn1JpnXJoq6ujsuGWFlZCX19fS64lOUCjDGGzMxM3L9/v82kDt2hpWDyUU1NTUhLS0NQUBBiY2NhZmaGl19+Gd999123Z8Tdvn07V7jbyckJP/74Izw9PQE8KNRtZWWF4OBgbv+0tDQMHToUZ86cwQsvvCB1rLq6Ovj5+SEuLg7l5eWwsLDA5MmTsW7dOpiZmXXnaRGicFFRUZgyZQqGDRuGnJwc1NTUYPr06eDz+Zg0aZLMo45NTU1ccFlSUtKp8kVFRUVITEzEsGHD0Ldv3yc9tSciWZLg4uLS4iwSSZK3999/H+fOnYOSkhJefPFFrF+/Xq6JylpC/SMhhJBHUUDZxQ4cOABLS0uMHz8edXV13JqiEydOQFNTEzNnzoSfnx9Gjx4tc6AkmfZVWFiI8vJy6OrqcuVIWroAk9TYzM/Ph6ura6u1y7oDYwwpKSkoLy+Hm5tbq3ecGWPYtm0bvv32Wxw/fhzl5eWIjo6Wac0NIaR3iI+Px9WrV/Hee+9BJBIhKioKISEhCAsLQ2lpKXx8fODn54fJkyfLPOrY3NyM4uJiCIVCFBcXQ0NDgwsuH54m+jChUIhbt25hxIgRCg9cJMGks7MzDAwMWt3v0qVLmDNnDrZs2QJ7e3scO3YMK1askPuaeEIIIaQ9FFB2k8bGRpw7dw4hISE4fvw4lJSUMGPGDMyaNQvjxo2Teb1kY2MjF1yWlpZCW1tbqtYlYwxpaWkoKiqCi4tLp6eCdQXGGFJTU1FaWgo3N7dWR1YZY/jll1/w1VdfybUoNyGkZxKLxbh58yaOHj2KsLAw5Ofn44UXXoCfnx+mTJnSZnKah4lEIpSUlHDBpYqKChdcSmrjFhQUIDU1FQ4ODjAxMZHzmbUtLy8PaWlp7QaTV69exYsvvohvv/0W77zzTpeVMyGEEEK6AgWUCtDU1ITIyEgcPXoUx44dQ1NTE2bMmAE+n4+JEyfKvG5EMu2rsLAQJSUl0NTUhJKSEhobG+Hm5qbQ4uAdCSb37t2LVatW4eTJkxg3blw3t5QQ0pOIxWIkJCRwwWVmZia8vb3B5/Mxffp06OnpyVwLWLIGUVIbt0+fPqioqICjo6NcSyfJQpLFtr3M2zdu3ACfz8e6desQFBREwSQhhJAehwJKBROJRLh8+TIXXFZVVWHatGng8/nw9vaWeU1RY2MjEhISUFVVBcYY1NXVuYQVrU37khfGGG7fvo2SkpJ2g8nuKspNCOl9JFPmjx49itDQUKSmpmLixInw8/PD9OnTYWRkJHNwmZ6ejtzcXCgrK4PH40nVApY1cVpXkTWYjIuLw4wZM/D5559j6dKlFEwSQgjpkbr3W5Q8RllZGePHj8e2bdtw7949/PPPPzA3N8eKFStgZWUFf39/hIaGoqamptVjiMVi3L59G01NTRgzZgwmTJiAIUOGoKGhAbGxsfjvv/9w+/ZtlJWVQd73DyRTbouLi+Hq6tpmMHn48GF8/PHHCAkJkVswuWPHDlhZWUFDQwOenp6Ijo5udd/g4GDweDypn0fbzxjDmjVr0LdvX2hqasLb2xt37tyRS9sJedbxeDwMHz4ca9euRXx8PG7duoXx48fjt99+g62tLWbOnIndu3dDKBS22bfl5OSgoKAAbm5umDBhAhwdHaGiooLbt28jMjISSUlJKCwshEgkkvs5FRQU4Pbt23B0dGwzmExKSoKvry+WL18ut2CS+kdCCCFdgUYoeyixWIyYmBhu2ldeXh68vb3h5+eHqVOncmuKxGIxEhMTUV9fDxcXl8fKi4jFYpSWlnLrLiV35s3MzGBgYNCld+YZY0hPT0dhYSHc3NzaHF0NDQ3Fu+++i0OHDmH69Old1oaHHTp0CP7+/ti5cyc8PT2xZcsWHDlyBGlpaTA1NX1s/+DgYHz00UdIS0vjtvF4PKmkHd988w02btyIffv2wdraGqtXr0ZSUhJSUlLkVnSdECKNMYasrCyEhIQgNDQUN27cwOjRo+Hr6ws+nw8LCwsuAMvMzEROTg5cXFweW4vJGENlZSXXP9bX18PY2BhmZmYwNjbu8gzTAoEAycnJcHJyarMeY0pKCqZOnYqgoCCsWbNGLsEk9Y+EEEK6DOuE7du3s4EDBzJ1dXXm4eHBrl+/3ub+hw8fZnZ2dkxdXZ2NGDGCnTx5UupxsVjMVq9ezczNzZmGhgabNGkSS09P70zTnkoikYjFxcWxzz77jA0bNoypq6uzadOmsW3btjFvb28WHBzMysvLWU1NTZs/VVVV7P79+ywmJob9888/7OTJkyw6OpplZ2ezqqqqdp/f1k91dTWLjY1lp06dYkVFRW3ue/DgQaalpcXCwsLk+r55eHiw999/X+p9tLCwYBs3bmxx/7179zI9Pb1WjycWi5m5uTn77rvvuG3l5eVMXV2d/fXXX13WbtK7Uf/YvcRiMbt37x774Ycf2Lhx45iKigrz9PRkX3/9NfP392efffYZEwgEMvVhAoGAJSYmsnPnzrHjx4+zy5cvszt37sjUv7b3k5mZyY4fP87u3bvX5n6xsbHMzMyMrVy5konFYrm9b9Q/EkII6SodHp46dOgQli5dirVr1yI2NhaOjo7w8fFBYWFhi/tfvXoVr732GhYsWIC4uDj4+fnBz88Pt27d4vb59ttv8eOPP2Lnzp24fv06+vTpAx8fH9TX13c+Un6KKCkpwcnJCevXr8etW7cQGxsLJycnrFq1Crdu3cIff/yBP/74A8XFxW1O+1JSUoKhoSGGDh2K5557Dk5OTty0r4iICCQlJUEoFHZ42hdjDHfu3IFQKISrq2ubyYD+/fdfLFiwAMHBwfDz8+vQ63REY2MjYmJi4O3tzW1TUlKCt7c3oqKiWn1edXU1Bg4cCEtLS/D5fCQnJ3OPZWVlQSAQSB1TT08Pnp6ebR6TPDuof+x+PB4PAwYMwOLFixEZGYl79+7h9ddfx86dO3HkyBFcvHgRu3btQkZGRpv9I4/Hg46ODmxtbeHl5YVRo0ZBX18fOTk5iIyMRGxsLHJzc9HY2NjhNkrKlIwcObLNZEAZGRmYMWMGXn/9daxfv15uayapfySEENKVOhxQfv/991i4cCHmz5+PYcOGYefOndDS0sKePXta3H/r1q2YMmUKli1bBnt7e6xbtw4uLi7Yvn07gAfByJYtW/D555+Dz+dj5MiR2L9/P/Lz83Hs2LEnOrmnEY/Hw7Bhw1BZWQkPDw+cOnUKEydOxN69e2Fra4sZM2Zg165dEAgE7V486evrw87ODmPHjuWmqGZkZCAiIgIJCQkoKChAc3Nzm+1h/7/mpUAgaDeYPH/+PAICArBr1y7MmTOn0++BLIqLiyESiR6rMWdmZgaBQNDic+zs7LBnzx6Eh4fjwIEDEIvFGD16NHJzcwGAe15HjkmeLdQ/KhaPx4OFhQVMTU3B4/EQERGBgIAAXLlyBW5ubvDy8sKmTZuQmpra7nryPn36wNraGqNGjcKYMWNgZGSE/Px8XLp0CTdv3kROTo5MQX1hYSEXTLZVpiQ7OxszZszgyoPIM1EQ9Y+EEEK6Uoe+sTpzVzMqKkpqfwDw8fHh9qe7mp2zfv16/P3333B0dMTKlSsRHR2N9PR0TJ8+HYcPH4adnR2mTJmCHTt2IDc3t93gUldXF4MGDcLo0aPh6ekJbW1tZGdnIyIiAnFxccjLy3vszjxjDHfv3kV+fj5cXV3brHl56dIlzJ07F9u3b8drr73WZe9DV/Ly8oK/vz+cnJwwfvx4hIaGwsTEBL/88ouim0Z6Aeofe47Zs2fjxo0b8PDwwNtvv41///0XAoEAS5cuRVxcHMaMGQN3d3esW7cOSUlJEIvFbR5PU1MTAwcOhIeHB8aOHQszMzMUFhbi8uXLiI6ORnZ2Nmprax97XmFhIZKSktqteZmbm4vp06dj2rRp2Lp1a7dnnZUF9Y+EEEJa06Fvrc7c1RQIBG3uT3c1O0dPT08q6Q2Px4O1tTU++eQTXLlyBZmZmZg9ezZOnDiB4cOHY9KkSdi6dSuys7PbDS61tbW5aV9eXl7Q19dHbm4uLl26hJiYGNy/fx8NDQ3IzMxEXl4e3Nzc2gwmr1y5gpdffhn/+9//EBAQ0C2p742NjaGsrAyhUCi1XSgUwtzcXKZjqKqqwtnZGRkZGQDAPe9JjkmeXtQ/9hxKSkpSiWV4PB4MDQ0RGBiIv//+G0KhEJ999hnS0tLw/PPPw9nZGatXr0ZsbGy7waWGhgYsLS3h5uaG5557DhYWFigtLcXVq1dx7do1ZGZmorq6GkVFRUhKSsKIESNaTHIjUVBQgGnTpmHixInYsWNHtwST1D8SQgjpSj3vNih5YjweD5aWlvjoo48QERGBnJwc+Pv74/z583B0dMRzzz2HzZs3486dOzJP+/L09MSYMWNgbGwMgUCAS5cuISsrCxYWFm1eAEVHR2P27NnYsGEDFi5c2G111NTU1ODq6orz589z28RiMc6fPw8vLy+ZjiESiZCUlIS+ffsCAKytrWFubi51zMrKSly/fl3mYxJCFE9PTw/z5s1DaGgohEIhvv76a+Tm5mLatGlwcHDAihUrcP369XaDSzU1NfTv3x8uLi4YP348BgwYgMrKSly7dg3x8fEwNjaGlpZWq/2sUCjE9OnTMWrUKOzevRvKysryON0W2039IyGEkK7SoYCyM3c1zc3N29yf7mrKF4/HQ9++ffHee+/h7NmzyM/Px6JFixAVFQUPDw94eXlh48aNSElJaTe4lEz7MjIygoqKCqysrFBZWYkrV67g+vXryMrKkpr2FRsbi1mzZmHt2rV4//33u70o99KlS7F7927s27cPqampWLRoEWpqajB//nwAgL+/P1auXMnt/9VXX+HMmTPIzMxEbGwsXn/9ddy7dw9vvfUWgAfv5eLFi7F+/XocP34cSUlJ8Pf3h4WFhVwTDJHegfrH3klbWxsvv/wyDh06BIFAgO+//x6lpaV48cUXMXToUHzyySe4fPlyu8nKVFVVYWFhgf79+wMALC0twePxcOPGDVy5cgXp6emoqKjg+tni4mLMnDkTjo6OCA4O7rZgUoL6R0IIIV2lQ0W2Hr6rKfmCkNzVDAoKavE5Xl5eOH/+PBYvXsxtO3v2LHfH8uG7mk5OTgD+767mokWLOn5GpFWSGpRvvfUWFixYgPLychw/fhwhISHYvHkzrKyswOfz4efnhxEjRrQ48piVlYWcnBy4ublBR0cHwIO1Y0VFRSgsLMTdu3dx6dIlFBUV4e+//8by5cuxZMmSbg8mAeCVV15BUVER1qxZA4FAACcnJ5w6dYqbPpiTkyN1jmVlZVi4cCEEAgEMDAzg6uqKq1evYtiwYdw+y5cvR01NDd5++22Ul5dj7NixOHXqFNVYI9Q/PgW0tLQwa9YszJo1C/X19Th37hxCQ0Px2muvQVVVFTNnzoSfnx/Gjh0LVVXVx55fUlKCxMREDB8+nBu5E4lEKCkpQWFhIWJjY5Geno6oqCjcvn0bgwYNwoEDB7q83qUsqH8khBDSZTpaZ+TgwYNMXV2dBQcHs5SUFPb2228zfX19JhAIGGOMvfHGG2zFihXc/leuXGEqKips8+bNLDU1la1du5apqqqypKQkbp9NmzYxfX19Fh4ezhITExmfz2dGRkZswIABMtVy27VrFxs7dizT19dn+vr6bNKkSY/tHxAQwABI/fj4+HT09J9aFRUV7I8//mAvvvgi09LSYra2tmzJkiXs0qVLXI3K69evs5MnT7ZZ0628vJx9++23bNCgQUxFRYXZ2dmxw4cPK/r0COkW1D8+nRobG9np06fZ22+/zUxNTZmRkRELCAhgYWFhrKysjNXU1LDk5GT2999/s4yMjDZrAYeHhzNnZ2emrq7OTE1N2dq1axV9eoQQQsgT6XBAyRhj27ZtYwMGDGBqamrMw8ODXbt2jXts/PjxLCAgQGr/w4cPsyFDhjA1NTU2fPjwVgt3m5mZccW9VVVV2Z49e1hycjJbuHAh09fXZ0KhsMX2zJ07l+3YsYPFxcWx1NRUFhgYyPT09Fhubi63T0BAAJsyZQorKCjgfkpLSztz+k+9qqoqdvjwYfbKK68wHR0dNnDgQDZ16lRmZWXF7t+/L1NR7lWrVrGqqip25MgRqc8HIU876h+fbk1NTezixYvsvffeYxYWFkxfX5/5+voyXV1dFhkZ2Wb/WFBQwDw9PdnkyZNZVVUVu3DhAgsJCVH0KRFCCCFPhMdYOwvnFMDT0xPu7u5cLTaxWAxLS0t88MEHWLFiRbvPF4lEMDAwwPbt2+Hv7w8ACAwMRHl5OdVu66C6ujoEBQXhwIEDGDBgAGpra+Hr6ws/Pz94eXlJTdXKyMjA1KlTMXfuXHzzzTc9MvU9Ib0d9Y89h0gkwk8//YSPP/4YgwYNQl5eHnx8fODn54fJkydL1eWtrq7Giy++CDU1NZw4caLNmr2EEEJIb9Ljrvg7U8vtUbW1tWhqaoKhoaHU9oiICJiamsLOzg6LFi1CSUlJl7b9aaSkpISMjAxcvnwZSUlJ2LVrFxobGzFv3jwMHjwYH374IS5evIiMjAzMmDEDL730EgWThMgJ9Y89i7KyMpKSkrBz507cunULZ86cwcCBA7FmzRpYWVlh3rx5OHLkCIRCIV5++WUoKSnh+PHjFEwSQgh5qvS4Ecr8/Hz069cPV69elUo1vnz5ckRGRuL69evtHuO9997D6dOnkZyczCUDOHjwILS0tGBtbY27d+9i1apV0NbWRlRUVLdn1+ttGGOPJdVpampCREQEQkJCEBYWhuLiYsyZMwd//vknBZOEyAn1jz1PS/2jWCxGfHw8jh49irCwMKSnp8PW1hY3b96Erq6uglpKCCGEyMdTd+W/adMmHDx4EGFhYVKZ5V599VX4+vrCwcEBfn5+OHHiBG7cuIGIiAjFNbaXaClDq6qqKl544QXs3LkTeXl5+PHHH7Fv3z65BpM7duyAlZUVNDQ04Onpiejo6Fb33b17N8aNGwcDAwMYGBjA29v7sf0DAwPB4/GkfqZMmSK39hOiaNQ/dr2W+kclJSW4uLhgw4YNSElJwZEjRxAeHi7XYJL6R0IIIYrS4wLKztRyk9i8eTM2bdqEM2fOYOTIkW3ua2NjA2NjY2RkZDxxm591KioqeP/996Guri631zh06BCWLl2KtWvXIjY2Fo6OjvDx8UFhYWGL+0dEROC1117DxYsXERUVBUtLS0yePBl5eXlS+02ZMgUFBQXcz19//dXpNu7fvx9GRkZoaGiQ2u7n54c33nij08clRIL6x96Hx+PhxRdfhL29vdxeg/pHQgghCqXIjECt8fDwYEFBQdzvIpGI9evXj23cuLHV53zzzTdMV1eXRUVFyfQa9+/fZzwej4WHhz9xe4n8eXh4sPfff5/7XSQSMQsLizY/Ew9rbm5mOjo6bN++fdy2gIAAxufzu6yNtbW1TE9PT6pMilAoZCoqKuzChQtd9jrk2Ub9I3kU9Y+EEEIUqceNUALA0qVLsXv3buzbtw+pqalYtGgRampqMH/+fACAv78/Vq5cye3/zTffYPXq1dizZw+srKwgEAggEAhQXV0N4EF2vWXLluHatWvIzs7G+fPnwefzMWjQIPj4+CjkHInseksiEk1NTcydOxd79+7ltkmy406YMKHTxyXkYdQ/kodR/0gIIUThFB3RtqYjtdwGDhz4WFFuAFzB6NraWjZ58mRmYmLCVFVV2cCBA9nChQvZhg0b2MCBA2UqDr53797Hjq+uri61j6RenLm5OdPQ0GCTJk1i6enpXfq+PIvy8vIYAHb16lWp7cuWLWMeHh4yHWPRokXMxsaG1dXVcdv++usvrlh8WFgYs7e3Z+7u7qy5ubnTbY2NjWXKyspcjT8HBwf21Vdfdfp4hLSE+kciQf0jIYQQReuxAaW8HTx4kKmpqclcHHzv3r1MV1dXqvC3QCCQ2mfTpk1MT0+PHTt2jCUkJDBfX19mbW0t9SVNOu5JL5g2btzIDAwMWEJCQpv73b17lwFg586de6L2uri4sA0bNrCbN28yJSUllpOT80THI6S7Uf/Ye1D/SAghRNF65JTX7vD9999j4cKFmD9/PoYNG4adO3dCS0sLe/bsafU5PB4P5ubm3I+ZmRn3GGMMW7Zsweeffw4+n4+RI0di//79yM/Pp2LhT6i3JSJ56623EBwcjL1798Lb2xuWlpZPdDxCuhv1j70H9Y+EEEIU7ZkMKDu75qS6uhoDBw6EpaUl+Hw+kpOTuceysrIgEAikjqmnpwdPT0+Z17GQlqmpqcHV1RXnz5/ntonFYpw/f16qFt+jvv32W6xbtw6nTp2Cm5tbu6+Tm5uLkpIS9O3b94naO3fuXOTm5mL37t148803n+hYhHQ36h97F+ofCSGEKNozGVAWFxdDJBJJ3UEHADMzMwgEghafY2dnhz179iA8PBwHDhyAWCzG6NGjkZubCwDc8zpyTCK73pSIRE9PDy+99BK0tbXh5+f3RMcipLtR/9j7UP9ICCFEkVQU3YDewsvLS+pu7+jRo2Fvb49ffvkF69atU2DLng2vvPIKioqKsGbNGggEAjg5OeHUqVPcBWpOTg6UlP7v/sjPP/+MxsZGzJ49W+o4a9euxRdffAFlZWUkJiZi3759KC8vh4WFBSZPnox169Z1ST3NvLw8zJs3T661OQnpKah/VCzqHwkhhCjSMxlQPsmaEwlVVVU4Oztz60kkzxMKhVJTgoRCIZycnLqm4c+4oKAgBAUFtfhYRESE1O/Z2dltHktTUxOnT5/uopb9n7KyMkRERCAiIgI//fRTlx+fEHmj/rF3ov6REEKIojyTU147u+bkYSKRCElJSdzFkbW1NczNzaWOWVlZievXr8t8TNL7OTs7IzAwEN988w3s7OwU3RxCOoz6RyIv1D8SQsjT6ZkcoQQerDkJCAiAm5sbPDw8sGXLlsfWnPTr1w8bN24EAHz11VcYNWoUBg0ahPLycnz33Xe4d+8e3nrrLQAPMhwuXrwY69evx+DBg2FtbY3Vq1fDwsKC1ok8Q9q7809Ib0D9I5EH6h8JIeTp9EyOUAIP1pxs3rwZa9asgZOTE+Lj4x9bc1JQUMDtX1ZWhoULF8Le3h7Tpk1DZWUlrl69imHDhnH7LF++HB988AHefvttuLu7o7q6GqdOncJvv/0GKysraGhowNPTE9HR0a22a8KECeDxeI/9TJ8+ndsnMDDwscenTJkih3eJEPIs6s7+UUNDAzt27KA+khBCCOmleIwxpuhGPM0OHToEf39/7Ny5E56entiyZQuOHDmCtLQ0mJqaPrZ/aWkpGhsbud9LSkrg6OiIX3/9FYGBgQAeXCwJhULs3buX209dXR0GBgZyPx9CCOlK1EcSQgghvdszO0LZXTpaINzQ0FCqOPjZs2ehpaWFOXPmSO2nrq4utV9vuVDqyEgEABw5cgRDhw6FhoYGHBwc8M8//0g9zhjDmjVr0LdvX2hqasLb2xt37tyR5ykQQroQ9ZHSqI8khBDS21BAKUedLRD+sN9++w2vvvoq+vTpI7U9IiICpqamsLOzw6JFi1BSUtKlbZeHQ4cOYenSpVi7di1iY2Ph6OgIHx8fFBYWtrj/1atX8dprr2HBggWIi4uDn58f/Pz8cOvWLW6fb7/9Fj/++CN27tyJ69evo0+fPvDx8UF9fX13nRYhpJOoj5RGfSQhhJBeiRG5ycvLYwDY1atXpbYvW7aMeXh4tPv869evMwDs+vXrUtv/+usvFh4ezhITE1lYWBizt7dn7u7urLm5uUvb39U8PDzY+++/z/0uEomYhYUF27hxY4v7v/zyy2z69OlS2zw9Pdk777zDGGNMLBYzc3Nz9t1333GPl5eXM3V1dfbXX3/J4QwIIV2J+khp1EcSQgjpjWiEsgf77bff4ODgAA8PD6ntr776Knx9feHg4AA/Pz+cOHECN27ceKzWWE/SmZGIqKgoqf0BwMfHh9s/KysLAoFAah89PT14enrKPLpBCOm9qI+kPpIQQojiUUApR09SILympgYHDx7EggUL2n0dGxsbGBsbc0XEe6Li4mKIRCIuS6SEmZkZBAJBi88RCARt7i/5b0eOSQjpOaiP/D/URxJCCOmtKKCUoycpEH7kyBE0NDTg9ddfb/d1cnNzUVJSwhURJ4SQ3oD6SEIIIaT3o4BSzpYuXYrdu3dj3759SE1NxaJFix4rEL5y5crHnvfbb7/Bz88PRkZGUturq6uxbNkyXLt2DdnZ2Th//jz4fD4GDRoEHx+fbjmnzujMSIS5uXmb+0v+25nRDUJIz0B95APURxJCCOmtKKCUs44WCAeAtLQ0XL58ucWpXMrKykhMTISvry+GDBmCBQsWwNXVFf/99x+uX7+OmTNnwsLCAjweD8eOHWu3fREREXBxcYG6ujoGDRqE4ODgx/bpaBr7lnRmJMLLy0tqfwA4e/Yst7+1tTXMzc2l9qmsrMT169fbHd0ghPQM3dVHfvvtt5g9e3aP7B8B6iMJIYT0YorOCkS6zj///MM+++wzFhoaygCwsLCwNvfPzMxkWlpabOnSpSwlJYVt27aNKSsrs1OnTnH7HDx4kKmpqbE9e/aw5ORktnDhQqavr8+EQmGH23fw4EGmrq7OgoODWUpKCnv77beZvr4+EwgEjDHG3njjDbZixQpu/ytXrjAVFRW2efNmlpqaytauXctUVVVZUlISt8+mTZuYvr4+l9GRz+cza2trVldX1+H2EUKeXj29f5Qcj/pIQgghvQ0FlE8pWS6Yli9fzoYPHy617ZVXXmE+Pj7c7x1NY9+ebdu2sQEDBjA1NTXm4eHBrl27xj02fvx4FhAQILX/4cOH2ZAhQ5iamhobPnw4O3nypNTjYrGYrV69mpmZmTF1dXU2adIklpaW1qm2EUKeDT21f2SM+khCCCG9D48xxhQ7RkrkgcfjISwsDH5+fq3u89xzz8HFxQVbtmzhtu3duxeLFy9GRUUFGhsboaWlhaNHj0odJyAgAOXl5QgPD5ffCRBCiJxQ/0gIIYR0nWdiDWVRURHMzc2xYcMGbtvVq1ehpqb22PqTZ0lrKecrKytRV1fXqTT2hJDehfrHllH/SAghhMhGRdEN6A4mJibYs2cP/Pz8MHnyZNjZ2eGNN95AUFAQJk2apOjmEUKIwlD/SAghhJAn8UwElAAwbdo0LFy4EPPmzYObmxv69OmDjRs3KrpZCtVaynldXV1oampCWVm500XHCSG9B/WPj6P+kRBCCJHNMzHlVWLz5s1obm7GkSNH8Mcff0BdXV3RTVKo9lLOP0nRcUJI70L9ozTqHwkhhBDZPFMB5d27d5Gfnw+xWIzs7GxFN6fLVVdXIz4+HvHx8QCArKwsxMfHIycnBwCwcuVK+Pv7c/u/++67yMzMxPLly3H79m389NNPOHz4MJYsWcLt017RcULI04H6R+ofCSGEkE5RdJrZ7tLQ0MAcHR1ZQEAA27BhAzM1Ne10rbCe6uLFiwzAYz+SNPMBAQFs/Pjxjz3HycmJqampMRsbG7Z3797HjttWGntCSO9H/SP1j4QQQkhnPTNlQ5YtW4ajR48iISEB2traGD9+PPT09HDixAlFN40QQhSK+kdCCCGEdNYzMeU1IiICW7Zswe+//w5dXV0oKSnh999/x3///Yeff/5Z0c0jhBCFof6REEIIIU/imRmhJIQQQgghhBDStZ6JEUpCCCGEEEIIIV2PAkpCCCGEEEIIIZ1CASUhhBBCCCGEkE6hgJIQQgghhBBCSKdQQEkIIYQQQgghpFMooCSEEEIIIYQQ0ikUUBJCCCGEEEII6RQKKAkhhBBCCCGEdAoFlIQQQgghhBBCOoUCSkIIIYQQQgghnUIBJSGEEEIIIYSQTlFRdAMIIbIRiURoampSdDMIIYT0MKqqqlBWVlZ0MwghzygKKAnp4RhjEAgEKC8vV3RTCCGE9FD6+vowNzcHj8dTdFMIIc8YCigJ6eEkwaSpqSm0tLToYoEQQgiHMYba2loUFhYCAPr27avgFhFCnjUUUBLSg4lEIi6YNDIyUnRzCCGE9ECampoAgMLCQpiamtL0V0JIt6KkPIT0YJI1k1paWgpuCSGEkJ5M8j1Ba+0JId2NAkpCegGa5koIIaQt9D1BCFEUCigJIYQQQgghhHQKBZSEkKdGcHAw9PX1Fd0MQshTICIiAjwejzJsE0JIOygpDyGkywUGBmLfvn0AHtRHGzBgAPz9/bFq1SqoqMiv23nllVcwbdo0uR1fFg+fu4qKCgwNDTFy5Ei89tprCAwMhJKS7PfxgoODsXjxYrqgfUKSf5ONGzdixYoV3PZjx45h1qxZYIx1W1senpaopaUFCwsLjBkzBh988AFcXV07dKwJEybAyckJW7Zs6eJWdo0fztzu1tdbMnmozPu2Nz107dq1mDBhwhO2iBBCng00QkkIkYspU6agoKAAd+7cwccff4wvvvgC3333XYv7NjY2dslrampqwtTUtEuO9SQk556dnY1///0XEydOxEcffYQZM2agublZ0c17JmloaOCbb75BWVmZopuCvXv3oqCgAMnJydixYweqq6vh6emJ/fv3K7ppz4yCggLuZ8uWLdDV1ZXa9sknnyisbV3VHxJCSHehgJIQIhfq6uowNzfHwIEDsWjRInh7e+P48eMAHowY+fn54euvv4aFhQXs7OwAAPfv38fLL78MfX19GBoags/nIzs7GwBw5swZaGhoPDZa99FHH+H5558H0PKU159//hm2trZQU1ODnZ0dfv/9d+6x7Oxs8Hg8xMfHc9vKy8vB4/EQEREBACgrK8O8efNgYmICTU1NDB48GHv37pXp3Pv16wcXFxesWrUK4eHh+PfffxEcHMzt9/3338PBwQF9+vSBpaUl3nvvPVRXVwN4MN1u/vz5qKioAI/HA4/HwxdffAEA+P333+Hm5gYdHR2Ym5tj7ty5XA060jJvb2+Ym5tj48aNbe53+fJljBs3DpqamrC0tMSHH36ImpoaAMD27dsxYsQIbt9jx46Bx+Nh586dUq/z+eeft/kakgL0VlZWmDx5Mo4ePYp58+YhKCiIC3hLSkrw2muvoV+/ftDS0oKDgwP++usv7hiBgYGIjIzE1q1buc9HdnY2RCIRFixYAGtra2hqasLOzg5bt27t8Pv1tDM3N+d+9PT0wOPxpLZpa2tz+8bExMDNzQ1aWloYPXo00tLSpI4VHh4OFxcXaGhowMbGBl9++aXUjaOcnBzw+Xxoa2tDV1cXL7/8MoRCIff4F198AScnJ/z666+wtraGhoYG9u/fDyMjIzQ0NEi9lp+fH9544w05vSuEENI5FFASQrqFpqam1J338+fPIy0tDWfPnsWJEyfQ1NQEHx8f6Ojo4L///sOVK1egra2NKVOmoLGxEZMmTYK+vj5CQkK4Y4hEIhw6dAjz5s1r8TXDwsLw0Ucf4eOPP8atW7fwzjvvYP78+bh48aLM7V69ejVSUlLw77//IjU1FT///DOMjY07fP7PP/88HB0dERoaym1TUlLCjz/+iOTkZOzbtw8XLlzA8uXLAQCjR49+bOREMmrS1NSEdevWISEhAceOHUN2djYCAwM73KZnibKyMjZs2IBt27YhNze3xX3u3r2LKVOm4KWXXkJiYiIOHTqEy5cvIygoCAAwfvx4pKSkoKioCAAQGRkJY2Nj7uZDU1MToqKiOjVVcsmSJaiqqsLZs2cBAPX19XB1dcXJkydx69YtvP3223jjjTcQHR0NANi6dSu8vLywcOFC7vNhaWkJsViM/v3748iRI0hJScGaNWuwatUqHD58uMNtIg989tln+N///oebN29CRUUFb775JvfYf//9B39/f3z00UdISUnBL7/8guDgYHz99dcAALFYDD6fj9LSUkRGRuLs2bPIzMzEK6+8IvUaGRkZCAkJQWhoKOLj4zFnzhyIRCLuJhzwoMbkyZMnpV6fEEJ6AlpDScgzorm5GZmZmbCxsZHrOsZHMcZw/vx5nD59Gh988AG3vU+fPvj111+hpqYGADhw4ADEYjF+/fVXbn3T3r17oa+vj4iICEyePBmvvvoq/vzzTyxYsADAg6C0vLwcL730UouvvXnzZgQGBuK9994DACxduhTXrl3D5s2bMXHiRJnan5OTA2dnZ7i5uQEArKysOvU+AMDQoUORmJjI/b548WLu/62srLB+/Xq8++67+Omnn6CmpiY1cvKwhy8obWxs8OOPP8Ld3R3V1dVSIys9mqgZKMsGDKwA5e75PM6aNQtOTk5Yu3Ytfvvtt8ce37hxI+bNm8f9uwwePBg//vgjxo8fj59//hkjRoyAoaEhIiMjMXv2bERERODjjz/mRgCjo6PR1NSE0aNHd7htQ4c+WP8nGZHv16+f1LTLDz74AKdPn8bhw4fh4eEBPT09qKmpQUtLS+rzoaysjC+//JL73draGlFRUTh8+DBefvnlDreLAF9//TXGjx8PAFixYgWmT5+O+vp6aGho4Msvv8SKFSsQEBAA4MHf47p167B8+XKsXbsW58+fR1JSErKysmBpaQkA2L9/P4YPH44bN27A3d0dwINprvv374eJiQn3unPnzsXevXsxZ84cAA/6yAEDBtDaTkJIj0MjlIQ8A5qbm+Hl5QU7Ozt4eXl1yzq+EydOQFtbGxoaGpg6dSpeeeUVbsomADg4OHDBJAAkJCQgIyMDOjo60NbWhra2NgwNDVFfX4+7d+8CAObNm4eIiAjk5+cDAP744w9Mnz691cyuqampGDNmjNS2MWPGIDU1VebzWLRoEQ4ePAgnJycsX74cV69elfm5j2KMSSUDOXfuHCZNmoR+/fpBR0cHb7zxBkpKSlBbW9vmcWJiYjBz5kwMGDAAOjo63MVuTk5Op9vWrUTNwG/ewHbXB/8Vdd+60m+++Qb79u1r8TOQkJCA4OBg7vOnra0NHx8fiMViZGVlgcfj4bnnnkNERATKy8uRkpKC9957Dw0NDbh9+zYiIyPh7u7OFZjvCEliIMnnQyQSYd26dXBwcIChoSG0tbVx+vRpmf6Nd+zYAVdXV5iYmEBbWxu7du3qPZ+NHmjkyJHc//ft2xcAuCnmCQkJ+Oqrr6Q+M5JR49raWqSmpsLS0pILJgFg2LBh0NfXl/oMDhw4UCqYBICFCxfizJkzyMvLA/BgSn9gYCDVmySE9DgUUBLyDMjMzMTNmzcBADdv3kRmZqbcX3PixImIj4/HnTt3UFdXh3379qFPnz7c4w//PwBUV1fD1dUV8fHxUj/p6emYO3cuAMDd3R22trY4ePAg6urqEBYW1up0V1lIMq4+nOWzqalJap+pU6fi3r17WLJkCfLz8zFp0qROJ+xITU2FtbU1gAcjUTNmzMDIkSMREhKCmJgY7NixA0DbSTlqamrg4+MDXV1d/PHHH7hx4wbCwsLafV6PUpYN5Mc9+P/8uAe/d5PnnnsOPj4+WLly5WOPVVdX45133pH6/CUkJODOnTuwtbUF8CCzakREBP777z84OztDV1eXCzIjIyO54L6jJMGF5PPx3XffYevWrfj0009x8eJFxMfHw8fHp91/44MHD+KTTz7BggULcObMGcTHx2P+/Pm957PRA6mqqnL/LwnmxGIxgAefmS+//FLqM5OUlIQ7d+5AQ0ND5td4tD8EAGdnZzg6OmL//v2IiYlBcnIyTW0nhPRINOWVkGeAjY0N3NzccPPmTbi7u8PGxkbur9mnTx8MGjRI5v1dXFxw6NAhmJqaQldXt9X95s2bhz/++AP9+/eHkpISpk+f3uq+9vb2uHLlCjcdDQCuXLmCYcOGAQA3IlBQUABnZ2cAkErQI2FiYoKAgAAEBARg3LhxWLZsGTZv3izzuQHAhQsXkJSUhCVLlgB4MMooFovxv//9jwtsH13npqamBpFIJLXt9u3bKCkpwaZNm7hRD8nNgl7DwAqwcH4QTFo4P/i9G23atAlOTk5cMigJFxcXpKSktPm5HT9+PBYvXowjR45wUw8nTJiAc+fO4cqVK/j444871SbJellvb28ADz6nfD4fr7/+OoAHAUx6ejr32QVa/nxcuXIFo0eP5qZ5A+BG+EnXc3FxQVpaWqufGXt7e9y/fx/379/n/l5TUlJQXl4u9W/ZmrfeegtbtmxBXl4evL29pUY6CSGkp6ARSkKeASoqKoiKikJaWhquXr3arWsoZTVv3jwYGxuDz+fjv//+Q1ZWFiIiIvDhhx9KJVGZN28eYmNj8fXXX2P27NlQV1dv9ZjLli1DcHAwfv75Z9y5cwfff/89QkNDuRFGTU1NjBo1Cps2bUJqaioiIyMfy9C5Zs0ahIeHIyMjA8nJyThx4gTs7e3bPJeGhgYIBALk5eUhNjYWGzZsAJ/Px4wZM+Dv7w8AGDRoEJqamrBt2zZkZmbi999/l8oWCjxYV1ldXY3z58+juLgYtbW1GDBgANTU1LjnHT9+HOvWrevQe61wyirAgnNAUMyD/3bTGkoJBwcHzJs3Dz/++KPU9k8//RRXr15FUFAQN7oeHh7OJeUBHkx/NDAwwJ9//ikVUB47dgwNDQ2PTbFuSXl5OQQCAe7du4ezZ89i9uzZ+PPPP/Hzzz9z07cHDx6Ms2fP4urVq0hNTcU777wjlRkUePD5uH79OrKzs1FcXAyxWIzBgwfj5s2bOH36NNLT07F69WrcuHHjyd4w0qo1a9Zg//79+PLLL5GcnIzU1FQcPHiQ60e8vb25z1tsbCyio6Ph7++P8ePHc+uy2zJ37lzk5uZi9+7dlIyHENJzMUJIj1VXV8dSUlJYXV2dopvSIQEBAYzP53f48YKCAubv78+MjY2Zuro6s7GxYQsXLmQVFRVS+3l4eDAA7MKFC1Lb9+7dy/T09KS2/fTTT8zGxoapqqqyIUOGsP3790s9npKSwry8vJimpiZzcnJiZ86cYQDYxYsXGWOMrVu3jtnb2zNNTU1maGjI+Hw+y8zMbPPcADAATEVFhZmYmDBvb2+2Z88eJhKJpPb9/vvvWd++fZmmpibz8fFh+/fvZwBYWVkZt8+7777LjIyMGAC2du1axhhjf/75J7OysmLq6urMy8uLHT9+nAFgcXFxrbbrWdbS5y0rK4upqamxR78Go6Oj2QsvvMC0tbVZnz592MiRI9nXX38ttQ+fz2cqKiqsqqqKMcaYSCRiBgYGbNSoUe22RfLZAMA0NDSYra0tCwgIYDExMVL7lZSUMD6fz7S1tZmpqSn7/PPPmb+/v9R5pKWlsVGjRjFNTU0GgGVlZbH6+noWGBjI9PT0mL6+Plu0aBFbsWIFc3R0lP0Ne8a01G8wxtjFixcf+3uMi4vj3muJU6dOsdGjRzNNTU2mq6vLPDw82K5du7jH7927x3x9fVmfPn2Yjo4OmzNnDhMIBNzja9eubfPf54033mCGhoasvr6+zfPord8XhJDej8fYQ4uHCCE9Sn19PbKysrjaZIQQQp4tkyZNwvDhwx8bUX8UfV8QQhSl5817I4QQQgh5xpWVlSEiIgIRERH46aefFN0cQghpFQWUhBBCCCE9jLOzM8rKyvDNN988lkCKEEJ6EgooCSGEEEJ6mOzsbEU3gRBCZEJZXgkhhBBCCCGEdAoFlIT0ApQ7ixBCSFvoe4IQoigUUBLSg6mqqgIAamtrFdwSQgghPZnke0LyvUEIId2F1lAS0oMpKytDX18fhYWFAAAtLS3weDwFt4oQQkhPwRhDbW0tCgsLoa+vD2VlZUU3iRDyjKE6lIT0cIwxCAQClJeXK7ophBBCeih9fX2Ym5vTTUdCSLejgJKQXkIkEqGpqUnRzSCEENLDqKqq0sgkIURhKKAkhBBCCCGEENIplJSHEEIIIYQQQkinUEBJCCGEEEIIIaRTKKAkhBBCCCGEENIpFFASQgghhBBCCOkUCigJIYQQQgghhHQKBZSEEEIIIYQQQjqFAkpCCCGEEEIIIZ3y/wA6bjJSrPM+MgAAAABJRU5ErkJggg==\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change wrap to 3 and Adjust dimensions\n", - "plot_results_panel_3d(cycle_mlr,\n", + "plot_results_panel_3d(cycle_mlr.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(12,6))\n", " );" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change wrap to 3, Adjust dimensions, adjust scatter plot and line colors, shapes, and sizes\n", - "fig = plot_results_panel_3d(cycle_mlr,\n", + "fig = plot_results_panel_3d(cycle_mlr.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(12,6)), # Panel configurations\n", " scatter_previous_kw=dict(color='rebeccapurple', marker='o', s=10), # Previous data point\n", @@ -589,10 +414,7 @@ " );\n" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { @@ -615,20 +437,11 @@ }, { "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change the viewing angle to 0 elevation, 0 azimuth.\n", - "fig = plot_results_panel_3d(cycle_mlr,\n", + "fig = plot_results_panel_3d(cycle_mlr.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(12,6)), # Panel configurations\n", " scatter_previous_kw=dict(color='rebeccapurple', marker='o', s=10), # Previous data point\n", @@ -638,28 +451,16 @@ " );" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 15, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change the viewing angle to +20 elevation, +60 azimuth\n", - "fig = plot_results_panel_3d(cycle_mlr,\n", + "fig = plot_results_panel_3d(cycle_mlr.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(12,6)), # Panel configurations\n", " scatter_previous_kw=dict(color='rebeccapurple', marker='o', s=10), # Previous data point\n", @@ -669,10 +470,7 @@ " );\n" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } } ], @@ -697,4 +495,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 24946cbb4d6748fb17f24b5c78c16c4c86743d68 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 24 Mar 2023 08:42:31 -0400 Subject: [PATCH 045/446] test: update test_controller_plots --- ...ycle_plots.py => test_controller_plots.py} | 78 +++++++++---------- 1 file changed, 38 insertions(+), 40 deletions(-) rename tests/{test_cycle_plots.py => test_controller_plots.py} (89%) diff --git a/tests/test_cycle_plots.py b/tests/test_controller_plots.py similarity index 89% rename from tests/test_cycle_plots.py rename to tests/test_controller_plots.py index b2a8a0fb..366a05b6 100644 --- a/tests/test_cycle_plots.py +++ b/tests/test_controller_plots.py @@ -5,15 +5,15 @@ from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score -from autora.cycle import ( - Cycle, +from autora.controller import Cycle +from autora.controller.plotting import ( + _check_replace_default_kw, cycle_default_score, cycle_specified_score, plot_cycle_score, plot_results_panel_2d, plot_results_panel_3d, ) -from autora.cycle.plot_utils import _check_replace_default_kw from autora.experimentalist.pipeline import Pipeline from autora.experimentalist.pooler.general_pool import grid_pool from autora.experimentalist.sampler import random_sampler @@ -29,7 +29,7 @@ def ground_truth(xs): @pytest.fixture -def cycle_lr(ground_truth_1x): +def state_lr(ground_truth_1x): random.seed(1) # Variable Metadata @@ -78,7 +78,7 @@ def runner(xs): # Run 10 iterations cycle.run(10) - return cycle + return cycle.state @pytest.fixture @@ -146,6 +146,11 @@ def runner(xs): return cycle +@pytest.fixture +def state_multi_lr(cycle_multi_lr): + return cycle_multi_lr.state + + def test_check_replace_default_kw(): default = { "subplot_kw": {"sharex": True, "sharey": True}, @@ -165,13 +170,13 @@ def test_check_replace_default_kw(): } -def test_2d_plot(cycle_lr): +def test_2d_plot(state_lr): """ Tests plotting functionality of plot_results_panel_2d. """ steps = 51 fig = plot_results_panel_2d( - cycle_lr, steps=steps, wrap=3, subplot_kw={"sharex": True, "sharey": True} + state_lr, steps=steps, wrap=3, subplot_kw={"sharex": True, "sharey": True} ) # Should have 12 axes, 10 with data and the last 2 turned off @@ -216,16 +221,16 @@ def test_2d_plot(cycle_lr): assert len(axes.lines[0].get_ydata()) == steps -def test_3d_plot(cycle_multi_lr): +def test_3d_plot(state_multi_lr): """ Tests plotting functionality of plot_results_panel_3d. """ steps = 20 fig = plot_results_panel_3d( - cycle_multi_lr, + state_multi_lr, steps=steps, - view=(20, 60), wrap=3, + view=(20, 60), subplot_kw=dict(figsize=(11, 8)), ) @@ -253,17 +258,15 @@ def test_3d_plot(cycle_multi_lr): ) -def test_score_functions(cycle_lr, ground_truth_1x): +def test_score_functions(state_lr, ground_truth_1x): """ Tests the scoring functions cycle_default_score and cycle_specified_score. """ - X_test = cycle_lr.data.metadata.independent_variables[0].allowed_values.reshape( - -1, 1 - ) + X_test = state_lr.metadata.independent_variables[0].allowed_values.reshape(-1, 1) y_test = ground_truth_1x(X_test) - scores_default = cycle_default_score(cycle_lr, X_test, y_test) - scores_specified = cycle_specified_score(r2_score, cycle_lr, X_test, y_test) + scores_default = cycle_default_score(state_lr, X_test, y_test) + scores_specified = cycle_specified_score(r2_score, state_lr, X_test, y_test) # Check scores are the expected values score_values = [ @@ -285,15 +288,13 @@ def test_score_functions(cycle_lr, ground_truth_1x): assert np.array_equal(scores_default, scores_specified) -def test_cycle_score_plot(cycle_lr, ground_truth_1x): +def test_cycle_score_plot(state_lr, ground_truth_1x): """ Tests plotting functionality of test_cycle_score_plot with a 2D linear regression. """ - X_test = cycle_lr.data.metadata.independent_variables[0].allowed_values.reshape( - -1, 1 - ) + X_test = state_lr.metadata.independent_variables[0].allowed_values.reshape(-1, 1) y_test = ground_truth_1x(X_test) - fig = plot_cycle_score(cycle_lr, X_test, y_test) + fig = plot_cycle_score(state_lr, X_test, y_test) # Should have 1 axis assert len(fig.axes) == 1 @@ -325,10 +326,10 @@ def test_cycle_score_plot_multi_lr(cycle_multi_lr, ground_truth_2x): """ cycle_multi_lr.run(6) # Run additional 6 times, total of 12 cycles X_test = np.array( - list(grid_pool(cycle_multi_lr.data.metadata.independent_variables)) + list(grid_pool(cycle_multi_lr.state.metadata.independent_variables)) ) y_test = ground_truth_2x(X_test) - fig = plot_cycle_score(cycle_multi_lr, X_test, y_test) + fig = plot_cycle_score(cycle_multi_lr.state, X_test, y_test) # Test line is plotted correctly axis = fig.axes[0] @@ -353,13 +354,13 @@ def test_cycle_score_plot_multi_lr(cycle_multi_lr, ground_truth_2x): assert np.array_equal(np.around(y_plotted, 8), np.around(y_values, 8)) -def test_2d_plot_indexing(cycle_lr): +def test_2d_plot_indexing(state_lr): """ Test indexing of 2d plotter. """ steps = 51 fig = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=2, query=[0, 3, 7], @@ -371,13 +372,13 @@ def test_2d_plot_indexing(cycle_lr): assert sum([s.axison for s in fig.axes]) == 3 -def test_2d_plot_negative_indexing(cycle_lr): +def test_2d_plot_negative_indexing(state_lr): """ Test indexing of 2d plotter. """ steps = 51 fig = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=2, query=[-2, -1], @@ -393,7 +394,7 @@ def test_2d_plot_negative_indexing(cycle_lr): assert fig.axes[1].get_children()[3].get_text() == "Cycle 9" -def test_2d_plot_slicing(cycle_lr): +def test_2d_plot_slicing(state_lr): """ Test slicing of 2d plotter using built-in slice() function. """ @@ -402,7 +403,7 @@ def test_2d_plot_slicing(cycle_lr): # Using Slice function # Cycles 0, 2, 4, 6, 8 fig = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=3, query=slice(0, 9, 2), @@ -414,7 +415,7 @@ def test_2d_plot_slicing(cycle_lr): # Last 4 plots fig2 = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=3, query=slice(-4, None, None), @@ -425,7 +426,7 @@ def test_2d_plot_slicing(cycle_lr): assert sum([s.axison for s in fig2.axes]) == 4 -def test_2d_plot_slicing_np(cycle_lr): +def test_2d_plot_slicing_np(state_lr): """ Test slicing of 2d plotter using np.s_ Index Expression """ @@ -433,7 +434,7 @@ def test_2d_plot_slicing_np(cycle_lr): # Cycles 0, 2, 4, 6, 8 fig1 = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=3, query=np.s_[0:9:2], @@ -444,7 +445,7 @@ def test_2d_plot_slicing_np(cycle_lr): assert sum([s.axison for s in fig1.axes]) == 5 fig2 = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=3, query=np.s_[-4:], @@ -455,7 +456,7 @@ def test_2d_plot_slicing_np(cycle_lr): assert sum([s.axison for s in fig2.axes]) == 4 -def test_2d_plot_plot_single(cycle_lr): +def test_2d_plot_plot_single(state_lr): """ Test query of 2d plotter for a single cycle. """ @@ -463,17 +464,14 @@ def test_2d_plot_plot_single(cycle_lr): # Using index fig1 = plot_results_panel_2d( - cycle_lr, - steps=steps, - query=[9], - subplot_kw={"sharex": True, "sharey": True}, + state_lr, steps=steps, query=[9], subplot_kw={"sharex": True, "sharey": True} ) assert len(fig1.axes) == 1 assert sum([s.axison for s in fig1.axes]) == 1 # Using slice() fig2 = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, query=slice(-1, None, None), subplot_kw={"sharex": True, "sharey": True}, @@ -483,7 +481,7 @@ def test_2d_plot_plot_single(cycle_lr): # Using np.s_ Index expression fig3 = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, query=np.s_[-1:], subplot_kw={"sharex": True, "sharey": True}, From ebf59dbb0c8af11bbddc754cac390199f524f616 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 24 Mar 2023 09:53:10 -0400 Subject: [PATCH 046/446] refactor: move controller.plotting module from cycle --- .../plot_utils.py => controller/plotting.py} | 103 ++++++++++-------- ...ycle_plots.py => test_controller_plots.py} | 78 +++++++------ 2 files changed, 93 insertions(+), 88 deletions(-) rename autora/{cycle/plot_utils.py => controller/plotting.py} (86%) rename tests/{test_cycle_plots.py => test_controller_plots.py} (89%) diff --git a/autora/cycle/plot_utils.py b/autora/controller/plotting.py similarity index 86% rename from autora/cycle/plot_utils.py rename to autora/controller/plotting.py index 59c7de07..20e63769 100644 --- a/autora/cycle/plot_utils.py +++ b/autora/controller/plotting.py @@ -9,7 +9,7 @@ from matplotlib.patches import Patch from matplotlib.ticker import MaxNLocator -from .simple import SimpleCycle as Cycle +from .protocol import SupportsControllerState # Change default plot styles rcParams["axes.spines.top"] = False @@ -18,41 +18,40 @@ def _get_variable_index( - cycle: Cycle, + state: SupportsControllerState, ) -> Tuple[List[Tuple[int, str, str]], List[Tuple[int, str, str]]]: """ Extracts information about independent and dependent variables from the cycle object. Returns a list of tuples of (index, name, units). The index is in reference to the column number in the observed value arrays. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run Returns: Tuple of 2 lists of tuples """ l_iv = [ - (i, s.name, s.units) - for i, s in enumerate(cycle.data.metadata.independent_variables) + (i, s.name, s.units) for i, s in enumerate(state.metadata.independent_variables) ] n_iv = len(l_iv) l_dv = [ (i + n_iv, s.name, s.units) - for i, s in enumerate(cycle.data.metadata.dependent_variables) + for i, s in enumerate(state.metadata.dependent_variables) ] return l_iv, l_dv -def _observed_to_df(cycle: Cycle) -> pd.DataFrame: +def _observed_to_df(state: SupportsControllerState) -> pd.DataFrame: """ Concatenates observation data of cycles into a single dataframe with a field "cycle" with the cycle index. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run Returns: Dataframe """ - l_observations = cycle.data.observations + l_observations = state.observations l_agg = [] for i, data in enumerate(l_observations): @@ -63,18 +62,18 @@ def _observed_to_df(cycle: Cycle) -> pd.DataFrame: return df_return -def _min_max_observations(cycle: Cycle) -> List[Tuple[float, float]]: +def _min_max_observations(state: SupportsControllerState) -> List[Tuple[float, float]]: """ Returns minimum and maximum of observed values for each independent variable. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run Returns: List of tuples """ l_return = [] - iv_index = range(len(cycle.data.metadata.independent_variables)) - l_observations = cycle.data.observations + iv_index = range(len(state.metadata.independent_variables)) + l_observations = state.observations # Get min and max of observation data # Min and max by cycle - All IVs l_mins = [np.min(s, axis=0) for s in l_observations] # Arrays by columns @@ -88,17 +87,19 @@ def _min_max_observations(cycle: Cycle) -> List[Tuple[float, float]]: return l_return -def _generate_condition_space(cycle: Cycle, steps: int = 50) -> np.array: +def _generate_condition_space( + state: SupportsControllerState, steps: int = 50 +) -> np.array: """ Generates condition space based on the minimum and maximum of all observed data in AER Cycle. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run steps: Number of steps to define the condition space Returns: np.array """ - l_min_max = _min_max_observations(cycle) + l_min_max = _min_max_observations(state) l_space = [] for min_max in l_min_max: @@ -110,17 +111,17 @@ def _generate_condition_space(cycle: Cycle, steps: int = 50) -> np.array: return l_space[0].reshape(-1, 1) -def _generate_mesh_grid(cycle: Cycle, steps: int = 50) -> np.ndarray: +def _generate_mesh_grid(state: SupportsControllerState, steps: int = 50) -> np.ndarray: """ Generates a mesh grid based on the minimum and maximum of all observed data in AER Cycle. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run steps: Number of steps to define the condition space Returns: np.ndarray """ - l_min_max = _min_max_observations(cycle) + l_min_max = _min_max_observations(state) l_space = [] for min_max in l_min_max: @@ -130,12 +131,12 @@ def _generate_mesh_grid(cycle: Cycle, steps: int = 50) -> np.ndarray: def _theory_predict( - cycle: Cycle, conditions: Sequence, predict_proba: bool = False + state: SupportsControllerState, conditions: Sequence, predict_proba: bool = False ) -> list: """ Gets theory predictions over conditions space and saves results of each cycle to a list. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run conditions: Condition space. Should be an array of grouped conditions. predict_proba: Use estimator.predict_proba method instead of estimator.predict. @@ -143,7 +144,7 @@ def _theory_predict( """ l_predictions = [] - for i, theory in enumerate(cycle.data.theories): + for i, theory in enumerate(state.theories): if not predict_proba: l_predictions.append(theory.predict(conditions)) else: @@ -181,7 +182,7 @@ def _check_replace_default_kw(default: dict, user: dict) -> dict: def plot_results_panel_2d( - cycle: Cycle, + state: SupportsControllerState, iv_name: Optional[str] = None, dv_name: Optional[str] = None, steps: int = 50, @@ -200,7 +201,7 @@ def plot_results_panel_2d( range of the observed data. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run iv_name: Independent variable name. Name should match the name instantiated in the cycle object. Default will select the first. dv_name: Single dependent variable name. Name should match the names instantiated in the @@ -260,7 +261,7 @@ def plot_results_panel_2d( d_kw[key] = _check_replace_default_kw(d1, d2) # ---Extract IVs and DV metadata and indexes--- - ivs, dvs = _get_variable_index(cycle) + ivs, dvs = _get_variable_index(state) if iv_name: iv = [s for s in ivs if s[1] == iv_name][0] else: @@ -273,16 +274,16 @@ def plot_results_panel_2d( dv_label = f"{dv[1]} {dv[2]}" # Create a dataframe of observed data from cycle - df_observed = _observed_to_df(cycle) + df_observed = _observed_to_df(state) # Generate IV space - condition_space = _generate_condition_space(cycle, steps=steps) + condition_space = _generate_condition_space(state, steps=steps) # Get theory predictions over space - l_predictions = _theory_predict(cycle, condition_space) + l_predictions = _theory_predict(state, condition_space) # Cycle Indexing - cycle_idx = list(range(len(cycle.data.theories))) + cycle_idx = list(range(len(state.theories))) if query: if isinstance(query, list): cycle_idx = [cycle_idx[s] for s in query] @@ -348,7 +349,7 @@ def plot_results_panel_2d( def plot_results_panel_3d( - cycle: Cycle, + state: SupportsControllerState, iv_names: Optional[List[str]] = None, dv_name: Optional[str] = None, steps: int = 50, @@ -368,7 +369,7 @@ def plot_results_panel_3d( Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run iv_names: List of up to 2 independent variable names. Names should match the names instantiated in the cycle object. Default will select up to the first two. dv_name: Single DV name. Name should match the names instantiated in the cycle object. @@ -388,7 +389,7 @@ def plot_results_panel_3d( Returns: matplotlib figure """ - n_cycles = len(cycle.data.theories) + n_cycles = len(state.theories) # ---Figure and plot params--- # Set defaults, check and add user supplied keywords @@ -416,7 +417,7 @@ def plot_results_panel_3d( d_kw[key] = _check_replace_default_kw(d1, d2) # ---Extract IVs and DV metadata and indexes--- - ivs, dvs = _get_variable_index(cycle) + ivs, dvs = _get_variable_index(state) if iv_names: iv = [s for s in ivs if s[1] == iv_names] else: @@ -429,13 +430,13 @@ def plot_results_panel_3d( dv_label = f"{dv[1]} {dv[2]}" # Create a dataframe of observed data from cycle - df_observed = _observed_to_df(cycle) + df_observed = _observed_to_df(state) # Generate IV Mesh Grid - x1, x2 = _generate_mesh_grid(cycle, steps=steps) + x1, x2 = _generate_mesh_grid(state, steps=steps) # Get theory predictions over space - l_predictions = _theory_predict(cycle, np.column_stack((x1.ravel(), x2.ravel()))) + l_predictions = _theory_predict(state, np.column_stack((x1.ravel(), x2.ravel()))) # Subplot configurations if n_cycles < wrap: @@ -501,29 +502,35 @@ def plot_results_panel_3d( return fig -def cycle_default_score(cycle: Cycle, x_vals: np.ndarray, y_true: np.ndarray): +def cycle_default_score( + state: SupportsControllerState, x_vals: np.ndarray, y_true: np.ndarray +): """ Calculates score for each cycle using the estimator's default scorer. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run x_vals: Test dataset independent values y_true: Test dataset dependent values Returns: List of scores by cycle """ - l_scores = [s.score(x_vals, y_true) for s in cycle.data.theories] + l_scores = [s.score(x_vals, y_true) for s in state.theories] return l_scores def cycle_specified_score( - scorer: Callable, cycle: Cycle, x_vals: np.ndarray, y_true: np.ndarray, **kwargs + scorer: Callable, + state: SupportsControllerState, + x_vals: np.ndarray, + y_true: np.ndarray, + **kwargs, ): """ Calculates score for each cycle using specified sklearn scoring function. Args: scorer: sklearn scoring function - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run x_vals: Test dataset independent values y_true: Test dataset dependent values **kwargs: Keyword arguments to send to scoring function @@ -533,9 +540,9 @@ def cycle_specified_score( """ # Get predictions if "y_pred" in inspect.signature(scorer).parameters.keys(): - l_y_pred = _theory_predict(cycle, x_vals, predict_proba=False) + l_y_pred = _theory_predict(state, x_vals, predict_proba=False) elif "y_score" in inspect.signature(scorer).parameters.keys(): - l_y_pred = _theory_predict(cycle, x_vals, predict_proba=True) + l_y_pred = _theory_predict(state, x_vals, predict_proba=True) # Score each cycle l_scores = [] @@ -546,7 +553,7 @@ def cycle_specified_score( def plot_cycle_score( - cycle: Cycle, + state: SupportsControllerState, X: np.ndarray, y_true: np.ndarray, scorer: Optional[Callable] = None, @@ -561,7 +568,7 @@ def plot_cycle_score( """ Plots scoring metrics of cycle's theories given test data. Args: - cycle: AER Cycle object that has been run + state: AER Cycle object that has been run X: Test dataset independent values y_true: Test dataset dependent values scorer: sklearn scoring function (optional) @@ -579,13 +586,13 @@ def plot_cycle_score( # Use estimator's default scoring method if specific scorer is not supplied if scorer is None: - l_scores = cycle_default_score(cycle, X, y_true) + l_scores = cycle_default_score(state, X, y_true) else: - l_scores = cycle_specified_score(scorer, cycle, X, y_true, **scorer_kw) + l_scores = cycle_specified_score(scorer, state, X, y_true, **scorer_kw) # Plotting fig, ax = plt.subplots(figsize=figsize) - ax.plot(np.arange(len(cycle.data.theories)), l_scores, **plot_kw) + ax.plot(np.arange(len(state.theories)), l_scores, **plot_kw) # Adjusting axis limits if ylim: diff --git a/tests/test_cycle_plots.py b/tests/test_controller_plots.py similarity index 89% rename from tests/test_cycle_plots.py rename to tests/test_controller_plots.py index b2a8a0fb..366a05b6 100644 --- a/tests/test_cycle_plots.py +++ b/tests/test_controller_plots.py @@ -5,15 +5,15 @@ from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score -from autora.cycle import ( - Cycle, +from autora.controller import Cycle +from autora.controller.plotting import ( + _check_replace_default_kw, cycle_default_score, cycle_specified_score, plot_cycle_score, plot_results_panel_2d, plot_results_panel_3d, ) -from autora.cycle.plot_utils import _check_replace_default_kw from autora.experimentalist.pipeline import Pipeline from autora.experimentalist.pooler.general_pool import grid_pool from autora.experimentalist.sampler import random_sampler @@ -29,7 +29,7 @@ def ground_truth(xs): @pytest.fixture -def cycle_lr(ground_truth_1x): +def state_lr(ground_truth_1x): random.seed(1) # Variable Metadata @@ -78,7 +78,7 @@ def runner(xs): # Run 10 iterations cycle.run(10) - return cycle + return cycle.state @pytest.fixture @@ -146,6 +146,11 @@ def runner(xs): return cycle +@pytest.fixture +def state_multi_lr(cycle_multi_lr): + return cycle_multi_lr.state + + def test_check_replace_default_kw(): default = { "subplot_kw": {"sharex": True, "sharey": True}, @@ -165,13 +170,13 @@ def test_check_replace_default_kw(): } -def test_2d_plot(cycle_lr): +def test_2d_plot(state_lr): """ Tests plotting functionality of plot_results_panel_2d. """ steps = 51 fig = plot_results_panel_2d( - cycle_lr, steps=steps, wrap=3, subplot_kw={"sharex": True, "sharey": True} + state_lr, steps=steps, wrap=3, subplot_kw={"sharex": True, "sharey": True} ) # Should have 12 axes, 10 with data and the last 2 turned off @@ -216,16 +221,16 @@ def test_2d_plot(cycle_lr): assert len(axes.lines[0].get_ydata()) == steps -def test_3d_plot(cycle_multi_lr): +def test_3d_plot(state_multi_lr): """ Tests plotting functionality of plot_results_panel_3d. """ steps = 20 fig = plot_results_panel_3d( - cycle_multi_lr, + state_multi_lr, steps=steps, - view=(20, 60), wrap=3, + view=(20, 60), subplot_kw=dict(figsize=(11, 8)), ) @@ -253,17 +258,15 @@ def test_3d_plot(cycle_multi_lr): ) -def test_score_functions(cycle_lr, ground_truth_1x): +def test_score_functions(state_lr, ground_truth_1x): """ Tests the scoring functions cycle_default_score and cycle_specified_score. """ - X_test = cycle_lr.data.metadata.independent_variables[0].allowed_values.reshape( - -1, 1 - ) + X_test = state_lr.metadata.independent_variables[0].allowed_values.reshape(-1, 1) y_test = ground_truth_1x(X_test) - scores_default = cycle_default_score(cycle_lr, X_test, y_test) - scores_specified = cycle_specified_score(r2_score, cycle_lr, X_test, y_test) + scores_default = cycle_default_score(state_lr, X_test, y_test) + scores_specified = cycle_specified_score(r2_score, state_lr, X_test, y_test) # Check scores are the expected values score_values = [ @@ -285,15 +288,13 @@ def test_score_functions(cycle_lr, ground_truth_1x): assert np.array_equal(scores_default, scores_specified) -def test_cycle_score_plot(cycle_lr, ground_truth_1x): +def test_cycle_score_plot(state_lr, ground_truth_1x): """ Tests plotting functionality of test_cycle_score_plot with a 2D linear regression. """ - X_test = cycle_lr.data.metadata.independent_variables[0].allowed_values.reshape( - -1, 1 - ) + X_test = state_lr.metadata.independent_variables[0].allowed_values.reshape(-1, 1) y_test = ground_truth_1x(X_test) - fig = plot_cycle_score(cycle_lr, X_test, y_test) + fig = plot_cycle_score(state_lr, X_test, y_test) # Should have 1 axis assert len(fig.axes) == 1 @@ -325,10 +326,10 @@ def test_cycle_score_plot_multi_lr(cycle_multi_lr, ground_truth_2x): """ cycle_multi_lr.run(6) # Run additional 6 times, total of 12 cycles X_test = np.array( - list(grid_pool(cycle_multi_lr.data.metadata.independent_variables)) + list(grid_pool(cycle_multi_lr.state.metadata.independent_variables)) ) y_test = ground_truth_2x(X_test) - fig = plot_cycle_score(cycle_multi_lr, X_test, y_test) + fig = plot_cycle_score(cycle_multi_lr.state, X_test, y_test) # Test line is plotted correctly axis = fig.axes[0] @@ -353,13 +354,13 @@ def test_cycle_score_plot_multi_lr(cycle_multi_lr, ground_truth_2x): assert np.array_equal(np.around(y_plotted, 8), np.around(y_values, 8)) -def test_2d_plot_indexing(cycle_lr): +def test_2d_plot_indexing(state_lr): """ Test indexing of 2d plotter. """ steps = 51 fig = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=2, query=[0, 3, 7], @@ -371,13 +372,13 @@ def test_2d_plot_indexing(cycle_lr): assert sum([s.axison for s in fig.axes]) == 3 -def test_2d_plot_negative_indexing(cycle_lr): +def test_2d_plot_negative_indexing(state_lr): """ Test indexing of 2d plotter. """ steps = 51 fig = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=2, query=[-2, -1], @@ -393,7 +394,7 @@ def test_2d_plot_negative_indexing(cycle_lr): assert fig.axes[1].get_children()[3].get_text() == "Cycle 9" -def test_2d_plot_slicing(cycle_lr): +def test_2d_plot_slicing(state_lr): """ Test slicing of 2d plotter using built-in slice() function. """ @@ -402,7 +403,7 @@ def test_2d_plot_slicing(cycle_lr): # Using Slice function # Cycles 0, 2, 4, 6, 8 fig = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=3, query=slice(0, 9, 2), @@ -414,7 +415,7 @@ def test_2d_plot_slicing(cycle_lr): # Last 4 plots fig2 = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=3, query=slice(-4, None, None), @@ -425,7 +426,7 @@ def test_2d_plot_slicing(cycle_lr): assert sum([s.axison for s in fig2.axes]) == 4 -def test_2d_plot_slicing_np(cycle_lr): +def test_2d_plot_slicing_np(state_lr): """ Test slicing of 2d plotter using np.s_ Index Expression """ @@ -433,7 +434,7 @@ def test_2d_plot_slicing_np(cycle_lr): # Cycles 0, 2, 4, 6, 8 fig1 = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=3, query=np.s_[0:9:2], @@ -444,7 +445,7 @@ def test_2d_plot_slicing_np(cycle_lr): assert sum([s.axison for s in fig1.axes]) == 5 fig2 = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, wrap=3, query=np.s_[-4:], @@ -455,7 +456,7 @@ def test_2d_plot_slicing_np(cycle_lr): assert sum([s.axison for s in fig2.axes]) == 4 -def test_2d_plot_plot_single(cycle_lr): +def test_2d_plot_plot_single(state_lr): """ Test query of 2d plotter for a single cycle. """ @@ -463,17 +464,14 @@ def test_2d_plot_plot_single(cycle_lr): # Using index fig1 = plot_results_panel_2d( - cycle_lr, - steps=steps, - query=[9], - subplot_kw={"sharex": True, "sharey": True}, + state_lr, steps=steps, query=[9], subplot_kw={"sharex": True, "sharey": True} ) assert len(fig1.axes) == 1 assert sum([s.axison for s in fig1.axes]) == 1 # Using slice() fig2 = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, query=slice(-1, None, None), subplot_kw={"sharex": True, "sharey": True}, @@ -483,7 +481,7 @@ def test_2d_plot_plot_single(cycle_lr): # Using np.s_ Index expression fig3 = plot_results_panel_2d( - cycle_lr, + state_lr, steps=steps, query=np.s_[-1:], subplot_kw={"sharex": True, "sharey": True}, From 06fdc8c5e59c8781ce918a2addd15a78e944dd1a Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 24 Mar 2023 09:53:24 -0400 Subject: [PATCH 047/446] test: update cycle_results_plots --- example/cycle/cycle_results_plots.ipynb | 316 +++++------------------- 1 file changed, 57 insertions(+), 259 deletions(-) diff --git a/example/cycle/cycle_results_plots.ipynb b/example/cycle/cycle_results_plots.ipynb index 6e99c8c5..1ba31d67 100644 --- a/example/cycle/cycle_results_plots.ipynb +++ b/example/cycle/cycle_results_plots.ipynb @@ -14,17 +14,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { - "collapsed": true, - "pycharm": { - "name": "#%%\n" - } + "collapsed": true }, "outputs": [], "source": [ "from autora.variable import VariableCollection, Variable\n", - "from autora.cycle import Cycle, plot_results_panel_2d, plot_results_panel_3d\n", + "from autora.controller import Cycle\n", + "from autora.controller.plotting import plot_results_panel_2d, plot_results_panel_3d\n", "from autora.experimentalist.pipeline import Pipeline\n", "from autora.experimentalist.pooler.general_pool import grid_pool\n", "from autora.experimentalist.sampler import random_sampler\n", @@ -36,17 +34,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Simple linear regression cycle\n", "random.seed(1)\n", @@ -123,26 +112,14 @@ }, { "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Plot cycle results with each cycle as one panel\n", - "plot_results_panel_2d(cycle); # Add semicolon to supress creating two figures in jupyter notebook" + "plot_results_panel_2d(cycle.state); # Add semicolon to supress creating two figures in jupyter notebook" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { @@ -159,29 +136,17 @@ }, { "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change wrap to 3 and Adjust dimensions\n", - "plot_results_panel_2d(cycle,\n", + "plot_results_panel_2d(cycle.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(9,4.5))\n", " );" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { @@ -202,20 +167,11 @@ }, { "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change wrap to 3, Adjust dimensions, adjust scatter plot and line colors, shapes, and sizes\n", - "fig = plot_results_panel_2d(cycle,\n", + "fig = plot_results_panel_2d(cycle.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(10,5)), # Panel configurations\n", " scatter_previous_kw=dict(color='rebeccapurple', marker='o', s=10), # Previous data point\n", @@ -224,10 +180,7 @@ " );" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { @@ -244,18 +197,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Loop by the axes to draw annotations\n", "for i,ax in enumerate(fig.axes[:-1]):\n", @@ -288,36 +231,11 @@ }, { "cell_type": "code", - "execution_count": 7, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 0.98, 'Last Cycle')" - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Querying using indexing\n", - "fig = plot_results_panel_2d(cycle,\n", + "fig = plot_results_panel_2d(cycle.state,\n", " query=[0, 2, 4],\n", " subplot_kw=dict(figsize=(8,3), gridspec_kw={\"bottom\": 0.25})\n", " );\n", @@ -325,7 +243,7 @@ "fig.suptitle('Cycles 0, 2, 4')\n", "\n", "# Last Cycle\n", - "fig = plot_results_panel_2d(cycle,\n", + "fig = plot_results_panel_2d(cycle.state,\n", " query=[-1],\n", " subplot_kw=dict(figsize=(4,4), gridspec_kw={\"bottom\": 0.25})\n", " );\n", @@ -334,80 +252,32 @@ "fig.suptitle('Last Cycle')" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 0.1, 'x1')" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Querying using slicing with the slice() function\n", - "fig = plot_results_panel_2d(cycle,\n", + "fig = plot_results_panel_2d(cycle.state,\n", " query=slice(0,5,2), # (Start, Stop, Step)\n", " subplot_kw=dict(figsize=(8,3), gridspec_kw={\"bottom\": 0.25})\n", " );\n", "fig.supxlabel('x1', y=0.1)" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "data": { - "text/plain": "Text(0.5, 0.98, 'Last 2 Cycles')" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Querying using slicing with np.s_[]\n", - "fig = plot_results_panel_2d(cycle,\n", + "fig = plot_results_panel_2d(cycle.state,\n", " query=np.s_[0:5:2], # [Start:Stop:Step]\n", " subplot_kw=dict(figsize=(8,3), gridspec_kw={\"bottom\": 0.25})\n", " );\n", @@ -415,7 +285,7 @@ "fig.suptitle('Cycles 0, 2, 4')\n", "\n", "# Last 2 Cycles\n", - "fig2 = plot_results_panel_2d(cycle,\n", + "fig2 = plot_results_panel_2d(cycle.state,\n", " query=np.s_[-2:], # You can use other list slicing conventions\n", " subplot_kw=dict(figsize=(8,3), gridspec_kw={\"bottom\": 0.25})\n", " );\n", @@ -423,10 +293,7 @@ "fig2.suptitle('Last 2 Cycles')" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { @@ -444,17 +311,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Simple multiple linear regression cycle\n", "random.seed(1)\n", @@ -516,71 +374,38 @@ }, { "cell_type": "code", - "execution_count": 11, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Plot cycle results with each cycle as one panel using defaults\n", - "fig = plot_results_panel_3d(cycle_mlr); # Add semicolon to supress creating two figures in jupyter notebook" + "fig = plot_results_panel_3d(cycle_mlr.state); # Add semicolon to supress creating two figures in jupyter notebook" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change wrap to 3 and Adjust dimensions\n", - "plot_results_panel_3d(cycle_mlr,\n", + "plot_results_panel_3d(cycle_mlr.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(12,6))\n", " );" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change wrap to 3, Adjust dimensions, adjust scatter plot and line colors, shapes, and sizes\n", - "fig = plot_results_panel_3d(cycle_mlr,\n", + "fig = plot_results_panel_3d(cycle_mlr.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(12,6)), # Panel configurations\n", " scatter_previous_kw=dict(color='rebeccapurple', marker='o', s=10), # Previous data point\n", @@ -589,10 +414,7 @@ " );\n" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { @@ -615,20 +437,11 @@ }, { "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change the viewing angle to 0 elevation, 0 azimuth.\n", - "fig = plot_results_panel_3d(cycle_mlr,\n", + "fig = plot_results_panel_3d(cycle_mlr.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(12,6)), # Panel configurations\n", " scatter_previous_kw=dict(color='rebeccapurple', marker='o', s=10), # Previous data point\n", @@ -638,28 +451,16 @@ " );" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 15, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "outputs": [], "source": [ "# Change the viewing angle to +20 elevation, +60 azimuth\n", - "fig = plot_results_panel_3d(cycle_mlr,\n", + "fig = plot_results_panel_3d(cycle_mlr.state,\n", " wrap=3,\n", " subplot_kw=dict(figsize=(12,6)), # Panel configurations\n", " scatter_previous_kw=dict(color='rebeccapurple', marker='o', s=10), # Previous data point\n", @@ -669,10 +470,7 @@ " );\n" ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } + "collapsed": false } } ], @@ -697,4 +495,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 132e4d0e7c46c74f79aa9d7d68cf9d4b61934bb2 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 24 Mar 2023 13:33:31 -0400 Subject: [PATCH 048/446] chore: update outputs --- example/cycle/cycle_results_plots.ipynb | 225 +++++++++++++++++++++--- 1 file changed, 196 insertions(+), 29 deletions(-) diff --git a/example/cycle/cycle_results_plots.ipynb b/example/cycle/cycle_results_plots.ipynb index 1ba31d67..f4624f8a 100644 --- a/example/cycle/cycle_results_plots.ipynb +++ b/example/cycle/cycle_results_plots.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -34,8 +34,17 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Simple linear regression cycle\n", "random.seed(1)\n", @@ -112,8 +121,17 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot cycle results with each cycle as one panel\n", "plot_results_panel_2d(cycle.state); # Add semicolon to supress creating two figures in jupyter notebook" @@ -136,8 +154,17 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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RKnsyTwH25s2j+kRxQ8dGDlt7qmJFRMpk1Y5Mxny3mQPHTwNwQ4dGjLu2FXX81StBpDozxrAoKZ0p87az+7cTmeYh/ozpH0uf2Pq6UCHiaHmZ9kIlN93+OS8TatW/6N2MMSxPyeCVBSkkHckGoI6/FyN7t+T2+CYObzegYkVESiUrr4gX5yTx1fqDADQK9mXS9a3pFX3xX4Qi4to2HzzJi3OS+XmPvTN1HX8vHu0TyV+7NMFTO/2JOAe/evYRlTMjK371LnqXn/cc55UF2/ll7wkAArw9uOfK5tx1RQS1vJ2jTHCOKETEqc3bcoTnvt9GRk4Bbm4w9PKmPNEvxml+kYlI5Th4Io9XFqQwa6O96Zu3h4W7r4jgvl4tCPTR4nkRp2Kx2Kd+lWLNytZDWby6MIXlKRmAPbfv7NaM+3q2oLaTzZTQmYaInNfR7Hyem7WN+dvSAGgR4s/UG9vSuQq2KhQRx8k6XcS7y3cyffXekn4KN3RoxOMJ0TQMrrjO1CJSwSyWC0792pWRy+uLUpmz+QgAHhY3br4snIevinTazXFUrIjInxhj+GrdQV6ck0R2fjEeFjfu79WCkb1bOnzuqohUnsJiG58l7uOtJTtKtirt1qIuzwyIpXWjIAdHJyLldfjkad5avIOvf7V3nXdzg+vaNeTRPlE0c/J+aCpWROQs+4/l8cx3W1i1MxOANo2CmHpj20rtTisijmWMYcG2NKbM287eY3kAtKxfi7EDYukVXXX9FESkYh3LLWDasl3896d9FFrto6R9Yuszum80sQ1c4++6ihURAcBqM0xfvYfXFqZyusiKt4eF0X2juKt7hMO2KxSRyvfr/hNMmpPM+n32Bbb1annx6DVR3NI5XLkv4qKy84v44Mc9/OfH3ZwqtAIQH1GHJ/tF06mpa03lVrEiIqSk5fDUN5vZeOAkYP+FNvXGtk4/NCwi5bf/WB5TF2wvmbvu42nh3h7NubdnC22eIeKi8ousfLx2L+8u38XJ36ZytmkUxBMJ0fSIrOeSo6T6bSRSgxUUW3l32S7eXb6TIqshwNuDZwbGckvn8CrvUCsiVeNkXiH/WLqTj9fap4W4ucFNHRszum+00y6wFZELK7La+HLdAd5esoP07ALAvinO432j6dc6zCWLlDNUrIjUUL/uP8FTX29mx9FcAPrEhvLikNY6WRGppgqKrXyydh/vLN1J1mn7FdcrWtbjmQGxWpMm4qJsNsMPmw/zxqLUkvVmjYJ9GdUnkus7OK7rfEVSsSJSw5wqKObVhSnMWLMXY6CuvxfPD45jYJsGLn3lRUTOzRjD3C1pTJ2/nf3H7Scz0aEBPD0ghl5RWjwv4oqMMSzdfpRXFqSwPS0HsK83G9m7JbfFN8Hbo/rs3KliRaQGWZmawZhvt3Do5GkAbujYiGcHtnK6BlAiUjHW7zvOi3OS2bD/JAAhAd483jeKmzqF466pniIu6afdx3hlQUrJphgBPh78/crmDO8egX81XG9W/V6RiPzJybxCXpyTzNfrDwL2IeJJ17emV/T5G0eJiOvam3mKqfO3M2+rvaGrr6c7917ZnHuvbF4tT2ZEaoKth7J4eUEKK1PtXed9PC0M69aM+3u2INiv+l501G8skWpu3pYjPDtrG5m5Bbi5wbCuzXgiIVonLCLV0IlThby9dAf//WkfRVaDxQ1u7hzOY9dEUT9Q69FEXNHOo7m8viiFuVvsFx88LG78tUs4D10VSWgNyGudrYhUU0ez83l21lYWbEsH7LuCvHxTW5fbX11ELu7MdqXvLN1JTn4xAD2jQnhmQCzRYQEOjk5EyuPQydO8uSiVb349iM2AmxsMad+IUX0iaVq35rQWULEiUs0YY/hq3UEmzkkiJ78YD4sb9/dqwYNXtaxWC+5E5H87Ab08P6VkLVpMWABjB8bSIzLEwdGJSHlk5hYwbdlOPv1pf0nX+WtahTK6bxQxYTVv5z4VKyLVyP5jeYz5bjOrdx4DoG3jIKbe2JbYBjXvl5tIdffznuNMmpPEpoNZAIQGejO6bzQ3dmysxfMiLig7v4gPVu7mg1V7yPut63zX5nV5ol80HZvUdnB0jqNiRaQasNoMH67aw2uLUsgvsuHjaeGxa6K4q3tEtdhjXUT+Z3dGLlPmbWdhkn2Kp7+XO/f1bMGIHs3x9dLoqYirOV1o5aO1e3lv+a6SHkjtGgfxREIM3VvWrfHbi6tYEXFx29OyeerrzSVXV7s2r8uUG9vUqPmsIjXBsdwC3l6yg08T91NsM7hb3LjlsnAe7RNFSIC3o8MTkTIqLLbxxboDvLNkB0dz7F3nW9avxeN9o0iIc+2u8xVJxYqIiyootjJt6U7eXb6LYpshwMeDsQNiueWycP2CE6lG8ousfLh6D+8t20VOgX3x/NUx9Xm6fwyRoVo8L+JqrDbD95sO8caiHSWNWhsF+/LoNVFc36GRpnH+gYoVERe0ft8JnvpmMzuP5gLQt1UoE4e0rhFbGIrUFDabYdamQ7wyP4XDWfkAxDUMZOyAWLq1rOfg6ESkrIwxLE4+yqsLUkhJP9N13psHe7fg1mrWdb4iqVgRcSGnCop5dWEKM9bsxRioV8uLFwa3pn9rDReLVCdrdmXy0txkth7KBqBhkA9P9ItmcLtGWHTVVcTlrNmVySsLUtiw/yQAgT4e/L1nC4Z3b4afl07HL0T/OyIuYmVqBmO+3VKyPekNHRvx7MBW1Pavvl1rRWqanUdzmDx3O0u2HwWglrcHD/RuwV3dI/Dx1FVXEVez+eBJXlmQwo87MgHw9XRnePdm/P3KFgT5eTo4OtegYkXEyZ3MK+TFOcl8vf4gYJ/X+tINbegZpR4KItVFRk4Bby5O5fNfDmD9bfH87fFNePjqSOrV0uJ5EVez82gOry5IZf42e9d5T3c3bu3ShAevakn9AE3ZLgsVKyJOyhjD3C1pjP9+G5m5Bbi5wbCuzXgiIRp/b6WuSHVwutDKBz/u5v0Vuzj1W1+Fa1qF8nT/GFqE1HJwdCJSVgeO5/HWkh18+7uu89d3aMSjfaIIr+Pn6PBcks54RJxQenY+z87cWtJHIbJ+Labc2JZOTWtuUyiR6sRqM3z760FeW5hKWrZ98Xy7xkE8MyCW+OZ1HRydiJRVRs5vXecT91FkNQAkxIUyum80Udq175KoWBFxIsYYvvjlAJPmJpOTX4yHxY0HerdkZO8W2iVEpJpYtSOTSXOTST5iXzzfKNiXJ/tFM6htQy2eF3ExWaeL+NfKXXy4ai+ni+yjo1e0rMfjCdG0Dw92bHDVhIoVESex79gpxny7hTW7jgH2q6xTbmxLbINAB0cmIhUhJS2HyfOSWZ6SAUCAjwcP9m7JsG7NtHhexMWcLrQyfc0e3l++i+x8e/+jduHBPJUQra3FK5iKFZHysNkgLxP86oHFckkPVWy1MX31Xl5blEJ+kQ0fTwuP941mePcINYYSqUI2m42MjAxCQkKwXGJe/97R7HzeWJzKF78cwGbAw+LG3y5vysNXR1JHu/mJVKqKzuvCYhtf/LKft5fuJOO3rvNRobV47JpoEuJC1UagEqhYESkrmw1+fBUOroPGnaHH4+UuWJKPZPPUN5vZfDALgK7N6zLlxjY0retfkRGLyEXYbDYmTZpEYmIi8fHxjB079pJPbPIKi/nXyt38a+Vu8n5bPN8vLoyn+scQUU85LlLZKjKvrTbDrI2HeGNxKgeO21sIhNfx5dE+UQxur67zlUnFikhZ5WXaC5XcdPvnvEyoVb9MD1FQbGXa0p28u3wXxTZDgI8H4wbGcnPncF2VEXGAjIwMEhMTSUtLIzExkYyMDEJDQ8v1WFab4ev1B3htYSpHf7vy2j48mHEDY+ncrE5Fhi0iF1AReW2MYVFSOq8tTC3pOh8S4M3DV7Xklsua4OVRcaOwcm4qVkTKyq+efUTlzMiKX70yTQtbv+84T32zhZ1HcwH7biEvDG5NaKD2XRdxlJCQEOLj40uuwIaEhJRr+siK1AxempNcclITXseXp/rFMLBNA12IEKlil5rXa3Zm8vKCFDYeOAnYu87f36slw7o1Vdf5KuRmjDGODkLOLzs7m6CgILKysggM1EJrp/H74gRKNS3sVEExryxI4aO1ezEG6tXy5oXBcQxo06CKgxdHU147p9+fxABlmj6SfCSbl+Yml3SpDvL15KGrWnJH16baya+GUF47p/Lk9cYDJ3llwXZW77RveOPr6c5dVzTj3h7qOu8IKgtFysNi+d/Ur9yjF50WtiI1g2e+3cKhk/Z5rjd1asy4gbEE+2lxrYizsFgsJVNE0tPTSzV9JC0rn9cXpfDV+oMYY+9SPbRrMx66qqXyW8QJlCWvU9NzeHVBSkmPM093N26Pb8oDvVuo67wDqVgRuVTnmhb2m5N5hUycncw3vx4EoHFtX166vg1XRoWU7TkqcPcxEbm4c00f+b3cgmL+tWIX//pxN/lFNgCubduAJxNiaFK3dF2qK2v3MRE5t/Pl9YHjebyxOJXvNhzCGLC4wfUdGjOqT2SZu84rryuepoE5OQ0ru4g/FBPGGOZuSWP891vJzC3EzQ2Gd4tgdN8o/L3LeI2gAncfE+egvHYN5zrpKLba+HLdQV5flEpmrn3xfOemtXlmYCwdm9Qu02NX9O5j4ljKa9fw+7zOPFXItKU7+ezn/SVd5/vFhTG6bxSR5eg6r7yuHBpZEakIv5sWlp6dz7iZW1n02zByZP1aTLmxLZ2alv5E5iwVsPuYiJTd76ePGGNYnpLBS3OT2fHb5hjN6vrxdP8YEuLCyrx4viJ3HxOR0rNYLPgE1OHVhalMX/2/rvM9IuvxeN9o2l1C13nldeVQsSJSQYwxfPHLASbNTSYnvxgPixsP9G7JyN4tLm2B7QWmmYlI5dt6KIvJ85JLFtsG+3nyyNWR3B7ftNzbll5smpmIVLy8wmKmr97LP1f8r+t8hybBPJEQTbcWl/63VXldOTQNrJRWrlzJK6+8wvr16zly5AjfffcdQ4YMOe/xy5cvp3fv3n+6/ciRI4SFhZX6eTWs7Br2Zp5izLdbWLvbfjLTrnEQU29qS0xYBb1nWrNSrSivXcPhk6d5dWFKyTx2L3cLw7s344HeLQnyvfQdgTS3vXpRXjuvwmIb//fzft5ZurNk+mZ0aACPJ0TTJ7Z+hW4rrryueBpZKaVTp07Rrl077rrrLm644YZS3y8lJeWsX1r162v6TnVSbLXx4eo9vLYwlYJiGz6eFh7vG83w7hEV283297uPiUilyskv4v0Vu/jgxz0UFNsXz1/XriFPJESXebHthfx+mpmIVDyrzfDdhkO8uTiVgyfsu3E2qePHY9dEMahdw0rpOq+8rngqVkqpf//+9O/fv8z3q1+/PsHBwRUfkDhc0uFsnv52M5sPZgHQrUVdptzQttQ7AYmIcymy2vj85/28uXgHx04VAtAlog5jB8Re0jx2EalaxhgWbEvntYUpJWvM6gd489DVkdzSOVxd512MipVK1r59ewoKCmjdujUTJkyge/fuFzy+oKCAgoKCkq+zs7MrO0Qpo4JiK/9YupP3lu+i2GYI8PHg2YGt+EvnxupQLeekvHZuxhgWJx9l8rxkdmecAqB5PX+e7h/DNa1ClddyTspr52OMYdXOTF5ZkFJyITHI15P7e7VgWNdm+HqpQasrUrFSSRo0aMD7779P586dKSgo4IMPPqBXr14kJibSsWPH895v8uTJPP/881UYqZTF+n3HeeqbLez87UpNv7gwXhgcR/1ANYuS81NeO6/NB08yaU4yiXuOA1DH34tRfSK5tUsTPN119VXOT3ntXH7df4JXF6SwZpd97aiflzt3XxHBiB7NK2SNmTiOFtiXg5ub20UX2J9Lz549adKkCZ988sl5jznXlZrw8HAt2HOwUwXFvLIghY/W7sUYqFfLm4mD4+jfpoGjQxMXoLx2PgdP5PHKghRmbTwMgJeHhRFXRHBfrxYE+ujERi5Oee0cUtJyeHVhSkm7AC93C7df3oSRvVtSr5a3g6OTiqCRlSrUpUsXVq1adcFjvL298fZWcjmT5SlHGfvdVg6dtC/O+0unxowb2IogP53QSOkor51H1uki3l2+k+mr91L42+L5Gzo0YnRCNI2CfR0cnbgS5bVj7T9m7zo/c+P/us7f1KkxD18dSePaWjtanahYqUIbN26kQQNdiXcVJ04VMnF2Et9uOARA49q+TLmhLVdEqs+JiKspLLbxWeI+3lqygxN5RQBc3rwO4wa2onWjIAdHJyKllZ6dzztLd/D5zwcottknBw1oE8Zj10TTsn4tB0cnlUHFSinl5uayc+fOkq/37NnDxo0bqVOnDk2aNGHMmDEcOnSIjz/+GIA333yTiIgI4uLiyM/P54MPPmDp0qUsXLjQUS9BSskYw+zNR5jw/TaOnSrEzQ2Gd4vg8YQo/LyUMiKu5MyuQFPnb2dPpn3xfIsQf54ZEMtVMRXbX0FEKs/JvELeW7GLj9bsJb/IPip6ZVQIT/SNpk1jXXCoznTmVUrr1q07q8njY489BsCwYcOYMWMGR44cYf/+/SXfLywsZPTo0Rw6dAg/Pz/atm3L4sWLz9koUpxHWlY+42ZuZXGyfe5rZP1aTL2pLR2b1HZwZCJSVhv2n+Clucn8svcEAPVqefHoNVHc0jkcDy2eF3EJpwqKmb56D/9cuZuc37rOd2pamycSorm8eV0HRydVQQvsnZw64lYNm83w+S8HmDw3mZyCYjzd3XigV0se6N0Cbw9tdSgVS3lduQ4cz+PlBSn8sMm+eN7H08I9PZrz954tqOWta3RSOZTXFaug2MpnifuZtmwnmbn2vkcxYQE8kRCtUdEaRr+1pcbbm3mKp7/dzE+77VuXtgsP5uUb2xIdFuDgyESkLLLyivjHsh18tGYfhVYbbm5wY8fGjO4bRYMgLZ4XcQXFVhvfbjjEW4t3lGxs07Tub13n2zbEUgld58W5qViRGqvYauM/q/bw+qJUCopt+Hq683hCNHd2a4a7fhmKuIzCYhuf/LSPt5fsIOu0ffH8FS3rMWZADHENNZddxBUYY5i/NY1XF6aw67fmrKGB3jx8dSQ3dw5X36MaTMWK1EhJh7N56pvNbDlk73B7Rct6TL6hDeF1tN2hiKswxjBvaxpT529n37E8AKJCa/HMgFh6RoVomoiICzDG8OMOe9f5M3+Tg/08eaBXC4Z2bYaPp6Zi13QqVqRGyS+y8o+lO3l/xS6KbYZAHw/GXduKv3RqrBMbEReyft8JJs1J4tf9JwEICfBm9DVR3NSpsRbPi7iI9ftO8MqC7SXTsP293Lm7R3Pu6RFBgJqzym9UrEiNsW7vcZ76ZnPJ8HL/1mE8PziO+gE+Do5MxAnYbJCXCX71wOK8J/v7jp1i6vztzN2SBoCvpzt/79mce3o0x1+L50XOYrPZyMjIICQkBIsT5XXykWxeW5jC4uSjgL3r/N8ub8rI3i2oq67z8gf6zS7VXm5BMa/M387HP+3DGPsV2ImD4+jXWg06RQB7ofLjq3BwHTTuDD0ed7qC5cSpQt5ZupNPftpLkdVgcYO/dApndN8o6gfqgoPIH9lsNiZNmkRiYiLx8fGMHTvW4QXL3sxTvLE4le83HS7pOv+XTuE83CeSRsHaBEPOTcWKVGvLU44y9rutJTuK3Ny5MWMHtCLIT8PLIiXyMu2FSm66/XNeJtSqXznPVcYRnIJiKx+v2cc7S3eQ/VuPhV7RIYzpH6sd+0QuICMjg8TERNLS0khMTCQjI4PQ0NBKea6LjeCkZeXz9tIdfPnL/7rOD2zbgMeuiaJFiLrOy4WpWJFq6cSpQibOTuLbDYcAaFzblyk3tOWKyHoOjkzECfnVs4+onBlZ8aukPCnDCI4xhtmbjzB1/nYOnrBfbIgJC+CZAbFcGRVSOfGJVCMhISHEx8eXjKyEhJQjb0pxceFCIzgnThXy/opdzFizl4Jie9f5XtEhPN43mtaNKninPheZyiplp2JFqpUzJzgTvt/GsVOFWNxgePcIRveNws9LP+4i52Sx2AuHyv5DX8oRnJ/3HGfS3GQ2HTgJ2LcvHd03mhs7Nta24iKlYbNhyctk7DNjyMg8Vr41K6W8uHCuERz/4Lp8uGoP/165m5wC+4ho56a1ebJfDF0i6lTEKyxXrOKadPYm1caRrNM8O3NryYK9qNBaTL2xLR2a1HZwZCIuwGKpvKlfZ1xkBGd3Ri5T529nwbZ0++Fe7tzXswUjekToYoNIaf3uxN3SuDOh5T1xL+XFhd+P4HTqcjk/pOby3vLNHDtl7zof2yCQJxOi6RVdiduJV+VUVqly+u0vLs9mM/zfL/uZMnc7OQXFeLq7MbJ3Sx7o1RIvD11ZEXEa5xnBOZZbwNtLdvBp4n6KbfbF83/t0oRRfSK1W59IWVXUiXspp4daLBaeenoMH61M4cOf0zgyZzsAzer68VjfaK5t06Dyu85X1VRWcQgVK+LS9mSe4qlvNvPzHvse7R2aBDP1xrZEhWrhrYhT+t0ITn6RlQ9X7+G9ZbtKpor0jg7hmQGxRCqHRcqnIk7cz6z/uOIxOH38vNNDbTZ7Y9bXFqWw+7e2AGGBPjx8dSR/6dy46rrOV9VUVnEIFSvikoqtNj5YtYc3FqVSUGzD19OdxxOiubNbM81pF3FyNpvh+02HeWVBSslOfXENAxk7IJZuLXVFVOSSXOqJeynWfxhjWJGawasLU9h6KBuA2n6ePNCrJXd0beqYrvNVMZVVHELFiricpMPZPPXNZrYcygKgR2Q9Xrq+DeF1/BwcmYhczNpdx5g0N6nkBKdBkA9PJEQzpH2jyp8qIlJTXMqJ+0Wmka3be5yXF6SUzGhQ13mpbCpWxGXkF1l5Z+kO/rliN8U2Q6CPB89e24qbOjWuvEV7IlIhdh7NYcq87SUbYNTy9uD+Xi24+4oIx1yFFZFzO880sqTD2by6MIWl23/rOu9hYejlTXmgd0vq+Hs5MmKp5lSsiEtYt/c4T36zuWRO7IA2YUy4Lu5/i2+1v7qIU8rMLeDNxan8388HsNoM7hY3buvShEf6RFKvlvcF73uxRnMiUgn+MI1sz/HTvL4olR82HQbA3eLGzZ0b8/DVkTQIKl/XeeW2lIWKFXFquQXFvDx/Ox+v3QdASIA3Ewe3pl/rsP8dpP3VRZzO6cLfFs8v30Xub4vn+8SG8nT/GFrWv3jH6gs1mhORSmaxcMQawNszt/LluoNYf+s6P6hdQx67JoqIev7lfmjltpSVihVxWsu2H2Xsd1s4nJUPwM2dGzN2QCuC/P4wJ1b7q4tUnYuMYtpshm83HOLVBSmkZdtzt23jIJ4ZEMvlzeuW+mnO1WguNDS0wl6GiPzP70c6Tp4u5r3lO/lo7T4Kf+s63zs6hMcToolreOld55XbUlYqVsTpHD9VyAs/bGPmRvuQc5M6fky+oQ3dz7dLkPZXF6kaFxnFXL0zk0lzkkk6Yl883yjYlycSormuXcMyL57/faO5+Ph4QkJCKvSliIjdmZGONb/8ik+7AeywhJNbYAWgS7M6PNEvmsuaVVzXeeW2lJWbMcY4Ogg5v+zsbIKCgsjKyiIwMNDR4VQqY+zbmT7/QxLHTxVicYO7ukfwWN+oi3ev1poVcSEum9e5R2HWg/ZRzFqhMPgfUKs+qek5TJ6bzLKUDAACvD0YeVVL7uzW7JIWz2teu7gSV83r/YeOcPMz75Betz3Gyz69K65hII8nRNMrqnK6ziu3pSw0siJO4UjWacZ9t5Ulv+0yEh0awNSb2tI+PLh0D6D91UUq3x9GMY9aA3jj2y188ct+bAY8LG787fKmPHRVS+peZPF8aVgsFk0PEakkxVYbX68/yFtLdpDWoDsAQW75TPrr5QxoU/bR0LJQbktZqFgRh7LZDJ/9vJ8p87aTW1CMp7sbD/aO5P5eLfDy0NUWEafy2y5BeSfT+eDXHN5/bQV5hfbpIv3iwniqf8wlLbwVkcpnsxnmbj3C6wtT2Z1p32GzQZAPd3UJZdiVMXh56tRQnIt+IsVh9mSe4qlvNpc0lurQJJipN7YlKjTAwZGJyLlYbYZvfj3EawtTSM8uAKB9eDBjB8ZW6Jx2Eal4xhiWp2TwyoKUknVldfy9GNm7JbfHN1G/I3FaKlakyhVbbfz7xz28sTiVwmIbvp7uPNkvmqFdm+GuDtYiTmllagYvzU1me1oOAOF1fHmqXwwD2zRQU1YRJ/fznuO8smA7v+w9Adibst7Tozl394iglrdOBcW56SdUqtTWQ1k89c1mth22X9XpEVmPl65vQ3gdPwdHJiLnsj0tm5fmbmdlqn3xfKCPBw9dFcnQbk3x9tCVWBFntvVQFq8uTGH5b5tfeHtYGNatGff3bEFtdZ0XF6FiRapEfpGVt5fs4J8rd2O1GYJ8PRk3MJabOjXWVVkRJ5Senc/rC1P5av0BbAY83d0Y1rUZD17VkmA/neSIOLPdGbm8viiV2ZuPAPbNL26+LJyHr4okLMjHwdGJlI2KFal0P+85ztPfbC5ZyDewTQPGX9eK+gH6hSnibE4VFPOvlbv518rdnC6yL54f2KYBT/aLpmldLZ4XcWaHT57m7SU7+Gq9veu8mxtc164hj/aJopk2vxAXpWJFKk1OfhEvz0/hk5/2AVA/wJuJQ1qTEBfm4MhE5I+KrTa+Wn+Q1xelkpFjXzzfqWltnhkQS6emtR0cnYhcyLHcAt5dvotPfvpf1/mrY+ozum80rRq6Ts8XkXNRsSKVYun2dMZ+t5UjWfkA/PWycMYMiCXI19PBkYnI753ZIWjyvGRS03MBaFrXj6f7xdCvdVjlT9NUQ1eRcsvJL+LfP+7hPz/u5tRv24h3iajDkwnRdHbgDn1q+igVScWKVKhjuQW8MDuJWRsPA9Ckjh9TbmhDt5b1HByZiPzRtsNZvDQ3mdU7jwEQ7OfJI1dHcnt806rpc2SzwY+vljSZpMfjKlhESiG/yMona/fx7vKdnMgrAqB1o0CeSIjhysh6Dl0LarPZmDRpEomJicTHxzN27FgVLHJJVKxIhTDG8P2mwzz/QxLHTxVicYO7r4jgsWui8fXSjkEizuTwydO8ujCF7zYcwhjwcrdwZ/dmjOzdsmpHP/My7YVKbrr9c14m1Kpfdc8v4mKKrDa+WneQt5fsIC3bPnOheYg/j/eNpn9VjISWQkZGBomJiaSlpZGYmEhGRoa61cslUbEil+zwydOMm7mVpduPAhATFsDUG9vSLjzYsYGJyFly8ov454rd/PvH3RT8Nq/9unYNeSIh2jHbh/vVs4+onBlZ8dMIrMi52GyGHzYf5o1Fqew9lgdAo2BfHrk6khs6NsLD3XlGLkJCQoiPjy8ZWQkJCXF0SOLi3IwxxtFByPllZ2cTFBREVlYWgYHOtUjOZjN8+vN+ps7bTm5BMV7uFh68qiX39WxRNVNIRFxUVed1sdXG//1ygDcXpXLsVCEAlzWrzdiBrWjv6IsKWrMi1URl5LUxhmUpR3llQSrJv3Wdr+vvxYNXteS2+CZO2+tIa1akImlkRcpld0YuT3+zhZ/3HgegY5Ngpt7YlsjQAAdHJiJnGGNYknyUyfOS2ZVh3zo8op4/T/ePoW+r0HNPGanq4sFi0dQvkXNI3H2MVxaksG6fvet8gLcH917ZnLuuiMC/jF3nq7p4sFgsmvolFUbFipRJkdXGv3/czZuLd1BYbMPPy50nE6K5o2sz3C2OnysrInZbDmYxaW4SP+22X1Co7efJqD5R3BbfBM/zTRnRgncRh9t6KIuXF6SwMvV/Xefv7N6M+64sX9d5LXgXV6diRUpt66EsnvpmM9sO24eir4wK4aXrW9O4tgPmuovIOR06eZpXF9gXzwN4eVi4+4oI7u/VgkCfiyye14J3EYfZeTSXNxalMmfL/7rO33JZOA9dYtd5LXgXV6diRS4qv8jKW0t28K+Vu7HaDEG+njx3bStu6NjIKXYeERHIzi/i3WW7+HD1npKmcNd3aMTovlGlv6CgBe8iVe7QydO8tTiVr9cfxGbAzQ0Gt2vIo9dE0bTupXed14J3cXVaYO/kHL3APnH3MZ7+dgt7Mu3z3Qe2acCE6+IICfCu8lhEqouKzOsiq43PEvfz1pIdHP9t8fzlzevwzIBY2jYOLvsDasG7SLmUNa8zcwuYtmwnn/60n0Kr/QLDNa1CGd03ipiwiv17rwXv4so0siLnlJNfxJR52/k0cT8A9QO8mTikNQlxYQ6OTETAvnh+YVI6U+ZtL7mY0DzEn2f6x3J1bP3yj3pqwbtIpcrOL+LfK3fzn1V7yPut63zX5nV5ol80HZvUrpTn1IJ3cWUqVuRPliSnM27mVo5k2RtO/fWycMYMiK3aZnEicl4bD5zkpTnJJbvx1fX3YtQ1Ufz1svDzL54XEYfKL7Ly0Zq9vLdiFyd/6zrftnEQTyREc0VLx3adF3FmKlakxLHcAp7/IYnvNx0GoGldPybf0IZuLTRvXcQZHDiex8sLUvjhtxz18bQw4orm/L1ncwIutnheRBzKGPjPqj2czCuiZf1aPN43ioQ45+g6L+LMVKwIxhhmbTzM8z9s40ReERY3GNGjOY/2icLXyzkbTonUJFl5RUxbvpMZq/dSaLXh5gY3dmzM6L5RNAjydXR4IlIKvl7ujLu2FQVFVm7o2Fjb/YuUkoqVGu7wydOM/W4Ly1Ls+7nHhAUw9ca2tHN0V2sRobDYxn9/2sfbS3eUTBvp3rIuzwyIJa5hkIOjE5Gyuq5dQ0eHIOJyVKzUUDab4dPEfUyZt51ThVa83C08fHVL/t6zhea8iziYMYZ5W9OYOn87+47lARAVWosx/WPpFR2iaSMiIlJjqFipgXZl5PL0N5v5Ze8JADo1rc3UG9vQsn6AgyMTEYAth7J44NNfAQgJ8Gb0NVHc1KkxHrqQICIiNYyKlRqkyGrjXyt389aSHRQW2/DzcuepfjHccXlTLJo7K+I02jYOZnD7hjSr68+9VzbH31u/qkVEpGbSX8AaYuuhLJ78ejNJR7IBuDIqhJeub136ztYiUqXevKW9pnuJiEiNpzkFpbRy5UoGDRpEw4YNcXNzY+bMmRe9z/Lly+nYsSPe3t60bNmSGTNmVHqcf5RfZGXKvO0MnraapCPZBPt58tpf2vHR8MtUqIg4MRUqIiIiKlZK7dSpU7Rr145p06aV6vg9e/YwcOBAevfuzcaNGxk1ahQjRoxgwYIFlRzp/yTuPkb/t37k/RW7sNoM17ZtwKJHe3Jjp8Y6ERIRERERp6dpYKXUv39/+vfvX+rj33//fSIiInjttdcAiI2NZdWqVbzxxhskJCRUVpgA5OQXMWXedj5N3A9AaKA3Ewe3pm9cWKU+r4iIiIhIRVKxUknWrl1Lnz59zrotISGBUaNGVerzLk5KZ9zMraRl5wNwa5cmjBkQQ6C6W4uIiIiIi1GxUknS0tIIDQ0967bQ0FCys7M5ffo0vr7n7jpdUFBAQUFBydfZ2dmler7M3AKe/yGJHzYdBqBZXT8m39CWri3qlvMViEhFKW9ei4jzUl6LVA2tWXEykydPJigoqOQjPDz8gscbY/huw0GueX0FP2w6jMUN/n5lc+Y9cqUKFREnUda8FhHnp7wWqRoqVipJWFgY6enpZ92Wnp5OYGDgeUdVAMaMGUNWVlbJx4EDBy74PF+uO8CjX2ziRF4RMWEBzBzZnTEDYvH1cq+Q1yEil66seQ1gs9lIT0/HZrNVQYQiUlbKa5GqoWlglaRr167MnTv3rNsWLVpE165dL3g/b29vvL29S/08g9s24MMFvzDIP4m/t/PGs+EV5YpXRCpPWfPaZrMxadIkEhMTiY+PZ+zYsVgsurYk4kyU1yJVQ1lSSrm5uWzcuJGNGzcC9q2JN27cyP799h23xowZw9ChQ0uOv++++9i9ezdPPvkk27dv59133+XLL7/k0UcfrdC4fIpOMKfZlzzotxjPw+sgL7NCH19Eql5GRgaJiYmkpaWRmJhIRkaGo0MSkUukvBYpHxUrpbRu3To6dOhAhw4dAHjsscfo0KEDzz33HABHjhwpKVwAIiIimDNnDosWLaJdu3a89tprfPDBBxW/bbFfPTzCO0GtUGjcGfzqVezji0iVCwkJIT4+nrCwMOLj4wkJCXF0SCJyiZTXIuXjZowxjg5Czi87O5ugoCCysrIIDAw890E2m31Exa8eaEhZxOmVJq9tNhsZGRmEhIRoqoiIC1Bei1QOrVmpDiwWqFXf0VGISAWyWCx/2v5cRFyb8lqk7FTWi4iIiIiIU1KxIiIiIiIiTknFioiIiIiIOCWtWXFyZ/Y/yM7OdnAkIq4lICAANzc3R4dxTsprkfJRXotUPxfLaxUrTi4nJweA8PBwB0ci4louuIOegymvRcpHeS1S/Vwsr7V1sZOz2WykpKTQqlUrDhw44LS/pGuC7OxswsPD9T44WGnfB2e+Aqu8dh7Ka+egvJaKpLx2HqV5LzSy4uIsFguNGjUCIDAwUEnnBPQ+OAdXfh+U185H74NzcOX3QXntfPQ+OI9LeS+0wF5ERERERJySihUREREREXFKKlZcgLe3N+PHj8fb29vRodRoeh+cQ3V5H6rL63B1eh+cQ3V5H6rL63B1eh+cR0W8F1pgLyIiIiIiTkkjKyIiIiIi4pRUrIiIiIiIiFNSsSIiIiIiIk5JxYqIiIiIiDglFSsiIiIiIuKUVKyIiIiIiIhTUrEiIiIiIiJOScWKiIiIiIg4JRUrIiIiIiLilFSsiIiIiIiIU1KxIiIiIiIiTknFioiIiIiIOCUVKyIiIiIi4pRUrIiIiIiIiFNSsSIiIiIiIk5JxYqIiIiIiDilchUriYmJFR2HiIiIiIjIWcpVrHTt2pWoqCgmTpzI7t27KzomERERERGR8hUr//3vf4mMjGTixIlERkbSvXt33n//fY4fP17R8YmIiIiISA3lZowx5b1zZmYmn3/+OZ999hk//fQTXl5e9OvXj7/97W9cd911eHl5VWSsIiIiIiJSg1xSsfJ7u3bt4rPPPuPTTz9lx44dBAUFcdNNNzF06FCuuOKKingKERERERGpQSpsNzBfX1/8/Pzw8fHBGIObmxuzZs2iZ8+eXHbZZSQlJVXUU4mIiIiISA1wScVKTk4O06dPp0+fPjRt2pRnnnmGZs2a8fXXX5OWlsbhw4f54osvOHr0KMOHD6+omGsUYwzZ2dlU0ACYiDgB5bWIiEjplKtYmTVrFjfffDOhoaHcfffd5OTk8Oabb3L48GFmzpzJDTfcgKenJ+7u7tx0002MGzeODRs2VHTsNUJOTg5BQUHk5OQ4OpQSe/fuxc3NjY0bNzo6FBGXpLwWEREpnXIVK9dffz2JiYk8+uijJCcnk5iYyMiRI6lbt+45j2/Xrh233377JQUqpZeWlsZDDz1E8+bN8fb2Jjw8nEGDBrFkyRJHh1YiJSWF3r17Exoaio+PD82bN2fcuHEUFRU5OjQRp+QKef17O3fuJCAggODgYEeHIiIiLsyjPHdaunQpvXr1KvXxXbp0oUuXLuV5KimjvXv30r17d4KDg3nllVdo06YNRUVFLFiwgJEjR7J9+3ZHhwiAp6cnQ4cOpWPHjgQHB7Np0ybuuecebDYbL730kqPDE3EqrpLXZxQVFXHrrbfSo0cP1qxZ4+hwRETEhZVrZKUshYpUrQceeAA3Nzd+/vlnbrzxRqKiooiLi+Oxxx7jp59+AuCuu+7i2muvPet+RUVF1K9fn//85z8A2Gw2Xn75ZVq2bIm3tzdNmjRh0qRJ533erVu30r9/f2rVqkVoaCh33HEHmZmZ5z2+efPmDB8+nHbt2tG0aVOuu+46br/9dn788ccK+F8QqV5cJa/PGDduHDExMdx8882X8KpFREQqcDcwcbzjx48zf/58Ro4cib+//5++f2Y6xogRI5g/fz5Hjhwp+d7s2bPJy8vjlltuAWDMmDFMmTKFZ599lqSkJD777DNCQ0PP+bwnT57kqquuokOHDqxbt4758+eTnp5ephOVnTt3Mn/+fHr27FmGVyxS/blaXi9dupSvvvqKadOmlfMVS7Vls0HuUftnEZHSMuLUsrKyDGCysrIuemxiYqIBzLfffnvRY1u1amWmTp1a8vWgQYPMnXfeaYwxJjs723h7e5t///vf57zvnj17DGA2bNhgjDFm4sSJpm/fvmcdc+DAAQOYlJSUC8bRtWtX4+3tbQBz7733GqvVetHYRVxddc3rzMxMEx4eblasWGGMMWb69OkmKCjoonFLDWC1GrN8qjH//Yv9s37Xi0gpaWSlGjFl2AZ1xIgRTJ8+HYD09HTmzZvHXXfdBUBycjIFBQVcffXVpXqsTZs2sWzZMmrVqlXyERMTA9ibhV7IF198wa+//spnn33GnDlzePXVV0v9GkRqAlfK63vuuYfbbruNK6+8stQxSw2RlwkH10Fuuv1z3sWnE4qIQDkX2ItzioyMxM3NrVSLbYcOHcrTTz/N2rVrWbNmDREREfTo0QOwN/gsi9zcXAYNGsTUqVP/9L0GDRpc8L7h4eEAtGrVCqvVyr333svo0aNxd3cvUwwi1ZUr5fXSpUv5/vvvSy46GGOw2Wx4eHjwr3/9q6RwkhrIrx407mwvVBp3tn8tIlIKKlaqkTp16pCQkMC0adN4+OGH/zS//eTJkyXz2+vWrcuQIUOYPn06a9euPatpZ2RkJL6+vixZsoQRI0Zc9Hk7duzIN998Q7NmzfDwKP+PlM1mo6ioCJvNpmJF5DeulNdr167FarWWfD1r1iymTp3KmjVraNSoUakeQ6opiwV6PG4fUfGrZ/9aRKQU9Nuimpk2bRpWq5UuXbrwzTffsGPHDpKTk3n77bfp2rXrWceOGDGCjz76iOTkZIYNG1Zyu4+PD0899RRPPvkkH3/8Mbt27eKnn34q2VHoj0aOHMnx48e59dZb+eWXX9i1axcLFixg+PDhZ524/N6nn37Kl19+SXJyMrt37+bLL79kzJgx3HLLLXh6elbcf4hINeAqeR0bG0vr1q1LPho1aoTFYqF169bUrl274v5DxDVZLFCrvgoVESkTjaxUM82bN+fXX39l0qRJjB49miNHjhASEkKnTp147733zjq2T58+NGjQgLi4OBo2bHjW95599lk8PDx47rnnOHz4MA0aNOC+++4753M2bNiQ1atX89RTT9G3b18KCgpo2rQp/fr1w3KeP0oeHh5MnTqV1NRUjDE0bdqUBx98kEcffbRi/iNEqhFXyWsREZGK5mbKsnpTqlx2djZBQUFkZWURGBhYoY+dm5tLo0aNmD59OjfccEOFPraInJ/yWkREpHQ0slID2Ww2MjMzee211wgODua6665zdEgicomU1yIiUh2pWKmB9u/fT0REBI0bN2bGjBmXtCheRJyD8lpERKojTTwupcmTJ3PZZZcREBBA/fr1GTJkCCkpKRe931dffUVMTAw+Pj60adOGuXPnVkG0F9asWTOMMRw4cKDUPRdExLkpr0VEpDpSsVJKK1asYOTIkfz0008sWrSIoqIi+vbty6lTp857nzVr1nDrrbdy9913s2HDBoYMGcKQIUPYunVrFUYuIiLivI7lFpSp+amI1CxaYF9OGRkZ1K9fnxUrVpy3W/Mtt9zCqVOnmD17dsltl19+Oe3bt+f9998v1fNU5kJcEXEM5bUIFFltTF+9hzcX7+CNW9qTEBfm6JBExAlpUnM5ZWVlAfaGbeezdu1aHnvssbNuS0hIYObMmee9T0FBAQUFBSVfZ2dnX1qgIuJwymuRs/285zjjZm4hNT0XgB82HVaxIiLnpGlg5WCz2Rg1ahTdu3endevW5z0uLS2N0NDQs24LDQ0lLS3tvPeZPHkyQUFBJR/h4eEVFreIKzuZV8i7y3dis7neYLDyWsQuM7eA0V9u4uZ/riU1PZc6/l68fFNb3v5rB0eHJiJOSiMr5TBy5Ei2bt3KqlWrKvyxx4wZc9ZoTHZ2tk5spEaz2gyf/7KfVxekcCKviLr+XtxyWRNHh1Umymup6aw2w2c/7+eV+dvJzi/GzQ1u7dKEJxOiCfbzcnR4IuLEVKyU0YMPPsjs2bNZuXIljRs3vuCxYWFhpKenn3Vbeno6YWHnH+r29vbG29u7QmIVcXW/7D3O+FnbSDpinzYVHRpAs7r+Do6q7JTXUpNtOnCSZ2dtZfNB+/TpuIaBvDikNR2a1HZwZCLiClSslJIxhoceeojvvvuO5cuXExERcdH7dO3alSVLljBq1KiS2xYtWkTXrl0rMVIR15eWlc+UecnM3HgYgEAfDx67Joq/Xd4UD3fNXhVxBVl5RbyycDufJu7HGAjw9uCJftHcHt8Ud4ubo8MTERehYqWURo4cyWeffcasWbMICAgoWXcSFBSEr68vAEOHDqVRo0ZMnjwZgEceeYSePXvy2muvMXDgQD7//HPWrVvHv/71L4e9DhFnVlBs5T+r9vCPpTvJK7Ti5gZ/vSycx/tGU7eWRiZEHMJmg7xM8KsHlotfLDDG8M2vh5g8N5ljpwoBGNK+Ic8MjKV+gE9lRysi1YyKlVJ67733AOjVq9dZt0+fPp0777wTsHeQtvzuF3m3bt347LPPGDduHM888wyRkZHMnDnzgovyRWoiYwxLtx9l4uwk9h7LA6Bjk2Cev641bRoHOTg6kRrMZoMfX4WD66BxZ+jx+AULlpS0HJ6duZWf9x4HoGX9Wkwc3JquLepWVcQiUs2oz4qTUz8Gqe52ZeQycXYSy1MyAKgf4M2YATEMad8IN7fqOVVEeS0uI/cozHoQctOhVigM/gfUqv+nw04VFPPWkh38Z9UerDaDr6c7j/SJ5K7uEXh5aOqmiJSfRlZExCFy8ov4x9KdfLh6D0VWg6e7G3df0ZwHr2pJLW/9ahJxCn717CMqZ0ZW/Oqd9W1jDPO3pvHC7CSOZOUDkBAXynOD4mgU7OuIiEWkmtEZgYhUKZvN8N2GQ0yZv52MHHujxKti6vPsta2IqOd6O32JVGsWi33q1znWrOzNPMVz329jZap9VLRJHT+evy6O3jF/HnkRESkvFSsiUmW2HMxi/Pdb+XX/SQAi6vnz7LWxXBUTeuE7iojjWCxnTf3KL7Ly3vJdvLdiF4XFNrzcLdzXqwUP9GqBj6e7AwMVkepIxYqIVLrM3AJeXZDCF+sOYAz4e7nz0NWRDO/eDG8PndyIuIplKUcZP2sb+4/bN8K4MiqE56+L06ioiFQaFSsiUmmKrDY+WbuPNxankpNfDNi3MB0zIJbQQG1hKuIqDp88zQs/JDF/m33b/rBAH569thUD2oRV240wRMQ5qFgRkUqxemcmE77fxo6juQC0bhTIhEFxdG5Wx8GRiUhpFRbb+HD1Ht5avIPTRVbcLW7cfUUED18dWTEbYZSxh4uI1DwqVkQcqRr+oT5wPI9Jc5JLrsDW9vPkyX4x3Nw5XF2rpUaw2WxkZGQQEhJyVu8tV/PT7mM8O3NryQWHy5rVZuKQ1sSEVdB222Xs4SIiNZOKFRFHqWZ/qE8XWnl/xS7eX7GLgmIb7hY37ri8KY/2iSLIz9PR4YlUCZvNxqRJk0hMTCQ+Pp6xY8e6XMGSkVPA5LnJfLvhEAB1/b0YMyCWGztWcO+jvEz777/cdPvnvMxz9nARkZpNxYqIo7jQH+oLXSk2xjBvaxqT5iRz6ORpALo2r8v461pV3BVYEReRkZFBYmIiaWlpJCYmkpGRQWioc+5298e8ttoMnybu45UFKeTkF+PmBrd1acITCdEE+3lVfAAX6eEiIgIqVkQcx0X+UF/oSnFKWg4Tvt/G2t3HAGgU7MvYgbH0b61Ft1IzhYSEEB8fX5IvISEhjg7pnP6Y19cOHcmz329j66FsANo0CmLikNa0Dw+uvCAu0MNFROQMFSsijuIif6jPdaXYJ6AObyxO5ZOf9mG1Gbw9LNzXswX39WyBr1cptyKuhut1RCwWC2PHjnX6NStn8vpwZhbfHvDmw/fWYoAAHw+e7BfDbV2alHuNWZnW7Pyhh4uIyB+pWBFxJBf4Q/37K8WXdYln8Z7TvLpwGSfyigDoFxfG2IGxhNfxK/2DVrP1OiK/Z7FYnHbq1xl169ajdsf+JOeFYvXwBeCGjo0Y0z+WkADvcj9udVizIyLORcWKiFzQmSvFizfu5u1VR5g+cxsALevXYsKgOK6ILMf0NRdaryNS3SQfyebZmVtZV9gMPCCqfi0mDmlNfPO6l/zYrrRmR0Rcg4oVEbmg9Ox8pszbzne/7QwU4O3BI30iGdatGZ7u5bxi6iLrdUSqk9yCYt5clMr0NXux2gx+Xu6M6hPJ8O4R5c/lP3CVNTsi4jrcjDHG0UHI+WVnZxMUFERWVhaBgdpZSapOQbGVD1ft5Z2lO8grtOLmBrd0DufxhGjq1Sr/NJESNXjNivJaqpIxhjlbjjBxdhLp2QUA9G8dxrPXtqJhsG+FP1916TMjIs5BIysi8idLt6fzwg9J7D2WB0CHJsE8f10cbRsHV9yTuMB6HRFXtzsjl/Hfb+PHHZkANK3rx/PXxdEruvJyzxXW7IiI61CxIiIl9mSe4oUftrEsJQOAkABvxvSPYUj7RljUfV7EZeQXWZm2bCf/XLGbQqsNLw8L9/dswf29WuDjWcod+0REnICKFREht6CYfyzdyX9W7abIavB0d+Ou7hE8dHUktbz1a0LElSzdns7477dx4Li9SWvPqBCevy6OZvX8HRyZiEjZ6SxEpAYzxjBr42FempvM0Rz7XPaeUSE8N6gVLUJqOTg6ESmLgyfyeOGHJBYmpQPQIMiH565tRT81aRURF6ZiRaSG2nooi/Hfb2P9vhOAfS77c9e24qqY+jqxEXEhhcU2Pli1m3eW7OR0kRUPixt3XxHBw1dH4q+RURFxcfotJlLDHMst4NWFqXz+y36MAT8vd0b2bsndV0RoLruIi1mzK5NnZ25lV8YpALr4p/Fih1yi+vWrcbvsiUj1pGJFpIYottr470/7eH1RKtn5xQAMbt+QMf1jCQvycXB0IlIWR7PzmTQ3mVkbDwNQz9+DZ2ov43qPtbhlhULerdptT0SqBRUrIjXAml2ZPP99EinpOQC0ahDIhOvi6BJRx8GRiUhZFFttfPLTPl5fmEpOQTFubnDH5U0Z3SeSoHWb4GCoGq2KSLWiYkWkGjt4Io/Jc7czZ8sRAIL9PHkiIZq/XtYEd21FLOJSft1/gnHfbSXpSDYA7RoHMXFI6//1P+rxeI1ttCoi1ZeKFZFqKL/Iyj9X7Oa9FTvJL7JhcYPb45syum8UwX5ejg5PRMrgxKlCXl6wnf/7+QAAgT4ePNkvhlu7/OGigxqtikg1pGJFpBoxxrBgWxoTZydz6KS9x0J8RB0mXBdHbINAB0cnImVhsxm+Wn+AKfO2cyKvCIAbOzZmzIAY6tXydnB0IiJVQ8WKSDWRmp7D8z9sY/XOY4C9x8LYgbEMbNNAWxGLuJikw9mMm7mFX/efBCA6NIAXr2/NZc20zkxEahYVKyIuLut0EW8uTuXjtfuw2gxeHhbuu7I59/VqgZ+XUlzEleTkF/H6olQ+WrMXmwF/L3cevSaKYd2a4emudSgiUvPoTEbERVlthq/WHeDlBSkcP1UIQEJcKOMGtiK8jp+DoxORsjDG8MPmI7w4O4mjOQUADGzbgGcHttLW4iJSo6lYEXFBv+4/wfhZ29hyKAuAlvVrMX5QK3pEhjg4MhEpq51Hc3lu1lbW7LJP4WxW148XBrfmyijls4iIihURF3I0O58p87fz7a+HAAjw9uCRPpGaIiLigk4XWvnHsh38a+VuiqwGbw8LI3u35N4rm+Pj6e7o8EREnIKKFREXUFhsY/rqPby9ZAenCq0A/KVTY57sF0NIgHYFEnE1i5PSGf/9tpJd+3pHh/D8da1pUldTOEVEfk/FioiTW5ZylIk/JLE78xQA7cODmXBdHO3Dgx0bmIiU2YHjeTz/QxKLk9MBaBjkw3OD4kiIC9WufSIi56BiRcRJ7c08xcTZSSzZfhSAerW8eapfNDd2bIxF3edFXEpBsZUPftzDO0t3kF9kw8Pixj1XNuehq1pq1z4RkQvQb0gRJ3OqoJhpy3bywY97KLTaT2ruuiKCh65qSYCPp6PDE5EyWrUjk+dmbS0ZHb28eR0mDm5NZGiAgyMTEXF+KlZEnIQxhu83HealucmkZ9u3Lu0RWY/xg+JoWb+Wg6MTkbJKz87nxTnJ/LDpMGAfHR03MJbB7RtqypeISCmpWBFxAlsPZfH8D9v4Ze8JAJrU8ePZa1vRJ7a+TmpEXEyx1cZHa/fxxqJUcguKsbjB0K7NePSaKIJ8NToqIlIWKlZEHOj4qUJeXZjC//28H2PA19Odkb1bMKKHti4VcUXr9x1n7Hdb2Z6WA0C78GAmDWlN60ZBDo5MRMQ1qVgRKQebzUZGRgYhISFYLGXvb1JstfHZz/t5bWEqWaeLABjUriFj+sfQMNi3osMVkVK4lLw+fqqQKfOS+XLdQQCCfD15un8Mt3QO14YYIiKXQMWKSBnZbDYmTZpEYmIi8fHxjB07tkwnNmt3HeP5H7aVXHmNbRDIhEGtiG9et7JCFpGLKG9e22yGL9YdYOr87ZzMs194uLlzY57qF0PdWuqBJCJyqVSsiJRRRkYGiYmJpKWlkZiYSEZGBqGhoRe93+GTp5k0N5k5m48AEOznyei+0dzWpQnuuvIq4lDlyeuth7IYN3MrGw+cBCAmLIBJ17emU9M6VRCxiEjNoGJFpIxCQkKIj48vuQIbEhJywekj+UVW/rVyN+8u30l+kQ2LG9we35THromitr+Xg16FiPxeWfI6O7+I1xem8vHavdgM+Hu58+g1UdzZrRke7mWfFioiIufnZowxjg5Czi87O5ugoCCysrIIDAx0dDjym9+fxADnnD5ijGFhUjoTZydx8MRpALo0q8P461oR11CLbWsy5bVzulheu7m58f2mw7w4J5mMHPv24oPaNWTcwFhCA30cGbqISLWlkRWRcrBYLCVTRNLT0/80fSTHzY/nf0jixx2ZAIQF+vDMwFgGtW2grYhFnNSF8vrn7ft5a1Uaa3cfA6B5PX9eGNyaKyLrOTJkEZFqT8WKyCX6/fSRDl268s+fM/l47T6KbQYvdwv3XBnByN4t8fMqf7pd6u5jIlI2Z/J67c/rsbQfxB2fJlFkNXh7WHj46khG9IjA2+PSthdXXouIXJymgTk5TRdxDcXFVqav2M77aw5z7FQhANe0CmXcwFia1vW/pMe+1N3HxPkor52fMYYF29KYMGsraTn2nO4TW5/xg+IIr+N3yY+vvBYRKR2NrIhcog37TzDh+21sOpgFQPMQf567thW9outXyOOXd/cxESmf/cfymPDDNpZuPwpAo2BfJlwXxzWtKi7vlNciIqWjYkWknI7m5PPy/BS+Xm9vAlfL24NHro5kWLdmeHlU3BXSc+1SJCIVr6DYyj9X7Gbasp0UFNvwdHfj3iub82DvSHy9Lm3K1x8pr0VESkfTwEpp5cqVvPLKK6xfv54jR47w3XffMWTIkPMev3z5cnr37v2n248cOUJYWFipn1fTRZxPYbGNj9bs5a0lO8gtKAbgpk6NebJfNPUDKmdHIM1tr16U185nZWoG47/fxp7MUwB0a1GXFwa3pmX9WpX2nMprEZGL08hKKZ06dYp27dpx1113ccMNN5T6fikpKWedjNSvXzFTg8QxVqZmMOGHbezOsJ/QtGscxITr4ujQpHalPu/vdykSkYqTlpXPxDlJJc1aQwK8efbaVlWyc5/yWkTk4lSslFL//v3p379/me9Xv359goODKz4gqVL7j+UxcU4Si5LSAajr78VT/WK4qVNjLOo+L+Jyiqz2EdI3FqVyqtCKxQ2GdWvGo9dEEejj6ejwRETkNypWKln79u0pKCigdevWTJgwge7du1/w+IKCAgoKCkq+zs7OruwQ5QLyCot5d9ku/vXjbgqLbXhY3BjatRmP9IkkyFcnNFI6ymvnsm7vccbN3Mr2tBwAOjYJZuKQ1mrWKiLihFSsVJIGDRrw/vvv07lzZwoKCvjggw/o1asXiYmJdOzY8bz3mzx5Ms8//3wVRirnYozhh81HeGlOMmnZ+QBc0bIe4we1IjI0wMHRiatRXjuHY7kFTJm3na9+2xQj2M+TMf1j+EuncI2Qiog4KS2wLwc3N7eLLrA/l549e9KkSRM++eST8x5zriuw4eHhWohbhZIOZzPh+238vPc4AI1r+/Lsta3o2ypU3eelXJTXjmWzGT7/5QBT528n63QRAH+9LJwn+8VQx9/LwdGJiMiFaGSlCnXp0oVVq1Zd8Bhvb2+8vb2rKCL5vROnCnltUQqfJe7HZsDH08LIXi2558rm+HhW7LalUrMorx1ny8Esxs3ayqYDJwFo1SCQiUNa06lp5W6KISIiFUPFShXauHEjDRo0cHQY8gdWm+Gzn/fz2sIUTubZr7oObNuAZwbE0ijY18HRiUh5ZJ0u4rWFKXzy0z6MsfdBeuyaKIZ2bYqHu7YJFhFxFSpWSik3N5edO3eWfL1nzx42btxInTp1aNKkCWPGjOHQoUN8/PHHALz55ptEREQQFxdHfn4+H3zwAUuXLmXhwoWOeglyDom7jzHhhySSj9gXPMeEBTDhujgub17XwZGJSHkYY5i58RCT5iSTmVsIwOD2DRk7IJb6gZXTB0lERCqPipVSWrdu3VlNHh977DEAhg0bxowZMzhy5Aj79+8v+X5hYSGjR4/m0KFD+Pn50bZtWxYvXnzORpFS9Q6fPM3kedv5YdNhAIJ8PRndN4rbujTRVVcRF5WansOzM7eSuMe+3qx5iD8vDm5Nt5b1HByZiIiUlxbYOzl1uq5Y+UVWPvhxN9OW7eJ0kRU3N7itSxNG943WQlupMsrrinWqoJi3l+7gPz/uodhm8PG08PDVkYy4ojleHrr4ICLiyjSyIjWCMYZFSelMnJPEgeOnAbisWW3GD4qjdSP1VhBxRcYYFmxL44UfkjicZd9i/JpWoTx3bSvC6/g5ODoREakIKlak2tt5NJcXZiexMjUDgNBAb54ZEMt17RpqK2IRF7Xv2CnGf7+N5Sn2vG5c25cJg+Lo0yrUwZGJiEhFUrEi1VZOfhFvL9nB9NV7KbYZvNwtjOgRwcjeLfH31o++iCvKL7Ly/opdvLt8F4XFNrzcLfy9Z3Me6NUSXy9tMS4iUt3ojE2qHZvN8O2GQ0yZt53MXHsjvqtj6vPsta1oVs/fwdGJSHmtSM3guVlb2XcsD4ArWtbj+cFxtAip5eDIRESksqhYkTKz2WxkZGQQEhKCxeJci1c3HTjJ+O+3sfG3BnDN6/nz7KBW9I6u79jARJycM+f1kazTTJydxNwtaYB9Kuez17ZiYJsGmsopIlLNqViRMrHZbEyaNInExETi4+MZO3asU5zYZOQU8PL87Xy1/iAA/l7uPNInkju7RWg3IJGLcNa8LrLamL56D28u3kFeoRV3ixt3dmvGo9dEUUtTOUVEagT9tpcyycjIIDExkbS0NBITE8nIyCA0tPQLWkt79ba0xxVZbXy0Zi9vLd5BTkExADd2bMxT/aIrpAGcM19tFqkol5rXUPG5nbj7GM/N2kZKeg4AnZrW5sUhrYltcOlbPSuvRURch4oVKZOQkBDi4+NLrsCGhISU+r6lvXpb2uN+3JHBhO+3sSvjFABtGwcx4bo4OjapXf4XWI44RFzdpeQ1VGxuZ+YW8NLcZL799RAAdfy9eLp/DDd1bIzFculTvpTXIiKuRcWKlNqZq5Fjxozh2LFjZb4qWdqrtxc77sDxPCbOTmJhUjoAdf29eLJfNH/pFF4hJzNljVfElV1qXkPF5LbVZvjs5/28Mn872fnFuLnBXy9rwpMJ0dSuwIatymsREdeiYkVKpSKuRpb26u35jssrLOa95bv458rdFBbbcLe4MbRrU0b1iSLI1/OSX2N54xVxVRU1ynCpub3pwEmenbWVzQezAIhrGMiLQ1rToYJGScsTq4iIOAc3Y4xxdBByftnZ2QQFBZGVlUVg4KXP1S6v9PR07r77btLS0ggLC+M///lPua5Glmdeu5ubG3O2HOGlOcklXaq7t6zL+EFxRIUGXOwJIS8T/OpBOU7CNLddKkN1y2soX27n5Ft5ZeF2Pk3cjzEQ4O3B6L5R3NG1Ge4XGCW91LxUXouIuA6NrEipVMSc9jMnB6U5GbJYLISGhpJ8JJsJ328jcc9xwN6letzAWBLiwi6+ZanNBj++CgfXQePO0OPxMhcsZ+IQqY4qYpShPLldv359vv31EC/NTebYqUIAru/QiDEDYqgfcOGNMSpiNEh5LSLiOlSsSKlYLBbGjh1brquR5Tm5OJlXyBuLUvnkp33YDPh4WrivZwvu69kCH89SdqnOy7QXKrnp9s95mVBL/VZEzriUvIby5XZKWg7PztzKz3vtFyBa1q/FxMGt6dqibqmeU2tORERqFhUrUmrlvRpZlpMLq83wfz/v57WFKZzIKwJgYJsGjBkQQ+PafmV7Yr969hGVMyMrfvXKHLtIdXcpowxlye3cgmLeWpzKh6v3YrUZfD3tvZDu6l62XkhacyIiUrOoWJFKV9qTi1/2Hmf8rG0kHckGICq0FhMGxdGt5cWLjHPOQbdY7FO/LmHNioicX2ly2xjDvK1pvPBDEmnZ9jVnCXGhPDcojkbBvhd8/HPl9aWOBomIiGvRAnsn5ywLcS/VhRa0pmXlM3leMrM2HgYg0MeDx66J4m+XN8XD/eInIuqbIK6muuQ1XDi392ae4rnvt7EyNQOAJnX8eP66OHrHXHw6pvJaRERAIytSRc411SS/yMp/Vu1h2rKd5BVaS/oqPN43irq1vEv92JrDLuI458vtd5fv4v3luyi02vByt3BfrxY80Kv0a86U1yIiAipWpBKd74qrMYYlyUeZOCeJfcfyAOjUtDbPXxdH60ZBZX4ezWEXqToX2/Z3WcpRxs/axv7j9tzuEVmP56+Lo3lIrTI9j/JaRERA08CcnqtOFznfFI5dGbm88EMSK36bFlI/wJtnBsQyuH3Di29FfJHn0xx2cRXVLa8BDp88zQs/JDF/WxoAYYE+PHttKwa0KcU24xd4PuW1iEjNppEVqRR/nMKx52AaX27N4sPVeyiyGjzd3bj7iuY8eFVLanlf+o+h+iaIVL5zTc2qUy+E/6zaw1uLd3C6yIq7xY3h3Zox6pqoS85t5bWIiKhYkUpxZgrHT4mJ1OnYn79+kkRGTgEAV8XU59lrWxFRz9/BUYpIWfxxatbuHAt/+/RHdhzNBeCyZrWZOKQ1MWGuM1okIiLOTdPAnJyrThcB2Lj/BOO+28TWI6cAiKjnz7PXxnJVjK6USs3mynlts9nYvvcw//o5g5m/7eBX19+LMQNiubFjo0uazikiIvJHGlmRCpeZW8Ar81P4cv0BjAF/L3ceujqS4d2b4e1Ryu7zIuJ0rDbDf3/az6sLU8jJL8bNDW6Pb8ITfWMI8vN0dHgiIlINqViRClNktfHx2n28uTiVnPxiAK7v0Iin+8cQGujj4OhE5FJs2H+CZ2dtZeshe9PWto2DmDi4Ne3Cgx0bmIiIVGsqVqRCrNqRyfM/bCuZu966USATBsXRuVkdB0cmIpfiZF4hLy9I4f9+3o8xEODjwZMJ0dwW3xR3i6Z8iYhI5VKxIpfkwPE8Js1JLtmutI6/F08kRHNz53CdyIi4MJvN8PWvB5kybzvHTxUCcEPHRozpH0tIQOmbtoqIiFwKFStSLqcLrby3Yhf/XLGLgmIb7hY37ri8KY/2idLcdREXl3wkm2dnbmXdvhMARNavxYtDWhPfvK6DIxMRkZpGxYqUiTGGeVvTmDQnmUMnTwPQtXldJlwXR3RYQKU9r5rDiVS+3IJi3lyUyvQ1e7HaDH5e7ozqE8nw7hF4uldO3im3RUTkQlSsSKmlpOXw/A/bWLPrGACNgn0ZOzCW/q3L36G6NC7UNVtELp0xhjlbjjBxdhLp2fZ+SAPahPHsta1oEORbac+r3BYRkYtRsSIXlZVXxBuLU/nkp31YbQZvDwv39WzBfT1b4OtV+VsRn6trtrpai1SM3Rm5jP9+Gz/uyASgWV0/nh/cmp5RIZX+3MptERG5GBUrcl5Wm+GLXw7wyoLtnMgrAqBfXBhjB8YSXsevyuL4Y9fskJDKP4kSqe7yi6xMW7aTf67YTaHVhpeHhZG9WvL3ns3x8ayafkjKbRERuRh1sHdyjup0vW7vccZ/v41th+09FSLr12LCdXF0b1mvymL4Pc1rl+rE0R3sl25PZ/z32zhw3L7urGdUCC8MjqNpXf8qj0W5LSIiF6KRFTlLWlY+U+YlM3PjYcDeU+HRPlHc0bXpnxbYVuVJhsVi0fQQkUt08EQez/+QxKKkdAAaBPkwflArEuL+t+6sqosH5baIiFyIihUBoKDYyoer9vLO0h3kFVpxc4NbOofzREI0dWv9uaeCFsaKuI7CYhsfrNrN20t2kF9kw8Pixt1XRPDw1ZH4e//vz4DyWkREnI2KFWHp9nRe+CGJvcfyAOjQJJjnr4ujbePg895HC2NFXMOaXZk8N2sbO4/mAtAlog4vDmlNVOiftxpXXouIiLNRsVKD7ck8xcTZSSzdfhSAkABvxvSPYUj7Rlgu0n2+VAtjbTbIywS/eqCrsyJV6mh2PpPmJjPrtymddf29GDswlus7NDrvVuOlyWutMRERkaqkBfZOrjIW4uYWFPOPpTv5z6rdFFkNnu5u3HVFBA9dFUkt79LXrxc8abHZ4MdX4eA6aNwZejyugkXkN5W5wL7YauO/P+3jtYWp5BQU4+YGd1zelNF9owny9bzo/S+U15omJiIiVU0jKzWIMYaZGw8xee52jubYG7/1jArhuUGtaBFSq8yPd8GFsXmZ9kIlN93+OS8TatW/lPBF5CJ+3X+Ccd9tJemIfRe/do2DeHFIG9o0Dir1Y1worzVNTEREqpqKlRpiy8Esxn+/lV/3nwSgaV0/nru2FVfF1K+c7vN+9ewjKmdGVvwcs+WxSE1w4lQhLy/Yzv/9fACAIF9PnkiI5tYuTXC/yJTOslBfFBERqWqaBubkLnW6yLHcAl5dmMLnvxzAGPDzcmdk75aM6BGBt0clN37TmhWRc6qoaWA2m+Gr9QeYMu9/jVv/0qkxT/ePOecufr/d6ZLyUmtWRESkKmlkpZo6M2/99UWpZOcXAzC4fUPG9I8lLMinaoKwWDT1S6SSbDucxbMz/zdaGh0awIvXt+ayZnXOf6cKWEumvigiIlKVVKxUQ2t2ZvL8D0mkpOcA0KpBIM8PjrvwSYyIuISc/CJeX5TKR2v2YjPg7+XOo9dEMaxbsz81bv0TrSUTEREXo2KlGjl4Io+X5iYzd0saALX9PHk8IZq/Xlax89ZFpOoZY/hh8xFenJ1UskHGgDZhPHttKxoE+ZbuQbSWTEREXIyKlWogv8jK+yt28d7yXRQU27C4we3xTRndN4pgPy9Hhycil2hXRi7PzdrK6p3HAGhW148XBrfmyqgyLnC3WOxTv7SWTEREXISKFRf30+5jjP5yE4dOngbg8uZ1GD8ojtgGFdu7QUSq3ulCK/9YtoN/rbT3RPL2sDCyd0vuvbI5Pp7l3CBDa8lERMSFqFhxccF+nqRl59MwyIexA1sxoE1Y5WxFLCJValFSOhO+31ZyIaJ3dAgTroujaV1/B0cmIiJSdVSsuLiYsEA+GNqZy5vXxderkrciFpEqsfHASe75eB0ADYN8GH9dHH1bhepChIiI1DiasFxKK1euZNCgQTRs2BA3Nzdmzpx50fssX76cjh074u3tTcuWLZkxY0alxNY7pr4KFZFqpH14MAPbNuD+Xi1YPLonCXEaMRURkZpJxUopnTp1inbt2jFt2rRSHb9nzx4GDhxI79692bhxI6NGjWLEiBEsWLCgkiMVkergH7d24Kl+Mfh5aQBcRERqLv0VLKX+/fvTv3//Uh///vvvExERwWuvvQZAbGwsq1at4o033iAhIaGywhSRakIjKSIiIhpZqTRr166lT58+Z92WkJDA2rVrHRSRiIiIiIhr0chKJUlLSyM0NPSs20JDQ8nOzub06dP4+p67iVtBQQEFBQUlX2dnZ1dqnCJS+ZTXIiIi5aORFSczefJkgoKCSj7Cw8MdHZKIXCLltYiISPmoWKkkYWFhpKenn3Vbeno6gYGB5x1VARgzZgxZWVklHwcOHLjoc9lsNtLT07HZbJcct4hUPOW1iIhI+WgaWCXp2rUrc+fOPeu2RYsW0bVr1wvez9vbG29v71I/j81mY9KkSSQmJhIfH8/YsWOxWCx/PAjyMsGvnr17tYhUqcrIa5vNRkZGBiEhIX/OeRERkWpCf+FKKTc3l40bN7Jx40bAvjXxxo0b2b9/P2C/cjp06NCS4++77z52797Nk08+yfbt23n33Xf58ssvefTRRys0royMDBITE0lLSyMxMZGMjIyzD7DZ4MdXYdaD9s+6Sivi9C6W12eKmbvvvptJkyZp9EVERKotFSultG7dOjp06ECHDh0AeOyxx+jQoQPPPfccAEeOHCkpXAAiIiKYM2cOixYtol27drz22mt88MEHFb5tcUhICPHx8YSFhREfH09ISMjZB+RlwsF1kJtu/5yXWaHPLyIV72J5fdGLFCIiItWEmzHGODoIOb/s7GyCgoLIysoiMDDwnMdccDrImZGVg+ugcWfo8bimgok42KXmdammf4qIiFQDKlacXGlOai5Ka1ZEnEpF5LXWrIiISE2gBfY1gcUCteo7OgoRqUAWi+VPvZxERESqG12OExERERERp6RiRUREREREnJKmgTm5M0uKsrOzHRyJiGsJCAjAzc3N0WGck/JapHycOa9FpHKoWHFyOTk5AISHhzs4EhHXckmbUlQy5bVI+ThzXotI5dBuYE7OZrORkpJCq1atOHDggH5JO1B2djbh4eF6HxystO+DM1+BVV47D+W1c6gOeS0ilUMjK07OYrHQqFEjAAIDA/XH1AnofXAOrvw+KK+dj94H56D3QUT+SAvsRURERETEKalYERERERERp6RixQV4e3szfvx4vL29HR1Kjab3wTlUl/ehurwOV6f3wTnofRCR89ECexERERERcUoaWREREREREaekYkVERERERJySihUREREREXFKKlZERERERMQpqVhxEtOmTaNZs2b4+PgQHx/Pzz//fMHjv/rqK2JiYvDx8aFNmzbMnTu3iiKt3sryPsyYMQM3N7ezPnx8fKow2upp5cqVDBo0iIYNG+Lm5sbMmTMvep/ly5fTsWNHvL29admyJTNmzKj0OEtDee0clNeOV53yWkSqlooVJ/DFF1/w2GOPMX78eH799VfatWtHQkICR48ePefxa9as4dZbb+Xuu+9mw4YNDBkyhCFDhrB169Yqjrx6Kev7APZuy0eOHCn52LdvXxVGXD2dOnWKdu3aMW3atFIdv2fPHgYOHEjv3r3ZuHEjo0aNYsSIESxYsKCSI70w5bVzUF47h+qS1yLiAEYcrkuXLmbkyJElX1utVtOwYUMzefLkcx5/8803m4EDB551W3x8vPn73/9eqXFWd2V9H6ZPn26CgoKqKLqaCTDffffdBY958sknTVxc3Fm33XLLLSYhIaESI7s45bVzUF47H1fOaxGpehpZcbDCwkLWr19Pnz59Sm6zWCz06dOHtWvXnvM+a9euPet4gISEhPMeLxdXnvcBIDc3l6ZNmxIeHs7gwYPZtm1bVYQrv+OM+aC8dg7Ka9elfBCRM1SsOFhmZiZWq5XQ0NCzbg8NDSUtLe2c90lLSyvT8XJx5XkfoqOj+fDDD5k1axb//e9/sdlsdOvWjYMHD1ZFyPKb8+VDdnY2p0+fdkhMymvnoLx2Xc6Y1yLiGB6ODkDEVXXt2pWuXbuWfN2tWzdiY2P55z//ycSJEx0YmYiUl/JaRMS5aGTFwerVq4e7uzvp6eln3Z6enk5YWNg57xMWFlam4+XiyvM+/JGnpycdOnRg586dlRGinMf58iEwMBBfX1+HxKS8dg7Ka9fljHktIo6hYsXBvLy86NSpE0uWLCm5zWazsWTJkrOu7v1e165dzzoeYNGiRec9Xi6uPO/DH1mtVrZs2UKDBg0qK0w5B2fMB+W1c1Beuy7lg4iUcPQKfzHm888/N97e3mbGjBkmKSnJ3HvvvSY4ONikpaUZY4y54447zNNPP11y/OrVq42Hh4d59dVXTXJyshk/frzx9PQ0W7ZscdRLqBbK+j48//zzZsGCBWbXrl1m/fr15q9//avx8fEx27Ztc9RLqBZycnLMhg0bzIYNGwxgXn/9dbNhwwazb98+Y4wxTz/9tLnjjjtKjt+9e7fx8/MzTzzxhElOTjbTpk0z7u7uZv78+Y56CcYY5bWzUF47h+qS1yJS9VSsOIl33nnHNGnSxHh5eZkuXbqYn376qeR7PXv2NMOGDTvr+C+//NJERUUZLy8vExcXZ+bMmVPFEVdPZXkfRo0aVXJsaGioGTBggPn1118dEHX1smzZMgP86ePM//2wYcNMz549/3Sf9u3bGy8vL9O8eXMzffr0Ko/7XJTXzkF57XjVKa9FpGq5GWOMY8Z0REREREREzk9rVkRERERExCmpWBERkWrpyJEjPP300/Tu3ZuAgADc3NxYvny5o8MSEZEyULEiIiLVUkpKClOnTuXQoUO0adPG0eGIiEg5qFgREZFqqVOnThw7dozU1FQee+wxR4cjIiLloGJFRERcxunTp4mJiSEmJobTp0+X3H78+HEaNGhAt27dsFqtAAQEBFCnTh1HhSoiIhVAxYqIiLgMX19fPvroI3bu3MnYsWNLbh85ciRZWVnMmDEDd3d3B0YoIiIVycPRAYiIiJRFfHw8Tz75JFOnTuX6668nPT2dzz//nDfffJOoqChHhyciIhVIxYqIiLicCRMmMHv2bIYNG0Zubi49e/bk4YcfdnRYIiJSwTQNTEREXI6Xlxcffvghe/bsIScnh+nTp+Pm5ubosEREpIKpWBEREZe0YMECAPLz89mxY4eDoxERkcqgYkVERFzO5s2beeGFFxg+fDgdOnRgxIgRZGVlOTosERGpYCpWRETEpRQVFXHnnXfSsGFD3nrrLWbMmEF6ejqPPvqoo0MTEZEKpgX2IiLiUl588UU2btzIkiVLCAgIoG3btjz33HOMGzeOm266iQEDBpx1LMC2bdsA+OSTT1i1ahUA48aNq/rgRUSkTNyMMcbRQYiIiJTGr7/+Snx8PPfffz9vv/12ye1Wq5WuXbty6NAhtm3bRnBwMMAFF93rz5+IiPNTsSIiIiIiIk5Ja1ZERERERMQpqVgRERERERGnpGJFRERERESckooVERERERFxSipWRERERETEKalYERERERERp6RiRUREREREnJKKFRERERERcUoqVkSkRpoxY0ZJl3MRERFxTipWRMSh7rzzTtzc3HBzc8PLy4uWLVvywgsvUFxcXKnPe8stt5Camlqpz3Exv3/tnp6ehIaGcs011/Dhhx9is9nK9FgqvirGmfdkypQpZ90+c+ZM3NzcqjSWMz8bbm5u+Pv7ExkZyZ133sn69evL/Fi9evVi1KhRFR+kALB8+XLc3Nw4efKko0MRqXZUrIiIw/Xr148jR46wY8cORo8ezYQJE3jllVfOeWxhYWGFPKevry/169evkMe6FGde+969e5k3bx69e/fmkUce4dprr630gk3OzcfHh6lTp3LixAlHh8L06dM5cuQI27ZtY9q0aeTm5hIfH8/HH3/s6NBqjN8Xjef6mDBhgqNDFKnWVKyIiMN5e3sTFhZG06ZNuf/+++nTpw/ff/89YL/SPWTIECZNmkTDhg2Jjo4G4MCBA9x8880EBwdTp04dBg8ezN69ewFYuHAhPj4+f7rK+cgjj3DVVVcB5x6JeO+992jRogVeXl5ER0fzySeflHxv7969uLm5sXHjxpLbTp48iZubG8uXLwfgxIkT3H777YSEhODr60tkZCTTp08v1Wtv1KgRHTt25JlnnmHWrFnMmzePGTNmlBz3+uuv06ZNG/z9/QkPD+eBBx4gNzcXsF/VHT58OFlZWX86gfrkk0/o3LkzAQEBhIWFcdttt3H06NGLvSU1Wp8+fQgLC2Py5MkXPG7VqlX06NEDX19fwsPDefjhhzl16hQA//jHP2jdunXJsWdGZt5///2znmfcuHEXfI7g4GDCwsJo1qwZffv25euvv+b222/nwQcfLCmmjh07xq233kqjRo3w8/OjTZs2/N///V/JY9x5552sWLGCt956q+TnY+/evVitVu6++24iIiLw9fUlOjqat956q8z/X9XdkSNHSj7efPNNAgMDz7rt8ccfd1hsFXXxRsSZqVgREafj6+t71h/hJUuWkJKSwqJFi5g9ezZFRUUkJCQQEBDAjz/+yOrVq6lVqxb9+vWjsLCQq6++muDgYL755puSx7BarXzxxRfcfvvt53zO7777jkceeYTRo0ezdetW/v73vzN8+HCWLVtW6rifffZZkpKSmDdvHsnJybz33nvUq1evzK//qquuol27dnz77bclt1ksFt5++222bdvGRx99xNKlS3nyyScB6Nat259Oos6cQBUVFTFx4kQ2bdrEzJkz2bt3L3feeWeZY6pJ3N3deemll3jnnXc4ePDgOY/ZtWsX/fr148Ybb2Tz5s188cUXrFq1igcffBCAnj17kpSUREZGBgArVqygXr16JYVtUVERa9eupVevXmWO79FHHyUnJ4dFixYBkJ+fT6dOnZgzZw5bt27l3nvv5Y477uDnn38G4K233qJr167cc889JT8f4eHh2Gw2GjduzFdffUVSUhLPPfcczzzzDF9++WWZY6rOwsLCSj6CgoJwc3M767ZatWqVHLt+/Xo6d+6Mn58f3bp1IyUl5azHmjVrFh07dsTHx4fmzZvz/PPPnzWCun//fgYPHkytWrUIDAzk5ptvJj09veT7EyZMoH379nzwwQdERETg4+PDxx9/TN26dSkoKDjruYYMGcIdd9xRSf8rIlXIiIg40LBhw8zgwYONMcbYbDazaNEi4+3tbR5//PGS74eGhpqCgoKS+3zyyScmOjra2Gy2ktsKCgqMr6+vWbBggTHGmEceecRcddVVJd9fsGCB8fb2NidOnDDGGDN9+nQTFBRU8v1u3bqZe+6556zY/vKXv5gBAwYYY4zZs2ePAcyGDRtKvn/ixAkDmGXLlhljjBk0aJAZPnx4uV77H91yyy0mNjb2vPf96quvTN26dUu+/uPrOZ9ffvnFACYnJ6fUcdYkv39PLr/8cnPXXXcZY4z57rvvzO//ZN59993m3nvvPeu+P/74o7FYLOb06dPGZrOZunXrmq+++soYY0z79u3N5MmTTVhYmDHGmFWrVhlPT09z6tSp88YCmO++++5Pt58+fdoAZurUqee978CBA83o0aNLvu7Zs6d55JFHLvjajTFm5MiR5sYbb7zocRXFZrOZUwVFDvn4/e+P0jpfni1btswAJj4+3ixfvtxs27bN9OjRw3Tr1q3kmJUrV5rAwEAzY8YMs2vXLrNw4ULTrFkzM2HCBGOMMVar1bRv395cccUVZt26deann34ynTp1Mj179ix5jPHjxxt/f3/Tr18/8+uvv5pNmzaZvLw8ExQUZL788suS49LT042Hh4dZunRpmV+jiLPxcGCdJCJOymazkZGRQUhICBZL5Q/Azp49m1q1alFUVITNZuO22247ax54mzZt8PLyKvl606ZN7Ny5k4CAgLMeJz8/n127dgFw++23c/nll3P48GEaNmzIp59+ysCBA8+7CD05OZl77733rNu6d+9epmkx999/PzfeeCO//vorffv2ZciQIXTr1q3U9/89Y8xZC7oXL17M5MmT2b59O9nZ2RQXF5Ofn09eXh5+fn7nfZz169czYcIENm3axIkTJ0oW7u/fv59WrVqVK7YqZ7NBXib41YMq+Hk8Y+rUqVx11VXnnOazadMmNm/ezKefflpymzEGm83Gnj17iI2N5corr2T58uX06dOHpKQkHnjgAV5++WW2b9/OihUruOyyyy743p2PMQag5OfDarXy0ksv8eWXX3Lo0CEKCwspKCgo1WNPmzaNDz/8kP3793P69GkKCwtp3759mWMqr9NFVlo9t6DKnu/3kl5IwM+rYk+DJk2aRM+ePQF4+umnGThwIPn5+fj4+PD888/z9NNPM2zYMACaN2/OxIkTefLJJxk/fjxLlixhy5Yt7Nmzh/DwcAA+/vhj4uLi+OWXX7jssssA+9Svjz/+mJCQkJLnve2225g+fTp/+ctfAPjvf/9LkyZNyjVyJ+JsNA1MRM5is9mYNGkSd999N5MmTSrzrlTl0bt3bzZu3MiOHTs4ffo0H330Ef7+/iXf//2/AXJzc+nUqRMbN2486yM1NZXbbrsNgMsuu4wWLVrw+eefc/r0ab777rvzTgErjTNF25kTRbBP5fm9/v37s2/fPh599FEOHz7M1VdfXe757MnJyURERAD29TLXXnstbdu25ZtvvmH9+vVMmzYNuPCc9VOnTpGQkEBgYCCffvopv/zyC999991F7+dUbDb48VWY9aD9cxX8PJ5x5ZVXkpCQwJgxY/70vdzcXP7+97+f9fO3adMmduzYQYsWLQD7DlzLly/nxx9/pEOHDgQGBpYUMCtWrCg5qS2r5ORkgJKfj1deeYW33nqLp556imXLlrFx40YSEhIu+h5//vnnPP7449x9990sXLiQjRs3Mnz4cNf52XBCbdu2Lfl3gwYNAErWiG3atIkXXniBWrVqlXycmZqXl5dHcnIy4eHhJYUKQKtWrQgODi55zwGaNm16VqECcM8997Bw4UIOHToE2NfkndnZTsTVaWRFRM6SkZFBYmIiaWlpJCYmkpGRQWhoaKU+p7+/Py1btiz18R07duSLL76gfv36BAYGnve422+/nU8//ZTGjRtjsVgYOHDgeY+NjY1l9erVJVc9AVavXl0y+nDm5ODIkSN06NAB4KzF9meEhIQwbNgwhg0bRo8ePXjiiSd49dVXS/3aAJYuXcqWLVt49NFHAfvoiM1m47XXXispmv64rsDLywur1XrWbdu3b+fYsWNMmTKl5ARo3bp1ZYrF4fIy4eA6yE23f87LhFpVt4vblClTaN++fcnGDmd07NiRpKSkC/7c9uzZk1GjRvHVV1+VXOHu1asXixcvZvXq1YwePbpcMZ1Zn9SnTx/A/nM6ePBg/va3vwH2Cw6pqalnjZyd6+dj9erVdOvWjQceeKDktjMjk1XF19OdpBcSqvQ5f//cFc3T07Pk32cKhTMXfHJzc3n++ee54YYb/nQ/Hx+fUj/HHy/eAHTo0IF27drx8ccf07dvX7Zt28acOXPKGr6IU1KxIiJnCQkJIT4+nsTEROLj4/90Bc8Z3H777bzyyisMHjyYF154gcaNG7Nv3z6+/fZbnnzySRo3blxy3IQJE5g0aRI33XQT3t7e533MJ554gptvvpkOHTrQp08ffvjhB7799lsWL14M2Bf9X3755UyZMoWIiAiOHj36p52cnnvuOTp16kRcXBwFBQXMnj2b2NjYC76WgoIC0tLSsFqtpKenM3/+fCZPnsy1117L0KFDAWjZsiVFRUW88847DBo0iNWrV5+1qxRAs2bNyM3NZcmSJbRr1w4/Pz+aNGmCl5cX77zzDvfddx9bt25l4sSJZf7/dii/etC4s71QadzZ/nUVatOmDbfffjtvv/32Wbc/9dRTXH755Tz44IOMGDECf39/kpKSWLRoEf/4xz8A+1X22rVr89lnnzF79mzAXqw8/vjjuLm50b1794s+/8mTJ0lLS6OgoIDU1FT++c9/MnPmTD7++OOSKY2RkZF8/fXXrFmzhtq1a/P666+Tnp5+VrHSrFkzEhMT2bt3L7Vq1aJOnTpERkby8ccfs2DBAiIiIvjkk0/45ZdfSkZsqoKbm1uFT8VyVh07diQlJeW8BW5sbCwHDhzgwIEDJRcXkpKSOHnyZKmmbI4YMYI333yTQ4cO0adPn7NGaERcmmOXzIiIM7JarSYtLc1YrdZKf64LLTK/0PePHDlihg4daurVq2e8vb1N8+bNzT333GOysrLOOq5Lly4G+NNC03MtlH333XdN8+bNjaenp4mKijIff/zxWd9PSkoyXbt2Nb6+vqZ9+/Zm4cKFZy2wnzhxoomNjTW+vr6mTp06ZvDgwWb37t0XfG2AAYyHh4cJCQkxffr0MR9++OGf/u9ff/1106BBA+Pr62sSEhLMxx9/bICSDQOMMea+++4zdevWNYAZP368McaYzz77zDRr1sx4e3ubrl27mu+///5PGwU4PavVmJx0++dKdq6ftz179hgvLy/zxz+ZP//8s7nmmmtMrVq1jL+/v2nbtq2ZNGnSWccMHjzYeHh4lGxoYLVaTe3atc3ll19+0VjO/GwAxsfHx7Ro0cIMGzbMrF+//qzjjh07ZgYPHmxq1apl6tevb8aNG2eGDh161utISUkxl19+ufH19TWA2bNnj8nPzzd33nmnCQoKMsHBweb+++83Tz/9tGnXrl3p/8NqmIstsP99Pm7YsKHk/9oYY+bPn288PDzMhAkTzNatW01SUpL5v//7PzN27FhjjH2zgfbt25sePXqY9evXm8TExHMusD/f+3Py5Enj5+dnvLy8zOeff15Br1jE8dyM+d0EbBERERE5pxkzZjBq1Kg/9XBavnw5vXv35sSJEyUjXhs3bqRDhw7s2bOHZs2aAbBgwQJeeOEFNmzYgKenJzExMYwYMYJ77rkHsG988dBDD7FkyRIsFgv9+vXjnXfeKZmKO2HCBGbOnHnOKagAQ4cOZc6cORw+fPiCI8kirkTFioiIiEg1cPXVVxMXF/enaYsirkzFioiIiIgLO3HiBMuXL+emm24iKSnpTxtCiLiymrGqTURERKSa6tChAydOnGDq1KkqVKTa0ciKiIiIiIg4JTWFFBERERERp6RiRUREREREnJKKFRERERERcUoqVkRERERExCmpWBEREREREaekYkVERERERJySihUREREREXFKKlZERERERMQpqVgRERERERGnpGJFRERERESckooVERERERFxSipWRERERETEKalYERERERERp6RiRUREREREnNL/A1kpnlXVaZAxAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Change wrap to 3 and Adjust dimensions\n", "plot_results_panel_2d(cycle.state,\n", @@ -167,8 +194,17 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Change wrap to 3, Adjust dimensions, adjust scatter plot and line colors, shapes, and sizes\n", "fig = plot_results_panel_2d(cycle.state,\n", @@ -197,8 +233,18 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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xZgNwNzZ+BAeLvw38/LvYfKXWEIo4KAO4K/Bo/P6dRORrapPBHsAM4HmUDErSUQyJA+uB3wBHEp4MHoPdykakrvR0mDTJbjVRUQH9+8PkyQ0f37UrvPhi7VTSn/7UbjORk2N//s1v4K237LYTFRW2YmmHDjZhvKTpOxKKI3Hie+wSlwOAf1KbDBYCjwMXxu6rXaY5+yRI3PN4POTn51NaWkpeXp7T3RFxlhcYAJwCTAL0V6JZFEdEIrADuA878yB0iVY/7Ly700m5qemKIZJsqk016a4Yj5uWAg8Dj2CvX4LysdPPr8HuDRJDGiEUcZDBXlPsCDyXFvgCeKFOWy6wBFs4RtckksQUQxwyA9gPu5VEMBkswN7FXwacQcolg5K4FEfqt9i7mBGLR7DEuyS2X3Q7cA+1yWA2dtnLOuBWYp4MghJCEUeVA3sFHuUO9yXhlAE3AEOxa3TW1Xm9Q5v3SKTNKYY4ZH/sXX2wRR5uAdYAV6JFWJJwFEfqV7SpCK/fS9Gmoth+0Y3YOJIOjAe+ASZifyFtRAmhiCSe2cBAbH1sP/ZfsL842iMRSWY76/x8EHAZ8FtsJcCJ2OldIpIUir3FLPAuAGC+dz7F3uLWf6gfW8/guTrtPYFpwFfYonjdWv9VkVJCKOKgHOxso6rAc2nCj8DFwAhgbaAtE7gfO/deJMUohsTYWuAcYDDh6wTBXrg9C/Ru606JRJfiyJ4mb5qMGzcAbtxM2TSl5R9msNPMBwPnYTd99NY55lygb8u/orWUEIo4yIWdIZCOlps06XXgYOCpkLZjsWsFb0ab6EhKUgyJke3Yi7b+wMvYKVx/r3OMrqAkSSiOhAuODvoCZT59+Fo+SjgHGIYtMhVcivg98O+odDVqFM5EJL79APwKOAvYEmjrgC0Y8yG2op+ISDRUYqef74eddVAdaO8MdHKqUyLSlkJHB4MiHiWcD4wETgDmhbQfCbwPXNDqbkaVEkIRB1Vh1xLfyJ6zkSQgA1tJNOg0YAXwexTBJOUphkSJwY4EHoQtVrUj0J4F/BFbMCbOLuBEokVxpFbd0cGgZo8SrgR+CRwBzAxpPxg70+kzYHg0exwdupwScVA18FDgUd3EsSmrAzAVe4f+eew8/H0c7ZFI3FAMiYLl2GrF51BbrdgFjAVWA/ei7WskqSmO1KpvdDCoWaOETwCvhfy8L/A0drroGOJ2Tq5W3Yg4KB34Q8jzlOcDirBz7fcNaT8FW9wh14E+icQxxZAoaA+E3vQfjr0yPsyR3oi0OcURK7SyaH1CRwkH5w6u/6BbgSex1yu3Y7fFyoh+X6NNCaGIgzKw+xoL9i79xcDnwNvAu4TfSVMyKLIHxZAW8BM+P2pf4GrgHewf5inE7V18kVhQHLEmb5qMCxcG0+AxLlxM2TSFqV2m2htHBcC1IQd0Bv4LDKFNNpSPFk0ZFRFnVQF3Y8sxfx5om0n4ukERkdbaBTwADGDP3bf/BCwCRqFkUCQF+YyPZWXLGk0GAQyGpaVL8e3vgz9jY8ePdQ46hoRKBkEjhCKOMsDuwPN2pOB1yOfY6RTLQtr6YadbHOVIj0QSSsrHkObwAy9ip3L9X6DtL8BtIcdo8zVJYYoj4Ha5mTlwJuW+uneLAqqxdQweg5z1ObjLAusMy4D/YZe6JDAlhCIOKqd2JqSXhLuh1HJl2Ln1j0LNzTg3cBNwB7ayn4g0KWVjSHN9hF0cNT+kzQVsc6Y7IvFIccTKdeeS666zPsWHTQTvpLboFNg5lucH2vu0UQdjSAmhiLStj4ELCQ+sg7Ebzh/mQH9EJPmsAm4G3qzTPhI7bXRgm/dIRBLNf7FxZFmd9jOx1YcPbvMexYwSQhEH5VA79TxlZix5qU0Gs4C7sPt+KRqJRCwlY0hjfsDesZ8CYduIDcRWzTjZiU6JxDfFkQZ8RngyOAK7bvBIZ7oTS7oEE3GQC+jodCfa2qnAb4FvgX8ABzjbHZFElpIxpDFVwDRqk8Hu2Dv5F0ADW4uJpDzFkYC6FYhvwG6FdQBwH3CiE51qG6oyKiKxswWYCHsU7ZoKfICSQRGJrm7YtcjtsdX/VgPjUDIoIg1bAZyFrW0QKh/4EphHUieDoIRQxFFV2BmTdwWeJw2DvUt/ELay37/qvJ6Doo9IFCRtDGmOD7FTQHfUaf8D8DW2QFWqVscQiUDKxpH12JoGA4DXsYXuttQ5pi8pUXbVZYxpfMMNSQgej4f8/HxKS0vJy8tzujvSTGUkYWWvdcDvgFkhbQdiN57XXfq4pjiSeJIyhjRlJXYU8K3AzzcD9zvXHamlGJKYUi6ObMGuBZyK3U4iqCvwMnYfwRSjNYQiDmoHXBHyPKH5gL9i9/YK3cbnt8AjKBkUiYGkiiFNKcEWjHmS8IIxHwd+VowRaZGUiSM7sFWGHyP8OmUv7I2lq0jZqjpJ/XsXiXeZ2PXKCW8ZdoP5z0PaemLvvo1ypEciKSFpYkhjyrEbyU/CDl8E9cAWevgtmoIu0gopEUeKsDesd4S05QDXYaeZd2z7LsUTJYQi0nIGW7jhPsKnXVyBncLVwYlOiUhS8AHPYC/iNoW05wITgGtJ2bv5IhIhL7XJYAbwe2yNg0KnOhRflBCKSMu5sMUbgsnggdjpXD9zrEcikixKgeupvYhzA5dhp43qIk5EGuIDdhG+GPIq4G/ASdgY0tuBfsUxTbIQcVAZkB54lDnclxZ7FLsQ+4/AIpQMirShpIghDemEHR0EOB1YCvwdJYMiUZY0ccRgq4UOxM4iCJUDfAU8hZLBemiEUMRhu53uQCTex/5rcUZIW2dgDSlQlkwkPiVUDGnIZuwG8ncCXULarwSOJCWr/om0pYSPI7OxU0C/CPz8NXZj+dDkT9cpDVJCKOKgbOC7kOdx60dsYJ0G7A0MJfyiTUFWxBEJE0Ma4gUexlb+K8dOQ/9byOuZKBkUibGEjiOfY2coza7T/hNgZ9t3J1FpyqiIg9KwhfJ6EMd/Gf8NHIxNBgF+AJ5wrjsiUishYkh9fNj1xgdgd8MOloB/EV3EibSxhIwjy4AzgZ8SngwOAP4DzAUOdaBfCSphfu8i0sa2AL8EfhF4DpCH3UriVqc6JSIJzQDvAocBl1IbW9phiz6sRNWJRaRxf8CuE3wjpG0/4DlsLYPR2NkG0myaMirioCrs/qgA12ArITvOYEcDbyB8v57RwGTsLUQRiQtxGUMashi4EZhVp/1M7DY1/dq8RyJCgsURgD7YaxWA7sAdwEXYqjjSIi5jjGn6MIl3Ho+H/Px8SktLycvLc7o70kxl2C21wC6lcXwp3nfAOGzxmKDOwOPA2eiOW5JTHEk8cRdDGlKFvYgL3U/wSOz6QVUmThqKIYkpruPIj9itrULrFlQBw4BzgPEk4MLH+KMRQhEHtQPGhjx3XCZQHPLz+cBfgAJnuiMijYu7GNKQDOxd/N8D+wITsRdzuskk4ri4jCNe4K/YglNnYbeLCMrAVhNV/IiauPm9i6SiTGC6050I1Rk7b+RW7FrBU5ztjog0Lu5iCNj69U8CpwE9Q9ovxl7AjcV2XETiQlzFkUps4bp7ge8DbU8DNwH9Q45TMhhVKiojkqoqgT8DW+u0/wZYgZJBEYmMAd7CFnu4HLi9zuvtgMtQMigie/Jhs9IDgaupTQbTsEtZNAM5ppQQisSZalMd+y/5DBgC3AZcW+c1F3G2gEBE4t5CYDi2+NTKQNszwDrHeiQiicAAr2G3ixgH/F/Ia7/C3qB+Els8RmJGCaGIg8qAjoFHGbDYu5gRi0ewxLskNl/oxSaAR2ODLMDLwNex+ToRia26MaTNfQtcABwOfBjSPhS7D1gfJzolIpFwNI6cjd3iamVI2ynAAuz1yYFt3aHUpIRQxGGlgQdA0aYivH4vRZuKov9FM7GbtD5Gbbnmw4H52M2hRSQhhcaQNuPBrjXuBzwb0t4XexE3F5sUikhCcCSOgJ1VEDQM+Ah4BzuLSdqMisqIOCgbWB14/pW3mAXeBQDM986n2FvM4NzBVJtq0l2t2FxnO3ZPwekhbVnA3cB1KAqIJLDQGBKsvL5towfP9gryOmVT0CMGC28Mdnro/JC2Ttg1g1eQAJuYiUiounEkZjFkKXZJSt+QtvOA/2Krmp+KisU4RJeCIg5Ko3Zw7rJNk3HjxocPN26mbJrCFd2v4Oo1V/P4/o8zMHdg5F/wH2wRh5KQtuOw8/H3b2XnRcRxoTEEYOHstcydsZpd3iqycjMYNrofQ4b3bejtLePC3kw6D5v8XQX8Edgrul8jIm0jNI7EJIZ8A9wJPI+dHvpyyGtu4MXWfby0nqaMisSB4sDooA8fAD58zPfOZ9K3k1o3hbSU2mQwD1vK+QOUDIokoW0bPcydsRr8hsLe+eA3zJ2xmm0bPa374C+BtXXafo2dMvoV8BBKBkWSQNRjyCZsxeH+wHPY2QWvYItQSVxRQijioGqgCLiybCWuOvOs0khjVcUqoHYKacR+C4wETscWkbmURv/Wt0mFUxGJmmAMKQJ+2F7BLm8V+Z1zSEtzkd85h11lVWxYtY21S0siv6hbjx0FPBK4sc5radhta1Q0RiThhcaRsorq1seQH4CbsTefp2D3JgXYG3sD6aBon4G0lssYY5o+TOKdx+MhPz+f0tJS8vK0WUuiKANyA88HLRqG27+r3uPcuBmcO5ip/aY2/GFrgX8Df6jTXo5dFNDEvPzF3sWtm54qCU9xJPGExpD1mzz854FPwW/I75xD6dZyPNsryMrNwL/b3/zpXzuAidgCVJUh7fOAn0b/HCR5KIYkptA48uBNs8jctbtlMcQLPAo8iC08FZSLrWVwPdpPME5phFDEQW6gz84F7PXjbFzG3+BxwSmk9Y4S+oCHsRVEb8Quzg6VQ7MWace0wqmIxIQbuyTnl0Bh9zyGje4HaS5KNpRSXbUbgyEjw9286V9VwOPYu/oPUJsM7h1oPzzWZyMiTgiNI8NG7d/yGPJzbHGp4MuZ2CRwLXAXSgbjmIrKiDhopbeYvb6+rFnLb4KFZsJGCZcCF2PX+ARNxFbqikBxAxVORSS+ZWGX5AQNGd6XXv0L8GyvYMfWMmY/vyxs+lfJhlI82yvCKwca4HXsFK81IR+Wid23dAKQ33RfYl7dVERiIiyOnNCHA/rtHVkMCboau22EG7vJ/B1Az8j6ojjiDCWEIg6avGkyLlwYmp65HTpKODh9sF2/M5HaufkuYDxwX8v6UbfCaaPTU0UkbhX0yKOgRx7bNnrIys2gdGt5zfSvrPYZ5HXKDn/DRYRvSwN2/fG9QO/mfWebVDcVkTbRZAzpmA2vYjeNHxDyxjOxxabGYvcojZDiiHM0ZVTEIT7jY1nZsmYlg0Fu3Ez5aordsPUeapPB/sAn2GldHSLrR0MVTltUxEZE4kZBj/AppKS5GHZ6vz3vup8Z8vw47IyDZ2l2Mhiz6qYi4qg9YojLxbBe/SgYkwe/ws4qCOXC3qxuQTKoOOIsjRCKOMTtcvPGwJkMSrN36+dV/8DasmXcuK5uOb9aPnzMT5tPcUYxgxls/wZPwO4BltmyfoSODtb0TaOEIgmhnNr9w77GLhkOFTqFNK9TNgU5efB/hCd7o7FTz8/ArgGKcGNoT6C6aWHv/OZNKxORuNJYHKmJIXMqyJuaTcFfQ/5Ov4PdQmJI6/ugOOIsjRCKOKi9O5ctLjdbXG46Z3Thxa0v4mriaszldzHl91PgJ8AC4G5anAzWHR0M0iihSGIw2K2+NgWe16egRx59+xVS8HIe7AdcVucAF/AkNjGMMBkEyOuUXTOtzO83DU9NFZG41GgcWQwFv8uj728LKfhfSGJ2GLaIXZTKDSiOOEsJYQx8/PHHjB49mu7du+NyuXjjjTcaPX7OnDm4XK49Hlu2bGmbDotjsoDiwCO9mVNITZph6ZCl+D71QSt3hwiODtYnOEooIvErNIZk1XeAAV7G7vt1PfAjMBN4L3p9aPbUVBGJS/XGka+B32ATv7dDDu4HvIS9IT2KFt1Eqo/iiLM0ZTQGysrKGDRoEBdddBFnnXVWs9+3atWqsH17unTpEovuSRxxY2MtAC43MwfOpNxXbi/i3sZW9jumzpsqICc3B7e7/kSuuUIri9YnrIiNKo6KxKWwGFLXXOy+pJ+FtLmA84GDo9uPPaam6iJOJGHUG0cuIDx27IPdOmIsMcseFEeco4QwBkaNGsWoUaMifl+XLl3o2LFj9DskCSPXnUtuSa6tFvoG0AtYRnihmIzofFdzKpy6cGktoUiiWQPcArxWp304dsPoGN3fCVYmFJEk8CdgJFCArRx6OQ1MQ4guxRFnaMpoHDnssMPo1q0bJ510EnPnznW6O9IGqrHV3qcD1Qa7judgbDIIsAF4Ifrf29wKpwbD0rKl+Iyv0eNExBlhMQTgMWwMCU0GD8au9ZlFzJJBEUlQO6H6Xpj+dUgcATgJ+Cd2U/nraJNkUJyjEcI40K1bN6ZMmcJPfvITKisrefLJJzn++OP5/PPPGTKk/tJNlZWVVFZW1vzs8agsbyKqwu7dCvCr0yD9nZAXuwB/A34Z/e91h05PbUKOOwe3q3XTUyU+KY4kvrAYAqQPoPaKrhBbdOoi9K+9xIRiSALbBUwG7oOqchh3m23+FZAOdnr5RU51Ttqa/omIAwceeCAHHnhgzc9HH30033zzDY888gjPPvtsve+ZOHEif/rTn9qqixIj7t1w6gZgNbg/CHlhLPAwsHfsvjvXnUuuOzd2XyBxT3EkwfnBvR1OLbA/ugFOxF7RHYRdPxjhvqQikVAMSUC7sUOBfwK+s03uTDj1v8Ax4FbMSEkuY0zzd8WWiLlcLl5//XXGjBkT0ftuvPFGPvnkE+bNm1fv6/XdlevZsyelpaVhhWkkjq3CFnf4MqStNzAVO29fJMYURxLY/4AbsLfyPyG80p8hapX/RBqjGJJA/MCrwO3A6jqvnYudTbB/W3dK4oVGCOPUokWL6NatW4OvZ2ZmkpnZws3nJD5kAssDz13AVcCfAQ3aSRtRHElAq4GbqV1nDPBv4BchPysZlDaiGJIADHabmVux+0qEOg24l0ZKFUuqUEIYA16vlzVr1tT8vG7dOhYtWkSnTp3o1asXEyZMYOPGjTzzzDMAPProo/Tp04dDDjmEXbt28eSTT/LBBx8wc+ZMp05B2sK+wH3YEcF/AkMd7Y2IxLOt2Dv4U7BTvoIGAJ0d6ZGIJIr7CU8Gj8VefwxzpjsSf5QQxsD8+fM54YQTan6+/vrrARg7dizTp09n8+bNbNiwoeb1qqoqbrjhBjZu3EhOTg4DBw7k/fffD/sMSXBe4AHgRsLW9JRfCYOuAtJgMZDjSOdEJG5VYCuHTgRC63V0A+6B8gthUKDmk2KIiOzBRW3yNyTw/GTCZhKUA4MCzxVHUpPWECYJj8dDfn6+5u3Ho5nAZcD/YaeF/rX2pTJqZ4h6gfZt3DWRUIojcWYmcAnwbUhbe+Am7PrB9oohEl8UQxy2GrgDuBA4pc5rHwM/o94N5xRHRCOEIrGyHbgeeDqk7SngNuyWEthtfT6h9rmISI18apPBNGwJ+Luxo4MBiiEiwrfY2DAN8GETw5MJT/6ObfjtiiOihFAk2gy2kteVwPch7ScCT1CTDIItE68p/CIC2P0D00N+Pgo4BztV9AHg0D3fohgiksK2YqeT/x2oDGn/DruhfDOrhiqOiBJCkWjaBFwBvBnSlo/dU/AiVP1PJAqqTTXprvSmD0wU3wN3AvOBzwhsKBgwHd2yF5FwHuAv2GsLb0h7Hnb/0WvRHqQSkXpmEotIizwNHEx4MjgGWAFcTL3J4G7glcBj954vi0gdi72LGbF4BEu8S5zuSuuVY7ea2Q9bPXQ+8K86xzSRDCqGiKQQg00E+2I3lg8mg1nYonVrsfsMRpgMKo6IRghFomU7UBp43gX4G/BLGh0VrATODjz3or+QIk0p2lSE1++laFMRU/tNdbo7LePDJn5/BDaGtOdik8QIKIaIpBAX8AHwQ+DndtjCU7cD3Vv+sYojot+5SLRcDbwEHISdxtGp6bekAceFPBeRhhV7i1ngXQDAfO98ir3FDM4dHPPvjeoU1fexd/IXhbS5gUuBu4DCyD5OMUQkMts2evBsryCvUzYFPVpXCTXm09f92CQw9MbyvcC7wK+xMaOZ6wTrE+y/4ojo9y7SEkuAx+u0uYEPsVW+mpEMAmQDcwKP7Gj1TSRJTd40GXdggZ0bN1M2TYn5d0ZtiurXwKnASYQngz/HxpPJRJwMgmKISCS+mL2aFx78lNce+5wXHvyUhbPXtvizYjp93QBvY/cNfK3Oa4cB67CzDFqRDIb2X3FElBCKRKISOzXjcOAabAGIUIqkIjERHB304QPAh69mlDCWQqeotsou4L2Qn4dgp37NwK49FpGY+mjdp1zdfhzf7/1/FPbOB79h7ozVbNvoadHnRS021PUxcAz2ZtFi7FZVdRf29Wz918Ss/5KQlBCKNNen2Dtz92KDswEecrJDIqkjdHQwKNajhPVNUW2xAcA4oBfwLPAlcELr+ygizfPPHf+gOmMXS476gLQ0F/mdc9hVVoVne0XEnxVpbNi20cPapSWNJ5/FwCjs3M25Ie25QEnEXWxUVGObJAUlhCJN2YldH/gz4KtAWzq2TPxzrfvoCmyOeVjguYjsqe7oYFCsRwlbNEXVB/wTm+xV13ntQWwM+S1R+9dXMUSkacXeYpYbO7Xzu4I1fJv/DaVby8lqn0Fep8in9kQSGxbOXtv4NNVV2P1Gh2DXBgb1x+5p/CXQI+IuNqpu//+2+Z+KIylOCaFIY97Fbgb9OHZEEOBIYCF2MXdm6z7ej50RsjjwXET2VN/oYFCsRglbNEX1PWAwturfHOCJOq/vRdSnlSuGiDQtNIa4/C4+7vUWpLkYdnq/iAvLRBIbtm30MHfGavCbPaep+rHFpA4BXg55Uy9sLYKlwC+I+v7F9fV/QVmx4kiKU0IoUh8vcAF2+saGQFs2dv+fT7FJYhRkATMDD+09LbKnhkYHg2I1ShjRFNUlwEjgFOxFXFAbzMJSDBFpXN0YYtIM3++zngHXdGDIiX0j/rwHv31wj7aGYoNnewW7vFXkd87Zc5pqGnaD+WBo6wL8FVgNXEjM9gGoL7al+32c+t2jiiMpTAmhSH2ysdM4gk7EXuhdBw0MVLSIG1t08KTofqxI0pi8aTKuJm6Ru3BFdZSw2VNUNwIXYedZzQw58AjgI+DJqHWpQYohIo1r6ObOC9XPRPxZxd5iVlWs2qO9oRtTeZ2yycrNoHRrOf5dZs9pqvcAe2NrE3wDXEWrZx411f/6YpufarZ8/ywF3mLFkRSlhFCkPm7sxVznwH/fB/ZztEciKcdnfCwrW4apma9dP4NhadlSfKb+UcRINTlF9dspcAfQDzu1K9i93sAL2OrDx0alKyLSCtFef1zf6GBQfaOEBT3yGDayH6xzUfLvUthYZ5pqP+A74I/Y4jEx5sT0e0kM2phexI9N+g7Drg8ksFnrgHT4P2K6lcRuaivRj0R/IUVCuV1uZg6cSbmvvMljc9w5uF2tv7cdWn2vPj58zK+YT/E7xQwuH2wb87Gl4a+kzedbKYaINCyYANU35TyYAE3tN7VZn9XQ6GBQaJI5OHewLSr1FAy5uy+9thXgSa8grzybgmF11iy2UcxoLLYZ3GzPG8ps4EtvMUfkDm6bTknc0AihpLavsdNBf4ed+lVVZ7PZGO8rWIndaujngeciEi7XnUuXjC5NPnLd0bm93uwpqndNsdWGr8FO9foDjiy+UQwRqV+01x83NjoYVDN9/XngIOD3wCYoqMqjb3khBcfkQVnzzyGaGott/rR0vtn/Mb7Z/zEmb36qjXsm8UAJoaSm3cADwEDsWh+A5cDbbbtZaxrwk8BDfxlFnBXRFNXuS/Et98Gj2DVADlEMEalfNNcf+4yP1RWrmzzOYFi6fSm+8332RlHQGdjiU88AnZr8mKhrKra5jCGnbDk5ZctZHsXp95I4NLtEUs8ibFn40JkT+wJTofjoYhasDt+sdXAMp05kY7cYEpHYqDbVpLvSm3Vs2BTVTdibRq9h1whOAMbXHpvjzsHtbniK6raNHjzbK8jrlB1xWftIKIaI7Kkl648bm3K+pGxJo5/1YO8HOXTcofAZ5JTn4PYHPusE4D7gp5GfQzRjSETT7we8FZXp95JYlBBK6tiFreg1idoyzy7slK97gFyYvLp2vUGk6wtEJL4s9i7m6jVX8/j+jzMwd2Cz3pNblkvupFy7xcyukBeexcaKZgzDLZy9lrkzVrPLW0VWbgbDRvdjyPDIy9uLSMtEe/1xU2sRX/rhJU7sciJsDTQeAfwZGEGL9hGMRQzJdedGbWq9JB/NMJHUMB9bNOY+apPBg7F7Cj4C5LZwI2oRiVsRTf+uBiYD+2PjRDAZ3At4GJhHs/7FbHQjahFpM9Faf9zstYi3FsMQ4N/A59i9YFqQDCqGiBOUEEpqyKB2Pn86cCewkLBpHBFtRB0lFcCwwKMiZt8iknpCK+o1emPHAP/Brie+gto7/BnADdi4cT3N3hus0Y2oY0AxRCR2tm308MjXf23eWsTdU+zN5zNpUSIY1NYxBBRHRAmhpIqBwC3YbSUWAncRdoEX7b2KmsuPHaT8NPBcRKIj9AZPozd2vgd+DXwV0nY2sBJ4CDtCGIGwjaj99WxEHWWKISKxsXD2Wp576BO+ql7R/LWIDYwiRqKtYwgojojWEEoy2oZd/3MX9i5/0O2BtnqWCkRzr6JIZAKvhzwXkdaru9/WHvuDhSrEjgD+GXt7/CFaVAAiqKBHHsNG92PujNWUbCglq31G+EbUUaYYIhJ9wWmbaVUuLn3wbn74dgf4YMzmn9CpqoPde/RybIG6wHYz0doLta1jCCiOiBJCSSYGeAm4GjvtKwu4I+T1jPre1MyNqGNUcbQdMCaqnygi9d3gceNmyoYpTH1pqp0KGnptdTNwOPYvYyumegUNGd6XXv0L2qTKqGKISPR5NlWw66sqCtfmk1bpojtZlGSVklGRS5ffdYEbiXj2QCTaMoaA4ohoyqgki++w+/ycS+0aoL8DTRcYi+peRSLirEanf++aT/F/iu12EqE60Op1P3UV9Mij74DCmF/IiUiUVUHer7PJWpNBqb8cP4bSzHKyemaQNzvbFp2KYTIYpBgibUkJoSQ2PzAFWzF0Rkj7L7D7DeY0/vaW7FUUTT5gTuChbWBFWq++4lBB7t1upvx+ChQBZW3br1hRDBGJsgwoGJ3HsB/6gctFSe9SON7FsFv7UXBYciZniiOiKaOSuL7Gzt//OKStK/Zi76zmfUS09yqK1C7svrUAXqB9VD9dJLU0Of27nY/5R8yn+ItiBreP7vRvpyiGiLSCAd4CTiT8L8+tMKSkL71+V4Bn77aZtukkxRFRQiiJx2D3Dvwj4RtHX0SLqgI6uVmrCzu4GXwuIi0XnP7d2Ii/CxdTzBSmEv0iUU5QDBFpoQ+BW4HPgInYSuRBXYAXoIA8CkjeRDBIcUSUEEpi2k5tMtgHeAIY4Vx3WioHWO50J0SSQEumf7dmxL/aVJPuSm/x+6NFMUQkQl9ibyjPCmmbhK0amu9IjxynOCJKCCXxuIDbsDWSRwL3oPkNIimuZvr34+W26MPe2Gqiv2GPf+laO/17sXcxV6+5msf3f5yBuQNb0WsRaTMrsdcO/67Tfgh225nkHwgUaZASQol/n2AD+aUhbVnAAmr2/xGRFGOA17ALX/a2TbnuXHJ/l2urC19JzC7wijYV4fV7KdpUFJP9SUUkitZj9yB+lvBd1/sAd2Ork0e3PIBIwlGVUYlfHmA8cAz24m5lndeTIBmsAE4KPCoc7otIwvgUu4n8r4B767yWjV0XFKNkMLRwTXB/Uicphog0ogIYAjxNbTIYLD73FfBbHEkGt230sHZpCds2etr+y+uhOCIaIZT49F/g98C3gZ+rgL9hg3gS8QPvhzwXkUasASYAr4a0FQHXAz3bpguhm967cTNl0xRHRwkVQ0QakY1dGxjcO/Bm4Cqa3JIqlhbOXsvcGavZ5a0iKzeDYaP7MWR4X+c6hOKIKCGUeLMNuBZ4LqQtBzu//yonOhRbmcC/Qp6LSD1+wI4EFgHVIe0HYzeZ36dtulF3WwsfvppRwsG5zmxjoRgiElAG/B27vKRjSPuNQDr22qJj3Te1rW0bPcydsRr8hsLe+ZRuLWfujNX06l/g6LYWiiOiKaMSHwzwAnAQ4cngCGAZNpAn4Rz/dsB5gYfuzojUUYndSmY/4FFqk8FCYCpUL6qG02izOun1bXofHCV0imKIpLwq7M2i/YGbsDEjVEfsGsKObdmp+nm2V7DLW0V+5xzS0lzkd85hV1kVnu3OTtRUHBElhOK8bcDp2GqA2wJtHYFpwEzswm8RSS0GOBZ7d7800JaNrRL4NSz+zWJGLB/BEu+SNulOcHTQhy+sPXSUUETakA94BjgQW2dgS6D9cezu6nEor1M2WbkZlG4tx+83lG4tJ6t9Bnmdsp3umqQ4JYTivFxgdcjPv8QWkLmQpN8h1YfdEunLwHMRCXABF4Q8Hwd8jd1mpkN4pc+2UN/oYJCTo4SKIZJyDPAGMAgYi60iGvQLYB72uiIOFfTIY9jofpDmomRDKaS5GHZ6P0eni4LiiGhkWOJBFvAk8Gvsnb2znO1OW9oFHBl47kXbKUoKW42tDNo1pO0yoBi7fnhQbXN9lT5juYav7trBupxcS6gYIillNraK8Bd12k/G1hr4SfM/attGD57tFeR1ym7ThGzI8L706l/gyHc3RHFElBBK29oN/AUYjV0vGHQM8A1JsZVEJFxA75DnIilnG3YvsMnYu/1PhryWXufngLau9Dl502RcuDCYBo9x4XKk4qhiiKQML3YG0Y6Qtp8CE4HjI/sopyt9FvTIi4tEMEhxRDRlVNrOIuAobNnnS9mztnGKJYNgC6iuDzwcrIIt0vZ2AZOwBWMex94segpY2vjb6q7li/UaPp/xsaxsWaPJIIDBsLRsKT7TthOuFEMkZeRirx8ABgD/we5JenxkH1O30id+w9wZq+NmT0AnKI6IRggl9nZhRwAeoHZy+jxsIP+ZU50SEUf4sRWFbwU2hLTnYAvINFFEKnR0MCiWo4Rul5uZA2dS7itv8tgcdw5uVxKWQxZpa+uwewfei60qHHQ1sC9wNi0e0ghW+izsnV9T6bNkQyme7RVxNWon0paUEEps/Q+4hPCiMYcC/6R2wrqIpIY5wB+A0OV4acBF2JtG3Rp/e0Nr+WK9hi/XnUuuO06rVIgkky3YJPAJ7DYzOcBjIa/nYOsNtEJopc/8zjmq9CmCpoxKrHiA8diy8cFkMB34E/ZiUMkgYAdPxwQeuxztiUiMXQ6cQHgyeAp2Kvk/aDIZhPit9OkkxRBJCj8CE4C+2D0Fg3uOvgxEeYu+eK306STFEdEIoUTfB9jiEN+FtP0UWxziEEd6FLd8wJshz0WS1lFAMF8biN08+qTmvz2eK306STFEEloZdgTwAWr3GwVb5vJ64Abs/qNRFo+VPp2kOCJKCCX62lGbDOZgK4CNhwZu7Ke0DOzMmOBzkaRQjr3Q6xzSdj7wKrZK4PlEHA/iudKnkxRDJCFVAVOxW0WUhLRnYGcT3Ap0iW0X4q3Sp5MUR0QJoUTfsdiAvgYb8JsoEpHK0rEFV0WSgg/4F/BHbMGoF0NecwNvtfBjW1DpM1WKuyiGSEKqJjwZTAMuBO4EejnUpxSmOCJKCKV1vsXuH3Yv4StSH8HeZtKGNiKp4X1sldBFgZ9fAq7FThdvJVX6FEky7YHbgKuwswbuAfo72iORlKaEUFrGjx39uxnYCewDXBHyeqYTnUo8fmBl4PlBqMqTJKDl2ETwnTrto4FO0fsaVfqsn2KIJKzLgKOBIU53RBRHRAmhRG41diuJ/4W0PYYN7vo/KiIV2F04ALzYm6YiCWELcAd2Cxl/SPsQbMGYE5zoVOpRDJGElYGSwTihOCK6CSDNVw3cj60QGJoMXgx8hpLBFioIPEQSxlRgf+x2EcFksCfwLPAlSgbbmGKIiLSW4khq0yW8NE8xNvErDmnriy1LNdyRHiWF9sBWpzshEqkCbBVRgA7YioDXEJPy8NI4xRARaS3FEVFCKI0z2IqBD1C7OU0acB1wN3ZbCRFJLWcBx2HnGN1J+PYSIiIiklCUEErjXMB2apPBAdg1Q0c41iMRcZoLmI32FhUREUkCWkMoTZuEnR56NzAfJYNRtAs4L/DY5XBfRCKiZDAuKIaISGspjohGCCXc28AObFQIygdWoK0kYsAHPB94/oSTHRGRhKQYIiKtpTgiGiGMgY8//pjRo0fTvXt3XC4Xb7zxRpPvmTNnDkOGDCEzM5P999+f6dOnx7yfYbYCvwF+DlyO3XA+lJLBmMgAHgk8Mhzui4gkHsUQEWktxRFRQhgDZWVlDBo0iKKiomYdv27dOk477TROOOEEFi1axLXXXssll1zCe++9F+OeYovGPIfdifSFQNtO4KnYf7VAOnBt4JHuaE9EJBEphohIaymOiKaMxsCoUaMYNWpUs4+fMmUKffr04eGHHwbgoIMO4pNPPuGRRx5h5MiRUe9ftakm3ZVuRwF/D/w35MVO2FtE50f9a0VEREREJM5ohDAOzJs3jxEjRoS1jRw5knnz5jX4nsrKSjweT9ijORZ7FzNi8QiWPLMEDiY8GTwbu1bwAmwVQYk5P7A+8PA3eqRI9LU0jkj8UAwRJymGJAfFEVFCGAe2bNlCYWFhWFthYSEej4eKiop63zNx4kTy8/NrHj179mzWdxWtK8Lr91JUUQTeQGN34A3gJaCwwbdKDFQAfQKP+n/TIrHT0jgi8UMxRJykGJIcFEdECWGCmjBhAqWlpTWPb7+tWwVmT8XeYhZULwBg/hHzKT6sGC4FlgNnxLa/0rCcwEOkrbUkjgBs2+hh7dIStm3UaEA8UAwRpyiGJA/FkdSmNYRxoGvXrpSUlIS1lZSUkJeXR3Z2dr3vyczMJDMzstKfkzdNxo0bHz7cPjdTpkxh6lFTW9xvab32QJnTnZCU1ZI4snD2WubOWM0ubxVZuRkMG92PIcP7xqiH0hTFEHGSYkhyUBwRjRDGgaFDhzJ79uywtlmzZjF06NCofUext5gF3gX48AHgc/uYnz6fYm9x1L5DRJLbto0e5s5YDX5DYe988Bvmzlitu/wi0iyKISLxSQlhDHi9XhYtWsSiRYsAu63EokWL2LBhA2CnWFxwwQU1x//+979n7dq13HTTTXz11Vf8/e9/5+WXX+a6666LWp+Co4Oh3LiZsmlK1L5DRJKbZ3sFu7xV5HfOIS3NRX7nHHaVVeHZrlUnItI0xRCR+KSEMAbmz5/P4MGDGTx4MADXX389gwcP5o477gBg8+bNNckhQJ8+fXj77beZNWsWgwYN4uGHH+bJJ5+M2pYTdUcHg3z4mO/VKKGTKrHLOC8NPBeJZ3mdssnKzaB0azl+v6F0azlZ7TPI61T/1HaJPcUQSSSKIfFJcURcxhjjdCek9TweD/n5+ZSWlpKXlxf22mWrL2ORd9EeCSHYUcLBuYOZ2k9rCZ1QBuQGnnux8/hFnNJYHAmqWf9TVkVW+wyGnd6PISdq/Y9TFEMkniiGJCbFEVFRmSQXHB1sSOgo4eDcwW3YMwFIB+4NeS4S74YM70uv/gV4tleQ1ymbgh71X/RJ21AMkUSjGBJ/FEdEI4RJoqG7cpetvoyF3oUYGv41u3BxeO7hGiUUSXHNubsvItIQxRCRxKQ1hEnMZ3wsK1vWaDIIYDAsLVuKz+w5pVRERERERJKXpowmMbfLzcyBMyn3lTd5bI47B7fL3eRxEl0G2BZ4XgC4HOyLiCQexRARaS3FEVFCmORy3bnkunObPlAcUQ50CTzXQm4RiZRiiIi0luKIKCFMEsGloB6PNndNJGUhzz1QTx1YSWQdOnTA5Uqce62KI4lHMSS5KYZIW1AcSW7NiSNKCJPEzp07AejZs6fDPZGW6u50ByTqEq2wguJIYlMMST6KIdLWFEeST3PiiKqMJgm/38+mTZswxtCrVy++/fbbhPpHpCkej4eePXsm3XlB8p5bsp4XNP/cEu3uvt/vZ9WqVRx88MEp/XtLNMl6XpC855bMMUTXIokpWc8tWc8LohtHNEKYJNLS0thnn31qpmnk5eUl3f/4kLznBcl7bsl6XpB855aWlkaPHj2A5Du3UMl6bsl6XpC855Zs56VrkcSXrOeWrOcF0Tk3bTshIiIiIiKSopQQioiIiIiIpCglhEkmMzOTO++8k8zMTKe7ElXJel6QvOeWrOcFOrdElaznlqznBcl7bsl6XkHJen7Jel6QvOeWrOcF0T03FZURERERERFJURohFBERERERSVFKCEVERERERFKUEkIREREREZEUpYRQREREREQkRSkhFBERERERSVFKCEVERERERFKUEkIREREREZEUpYRQREREREQkRSkhFBERERERSVFKCEVERERERFKUEkIREREREZEUpYRQREREREQkRSkhFBERERERSVFKCEVERERERFKUEkIREREREZEUpYRQREREREQkRSkhFBERERERSVFKCEVERERERFJUixLCzz//PNr9EBERERERkTbWooRw6NCh9OvXj3vuuYe1a9dGu08iIiIiIiLSBlqUEP7rX//igAMO4J577uGAAw5g2LBhTJkyhe3bt0e7fyIiIiIiIhIjLmOMaembt23bxosvvsjzzz/PZ599RkZGBqeccgq//e1vOf3008nIyIhmX0VERERERCSKWpUQhvrmm294/vnnee655/j666/Jz8/nl7/8JRdccAE/+9nPovEVIiIiIiIiEkVRqzKanZ1NTk4OWVlZGGNwuVy8+eabHHfccRxxxBGsWLEiWl8lIiIiIiIiUdCqhHDnzp1MmzaNESNG0Lt3b2699Vb23XdfXn31VbZs2cKmTZt46aWX+P777xk3bly0+iz1MMbg8XiI0oCviKQgxREREZHU06KE8M033+Tss8+msLCQiy++mJ07d/Loo4+yadMm3njjDc466yzS09Nxu9388pe/5LbbbqO4uDjafZcQO3fuJD8/n507dzrdlTDr16/H5XKxaNEip7siIk1QHBEREUk9LUoIzzzzTD7//HOuu+46Vq5cyeeff8748ePZe++96z1+0KBBnHfeea3qqMTGli1buOqqq+jbty+ZmZn07NmT0aNHM3v2bKe7VmPVqlWccMIJFBYWkpWVRd++fbntttuorq52umut5gMWBR4+R3si0nKJEEdCrVmzhg4dOtCxY0enu9JqiiEiItJa7Vrypg8++IDjjz++2ccfeeSRHHnkkS35Komh9evXM2zYMDp27MiDDz7IgAEDqK6u5r333mP8+PF89dVXTncRgPT0dC644AKGDBlCx44dWbx4MZdeeil+v5/77rvP6e61yi5gcOC5F2jvYF9EWiJR4khQdXU15557Lscccwyffvqp091pNcWQ+FRtqkl3pTvdDRGRZmnRCGEkyaDEryuuuAKXy8UXX3zBL37xC/r168chhxzC9ddfz2effQbARRddxM9//vOw91VXV9OlSxf++c9/AuD3+3nggQfYf//9yczMpFevXvz5z39u8HuXLVvGqFGjyM3NpbCwkPPPP59t27Y1eHzfvn0ZN24cgwYNonfv3px++umcd955/O9//4vCn4KzXED3wMPlcF9EWiJR4kjQbbfdRv/+/Tn77LNbcdbxQzEk/iz2LmbE4hEs8S5xuisiIs0StSqjkli2b9/Ou+++y/jx42nffs97ysGpVJdccgnvvvsumzdvrnntrbfeory8nHPOOQeACRMmcP/993P77bezYsUKnn/+eQoLC+v93h07dnDiiScyePBg5s+fz7vvvktJSUlEF2dr1qzh3Xff5bjjjovgjONTDrAx8MhxuC8ikUq0OPLBBx/wyiuvUFRU1MIzjj+KIfGnaFMRXr+Xok3J8/+ZiCQ5I0mhtLTUAKa0tLRZx3/++ecGMP/+97+bPPbggw82kyZNqvl59OjR5sILLzTGGOPxeExmZqb5xz/+Ue97161bZwBTXFxsjDHmnnvuMSeffHLYMd9++60BzKpVqxrtx9ChQ01mZqYBzGWXXWZ8Pl+TfReR5kvmOLJt2zbTs2dP89FHHxljjJk2bZrJz89vst8ikVi4c6EZsmBIzWPhzoVOd0lEpEkaIUxRJoKy8pdccgnTpk0DoKSkhHfeeYeLLroIgJUrV1JZWcnw4cOb9VmLFy/mww8/JDc3t+bRv39/AL755ptG3/vSSy+xcOFCnn/+ed5++20eeuihZp+DiERfIsWRSy+9lN/85jcce+yxze6zSKQmb5qMGzcAbtxM2TTF4R6JiDStRUVlJPEdcMABuFyuZhV8uOCCC7jllluYN28en376KX369OGYY44BIDs7O6Lv9Xq9jB49mkmTJu3xWrdu3Rp9b8+ePQE4+OCD8fl8XHbZZdxwww243e6I+hBPdgHnB54/C2Q52BeRSCVSHPnggw/4z3/+U3MjyRiD3++nXbt2PPHEEzXJaaJRDIkfxd5iFngX1Pzsw8d873yKvcUMzh3cyDtFRJylEcIU1alTJ0aOHElRURFlZWV7vL5jx46a53vvvTdjxoxh2rRpTJ8+nXHjxtW8dsABB5Cdnd3s8vJDhgxh+fLl7Lvvvuy///5hj/rWIDXE7/dTXV2N3+9v9nvikQ94NfBQyXhJNIkUR+bNm8eiRYtqHnfffTcdOnRg0aJFnHnmmZGdeBxRDIkfoaODQRolFJFEoIQwhRUVFeHz+TjyyCN57bXX+Prrr1m5ciV//etfGTp0aNixl1xyCU8//TQrV65k7NixNe1ZWVncfPPN3HTTTTzzzDN88803fPbZZzWVA+saP34827dv59xzz+XLL7/km2++4b333mPcuHH4fPVfzjz33HO8/PLLrFy5krVr1/Lyyy8zYcIEzjnnHNLTE7usdwbwt8Ajw+G+iLREosSRgw46iEMPPbTm0aNHD9LS0jj00EPZa6+9ovcH0sYUQ+JDcHTQVyctDx0lFBGJV5oymsL69u3LwoUL+fOf/8wNN9zA5s2b6dy5M4cffjiTJ08OO3bEiBF069aNQw45hO7du4e9dvvtt9OuXTvuuOMONm3aRLdu3fj9739f73d2796duXPncvPNN3PyySdTWVlJ7969OeWUU0hLq//+RLt27Zg0aRKrV6/GGEPv3r258sorue6666LzB+GgdGC8050QaYVEiSPJSjEkPgRHB+smhFA7Sji131QHeiYi0jSXiaQqgMQtj8dDfn4+paWl5OXlRf3zvV4vPXr0YNq0aZx11llR/3wRcZ7iiEjkir3FXLL6kiaPe7Lfk1pLKCJxKbVupUrE/H4/33//Pffccw8dO3bk9NNPd7pLScUPfB14JPZqSJGGKY7EjmKI8yZvmowLV6PHuHBpLaGIxC1NGZVGbdiwgT59+rDPPvswffp02rXT/zLRVAH0Czz3As0vqyOSOBRHYkcxxFk+42NZ2TIMjU+2MhiWli3FZ3y4XYlbGVtEkpP+VZZG7bvvvhHtNSaRy3e6AyIxpjgSW4ohznG73MwcOJNyX3mTx+a4c5QMikhcUkIo4qD2wA6nOyEiCUsxxHm57lxy3blOd0NEpMW0hlBERERERCRFKSGMsokTJ3LEEUfQoUMHunTpwpgxY1i1alWT73vllVfo378/WVlZDBgwgP/+979t0FsREREREUllSgij7KOPPmL8+PF89tlnzJo1i+rqak4++WTKysoafM+nn37Kueeey8UXX0xxcTFjxoxhzJgxLFu2rA17Lk6oBC4MPCod7YmIJCLFkARSDixyuhMiInvSPoQxtnXrVrp06cJHH33EscceW+8x55xzDmVlZbz11ls1bT/96U857LDDmDKleWWqY71/mMRGGRBceaIKgeI0xZHEoxiSIGYAVwG7gFWoEpCIxBUVlYmx0tJSADp16tTgMfPmzeP6668Paxs5ciRvvPFGg++prKyksrL2frDH42ldR8UR6cADIc9F2pLiSOJTDIlz64FrgP+EtN0BPOZIb0RE6qUpozHk9/u59tprGTZsGIceemiDx23ZsoXCwsKwtsLCQrZs2dLgeyZOnEh+fn7No2fPnlHrt7SdDODGwCPD4b5II/zAv7BDMElEcSTxKYZET7Wpjt6HVQETgYMJTwZPBC6P3teIiESDEsIYGj9+PMuWLePFF1+M+mdPmDCB0tLSmse3334b9e8QEeALYChwPnC/w32JMsUREWuxdzEjFo9giXdJdD7wZeBWoCLwc1fgeeB9oH90vkJEJFqUEMbIlVdeyVtvvcWHH37IPvvs0+ixXbt2paSkJKytpKSErl27NviezMxM8vLywh6SePzAxsDD73BfpI5K4GLgKGxSCPAQ0PDAfcJRHEl8iiHRUbSpCK/fS9Gmouh84LnAkdirrGuArwJtruh8vIhINCkhjDJjDFdeeSWvv/46H3zwAX369GnyPUOHDmX27NlhbbNmzWLo0KGx6qbEiQpgn8CjooljpY1lAKEDZgcDb2Pv9IvECcWQ1iv2FrPAuwCA+d75FHuLI/uA3cCsOm1u4J/AAuBRVERGROKaEsIoGz9+PP/61794/vnn6dChA1u2bGHLli1UVNT+U33BBRcwYcKEmp+vueYa3n33XR5++GG++uor7rrrLubPn8+VV17pxClIG2uHqjvFJRe28ENB4L+LgOFOdkikfoohrTN502TcuAFw42bKpuZV9wZgHvAT4GRgbp3XDgUOi0oXRURiSglhlE2ePJnS0lKOP/54unXrVvN46aWXao7ZsGEDmzdvrvn56KOP5vnnn+eJJ55g0KBBvPrqq7zxxhuNFqKR5NAeqA48VC7eQWuBM7Gl4UMdhB0lvBqVcJS4pBjSOsHRQR8+AHz4mjdKuA24BDgaWBxoGw9oIy8RSUDahzBJaP8wkRYowxaKeRC7ZnA/YDmQ6WSnnKM4IqnmstWXsci7qCYhBDtKODh3MFP7Td3zDX7gKeBmYHtI+2HA37EFqEREEoxGCEUk9RhsFcCDgHuxySDYBHG1U50SkbZUd3QwqMFRwkXAMOBSapPBDtg1gl+iZFBEEpYSQhEHVWJnGY2nNieRGFsCnACcQ23RmHTgJmAVMMChfom0gGJIy4WuHawrbC1hJXAtcDjwWchBv8HGjGvQIk4RSWhKCEUctBs7y+jvgecSQ9uBK4HBwEch7aOAZcAkQLMkJcEohrRMQ6ODQWGjhOnAfGr39egPzAaeA7pF/t3VprpFfRYRiRUlhCIOSgfuDDxUsyTGrgWKqL2o2w/4D3YriX4O9UmklRRDWmbypsm4mtgU0IXLjhKmYTPuPOA+bBGZE1v2vYu9ixmxeARLvEta9gEiIjGgSQ4iDsoA7orxd1SbatJdulTkLuy6wXbAbcB1pGzxGEkebRFDko3P+FhWtgzTRElQg2Fp2VJ8xod7oNtOMW/lLIKiTUV4/V6KNhXVX7RGRMQBSghFkthi72KuXnM1j+//OANzBzrdnbazEfg/bEn4oL7Av7CFH3o40SkRiQdul5uZA2dS7isPf2EmcAfwHXCA/TknKwe3K7DOsJXJYHCaKlAzHXVw7uDWfaiISBRoyqiIgwywI/CIxf4voXejU0IlMBE4EPg1tmpoqF+iZFCSSqxjSLLKdefSJaOLfWzsQpdfdKHL6C50Ke5Cl61d6DK/C10WdCHXnRu17wwtYhNWtEZExGFKCEUcVA7sFXiUN3FspOq7G520DPAWcAhwKzYR/Bb4i5OdEom9WMYQgG0bPaxdWsK2jZ4YfLrDKoE/Awdj40fQCGApcGz0vqpuEZsGt7YQEXGAEkKRJJUyd6NXAacBo4FvAm1p2Iqi4xt+myr9iTRu4ey1vPDgp7z22Oe88OCnLJy91ukuRc8sYCB2PfGuQFt34EXs1NEoF5qqb4uLpI7LIpJQlBCKOCgHqAo8cqL4uYl+N7pZoxIe7N6BA4B3QtqPA4qBx4FO9b9Vlf4kWcQqhmzb6GHujNXgNxT2zge/Ye6M1Qk1UthgHLkfOBlYHfjZjS0ytRK7P2njxUcj1tAWF4kWl0UkeSkhFHGQC1sqPp3oXoMk8t3oZo1KfIxdJ/ggEBzo2wd4CfgQe+e/ESm3tlKSVqxiiGd7Bbu8VeR3ziEtzUV+5xx2lVXh2V4RxW+JnUbjyBnU7tFxNLAAO708RvuQ1hePgxIlLotIclNCKJJkEvludLNHJfpiRwjBbh1xO/AVcDZNXhWn1NpKkRbK65RNVm4GpVvL8fsNpVvLyWqfQV6nbKe71qQ94kh1nThyEHAP8BTwP2BQy7+nqZkMDcXjoESIyyKS/JQQijioCrgx8KiK0mcm8t3oBkclttYZldgH+CMwBjvN626gffO+I2XWVkpKiEUMASjokcew0f0gzUXJhlJIczHs9H4U9IjRMFoU1cSRDjmk/c9F/v9y2LWzzujmzcA4WnwV1Nz1lZM3TcbVxF0qFy7FIRFxlPYhFHFQNfBQ4Pld2E2mWyN09Ks+oXej43H/q9BRifzOOZR+X07W9xnknZsNnwH5IQffQsQXc3X/fOL9z0OkKdGOIaGGDO9Lr/4FeLZXkNcpOyGSQYC8jtlkbc+gdG45+eU5lKaXk7U5eqObdUcgS7eWM3fGanr1Lwj7M/IZH8vKlmGa2BDEYFhathSf8dXueSgi0oaUEIo4KB34Q8jz1grejW7sAiR4N3pqv6lR+MboCo5KzJ2xmpLFpWR9k8Gw9f0o2JFnp3g9FHJwC+7sB0cHQ6dvBUcJ4/HPQ6Qp0Y4hdRX0yEuYRBCAhVBwRR7DVvVj7t6rKckqJSstg2EDoze6GRyBLOydXzOToWRDKZ7tFWHf4Xa5mTlwJuW+pjcEyXHnKBkUEccoIRRxUAa2Lko0JMvd6CH79aXX2gI8H1SQV51NQVXgAsuL3W+whZUzGho91SihJLJoxpCEtgO7lvjvgB+G0Jde5QV4RleQd1s2BYdFL6ndYyZDI+src925Ud3cXkQkFpQQiiSJhL8bXQ5MAh6Agl15FARL/h2F3ULiiNZ9fH2jg0EaJRRJUAZ4DjtMWhLSfhAU/D2PguOjP7oZNpNhQylZ7TMSZn2liEh9lBCKOMgAuwPP29H6svEJeTfaAK8CNwDfhrQXYhPE82l1+atEX1sp0pBox5B4VW2qSXfVMyn2W+ASoDLwcw5wJ3At0V1QWUeirq8UEamPqoyKOKgce82SEXiekrZjL+iCyWA7bHK4ChhLVKKUKv1JskqFGLLYu5gRi0ewxLtkzxd7YQtMQW3V4ZuIaTIYVNAjj74DCpUMikjCU0IoIs7aG/hT4PlIYCm2eEx+g++ISEvWVopI/CjaVITX76VoUxG8xZ6Z783Ae8Dr2ARRREQioimjIg7KAX4MeZ70fMA04ExsIhg0HrtZ9MlEfc5bwq+tFGlEsseQ0One873zKb69mMGfDYZ7Qw7KxsYOERFpESWEIg5yAR2d7kRbmQtcBRQDC7HVAIPSsaODMZKQaytFmiHZY8jk7ybj9rvxpflw73Yz5fdTmHrVVLgU6O1070REkoOmjIpIbG3CFob5GTYZBJgK/J9jPRKRBFD8QTELyhfgS7PTuH3tfMw/Yj7FrxVraqiISBQpIRRxUBVwV+BR5WhPYqASWyW0H/CvkPaBwIfo7r5IFCRlDPkO+BVM/noy7t3hU7jduJly4JTkLacqIuIAJYQiDqrG1lP5U+B50ngbOBRb/a8s0NYJO010AXCsQ/0SSTJJF0OKgP5QvKaYBUcswNcuvMhT6BYxIiISHUoIRRzUDrgi8EiaBb1jgZ8DawI/p2FPcDVwOUl0oiLOS7oY4gHKYPLvJ+P21V/gyY1bW8SIiERRUvz7IZKoMrE3xJPKMOCZwPNjgb8Cg5zrjkgyS7oYcgMUf2FHBxsSOko4OHdwG3ZORCQ5aYRQRFrOsOeeYBcDo4EXgDkoGRSRPfmAycA9ddozYPLEybiaWCTowqVRQhGRKNEIoYi0zALgauAAYHpIuxv4jxMdEpGEMB87fXw+9irkTOyaY8BnfCyrWIbBNPoRBsPSsqX4jE97h4qItJISQhEHlVG7h9gOoL1jPYnAVuCPwJPYEcJPgd8DP3WyUyKpKaFiyI/AbdiRwWC+txt4h5qE0O1yM3PgTMp9dace7CnHnaNkUEQkCpQQijhst9MdaK7d2Au5O7BXnkEHYqd/iYgj4j6GGOBZ4Ebg+5D2Q7CVh+tUHc5155Lrzm2r3omIpDwlhCIOysZuuRV8Hrc+wE4PXR7S1gG7+dmVQIYDfRKR+I8hy7ElUD8OaWsP3AlcC6Q70CcREQmjhFDEQWlAD6c70Zj/A24AXqvTfiEwEeja1h0SkVBxHUM+BoYTPoT5C+ARoKcjPRIRkXqoyqiINOxjwpPBI4DPgGkoGRSRxg0F+gee74ddK/gqSgZFROKMEkIRB1UBDwYeVQ73pV6/xV7UdQGewiaDRznaIxEJEVcx5Mc6P6dj1x3fBSwDTmnrDomISHO4jDGN13aWhODxeMjPz6e0tJS8vDynuyPNVAYESyd4cbhC4HLsdhETQtoMsB7oBOQ70CdpU4ojiScuYsgu4H5sVvoJoL3iRUQSitYQijioHTA25LkjdmALPBRhq4UOBY4Peb1P23dJRJrH8RjyLraw1DeBny/HbkWj+UciIglDCaGIgzIJ39O9TfmwawEnANtC2v9CbULoauM+iUhEHIsh3wLXEb7GuB12C4lqbMdERCQhKCEUSUXzgKuABSFt2cCt2KqiIiL1qQYeBf6Ena8adCx2T8FDHOiTiIi0ihJCkVSyGbgZu0l0qHOAB4Bebd4jEUkUH2P3FAzdj7QL8BC2AJVmFIiIJCQlhCIOKqN2D7GNxLggxBpssQdvSNsA4K+ErxkUkYTRZjHEYKeIBpNBFzY5vBfoGKsvFRGRtqBl3yIOKw08Ym4/4MjA872AvwELUTIokuDaJIa4sFNCXdj9SL/ExpCOsf5iERGJNY0QijgoG1gd8nzbRg+e7RXkdcqmoEcry/5vArqH/OzCjgb+DbgHKGjdx4uI8+rGEIhSHPkSu4/gYSFtRwFzgGGAu2UfKyIi8UcJoYiD0oADAs8Xzl7L3Bmr2eWtIis3g2Gj+zFkeN/IP9QL/BlbLfR14NSQ1w7BbhQtIkkhNIZAFOLIduCPwFRsMvgl4cnfsa3usoiIxBlNGRWJA9s2epg7YzX4DYW988FvmDtjNds2epr/IQZ4DjgQu0l0FXbNT1Useiwi8aZVccRg96/oD0wJ/FwM/CuGHRYRkbigEUIRB1UDT2C3AcysqKb7PnmkpbnI75xDyYZSNqza1rypX8XYbSTmhrRlAGdh9xsUkaQUjCEAI7ZXsMtbRWHv/MjiyFJsgZhPQtpysVtL/CbGJyAiIo5TQijioCrgSoAeeTyYn0Xp1nLyO+dQurWcqordfPjyCvy7/Q1P/doG3Ia9IjQh7aOxU0b3b5PTEBGH1MQQYP3e2WTlZjQ/juwE7gIeI/zG0a+AR6gtXyoiIklNU0ZFHOQGfhl4DBu1P6S5KNlQSnXVbgyGjAx3/VO/DLY4zAHYtT7BZLAf8F/gPygZFEkBoTGksHsew0b3a14ceR84CHvjKJgMHgC8B7yMkkERkRSiEUIRB2UBrwR/OKEPB/TbG8/2CnZsLWP288vI75wTNvXLs73CTvlyYS/cdgTemwvcAVyDnSoqIikhLIYAQ4b3pVf/gqbjSKc82BzyIX8EbgQy2/oMRETEaUoIReJIQY88CnrksW2jZ4+pX1ntM8jrlF178CPALOAcbBGZbq377qhueSEijmlWHOkBjAe+AR4HWlDQuC7FEBGRxKSEUCQOFfSwU7/mzlhNybpSskoyGPazfuEXWftjL+aiMLUralteiEjcqIkj/1xNyfxSsn6SwbDTQ+LIw9irAFfrv0sxREQkcSkhFHFQObV7iH0N5IS8NuTEvvTaUIDnvgryNmRTsCzPTgkNPSgKyWDdUvWlW8uZO2M1vfoX6C6/SJxrLIawAYYU9aXX2wV40ivIOyWbghND/k6nR6cPiiEiIolNRWVEHGSATYFHaJFQVgAnQ8FFefRdU0hBVR5sBD6Ofh88gVL1oeuMdpVV4dleEf0vE5GoqjeGVAGTsEVjXoeCqjz6lhVSMD+vTqCJDsUQEZHEpoRQxEFZ2C0EiwPPKcVuJj8QWwUwaDiwBDgl+n3I61Rbqt7vN/WvVxSRuLRHDJkDHAbcgh0+BCgEngPeJCrTQ+tSDBERSWxKCGPg448/ZvTo0XTv3h2Xy8Ubb7zR6PFz5szB5XLt8diyZUvbdFgc48Zeux3mB/dT2G0jHqW2DPy+wGvY4jEHx6YPwXVGwVL1pLnC1xmJSNyqiSFbwP1b4ARgZeDFNOwmhauwG8zHIBkExRARkUSnNYQxUFZWxqBBg7jooos466yzmv2+VatWkZdX+w9oly5dYtE9iUe3A/eF/JwNTAD+EHgeY6Gl6lUhUCTB7AQOBX4IaTsKmAwMbpsuKIaIiCQuJYQxMGrUKEaNGhXx+7p06ULHjh2j3yGJW9XYmVxcD+c9Duk7gV8BDwK927YvwVL1IpI4qoHnOgB/g/POh/QO2PWDF9Pmc4AUQ0REEpMSwjhy2GGHUVlZyaGHHspdd93FsGHDGjy2srKSysrKmp89Hk9bdFGioQpbDvAQ+3QcwN7wq8chvRd2ypdIG1AcSVA/AB2AjJAY8mv41SJIvwHo7GDfREQk4WgNYRzo1q0bU6ZM4bXXXuO1116jZ8+eHH/88SxcuLDB90ycOJH8/PyaR8+ePduwx9Ji72ELxowAdtr1P6cGHu6xKBmUNqU4kmD8wFPAgdg9BKkTQ+5HyaCIiETMZYyJQRFqCXK5XLz++uuMGTMmovcdd9xx9OrVi2effbbe1+u7s9+zZ09KS0vD1iFKnPgGuB74T0jbTdipXSIOURxJIIuBK4BPAz9nY4vHtPHUchERST6aMhqnjjzySD755JMGX8/MzCQzM7MNeyQt4gUmAg9h53YFHQ2c40iPRGoojiQAD3An8Di11YcBTgf0qxMRkShQQhinFi1aRLdu3ZzuhrSUAV4EbsRuKB/UDVswJoYl4EUkCRjgZey+pJtD2vsBRdhp5yIiIlGghDAGvF4va9asqfl53bp1LFq0iE6dOtGrVy8mTJjAxo0beeaZZwB49NFH6dOnD4cccgi7du3iySef5IMPPmDmzJlOnYK0xhrgIuB/IW3pwA3ArdhiEAHlwKDA88VATpt0UETi2irs/oHvh7RlAbdht6IJGRlUDBERkdZSQhgD8+fP54QTaquDXH/99QCMHTuW6dOns3nzZjZs2FDzelVVFTfccAMbN24kJyeHgQMH8v7774d9hiSQXGBRyM+nAY8AB+x5qMHmj8HnIiI8S3gyOBp4DOiz56GKISIi0loqKpMkPB4P+fn5KgYRLx4GpgCPYhPCBviAzwLPf4qtGCjiFMWROFEOHBx4/lfsesEGKIaIiEhrKSFMErqQc8hHwN3Aq8BeIe3V2BLxKvogCURxxAHrgS+As+u0L8eOCGoOqIiIxJj2IRRpie+Ac4HjgQ+wVQBDpaNkUEQaVgnchx0JvAC7NU2oQ1AyKCIibUIJoUgkdgF/xm4M/WJI++fYUcEI7QZeCTx2t753IpIIZmMrwfwRqMAmh3e17KMUQ0REpLVUVEakOQwwA1sCfm1IewH2Lv9FtGjxTiW1M8W86C+kSFLbjK02/EJIWxpwFfCnln2kYoiIiLSW/u0QacpXwLXAeyFtbmA89q7+Xnu+pbnSgONCnotIEtqN3TvwdmBnSPtQ4O/AYS3/aMUQERFpLSWE0ma2bfTg2V5BXqdsCnokSMGKSuw6wZKQthOwlf8Obf3HZwNzWv8xIikj4eLIeuBMwrei2RuYBIyj1VmcYoiIiLSWEkJpEwtnr2XujNXs8laRlZvBsNH9GDK8r9PdalomtmDMFUAv7HYSvwBcTnZKJDUlZBzpip3LGXQpMBGbFIqIiMQBzTCRmNu20cPcGavBbyjsnQ9+w9wZq9m20dPiz1u7tKRZ74/kWMCWf/++TttlwOPASuCXtDoZjLhPIhLVOBLTGFJXFvA37LTQecATRCUZVBwREZFo0QihxJxnewW7vFUU9s4nLc1FfuccSjaU4tleEfGUr0hGCCIaTSgBJgDTgIuBJ0NecwNX1v5YbapJd6VH1O+G+nT4GQdyxQl9AHutmN2iTxVJftGKIzGLIWCnhV4DTAEOCmkfCZxE1G7BhvYrba8snrjjOLI7ZCqGiIhIi2iEUGJud7UPv9+w9VsPfr+hdGs5We0zyOsU2aVLJCMEzT62GvgL0A+bDAI8BRTX34fF3sWMWDyCJd4lEfW9oT59+s4aFgOLsfvYi0j9ohFHYhJDAEqxieDhwMfYglOmzjFR+te2br+MgdUdMhVDRESkxZQQSkwtnL2W955ZQrmnkk3rfmTN4i2Q5mLY6f0iHh0MjhDkd86pGSHYVVaFZ3tFy46dBQzEloEPXuPlYxPEBgrGFG0qwuv3UrSpKKK+N9Qn344Knl73IzOxM8tEZE/RiiNRjyEGu4VEf2yhqWBGtpk9p55HSd1+FXTM5JK/zOPpdT8qhoiISIsoIZSYCb2Tvd+gQrr33Yv2eVmMvGAgQ06MvBBEXqdssnIzKN1a3uQIQaPHrsVW/TsZu6UE2HWBlwCrsVtM1DMjtNhbzALvAgDme+dT7G1gGDGC/ufkZHBqhpuTaNE2hiJJL5pxJGoxBOya4uHAb4AtgTdlYwvGLAYKGz6f1qz9q9uvnd+XM3BDKadmuBVDRESkRZQQSszUvZPdeZ880twu2qW3/LJl/0FdqK7aTcmG0kZHCAp65DFsdD9Ic4Uf+3IeHAy8EXLwUOBL4B9Al4a/e/KmyQQvudy4mbJpSkR9b7BPiVA6X8Qh0Y4jrY4hHfPseuNBwIchbzgdmyTeAmTU/90LZ6/lhQc/5bXHPueFBz9l4ey1EfdfcURERKJNRWUkZkLvZOd3zmnx2kGoU0ShXRqHDt2HI0bu1+hF0JDhfenVvyB8z7IN2L0FwZaDfwA4jyZvjYSODgL48NWMEg7OHdzs86jbp4498ng78NpI9BdSpK5oxZGoxZBRwLshB+2LrUL888a/v+7av9Kt5cydsZpe/QsiTuZC+5XTKZsFgTiiGCIiIi2hEUKJmWjdya57IZWR4WbNkpKm3wgUdM+j74DC2u+8ADgGuBE7PfR8mvW3IHR0MKglo4Rg/1yCfarEXkf+nNo8VURqRSOOtCqG9KgTQ24OvJAB3AYsp8lkECJbvxhJvzr0yFMMERGRVtHNRImpeu+wR6hF5ea3A3dgi8U8E9Kehp3mFcFss7qjg0EtHSUMlQb8JOS5iOyptXGkxVtWVAI/AN1D2o4HHgRGAwc2vw/RnDERSjFERERaS/9+SMztcYc9QpEUgsCH3QPsAKAIeBb4pM4xTSSDdYs+1Dc6WPtRLRslDMrGLl/8Eu0fJtKY1sSRiGJI0PvYKsRns+d+Dn+gyWSwbhyJ1do/xRAREWktjRBK3AteSM2dsZqSDaVktc+o/0LqE+Aq7ObQQe2xVUV/1rzvqrsRdeczfSzosOfoYFA0RglFJLaaHUMANmK3onkp8PNq7CyDC5v/fQ1taB+NGRMiIiLRpoRQEkKjF1IbgZuA5+u86TxgEtCjed9RX9GHZ73TcHVwYfbYZbqWCxdTNk1har+pkZ2UiLSZJpOx3djiMHcA3pD2o4Ehzf+eporHBB8iIiLxQgmhxKVtGz17XLjtcSG1C3gE+DNQFvLmwdgLu2GRfWfddUYdOmextfO3jSaDAAbD0rKl+IwPtyuyUvgVwIjA8/fRlC+RaKobRxpMxuYCVwBLQtoKsDeULiSixRUtXq/YQoohIiLSWkoIJe40NN1qD+8Ct4b8vDc2ObyEFu3yXrfow86tuzjnlQmcdtUAOnXt0Oh7c9w5ESeDYJcmfRryXESio1lxZBt2dsG0kDYXcBlwH9Ap8u+NVfGYhiiGiIhIa6mojMSVutOt8BvmzlhdU5ghzBnYin9pwJXYtT6/o0XJINRf9OGEUQPp32s/umR0afSR685t0XdmAq8HHpkt67aI1NHsOLKa8GRwMDAPW5iqBckgtP3G8YohIiLSWhohlLjS4HSrbysomJUXXtjBBUwGqrDVAKOgrYs+tAPGxPQbRFJPs6dtHg2MA17Dzi64nBbfUArVlnFEMURERFpLCaHElXqnW/2YQd4vs23xmO7AySFv6B/9Pqjog0hiqzeOZGSQ90o2HEx40vcgdnpo1+j2QXFEREQShaaMSlwJm261vBTmuRg2ux8FGwMXVrdAEzVeEooPmBN4+BztiUjyCIsj/1cKm1wMe7sfBffkwZN1Dt6bqCeDbUkxREREWstljEmiy+vU5fF4yM/Pp7S0lLy8BL8r/T1su8GD5/UK8qqzKagKnM+ZwMNAHyc7F11lQHD1oRe7baKIU5IqjgDbPvLgua2CvC9C4sg+2L1J053sWfQohoiISGtpyqjEj2qgCLgLCkrzKCBwAXcQ8BhwkmM9ixkXdgZb8LmIREEZcA8UPJxHwe6QxPZM4FGSJhkExRAREWk9JYQSH7YDxwArQtrygD8B40mqC7hQOcBypzshkiwM8AZwDfBtSHtf7N6kpzrQpxhTDBERkdbSGkKJD3sBvQPPXcBF2JLw15K0yaCIRJEPuxXNWdQmgxnA7cAykjIZFBERiQaNEIozdmE3zQrOcXIBj2AXwTwEHOlQv0QkMbmBHiE/nwz8DTjAme6IiIgkCo0QStsywCtAP+A/dV47EPiYlEoGK7BLI08KPBeRCNQtiXYfdnP5l4F3SYlkUDFERERaSyOE0naWAVcDHwZ+vg4YCWS1XRe2bfS02abzzeEH3g95LiLN8B02fhwLXBXSvhewgJhXV4mnOKIYIiIiraWEUGLvR+BO4O+Eb5R1ALCDNtsDbOHstcydsZpd3iqycjMYNrofQ4b3bZsvb0Am8K+Q5yLSiGpsxeG7sJVEZwK/BLqFHBPjZDDe4ohiiIiItJamjErs+IAnsInf49Qmg32x00Xfpc2SwW0bPcydsRr8hsLe+eA3zJ2xmm0bPW3TgQa0A84LPHR3RqQR/8NOB70RmwyCzYBWt10X4jGOKIaIiEhrKSGU2JgLHAH8Dvgh0JaDXeOzHBhNm26a5dlewS5vFfmdc0hLc5HfOYddZVV4tmvVjUhc+x64EDs9NLi/ggu4HFgFHNd2XVEcERGRZKSEUGLjEaA45OdfYy/eJtCmawaD8jplk5WbQenWcvx+Q+nWcrLaZ5DXKbvtOxPCB3wZePiaOFYkpfiAydhiU0+HtB8OfI6dgr5X23YpHuOIYoiIiLSWEkKJjYewid8gbOXQF4B97EvbNnpYu7SkyWlW1aY6at0p6JHHsNH9IM1FyYZSSHMx7PR+jheE2IUtqnpk4LmIBPwFuAK7zhggH5sEfg4c0fw4Ek3xGEcUQ0REpLVcxpi6hbslAXk8HvLz8yktLSUvr40vTt7Gln//eZ32+dg1P+7apuYWZFjsXczVa67m8f0fZ2DuwKh1NZ6qAwKUAwcHnq/AzqoVcYqjcWSPzgD9gc3ABcADQKF9yenCLvEURxRDRESktTRCKC33NXAaNhH8PXZT+VA/ISwZjKQgQ9GmIrx+L0WbiqLa5YIeefQdUOj4RVxQDrA+8NCFnKQsA6ys05YH/BP4CDtlNJAMxkNhl3iKI4ohIiLSWkoIJXI7gVuAQ4D/Bto2Ulv7vAHNLchQ7C1mgXcBAPO98yn2Ftf3cSKSDJZhC8Mcgd1fMNQobDGZEJEUdnFiWqmIiEiiUUIozWewSd+BwCTsnmBg1wa+gK0o2ojmFmSYvGky7sDQohs3UzZNieppiEgc8AI3YaeV/w+7lcR1tS83tIa4uXFk4ey1vPDgp7z22Oe88OCnLJy9NkYnIiIiktiUEErzLAB+BpyPXdMDkAH8EfgKW0W0iW0kmlOQITg66AvUy/PhS+pRwl3AmMBDBSEkJRjgNeAg4EFgd6B9P+Bi+3SxdzEjFo9giXfJHm9vThyJh2mlbUUxREREWkv72ErTpgMXYS/kgs4AHsZexEVgyPC+9Opf0GBBhuDooC+kgHpwlHBqv6kt638c8wFvhjwXSWprgKuAd0PaMrHb0dxMzZY0oWuI6/t731QcCU4rLeydXzOttGRDKZ7tFXGx7i+aFENERKS1lBBK004G2mOneB0IPAaMbPnHFfTIq/eiLHTtYKjQUcLBuYNb/sVxKAN4IuS5SFLaBdwfeFSGtI8CHifsxlJ9a4jr+3vfUByB8Gml+Z1z4mK/wFhRDBERkdbSlFHZ09Y6P3cHJmJHBJfQqmSwMaFrB+tK1rWE6cClgUe6w30RiZnd2IqhwWRwH+Df2C1r6swyiMYa4njcLzBWFENERKS1NEIotf4PuAG7kfxqoGPIa1fG9qsbGh0MSuZRQpGkl4udWXAOcD1we6CtjrpxoDV/75uaVioiIiKWRggFKoA/YTeBfg07Qvintu3C5E2TcTVRlcaFK+lGCf3A8sDD73BfRKKiGngIWFen/UzsjaZJ1JsMQv2zBFozOyCe9guMFcUQERFpLSWEqSy02t9d1Jao6wIc1nbd8Bkfy8qWYcKq1uzJYFhathSfSZ7SCRXAoYHHnruoiSSYj7Gx40bgmjqvuYA+Db+1boXhoGSvNNxaiiEiItJamjKaqpZjL9hmh7S1A64G7gDy264rbpebmQNnUu4rb/LYHHcOblf96wwTVYHTHRBprRLsnoLPhLS9BSwFBjTvI+qrMByUzJWGo0ExREREWkMJYaopBe4E/kZ4jfKTsGt8DnKiU5DrziXX3cA8siTWnj1r+IgkDB8wFbgVG1uCjgD+TrOTQa0hbjnFEBERaS1NGU01VcDT1CaDfYA3gPdwLBkUkQT0BXAUMJ7aZHAvYAowD/hJ8z8qVdcQi4iIxAMlhKmmM3A3kA3cA6zAbjLf+LWYiEite4CfAqGDehcCXwG/gwZ2j6lXKq8hFhERiQeaMpqKLsdW/NvH6Y7ILuDiwPN/AlkO9kWk2QZDTf52KDAZ+FnLPirV1xC3lmKIiIi0lssY0/htWUkIHo+H/Px8SktLyctL3hLryaaM2gr8Xux6IBGnRBRHzscmhlehHdEdpBgiIiKtpSmjMfDxxx8zevRounfvjsvl4o033mjyPXPmzGHIkCFkZmay//77M3369Jj3U5yXATwSeGQ43BeRiDyL3WReyaCjFENERKS1lBDGQFlZGYMGDaKoqKhZx69bt47TTjuNE044gUWLFnHttddyySWX8N5778W4p+K0dODawEPX1SISKcUQERFpLa0hjIFRo0YxatSoZh8/ZcoU+vTpw8MPPwzAQQcdxCeffMIjjzzCyJEjY9VNERERERFJcUoI48C8efMYMWJEWNvIkSO59tprG3xPZWUllZWVNT97PJ5YdU9iyA9sCDzvhYbspW0pjiQ+xRAREWkt/dsRB7Zs2UJhYWFYW2FhIR6Ph4qKinrfM3HiRPLz82sePXv2jGqfqk11VD9P6leB3QqyT+C5SFuKdRyR2FMMERGR1lJCmKAmTJhAaWlpzePbb79t1vu2bfSwdmkJ2zY2PBKw2LuYEYtHsMS7JFrdlUbkBB4ibS2WcUTajmKIiIi0hqaMxoGuXbtSUlIS1lZSUkJeXh7Z2dn1viczM5PMzMyIvmfh7LXMnbGaXd4qsnIzGDa6H0OG993juKJNRXj9Xoo2FTG139SIvkMi0x5bNl7ECbGMI9I2FENERKS1NEIYB4YOHcrs2bPD2mbNmsXQoUOj9h3bNnqYO2M1+A2FvfPBb5g7Y/Ued/iLvcUs8C4AYL53PsXe4qj1QUQSW3PjSPBYjSKKiIjEPyWEMeD1elm0aBGLFi0C7LYSixYtYsMGu/R/woQJXHDBBTXH//73v2ft2rXcdNNNfPXVV/z973/n5Zdf5rrrrotanzzbK9jlrSK/cw5paS7yO+ewq6wKz/bwVSeTN03GjRsAN26mbJoStT6ISGJrbhxZOHstLzz4Ka899jkvPPgpC2evdajHIiIi0hQlhDEwf/58Bg8ezODBgwG4/vrrGTx4MHfccQcAmzdvrkkOAfr06cPbb7/NrFmzGDRoEA8//DBPPvlkVLecyOuUTVZuBqVby/H7DaVby8lqn0Fep9opqcHRQR8+AHz4NEoYY5XApYFHZRPHijitOXEkklFEaT3FEBERaS2XMcY43QlpPY/HQ35+PqWlpeTl5dV7TM3an7IqstpnMOz0fgw5sXbtz2WrL2ORd1FNQgh2lHBw7mCtJYyRMiA38NyLXQ8k4pRoxJG1S0t47bHPKeydT1qaC7/fULKhlF9cfRR9BxTW+5nScoohIiLSWioqk0KGDO9Lr/4FeLZXkNcpm4IetRd8oWsHQ4WOEg7OHdyW3U0J6cC9Ic9F4l1jcQTCRxHzO+fUO4oo0aMYIiIiraURwiTRnDv7jalvdDBIo4QiqaG1cSSoqVFEERERiR8aIZQGRweDNEooIpFoahRRRERE4oeKygiTN03GhavRY1y4VHE0BgywNfDQUL0kk4IeefQdUKhkMMYUQ0REpLU0QpjifMbHsrJlmCYuJQyGpWVL8Rkfbpe7jXqX/MqBLoHnKgghIpFSDBERkdZSQpgkgktBPZ7IS7u/uu+rVPgqmjwu251N2c6yiD9fGhb6p+mBelZwSiLr0KEDLlfjo+/xpDVxRJyhGJLcEi2GiEhiUkKYJHbu3AlAz549He6JtFR3pzsgUdfa4ixtTXEksSmGJJ9EiyEikphUZTRJ+P1+Nm3ahDGGXr168e233ybVPyIej4eePXsm3XlB8p5bsp4XNP/cEu3uvt/vZ9WqVRx88MEp/XtLNMl6XpC855asMUREEpNGCJNEWloa++yzT81Ur7y8vKT6xzMoWc8LkvfckvW8IPnOLS0tjR49egDJd26hkvXckvW8IHnPLVnPS0QSi6qMioiIiIiIpCglhCIiIiIiIilKCWGSyczM5M477yQzM9PprkRVsp4XJO+5Jet5gc4tUSXruSXreUHynluynpeIJCYVlREREREREUlRGiEUERERERFJUUoIRUREREREUpQSQhERERERkRSlhFBERERERCRFKSGMc0VFRey7775kZWVx1FFH8cUXXzR6/CuvvEL//v3JyspiwIAB/Pe//w173RjDHXfcQbdu3cjOzmbEiBF8/fXXsTyFBkVybv/4xz845phj2Guvvdhrr70YMWLEHsdfeOGFuFyusMcpp5wS69PYQyTnNX369D36nJWVFXZMov7Ojj/++D3OzeVycdppp9UcEw+/s48//pjRo0fTvXt3XC4Xb7zxRpPvmTNnDkOGDCEzM5P999+f6dOn73FMpH93YylZ40iyxhBI3jiSjDEEUiOOiEgSMxK3XnzxRZORkWGeeuops3z5cnPppZeajh07mpKSknqPnzt3rnG73eaBBx4wK1asMLfddptJT083S5curTnm/vvvN/n5+eaNN94wixcvNqeffrrp06ePqaioaKvTMsZEfm6/+c1vTFFRkSkuLjYrV640F154ocnPzzffffddzTFjx441p5xyitm8eXPNY/v27W11SsaYyM9r2rRpJi8vL6zPW7ZsCTsmUX9nP/zwQ9h5LVu2zLjdbjNt2rSaY+Lhd/bf//7X/PGPfzT//ve/DWBef/31Ro9fu3atycnJMddff71ZsWKFefzxx43b7TbvvvtuzTGR/lnFUrLGkWSNIcYkbxxJ1hhiTPLHERFJbkoI49iRRx5pxo8fX/Ozz+cz3bt3NxMnTqz3+LPPPtucdtppYW1HHXWU+d3vfmeMMcbv95uuXbuaBx98sOb1HTt2mMzMTPPCCy/E4AwaFum51bV7927ToUMH8/TTT9e0jR071pxxxhnR7mpEIj2vadOmmfz8/AY/L5l+Z4888ojp0KGD8Xq9NW3x8DsL1ZwLuZtuuskccsghYW3nnHOOGTlyZM3Prf2ziqZkjSPJGkOMSd44kgoxxJjkjCMiktw0ZTROVVVVsWDBAkaMGFHTlpaWxogRI5g3b16975k3b17Y8QAjR46sOX7dunVs2bIl7Jj8/HyOOuqoBj8zFlpybnWVl5dTXV1Np06dwtrnzJlDly5dOPDAA7n88sv54Ycfotr3xrT0vLxeL71796Znz56cccYZLF++vOa1ZPqd/fOf/+TXv/417du3D2t38nfWEk39PYvGn1W0JGscSdYYAskbRxRDwiVSHBGR5KeEME5t27YNn89HYWFhWHthYSFbtmyp9z1btmxp9PjgfyP5zFhoybnVdfPNN9O9e/ewfyxPOeUUnnnmGWbPns2kSZP46KOPGDVqFD6fL6r9b0hLzuvAAw/kqaee4s033+Rf//oXfr+fo48+mu+++w5Int/ZF198wbJly7jkkkvC2p3+nbVEQ3/PPB4PFRUVUfn/O1qSNY4kawyB5I0jiiHhEimOiEjya+d0B0Qidf/99/Piiy8yZ86csMIJv/71r2ueDxgwgIEDB7LffvsxZ84chg8f7kRXmzR06FCGDh1a8/PRRx/NQQcdxNSpU7nnnnsc7Fl0/fOf/2TAgAEceeSRYe2J+DuTxJdMMQRSI44ohoiIxI5GCONUQUEBbrebkpKSsPaSkhK6du1a73u6du3a6PHB/0bymbHQknMLeuihh7j//vuZOXMmAwcObPTYvn37UlBQwJo1a1rd5+ZozXkFpaenM3jw4Jo+J8PvrKysjBdffJGLL764ye9p699ZSzT09ywvL4/s7Oyo/H8QLckaR5I1hkDyxhHFkHCJFEdEJPkpIYxTGRkZHH744cyePbumze/3M3v27LA7waGGDh0adjzArFmzao7v06cPXbt2DTvG4/Hw+eefN/iZsdCScwN44IEHuOeee3j33Xf5yU9+0uT3fPfdd/zwww9069YtKv1uSkvPK5TP52Pp0qU1fU703xnYLQwqKyv57W9/2+T3tPXvrCWa+nsWjf8PoiVZ40iyxhBI3jiiGBIukeKIiKQAp6vaSMNefPFFk5mZaaZPn25WrFhhLrvsMtOxY8eacuLnn3++ueWWW2qOnzt3rmnXrp156KGHzMqVK82dd95Zb7n4jh07mjfffNMsWbLEnHHGGY5tYRDJud1///0mIyPDvPrqq2HlxXfu3GmMMWbnzp3mD3/4g5k3b55Zt26def/9982QIUPMAQccYHbt2hW35/WnP/3JvPfee+abb74xCxYsML/+9a9NVlaWWb58edi5J+LvLOhnP/uZOeecc/Zoj5ff2c6dO01xcbEpLi42gPnLX/5iiouLzf/93/8ZY4y55ZZbzPnnn19zfLBc/I033mhWrlxpioqK6i0X39ifVVtK1jiSrDGkJeeWKHEkWWNIsC/JHEdEJLkpIYxzjz/+uOnVq5fJyMgwRx55pPnss89qXjvuuOPM2LFjw45/+eWXTb9+/UxGRoY55JBDzNtvvx32ut/vN7fffrspLCw0mZmZZvjw4WbVqlVtcSp7iOTcevfubYA9Hnfeeacxxpjy8nJz8sknm86dO5v09HTTu3dvc+mllzryD2ck53XttdfWHFtYWGhOPfVUs3DhwrDPS9TfmTHGfPXVVwYwM2fO3OOz4uV39uGHH9b7/1bwXMaOHWuOO+64Pd5z2GGHmYyMDNO3b9+wfdGCGvuzamvJGkeSNYYYk7xxJBljiDGpEUdEJHm5jDGm7cYjRUREREREJF5oDaGIiIiIiEiKUkIoIiIiIiKSopQQioiIiIiIpCglhCIiIiIiIilKCaGIiIiIiEiKUkIoIiIiIiKSopQQioiIxKHNmzdzyy23cMIJJ9ChQwdcLhdz5sxxulsiIpJklBCKiIjEoVWrVjFp0iQ2btzIgAEDnO6OiIgkKSWEIiIicejwww/nhx9+YPXq1Vx//fVOd0dERJKUEkIREZE2UlFRQf/+/enfvz8VFRU17du3b6dbt24cffTR+Hw+ADp06ECnTp2c6qqIiKQIJYQiIiJtJDs7m6effpo1a9bwxz/+saZ9/PjxlJaWMn36dNxut4M9FBGRVNPO6Q6IiIikkqOOOoqbbrqJSZMmceaZZ1JSUsKLL77Io48+Sr9+/ZzunoiIpBglhCIiIm3srrvu4q233mLs2LF4vV6OO+44rr76aqe7JSIiKUhTRkVERNpYRkYGTz31FOvWrWPnzp1MmzYNl8vldLdERCQFKSEUERFxwHvvvQfArl27+Prrrx3ujYiIpColhCIiIm1syZIl3H333YwbN47BgwdzySWXUFpa6nS3REQkBSkhFBERaUPV1dVceOGFdO/enccee4zp06dTUlLCdddd53TXREQkBamojIiISBu69957WbRoEbNnz6ZDhw4MHDiQO+64g9tuu41f/vKXnHrqqWHHAixfvhyAZ599lk8++QSA2267re07LyIiScdljDFOd0JERCQVLFy4kKOOOorLL7+cv/71rzXtPp+PoUOHsnHjRpYvX07Hjh0BGi00o3++RUQkGpQQioiIiIiIpCitIRQREREREUlRSghFRERERERSlBJCERERERGRFKWEUEREREREJEUpIRQREREREUlRSghFRERERERSlBJCERERERGRFKWEUEREREREJEUpIRQREREREUlRSghFRERERERSlBJCERERERGRFKWEUEREREREJEUpIRQRSUDTp0+nY8eOTndDRJLAnDlzcLlc7Nixw+muiIgDlBCKiLTChRdeiMvlwuVykZGRwf7778/dd9/N7t27Y/q955xzDqtXr47pdzQl9NzT09MpLCzkpJNO4qmnnsLv90f0Wcme4Fab6jb5nuDv5P777w9rf+ONN3C5XG3Sh6Dg/xsul4v27dtzwAEHcOGFF7JgwYKIP+v444/n2muvjX4nU0Do76G+x1133eV0F0XEYUoIRURa6ZRTTmHz5s18/fXX3HDDDdx11108+OCD9R5bVVUVle/Mzs6mS5cuUfms1gie+/r163nnnXc44YQTuOaaa/j5z38e86Q4USz2LmbE4hEs8S5pk+/Lyspi0qRJ/Pjjj23yfY2ZNm0amzdvZvny5RQVFeH1ejnqqKN45plnnO5ayti8eXPN49FHHyUvLy+s7Q9/+INjfYtWPBSR1lFCKCLSSpmZmXTt2pXevXtz+eWXM2LECP7zn/8AdsRmzJgx/PnPf6Z79+4ceOCBAHz77becffbZdOzYkU6dOnHGGWewfv16AGbOnElWVtYe07euueYaTjzxRKD+EbXJkyez3377kZGRwYEHHsizzz5b89r69etxuVwsWrSopm3Hjh24XC7mzJkDwI8//sh5551H586dyc7O5oADDmDatGnNOvcePXowZMgQbr31Vt58803eeecdpk+fXnPcX/7yFwYMGED79u3p2bMnV1xxBV6vF7DT1caNG0dpaekeoxbPPvssP/nJT+jQoQNdu3blN7/5Dd9//31Tv5K4UrSpCK/fS9Gmojb5vhEjRtC1a1cmTpzY6HGffPIJxxxzDNnZ2fTs2ZOrr76asrIyAP72t79x6KGH1hwbHGGcMmVK2PfcdtttjX5Hx44d6dq1K/vuuy8nn3wyr776Kueddx5XXnllTcL6ww8/cO6559KjRw9ycnIYMGAAL7zwQs1nXHjhhXz00Uc89thjNf9/rF+/Hp/Px8UXX0yfPn3Izs7mwAMP5LHHHov4zyvZde3ateaRn5+Py+UKa8vNza05dsGCBfzkJz8hJyeHo48+mlWrVoV91ptvvsmQIUPIysqib9++/OlPfwq78bNhwwbOOOMMcnNzycvL4+yzz6akpKTm9bvuuovDDjuMJ598kj59+pCVlcUzzzzD3nvvTWVlZdh3jRkzhvPPPz9GfyoiEkoJoYgknW0bPaxdWsK2jR5Hvj87Ozvszvfs2bNZtWoVs2bN4q233qK6upqRI0fSoUMH/ve//zF37lxyc3M55ZRTqKqqYvjw4XTs2JHXXnut5jN8Ph8vvfQS5513Xr3f+frrr3PNNddwww03sGzZMn73u98xbtw4Pvzww2b3+/bbb2fFihW88847rFy5ksmTJ1NQUBDx+Z944okMGjSIf//73zVtaWlp/PWvf2X58uU8/fTTfPDBB9x0000AHH300XuMXARHLaqrq7nnnntYvHgxb7zxBuvXr+fCCy+MuE9OKfYWs8Brp0jO986n2Fsc8+90u93cd999PP7443z33Xf1HvPNN99wyimn8Itf/IIlS5bw0ksv8cknn3DllVcCcNxxx7FixQq2bt0KwEcffURBQUHNzYPq6mrmzZvH8ccfH3H/rrvuOnbu3MmsWbMA2LVrF4cffjhvv/02y5Yt47LLLuP888/niy++AOCxxx5j6NChXHrppTX/f/Ts2RO/388+++zDK6+8wooVK7jjjju49dZbefnllyPuk1h//OMfefjhh5k/fz7t2rXjoosuqnntf//7HxdccAHXXHMNK1asYOrUqUyfPp0///nPAPj9fs444wy2b9/ORx99xKxZs1i7di3nnHNO2HesWbOG1157jX//+98sWrSIX/3qV/h8vpqbaADff/89b7/9dtj3i0gMGRGRJLLg/W/MX695xzxw8Zvmr9e8Yxa8/01Mv2/s2LHmjDPOMMYY4/f7zaxZs0xmZqb5wx/+UPN6YWGhqaysrHnPs88+aw488EDj9/tr2iorK012drZ57733jDHGXHPNNebEE0+sef29994zmZmZ5scffzTGGDNt2jSTn59f8/rRRx9tLr300rC+/epXvzKnnnqqMcaYdevWGcAUFxfXvP7jjz8awHz44YfGGGNGjx5txo0b16Jzr+ucc84xBx10UIPvfeWVV8zee+9d83Pd82nIl19+aQCzc+fOZvfTSZeuutQcseAIM2TBEHPEgiPMZasui+n3hf5OfvrTn5qLLrrIGGPM66+/bkL/yb/44ovNZZeF9+V///ufSUtLMxUVFcbv95u9997bvPLKK8YYYw477DAzceJE07VrV2OMMZ988olJT083ZWVlDfYFMK+//voe7RUVFQYwkyZNavC9p512mrnhhhtqfj7uuOPMNddc0+i5G2PM+PHjzS9+8Ysmj4u6h40xPZrxGF3Pe0c3870Pt76bDf09+/DDDw1g3n///Zq2t99+2wCmoqLCGGPM8OHDzX333Rf2vmeffdZ069bNGGPMzJkzjdvtNhs2bKh5ffny5QYwX3zxhTHGmDvvvNOkp6eb77//PuxzLr/8cjNq1Kianx9++GHTt2/fsBgpIrGjEUIRSRrbNnqYO2M1+A2FvfPBb5g7Y3XMRwrfeustcnNzycrKYtSoUZxzzjlhhRoGDBhARkZGzc+LFy9mzZo1dOjQgdzcXHJzc+nUqRO7du3im2++AeC8885jzpw5bNq0CYDnnnuO0047rcHCKytXrmTYsGFhbcOGDWPlypXNPo/LL7+cF198kcMOO4ybbrqJTz/9tNnvrcsYE1bE5P3332f48OH06NGDDh06cP755/PDDz9QXl7e6OcsWLCA0aNH06tXLzp06MBxxx0H2Klp8S44OujDB4APX5uNEgJMmjSJp59+ut7/BxYvXsz06dNr/v/Lzc1l5MiR+P1+1q1bh8vl4thjj2XOnDns2LGDFStWcMUVV1BZWclXX33FRx99xBFHHEFOTk7E/TLGANT8/+Hz+bjnnnsYMGAAnTp1Ijc3l/fee69Zv+OioiIOP/xwOnfuTG5uLk888YQz/294gI3NeGyt571bm/neNpjwMHDgwJrn3bp1A6iZor148WLuvvvusP9ngqO25f/f3t2HRJntcQD/jlNTqY0ZTTctXyqNHEubUcvc1FqlKfJexQRZDSWsNSvEbkoIJVgZmi+rBrkVlBjKiFAZFqW9aKagiUStTmlUZm4uq6stk5uVev+YO89tMist7W7z/YCgD8/Lceb4zHw95/ymrw8ajQY2NjawsbERziGXyzFjxgyDPmhnZweZTGZw3a1bt6K8vBwdHR0AdFPi9QWSiGj8TfraDSAi+lL+/OMvvNS+wj/sLGBiIoKFzBS/PXmOP//4C7PmSsftumvWrEFeXh4kEgmsra0xaZLhrdXMzMzgZ61WCzc3NxQWFg47l/6NkoeHBxYuXAi1Wo2YmBicPXvWYE3eaJmY6P7/p38zDuim/b1t/fr1aGtrw8WLF1FRUQE/Pz/s2LEDGRkZo76eRqPB/PnzAejWLwYEBCAmJgYpKSmYOXMmbt68iaioKLx69WrEUPHixQuoVCqoVCoUFhZCJpPhyZMnUKlUf4tiFHm/5kEMsRAIAUAMMX7+9WccW3Rs3K/v4+MDlUqFxMTEYdNstVotoqOjERsbO+w4W1tbALrKnsePH0d1dTUUCgWkUqkQEquqqoRwPlr6cKDvH+np6cjJyUF2drawzjQuLu6jz7FarUZ8fDwyMzOxcuVKTJ8+Henp6airqxtTuz6LFMDcT9hPNsK2Tzl2/G5hgsmTJwvf68OYvmKwVqtFcnIygoODhx03derUT77Gu/dDAFAoFHB1dUVBQQHWrl2LpqYmXLhwYbTNJ6IxYiAkom+GdOY0TDWX4PnvfbCQmeL5732YaiaBdOa0cb2umZkZHBwcPnl/pVKJ4uJizJ49G1LpyO/ywsPDUVhYiHnz5sHExAQbNmwYcV8nJyfU1NQgMjJS2FZTUwO5XA7gf0Hz2bNnUCgUAGBQYEZPJpMhMjISkZGR8Pb2RkJCwqgD4bVr13D37l3s2rULgG6Ub3BwEJmZmUIwfXedl0QiwcDAgMG2e/fuobu7G6mpqcKoQ0NDw6ja8rW8vXbwbW+PEirMFePejtTUVCxbtkwoZqSnVCrR3Nz8wX7r6+uLuLg4lJSUCGsFV69ejStXrqCmpga7d+8eU5v060X9/f0B6PppYGAgNm3aBEAXQFpaWoS+C7y/f9TU1MDLywvbt28XtulH2Cfcv//7NRbnP77L/wOlUon79++P2GecnJzQ3t6O9vZ24e+1ubkZvb29Bs/lSLZs2YLs7Gx0dHTA39/fYKSRiMYXp4wS0Tdj1lwpvvvnIsBEhN+ePAdMRPjuX4vGdXRwLMLDwzFr1iwEBgaiuroajx49QmVlJWJjYw2KgISHh6OxsREpKSkICQnBlClTRjxnQkIC8vPzkZeXh9bWVmRlZeHMmTNCcZZp06bB09MTqamp0Gg0qKqqGlYhMikpCaWlpXjw4AGamppQVlYGJyenD/4u/f396OzsREdHBxobG3Ho0CEEBgYiICAAERERAAAHBwe8fv0aR44cwcOHD3H69GmDapUAYG9vD61Wi6tXr6Krqwt9fX2wtbWFRCIRjjt//jwOHDgwqsf6a9GPDr6PfpRwIixduhTh4eHIzc012L5nzx7U1tZi586duH37NlpbW1FaWioUlQF00wctLS1RVFRkEAjPnTuH/v7+YVOU36e3txednZ1oa2tDRUUFQkJCUFRUhLy8PGH6s6OjIyoqKlBbWwuNRoPo6GiDypSArn/U1dXh8ePH6OrqwuDgIBwdHdHQ0IDLly+jpaUF+/btw61btz7vAaMRJSUloaCgAMnJyWhqaoJGo4FarRbuI/7+/kJ/a2xsRH19PSIiIuDr6wt3d/ePnj8sLAxPnz7FiRMnWEyGaIIxEBLRN0XptwA/JHhhY+wK/JDgBeX3C752k4YxNTXFjRs3YGtri+DgYDg5OSEqKgovX740GDF0cHDA8uXLcefOnRGri+oFBQUhJycHGRkZcHZ2xrFjx3Dq1CmDKpAnT57Emzdv4Obmhri4OBw8eNDgHBKJBImJiXBxcYGPjw/EYjHUavUHr3vp0iVYWVnB3t4e69atw/Xr15Gbm4vS0lKIxbpA5OrqiqysLKSlpWHJkiUoLCwc9pEIXl5e2LZtG0JDQyGTyXD48GHIZDLk5+ejpKQEcrkcqampY5q+OtHeXTv4roleS7h//35h2p+ei4sLqqqq0NLSAm9vbygUCiQlJcHa2lrYRyQSwdvbGyKRCKtWrRKOk0qlcHd3f+/Uv3dt3rwZVlZWWLx4MWJiYmBubo76+nqEhYUJ++zduxdKpRIqlQqrV6/GnDlzEBQUZHCe+Ph4iMViyOVyYepwdHQ0goODERoaihUrVqC7u9tgtJC+LJVKhbKyMpSXl8PDwwOenp746aefYGdnB0DXX0pLS2FpaQkfHx/4+/tjwYIFKC4u/qTzW1hYYOPGjTA3Nx/2/BPR+BINvb2ghIiIiD7Ljy0/olHbiCGM/PIqgghu5m4TspaQ6O/Cz88Pzs7Ow0a0iWh8cQ0hERHRFzIwNIBfXvzywTAIAEMYwt0XdzEwNACx6P1TS4mMRU9PDyorK1FZWYmjR49+7eYQGR0GQiIioi9ELBKj3KUcfQMf/jgNADAVmzIMEkFXZbSnpwdpaWnDCiAR0fjjlFEiIiIiIiIjxaIyRERERERERoqBkIiIiIiIyEgxEBIRERERERkpBkIiIiIiIiIjxUBIRERERERkpBgIiYiIiIiIjBQDIRERERERkZFiICQiIiIiIjJSDIRERERERERGioGQiIiIiIjISDEQEhERERERGSkGQiIiIiIiIiPFQEhERERERGSkGAiJiIiIiIiM1H8AkcgfjK13XtUAAAAASUVORK5CYII=" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Loop by the axes to draw annotations\n", "for i,ax in enumerate(fig.axes[:-1]):\n", @@ -231,8 +277,33 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "Text(0.5, 0.98, 'Last Cycle')" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Querying using indexing\n", "fig = plot_results_panel_2d(cycle.state,\n", @@ -257,8 +328,25 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "Text(0.5, 0.1, 'x1')" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Querying using slicing with the slice() function\n", "fig = plot_results_panel_2d(cycle.state,\n", @@ -273,8 +361,33 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "Text(0.5, 0.98, 'Last 2 Cycles')" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Querying using slicing with np.s_[]\n", "fig = plot_results_panel_2d(cycle.state,\n", @@ -311,8 +424,17 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Simple multiple linear regression cycle\n", "random.seed(1)\n", @@ -374,8 +496,17 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot cycle results with each cycle as one panel using defaults\n", "fig = plot_results_panel_3d(cycle_mlr.state); # Add semicolon to supress creating two figures in jupyter notebook" @@ -386,8 +517,17 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Change wrap to 3 and Adjust dimensions\n", "plot_results_panel_3d(cycle_mlr.state,\n", @@ -401,8 +541,17 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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VmG0skcRH3/cZGxujUCjwzDPP4HleN4Zls1my2SwbN25EKcWVK1c4dOgQ9XqdCxcuEEUR5XK528HM5/MmwTQYDLcls/02khi2XB/NxW6/UILZ6XRot9smwTQsCZNQXgdmV917X3TLSShX+mJdaoJpLKYNBsONZq5iG8C5c+e4cOECuVyOp556qut8PRdCiK7RxMMPP4xSikajQaVSoVKpcPbsWZRS1ySYyeFpoQQzuSYzQmAwGG40vS7XMNN4Z7kNiZWc6WaPAvSOEJgZdcNCmIRyDZmv6t7L9ehQLkZvgpk8npQS3/f59re/zX333UculzMJpsFguK7MVXUPgoADBw5QrVbZtGkTURTNOEDNx+xCXT6fJ5/Ps2XLFpRS1Gq1rkT29OnTCCG6yWW5XCaXyy2YYAZBwOHDhymVSgwPD5sE02AwXFdmu1zPjjHLPT/C6s+QC82oX7hwgU6nw1133WVm1A0moVwrFrJz7uVGdCiX8phJglmv17vX5Pv+jOqTSTANBsNaMF/VvVKpsHfv3q7E9eLFi0xOTi7rcedCCEGxWKRYLLJ161aklNRqNSqVCleuXOHEiRPYtt1NMPv6+shkMtckmJ1OpxvTzYy6wWC4Hszlt7Gc82Ni3jjX7dea3vjY27Gca4RgtkTWcGdjEso1YK6q+3zcjA7lYszXwex0OguuKTEHKIPBsBhz7ZYEOHnyJKdOneLee+/trgmZKz4uZE62VCzLolQqUSqV2L59O1JKpqamqFQqjI6OcuzYMTzPmyGRzWQy3YPaYiMEvQmmmVE3GAxLZanNCLg5HcqlMNcedTOj/ubDJJSroLfqrpRa0pDyze5QLvU5kxd9r8X07ATTLMk1GAzzMV/VvdPpsG/fPlqtFk899RSlUql7nxtVcLMsi3K5TLlc5q677iKKIiYnJ6lUKly+fJmjR4+SSqWQUuJ5HoODg6RSqe41LtUErfcAZeKjwWDoZSG/jbm4ETOUy2GuzqiZUX/zYhLKFSKlJAzDJVWVernVOpQrdQAzS3INBsN8zFd1v3LlCvv372dgYIDHHnsMx5n5FnSzCm62bdPf309/fz8AYRgyOTnJ0aNHqVarfOtb3yKTycyQyHqe170OY4JmMBiWylL8NubiVu1QLsRCM+pzKTxMgnn7YhLKZdJ7WEiqM8s5GNzqHcrFMAmmwWBYiLmq7lJKjh07xrlz59i5cyebNm1a9Yw5XL/DkuM4DAwMkMlkWLduHcPDw12Dn7Nnz3Lw4EFyuVzX4Kevrw/Xdbvfw3wmaGZG3WB4c7McietsbrUO5UqYK8E0e9TvDExCuQySQHDo0CHK5TLr169f9h/5rXJg6mU1L9SlJJgjIyMMDQ11nWRNgmkw3HkkVfezZ8/SaDS47777EELQbDbZu3cvUkp27dpFPp+f9zFu1YKb4zgMDg4yODgIaIOeZEXJ6dOnOXDgAPl8foaLbO8haKEEs1Kp4Hke/f39JsE0GO5goihifHyc48eP88QTT1z38yPc/A7lYiym8Gg0GnQ6HdavX29m1G9xTEK5RHqr7q1Wi2w2u6I/6OUemG71YDCbuRLMs2fPdpNJsyTXYLjz6K26+75Ps9lECMHIyAgHDhxg48aN3H///TMODnNxKxbc5sJ1XYaHhxkeHgag0+l0O5jHjx+n1WpRKBRmJJi9h6DeBHN0dJRCoUAulzMmaAbDHUiv30YURdRqtet+fkxufz2Zz112NcxOMGu1GhMTEwwMDJgZ9Vsck1Auwnxa9/kWbi/GrZYkXu9rSb7f5HC00JJck2AaDLcfs12uLctCSsmBAwcYGRnhoYceYv369Ut6rJV0KK/HoWa5pFIp1q1bx7p16wBot9tUKhWq1SpHjx6l0+lQLBa7yWWpVJpxCEpGBGaboM23RNzER4Ph9mCu3ZJrdX4Mw5BTp05hWRYDAwPk8/lr4sKtdN5cKcn5EMyM+q2MSSgXYD6tu2VZK36R3ukdyrnoPfDNN6A9O8FMDk69+nkTIAyGW4f5XK5932diYqK7WzKTySz5MW81yetKk9V0Os2GDRvYsGEDAK1WqyuRvXTpEmEYdhPM5FAEZkbdYLhTmM9vI3ldr4TeIlq9XmfPnj3dROrs2bMIIbqqiMRg7HY/Q86OwWZG/dbFJJTzkPyBzuXAdaM6lDfqBXAzZRGLWUz3BuHZ+nkTIAyGm8NcLtcA58+f58yZM6RSKZ566qllO/WtdEboVo8FmUyGTCbDxo0bUUrNSDCnpqaYmpri6tWr3cNgoVDovu8sJcGca4n4rf4zMRjuVGY3I3pfx6s9PwJcuHCBI0eOsH37drZu3dqNmYk8dGxsjOPHjwNw9uxZ2u02/f393dVHtxNSygXPj7DwjLpJMG8cJqGcRdItS6pKc70x36gOZXI9tzvLOfAtJ8E0S3INhhvLfFX3IAg4ePAglUqFbdu2MTk5uaLX5K1YcFtrhBBks1my2SybNm3ijTfe6Jr4VKtVzp07h1Kq6x7b19fXlbLNl2BKKbsJpplRNxhuHovtllzN+TFJRI8dO8Zjjz3G4ODgjOcqlUqUSqXubt1XXnkFx3G4cOEChw8fJpvNzlh9lDhTr5QbUcxb7vkRZiaY8+1RNwnm2mMSyh6WultytRr4pd73Rkm6bsRzrPR7MQmmwXBr0CtxhenXZrVaZe/eveRyOXbv3s34+DiVSmVFzzFXQrlYknknFN0SieyWLVu6crZeF1khxIwEM5fLLZhgJiMExgTNYLgxLHW3ZBLPlnsuqtfrvPHGGwC8/e1vX9AtG+iqFjZu3Eh/fz9BEHSNwxJn6rmMw2411uL8CNcqPMyM+tpjEkqWv1tyNbONpkO5OuZLMK9cucLp06d57LHH5qw+mQTTYFg5vVX33jny06dPc+LECXbs2MH27dtv+Iz59eZmVOCFEBQKBQqFAlu3bkVK2U0wx8fHOXnyJLZtz9iBmbiOzxcfgyDgG9/4Bk899RSe511zgDIjBAbDylnObsne1+ZSX3MXL17k0KFDbNmyhUajsSzpahJPXddlaGiIoaEhgO7KokqlMsM4rL+/n76+PorF4i1xbroe50e4NsF89dVX2bx5M4ODg2ZGfYW86RPK+aruC2FZVjdwLJf5KvDz3fZGcD2fJ/ler9dzzK7SJ06yvQ5gQgiTYBoMK2C+qnun02H//v00Gg2efPJJyuVy9z6rUXAkz3k9b3+rsdiBybIsisUixWKRbdu2IaVkamqKSqXC2NgYJ06cwHGcGVK2dDo9IzYKIYiiqJs8Jr9TM6NuMKyOhfw25iL59+T2CxFFEYcOHWJsbIxHH32U/v5+zpw5MyPmLdb8mI9UKsX69etZv379NXPdFy5cQEpJqVTqJphzOcgu9hxrwfUs6vXGuSQ+JgVRM6O+fN7UCeVcVfelcD06lPO9aO6EwxJw3RO4ZHB7rgp98nsOggDgmgSzdwG5wWDQzFd1Hx8fZ9++ffT19bF79+5r5nBWEx+X0928U16zyz0wWZZFuVymXC53Z6WSBPPy5cscPXoUz/NmJJhJPEwOQ8s1QTMFOINhJkvx25iL3tfeQiQurq7r8swzz5BOp7uFut77LvY4S4mns+e6lVI0Gg0mJia6Etkk7iQOsplM5oaNTN2I+NO79grmn1E3Ceb8vCkTyqVq3edjOXOQc9139otwvgPFjaj8XG+ud4cyYb5qX/Li772e2TuMehPM3gq9wfBmZa6qu5SS48ePc/bsWR544AE2b948b9wyHcqls9oKfCJ/7evrA3SlvVqtUq1WuXjxIkeOHOlK5MbGxujr6+t+bmbUDYbls1S/jblIXjsLxchE4rpt2zZ27NjRvU/yHNe76CaEIJ/Pk8/nu7L72Q6ynufhOA6e59HpdK6bg+yNTChnz5DOJ5GVUtLpdMyM+izedAmlUoparcbly5fZunXrin7xd5rL6+0seU1YinwkuY7FEkzLsq45QL0Zg4PhzUdSbDt27BibNm0ilUohhKDVarF3717CMOTpp5+mUCjM+xiriY+z7xuGIefOnSOdTl/jSninzlCuFtu2GRgYYGBgANA/w9HRUY4ePcq5c+c4dOjQDLfHcrmM53nA4gkmzO2QaBJMw5uB5Lxw7tw5crkcxWJx2a/dhZLC2RLXZN5xrutYzjWvlrkcZCcnJzl16hT1ep1vfetba+4gm3AjYjAsvJ4kYfYowGwTtPnWlLxZEsw3VUKZVN0bjQanTp1i+/btK3qc1bq8Ji9wpRQXLlzg7NmzM4ahe9/cb3dutYRyNr0JZnKtcyWYxmLacKfTK3E9ceIE69atI51OMzo6yoEDB1i3bh07d+5c1AlwrTqUieQL9Guy1Wp1XQn7+/u7Lodv9g7lYjiOQ6lUwnEcnnrqqWvcHhuNBvl8vitnK5fL3cPgQiY/Zkbd8Gai12/jwoULbNy4kVKptOzH6Z2h7CWJd47jdCWuc913OU2J6xVXbNumv7+fiYkJisUid91113VzkL2RCeVK9ibPFR/frDPqb4qEcrbW3XGcVR14VjMjlFxPGIYcOnSIq1evsn37dlqtFmfPnuXgwYPk83n6+/tXfZ1LuY7rza2eUPbSK22AuZfkKqWoVqsMDw/jeZ5JMA13BL3xMXmDTKrlly5d4sEHH2TDhg1Leqy1UHBcvnyZAwcOsHXrVrZt20YUSkbPVRm/MsHVSzUuXbrUlZtduHCBoaGhGaYRY8EYdVnn7tTdK7qOG8mNODD1xsfZbo++71OpVKhWq5w8eZJmsznjMJgkozD3AWr2jHq1WqVUKpHNZs2MuuGOYLbfhm3bqzqbzY6Rly5d4uDBg2zdupV777130bPMraJyW4qD7JEjR/B9f8UOsjdKJbJWZ8jFRggajcaMIt+dlGDe8QnlXMYSq3UhXG2HMggC/uf//J+4rsvTTz/dbYfvb+/nQvMCD4YPMjExwcjICL7v893vfrf7QiwUCrdV9fd2SihnM1eC6fs+Bw4c4Jlnnuk6A5sOpuF2ZT6Xa8uy2Lt3L67rsnv3brLZ7JIfczXxMbmegwcP8sgjjzA0NES71eHEd0cZPTsFgO1kuOvhLZQ2eHz7299mamqK8+fPY1lWtxr+S8EvcVVe5Y/v+mOy1tKvfS5uROy6mc/heR7r1q1j3bp1wNzrBGYnmElMnGuE4Pjx4+zYsaP772ZG3XC7Mp/fxmrPkImKI4oiDh8+zOjo6IIS115uB+OytXKQheufUJ7zz/FzF36OD7gfYJe1a00fe64E8+LFi6RSKTzPu+Nm1O/ohDKpus823kmCwUr/UFfToazValQqFbZv3869996LENqCP1Qhnxz5JFfDqzyx7Ql2rtvJxMQEhw4dYt26dUxMTHDu3DmUUl3JV+/+sdVwPV+s17PD2suNGNxO3gSEEF1ZclKB6nQ6+L7PSy+9xG/+5m/y0ksvXddrMRhWS1J1T16jyUHlwoULBEHA4OAgDz/88IpkQCuJj61Wi4MHDyKl5Hu+53vIZrNaETDWZPTcFOV1OVzPpl5tc/F4laHNWwHYuXMnrut2TSNeGn+JM9kzoOBzxz7HD/f98IxRguVwo1QcNyKhXKrkrPcwCPr3ksjZDh8+jO/7lEqlrkS2VCrNMA2JogjP83Bd95oZ9VqtxtNPP83Ro0cpFovX7fs1GFbLfC7XsHpZv2VZNBoN9u7di+M47N69m0wms6xrS1gsdtzskYCVOsj2zppez/j4X8f/K1ejq3yl/BX+sfjH1+15YPrvxnXdbnycPaP+oQ99iF/5lV/h3e9+93W9luvBHZlQzq66zx6I7e04reQPdSXVKSklR44c4fLly+Tzee6///4Z1/Di1ItcCa9gYfH747/PL2/45e51b9q0qftCTBLSK1eucOLECVzXnZFgXi+nrZWSfH+3Y4dyLqIouuaNBaYdwKrVKo1G47pfh8GwUnoP+b3Ftl4ZfiqVYvPmzSt6Ta0kPo6NjbF//34GBgZoNpszOqJRKFFS4Xo6brsph3bdJ4qmLfQT04hiscgnz30Sy7eQQvKi/SKPn32c8GDYHSVY7UzPWnMjimGr6YJmMhkymQwbNmy4pttw6dIlwjCkVCp1O5hRFM3bwWw2m4yMjCzr8Gww3GgW2y25WsmrUop9+/axbdu2JUlce1nObVc7nrXU51ju7ZfiIJvEk2QW8Xpw1j/LV+pfAeBC6gL7/H08mXryujxXQnKGhLk7mIna5nbkjksoZ1fd50pmem2bV2rkspwXaavVYs+ePSiluOeee5iYmJjx76EK+a/j/xWBQCJ5qf4Sz/rPUhbla563d8F14rRVqVQ4f/48hw4dIpfLdRPMcrncnX2Zizul+g43NqGc7yCa6ONzudx1vw6DYSXMV3WfnJxk7969pNNpdu/ezXe+851VG48t5bUvpeTEiROcPXuWBx98kGKxyNjY2Izb5Eop0nmPiZEGmbxLrdJmcFOeVNbtfk8JLzde5pR/qvt5mzbnd5znI/mPUKlUmJiY6Mo4kySov7//po4S3OgZytUwV7eh2Wx2E8zz5893XYIHBga6YxrJ99doNMhkMrdMMm8w9DLbb2M+d86VSl4TiWsYhtx3333cfffKZrxvlPJrMdYids3nIJvIY2u1GlNTU7RarTV3kP398d/HxiYiQijBC5UXeFv+bdc1Hi90hgS6Jmm3I3dMQjlf1X0ulrIHaCGWE0yuXLnCvn37WL9+PQ888ACXL1++JpF7sfYiV6IrM772++O/z89kf2bBpC9x2urv7+eee+4hCILuG/vx48dpt9sUi8XuoWk5g9BrxY1MKBdKnteKOzkYGO5seo0lkviolOLMmTMcP36cu+++m7vvvnvVM0JLlSp1Oh327NlDEATs2rWLfD5Po9G4JublSinufXwd5w+P47cjhjcXufuRIWz72hj/pckvzfhcofiryb/iR/p/pDsn2Ntlm5iY4Pz5891RgiRW9o4S3AhDiNsloZyNEIJcLkcul2Pz5s1EUcTXv/51+vr6mJyc5OzZswCUy2UymQzf/e53yeVyq/5+P/3pT/Nnf/ZnHDlyhEwmw+7du3n++ee7yp+5eOGFF/ixH/uxGV9LpVLdheWGNzcLSVxns5L42Ovimk6nV+QQmzx3Lwu9lm5Eh3KtmX2u3bNnD+l0GiHEmjrIXvQvdruTAEooDnYOsq+9j0cyj6zltzSDO/kMeUcklMsJBMm/w8oTyqW8SGdX3jdu3Djvffe39s/4XKHY09qDyC3vTdd1XYaHhxkeHgaYIU3av38/UsoZOvXElvp6HmZupR1Ca/U8CwWDZrN52wYDw53JfMYSvu+zf/9+arUab3vb2+jr6+veZzUJZa98Zz7Gx8fZt28f/f39PPHEEzNcROe6X//6HP3r80ShxHFnxvfe2//k4E/yvZ3vnXHfze7mGZ/P1WWr1+tMTExw9epVTp48ieM49Pf34/t+d7blenE7J5RzPQ/Ali1bcBxnxpjGK6+8ws/+7M8ipeQjH/kI3//938+P//iPr+h5vv71r/Pcc8/x5JNPEoYhn/jEJ3jPe97TVenMR7FY5OjRo93PjTmQAeb325iPxAV7qcx2cf3Wt761qi7jUu97J/x9CyEoFAps2rQJmNtBtldyv9TGiStc3p59O23VRkaSqdoUg+VB8tb1Pb8tdIaMooh2u33bniFv+4Ryrqr7YiQy2OvVoex0Ouzdu5dOp3PNEvC5Dkz/1/D/xY8PzHxjzdt5wnq4qupSMvuycePGeQehAUZGRhgaGppz79FquRMlrws9j5G8Gm4l5iu2TUxMsHfvXsrlMrt3777GsGYtEsq53jiVUpw6dYpTp05x//33s2XLlhnxYaHupmUJLG/m482+zfbUdranti/repMDS6FQuGaUIAgCjhw5wrlz57rzl319fWuqhrhRtvg3MqHsnaHsHdPI5XL88i//Mo8//jjHjh1b8fP87d/+7YzPX3jhBYaHh3n99dd55zvfOe/9hBBdsyGDYTG/jfmwLGtJhaZeF9dHHnmkW+xfTedw9vVdvXqVVqvFwMDAnB4at9oM5XKZ3SxYioNs0jhZyEF22B3m+U3PA7p7/PqZ13nXQ++6rt8LLHyGrNfrACahvNHMV3VfKquVdM33Ik0Oav39/Tz++OPXHDzmuq8jHIacoWuuvyZqK7q++a559iB0pVJh7969jI6OcuLECdLp9IxD01ro1O/EhHKhDmW9Xr9tg4HhzmKuqrtSihMnTnD69Ok5E7qEtZK89pJ0ROv1Ok899dSckq+VOPut9YGpV3I1Pj7O1q1bsW2biYkJTp48SavVolAodGNlr8vpSriTOpSLFXY7nQ4DAwN84hOfWNPnnZycBKC/v3/B29XrdbZt24aUkscff5xPfepTPPjgg2t6LYbbg7lcrpfKUuJjo9Fgz549WJZ1jYvranf1SimRUnL06FEuXrxIJpPhyJEj5HK5GWe4G1Gout4sFB9X6yCbcKPiIyx8hkwMHW/XM+RtmVAuV+I6F8uVLMy+7+xgopTi9OnTnDx5csGD2nIrU9frBZsMQgM88sgjCCGoVqtMTEzM0Kn3HppWolO/rRPKKADlg+WCpTs4S9G/J/vcDIabwXxV93a7zd69e/F9n7e//e0Lrm1YbXyEmbKsyclJ3njjDYrFIrt37563WLUSx8DrjeM4DA4OdnfEtdvt7vxl4nJaLpeXtFNtNks1L1ot1yU+KgWyAyiw9WF5MQXH9Si4SSn5+Mc/zjPPPMNDDz007+3uv/9+fvd3f5e3vvWtTE5O8uu//uvs3r2bgwcPsnnz5nnvZ7izWI7fxnwsllBevnyZAwcOsGXLFu6777455x5XU7DzfZ/XXnuNKIp46qmncF2XVr3DyMWrTFUmGRs7iu/73Z2G+Xx+hjnW7cRy4uNyHWT7+/tJpVK3VEKZTqdviB/I9eC2u+ooihgdHeXKlSvcf//9K36BrGWHMggC9u3bR61Wm7fy3nvf5TzPjUAI0T00DQ4OAtM69YmJCQ4fPkwQBDMW0S41ON3QhBIgaoP09UEn8hHK159HHZ0cyhAh2yglEWETUBDWQNiIYBIlHEQwCcKCqAVOgeiufzLjeRaboTSSV8PNQkpJo9HgwIEDPProo115f7KWY3h4eMbM4nystoKeXItSinPnznHs2DF27NjB9u3bFzWRSO671IPe9aySz/XY6XSaDRs2dNdoNBqNbqxMKuK9q5yWsiLjpiWUMtRxLvJBtuL42QYZQNgEFSDCRs/nvr5N1AIERC3Uth+BnN4JuljB7XrEx+eee44DBw7wzW9+c8Hb7dq1i127pheX7969m507d/I7v/M7fPKTn1zTazLcmiTNiGRlx0qTrPnOj1EUceTIEUZGRmZIXOe6/0rjlpSSgwcPMjw8zIMPPohSirELVU7vv0pzqoPjpth4z70M3ZXj4MEDtNtt9uzZA6BnDPNlooaDbblk8h6DmwtY1q2baK5Grr+Yg+zhw4e7Hc7kb2OtHGTnIilmLJRQrsVu+ZvFbZNQ9lbdk2RnNT/01c4IJfednJxkz5495PP5OWeRZrPcytTNcuiarVNPrOEnJiY4c+YMQohrDk1z/T4WDQYy6CZ/+rDS0UlgFIDq6H+POgjZARXqw4wKEWEbJRQiqIHlsLF6mLwawqmBcouIYArplrCCSZRbhGAKZZcQqoWy0lgqQAkPUCAEguTaQ5Tl6c/cEtHGD3er77C0GcrbVa5guH3prbqHYdhdvaGU4siRI1y4cGGGOdhirCY+JvcPgoBjx45RqVR44oknFpUjwtyJ1VIS0JtFb0V8y5YtSCmZmpqiUqlw+fJljh49SjqdnhErew8sSXxf8fehVJwEtnTRLCmoRU2QASJqomREduI46TBEnH4NlNRFNCXjDmMSzyQIW8dZ4el/szP68Zw8SB9lpxEyQFlZBCGq77FuMglLU3CsZUL5Uz/1U3zpS1/iG9/4xrK7jK7r8thjj3HixIk1ux7DrUuv38bExAQbN25c04bEQhLXpdx/MRJH7na7zdatW9m5cydCCDptnzMHr9KuBwxsKNBuBlw4VqE0lCOdTjMwMMCmTZuo1WpcGbvKnq+fYuTMJI7jkMtnue/xjTy8664VJ1K3kwv2XJsRqtUqly5dIggC/v7v/35NHGTnY/aM+Wzq9fpt3ZC4LRJKKSVhGHYlWI7jrFiOlbDaDqWUknPnznH06FHuuece7rrrriUbAi3nea4nS01WZ1jDb9qIDFvUJyeYrIwxcXE/Fw5PkHItysUMxUKGUj6FY4GQbbypCsOtM1gXLiCCBkoI3QG0XPAr+qAS1sAtIIJaNxlUdgFkE+UUEFELZWf1Qcb2EDJEWQ5CRShhg/TxyWAJC+WVQXaQbj8iqiOdAiJogJ1ByBoIFxE1AYEQoT5UWS6EPsrLIaI2ys6BCpD974DMhhk/ixt9YDIYFmP2CEByMKjVahw8eBDQ3Zjl/F2uRvKasGfPHrLZLLt3757TLGIuejuUS+VWssVP5nXK5TJ33XUXYRhSrVa78zy9lvf9/f1aHqsCRFgDKXV3MGrr5FC2dFIYNkBGEMWdwij5vKk7jEjoFsRU/N9xcihDhJ3CaY4irTT4EcpOIZSlC2eWi8JBqEDHwagNTg7CBsopIcJJcAqIoIpyClj+BMrOIcIJSA3C4DMzvv/FFBxrVXBTSvHTP/3T/Pmf/zlf+9rXuOuuu5b9GFEUsX//ft7//vev+noMty5z+W3Ytr2q+DY7Pl6+fLkrnZ5L4jqb5TYWgiBg//79TE1Nkc1mGRwc7MbKoBPRaQTkyimEJcjkPRrVNn4rnLH6qFgs0pkS5Owmjz+9mU7Q5srlCoe+c4YrjXP0D5a6ydZS58Jv9gzlanFdtzvK4Ps+b33rW9fEQXY+esf05iKJjze7ULpSbumEsrfqnvxRCSGwbXvVi11Xk1AmAerEiRM8/vjjDAwMLPm+N2WGstvt87v/F7KD8FsUoxNYlQwWIUqFOqFC6QMOAhHWUMLWSZ7lIvwqtlugP5iizy0hCpNE5QKd+hj1tkNnYoJTHUEuZeNlS7iWxJZtRHtUy0lViBKeTg69vvhjPyJqx0lgTSeF/iS4BayginJLWEEF5Rax/Ik4SayirAxC+fpxZQcQIEN9QEKCnQUBys3rRqTjohTxNcT3cTI6ofUKWMFU/FxVZOkhVP/j1/4oo2jBSp7pUBpuJAu5XL/22mts3ryZ+++/f9lvgquJjxcvXkRKycDAAA8++OB1LaLdiDfeRZ9DyWkZqOzMSAidyGcoajFYCBGZBkHo06rupzHWonZqlIkQ7pJ1antfJp8CL9evE0g3B0ED3LyWmrpF/bhxx1BZKYRwUE4aQQgknUUnvp0HYQOcPCKcQlopXFUHMaSTQreEFUzESWMFnIK+vfAgCsHKIoQCtwQopNunC3h2AYSemVQb/1ewZh4hbtQM5XPPPcfnP/95/uIv/oJCocDIyAgApVKp2xl69tln2bRpE5/+9KcB+NVf/VWefvppduzYQbVa5TOf+Qxnz57lJ37iJ1Z9PYZbk/n8NlZ7hkziY6/E9eGHH16yf8JyJK+1Wo033nijW5z7zne+M+O+bsomlXOpTbRx19u0GwG2bZHKOlCbeYaMQomSCjfl4KULeE6GZq3DI09uph3UqVQqHDx4cMZceH9//5rsjl0pN2JsKolba+Ugu9DzwPwJ5e0+MnXLJpSzA0GSTAKrri7Byg9MyXJapRS7d+9e9qqNuRLKa76mJEQdrLCGG01C86JOfroJYSwTVaHu3imFiBooLKxwSnfv/Io+dPjjKCcfJ0ta+ombh6CBI9IMdo5gV1oIJcHxIIqwLL0/TBe9Y9dFy9MHCrcPVAflleMksIQVNkjnh8hkG6jBe5CdKZqBRac+TrUlEUGd8+cU+YxNqtBP2rXASoEM4iQzQllpQOqOpApRbhmhQn2QkW2kU0REHd2pjFrQk0QKQizpYwldvReWB2GAcrKIyEc5WX3NIoPA13ItFaCcPKgAnH4EAdLrR8gOMrsZue775/z9LVSBT6TBt3NAMNwezOdyHYYhhw8fBuCBBx5gy5YtK3r8lcTHXot8x3HmNSZbiJvWoYw6cXcwSQj1LGGufQi3chUReNPJovQhbIGKIKzHF+GDlY47fBmdBDo5RFhH2QUdq+w0ropwcymKORs1VKbTbnDm/CU6fsjEpI/NJKlsiXy6TTrbRypsgFPUBTS3hAgqKKeok8FuUliEqK47i7IDdkq/h7gFkBLp9qPkJSKnDEiUW4Koo5UYYSNOQhvxD0JeI3kVURPsWPLqxEqR/ichc+36jaXMUC6nADsfv/VbvwXA937v9874+u/93u/x0Y9+FIBz587NOLhVKhU+9rGPMTIyQl9fH0888QQvv/wyb3nLW1Z9PYZbj4V2S672DGnbNmEY8uqrryKEYNeuXWSz2SXff6kdymR/5V133cU999zTPQf3xjzbsbj7rUOc2neVymgDx7XZsnOA8nCWC2Mz42+hL022lGL8Up1M3qNebTO8tUixL0efXZh3Lty27a6qIjGyuVHcrLVK8+0rThLMXgfZxUa/EpLz43y3MZLX68BiuyXXQo61kgNT8uLeuHEj58+fX9HexgU7lO0R7HP/TSeEQR1PpdnqH8a6dEHLRJ0ihE2UGydKdhpUhBA2KLAEKASgUNiIqANun06SvD7dfXT7EFEsMW1XkXaqWxHX84hpCGsIKz09MxNFKMvRnUshUMLRyioro5/LLcTV6xIiCrDSfRTcDvncVtK1CSoVQSnv0OyETF44icQhl3FJZfJk0x6ul9a/Y2GhhIUC3VkEXSW3PECg7AwgYpkW+jmxEDIgJIWF1Ie4OHnudhz9CX2N/lXd3Qwn4u5mW38vQurnJEJZKeT6D4A9dxdyKWtDeveOGgxrzXxV96mpKfbu3Yvned03upWSHJiWSrPZ5I033sC2bXbv3s2rr766ooLdimcoVaQ7hGGcGMqZiaHozha2tGIjagAKghoI0dPNq6GcIiKcQjkl+jsHcWrbIRBxt7CjY6IKUMJF2Cn0DGJaP57laOmqndVqCSutpavC1s9puRBWQaQQso5j2biyzoZ1WxBIWp2IVqvBVD3gytWzCMsll2uSyRbI4eN4ZYh8lFPQ7wV2RieTwu2aixH6aMOceD4T/f1ZbtzFJA2Wg8JFuI6OoURd6auy8vF7RXnWzyOvFSOZjTC469pfBksbCVgryetifO1rX5vx+W/8xm/wG7/xG6t+bsOtzVJ2S672DFmtVmk0Gmzbtm1FCpDFlGpSSg4fPszIyAiPPvpoV5Y5331Lg1ke/p4ttBs+jmuTLXpzrm/KlVLsfHojp/aO0WlFbLirzD2PrcO2rRmPP3suPDGyuXjxIocPHyaXy9HX10er1brujqS3igt2777i2Q6yo6OjHDt2bE4H2V7u9JGpWyqhXOpuyRstee19cT/yyCMUi0XOnz+/oj/0eQNJMIV96Uv6gCADnfh1pvBFHhE24mr0ZDxjWIur3zWdbEXt+CADwrJQSoAltFRVxSYzMkJZWSDQBwQVErl9CEaRTglbtbXsKX5swnqcnNXBySKiuk42gwa4WYTsoOwMIgpQdmq6o2nZ2iHV1sl2JDIou0OxfwMFJUFuoOP7tJo1Gs0WlatjCCdFLm2TzpXJpizsVD6+jiy0tRxVRG2Uk9fPa6VBxbIvFaCcnJZHu9pdV3oDCOnrjmPcRe3OYUYtwNbGPypemBt29PcYTBJt+kFIzV9BN6Y8hpvJfLslz549y7Fjx7qV7K9+9atrIulaCqOjo+zfv59NmzZ1D1crtcWf6xA0I15e+SaidTHuDjbZ1DhK6vQ3EZ41HQvj2Kg7epNxB6+pC3JRU8tGE9m7cHQhyy0jkCi3HMffMoQNApHDimrgrNOqjx6pqO4SFiGYjCWpcUyMC1M69joIGemYSKTHHa34QGG5EIQIFEJFoEIyKYeMm6G/XEBFBVp+RLs+xVSlydVLTex0gVwmRSZfJJtOIWwPIbweMx09U47l6OQ5LhBKK42jmmANTHc5w56PdgEhJ7XUNYwVICrUnUkh9M8DUF4/asMH4u/nWpZyYDIFN8P1YvZuyV5lWy8rPUNGUdTd/eh5Hjt37lzRdS4keW21Wl0V3Fydz9lnyOT7S2UcUplr957Ppn99nr51OaJQ4riLG84krtV9fX3cfffdBEHQ7dIlBmSTk5Pd7mWhUFjTFRy3SkI5m/kcZCcmJmY4yCY/u76+vjv+/HjLJJTL2S2ZBIPV/KEtNaA0m82u5XLy4vZ9H1jcgGAu5kwoow7W5b9CRU2EcFHCAiTSyoCSes6lR5Kp3LJ277OzWo5kpXVV3nIhChACiAQQgfAQtFF4CNXUzqYy0AcdOoTK1QcpO9cjNdUHKiF9PUMTtcAuxOY5cSLrFBDBlK7qB5M6CW1XIVVA+HXwclqiioXuOTp61sfxSFsOXrZInwqJpIXfrlFv+lQnJ7nUici6V0hl+8inmqQKwzjx4bA7Rxkf5IQ/jnTyiLCClBG2bKJUFpSKO5gSZbmABXYWhdBzR91E2+n5eTaQ/U+higu/QSx0YEokr7dzQDDcmvRW3RN5jhCCIAg4cOAA1Wp1hpPqWphOLBYfpZQcO3aMCxcu8NBDD7F+/bQEcrVrR+a8b3U/YuLbKCvdLZSBRApPyzyJZ/6kr2WdYUsnk914NTFDNir8iu46+nWEm9WJmJ2ia3Bj51BYSKcMCD3jrXyU1w9BQ8v/g0lw87EktahjVK8k1Y8lqUFNy/lpx+qIAGXnUVGLwCqBnUKJnC502VmI2givRNaqkc1vZSCYIrS20qmNUfctqqOnGYlS5LwQLzdELiXxckNYshl3RwOwcoAeU1DUiZw+UEp/H7Iz/TNKuqgQz9qHYCsIkuS8oa89rCHXvRvSQ9f+bnr+JsxaJcONZj6/jflYSXxMzoJCCB5++OHuaMFKmC++Xr16lb1797Ju3Tp27tw552tpLXw4hBBLSibnwnVdhoeHGR4e7iZi+XyeSqXC+fPnAWbMXy4mA13K9d+IhHK1z9HrIAt0HWQrlQqnTp2i2WySyWSIoojx8fE5HWRNh3INkFLi+/6Sd48lGX4URStuty/lwJTsbtuwYQMPPPBA93nnqqIvlbnmJa1Lf45oXYjnCju64ozAUr6+rfLj5CdECW3CoJx83J0rdecBhQpRtouQga4gKxVLnARCtcByEVGdjhBcqB1jW+otuLR0py5qXtv5szydbDqF+CBV1vMz7gBCtmL5bFMf0sIpVErLTKVbwvKrSLeEE46BsBDBBNIt69vZBaywiXIyWCIglSuRyYZIMYQKfZrtgFZjipFKQHTlBJ6XI5/tkM4VyVqd+HCjD0IiagMWNh2EinRVXQbT0l0nB1FbHyBlS89PSh9peVjdn2eEzG5FDf+DRX9/Cx2YOp0OYRiahNKwpsx2uU5iZKVSYe/evRQKBZ555pkZK4tWK+laLD4mu82iKGLXrl3XvAmudnH3NbG1PYYY+VuwUoigCk4Byx/XooiwBqTjmb80OiEU2jhGCC37J+omhNIdQER1bQqWJIThlE78/GTGfBLsDI6sI1QJIhXHZXR31I4lrm5RJ2len47Pbl+c0Ja1U2pSiHNycUewEHc49fUrlSalJnUMlnWw4sTWSsXFxDKCEOkOYKuATN9WsrIDw+sJ2lM0OyGd+jiXJ0OEvEwqUySbdshkC3iuVosIFEQNbOVAaCEcoWcssXQia7lawSJiyavjIqSP8rJ6Z6/bB2ENldsG/U8u+Lu7UaY8BkPCQn4b87HckaeRkREOHDjQVWHU6/VVKUBmx0elFKdOneLUqVPs3LlzwRU4y0kol5t8rgTXddm0aVN3zjCRgY6NjXH8+HFSqdSM+cvlrie5VTuU0zt8W1r5ErUhbCJkG8IGnvQZjhoMbXkH3HcfnU6Hs2fPMjo6Oq+D7FoklJ/+9Kf5sz/7M44cOUImk2H37t08//zz3H///fPe54UXXuDHfuzHZnwtlUrRbreX9dw3NaFMJK5JVWmpi6yTA/31knRJKTl+/Djnzp3joYceYsOGDdfcd6XPf80LfOT/QzTOx4lOKz5I6BkYoSQuHT0nGbRiiWldf5RtpEjHJjOePgCIFKgI5WR0d47YSEcAKpZV2Sm+W/t79rUP8J64GypkC+w0IpzScrCgElfRW/qjbKCslJ7dsfShQ1ppLKKusY1y+hAEKLcfoTqx3LRBaBdxGEW5G7V7ateCPofwqygnixVUwMpiyQZYKQoZQT47wBAQhBGtVptmo87k5ctEUUQmmyObTZPN5ki5GZSwCaWjV5Cg4lnT+gyZsBVUkG58mLNzWGFVrxBR+jnV+vdqudgiLNShbDS0sYU5MBnWgvmq7kopTp48yalTp7j33nvZtm3bNXFzrVwM52IpVfQ17VBGHZ1MOjmIolg10Ua5JWzOI+31iLAajwPo9RZJQiiCjjYbkwoRy/GFCvS/IXVRjKArk1deP36jTqeTwrXqhCKDJVtgJeMAeW1sZme1MY+t46KIZbT0nknsrE5oRRkhVGxoFqKcUtfghlYDJVytMBGWnofEAvR+SEG84gihY7gd6ZUhjoWbylJMO4hSCaVs/E6TRqtNo1nnSvUqtojIZArkMoJQpbAJ9LhCPFvelb76cYIbjKMs3ZHESiGI4sH8CNxiLHVd+P05iqIFDTtu9wq84dZiMb+N+Vhqh1JKyZEjR7h06dIMF9e12NObxDjf99m/fz+NRoO3v/3tFIvFBe87n+T1ZjA72UvWkxSLRbZv304URVSrVSYmJjh79iwHDx6csTapVCotqvK7IQll2MGRDWhd7iaJImzquBw24uRRr3YSYVV7lPgT3TV3JLvOnRJCNrur58jf1109l0qlKBQKNBoNHn300WscZH//93+fAwcOMDw8zJ49e3jrW9+6Iunw17/+dZ577jmefPJJwjDkE5/4BO95z3s4dOjQgrG3WCxy9OjR7ucr+ZnftIRyvqr7UujtUK6U+QJCu91m7969BEHArl275kwOVuJE2HvfbjAYfxVqx1BuQRs4OMXY7CGtXVeFj8SByAfbjWWuab0bzC5gB+OxOUNV3zdqxDLY6TlDnTTpVR2KiIZq8t3OUSJh8Yq/j7epx/V9iLTJTuRrmazq6DmaqAGWNz2vGTbBSmFFLX074ccmPR2dnBL2OLdmUKpNaBVACaRb1gmvW4w7nTlEFICIk2gh9KFMRQhLgQzw7BReBkqFYQiatFWKTqNCrdWhOj5GJFIUsi4RLlKGSDuPpSKkU8Ai0i6xRLo7KgOkW0DI2F5f2Hrf5KYPxfb4i7NQQlmv17vOYAbDaphtLJEkk+12m/3799NqtXjqqacoleb+u70eklelFCdOnODMmTNrWkVf6L71ep1zL/8nUv450vl+cmmPdK6ILTxCGfHl3Hd4yG/wVHa3LnS5/QjZBLfclegT1mIZaCuONUF3BlDHWRdBByyPydEql07WCP0A4XgoL2BoeCMg9HqjnpEDelyzRTDVY15TAL+qv+7H7qtBG7x8LCdNg7BRVgplRUQi051VRM6ag5RtbeAT1bqyU+UUe4zH4jGAsEIqVSLlhFDeiopatAJFq1FjoubjdzqMBCkKwRUymTxZK8RO5kXtfOy47QEBehhf6blzK147svED4JUX/d0tNhJgZigNa8FS/Tbmw7KsRU3Heseddu/ePeN9fbUJZdKhnJycZM+ePeTzeXbt2rWk7t18mwKWetsbiW3bDAwMdJ2dfd9nYmKCiYkJDh8+TBAEM1xS51rDsayEUqnYjK3Z0z1s67Oz9BFhXRfIgkkAhD8BwmFg9CShlcc67emzYDAZq/9acXz09flXhdrMrFuE7Oj3hbChjR+DKd3wCafAG0Cuf/eMy+stfMx2kM3n8/zKr/wKly5d4h3veAef+tSn+Omf/ull/8z/9m//dsbnL7zwAsPDw7z++uu8853vnPd+QogZYysr4YYnlMvVus+FEOK6SLrGx8fZu3cvg4ODPPHEE/PKaddE8jp5BK58M5a5tkCk452KnjZ1EC7YAomNsrMgFDhxJ9IpIqIW0i3qF4iT75kvrGjJaWIYEU7qg0hUR9l59tRfIxIKWyquRBWuuFfZIXaihA1YcaIFghRKSZ3IKhl3TiPAiT8CBLryLyL9QhbaYbZbTbe0dEqoCKUisCxQQh+YLD1XqbpdgzCebQxQZPXPwsnMlNR6RTLBJOn+dZSDKaQ9hN+4Sq0jSHGVixfO4lqQyuTI5fJkMmksJ40iiruPAoELlqW/XxWh+p9C5e5e8u9vIUlXMj95uy6lNdwa9Fbdk1gHcOXKFfbv38/AwACPPfbYgnL/tY6Pvu+zd+9eWq0WTz/99KJJwWoOXEmMHB0d5cx3/4S7UqM4xY10amNcqQOXz+JlioykzhEyxf7O6zwSPkjajmcQrbSWi7olLUP1+nVX0tLrMohl8Agnjr0KVETYbnHl7FUsFVHsTxG2m1y52safmoDMkH4M4eqClJ0FIj0GoCJ9uFDxISNqo1L9iKCu/z3QO3VF11QtNvfxx4EUHnVAxYlvARLDM+VrsxwVIL3B6ceXQc8cZJ+W1jpFCKd0shpOIiyXrBuQLecZKEsunK+RzduoqEHl6hRX/BZOukQu65HOlsmkbYSbiyWvurAp45+nLD4I5UeW9LszM5SG681y/Dbmw7ZtOp3OvP8+W+I6+z0/6TCutHtmWRaTk5O89tpr3HPPPdx1111LfpzlSl5vJTzPm7HnsdlsdhPMZA1Hklz295VIOxI7nMBqXwApppPDqKONImWo46aS2jRNWHFjJTfdYAlbWpVGGBfNpI79SH3OViGB0A2IxE9DOXlENKXvnyj2gioID4iVevjxaJmMNxCgz9xIlFVAbfqw9jXpYb6CmxCCxx57jFQqxT/9p/+U5557jiAI1uRnPjmpk+dktnM+6vU627ZtQ0rJ448/zqc+9SkefPDBZT3XDU0ok0Wh58+fZ+vWrStKJhPWUtLVq19/4IEH2Lx586JW9atxMfSiq3DxdS0z9SsIKweJYURUj/+YW4Cr51+EpQ89It7fCFoyKmX8AvCJvH6sqKXngsK6nuGJ3QetYBLplmh3LrCvfYiSDKhbNqUo5HjmBLvDx3Q1PWppGZbsoERKy7SEE9vf2925G20xL2I5rV7boSxb71pLuphOVn8UFq5q6p+nX9W70fwJcEpx1T2n53Tihd3SSmMlkloVxpLaSM8SqcSYyI87jh28/AB9qQ6TEw7btmzG7zRotiMmxy9zObTJeRIvO0g+FeHlh7HjvW0imkJltqEG5ra/n4+FDkz1ep1sNnvLBXHD7UFSdT99+jSDg4NdM4NeCf5b3vIWNm7cuOjf2FrGx0qlwp49e+jr61s0kU1YzQwlwNmzZ5m4eIBHh6bIZnegog75wn0MyBZBaNGYGuENeRRPKXwh+faVb/Jo5gmy2Qy2JfS8uF+LZa9NsHNaRZGs/HByem4wnqtEZAhaLdodl2zOQ2LjpEHJGqEf6a6mUiBCXRSTLZJYqA3H9CFFiAiwddLpZBFK6c5eYobT3a2r5yyVX9EdyrAWJ4NTYMdzopanu62Wq5UiwkZIBZaNEEl3RYKd1sU4p6ClWI4bdzlDXcSTAaFIk0l5ZAqbGAwnCcjRqY9S60Bz5AS+8sh5Ci/XRy4lSKULCBo6OV//vyz593anuxgabi5SSi5evIjrupTL5TU/P0opuy6us43Geukde1quMWMURVy5coVGo8ETTzyx7L2sa2HKc8OJApDNnu5hGytskpM+ebfG1qEQVZ6k1WzSmrpEc7TNaOsKlldgkz9FeGov5FyEk+2OLBBFepxBSRCWNmlTShfXVKiTSunr+ClbceOiEY8WtHRjAQeEQCXr9wS6I4nSpmUyig0rI3TDBZA+QgkI23E3sonyijqmW1l9Xh1+x5zmZUtZO5fP53Fdd9mzpnMhpeTjH/84zzzzDA899NC8t7v//vv53d/9Xd761rcyOTnJr//6r7N7924OHjy4oBJpNjcsoUyq7q1WiyNHjsw597Mc1kLS5ft+V79er9cXlJDNdf8VdSjDKYb9l8G+NzaLKUCnZ8dXd29iEatd1d3CTkW7p4ZNvVg6ClCWi0IilN4LaSkV76WU2tFQhTq5lPFco2xzVE7gqohJ26YQSeqOTSGapCJ8BoIq0i12n1skiVcYV9WjaUMbZad09dzyYiOgbDwbNPt5+1DyMqFd7BpViHAKEjt/t6S/53iORzp5LH8CnCxWUNcJt9KdBCVClEK/wBG6G2q5gIMUEJLG9nKk0yWyxQDFVqKgTqMt6TSucmk8QowexMsUyKWnSOeHcO7+4LL+BpPu+kIzlKb6blgJvVX3U6dOkc/nyWazNJtN9u7di5RyXgn+XKxFfEyS2xMnTnDfffd1i4BLvf9K4qPv+4RhSOXqJd6+4SKe6yCjFkrZ3e6j50Yczk3SqncIbRsX2GedYePk3URXKqRdm0wmSy6bIpO2EJaj51+6hj5Z7fDq6N20OCWQDexUmlQKGnVBviioNz0QLla6rBMrYceGX4kk1dYdT2HphJOk4+npA5TlxEmdA1GIsD0gQlgpnZwCyvK0E7VTiAuGjk5YHa2o0NJcKz40uRDPfIuwruNtvBalV6GinJKW4jqFeKWU3lcsnYI+MLl9OEpi920hpyIU6wg6bVqtJs1mk6mJKaSokE8L5KYPU/AtMvbSOjELHZh83ycIAiN5NSybXr+NS5cudefwVspc8XEhietsVppQJs8RBAFDQ0PLTibhFuhQRm09Py5buO1zpBAwPq6LXtKPZw478UdfjwckCZmK4m0EWoVhEenxLEKE8Mg6EZmBYQb7A0K5iXazysXLksnqVcau2hRSEV5ukJxXJ5Ub0F1JOx+bRObi+KwNzbB0YU1hxSuVhN7fLmwtZcXrNlGssIaVLiH8GsrNx3veUzrZFG583Rkd552M/v7csm6KeKUe9ckEqvQQlN86549uKQqOtYyPzz33HAcOHOCb3/zmgrfbtWsXu3ZNN1d2797Nzp07+Z3f+R0++clPLvn5rntCOVvrnmTdq3FohbWRdLXbbV5++WWKxSK7d+9eVkVgRQemqI0z+jfx4UBo6ZWM4t1nHZQ7ALKlK9lRHemWcdR5lFfAChs9uycz8V5ITy/txtYzL8IGJVAi0i8iYhlpvBftofQD9NtlIEJZNsrvMHq5Qt9QHmmlEbKpO39RfXqHm1vE8meu67D88XhtxwTYRYSc0hX/qKXNe6Svl34TIkUKIVpIO6+vwynFErH+WLqlO43SKSAiv2tyoQ9pep4SYevdaE4Wwsn4IBhb/0eTKDI4og1CYEUdfUBTPrabpuQKVCGrDX78kGazQbPV4tCV7XQmXp+xhHYxe+teic1c1Ot1crmc6VAalsVsl+vksHP58mUOHjzIxo0buf/++5d1cFltfJRS0m63OXv2LE8++STlcnlZ919Jh3Jqaoo33ngDATwycB5PdIA0qLZ+I5cWqBbK8jhS+xYCRVEpGkLgEXB1sMbbvIdptANajRqXrtRQ0QSZbI5cLkM24+B5ujOJndNVaiejk007iydqrL+njyunLtGY8kh7bYrDLvlMEyhoaWly2HAysaur3umryGpTMjLx6o9MdycmUSNetzTVNXHQc5BVwMFTNUDFUli9K7N3565e4xQX61IDcZezf1qxESXSVz3WoLudnu52Wg5EdYTsYKsWhIFOeGUHYemDkXAKeK7AzQxRKgVIsR2/3WBSDnCxXmTy1VfxPG9aitbfP8NRuJfFZszBmJYZlsdsvw3HcdZ8D3myS3fjxo0zHP0Xun9ybUtlbGyMffv2sXHjRlKpFLVabUXXvmYdSiW7iWH3YywpJeroXeTS10oz2dGO+pHu9CF9lJ2hr3YaO+jHsjJdIxoldKMB4cRnz0x32wBEOv5aXqxoc/RcuJXSZzk7jYU2mLRFRDbfh2CMjVvuR6mARiug06hwoRpgqyt42T4KqSlShWFSfkXPtfsTeg4yrE/vXo/3Aks3p939vb54o0EfQjYJrTwp2uAOdZsdotvgqcTNkmQTQqjjq5LT3VCvXyfJ6Q2o9e+b93exlD29a+XB8VM/9VN86Utf4hvf+MayuoygnXsfe+wxTpw4saz7XdeEci6t+1ollKuRdCmlmJqaYnx8nPvuu4/t27evaI5zWc+vJJz/c6zWZT2TGPlxxVlSr9ewLJtsxoklWVE899jBJ6e1+m5fPFdY0ImbndUvcDte5i08/ZiWjYhkjxufAgRCWDjKYqszrJNNIQjcCD+KX9xESDsbP7c+JCXOh9LrQ0QdpFdGhE2kU0QEDe14GNW1rCycAit2irVSWtZgeVhhDSElVtTU1XHh6tlMKeK9kEL/HKx4wbiwECKaToTRnQnl6he+7nJOLy2XTh7RqujgFdb0jFPY0bErrkwJKw1RB8/L4zlpind/H8N9b+vaW1+5coXjx48vemhK/o7nCwhmPsiwXMIwvMbl2rIszpw5w+Tk5AxnweWwmvg4NTXFoUOHUEqxe/fueZOHhVhuwe3SpUscPHiQu+++m+qJ/w87nIRcOXY0deN4CSht+DWc2koprFPzA9Y7FqGbYr1ysB2HUrpFsbCedf4kHZWmVZug3mgyMTYCboZ8OkUmXySXTWHZru74KQVumWJfSPqtdxM0GtheitHRE1r5ETtgC78Cdi7ucOZ0cc0pIMIJsPMIVY8r2clOy2SWs2fO0h3Qs4leP3QmCKxCnEQmpg56ryXx/I5+3rqOr7IeS7gifRhTke5oKglWWo8eiHwcR1NxYS4iUGlE0gmNmrqwlxTnwqp+Tr8KVhorqpJO50jd848YtjMzFnafP3++6xaYxMpyudx9L1+KC7YxLTMslaTY1uu3Ydv2ooY6i5EU7ZYqcZ1Ncl5cStGu18zswQcfZOPGjZw5c2bNXLBHRka4evWqfj0Ws7jRVQjbCNkk1zqO2+ogLh/XI1BK6URJKd1RBPQ5KYi3CLS0aq67uiiKnf3jJkXiZm3ndCNAZHBlA2UlCVjSeIgbE04u3nueimfQPUgaHkqiJftWHM9iFYqw4oaJGyezEUK2cNws5YILxQLrlKLt6/Vy1WYbf+IMwklTyFRJ58rkogmsdN/0NcVx2gqmYllqfGYmjBPecSKnrH+uXr+eg3eK2vHaTncbOPqMaYOUunljufHomVbMqY3v12fyeYiiaN7307UyLVNK8dM//dP8+Z//OV/72te46667lv0YURSxf/9+3v/+9y/rftc1oUxedLNnJYUQq6qew8or8GEYcuDAASYmJiiVSiv6YcMKEspLfwP1k+CksWmjUKiwzsjoGK1WCyUlUtnkcmnSuRL5jAdORg8S48TS0pSuitsFIJiWmLqxftvJxTruaZdWZEe/MFUI6BeCEAIkWDLEJpYkAFZsXKM7nAr9P0t7Vliuzk3tHKBizbjU8ioV6sNU4hSbDD0rUEJhWSJ2cU3HJkJJpzGP8Kd6pFsFPRBt5fValGTeKTEqsmKrfycefPb6EEoS2QUQ1Xgw2pr+GUQt/Rxdm/xJZPEtqP4nEXCNvfVih6bZJimzSTqUBsNSSQ4GSTJZq9VoNptEUcQzzzxDJpNZ0eOuRPKqlOLChQscOXKEjRs3cvny5RUlk7D0+Jgc6C5dusSjjz7KYLbJmcNvgNwOfmxmE7b0TLfy49nukO8tfQCQnDtzhoHiEPlsuhvvlFvSBxG3SDqcIjWwnr5wisjegF8fpe471MfPMDri6Bnr3AC5lEUq349FhJdK4aXyKFyisax2Y3XTOumMYw9OPp6viTuRltudqyRq6I+0ATues7QRUfwxWQmiFJJ4T6SVRQlLu9Mq4v2W6HgrRLz30tKHGWGD6uh4GsXS1zCRvlZ7RglKEFTBKWKplo7hMtAKFyQ4JRTEuy6VTjZBzxZt/IA+PHLtwm7f97urAI4fP0673aZYLNLf308YhvMWZ5ORgJVY4RvevMw2b3QcZ0FDnaVg2zZBEPDqq692C2fLKXQk54DFYtx8ZmZrsae3NxkeHh7m9KkTVKp/Q9Gtk8nkSOfyZBp1XViqVXWiI6PYgDGOXaiu2aKIkpnASW0GlpiHJZ0+Fe/5VTZYDkpYKGwiK6dnF92SPr+5+bjLFyvJYgMy5fbpsS07HxuPeXpnupUBEcRJZrxL1NIFAwXYVoQQkZ4njw0fERZpzybtFOnrLyOj9bQ6bVrNNhMTk1z2fdLeFNlsgVy6Sjqb000Ny9HJYbyZQBtFKmTUwVZpQOrOo+XFctdYTWHp86wQQhftkvGzsK4TT38Stf49kF64+LuUDuVqz5DPPfccn//85/mLv/gLCoUCIyMjAJRKpe554tlnn2XTpk18+tOfBuBXf/VXefrpp9mxYwfVapXPfOYznD17lp/4iZ9Y1nNfd8nr7Ep1UmFabUK5kseo1Wrs2bOHVCrF3XffzcTExIqff1kV+Cuv6GTS60f4DUIy+O0Ol0ZGcSzFls1bEEIHn2arTXPyClevRKRt3V3s1C5hFddh+ZOxFKoR74fsaImpCmJpVKhNdaJQJ2Uq1AchGeodleikUjseWqjQR6l4z5jlda2RRdTqymmxvHjPWgpCvTtNxC6EWpcey4SFrWOTrY0jpCWwVERk5bW0wC3F0qxyrHfvizuN5WlpQbynUu+KzMf7KdMgG7F8AhL3WL1mJQ1RhFICSwh9wCIexibS61hUbLxBiMxuQW6Yu+Ky0KHp2LFjdDqdroPr5OQkhULhmoORMZwwLBfLsrqx5Pz58xw5coRUKsW2bdtWnEwmj7scl7gwDDl06BBXr17l8ccfx/M8Ll26tKrnXyw+djod9uzZQxiG7Nq1i4ynUCf/iEh4ev4mOxDvk8xjdSbAK3dl98KfACePLVu6WixDsONkC6ETNERsuhMi3QEs5ZMqbiItW6jBIWS7Sr0j8OtjXJ60sEcu4uYGKaQiUoV1eNQQKOywilDDcZKa0/E12fVrpREi0FV8Kael+sLS14QdV8NdiEKwXf29OTktKUPgqiYIqavnyWoQtxRLbIs6XibxPV57pLz+WFWSdD1j11enDFFsdZ9U+aMGQkls1YFIxHJYLce1okZ33Yl0y7oiX3oY8vM7X3uex/DwMMPDwwDdfWoTExOEYdg1cEqKcckYwFqMBKxkaTfAF77wBX75l3+ZM2fOcO+99/L8888vu/puuPHMVcBdi/Pj5OQkjUaDrVu3LkniOheLxbhqtcqePXsolUrs3r17hiJvtXt6wzDkO9/5Dr7v8/TTT+M4DvbYlwmvFmi2PNrNKa6eqdKuR7g2hJ2IoaEUdqasvTtiKT2Wg+5QqtgAUe/JFVFrWp7v6t3hiZxUqxu0oSJEWgwnYnMbJ0kuy3FxT8cULfGvxI813uMVok0kZbcZktL+G7YeKZC2h1SOTmhFGLu46ngqorbuooZtbMsj7ylymTJDfXlCHJrNOo1Wh8tXJomiCTIZj2w2Ry6bJuVpw0sl9Do+hKX3DRN3WpO5TKcEMt4BT4C0M/os6fXsW5dtVOkhVP/bFv3d3YgZyt/6rd8C4Hu/93tnfP33fu/3+OhHPwrAuXPnZvzNVyoVPvaxjzEyMkJfXx9PPPEEL7/8Mm95y1uW9dw3ZQ/lWkkWllPhuXjxIocOHWL79u3s2LGDixcvrskeoUWZPKz3TSZdQkcfEk9fGKWvVGRoeB0qCpEo0m6WVDaCgXXIsEOj1aEzcp6xiQbiyiHcTIl8eoJ0fpCUXdGV52Q9iD8BTlEPFTtZLQ+1YsdWK61fpEnCZWV0pcrJo4SjX+xxgiaiTvxiauuOZ1Dvzm4qp4AVJvvPqjM+iqCCdEqxZKuIFTVRdhZLdohEnABaaX0IsjJYKKRbmjYNIkR6fSADLamVoU4mideMCEfLaJ187GJbxAqm9GM0R/X8ZDCuJbCJU2J8wFNCf5Tr36cT5CUw16Hp0qVLNBoN9u3bh5Ryxv6kXC7XXRtyvVjpYcpw6yKEIAgCDh48SKVS4fHHH+f8+fOrnhFazqGrXq+zZ88eXNdl9+7dpNNpGo3GdY2PiXNsf38/Dz30kD5gnfsTXR0WKW0gY3n6wCB9pNuPJTsot1/PPHq6ECWdDFZUA2t97CKdgyCWKYWduLIstSuqEAi07EqgsFN9lFISVepjSIW0OhHtZpVqM8CfOImws4ioRT0o43aqsYRqsmsippxkPVMyZ5PsLytOjyWoJJ4G3RUjKjWou61eP6pTwbeL2jQnKbI5pa5iIymuaQlsIn1NaelrvO8XYekim5I6VlquVmgIq+vS7ZNF2BmUl9GdCK8/XsnUB2FNF+GCSfDKqPX/v2X9rjOZDJlMhg0bNjA2NsZDDz1Es9lkfHyckydP4jgOSilefPHFZc/izmYlS7tffvllfviHf5hPf/rTfPCDH+Tzn/88H/7wh/nud7+7oPOh4dZkNQll0tW7cOECrusu+8C8lOtIioNHjx5lx44dc45UraZDGQQBIyMjDAwM8Pjjj2uDydFXoXYCJ12imArxnAL1qxN0ahO0laJTu8z4uEe5fJFUbih2vR/CDurdxFA3B3RKoKWqISrVrxNQrx8R1oisApUzF6hUHFLuZULPJuPWgFhJYmf1mIIdjzIhUG7s1O/1dRUkusNX6M6N6+Qy6WTGclkrD1GTCAdLgLRzWCqIk9VAbwqQLd1kCWv6Y7yf1wmmKBSKFNOghofwW1M0O9BqTjE+MYktJOlskXzWI5MrouREvGc4MY+MFXNd35AJlBuv57MzWCo29xEBOEXdnVwCC7lgB0FAp9NZdYdyKYWKr33tazM+/43f+A1+4zd+Y1XPCzcpoXQc54Z1KKMo4vDhw4yOjvLoo48yNKStfFe7mHZJFabmebj4RS0dCqoop0B17AwKwYahIuW+jSDbhMLVBw88BAJlOViuTd7Jcnl0nHu2biOSekl1rdXh6nmtF8+nq6RzfeTlFMIrxA6qhR7ZU1LNH48ln/G6jjBZ19EhEjaoAGl5+sXqJDOccbUqpR1bp010BrQc1e1HyMb0ISheT6Lcgt6F6ehDkFTgqCmEKmopmOVhxcu7LRFXxiypuwy2he4uxH+Wlt55qUQyCD0QG1T0QzyDJKIWkZ3HZjKeK40NfVDayMPOYoV1oo0fgtS1Ns5LJZPJ0NfXx9jYGE8//TT1ep1KpdI9NP3d3/0dr776KtlslosXL7Jp06YVP9d8rOQwZbi1mZyc5Nvf/ja5XI7du3eTSqW4fPnyqgtuSx0JuHz5MgcOHGDr1q3ce++93Tc727ZXvWdtrvjYe9i69957u27favRrMHUE3KI2qZFlCPQqDSUDULHckwhiW3fp9gGTRE4JLcPv1/bwbhxrnJx2O3XzuitoZ/TXRSqW4MfJJgCCjGuRKZXpKwukHKbVaXHxUsDU5BTj4xGZdJ1MtkA+08LL9OlDhdsHUUPPHQU1cLVkDDs3PQfpV6adZe0MIpqK1RRtpFLYsUOsUFFX4qqcslZ3uWW08Es7tOoZpmSXr6PdZO1Mt8imDX9KMxLeyMqTooYgiuckvbi6H+8UtjO6Um85qA0fXHLRba7fLehRgsHBQbZu3dpd3v7KK6/w4osvcvz4cR544AF+5Ed+hH/9r//1sp9jJUu7P/vZz/K+972Pn//5nwfgk5/8JF/+8pf5z//5P/Pbv/3by74Gw81lpQ2JxDFbKcXDDz/MwYMHV3UdcyWFidJjfHycJ554Yt7dfyvtUF68eJGxsTHK5TKPPvqovoapk9hjL+lRoli1VqtM0WoKMqUclgBLpsh4UBws0KmPc3k8who9gJMdoJC6qk1trNj4UemZR4j07kcRG9DYBcZOX+XSaUEm06FRTyGDq6Qz6ymX6nH3cVI3NIKa9gOJwvix9Mo5cHV866rHYnMct6wLXN3xp7yeHxcZUmjzIiusxSMGndgUJ1npEXRXMenCY3vaa8MpIoJJvEyRlDNFubwJ/EnakUe7XqEy1eLK6AiRsBDVcaCPXDbCcsszDTOdXHy2tLXhkIo7s1EbueWH4pGExVmKadnt7IJ93RPKuQ4jayFZWMqBKbFoFkKwe/fuGRKytdzTNid+Bc7/uV6AHTaRIs3Y+aPUfYsUdQrFHYhgXK/qaI8T2ToRw4uNGZycftNHgozwvBSe20epz0KpDbRaLZrNJpVqlcudgEyqSjZXJJdpkMqUsWQndmxtxCY6tWltu6M/IlKkqWtZSTAxrXl38rGOPqflClYaIh8l9DJX7eAaxQ5actrEp9tpLEMUxEGjjhTxOhNs4t0fsYmQdm4VYb0nEdZyNuWW9M/QLiBkLZZANOLFtPHgtop0N0Pp7qeyUiAclOXqXZZ2Hkv6RP1v11bOqyQJBkIICoUChUKhe2hyHIf9+/d3V+J8+tOf7h5g1oqVHKYMtzae57Ft27YZa5RuxEiAlJIjR45w6dIlHnnkkW4nPmE1e9Zg7sNWUtwbGxubcdhStZOo8e+CNwDhFJGd14YR7gbwp2JzmlrsyBrEBSMQSiJkoGdrVErPjSdGEnaxxxU10MmmCkCUdDIZV7+x07qj6WTizzMQtbDsFLmUwrMlG4by2G6aVmOKRnuKS+MNlOWQyaTJ5ovkMhlc2wavqFcaue6seUstA+va2Sd7hWWIpUIc1QEi7bRoZ/X1uHlEoOcj9V7fAqi2LprJDkok8/NlBDIu+MUxWCZVfB9l51BhG6lsLCJ9PULow5ul1RwqUboMPA25Lcv+Xff+fmGmaZllWfT19fH+97+fer3Ob/3Wb/Gv//W/7h6eVstSlna/8sor/NzP/dyMr733ve/li1/84ppcg+H6Mdf5cSUNiV4X1/vvv59Wq7UmKpDex2g0GrzxxhtdpUcqlZr3vsvtUPbOmg8NDXX3XavOOJz/M5TlafWYU9Ar2JSFZ7UIhN4Xbtl63rFQyFMoFBiUEZ1Q0qpXqLUCxs+fRFlpiukrpPMDZNMdbCcuwFkOCguJoDraJJ1VeLk8aSSXzmRo12OX00TiGpvx6G5hXp89nayONVYaEQbx3vF4hYiIu6J2FlDTscvJg98kFCktS7VTOkm00/ocG28mUFYq3ssbe43EcVa5pe7IE6oTK0fa4GTIWAGZgX76lERGinMXzuOIDpUrFxnzO3iZEtlsimy+RNpzEbFxpBIOkKyIaqOG/wFkNi7597gU07LbuTlw0ySva3FgWugFmQSQTZs2cf/991/TZl5th3JB6+awBRf+snvw6ag0F8+dwXbybN86yLGTZ1FRR3f/woZ2UfUr4BW7LXsRVFFOHk81QKh4XUgaQj3PmEsJspkyor9IEOk/xla7xcVqBdRobJWfJZvL4woRy61ClFfWrltuCdWuE5CFqBNLRVuxo1UzrmLHjoJhLTa8aINlaZdaKRCWpYvnsaxMS/GjeJbJAhwkDpblgOUhLRtLRUiyWERIqw9LdbryK+n2xetSijrJtDMg67FeXrvECiV1t9JKQdRCxPs7hRC6Q+vk9W4gK4MVtVCpQdTQu1b8e+5lvmBgWRbf8z3fwx/8wR/wjne8g3/xL/7FsubXVspSDlOGW5tsNsv27dtnfC0xjFgNC8XH3kr9fGYUq00oZ1fgW62WXgkSF/fSaV3Rlf4UXH5xeubQzgNjhHYZEJAahKiFdMp6BjBZ92G5INtYFvpgIjv69rKj44XUSaLwp8DKIZSexUS1dRIpk3nE6WRTS+8DlFXWC7GdEnAJ6eRJ2SGF8nqKUQ25YTNB4yr1jk178jJXxywyToCXGySfkqTyQwjZBregC3F2DkGnu2hbWbnYXC2HFBMEdgGErU3GeuRlyi3rGVInr51Yndx0lzOs6tisQkiMfaRC2K5We1hprf6w0yghCUQWnOz0Oqdk9tLp10XCzBbU8Ori5FLWKpVKJT70oQ+t6nkSlrq0e2Rk5Bqn5HXr1nXNKgy3NrPPWss5P/Ya1zz44INs2LBhxmOsVIEBM8+QIyMjHDhwgM2bN3Pfffctae3IUjuUyax5EATs2rWLc+fOafVI1IYLfxHHs3Y8+9hAuSUK2TEm7RS0rtBRGbLpKYr9Q4iwg7L0vGPKtUn1DdNXjpBiM+3mJPWmT6UywSU/IucqMvkS2bRFKlPAkhLH8Ql8hYMiwsa2O0gK8b7dVCzdj00iPb0GbzrJTCSphTjJ1DJ77S7ro9zstJ9HrMBQVhqpXHCLejuAHasqLDeW+6MLhUoCYfxRF+wUqVh+m9JfsxyUyMT/7WnVCz7C1f4cxfI6sl5EQIZ2bYy6r7h68Tg+GQophZsdIpe1cdNF7Uab2wYDTy3rb2ahhLLZbJLJZFb0fnurcNMkr9dL0iWl5NixY5w/f56HH354XhvotZC8znl/GcGFP4f2KNgp6lNTXLw8SrmvyPDwelBCV5TjN3xt497pmSOMzRW8Poha+CKnW/ypojZxcLKxqUM2TvzSuKJDuZilnHdQQ/34rRqNjqQ+eYWrY5dxHZtMvkg2nSKTK2IJgUCA5emKup3TL0CRjx0HUygVIWwHhUJYtpZbSdBdxlhyECW7IZvxnOXU9I7IZMmrkjiyhhIDWGEjnqtsx060cbdT6qqSQHblbNrBVoHjalMvQn1b5aNEdoaxT2jl8LgM1kZt7y88nZBaHnLDB6cltKsk2RM4H4lD12rnhJZ6LUs5TBluP2zbptVqrfox5oqPY2Nj7N+/nw0bNixoRrGSPWu99Dp5j4+Ps2fPHtavX8/OnTu7j62URJz/M1SnGr9GFSiFRYgdVkGmQLWAVGw4k51enUSAskpIKtpl0M6gNaLx92zHhwnLi01xbD17KTztHmunEX5Ddz/DcR2/gpr+KBvaeVAGREqvFNFOqCHS0omYm99If85HDQwzHDZptCM69auMVCRi9CBOpp9CepxUYZh0NAG91vX+ePdwhQxxpV78LaIe0zQ7qw9HThElhD6UJe6v+iccG6FZ0/Plc+y6tIIJQpUmRV0nn8lqJSLdrLS1qZpa/32xpHjlJPNB8x3Q13rGfKlLuw13FkuVvLZaLfbs2YOUkl27ds3o/CSH9pUWzGD6DHr06FHOnz+/7LUjS4mtk5OTvPHGG5TLZZ544gkcx9H3jUK4+CUdy4SLwsECbcZFRGZoPRuY5OwpH0/VGdy8hXI/sTpB7+zWhzoVO2i3yeSKZLIRDA0TBW3qrQ6t+iQTk02EnCCby+MVbFojHeqVCCUhlbUolYI4tug9jVYYaJdUqZ39IULaBb2xwBvQyW/cPJg2IStMN1P8xLxnEkUaWwTdgqOyU7Hzfyb2BbFj52o7nluP5f92vudxZyrfRNTQOzPxY5+OEF9lEbaN9Mo40ic/sI1C1EKK9QTNcZodRacxRmUiwrW0M7i660P0+f6ynNAX+nu7E/aY37aSV9u28X1/xtfa7TZ79+7tVnIWevO6bh3Ky38N9VPg5JgYPcv4ZJONw/0UimWdgIGWaYWt+NDj64Xdsh2vBQmQIoWlQpSVxQIip4gjmO4yumX9onKK0yY6sQmOCBukcn2kUg1kQeKFdbLREO36OFevTqEun8HNlimkBXaqhCObOnmM2vFAdYCyUyBld02JrgqFKCcVz9xox0Fp92FJrV+3VJwUy2bPXGUJW17truzQ8tZJ9L7KuOup2vpshKUDg60DBY62qVak9SHSSuu5SSvd/dkIJVFOAaUmCJ0iWDZYRb15U0VEG94PXnnFv+PZ3AjL56ViDlN3LmsxYz47vkkpOXHiBGfPnu3uQluIJG6vNEYmz3/69GlOnDjBzp07r1murEa/1l0Nog8fiTMqyGQ1Bo52KxVuPAOexwrG9XxgqF267aiuJaBRJ97jG8cHmcj0k/kdFVvOO1py6sQGElZKF+0sJ47RVpx8OjiqDrJvuiModVIqVEt/FG2E5VDIuRSyWxi0BH4Q0mw2aDTbXLl4CRDkMg0yuRy5dAfXzcUVfd1lFSK27peBfk8I2/GohJa+WkG968Sq3LJ2f3W0cZruRGpjCC3z6gcirXqREcopIf02kcjq70t4sfNrU8tqwynk0Dshs7SD8EIsdjhfy/i4nKXd69evZ3R0dMbXRkdHl3z4N9xaLCU+JoWz9evX88ADD1zzd7laBUbCiRMnEELw9NNPL6tYspQO5YULFzh8+PA1xj5CCLK1V0CO6vVFnUmEcHUtzbLjOb+IwlCZDXQIZZ6+4QJg6Xn0+Jyp92Cgi/O2F3cL9RnNdvKUcopScStK+rR9SasxSSDbkI8I2jbZkkMmY2OnPa0aw0LIjn7+sKaLeWFDm9igPUIQPt39j04OoaK4iaI9Maww2TU+pWNcc1zHcF8X5bp+IInLrAzASYEM9YaAOfan6+R1ep5SJ5mV7pymsrO4qgZqQG84ELY+7woXIQRefhAvp6B/ECklnVaDy+73cPXSFQ4ePUU+n5+xXm6hv6eFTHlu5PnxenHHSF7Hx8fZu3cvg4ODvO1tb1s0SKxFQnnN/a98C2qniOwSIxdO0/Ijtm7eTCqb1weWeMm0bSn0wLM2hhCyAbjx3GJGzxLaOYSq6dGbxNpddpBCV2ik8LDiXUJ6RUZRrw/x+hAqIHAL/NX4F/AV/HjfPyZfuIfBqEUnsujUr1JrQVg5Q2RlGL98Ei8/TN4bR6T7sPyKnu30J7SLqj8B8ZAzVg5kQye7shN3DZPFtVHsbBib+qAIrDy2sLp6duEU4u5nBqWElltZnu62uvoFL92SNvhxilihXiXSXfAdVPXPDAmRRNgeImhiQTyQrp0VZfkxyO9Y8e93LpaSUN6ItSHLOUwZbj/WeoZytmRqKX+jS92zNh9KKcbHxxkfH+fJJ5+8pmsvp47A+Le1OkK29Ufl6wOS8JBWNt55S9yBC+M5wlDPFUZ6n5nDCNJeF+9dLGoXPid2C+x+XoSwqrt7shUX4No6ZlkijmGhdu1LZhxVBAgikdb7G5NuILGcX/pgydggIgVh/DFo49kZvKygXOhH+Sna0qVVrzA1WWN8tI7t5snltbugFC6RXdLmQ3Y+lt6mp6W4sseJNTY6U3ZWx0HL0/t8sYF2PKfpgPQR8SyoVsC0kMIBJMrNxQW7MogQlbsbBnev6Hc8mxsRH1eytHvXrl185Stf4eMf/3j3a1/+8pfZtWvXqq7FcGNYjuS1V6H20EMPdSWus0n+TqMownXdZV9TpVJhamqKYrHIk08+OWMlyFJYqEOZzLdfvnyZxx57jMHBwRn/nmoexmsdg75tsemYdn8WdpzYSYUUjnbqByylxwBQAcrJYMlAxzQi3akUkS4+2em4sZDSCZ2di89+BbJ2m0z/IAPlDuGmLbQaVepNn/pUlSsTDRrNgEw2Rz6bJpV2wBJxLHW0PBSBoIkuEDb19aB0siiS24X6GpI5ShUSOWUspteLaNOe2GPEr2rVSmcC5WjzRWVnsOS08k0/j4x3t8v4caPuGjuSXeeALWRcpNNFva65kKtnL5WTxRIRmc3v4q7BXdyFXi+XrEw6evQovu9TKpW6a5MKhUK3EJDsDl1ohtJ0KFfAWq0NSTTwp06d4tSpU+zcuZNNmzYt6Rey5i6v1YMw8W06vuTixTNYboa7tg9jO6l4XUgu1ncLJB5SpHSlGBU7//nx/GL8USZy1pY+6IQ6ybSCSte+WM8Lxu39qBYbNuidZUcbrzMpW3jAXv84T2QeRjk5PDvE699KkYCWP8zFc6dRqUEmq+Ncbkty6QqpbB/5dIVUrk87a/W6d0WTsUxsUtvXR1P6RRu1YqMcpQtfjl4VIlSk5yyF0F1PkdKuisJBiBBFQctZnT6958cb0N3P2FVWOSUtAXOyejZI2NPP5+p1JtJO46pxsDZiBRPI3HbU4DtW/Ludj5udUK7kMGW4tZlPwbFW8XF8fJx9+/YxMDDQlUwtlZXGyEajwYULFwB45plnrjGnUH4FceGvtMFBMKljo39Vd9mCq3pXcVgFCtqgJpaf4iQOpy44NgoIRAGUpQ3IktUbMnFfbcb7HOtaip8UqYLYATWs9Lhhl+LYWoJwEmUVECoxs1Fxoayn8+n1x8qJTM++Xx/cctz9K0E4hUiVyYaTZIY3MxBMElob6NRGqXUk1ZETtCMPT3SoTNjkPIWbKeqYaXm62k8yM5TVc5ZOCYVAuC666xqbnclO3HlsdSv8OEVEOIkUHmlR1462fjWWtrZAiFjquvwdfHOxWHys1+urdjBcydLun/3Zn+Vd73oX/+E//Ac+8IEP8N//+3/nO9/5Dp/73OdWdS2Gm0PSTJg9gtIrcd29e/eC3R4hxAxZ/lJRSnH27FmOHz9OLpdj48aNy04mYf4O5ezdvLPn21X9HNn6d+ngATKONx1dZFMBiQvp3039Dyzh8bh6kAA3lnsWsPxqXOz3tWxU6Y6hsvXPQbmpeMd3IY6jiTlYrtvdc4IK+XyRQqrGxSBL2gPLzdNq1jhfqWIJSOfyFHJpMtk8jq2ltfr8hpbbx6oMofTsNyKegQcd4y1Xj2GpEIWt/9vN67lwrzw9A5+4XMfu/jpmqvjcGcXeH0G8Si82A4pNIZWlC3BCRbHkNYVy9aoo5ZV7/Ewmp6W4+bu0eVmM53msW7eOdevWoZSi1WoxMTFBpVLh3LlzCCHo6+vrdi+BRRPK25mbInl1HGfVphPJ4u7XX3+dZrPJ29/+dorF4uJ37Ln/aroAMw5bjbNw6S+ptyLGLp4i37eR4b6srhaHdV1pD1ux5XsnrhbH1WSl9IslWZnhuPq15mhzhVBktVLLLurVIo6urmNntQOXnYmTyUzc4cwi/at8p/U6ZRnRtCyO1V7hEfd+HJqxW2odZaewpY9SMNhfgIEyURjSaLVpNepcHG2BqpDJ5shmJdlcjpSw9UxPbG8vkLqakwxYCgdkMlfZALeALRsIazDuevasMgkmdOcxnNDfg2oBOsnU5SKFQukAmTy2HcVBMELZenWI8vpRaozILmvJV2oYufEH4hmBtWWh6pJS6rrvoVzKYWotOHPmzJzJ6rve9a5r9hcZVs/sCvxaSF6T/Zbf/e53eeCBB9i8efOyK58rSSjHxsbYt28fhUIB13WvTSZliLr4P7SRhGJasumVdZfP7cNSY0T2oE423RKiUwUrGxuVxTt33TwibGCLABHUEDILykeI2L5dqen1F3ZezyE6fQgRaSdUFeikU3biDmBTP3Yw1TMPnsOjjhLr4yS0gOVf7fmY1wU2K55nt9O6c2mn49GAYnwQ6gMk0u3DVhHZ0mayhCg1xMTVMZpNm3azRnW8jrAmyKUd0vkyWU9gp2Kpa+/OtmAyHiNIuq/aCVcnlSk9z2TrOKTcMipsEVp5/f6TGF5IHzX0PZAaWNbvdyEWknOBnqFcrcx0JUu7d+/ezec//3l+6Zd+iU984hPce++9fPGLX1zx7LmJjzeXJIHr/XtbTOI6F8t1+g/DkAMHDlCtVnnyySc5ffr0ilZ/wNyxtVqt8sYbb3R3887+HpRfhfN/AirEki2deKkO2C5E7bjbFnFRXuVIcA5bwVZ7B6UInRiGNZSdzBSWsIKrM2SgyE48iw4IK173IVBe7JbqlWMJaSleU1TAUSNY7hB9ORWv5KjSCj3a9QkmKh3Cy5ewU3kK+TSZbIFMyu0qTbASpYetZ+QtT28VsDN6ztHJYAV1LBEhOlOxusLXSaHSZ09lo8+IwtHzlIn0VgW62xk2ug0RYrNL3BJ0V+lNIu08Nm3dtZX6folLrOiuNglR6WHUhg/Ne7YUQpDNZslms2zevBkpJbVajYmJCUZGRjh27BgAJ0+eZGBggHK5PKM7Xq/Xb4jC7Xpy0zqU7XZ7VY/R6XSoVqsMDQ2xa9euZcsWkgrRSl2+uofAzjhc+CJXJhpMjl9meMM2ijk7Nnmodvc/Kk9LR5VXIh3v1aFTBa+o95dZGS1RcNJdFyvtwiBQWLqKLNL6c1ub5yjLjecI9WJsLYsNOBicpRM1iSwLVyqmRIcj7T08lHlE72kTFiJsggpxha8r6ygcoJS1KaWLqKE+/HaLRieiMTnOlatXcC3I5MvkMx6ZXAlbiPjwFGr3LBV25ytV3GH0KeARId3B2Mm12F2Aq/XqWoqhA4SlJWROFqtbHZqIE9HpBbjSzukOrnCAEBn5iLjKHq1/r+54XAcWk8dc74CwlMPUWrBlyxYuX77c/XxkZIR3v/vdZjXJDWK1klff9zly5AhKKZ566ilKpdKKHmc5CaVSipMnT3L69GkeeughfN9nfHz82hte/v+gdjKOe01w9dw2wuoa80iR0duF3H5AgtcPYbL7NnGFboBbwlaj3fVI07sg81oKmswhxlIuLRENYmlYlHhS6Nhjp/Q1uCUUaIk/kkDk4gJfHGPtvD50WGkdq7D0HDhKdyaT/bcyiguGgZ4Zkh1Ech9XG60JJ4fluNipPBs2DCOVQ7tVo9HqUJmscakdkknVyOQK8TqoEnbUiRPfuj7wRY1YgttCS9hiGa6T6/5MhF/VRmxJAhpMQXYL9D2+or+L+bgRM+YrWdoN8JGPfISPfOQjq3ruBBMfby69clXbtrsS16XMhs9+nKXG2Xq9zhtvvEE6nWb37t14nrcqldvsIuJ885IJKurA2T8GGWERm9HErvZ0JkDkY0lonm/XX0YJCwTskwd4RnwPYOndtombdVTXCaI/nWhNG9fk4wQuVmPYsYEkKnaJFvGIlSSwiqRUhPIGdcLqlciKKTLDm+gPpgjF+tgxNeTqpRN0VJp8xiaT6yeX8XBSudh1Oh+b9vTFWwjKCNkmcvLYjIIXm5h1k8KksFiC2IBMyHacbMbxWUVIr4wVxco32QGvP04y41V6ThqCSQQKO6zHyrq2fk8QsYTYideZDP8vOnYvEcuyKJVKlEol7rrrLur1Oq+99hqWZXHq1CmazSaFQoH+/n48z2NycnLV8fHTn/40f/Znf8aRI0fIZDLs3r2b559/nvvvv3/B+33hC1/gl3/5lzlz5gz33nsvzz//PO9///uX/fy33QxlIjk4ffo0nud1l7sul2mnwVUs7g4bROde4tKFS3Q6HTbf/SBpz4k7df70bp7YsVW5/RDWCEReH3wKQwg/3gvpV8Er6RdNrA/HyeKqJkKGsW48ow9UdhYiH+yUtlK2bH0wiq2RD/kn6AiBAkJLgIIDwWkezL1dJ6ICQCItn0jZXfdULE9Xuhxd2UllC6S9Ov1921HBFM3AoVMbY3zcRo2cxskMUEhF2snQiWLHrka8H6gdG2KEKJGO91bqhBg7qwOepWVbggiFHQcWPZspvX5E1EJ6ZUTQjNeaNFHCizX5sZwhagI2rqoj+z+gD0rXiSiKuusO5uJGSF5vBLZtdzsJ7XabD3/4w+zatYtf+ZVfuSHP/2ZnNfExcQVM/g5XIzFc6MD00uRLvFZ/jV/Y+AuEYci+ffuo1+s8/fTTFAoFzp07d819VfUAqrJXdyOTSnEwGUtdp7oJoCUC3fGL6nHCp/SaItWK7eKTLqAkFPr7k96glqB6091HITv6OaIG2AXtcOrkpzt+8Vyi7oTm9c5LJx/vxc1pua2K9AGDjJZbOSlEhDaxkGHX9EfPXwZoF+xAdw2Sin/U0N9jOBWvhprUUv6ggsLClVVQQ9jhBNlMjqwbQt96Ir9Jsx3SbtcZudxEYpFNp8nkiuQyaVwnixDRrBnQIDbs6ehDVNQktPLYTOmDYdTU9vkb37/mKo6lmPLc7hV4MPHxRjP7fJbMdyd7xqMoWlTiOhdLjbOXL1/mwIEDbNu2jXvvvbd7PatNKBPZ7uHDhxkZGeHxxx9nYOBaxYBSCi79TXdeWzlZlF/TZmZBE5wSolNBeSXGmoe5EF2iGGp12rg8R0VcZb3qiwvwNiBjQxwZm3cFOtHqkcurOIGbnlnM6vOXlepZESIRQs9BomTsMKvic69uKjhRk1zfRvJBHTU8hN+coNGxaNcuc/WqRdrq4OWGyGUs0vlBbOIZxtgfBFUnEHn9NpAa0IVBty9ej5K4WcfNGyfbNdkRYQVEWn9PwkVIPW6GiuLOq9VNiqWdI1SOlrxabuwWm9WeIYnkdeh7IL/6ESPbtrnvvvsA3RSbmJhgYmKC3/zN3+QP/uAPGBgY4D/+x//IBz/4QXbsWL4HyNe//nWee+45nnzyScIw5BOf+ATvec97OHTo0Lyvj5dffpkf/uEf5tOf/jQf/OAH+fznP8+HP/xhvvvd7y5bxXHTJK8rmRFKJAeVSoUdO3Zw6dKlVe0QgsVlOvMhVERq7G84VzmDk85x15YN2K5Ndx+OsLtteVQ0PQPoFBACIqcU68EHEGErfmHHi2FlPIMT1QlFBlQL3IG4QpO077VBA04e/KruygX64wdSuxjPBNjSJ7LTWJFPKbVueumr9BFWCkFAJFIIy0GJdNxhLM3oMCZrTEj1k3PaZPP30B828aMNtOtXaLRtqheOI0WKXMbVsoZMB8d2EUJLMyyVgmAK4cRLu53C9IB1ItuVsUmG9HWvVIVx8oienRLaEENZekZAYndNiZS6SCt9P6WBt6/ob2GpLFSBvxGS15vBP/kn/4RarcaXv/zlFb1ODIszl+nEcuOjUopz585x7NgxduzYwaZNm3jppZcWXXWzEPONBXRkh89c/gyT0SRPu09jHbHIZrPs3r2728GfPSMk21dh9Bta7oQCb0DHG28glrz268TNLSK4otcqxTvERBg7ryqpv6Yg3nmBQx1k7AJoZ7QCw0rFM+ip2Dk1F0u2+hAycQEMtGqCOAnt6XzqvWlT4JVwOdt1p9ZmP/G8ZfIxmIjnMmM5blSjIRRfnfwLni6+i0FR0kZmSB3TUUinD4Ge90RdJbLi2SAnT3fZtwLbsikU8xRzLoPrNhM2x6n7Nn59lPFxi5To4OUHyXuSdGEQR7V08U614lkqGXcWQAlPz3+KEDX0zjV1v05YiuTVxEfDWiCE4Lvf/W53FdFKd+UulBD27rB85JFHGB4evub+Ky38Jc/92muvdWc+5x1bGfsaTJ2Iz5ABYCGVA1ix0VhcQFM+3/KPkJKKuuOQD0Oats2ZcB87uVcXyuJxK2U5KGUhRATJecvOTM+hq0Cv+IhasYFNU5v0JO6rYR3lZLGjBpZy4lUdaW3uY3lopYbUzYWko4nCyw/jZXzo60eFLZptn06jysRESDR6GTedJ5d2yOTLpByBiiSO6OjkNerE899SxzLQ85RKxslh2DWFBEerNWSIEHZsspOJu5LJ9et9khIPS6DvKwKkU9R70j29Ior8vajB71nR77mX2QW3VCrFhg0b2LBhA5/5zGcQQrBnzx5efPFFWq0Wv/iLv7js5/jbv/3bGZ+/8MILDA8P8/rrr8+roPjsZz/L+973Pn7+538egE9+8pN8+ctf5j//5//Mb//2by/r+W+bDmWtVuONN94gk8nwzDPPUKvVusYPK2FVe9aUIlP5O9rVIxQHNzGUzEvG1V+6j2kBEWBDJHUHMLbF18PDGZB6plJEAdJOY8WyKqFCvftMjCCdsn7auG2vD0DxLh/ZjF/w9a4mPJ/ZSDGYQqa0W6rMFLGCKaTrdA0oRDAOpEkpLb/tmu6EjTix0w6uRIGuwivZTfCUncW1we3fTAFAbaLVbtFsNpmYauKPTeClMuSyHr5MkU306GFNS8lieYUVVJHJvJKV0v+OFUvRYjv/sKWTZ39yRkXKippacy/bRCJDs7T2JjyzWSihbLVaSCnvqAPTr/3ar/Hiiy/y2muvrdpMw7B0HMdBSrlk9URvoe1tb3sbfX193bgWRdGKTCNg/gPXFytfZCqaQiD4zYu/yb9d/29nVO5n31fJAHH+T1HBVOwqGK8IEilQTW04Q5wAqlA7vCrA7QMC8NLgt7vri3RyGYKwkLhapSHiziDozqAQsRzU1jFEuIioDiKOM8Kj05gikg6pNDiptI7JVibeh6sPQL4qACKeuwzjj74275GBdg+M2igr032+A/WXGQkusKf2Td5deDfgx8VFbRAhrJReF2KlEbKBEJ6WaYkswlL6o/JjRUrYNSpzchvoywWo/iGGwg6tdodWo8bEZBv/ynFSnkMmWySXtkjlB6bdwYOrCCuO/YV7oe+tK/p7WIxbaa3SjcDExxuPlJLjx48TRRF33313t9uzEhY6h7bb7Rndz9nmOLC6DmWtVkMpRSaTmXNeMkFV98PYN+O5P706ww6mdKE9ascjQ/HWAOGy2VkPCGwkoWtR8jtkrKHYxTXZXZ6Kx5xsQOlCnnCAljZNFAHgovc0pgCpV3LEiauWzZa1+kFk41GtgWnznnBq+ixppyGKDdWSzqZwdMHLzZFz8+QKffQjCXyfZqNOs9WkcukSlgpxU9pMJwraOFbcoJEd3RgJw9ioLYgVK5aeg1SRnssk1PsmlQ9WWRtWen1xN3N6P6XVmgAUdljRc5uxC60l9Ao9teF9a2JctlB8TPKRXbt28R//439c9XMlTE5OAtDf3z/vbV555RV+7ud+bsbX3vve9/LFL35x2c93QxLK5dg+z8XFixc5dOgQ27dvZ8eOHV3Jw2pNdWD5CaVSigtv/HeiqdNkCusZGhyaPsw4uXifmRcfmIQenCZut8eufW5ccdFzPimsoE5kpbTZgpVFyEldDYr02hARtVAU9QsptkOWIo1FiLRzcRJaBKIeyddAfPAZ0G6Fbj9CtrrzRtIpIpoTRFZGS7GsrK5gWSl98LK8uCJkdavcIpYMCOFOO2dFbXByZFMOmcw6Bvt8QmxajUnqDR8/6DAyHlJvXySdzZHPdnDdPqzYpELE5hUKibAslOXoYGdlQU7vJMLVSXFXihDbYgNUcu/AdeaXoq4VC3V7Go0GwB2TUP7pn/4pv/qrv8r/+B//g3vuuedmX86bit4ZocWSwVqtxp49e7qzPYkJTrJgfs2Mx2I6ssMLV17QhlnARe8i1Y3VOWVpScxXl16EoK4r7FEbvXojVnJggWzorlxYAzuNI6eAfgiugpPnyqUT1Ds2+YwgnR8i46HHAqSvO3KWpw8IKopln4lCRK/+6CahQgAWCovRM1Wqo1NEkUUmJ9iwY5BMzgIRxPOVNsgIW7QRYR1hSx377LjrmcQbW++01MU2l4bq8HrnKL4lOBqe5620GbbL+uAXtcHRypNEAitFGkdNgTWkXWedvN63aetVADoONwBLX76KEEIb7+SyWXJpFzW8kShoUm9FtJtTnL/Swho7QypbIJ9VRORBTKFSQ6gN37/iv4fFWEzBcadIXsHExxtJEltarRZ79+4lDEOy2Sx9fX2retz5zpDJCrqhoSHe8pa3LJgErCS+nj9/nsOHDwPw8MMPz3umUM2LMPp18AZBNsHrg6CmXf5bY3FhrgnKQsgIATyZeZynkDrhUiET1Tp+u4l0cnp/t1uKjXDiXbhCxAmk9usQqoN2jdYz2iKK90iqFsrKxsmZF8+T5xBinMjpQ5v3JCZnZd3gcIqoYII/qf8dj7l3c0/u8dhYLNkDWUIo3flEShwvS8nzKPavAxnQ7oRMVq/S6ShOnz2P42UpZASZXD9Ztzk9IuaWwJ/22ND72Sd1Y0a2YolrqIt+SsZJcRRfb0ho5RHiSpxoS/SsqKXvu+l/1dLiNWApCo7ZK2JWg5SSj3/84zzzzDMLSldHRkZYt27djK+tW7eua/q4HG7ptSFRFHH48GFGR0ev2cezXIeu2SS20ct5jCAIOP7an5Ke/BbZQpmUTfziCmK3wniAV0Vge/rQpCAeBEIfbiShSKMQsetrvHsn7IDtxZItL07qUriiBYi4i5eBaBLsDFZYjz82YqlBWydjKkCpeC8ZNnqRrBvPMHp6hjLe7ybdIpKq7oQqLcdVSiJsR1+tbWuLeunrA1PUiuVftXhH5JTeV9l1DdMOrm4wiZ3PU0g3uODb5LMuWDbN2lWqoy0sL00u5ZIp9pNNe1hO/HOw0np1iJWN5az5brdAge5yIvQMEHrPUTT0D/AvQOoGyI0WOjDV63Vs215wxvJ24cCBAzz77LP8wi/8Ag8++GA3sHiet2Cly7A2JH9jYRgumFDOVWjrZS1WI82+/59c+RMmo8kZX/vc2Od4Kv/UjK91Z4Qm9sDUER27VKDdR5Verh1v4o4NEIhjqUTaeVQUEFLk8pkTRMqiL2/R8C3ql44TiAzFtCKVH8aOqig13LP6Q68EIV4RImQs1Ypnz4X0qVY9xi51cNMl0jlFdSJEnW5x1yMbsAh08ic74Ohupa5yd+Jxg6kepURsJd+ziuTQ1DdRQtEXSeqWw77aN3l3+UN6zZGVir93bfKAU0Sqq0i7qF0V3VLcmS3GHc2Mno+XxGqNes8MaEnLcF29a1PYOcoZH3J9rB8I6PgBrXaH9tQojY7EVj6nau8kN9Gkry+1ot17i7HYDOVarA25FTDx8cZz5coV9u3bx7p169i5cyevvfbamu7qBV30OH36NCdPnlySM7ZlWfi+v+Tnk1Jy6NAhRkdHeeSRR3jjjTfm9UVQ/hRc+CtAxrFAz4zjlUGMEljx68jOosI2YShxbL3XG+zY0TWNE03RUbZe/2bn4+5eetoFVQbak4Ok+BbL5C0rLvZ7cZLp6AaHldJqNjuLiKZQSmHJNhDH9TiRVbZeB7cvusxYOMqX5RTbMjv5/7P33zGSZdl9LvrtfUycEyd8pKnKLN/ejGuOq6YbSiLnDZ1I6lIX774nkiIESAAliqIAQhIIPOGCuAIewUsBuoIoiRRJCaSk+ygNecVLSUOj4UxzTE/PdLWvru7yVenD2+P2fn/sHZFZ3eWrurqnWQsYnMnsjMiojIgVe631W9/P9RuWKlu90o5jr32TLQqLXkZerpDGU/YfeJTJuM9onNLf2iLNNaVwSBjViIIRXlBD5hNL3p4NRuzgIRuaf5vAqAcdD1SOtmd1jaHEKtdYQ5l9+Qy18K1QunvNonut4Pipn/opXn75ZZ555pm7dp83ineloLwZLP5oNOLEiRM4jsO3fuu3vu2gfjfMv2/lwDUcDnn12f+bA/kzLB85Rnf7ApkuIpKW0W8nLdNByvpmTygdGbDDzEZDxYYahQZc460jfdCOsWjEMwNNaYhawi2gdU5GiEJYbbjx3DHSAd+AeYRj5Qti90pqJqMiMXnC2noIHKMpn3XZlcBRMSZpJeZ3W2yy0KlZalap1ZqnaKdm5Q5NU/h5DQPSKTQReWymnyoxJNfcyCW0Bs/zicoNauUA5UTEwx2GsaS/c5GN1KdUyPCjBUoFhV+sIdXIJAJtukUIz3pRRkbTLyOEnqArj0HtSdSFF+/J/sr1EsJ4PCaKovfFHs1zzz3HeDzmF37hF/iFX/iF+ffvY/HfmbhaIXi96eLeRtuHP/xhFhcXr/pzd5oj39q06/V6nDx/kkKxYF7n9mHHKn7bbaWUuFkLsfanRvaUdmxebJnp3IzQl3QspMfmS52hkaS54uyFixTDEiv7VlF5TlmC1geIpyNG4wm9XpfJ1GGSbpNmDUphm6BYtZYfJWRmpE27hOiW6ZqPt9BKUwqGpJSoVsZMp0X0tAtBxRR/jsmFqQ4NeMI11Fc1y31+wyhFvKZdP6gxTtt8PX0dP8+ZCkmoc84m52nHF2m4dsqgdqWvOAWcfICQoV05mFFqq0ZG65ZME8+xu1Nuhbk5N1g7Em0KaKGBkplaCIdCVKEQTNCNFXo7F2hly+TRQ5w9e5ZXXnmFcrlMs9mcm2/fjbx1o4n6+2WH8n5+vLdx6dIlXnrppSsornfLq3eW39I05aWXXmIwGNw0GftWzo8zCe1sX3L2PrlaQalVChf+o4GFwa7aQobG3kgb2CFOQJ4mXN7sMuz38DyPKAqJigFhWMRRKUoGOLqHlst2Klg2eXBP4WameOaMqp2CgfSIAMiNw8CsqMUzQxO7EoVwcPQU8K26zQc1sqtIEzLpcmL4ZRQa1JiX4zf4sPNha8exx2vcb5gdzZkdiRuZYlWGyKyFEBpPD/CigEog0Av7iNOM8XjKeNSltTXB8TzCYkgUlYnCIlI6lhqOncR6prnoXgnbwasi0y2EkPbvYaeawX64C3uTe+NmCsq71XD723/7b/P7v//7fOELX+DAgQPX/dl9+/axubl5xfc2Nzdvy+LpPSl53djY4OWXX2Z1dZVHHnnkqh92s2Rwu5TWvfdxo9jY2ODkC8/wROlVFhYeNsWTY9Ht3j7TOfaqttOy23GZ2YSIeFZ0Gg28rwegGwam45UMqUvsTjoFGRozpkc45pAgPVDS0FEdu5AtZkaxAo22tFRpwQ6W+Cdc6/ETIrKR9Yg0Rt8ybqFmUluvsusVaYETs2JZJjuGsppav0sd26lnbAew5nlAYIhfwjXro9JBC5fcraClC24TqVKC6kFCFaOX9pPHI4aTmOmoz+XOFC06RGFhvgvkFiqm2zabCnj2gBgso5a+GzBJ+d0uKIfD4ftmP+gnfuIn7qoNyf249bhW0208HvP888/jOM71QQ7cGTRidvtZfpxNQ//6A3+d//Xo/3rDnCt0QnX0ZfTSftPx9momv3l1s2vu1XaLpznxtWvzaJveVLLcCKgt7jfTRlGAdIKQAYGrCBo1mrWIyxs7SKHJ85y1jS5adAjDiCiCYlTGF85cWq+9mgGSBTWk2GCSBPj+hMFIUqrESK9p5KjCNfJb4eKJsbH8yM00Vdj1BSEEKI2Q1vJISGJSCrKEIkZJ89kihMvEca0kN0M7VkXiRog8JpMVIyfz6mYFwivP/YRF2jXT0qyPqd7F7m55PjHTSistI7HSr9zupNrVC6EzcrfOtPydPP7QQ4A53M7oghcvXgSgXq/TaDRoNBq37Wl7M5LX90OOvJ8f720sLy9TKpWuaEbcDa/eWX6c8TmKxSLHjx/H9/2buv3Nnh87nQ4nTpyg2WzyxBNPXHH+fRsJW2u4/PvWSs7aVswk+LlRpbmqDxrS4SYXNwd4IuXI0WNk8YjBNGNrp02WtQiDACkFiiIgLGtjaveqB3N/xpm13Sz34lZhRsLODczG0FF9hBaWqZGjNeQiQAvPngvVnkEAvDF5mSQfEWLGKC+PvsKT7hE8NzRzAjkjryqT34UwuWzWJEMZCyms5RPmx4VwCNyMQq1GozxBySWmwzajBHo759nIPEJfEEQNSkUfP6ggRYpyq0i9C2WjYOyoDAW7beyllNlL1as/cFf2JvfGzVCw74at0t/5O3+Hz372s3z+85+/ql/uW+P48eP88R//MT/zMz8z/94f/uEfcvz48Vv+/e8qlOetxeBeqtaTTz553Qp5L6X1bkMnZqG15o033uDi2df56NIlqsUFs4fjBAgxJLe+kHjW+LXQNBCbQgPyxFyzicEoZyOrge8bzXY+Am+/6ZK4JWTctZrwru2etMEr4+kB6CX7Rjd0VNwSZENj9JpN7fTSLipbOZlQZiHZHF7sYcaf0bsMYj936wjRm9ubKNspUl4NkY1QTnmP0ewEpGuQ/bPmgE6Nt1o23uO7abyMTGHaQWuNm/VA+WZyIHyEGJu/m0pwXJdqtUi1UjKgjMmQ4SSmNxiwvpMSej2CYolScUxYrCG1RhcWyFd+eN6Bul1S763G9RLC++WwdD/eG3G1ptvm5iYvvfTSdRttN7qPW4nZgevVV19lfX39bWsH14vCzh/ipdugl6wvovUyEzPPRyz1VO36TXoNtrfW6U49aqUCtaWju8Vn3DV4/Ky7p7lUwWeMFzaoRS56qUk67jKKNZPuGq0tKLiKoNSk6DuE5QZCa6qLEaNuhfZmwngAhVKJxaM1C3hQdm/c/N1y7aFkaCW57txAfFd+utvsqruL/GTlh8yBLevvXm0+NyCIjs2VQ0tztfAzckOjFQ7arVofOc8QCskwdiTWpsRaUhmfyer8oDkrglEj8/dUGqGmjMpPI9gtEoMgYGVlhZWVFbTWc/Ptzc1NTp06RRAE8+llrVa76c/X6xWU0+mUPM/fF5LX+3Fvw/f9tzWw7oZCzXEcer0eZ86c4ejRozzwwAO3NJy4mfPjxYsXef3113n44Yc5dOjQ/P5n17dNKLf/DEYXjcSVDAPH0SZ3OgXIY5RbRqeXOHs5ph5JGvseQMcd/KhKVOijm4dJxz2GiabfaZFkgrNnzxFFJYpRRLHoIX17ZvWboKbmbJiN5t6UJmd1dovMuUflrBgtQR5buzeBdnzIMzMQ0Rk5gq9MTjCVAk9rciGR+ZST2Wk+IB7chfa4RQvzKSKmI7RfNgMQN4I8NzYe0kE5IVIrI8/ViV2hMPuaMh8RVvdTzHroxUfJJ21GiUM83GatC64+g1+sERUkYamK5/nmCCswhTo5SgZ25Uqjlr/bfjbd3bjROfVu7Jj/1E/9FL/927/N7/3e71Eul+dy/Gq1Om8U/tiP/Rirq6v8k3/yTwD4u3/37/Kd3/md/NIv/RLf933fx3/4D/+B5557jn/1r/7VLf/+d62ghCuLwb1UrePHj9/wcD67j7u9IzSLNE154YUXmIwGfNuhSxTUGJQHeb5bWKnUFHBamw/yPDGHkdwmApXbPUlrG2I18EJsk7lWruSZN7Tym0gdW3LrZJeiJctGDhAuGGKrV929JlfuLu5OFtvzyaLpPnUs7Gds5Kz5EC19pJqitJVY4M5tRQQYeakARBkthDn84FwBvpjTCP2a9Yw0SUl5VVuIRjj6MsgyQk/tXzYzfyfX7n/axKXsvyMIK4Rugq7tR6VDxpOU8XTEzvoWqXaIQo985fsppz6Rr+fT73s1obwelCeKotuelt+PP79xtdfMXkmXUopTp05x6dKlGzba3nofd5IfZwci3/c5fvz4VUmHV73dzrO4o9Nk0hY58wlayfpNhtZTzeZIIVBKs7G+xiTOqJUCXLdgfRyN75l2K0Zy6jevsP5IZR1XYwnYU7zSPurhEJrLqLjDKJHEgy02BwK5tYYXLVAuKJaOLNFc7JHRJPTHuJFvgRHV+ZVshMJDqhjt1EzTzi1b6JmV+/sNg9q3iHk1h6HNABUVc/ByQktldAwUwwKJZD5FeNEez7eu3cns7ClEK1YybCFowjdXbPHmhGbHXfomN+/19a19iHh4AHmNw7cQgkqlQqVS4ciRI2RZRqfTod1u88YbbzCdTqlWq/PpZblcvmaOu15B+X6Dlt2PdzfutKBUStHtdhmPx3zkIx+55trA9eJ658fZvuTW1hbf8i3f8rbd2tl7aO/tde9VYxHilCHrml3qWQNr5jcuC4zGHUapz77lJo3GEjqfkM2J/4Zi6kU1Gn4fKZpMBzuU64tMhh1a2302kpgwKhOGIVGpYgp2ra03pbUzUgndTkAWd/DCkGrVWNnNJ5pWjecpA3SUcdsCFAemGFRTKt4CoQoxp0vIXIkvI7a3PCb9NtIrsLg/xi9ZuE6hOpeh7u5VGimmTCzEJx/YotLsdc7gQGYVwMB2nOIilSCDapXFPCWOY0bjIYNhn+12n4KrKERNSgEUSouItGPWTNIuuv5RqNw+Ofh6cS92KP/Fv/gXAHzqU5+64vu//uu/PldVXLhw4Ypz7NNPP81v//Zv8/M///P8o3/0j3jooYf43d/93Vv2oIR7KHm94pe6u5Ml13XZ2dnhhRdemC9c34yn0N4J5e3GtRLCTAIRFYs8fWgbZ9Izeyz52B5wYgP10am1AdFAamUJmZWnjizi2KDq0Yk17h6iAZmNgcC8MYSLyBMLzcktlCZDOSUEG+SO9S+z+HhzTSzCfjZZnBhpVz42BNdsbGSq2dBMVPOxhf2MjRxWjdE6xSU100dhOuBC+pZGWEQkI9OZysZWWz5Cy5K5rQxNp1x65sAnA1NsytCAc9wSWghSiuYwJQNbiOYot4ScT0RjW4hOUG4ZkcdzQpmUDqVKRDnyUcsHyCZtOs6jrE/qvP7cc3ieR6PRII7jOzo430zM5NX3J5T3417ETNJ1q422vXEnh65Op8PW1hbFYpFPfOITN+3zpsZr0HrWyEt1D7wFIIXCgsmBXgP01JDz7GEpnfS5tLGFLxVHjhyitbOFIDaFl0pAC7uT7SLyzBRUlgjrqAFKW8WHtREy3esMWahT9nNK5RoLOmOS5ExGfTrjKUn7HI4XUi7myEIVJ59a8urQ+EGmfXCKeAxB7jPdeidAZF0rJR0BLoKZFBZAm4miyncPgUgQrin43Bxtb6PxEKRksoon9XyfyNC4p6agzae2EN3Nz2aHyTNNRtfKY/fAiMzVWjH5NVj6dlT/3E033FzXZXFxcX7Ankwmc3nshQsXEEJcIY/dyza4kYJDCHHbctr78ec3btRwu9WYTCY8//zzJEnC8vLybRWTcO3zY7c94P/69WfobyUce+QAPP52Ce0MCjknYU/W4PJ/NTkoNVR7ks7unrlXRscdNlsDht02QSGkUasBGUq4/JfeZzlaeJgPBo/ZJldipo+jDXJpvB2L0SEWsiGJ8piMWowmY/rtTYTjEYWBLTKLCCfn8uku7ctj8lwjnQELB6qsPqjnZ0/tNyCfkooSxXyC9pZ3FWppF+lV+CvF77wSYKYmXDyTcP70RbLcQege7c2IRz9awAuqCJXP8yD2TJs7ZRy1ZSFARgYssoEprnVsWSFY2I5vm48zMKbJTX4UUiiWYfEAKksYTWMmwx5rrRS2TiG9CJkPmYojeMt/gXdqJHAzKwF3quC4FuRpb1xtz/tHf/RH+dEf/dE7+t3wLk0oZ9CJLMu4cOEC586d47HHHrvh8ujeuFvWIW9NCBsbG7z00kuGnFi5jOhumDe5zqy0NTNFpUzIyK22e2YNos0LHbWrfxcekFoQg/G38ZgaG5BsFwuv8UyRKQOkGtrrGFRuqFWqgmBsO8+phfqYq5kaFizB1X5gWwNx7ZQN7Mf17N/NNwRXodBSorW0fmcJyJLxUvMqkA3QfnV3vzLtorzyngNL1yxnZzOarDGyFdKDLDYFaTo208N8jKZo/p3S+Pxo4YFKjLRL5aaIlALwjcxB5wZSoVO0Y7r/bv0Jmgd+hKYwz3uv16PdbpMkCa+99hqXL1+eH3QqlcpdnVrOXmfXOzDd777fj7sVjuPQ7XZ55ZVXbqnRtjduh/KqtebChQucOnWKWq1GuVy+6d+rswniwu+gs6nJXToFa4GBGhnVgxpZYmEKbpHxsMeFyy3q1SrLyysY+ZHplOOWzMFAa9OzmxFPhdgFVQhwtLX6yCfmgQhThBrStbB7j5LQUYSVkEYlQKka40nMaDxia3OdPMsIo4hisUhUCvC9ClpIMiKTwz3rjel6Jt3n0kpgDZ3bNBuLb9n3rtpDVtVMHN0yMjPgB2n3JB3VRegQkRiPTiFzC5FwAccoRkRmbUmy+fqCcma+mE07MbX+cE7JUhoL6P3fD9JHKXXbVNcwDFldXWV1dRWlFIPBgFarxdraGidPniSKonnOzbLsugqOUql0X8FxP+5KuK5LHL8dBnajmNFi9+3bx+LiItPp9MY3ukZcLb/ubLf49//7n9I5n1OuRLzyzBrb50f8lb/3cUq14Kq31+kQ1v4rM89H/LpRv/lNA3X06+Rxl0sbHUgHLC3vo9/dNjk0U5ybvslafI5Oss7D/hEK2rOAHGOrhIgNjV9naL+GpzK8wiGqakrOAeJhm9E0o9PaZCMDJ4f2ZYgil0IYkMYJvY0WjQVJWC3a/DLLURolI0BaSFhuc1Fi8mBuV7LyIWnu0lvbwCu4VCNNrlxG3Q69DZfFA8bvV9icbnY2PZSeopyiPXN7pmByTA4Wud1Jzydz2I7eA9sh3qPcs+ti0i1SCRzKxSWWgCTJaXc7DEeaZ9cPQusr83zWaDRuep/2ZkIpdUNo2Tf7UOJdKSjBvJleeuklkiThk5/85G1V5ncq6brCfFtrTp06xcWLF/ngBz/IsrcG61+y+4oDs9+STyxpyxrBqtySW1PAB3LbObE4fKF3OyhaAQGgyGRoCik3MsWUUzRF2Mx6ZAboERJHZAi0AeFogRA5oA3lK1fMdmuEdCG3E8ZsCl4RkcWmW5NNEZ75HaZ4nKGjY3IZWA18yUi23AqS3OxR6j2SLq9uOuteDa1zhFMyICAp0dKxtieR9Y6sWO/IMp6+AM4SIuvZSekAcBEiewtgooxIBrtQILeCTHeMd5AagRui9n16vijtOM78Tb+9vc2xY8dQStFut3nppZfQWt8V0MQsZq+TPw9Qnvtxb+Oth2ytNdPplE6nwxNPPHFLjba9casTyjzPeeWVV2i1Wnz0ox9la2vrpm+vtUZf/n1LL/VNUafsKoBKLOjGkgCt7LW3dZbN9pjVhYhyc8kUn14VT/VJhc27bsnmXet95paMMsINEToll2VyxBxTbySlGUhpHot0dwnb+cQcRrIR0osokVIq74dsSKJ8psNtBqMhve0L4FeoBNrkurRrvOCyoQVVTHatSNzIrD+4ptm4my/3XjN7yDI4fWa7QIDWEoFrTbjLbytI54Vo2jIHs6RvJpQ6t2ALW9R6JTvxLYGeousfgeggcH3/3FsJKSXVapVqtcqxY8dI03Quj3399deZTqecOXOG0WhEo9G4ooCc5cf7BeX9uBtxq7lNa83p06c5e/Ysjz/+OKurq5w9e/auDSRm6wHPf+UVpjsO+w5UKZYLqFyxdanPhdd2ePz4lXlcCIHOU7jwWZhs2Qkb4Fh5q4U0xtMxFy51CAOX/Qc/wHjQRYmigdjg8uzoa7hCkpJycvI8Hy5+1EB1ZICT99BKIdOhgeuoXR9J5RSROjX7h5UULVbJ4jGbawPSeJ2uyvGnQ6RTRCQJWb5gFW4Fc8aUPo6eIOfDgsBOCqWFSTqGHiuEaWxlGUkW4DiCXAtjBacFWptcrd3QquGKlnpdwsl6CMSuXZOaopzQTEndIujUnBm1dRuw1FhUDF7N5uqiedxIsyqglYFi5zG+X6TkpXRqn+Dpj30v3W53rsZ49dVXKZfL8zNktVq9ozya5/l1C9T3w1DiXSkoO53OfB9tL0L5VuNuQSeSJOHFF19kMpnwyU9+khKbcP4PzGEm7YO/h9xqvXI8tWFqxdQgmM1hqAJxG/yqvZ39kHdDuw8UglII4aC1sDRUAdoUn1q6CHzjUWm76rkooGRg9mRsB0cj7Q6jsQwxbxiLl89H4O/6pcnUekXO9i3n+5UtED4F3Tfek2kbnKKZjgofOTMdF3biKszE9WvTl+mlW/zF2vdai5HQFqIlI9O1JC3tmm5VQgktHLQXmQOQ4+7ZwfTMocwCJmYwIO1EkI8NMZYchEKt/OA1DWaVUhQKBWq1Gvv3778CNLGxscGpU6cIw3CeGOr1+i1Pe2av12sdiN4vSPz78e5GkiS88MILxHHMoUOHbruYhFujvI7HY06cOIGUkuPHjxMEATs7O6RpelO31ztfhtF5a3ERg1sixzHvWW1zCbZDL3zW19cZjFIOHlglLJbNXrVXgXSEkkWcbADOgkHnuyXE1Mi/ZtYfMjUEal91EaJuJ3/Gf9IoLMZor2Q75BZO5lcNrKxgVgd0YcECbhbwdYIXHqOSx+QcYjpsM5zkOGxwbq1LOegQlJpEhRZuYGRdOIG5Sg/0BHAQuVGqiCtWCIxiw1xLNm8WLeU1wnOc+aRxVoCaBp61CNHKqjUwnyHCsX/joim6vRkEqGygRYVFWPz2+XNztwrKt4bneSwtLbG0tITWmi9+8Ys0Gg16vR7nzp3DcRzq9TpRFHH58uW70nD7whe+wC/+4i/y9a9/nfX1dT772c/yQz/0Q9f8+c9//vN813d919u+v76+fltY/Ptx7+NOJa+z8914POYTn/gElUoFuHsU7Bm4bHt7mw984AOc/8LLu49ZiDlT52q3lxt/AOm6PRvGdm0qx6jcPIbdTS5v9mjWSywsHgRStFNAo0AWeWP8Kmt6iCuM+P7Z6cs8Fn4EfwZmlAUc3TFny8xS+tOuUUmkbdPEmu0l5jFuIWJxv0NvQ5FNp2jHZToaoqTP+tY6nUmZcjimGNUouAm5CGzRVjOwnvkeeHluUyKyCdot4/sZ5UaJ9nqHNAtQWYr0KxTrZXQhMkBLr25hO3VEPiJzyrh6G+0esJ6/5V17j7SNdkKklcLKHONyIK3/uwDtFADXkFuFxPjoOWb9wjfnzWnxSWL1IFLK+RkRII7judz/5ZdfRik1H1I0m81bHlJcT/KaJAlpmn7TQ8vu6Q6l1prz58/zxhtv4Ps+R48eve1iEm5P0vXW24/HY7785S9TLpf55Cc/iZd34OLnDN5ep3bnJ5l3PYx2fELu1pFqDe0fsR/qNXOY8St7FpevXGA2WvgKXj4AysZGxC3T3zlPnPmUiwI/WrD7MaX5bqPQKZqyeaPJwEgZnNB6RJZsB7y6pxOeGkiF3uMV6TesR6TZ0VFeDZIumTD3Mzfdnk1WlZXt5mPr6TZg6DicHPwZPSn44PgkS+FDiKRlisnM7BhJNQQco0PXCqFTpBojMLun2oIljN+m2RtFZVayizHqFdIU3dIuXdc/ho4OX/N5fOuB6XqgiVOnThHHMbVabZ48bkaKdTOErvsTyvtxJzHDy9frdZaXl+8oN8LNKzhmO+z79+/n0Ucfnb/Obza/6uE5xOb/MLuHSQu8Kk62Y/5jYn0nM5P78lGHyzsDdDbh6NEH8YSlqmI62ngltJyQy7KRmvo1UAa4IIQtsFRqr1Nyp2QMrZ391gO4aIvQIiKxIJsZ0CYxTTORmAmpyAeGiJ1b39t8AsLDETFRVCIqOZzqtVhdWSWOY4bjCds7Exx3QFQsEkUOxbCIlK7dbbTTWOnZRmLVPqaK2XV0q7uHuaSNdoq4eQ+pK2aFwPTiTSHq2IOaW4J0aP4O+WjuxYuwfsRuBFpYL0qJ9uvolR+cE7CBewItm+2D7d+/nyiKUErNVxI+97nP8XM/93OUSiX+wT/4B/zAD/wA3/Ztt+fxNhqN+NCHPsRP/uRP8iM/8iM3fbvXX399XkgALC0t3dbvvx/vjbjZYUKv1+P555+nUqlw/PjxK6Tfd0PhlmUZzz77LGDgJp7rc/CRdd58foPJMCGeZjT3lzj4SPNtt6+mryEmQwirFtxYsnJ3QCvaO1vsdLqsLDcoVxch74Jb5uT4i3RdzbF0kT8bfJlQ5UyEoamORcJL8St8S/ETaCcHkZDJIkgHLcu2mWUbbdJIUbVTtPkpQiQd/EKJow8JLp7zUMmIYLHGwWNF3FKR6bjPYJTQ7l5E4yNlxiivEeYKx+5Valuoaa82l/zPQGdHHx7helWmXdMYPHDYJar4phiVoSn0MNRrA9+Zkjqmmab9ujkrehXzd3ICa1UiTeFuG3ciG1pLlK5tUlo6bT6wHubT3XNnYZlReBzZH73t+SkUCuzfv38+pBgOh7RaLba2tnjjjTcIguCKIcWNPq9vZDsH3/zQsns2oUzTlJdffpler8fHPvYxXn311TsGqdzphDJJEs6dO8exY8cMMjobwaX/DNnEHnK0kW5hMfLCny/7SjEh1aFpPXlV8wIvNM0b1m/aoq6x+7WaGk18PiJzK9a4ej9ba2/Sn2jK/oRLWw6eWscrLVEpdCmUl/H1AE3Tklxn3pBlc3WtWffsa6eEUENLck0s8S81XRuhbJfMTjbRaOmRC8/s2wjHdMSFPRw5kfVNszICr8Hzw/9BR0p8rfli/Ap/pXDEgHTmBaltCzkF+6au4OoJwinOF7Nl0pknMKPBT+bSV5FN7FS1M5+mqvJD6MbHrvs83qgD/1bQxHg8nneeZp30G+nmb+QhNBwOaTbf/qFxP+7HzcTZs2d5880353j5kydP3hUs/vXuQ2vN2bNnOX369FwGtjdupqDU6RC9/ofg1sz7eJ7zGgi9YX0nR+DViIdbXNzoGcrq0YdNQedWLcG0ArGBT7j5AKU940vphCYf4xkJqxtaOI1jlR3SKDikB9LsPYrZodGr2L1H421mdjI1yKL5Wrh2P1MbpYjKdotBa8MhBRTEhKhep1mW5HKZeLjFINZ0t86xnhcoFXIKpSVKgbRwicSsL6jUEBtRtuDDFMJgyK1CkokivlWgIDzzOWF32fWsSWkPR1oGRvkiHNBjO/mdFZ7GU04tfieEy1c8R+/UhPKK14HWVzTdpJTU63Xq9ToPPPAAUkr+5b/8l+zs7PA7v/M7t11QfuYzn+Ezn/nMLd9uaWmJWq12W7/zfrz78VYv8xv5UGqtuXTpEidPnuSBBx7g6NGjd916ZDgcEscxi4uLPP744/PX/qd//INUmiEb53pUGiGf+L4HqTSvnGbp3uvU0pdQ+qhVrwWQp3bnOWVtfYfJZMrBQw8SBqHJJV6F3nSLL+WvUZI5ffFJVkTIhtdgMUsZuR6lPCMQBVvAVYwCTGO9yV2T34REi5JRxc1ANl7VWN1ZMGJYr/NQeUqmGrgyQXghIh/jVypUoxFKNpiOOmxs9ZkMtjndbVEMfMKoQqkY4gcREoX2GkBmrjrBiZocfXyKoolUpoFnvH8929iToKemQAREPsTR2oAgsbAdGYLQKGk9L8XMHi8y/wY79NF+w1JnLZxS+OYzR7hWAitQK9+LWh/cMD8KISiXy5TL5fmQotvt0mq1ePPNN6+gYTebzasOKW6Ggn2zJPX3atyTgrLf7/Pcc89RLBZ5+umn8X3/jihds7hdycJsX3IwGLB//34efPBBUCnds79CLZ11mBN2jU3Vnt0cA5MR5LhqaMETlv462xFSE8A33Rb783NIjxMh2CIj4MKF82TK59ChZaT0WCBnEmdMRj22Bznpzhk0IU6/g1NfoiCN745QU0tEtR3sbGxNWe1EM4+Zwyi0QkjHJquC8cN0i8Y4F4+CHoAAmXaM5DRpm0OQ7ptujrUYGWctXhy/hINGobmcrrGWbbO/cAhzB6YQ1cLuYvoN0DlTSiDEfFKqvSoahRDhHG2vRWT/TTXrg1k1BalfR+3/gXlyuVbc6oGpWCxSLBY5cODAFZ306+nmbwb5fPjwtaeo9+N+XCsGgwEXLlzgYx/72PzQ6zgOSZLc0f1eLz9mWcZLL71Er9fj4x//ONXq2323bpRftVZw8bMw3TIHImV3yZVGaI3CFmlOSL/fY22ty+Jik2ZzCchAWgKsv2AKqULDTB3dGsQ9o/rI+kbyGnesT++V/o5uPgBZmcurZNqzMisjvyIdWPz+xO6sW0iESkwjTiWmM69StD+7zkiGTWDTYPRRKK+BVBPC6gphPoKFRdJph9FUMR3vcKmV4jqXKRYjwmKRMCggHQPHwJntB5XmzTaRdgGBkw9Al03h7RSsvUjBNi+tN6VTMbvqTmEPiM1FkNLWCf+j+zt8R/NHaS683Yz6XhSUN9oxBzh48CC/9mu/9o4+jmvFhz/8YeI45sknn+Qf/+N/zLd+67e+K4/jftyduF4xuFeC+tRTT12z0Xsn58cLFy7w+uuv4zjO2+wVipUCf/F/ubblgp5uwqXfNSs/ad80gGKzMpWNt7i40cch4ciRB/GEhdQICUi+PHkBEAwcyddGX+PTS/8LqJhUSVOQOUXDr3BL1uPcx1MtkCs2R5YQcc/4PVqmBlqBdq2qwUG7AWiJ8H08NMaNYOZrnllgZEqxWCbw+5TLDcLQZzyeMh53WG+vI6QgDIqEpRJRUER61rMdbUFjhr1hvlcwV52BkIg8NRLdfIySRVw6Vqpr1xmSlh04dOa+50YCO5zDHhGeBfvYXVO3Yn+Hb4vWFL38F6DQRKneLedH13VZWFiY+zJPJhNarRbtdpvz589fIZ9tNBoUCoWb8jG/F/Z372Tck4JSa83q6irHjh2bV+036jDdTNxOh2m2ozSdTllcXDQdAa159c3/L3+09mv8P2rfz8PhMQvbmXXnrSZb2o62dJF6gkLaF69jZUgG+GCofwMLoJiYQi4dWUPsAVol9HfOUwhrHNpfRwtNng3B8Sn6mtCv06hDlisuXLxAkikuX76I1hCVS5SKIcXIx3GLcwNsgLW8xecHX+SHKt9P0UJ2tLD+km7R+Kj5VSN38OqIpEUiSsZvzbV0LLdoOkbSN3RZHISa8tLoOVwUnlJMHYeqynh2/Cw/LEq7b+qZ96UbIVLTCXL1xEjJtLO7O6QyK9mNDYVMGxqZQO3ajngV8pUfnqOfrxVa6zuSdL21k54kyXx6+corr5DnOfV6/armynvj/UDouh/vTlQqFb7927/9itfw3TLuvlrTbjgc8vzzzxMEwbzBd7W4oXH35hdgfNHsSSZmkkY2BLeIzMZINCod0O4O6HY7HNy/j6hUhKxnp3G2AafHtkM/BlFA6oQEqwZxqxa80DSwML+xBxaWkTo1PPI9Un6ze6PdmilG/Rm2fiY3rVhStVV3uGV7LdlmWoRIWlYSOwSVGUy9KCCkpc3qHPBBOnhhnWrRo1ZfQCEZD4f0B2OG7RFJ0qdY9AmjKuUwxQ0ahnDt1cwE0okQ2QZa1kwTUmszrVDpbgFqC88rSbE9e1sD6Hlp8EXayQa/5l3k58Tb86BS6h2H4dyIgv1u7Zjv37+fX/mVX+GjH/0ocRzzq7/6q3zqU5/iq1/9Kk899dQ9fzz34+7EtXLbeDzm+eefx3Ecnn766Stsba52H7eqktsLLnvyySd55ZVXbun2OhvBpd83BZ90yJ2KJbrWmQy2ubTRoRpqFlceMnnHLRuVghPQm6xzZvIKvtbkUvDa6GUOeQc5Fj7IzI9RoOZARe3VEZMWibCNM+sTOfN7nBFRjTzUFpvzid4I7UVG6WYLTLOHKAAHIY3XbYZhfDh+SNkvU6lUUcIhHplGW7+1yVbmUPS13UHX+FFz9/fn1opOxwa2o2awnQTlVYFtUmmanTObPLy6HaZERlUoPTNM0SDQdvBTgGyyW2h6lV2rpXyCLj8C1Q+Y+70LDbcwDDlw4MB8SNHv92m1Wly6dInXXnuNUqnEdDplNBrRbDbf9vveL9Cye1JQ1mq1tx2279aB6VYSQr/f5/nnn6dcLnP8+HFOnjxpsM2b/4MXtn6XVAi+OniGB8OHDZTG0rBMcTlb5lWAQDs5GUO7wzL7DWr3NpbgimM7QE4IKmcwTonHQ4rFIisri+a+rJel0ClobUmuCk86BDKlXqsTBh5xohiPunTbQ3bWz+KFVUoFSVBZpCAznhn9GeNkk6+PT/Ad0UetV2SMkoHpuAuD69cyROicXBYR2hxSQBjZg8DIDwDErlG2citEWZXUEVTRJFJQEgWUX7Oel3b5WhbMNFRgCm8ycANkNjI0rtmhLjEL1SLvWvBPbA5vThGRjcj3fwbCG0MTZs//3ers+L7Pvn372Ldv31w33263WV9fn+/bXk03/07vUN4qjOJ+fPPEzAJpb7xTDbetrS1efPFFDh48yEMPPXTd9811jbsHp6H7opWVKigsWtBB0+6e11BijUubA7JkzMEjD1PwxB7qq7DKA2u3pGJTUIkxktxaJsUw83vMpuD4Rk4qfIQ2/r6OGqGwBwqkyaEz/P5s98atoQXmgIUySgih7R5mZoE3ykwDdW6720a+L4QwXpPSNc1Cb3bomhV6lbkHZPvsRc6cjHEZ4lfqHHssJBUO4+EWF7YzXFcShiHFUpUoDJCOTy5CtIzs7rhnpVsV4zMsZ37DdWsNYlYrzK6RBhy6asLZ6Rt8vVLh1fir/Gh8lqOFo1c+V/dgQjl7nV3PNuTdaLg98sgjPPLII/Ovn376aU6fPs0v//Iv8+/+3b+754/nftxe3IzkdZbbVldXeeSRR274mr/VM+jMv3IGLpsBeW42tMrh4u+aaaQToNFWhVCi2+2wvtFhealJo7locmShYXexK5CNeHb6Ep5WxFJSUJpMwJ90/m/i7Nt4tPCYPW/6SLRVLyh7ne0j5nPWxlv9JLVfN7JQzwAdmdtvGCDlFbuIs73IfIRGGgijKBhWh1NCkFKorhKWE/TSKlkyYThJiIdtWp0MT+xQiBqUC0PCyjJO1r0StpO00W4RmXXRWYyrE1ApQsxgO8rsQAoH7TrADPLoms8eq3gzYJ+B3eGc7Yh2odBA7/vu3eflLu+YSymp1WrUarUrhhQnT57k4sWLnDt37goHgmKxeFfy43sBWPau2Ybca8nr+vo6L7/8MseOHZtPSqWU+MMXuTD4b2zlbeoqpy16XBh+gyOlD8+nbiRt24Xvg182ElMpkCo2L3BlLT/IjVQJZQ5OtqgyPosOrfYWOztt/KBCWGmAG9n6NEfNlrGlRGINsHVqLEa0Bq9EIKcE0SEa2ZhU+0wHWwxjQe/SG2z4U6aFNWLpcH78PKPihyilUwPMSTuWANu1ktwxCAcnn+AyNYcxZRDVJoEZVL9Z3h6jnYinww9zPPqEmS4KB4QysgitzQFHSEOila6VY3mgE6aUEUJeSS/UZrFaa42QwvhOqtRQGdUYVfswuvaRm3peZx8y78SBaa9u3nVdtra2OHjwIO12m9OnTzOZTKhWq7z66qtkWfaO6t9vF0ZxP74542403PbmR601b775JufOneMDH/jATX1gXKug1EkPcfF3zYFoti+Y9y0VuwVumWzSQiuByMccOvowrkh2SYZOCUjt15n1l8yt1UdO7sQooayMNgMEigyhJALHgmsECI2cFaR5PPdem19FwfpEFiCZsHExYdrrIYMSq4d93Khq8p0X7SFWa6OW0CnaraNYQ3kNs29ksfRGvj8jsiZor053s8PrL4wRJGjXZ7zW4bwu8ujxA1TLPloGxKM2w6mgv3OJjcyj5OekqmAkXIR7rEEGtqk3Ng1G6dr90T2EWBWDV+YbvT/jUiHk1bCIg8Nvtn+Tf7z/H1/xfN0ryavjONfssA+Hw/cMcOLjH/84zzzzzLv9MO7HHcTe/Ki15o033uD8+fM8+eST7N+//6bu41bOj61WixMnTrBv3z4ee+wxpJRzD8ubfn+t/QEMz5p8k/ZxhUJnI7bWO3R7Aw6t7CMq2ZwkC/N1AVQOXoWUnKlbxFWKWGgEKamQfG30DR7wHsXRGqmMRFboGJDIfIzUqSmoZAGhx8YyTk3sOlY6l9cbj9tdFYj2GzbPGAq/9uoG3rOnqPT1AC3Lu6qPbKb66JgCLh/jegE114HyYZZ0ziROGI+G7PQz0tZZfD+gFKUUoxKBzK16zeR9ITRSmCGOyIZ24jh7DLNCt2sUKNbnHJWYwYaV6cKe3XW/hl79IfMZYeNG/pB3GrMhxalTp/jQhz6ElJJ2u83Ozg5vvvkmGxsb/M7v/A5BENDv96+6fnIz8V4Alt1TyuveuFcTSqUUp06d4tKlS3zoQx+64o8VpBcJB8/x37yX8TV0pUNRaf7H5EV+Inhod4dn1uGYyae8Ku54A4U771Ize3En7T1XQ3JVTonti6cYTODoap3tXoJMB2hdhWxMLkMECbkOkHlMJgMkiXnjg5kkgvU8U8a2RKe4zYOUtELrA3yl/e/p5i4Omo4UfHH7C3zMf5pSMcYvlMzysgzsZMAAeWBMLgpWYlWye0m7cgiZdlBuybxxncBIrHARQs9BOmQThGfe5GpuIms8JJUs4qsBQiUGpz97DDP/NmK0U7CaeR/I0P4iavm7r/pcXuv5hXemoHzr77mabr7dbvPP/tk/49lnn+WFF17gc5/7HL/wC7/A0aNHb3CPtxa3C6O4H98c8dYO/N1ouM3yY5qmvPDCC4zH41vy/L1aftUqR1/+L8xMrXFc22Uv2mIuZDLY5vJGC09MWF55HFfZYjNpm257akiwMyLsbs401iBuPmSKtLvhESqfoJ0Qnccgi8b30akgychkGSly02HXKRoLaPDMniRODfIJJ09M2DqzzjTxCf0OWxebPPUdAqdYt93rmaTUQsHcEiLeQZDzwuCPWA0fZMVpGqADxmdYENvJpmKwMyRNc6oNn1wVEFKyvSM5ljr4RQMuK1QOEFQyNCtk6ZTReMJ4e4vtXk6rf5ZiMSSKoBh4OK7dpfRKcyuUmTebzLpoWaA/vciZ+HX+rLkIQpCT8/nh5zmfnOewv7vPfa8mlDeiYNfr9Xf0MdxsnDhx4qaLjvvx3oxZbppMJrz88stMp1OOHz9+S02Lmzk/7nUmePTRRzl48OD8v81e7zfz/tI7z8LwjKU1J+CV0XqdVnuA0AlHDh/E9yyUS7hW5hranFmCbMj31o1V2yTRPLf2Zb5aPIGnNC0R81ryBk8EHyBHobUwjTYBSrhWQGemeCAQagoIywlx7YDBAz1GSx/JFI2HEPa/68wAwVB2wpnPgZOpNEWodusINZkXkUjXylEduzolgRgBRL4gcgMWG2WyNGYYaybDPp3uAKlzglKFUhQQRhWUmJA7BZAu2rFOC/MCt2KkuW4Rkfd217SENP+2PT7nBlg5MWfL4N5Dy2a/x3VdisUipVKJQ4cOkec5L7zwAq7rcuHCBZrNJn/zb/5N/vk//+e3fP/vBWDZuzahdF33pj3OrhU3Kkr3erodP378ypHyZIPy6Mus6R266TZagIdkLDRx3uGi6nOIyNqHZJbcmqH9BVAxym+g6e8SXL266b57dbMP49chG5KJEhsXXicj4uiBEk6xidd5HeUsQNxBuVU78q/ipl1yp2wLuTLEO2itkGkbpZZtwWd3Z0SA0GMQLpfTi3SyTQpCoJVGCMl5cYaHJ4/RaSVIt0AUFiiWahRDD+lGRvLqlIG+0dbrnHxvZ0rncy80ZtNEx9njIekbWYFfN1NMr2a8JN2y+VpaKJEQCOmYfUnrizkvVr2ykSA4hvCKdMlXvs90zm4yZh8I92JH6K1JJwxDVldX+Tf/5t/woQ99iJ/+6Z+m3W7f36W8H3ccd0vyGscxX/rSlyiVSm/D5t8ohBBvLyg3/wRGl+y+5NjuXOdmCqgk7e6Q7a0Oy/sOsL6+bhQaXhFQ1oIpMyTYPNnNlW7NwsIqkPbRThEn30bLfei4jfbK+Lnx0yXpohxjT6LcEk7WQTllmO6gvApO3poTT3EiyEeMRpLNs9soAipVSZJV6G6N2FxvsvKAkcKCNrtHaLN/iUK7FXbcV/hG+hon1WX+X9W/aiA7lgI7AwOJtI/0i4TeJnleI/CG9McupWCII7XZzZQ+Qo/AWoN45NTKAeNOSqm+gi8TBhNJt73JeiKIQoegWCUqagpB2UxO3YpRh9hrqoacrj9MqRAyO0Z7wiNRV8Kc7lVBeT0gz2QyuSNP1VkMh0PefPPN+ddnz57lxIkTNBoNDh06xD/8h/+Qy5cv82//7b8F4J/+03/K0aNHeeKJJ5hOp/zqr/4qf/Inf8LnPve5O34s9+PexVs/32cTpa985SvU63U+8pGP3PKUaXZ+1Fpf9fywd19yLzBt7+2BGxelg9Ow/YxVTmTgFIjjMeNEEnouB48+iCPBQGMUoGxeTUyjLhub4jLtg1tGpFu8XniDRq4YSEkty/ja9Os8GTyEEBHoGCXM+SvDJ9euGVIIEEIyZ4Oo3P5tNTO/TKFzIDe7iNp4ocOeq3Dn0lqUQmtlXQSEmQ5KxwwrtACZm/tVuYXtJBa2MzFFYDbAKVSoyT61ygF00mOaF5gONum0J7TWz6BkmYKvmExCwoJnOSYp2rI1ZutauI59HOZ3GRBayRaeNdOMqz4GjbfvTd8rCvbVoDyO4/DUU0/x6U9/mjiO+fVf/3VardY7+ljeGncTWPauSl5nkoHbjetJFmb7kpVK5e3JJh3Ahf+IVFMayufbKp9AqIRcOjjWE3EJzxx+8rGVdI2sDGFk/RZHZEqaInK2Y+kEtlsfAopJ5nH50lmCsMnhfUsIx/jrpE4VqXJyr2newNYzkkITRyUQLEA2YaMTQ57ghfvQmUkSIpmYAo0U6+6JozVNt4mrMmLHIVAZuRexvxZQ8A4bzP3Uobd9hs3MpxQogtIiLhPQmTXn9pEqs4lAglLgzqaJxoR7DtKRAcYT00w3NRao40R2AhqhhYPOUxJKBoXtlI1MzG8g1My4Owe3bNRrwiXf9z1mB+sWYpYM7kVBeb0D02z6c/z42ymL9+N+3GrcDQVHr9djMBjw4IMPGlukW3yPvFXyqnuvwc5X9kwV91zjFhutCdPBJocOPkTo5WyRQdIFp2qgZY7F32MJpnrmP6mtekIaCI/okFBCaQl+HYk2V60haCJVhpJ1Ou0tumPN/iWXXAaIdESGi0hHCCHnaHidjHFESsHXuBKk5yDcGJ2OIRXmceXG3mi2J2ngaiUuBqfIEEyyPifzSzwujxh5lU52kfh+nYUDCWtnFxh0JgxwcBzBwiMHcH3PEBSFNGAy6VvibAmyAako4uoxQWmJIBjBwmGyeMx4EjOZ9NloX0bLgHIo8UuLlPwcETSQaYdm+XH+t4P/nxs+j++FgnIGnbjTeO65567Y+/nZn/1ZAH78x3+c3/iN32B9fZ0LFy7M/3uSJPz9v//3uXz5MsVikQ9+8IP80R/90VV3h+7HN0fMLEEADhw4wIMPPnhbn/97J4xvfe2Ox2NOnDiBlJKnn36aQqFw3dtf87HGO3DxPxtORNIDr8S4e4mLmwNCOaXaOISjpyAiuzplc6NwwHHMeUr6prjz6qAS1sWURAxJhCRSOSPHoZQNOK82eFAuov0qTtJlmLgMts9Srq8i0oEZUqixIcGq2HjdkqOdgin6XENznfvpCtdAckTBgBydYFd2mg3RXglPDRHMJLBl671eMUAxp2TOi3Z1wcB2UpRXQ+7Z41R+00hr/QahmhKEh6lnY7J8ge2tNdJcsbl2DoEmLJaIAo+g1MAl3rUE8Wrz9azdFbTM7I6i0eEKet/Vp3fvhR3zGdTx0KFDHDp06B19LLN4J4Bl3/SS16tNOdfW1njllVeu2JecR57Axd8BnSOki0PIt0QPwLwjIzC7kI4tFj1TVDq+pZ8WIO0jhMDXIwNHyHvGGycfzg8ng0nG1vo56s0VmtUikEI2BeHhiZR2a0A8HVOMSpRKEa7jgchAOOR5yqXLGwghOXjkIaRjukIKiVaZwfFbbLwkZ6nwCH8leACEhyRDCQ+ppkYym08IqiuEpRi9tEQ2HTCcxIyHXSZTI9naaXUoFiuEBYHwIkv4MtNE5VWRSXc+VTRkra4ly1qQhnQhTRBuEZHa2yZtMqdEQB/IjWxWuOa5mBWrOkYLk9x09QNQfvSWXwP3gmAINz4wjUaj98yO0P345ourSV5vNz8qpXj99de5dOkSQRAYW6TbiL2SMBW3YfPzRnmhlJky2mZYlk64uD5CqgkHjn0QV2YgyzjigsG151O769KzgIm+pRf29xBiI0iHIEO+En8ONy/jXOgTlhuUAgcvqCCyGLwQnU3ZbA8YD7ocPHiEwJdoEaDz2Ei8VIbCAXKE8ChUCohClWF/jOMXIDPd68pCGfwq5OM5ZGLXqqTM+vgk2+6QqsqZSIdX+1/k0cYBZNbBfHROMG19h4Kb8qFvW6B1fps4K1Cp5tQP1iEd7N63W7Q08II99Bi4jpYl0/m3k0c3KFAuOlRUjsIhng4YjmO6nQ4bSU5Y6FGM6jjLT1O5xnTlra+He7VDea24W/nxU5/61BXvk7fGb/zGb1zx9c/93M/xcz/3c3f8e+/HeyOyLOOVV16h3W4jhGBlZeW2P//3Thj3vnZn+5L79+/n0UcfveZ7RwhxVRXHLHQ2gYu/t8vX8Eq0dzbYavdZXSjRHYc4Wd8oNPY26Ga50THTRqQdVEjz71x0Fnl4/GEcH3w/ICj4+H7IQfeAsZHLJ3RGgvb2WRaWj1IrgnJK6KRnFGRpG+VWjALOq5rdba+KzIeGFJtPzbCADOUUjQ2cV7FF4MKupZKKjeSVHO0vvm1lylC1y4jErhBYD3Kp2iACpB4bEJmYsJVt03AXcKVnYTsRjiuRhRqBdFho1pjGKZNhl9YgIds5jVOoUAn7hKUmIV1Lvu6aAjzrW+ib8ZBXB37EqGiuEu8FCva7cX58J4Bl76rk9W7sCO2dcu7dl/zwhz88N7Kfh1aw/gcGriMDEB65tt6SOJjRvjA1paX8GXJrYDtEwS4cIZ+QisD8iBOZzo5jOjCt3ojOzjbL+w9Qnr1IdGY8tFXMQqNKpRwxHg0Z9tq0Ni/hF4qUIh/Xj2hvr1MoVlleqCAc5mhnN+tbb7U+SpbR2cAQAvMuSkYI3SeXIVIP7W7i1JKvMjQOoHELRWpBhXq9znCUsLV5mUxGbLY6pBmUiiOCqEK5mOJ6jbmnpAHp1NA6R7iRATLqHO14u0hqNUZbqa9yy5AMyTHAndm+1QyFvyt97aLDA6il2+sY3yv9e57nV+1Szh7Du4XFvx/vz7jdHco4jjlx4gRpmvLEE0/wxhtv3PZjmE0otcoQF/8TOunYnRploNdCEk8nXLy8STEssP/AYYTIjGJDp2TM8mYVUJYAmxnfSZ1CYXZtGnm8V+fc5HVOsk4UbvKE/2Gmwz7bO5rQWadQXqLk92kNcmQ24NCRh/H0GKSxBMGr4qQ9c1hKujYHtRFumQ8dj3j1axoVd8m9iIefqlJuRkZOJkwOQ4YwU00AzyQvkwnoOw4a2NBjTqUXebTwkGk46gxwQSdoL8KXU/Y9dtR0zGdev671hZtZSgGgmVmDuHoEzoI9fFUt3KKMTHrm8JX3CT2PsOpDpUmmHCbjHuvOh1h/5TRCnJnTApvN5lUtYO42xfBqcaMdyvv58X7caQyHQ06cOIHneTz99NM888wzd3SGnB3u8zzH8zy01pw7d44333yTxx577KYk2tcEl2kFl/+LaZY5PiqD9a1NRsMxhw8dIQwjeuOzpE7NNOgKDchmq1KD3QacVzaNNrcIWR8lfEKR8h0HPspkMGAUJ4y6I7QQ7BR3iMpTxuOY0XDA/oMPEQUBWkgEOSJYROoE5S7iZmPyQgPSAbkTIeIumVtCxm3DCkk64FtrJc9Cdxy7v+2E5qwnfIRW5MLfA/VRdoVAmbOgzg1jRJlpoc5TslTi+rl5TMKlk6zz2cF/4YPuAxyvfY/llFhvYa3w9BQhmhT9jLC5TKOZk6lVJqMhw9GE9to6GoiiCVEYUow8XGl8NYWKzdkyvPbO9L1quF2N5j6L98pA4k6BZfesoLybHfhZ7JW8JknCiRMnSJLk7fuSs9j4HKL7ksGvJy2k1Dh5H/S+3TdvNjbd8iw2U7Q8BteCa7T9sBbSGLMq48ljdN0OSmvWNjaZTGIOHn2IILDjdm0mirnGwHEcScFTFKImdZWSKcF40KHbHxN3NtEiJBCaYeIQiQmOXzOoeq9mDh5+DSftoQtl2wEvotMhSvqQDVDCgTwxHTSrORdOaA84BrJjTMG7COmwv+aiFg6TTdoMY5j21mhvJBR8n6AYUixVKBZ8cEPrbRkaT0snADsJhcxIX7UyZrJSomVIRgHcoiG5WpkDOjWL1eSoYBm18kPmgHYbcS8Xqq8nV9BavycSwv14f4TruqaYu4kJ1Cy63S4nTpygVqvxLd/yLYxGo1v2Wdsb84Jy449ME84tmUOPDEBNGAxHbK5fpNFcplEvA5ntyY1BS3w5QeVTSM3eEPnA+k0OrJKjb+yV0iHIAJX1+PLwWQIFE6E5577B8YPfjtI5w3HMsNfhYmeEQBGWGoyGI4qlKj7aTkxT8BpIHUOhgVQxOqgjswlRs87HvrtIkiziuiCkQKVDszYgc2sN4lsqbAjZiGHepZobSVmU5wwdh2m8BsFD1retbDrhTmABFD5iRpvVyjQYHQ+Ea/H9maF3k6OdMkKnxKJiphwz+rX13ZzRCZGenWiOwavgpV3c1Y9TWv1+HlCKwWBwhd9ZuVym2WzSbDYpl8vzCcq7LXl9t2xD7sf7I7a2tnj++eevsDu60z3z2YQxz3PyPOfll1+m3W5fdV/yWnFNa6X1/w7918EzxOvLGy2Ezjh66CCeB+RT49CmYhBVu1tprD3wTA6YWzD5hs+Ry8hAEN0INx9TbixQziewvI/ppMdgFNPauIBSgkLgk4xauCqiEIT2wRrbDWnXs1wBODWz3+f4RgUna5Bn4JQRaQxOhMymNrdNzNl3ZtGkRkg9xdETRG6ngSpHOL4lrBZt4ywAoWhtppx5fo04hmLJ4cGnVqk0XL6UnkSheS4/ywfyLiVv0Z53Kzj5zhwYaSS4I7C8E68oqJTq6CxgmrlMRi06vZSdrcs4foVS5BE0HyGofYTrZb/3Qn4cDoc3Dct7J+NOgWXv6g7l3aK89no9nn/+earVKk899dTVl7N3vgqdF0z3JB+CV0eqS2QiNC9ef3dUj/XhmReZM9PutGdphV0EBVw9BBQkHTIKbFx8k0yGHFup4fg+JGbkr9OhpfONjM+P1XoLNUHLEJcpmfCI4xGL+w7hu4LBaEprZ5vLSU5U7FKMypSjCV6hhtQWHCE0FBoIpRCBjwS0ClEatEpRtntufHlScGtGCmvNv3O3gqO7KDdC5BO8Qol6QUE5QOEwHXYZxtDevMiGDigXlJ0SDJFBzXSxpA+6b/7G0oU0RrglRNIjo0iBIQJtOl1OiFADs09kpQh6//eCX3n783WTcS8nlNeTKwDvaEF5IxjF/fjmjrcWjXu75zcDm7h48SInT57koYce4vDhw/Nu6J3kWCklxeQMdAdWnhmb/KcTtjoxvU6L5QOPUInCOSzBTO6M0iOngBa+lbdjCjWhTXGplZlk5jEgUPmEi+klBukmAgg1vDJ+gQ8XHid0Qjw9Zjru0KyWKFeqTMYjhr0tdrYu4Hs+URRRDIsEUdnQDKUBXAgpEK6PlAVQBWTgoPLUFHkqRWnrsyscHJ2DWzTeuYUFfrz+47xx+jwPHFlBekVEPjaNuWxkmmn5xMIsUjutdMz3Zl6V1sPNyMrs5DFrG9uPvA/Sw1VDZB4gcsfsLbklg713ygg1BlnctVpRGbqwhN73PfPnp1qtUq1WOXbs2NzvrNVq8eKLL6K1npNV71QNdKO4Xn7UWjMajd4TB6b78c0Znufx5JNPXmF3dLfOkLN9Sdd1r7kvea24WkGpW1+H3uvgN5iOu1y41KIcOiyvHEbmU5Mz8gQpMlNwZUNr4WH4HCR2IqlGIEO0ilF4aJUi/LJVw9VM8enWQCfIoM546xxeWGepUWaS5EwGO7TafTyZE5ZqRKFHMaoaXofjW+m/j8hTHCcAjN2G1pihhPBQ2jHrBFogpQYhkcKq93TOoO8w3Orj+RkLSw6VfQu7uc/SqUk6jOOQC8+/zmToU44Sei2PM8+dYt+3LXJp8jqZEHhK8bXxi3xX5bvmPI5URkjhGSkv0vy9pN3tdIqG6lqoEYouwcIBGtmAjEWmozbDqeLZc4u8+R//K/nEYflgje/6n5+k2rjynPZeoGCPx+Pb9n6cxXsBWPZN7UM5SwbPPvssDzzwAEePHr16N7//BrSftYeh3Ozx6AztVVF6+0qCq+1yQ2akWCqxHSLbKcqNLEEkAxIRofOEaR6wcekNCsUGhxcjhG+KLeVVTVHplI2006/aznYFYX17RNJiqztl3Nvk4IEHCD1jQhv5ChYOkMZDRuOE8XCLS9sxjutavHyFsOAivCIimxr6q5qCU8RVUzO1zMcoQlBTcgqIPCYTDiJLELKA0lOUCMzBzpUWFW2oXULnhI0yoUqBgyTxiOEopj/osTlJKHgDilFEMXLM9NKajmuvYfDMXh3iPqkIDQ1W+pg9VWxHf0i++J3o6M7sNd4rBaXnebf0QXSrcSMYxf14f8XstZZl2XULSqUUr776KltbWzz11FM0m7tQqzs9cMm0zULyLPA4pF1wi6i4xdr2ADXtcOjwIxTkFAj2WIAY6w/Snjkk5ROQZTPZdGaKjxDyFBwPlSdoaYrQZ6YnmAhBLgSO1igUJ+KTPKY/wPbGNgtLB6hXIpAugR9Qb66Qp0OGE8V0uM3lwRRXXaJQWqQcKMLKPpy0b/YkrTm3TLtI2yxUboRIe+ROhJN0UE6ISPvksoDQAxAOLikSgaNn3sIOyMCsFAhjdyRUhnZcS8guGoNvC1oznpXmswaVm4ORxuziC0Pc1W7ZfL5YU/H5nrr0zTRXAygj31r9fvN3vErM/M727duH1prBYMD29jYAzz77LKVSiWazSaPRoFqt3tW8ea92KO/Hn89oNBpXeOXB3SkohRC88MILrKysXHdf8lrxNnDZ8DxsfQGER7/X4vJ6i6XFBs3GIrCnEPQKaOmjhLfbYJOeYXxIK4+XATrpopwQsjbCK89t2ci65hybj5mksHbpPGG5zv7FBkL6FKKMWmMZncfm/DjqsbEzRm+2CcIy5aJDsdzAy2JrTTRgmhQYbF5Gu2WaixoZVnGSvqFqpz2UW51LZCGmvZWz+fqIPPXwvJT1Cz6Pf7RIbWWW+5rGD9NrMFrbotsv0GhoFBG12pTxUPPi4Eu4CBw0QmtOT1/l44VHiLxFRDJG6BxHjxA0za67G+36TJKjnZLZQ3drZmXSq+IAUW0Vb/H7eeN/O8351/qoXHH+RI/Xv3GZ7/jrB1haXqDZbFKpVO7ZSsA7reB4LwDL3jXJ653KFZRSbGxsMB6Peeqpp96+LzmLyRpc+s/mw1tNLIZ5AE4RJx+gcgWZRcxnM0mWPfxkE0t2tWbZOrEk1wwtQySK3lixuXGBZnOVZrOGEI5ZAvYaaJWSuw0cMlShgVD5rrTJa6CyKWtbI7J0ysrhx/BdiRZGNpBIF1+D50dUCyVq1TIKj8nQdF5aW5eYqgKVQFMoL1MO2jhhw+7imIIVN8LN+iALOGqExkHr1HjJaQ89bQMKHdul7KwFfs3YlLhlRNy21LEJBVdQqBVoVprkwmMy6jOcKLY2LpMql3LRJYxqlIrKeKjpHI2LxjXTDWn86rQTIXRGXn4c3Xz6tp//va+D90JBGUXRO7rYfSMYxf14f4WU8oYTxul0yvPPPw/A8ePHCcPwbfcxw5Xf6ntE5wly83NkFAw4wS+QxlMuXu7iuYLVo0/iCAXSWn94VWsjYg4deGV8fR4lFneBE2lnD3iiYvKOUzKQG6/GsvbJ3DpRnjJyPYp5SjLJaXfPsbL6IFGAhaPFpoOvExy/RtVPqdaaLKmU8TRlMuqz2ZuQbZ+hEEaUixlRqUJBa4RvSIl4VaQt4lyVQ6Fk/lZ4Bj+vFFkucUlQSGQ6mBek2qvu+u0mLVMApn1rf2QKUWH+iEb+lU2NtFdNwCnvfg7pKakuIMjsHr4tLoULjmMQ++RoHFOk1j4EpWM39fwJIahUKgRBwPnz53n66afpdru0221efvlllFLzvctGo0EQXL1Ivdm4Dy27H+9kXO2z9U44HLN9yTRNOXr06BVwkluJvQWljttw8f8HWrCzdYZ2P+bQvgpRtXFFAYhTtMoMz+xPzsiuSCzqAmQBlaemAaUS8CuWtGrPqW4R0h6DiWZn4yz1hYM0So6RpKZttFOGvINwI0qhoFRaYWkxJskEo0GX7ihhs3UO1wsoRVPyaYE3nj3DsK/w3TZRo8K3fJePHzUMOCho4KQ9cr+ESE2R2blwjizVLCwmIEvEwx6b5x0aiwKNh5SpOc/qFOF5CAHT2MfxXZKpJnFS3hCXUELgIEglBFrxcn6ZT7p1tFdDqm3jP5l2bZ7t2kbbdBe6o1K0E0I2NmdfNUYvfDtnT/tcfL1NWPJwPYcsyRltZTiTCnEc89JLL6G1RmtNp9OhXC6/Y0OBe9Fwey8Ay74pJa9xHM+NuovF4rWLyaQHF/+TlROk1v7DUrPyCcIJcPSOpULZvZ50AF5o5a1FkwicyHTonWj+tZMNEFrRXj/F6spRSoFjdPD5wMgG8ilaeDhMQLhIlZjJn8hAa9Is5vLly7hegYOrB3A8az0iHAZ6wG+2/j3fUfokH4i+xRwmpKFthbVViuRofZA0GdMfTekPumxspQR+n2K5QjkcE4RV66lWNoAh1zMOaBZCNOjusNlOWF4oodw6IhuQyQgx7YEbIDNjTgtqDhya0V/dtE+p0qQc9lCLx0gnHYaTjFFvg/bmFK9QpBS6uEEdn6HxokzaKNckI7wyev+nbUK4s7iXO5TXSgjD4ZBisfiOP4b78ecrrpcj2+02J06cYGlpiccee+yqr829FMNbfo+s/QFyfBEpFCruEOcO6xfOUKovs9ysWFsy41mGcAFtpK1gKIUIUlkybDO/YX7Wb9pJXAOVJyi3itSp9aWc8hebP2I9HguodMjGdpds0mP50FEKbmYaU2lv9+oEhrAtfNBjhHCJvJyoHrFQK5LlGaPxmPGoTbe1jiMhjKqUQo+wvGB+t1e004EQoac4bh10wnjqsb5xnqByACE9chGYRpioIJVCuAZQJt0qvdaY7sYUL8zZd7hovCrV1BSP+Qhm6xRuCZH1jFw264OQODo2MjgKc2CZUbHMZLIlZNYzgIul77i155BdS4NCofC26WWr1WJ9fZ3XX3+dYrE43728nenlzUhe7+9Q3o+7Gbd7hsyyjJdffplut0sYhjQajdt+DPM983wKl36PXLusXb5IkuQcPrCfQriHZj2XgLbNPrTuo8Xu13s5HrkM0SpHOI5pSgkHnCJJnLN9SZOnPbSbkage+1Yeolz0TR7OJ6YIzYZGCTIHhHXBLeKLMX6jRr0yIhcLTAc7DCYZbzx3nn5HUCwLwGew0+fsySKPPBWZx6Y0+E0cMnCaqHRKq6NwHIWWVSQ5SruoLCHPM4QjIbfsjnxIYylk5QBsXIrxky5ZXuTwAx6HKp8hzQekjo+rMjLhcrRwEC1CQJOKCE9Ik/+U2vWdxJxjjRd6FZEOdwmzpWPo5idJT62hlUYak0+kI9EaylGVxx5fmefBEydO0G63uXTpElEUzRttd1PFcTO2c++Hhtu7XlDeCnQCmO9L1mo1jhw5wsmTJ6/+g3kMa//F/P+ZvEhI+/8xMoNkRELR/DfPopn9mimgvJqRv7o1S3at2GsZnSes7wyAnOXVY0RhAbMTmKKFi1YJIBAoBArIQWcI4UGeMkk1m5fPUiw3WWqUwXPnJFeRdnlu/HVKasJXx8/yhHcQx2sgs5bpiKdttBshsx6+V2SxLKGyRK4yRuMJo9GYtdYGSnpU7MEpCl0cvziftHZb22y1O6ysLFMqVYw5rSyhNEb+ICS5ctDCQ+oM7UQ45FYanKL9OkIrlF9HKIUfLVKPBA2dkynBZNRjMEqZbm+S6RIbaxcISxWiIMH1QvLVH7ZenXce90KuANfXwM+6S/fCvuR+vD/jZq2VtNacP3+eN954g0cffZSDBw9e8z5vdQ9zFqr1deifQhYaSH2ezjCnv3OOhZWjVIvCFGHJ7JA065bPQDsjk1uzGFdkiHwAmSFNo1xQmd3xVkjHMwbaM8WYViAc0ixj7fIOOA6rhx/F9QxFELvXg5BmUqm1JXQL5l6PatfyyfVDqq5Htb4PlQ6YJJJJf5PNTorYegk3WqBS2CGsLuPnbZP7kjajWLO1fp5abZFGPUSIxE56jeqCJDby2HzE+TMZp750itEkIvCnvLmwwLd9/wGkH5jPDycErczEQLhmp154COGjpYcSvjksSYlyGhZc1kCo1EjbtEY7EXrlB0zj8xZjRhfc+/qaTS8rlQpHjx4lTVPa7TbtdptXXnmFPM+p1+vzAvNmppd5nl+VMAu70LL7O5T3427G7RSU4/GYb3zjG3iex/Hjx/nGN75xx3vmKs/g0u+TjLa4eHkTzxEcPnoM13FMjvIN9RS/vgvbURNyp4aTj8DfZ/x6bVGZOyVIOkaan/Vtfh2SpC6vfukMnW2Fyqco4fHYx5colyKbO6X5WaQla2vwZh6WhrRqlBIpyCKOTokqdaKy4k21RbmcGSGZmpKR0d1co72VGHWH59icm5Moh7WL56gshHSnMBpopIA0Danua6K9KloZFZ9QKThVpI459vFHqSyuM5kuUYpimoeXcdQYXTiEyKe7BHENaOORLnWCm2egyoh8YmFmEzOpzWO0MCRx7ZZAaHSwz+RKITj4SINSPWDYmeIVHNI4p9wIOfCw2Suf5UEpJY899hhhGM530Gd58G6pOG60Q/l+abjdU8nrFb/YHnBu5bBz+fJlXn311fm+ZLfbvXoy0Aou/ScYntv1/nICSKe2axODEyDziZnY2a64eaN55o0nJOako+0kTYB0STPNpUvrIBwUHn5QNvIkYTwitRIglTHWRhu5ks7QFjPfH+dsblyi0TxCs1YyhwQV2w5MTF96nJq8wkS6hPmEF7MLPCVDg2TOJwZskxt/TKGtB6eQOEJTrjSoRCP0ygrxcIfBVDJoX2AjcSkVcoLSAtm0x3CacXj/IkHgQG506jgFnHyC9qwtiVOCtIOSJWS6Q+oUcFSKkI6V9SprVD61nfcR2i3hMqZUrlAu5UymIZfX1vF8n253yPZ0k0nlYwROzMJCf04hvJN4L0le78f9uJvxVknXrZIIZ++tWzkwqck6bD1jYBA6JRNFOu02K4cfN40za0NEsGD3ymckQktZdWpzSakQWyhpu64ygnxkdrbzAcIzOYN5N70E2YA499i8/CZ+scm+ZoRwpd1/LENiyKprr53l0tmMgjth38OH2LcqzJ5kOjDT0Xxi876VxqoU6VWJvIwoepAmOUmcMRqP6A1GbLTP4/kFSlECAjqtDvv2HaZatXYnGAiE4/qQjw2pOumRUOaNL3+NOAkoBmOy3GXS3ubCax5Hnlyyyo7QQNjcsqVrzyaPRi6LzpBpF2TRNB91bgzH84n18BxA8zgUb2xhcNXn8ybyo+d5LC8vs7y8jNaa4XBIq9Vic3OTU6dOEYbhvLis1WpXvb/rKTjuBbTsfry/41oNt1uRvG5vb/Piiy+ysrLCI488clNrBTcKKSVu6wtMxi9yabNPreSwsHzY0kgLZldcuvYM6exCvGSAFB0yHdqi01gn5W4NnScIv2kgaF7NNOm8MttvnKOzrQjCEYkK8XTK+hsDVg/3EG5k8qgTmvOsG1inAktalQXzu6VtyAnXNOGsX29QqdFa61Eo+kZmr3MqzSKTTNC7fAElAyqhwAkbDFqXCCvLHD5Q4HItp3NpnSQvcexRwfKDRu2mZQmdj1CiAPmEXLoIUhaPLSGFRAthro5npf4ORr46mzzGBrqjt8idBUQ23ONtGVlfc88Ay7Be6CpFHfp+UzQDlWbI//MfHuf3/o+v090es3ykyl/+209Rblw5zJjlyGvlwbuh4rgZBcf7IT++qxNKuLmCUinFyZMnWV9f5yMf+QgLCwvz+7g6svm/weiCNdEe2E56/y2HlyHCLSDzsXmT7e20u0WzV2l16rMO0TSTbFw8TVBqsm+hzKkzAyNXcOoQD9BOZMxhvTIitaSrzEB5SAe0ejHd9jr79x+mXPTQwjPSKOGZw4/w+PrgCyQCHDSxgK+NvsYHCk/gCsdgmIUwt5sd7GYwCKdoaIVOA6ly/PIqC+UElpbJkimD4Yh2a4csy3Edl+4gpqgLlIo+wi2aN3ChbmRXfh0nG6ELVWQWc4E2f9T5Q74/+jT1wioys4TYtG8Mb5OOsSPJeoBj4EAqQwhJQUxpLjzIQtonKX6UTf/puXGwEGLe/Wk2m3ied8uvo/sF5f14v8beDvx4POb555+/JRKhEOLaOfIqofMp4sJ/QucT8jxnbX0DV49Z3X+EtNfjzZMJIp9QatZZXPGQfmk3r6bWkDvrz31612SLNEuoOE+gshgtS6aBV2iYJta8GDXXfuKzuXaRWvMwC40SQvh2f6hh8qRf5/yLF3j5y318d0KcFmhvnIRvf4h9B2YrCtZjOO3v5nXhAIk5uAkDHCt4HoWSQ6O6jEqHDBNBf+cyo8TBFxOGoxEyHxJWlnGx9ib5GBzP2JJ4RdJhzHjioXDI44BcO7ha0el4HMAHGRlEv1s3kjBv1893BuhRWprDnxOa3Ur7NzS08QEUltC3IXWdxa3mRyEE5XKZcrnMkSNHyLJs3rV/7bXXSNP0iunlbG/3RvnRcZx3FFp2P/78xc1OKLXWnD17ltOnT/PEE0+wsrJyxX3cibVSMXmT6egFtgZT9i81qNWXbGEXWbVGYAo86Zlchx1OMEGQ4uRTUHV0PkTJAqipaSjpxA45ckt+zuiPQ5Kkg/YrVCKf8VhDkpHLKi5TM4W051uR9XfPoW7ZSO3dsqVOlxFJ2w4PuuBVeexjVZ7/QkoyHJFmPsuHijz41AN4nkJxhOloQLs3YLS9g6KIk8S0xyFLh31WH/4gYvZvzUbGXk6NwXVxdGr3FGPD7hAuOo/RbojOTb4TaoJwzW65ge7EFuKYkxEghTByVrTJnVrvSl+18T0WaoJa+A6IDl/x/Bx6rMnf+effQ54pHPfqefBqOfKtefBOVRzfLLYhdxrvWkEppZx7AF0vZkbdWZZx/PjxK3bVrppQdr4KgzftiH9P57zQMC8+f3atgxoRCyPxNHKEzEhe1eyamA6RiulPHTbXztFYPESzGhqojziPciuobIxyykhlddxZf45N1p4x2d5ojUlGOxw4+CCBm6GltCS/ANQAcJmqPqcnLxMIcDRkQuBmI05NX+MJ7wjaK1mt+IwSWzXX2ddOhMwHIAtIPQYECCMbGPZ7FBzNoSMPkk6GDCYZra01LmeSctEjjKqUI43n+1aiFRiJlgNf7H+Dkcj5Un6Sv+wdQzkBUufkoma8i5yKaRLJotkXFQrtuuhsSCINmU2FB5EHf4D90mf//v0opej3+7RaLS5cuMBrr71GpVKZv0FvVkI6k3S9kzEDm9wvKO/HOxXXk7xerbN+s3ErsjC99t9BKaaZx6WLa4TFEmqaMx7CxukpKsuRrs/gbAetG+w7ZDvfM4VHNrKEwgETcl51vgxJwKHpMtKxthtuaORd7swX11y7gyntnTWW9x2kEgpzkMo7c/ksuKCnnH+tjZQ50g0peILRxOX8yR77jh69UhIrPdu1tnYes11PnZqf0cnckkMETaadc0xUyOEDNbQsMOlv0xmkbG2/gl8IiaIixWJIIYgQKgE3ohjEuJ6D54wZT0PK4YhJHLCwECOFgqyDFi6onFyAkB6oBOGXEPnI7EnqifmZfGJhE9bnUytwS+jVH7htj16484ab67osLS2xtLQ076S3Wi22trZ444035jto0+n0mvcxy4/3ovF3P/78hOu6xHF83Z/JsoyXXnqJXq/Hxz/+cas62I07YXmowQWC3pfp53D44ArFqAZkVm6agqxZmWnFSPaltAUQoJXdBJQolaARRi02IzvLwDBA3BCSAeNEkGWXkK5L6GRMpy4qmVJcbuA6OYiSXdGqAplRvGm7pqQStN1TN7Z5I1OY2d1OkXaJ6jWOf7em39+HK0ZEC4tI1QUdIbM+WZKR9NfZv+8gYcFjMI4ZD7vsbE3wPJ+oGFIqC4phCSEdu4pgrOGEhYohXMMW8SJ0OkC5ZudRuSUDgPRKyKSDlgXTtBMSR09xlESoGSyzZOz2nJJ1DzB2Vjo8CAvHr/lcXauYnJ3tbnSGvNb0cmNjg1OnTs2nl41G46oqjhtBecbj8fviDPmuSV7hxpSu2b5kvV7niSeeeNskc74QPdvD7L0Gm39ouh6WXCqSHYNsTzp2Ytkz3eZsiBQujhoz9w+ToX3RhmaULn1QKVvtId32JvtWj1IpWZsNnZFSROU5uV8xxEOLhNZe0yLk6+RpyqV1A/BZOfIkrisMRVArU2xqDXggwFGCR0pPkeYTciFxycmEy5LbNHuL+ch6SA5NMZkNDTU1HdouTWylFWZnUzsh+bTHxc0+oZuyePAhnHyAV6oSBX304jHSSY/RJGU83KGzNcL1Q0qhS7GyROjlnNcd0mQdIR26kzdohessOhVAWJlvgpIhpMO5DUrmlpCqj84Unp5AnqIOfuaKHSApJbVajVqtxgMPPEAcx7RaLVqtFufPn8dxnHlx2Wg0rjnFvhcTyhkJ7HpQnveDXOF+vLdCSsnGxgbtdvttnfVbuY+b6cDrna9A90UGE8H22mnqCwdpVgu8MR4x7fVIEk297qJEgelY0d5O2XfYA8czhyTpgZztL+Y8N/wzBo5A6QkvZxf5kP8BA5cAoxhBgxuhlWKrPWA4HLKyesRMvPKU3VUD7GHMNQciDYFvaIehMyGduvjOwPy8tQYh6RgJbNw2Hrfz77etdLZtSItqgNIO62uniZOMwwdX8R0XHJ8wKNCQHmkyZTRJGA37bK/1cWSPYqlMqSgoRk0+8t3w1f96DilyRtOQ/UcrHHziKNIBLUpo4aLyKVoUIB+ROxVEbCaeIumicZBqMi92hYrtCsEQtfQpCJZv+TnfG3czPwohKJVKlEolDh8+TJZldDodWq0W4/GYU6dOsbOzM1eczJq/dyM/fuELX+AXf/EX+frXv876+jqf/exn+aEf+qHr3ubzn/88P/uzP8srr7zCwYMH+fmf/3l+4id+4o4ex/14d+J2JK+j0Yjnn38e3/d5+umnr7rje7uS12S4xfqX/nd0nrFYi3CEx/rrZxhPC5SKMY1Dq3jOyErwp1aKalatjATVBxmYglKExi9X6N3zoFY2R6X0hgk7W5c59PBBqtGYy2dGeHpEebnMIx8JAWUUIk5oFHkyBGWhPDMbkjy2U9LM8iu0yY1KmyKTHCdqUg9zENbGyC1BntLqjOh12+xfWSWMiqAyGvUKjUqAEj6TQZthrGitv8G6KlAOISgtEoUCN2jskm11Yqav5FCo4GjQThOlc7RTQ2vIZQTanpGFh9IC5ZpiWXu1K+BmZp2gD06IWvnMbUEeZ1TU6xV7b42rTS9nefDVV1+9YnrZaDQIw/C6O5T3Ja93Ka7XHbp06RKvvfYaDz74IEeOHLlmQgFb/ccbsPb7xgA6m8wlQ9qzMizrpWM8d0xnRiYdFK7F2VfstWwPICV03GZ9e0g6aXP40IOGNKg1pG20E1J0JqxdOk85cgmjOsXANYQ/PTJwicmUtcsb+GHAvsUFhMwRaYJ2Q4OAdosIO/4XKsaVId8WfRJEAMRGg68TTHfeLDlDjpaRWUB2y+YqIvMzZIBrvCSdMtPpkItrA6rliMXlFSu3sp0rr2p2tsMGtVBSa+TkWjIZ9hiME3rra6TK4evlZxhIF1fnTIXDs+Pn+L7y9xgftnyM8OvIrI8O6pAOUV7RUgshzVIkGcnCXwDXAHyu9aYqFAqsrKywsrKCUoper0er1eLs2bO88sorVKvVeYG516LjXpnSAtf8Pe8XQtf9eO9ElmUMBgOUUnziE594mwfbzcbNdOD16CJ684tsdxP67XWWVx+gFGhj/cEZlNyPL7ZQYgFPDBjnPpHfB7lscqZbmVuBkLaZOB5nBs8hJFSV4muDL3N0WqNU2YejrTQrH5OJIptr54kzycGDB/ALZaMKKRivMWOlkVqQRAJOnX0PDHn9OfBcxTAL8dycxiErcyo0zMHJr5urVzHTTS/atYDKJmaCqTPyPOXy5jqOSjh86AHz2JzAfk4YD2HPq1BjSq2yD50MmCSCyahNZ6fN9vpZgkKB4z+4SD5KKZTrLCwJQzjMhginhFBjpGf2ObUbIrTxGVZI0twjo0BOgHBCBDnCjxA6R0UPQPOTt/WcX/HcvoPQMtd1WVxcZHFxkW63y8GDB8nznJ2dHd58802CIMB1Xf70T//0tl+/sxiNRnzoQx/iJ3/yJ/mRH/mRG/782bNn+b7v+z7+1t/6W/zWb/0Wf/zHf8zf+Bt/g/379/PpT3/6jh7L/XhvxPVy2/b2Ni+88AIHDhzg4YcfvuZ74HYkr/3uDpe/9E8pFwqEJZ+cAhdObtFrZRS8Cd0th3h8ngNPHjHrQDOrJL+6x6+3jeMI4t46a3lKKXQoVpfwHW3yFClaeGxvt+h2hxw48BDFYpFqU7D8YEqea4JQIqUwDTcntI033+5oOiZnaozaQ2O+RmOUa5rZBHG+PiVDezX7lkp4bG1vMRzHHDz0EIWgCOTgOEAKThGpEqLGAaJ8jN53gGTcZjhRDHsbbG1JQn+NsNQgCgYEYRmwE2Vhp5fSN7Jfr4TORminjM5GKBGi8ylx7qBVRiarSJ2bv50A7dbMpFf66P3fa4rf24jZc38nKjfP866q4ti7gy6EIIqiq55Xp9MpeZ7fl7zeaVwtIVxrX/JatwfIJy2cjT8wbwgBSGse7dkPMddStrwapjNjiir8BlpvouyOy5zC5dXJ0gkX13o4Imf16BO4QtvOT2pf7Cmrhx9iMhkxHo3Z3tokzzPCYolSVEA6BbY3L1GuLbFQK4JXtHLVsvEqs5JY3KpZNJ4vHJcRWXt38VgGoMe7BCyUXUZODKAnG1utfMfIHbIO2q0w6V/k8maPpXpItV5BZH3zN5EO5JkpZq1mXWRjtFvEVVNK5SqlUgpLDc6MztIe9tBaoDQkUnM2Ps9lf8BK0beWJLm1CQDcMlI64OUMBhN22ttUDn4KVXkcved5ni3DXyvJSymp1+vU63UefPBBJpPJfI/n7NmzeJ43Ly6zLLtnBeX1JK/3bUPux53E3g+04XA495dcXV29o8P4jQpKlY5RF3+ftbVN4iTlwOFHKAQBs4NHIio0qwUG7QV6rQkaH9dVVFYPmTvwG7twHjUFr8GJ3p+wIyDSip7jUFITvjE+zeHtFm6xQSXo4EVN2hsvIwo1jqyUkH64O0mMW7ax1zINwLhlisq0zcMfWcBjxKUzEzw3ZeXhZQ496JgCMpuYx6RzSwuUtmvtgtTWQDwBEZDEQ86v9YgKIcsrD+OQgyiaA5XXAHK7HpHbzw2F8CsUPYy8Sbok8YjhOGMybDFyFXmyxla3QbkwIYjqyGxg8+3E7JQ7AY6agl8mGe2wvjmgWRI4rkAkLWM3lQ4QwkEd+KsIDfIO1fz30lYpiiJqtRqHDh0iz3M6nQ5f/OIX+bVf+zW2t7f5nu/5Hv7aX/tr/NiP/dgt3/9nPvMZPvOZz9z0z//Kr/wKR48e5Zd+6ZcAeOyxx3jmmWf45V/+5fsF5Tdp3IyXudaaM2fOcObMmZtSddyq5HVjfZ2Nr/8Kq1XNwtJB1tcuMhnm9FoppVqIdCPcJGdnR9EcpkTVpl2dapg84DcgG5K7VSrFLuGxx5kONulPcjqtlxB+jUqoKZSXGLTeZJI5HF2p4hUL1tIuouDbZptK7MqUtmq6zMrjFdbXCVN95ZaAbWGTKjZDi3w054Ngz6XmLNkjlyW2Lr/KVAUcXangBAW7XjWzQamYFQenYKefPkIrCsU6hSKwsECWZozHY4bDIe1uD+hRKoYUy1VKIThuGdTYAtV6CK+CTPtorwhxn/XNFgWZEhWqiGyCzhOUExpFh1dBpANYeBpKR2/6+XtrzArKd1LF0W63OX36NK1Wiy984Qtv20Efj8fAnUHL3isKjndV8vpWycL19iWvFlJKpE4QF/4DMLJF1+zNo8xhQmlwpH3TyTmYAeMKaTrG+cT8rKNBKSbJkMsXL1CMKuzft4BwXFOEIdFaooSLkBIpPKKSR7G8yIJKSdKc0aDHdmdIlg1w3ApCekyICJGoQhOpDBZe6sxAGkiNjFXnlvSaoV1DF9RuCVCgw3lHx9BdYyPnzSdGCpuN7O7mGO2WGHQ22Nxus7zUpFIq2b+D6YyLfGQIjFnPSFTT3q4xt/AQ+djKLXwW3AKPFB/Bz6aMHR8vHjPRLsO1c5wVO5SKAcViSBjVcEjmJMPuGDobZ1g++EFKT/7PKC3mWvWZRHn2vAsh5vu013pTh2HI6uoqq6ur5Hk+n16ePn2a8XhMEAQEQTCXWd3tncqZXOFa9zsaja7thXo/7sctxMbGBi+99NL8w+h6RsU3E9eTdGmtSc78n2y8+TzCiziy2sTxhbUAMbRrX8YUCgkPPBbQa4HKc4qVEtVmBlpAMto1msZDM+LU9CwFINYCFxgj6EVdDix/G6PRgE5vQNy+jJQhFc9nnIcUNUi/bnKPLeDwqyaPuxXAwCkkmgc+cpAHnnJsJ943haTjmQOOV5ofkMwBrLrH/80UrPHgMpc2ejRKDo2l/dYs2zP5FXYPX47d25nB3PyqhRAZf0m/ENFwp1A7jFK5OTyNYtZ2+uRbQ5MfS3VKRQ/PrzCzEZlMRlxcG1CvVVhYXEQAyimgEKBS8sYnUU4Vsuym8uP14l5Cy/b+HsdxWFhY4Id/+IcZj8f863/9r/mBH/iB+eHpnY4vf/nL/KW/9Jeu+N6nP/1pfuZnfuae/P778c7HW4vB2b5kv9+/aVXHzUpetdacPn2a7qnf5dGFmKi2AknHnAPzPo4QeGKMyYEZjtCm0aZikydJQRTQdiCxEa/xZnKObyt9En/xGBWdkOtDTAYdusMJ7UvnyfGoliTTPMBJ+shC3XI59gwhkvaV17Rj1Xk25+Wj3eLTUvlxS3YSWTePq9Cw+5amOZiKMpcvnkU6ZQ6u1nG8ImQjtFOCdAbfGZjzdjY2Q5xsNin1jMzWCXHVmEqpRCWEl2o7VFIfJwnotjdZjzVR4FEsVSlFDn6halZL3RpZnnFpo4fjBKweOIx0C+hsYnYt8xHKNcWkClbIaseRdqhwO3lu9vn6Tqo4lpaW2NzcpFqt0mg0rthBdxyH3/zN32R5efmOzq3vFQXHe2ZC2e12ef7552k0Gjz55JM3pWkWWrE/+zI6KUJQtsRUQ8qaH3JmlK1ZJ2XPVeRTU1vmEyuLGtEfpWyvX6CxuEKjEphiLDF7lzqegW86tkPSRrtlZNoGr0og+vS1ws27LK0cQ6R9+tOYwbmX0U6JcgBBeZEokGingNQTwEGI1JCrtIdQCdoNELmdQKoMLYPdrhKxXUROzFWlxgRWa5QIaLc6tNtDVg8+QDEsoIS1LXFLxtvMaxgs9Pxaw2hfK5i3lsZIbFPKxaN8d3AALXxDHRMGzayXNZNJzHjUZ7OToDbP4IdVytGEVDn0O1usHHiE8LEfB+HMu+x7JcozD9LZ/5/FjaaXjuPQaDRoNBo89NBDvPTSS+R5Trvd5syZM/i+P+/+1Ov1W9LGXytutFA9HA45duzYHf+e+/HnN7TWvP7661y8eJEPfvCDLC8vc+rUKZIkuaP7vZ6kq3f6v7J56uuUK/vYt9Qwu+FY78Q8MTs4wiUXBaKoQFC2klTHwmNm0ikB5DlKCnSW8D/VfpBx3kVL1+YvlwpFfCcnJkYlQ5abZbxCifGgw9ZaB6GmBKU6pcAlrDRwdWKKw3xs1hVUbAu7id3HmYJjc75fM5NHuwtkrspOGi2ATRsfuOGwz9p6n4XmAo1G3f4lpNnRnBWo+RT8yByW/Lol15bN58BsT0kYy6XZJFSqKaVShVIwRO9bJh1tM4glk+4Ftjch9DTFqILjSNqtbRaa+2hUfPM40wHSducpHUYufQKNKdJuNT++Ne4FtAyuTzEcj8csLCzw0z/90+/445jFxsYGy8tX7p8uLy/T7/eZTCZzOu39+OaNvefH0WjEN77xDYIg4Pjx49f0RL3afdwox84K1az1Ih9YVRSCQyYf+XUkQ5yoTLk+pdPKCQojktilvuQSllx7JnVAKZTWaC1AZXxh8EcMkh0OuUsc9lZA+jjEOH5AMtkmLDepV8sMJ1N2WjtcTnJKxSFhuUYlSvA8YyuivYaho3p1yAwUctb0epvyLWnZa9sMK7KWpdFaCBAZcQprly/hB0VW9u/bXUt0Igs7s0X6jIdhhxy7q1mxaeJlQ5OX0y4dMr7e+2OkW+H/Xf1hZOMgWTJmNJkyHrW4vHMB6QaUQgev2KC3cxmv2GRlsWL27rMJQhZwdAZ+hFQa7S2Srf5lpOPe8oBib+R5/jaf3nciZvnxrdPLM2fOMBqN6HQ6LC4u8sM//MP81m/91i3f/3tFwfGuFpQzycJsX/Khhx7i8OHDN//kbv4RBTEgF4um8JuZtzqVXV25ysC32nAnMBNKx1L0/AClL5PLClLAdjel195m/4GHKUXB3NtGezV0PkF5NaSKwW+Y6aA3A+XU0OmQte0x+bTL/iOPUhAZurSfcjZGLS8wHXUZjmNa25fYzHKiYokoKlAs1XCFnR5mM8uRvpkizhNCZ48Utmg76r45VAEIB50lbLcHTIdtDh16iMCJ0QRIe1uZtOa/AyERMymEE+zxSdtrdRIgE0NtFCI1k9MZzloWiYKMYrTCYh4T55rxsE+n3SXJc3zXZ50PU+ln1OtvP2jsPRDNJpZ7C8y9MoTZm/168tjZcvRMZtVqteaH8VqtNi8wb1eWeiNT2slk8r4gdN2Pdy+SJKHb7fLJT35yLn1xXZfJZHJH93s1SZfWmsun/ozpyd9hcekwtZIEhJWYzsBlJesb6Vs/x7r1NyvZ4q1mJ25F0CnKC9B5iigUKWpF0V+0REPjv6txae2s0+pOWNl/gFKpDjqmVG6yqCZMEpgMdmj1M/LtU7jFBtWgS1jdR0H37P5R104ezY672dssmq9lYORTc5WKxfOr3a55b5jQ3r7I/v1HKQf2M2Yu3xqYQ1M2NPeVTawnsd1lF8LIbmf0WGEBF7P9IzeynzXGEsUv7aMZ5dBokuU549GIdrdPPOmDLBEnKb1piZKIcbwyZFNzHyvfj7T58q0NuNtRd9yoGXY34mYo2Pd3zO/HncbVJK9ZlrG1tcWLL77IwYMHeeihh+4qBXsymfCNb3yDoujykX0bOO6eAYVKQQRIN+HA40fxz7eYThS1yGHpcB3pgpGcKpRWaJuXzmUX6KYthBD82eYXkKO/xIGH6oymMdsbl6k1FmlUI3A8igUFjVXS6ZDhJGPc36C1keH7LlGpQqkUEQQlA7pxzPkm1RHrr7dIYk1z34j6Pqt884zXuVlVmk0sk3kOm07GrG1sUq1ENBcWbDNvZgVSRKQjew4150Rjj1KxP2fvSxbsWTvCNPUqfLX73+lJB9SQk/kaj3tP4AYu1bBGtZahcJkOW3SHGb2NC+SiQCXr0x96REGC61t5Lgq0h1Qx90STuQABAABJREFUevWv4kdGFXatAcXes+P1gDjvlu2c67o8/PDD/L2/9/f4+te/zh//8R9z+vTpd/yxwDun4HhXC0opJWtra4xGI5566imazebN33j7GUTrWYSQiLQFwZIlu87G/1XTkZkvRFdMh3nm72VJekJIVNLl4uaULO5z8PAxAk/bDrSh86lsgpY+Umd2pzFFy4K9FsnShEuXtnEcwerhR3Acx2CghTaTQeEQlguEFSNbjRMjjW0NJ6zvXMYrFKmUEoqlOiHCSmEztN+wU8UaaLUrhXVKFj3tgnTRWcyl7SE6mbBy5Ek8maJlzZpjlxFWgiC0MsWn9A1y2S2Z5OCaHUvtFs1+pzSTUgAtHFNw7vXWTLumOE17ICS+lPQnPbSAoyt1xqWPspYf5LXXXiNJEhqNBouLiywsLLzNr2f2Zr7W4Wnv0vTVDk97E8JMZrWwsIDWmvF4TKvVmkMibsag+2pxMx5C9wvK+3EnEQQBn/jEJ6743p0g7WfxVsqrUoqTL30N58L/yeqBBygWXCtdMv5hc3Ps1JBIC4zQomQp2TO/ycpu8Rm3DIUv6SC8KmJOVG3NyarKKbGz/hqDWHJ4f5VCceZXWTSofCeg6KcUF1dpNnOSfB/j0YjhcMzWuXM4nkcpiimVKoSeQPo1uyNfM/8o1/rXSteuBig7FUjB9dH5lO1+yqC9xb6DjxL52k4ah5b4PTDT2XyEKULj3X2jmeR1Vngm3V3IhhuZ7zsFc19gVRyJvd8ReBVcNTKk6GmLlf2H8UTKcBTT3bnM9nSKH1UpFX0Kh3+QomekX299Dvc24Pb+70bTy3sxoZy9vt5Ltkr79u1jc3Pziu9tbm5SqVTuTyffJyGlJEkSXnjhBZ588kn2799/W/dxrRzbbrc5ceIEK4slHnZPIbRjrZEKxr/RiXD1gEz5BE6PQ48s7KoZ1ASIQE3JCdBqinAKQM6fjp8jFhIlYBIO+OpLb7K5cYDKAc3+/UcolyNA2zxWAK3wCiH1gqZejVBaMx6NGY1GrF/eRiCIopBiVMJzfL7xRxcYtCZoLTjjwGPHj3DwwQo4wpwbhQP4tjlmmn6Dfo+19R7LiyvU6gu24IzsJLZmJK9eHWbnxbRvp5xdU8imPbt+EMPMa1PntLMha+PX8BxJkCu+1P0Kjy4cRPpV20QLkJYoOx5doNJYpVqtMByN6Q2GbGyN8fwC5SgkKlUJCw4sfCuUH7riObzagGJvnpz93FsHFO8FH/OZZcjjjz/OE0888Y4/FnjnFBzv2g5lHMd0Oh0Ann766Vv7B/RehZ0vo/06nrhM7lTA7hOS9uceO0ZrbjXnyR7tuV81yHavii/GrG12KMiY1SMP4aiJgfqkXZRTNAcpp4jMDJ5YZH3bhRmD8JhOu6yvb1IsFllabJhpXja2h4ypLbxmJrNmbzGQI4Jmg2a1TyaWmfQ36Y8n9HcuoUVIuewTRlWiMDB1o+MZCeycChuZIs8pkiVDLq1t48qclYNHcRyF1qZLrzGHRS2VfcymABYqRTtmL2l3X9PSs4RjDkXkaOEhdIqWTVPgenUEyspkMXI4JJvrlxjHPocO7sOtHqG6+oNUbTdxOByys7PD+vo6J0+eJIoiFhYWWFxcpFKpvO3NfL3kcLXD07WmhzOqVhRFHDp06ArE/cyge4a3n6GdrxU3KihHo9H7gtB1P97deGsH/m4UlHvvI45jTjz/DWr9P+LIoRW8gvUuEx44rjloSN9KmwogIHMqKO3sAZo1zNTPM11v5VbRKkX4dYROd/1/3RLkUzJRYOPimyjtcGh/E88PTUd7JhlF2iuQZ6AVvnTwI49aZQGVlRnHOZNBm831LiKfUCg1qYSCoLKMq8a2STgr+HoW6NMFt4xOuqxvD4gnfVYPHCYIHMOq0DYnCs8ciOYTB0tJdGeTx9D+ey2op1A3X/sVux1QNtNL4e3i+f2ilc3WIBvT7id0WpvsXzlAsRhCLglKTRayIakoMult0M7+/+x9d3hc5ZX+e+/0Pupdsty7ZEm2Mc0mFIObDAmksQayYVMw2QQSEsiGbEhC2MASCJCQ8gOTtjQbbDDd2KYZbKvLarZ6m977zL3f749bNJJVRlYzMO/z8AhJo9EdeebMd855SyraW/2Qd36AtLQ0ZGRkIDU1NSF2x3ja9InYFdOBiVyw52JDuWHDBrz22mvDvvb2229jw4axs+qS+PQgFouhra0NLMvioosuOuf337EkAb29vWhpacGSRfNRQD4GIlIAEn6hwGdLshGwMj2ogAuQ5fA1SEgTMABRJxhaByrGObvSMRfaGRtI1AKKlkDPMPBJJPAvOg3ThwYsWl0InYFngtBKcFmWMu4jZKI/CA0GWqMWWgMLAiAU8MPrD8Fud2DgTAgeuw+EUkKlisDvl6OzugN5CysgiXl4Jppr2OLFY+2C2RlGfoYGGqMRYDgWBVenFbwmUg0uY1PLybOEM6DwkZaCO0DGRGkUaDUOt78Oj1ECPcvCJ5FCx3pwfKAZF+Qu5R5j1INAIASTyYSMFCMMaZw3iCLFgHQdjRidi5DHBE8wBnP/GYQpIyIRA9KZAaSnp59FbZ7sgmI2TB2F6xgvdi4+ueDTjDnZUAp6ScGtc1LNZKAPML/LU60YMLQaLMvwga7cih2E8Loalgt2FQJeBeEx//VgwIsQ0UAnlyMnfxFosIBUCUJiIBLuoASZARRheGMIFkSiASgWIGp4fV6YTVakpGYiLUUvUquIVMdtAGUGrvGT8x9lRn4jaADFeEGkWkhjAegMGdDpoyAkFcFgEH6/FzarFdZYEApNKvQqCip9NqQxoUnmaKwxvxl9JifUCgqZ2XmgEAZinKEOoiEQmXbI/TXqAJHqQUccQ+Y8FE914zMrqZhwOOOcYukoT5NlfABFgaIkXHGRcdtdzgmsFRFWjuJcIyQyKdjszRCI9/F5PcXFxYhGo+LGsLa2FgCQlpaG9PR0pKWlTbo4RKNRRKNR8evjUb/iLe5Hs3YWgmnT0tJgMBjOmvJP1FAmN5RJTDcmyulNBEJD6fF4UF1djXxZG4qzCCiFeijyQ4z+4GOTovz2MBqAlIpxmV+sepgWnVAyECbCufuxUVDg89UkvCGaVIZwlEHfoBUqhRpndJ04GazFteobQLECVZThGlnhczAAJLxzNEeVpRUaaOVRaA2ZSGdCCIYJgj47rJ4IGGsbZEo99Bo/1No0yGMBUBI1v0WQg436MGgygWUJCvKyIZVLOSorn0PMPVYXd/gLO+I2qxogEr95pIaMeqQqbmAomv4IzSwfN0UrgYgXoGgQhGG32eD0hlCQXwClhqOcQZbC04YNkIGFLGM+9MU3ooBSwul0wmazobW1FeFwGCkpKeIAbuT75Fj1MZ76JejDYlMwrpgIEzWUPp9vXLf2RODz+XDmzBnx887OTtTW1iI1NRWFhYW4++670d/fj7/97W8AgG9/+9t4/PHHcdddd+Eb3/gG3n33XTz//PM4ePDglK4jibmDcOAWXLCF88JU3ntHbijjUwbKy8uR4j8Kp60ObgmLeZIs3ima5eoVJQVF0YiBH8LxZ07IU0BYBqw0BYQJAfI0/jyYAleoER6JBDqGhV8igY5l4NVFMU8bhkyhG2LXRUf5KOXNduQG3mFVDYqNQKUxQqWOAFnZ8Ay0wxQGFCoWLp8KUoqB06OExx2AIS0VFIlwZ2AmBFZmhN3cA5c3jIJsI1efYl7ed8TDpwnw9Y9EuEEaLePrvJKvg1qeiaGL07tzme6ECWNQYYcEgJeiQYHATUvQ7u/DBTKOjeP1hDFgciEnMx96YypHyZWncBtRqQ6SmB8aQxo0uihApcGTcT2s7hh6e3tx6tQp6PV6sT7qdLqzmrKJFhThcBgURc1ofQTGX0rMxcBtphgcs95QCpOfRYsWIRwOT+7AFHYAPc9xL1o2BlAyyBHiLeOFF0CEt1CODJlMgJ/yUFLuRQEaXq8bJpMZcppGqiEdNBsSqVKEjYKFDDSJAqD5jZ6EM5iQKAEmAIc7BKdtEFnZRdBpZSC0gjPU4QNcCZ+hRiRaADH+c5bnr3M5kaAojjZA0QCRAZQUKokKKkMW0tkYQhEWAZ8LNl8IEXsnZHIV9NooNDo9SDCEvkE3jHoj7zBKgVDs0GPls9xYeRpHX5UZOT2oVMdNmCSCRjQGUGqu0MhSQbEBjtLAhrkmE4Tfcsq5mBFZCijGgxilhqm3DYSWoTA3AzRNgc2/jjusjQGZTIbs7GxkZ2eDEAK32w2bzYbu7m6xOAjUWK1WO25xYBgGp0+fRigUQkpKyqSMK0ZaO8cH0546dQoMw4jby7S0tHGLgUCtTWqEkphuTBfl1ev1oqenB4tzJSigQwCdzk2S5WkQIz9IFFCkDTU8vNkNRTkRo7VckyczAFEvWAlH9SRSHReNITZXQ01W0GNCn8WHNJ0UdLoOnZZqeCU0+nz1KNCs4JpYeXwj5+Dv38E3Z+4hnaRMA4TtoCQqqKURqFOMSDMSRJk0BAIBeL0+WO2dkEkoaHQp0KqVkCpkGBg0gZJokZ+fCYlEGaf55B87iQ1Fg4hRUryzrEzLb2xlcTRWLW/glsLRY2U67lBF87oqiubYXmBApCrYBjrhDVGYl2uETMXThmkFQNz8Pw6/0Sy8CpBqIAHOouxbrVZYLBZx6CV8fzTK/sjDk8lkwsDAAJYvXz5pbfpkINTHsSbs01EfT548icsuu0z8/I477gAA3HTTTdizZw8GBwfR09Mjfr+4uBgHDx7ED37wAzz66KPIz8/HX//612RkyKccgl6ysLAQxcXFOHTo0JS28PEbykgkgtraWkQiEWzYsAEq30kQVwNe9R9GLGLHDVnfhCYa5gdSbkCihIT1gyZhrqniySUsaHBEE4qLURMMDcFgje4SFIZW4sShbkSjYVCUBNmEgizDAKVGCiLlmypZCm+2Y4wz2+EkW1TEGWe2oxfjPKiIAynpGhg0A4gwKhjUIfgDUhgMYVhtA3A4+qDSGqDVqKFSaWA19yEYiKCgsAgKuYK7eFrPudJKBdMdXj5Aorzzt5Aj7OfNd7hhmkiBjTp5yYQbgATz310JRhYBy0ogoWOIMnIU5emAZSw89n6Y7CHkZxuh0XOPmzuT8md3iuZqLi3lqLlZV0JvKII+HViwYAHC4TBsNpt4hoyXPKWlpUEqHd7ejBzAeTwenD59Grm5uVMy9kkE4z1HP0sMjllrKFmWRWNjIywWi6iXbG9vRzgcTuwOYkGgdy8APluMlgAgILQcLCF888jyHwnXXFIAl0VGuBcJIQBYDNotCLrcyM0vhNXcDwp8Ng+tBBv1g0hUoBnOgp7TDeq4mA3+4GN2hBD2mpFXsBgqaQSEUovmN9wkic+QlAhiYil3bQCEUG0uZDskRnYMZUlyv4NI9VDRPihT9EjTyxAjRgT8HgT8LvTbTaBIBEpVCjRybiJNMz5OE8m4eVqrBwRS0KIWUsrTV1Vc7Ait5mmzSoCNcFRXNsy5kInUVwUIeLdHEuEKBokhysrR298PuVyH3JwCUBQLNm09oM5P+PlAURSMRiOMRiMWLlyIUCgkFofOzk5IpdJhxSG+oSOEoK2tDTabDWvXrhV1medqXDEymNbn88Fut2NwcBCtra2QyWSQSqVwuVyj0nSTphNJTAemm/IqDG1cLhfKVs5Huns/d/8RH79p9AJSJf+5CmDiNIUSbhNHKBm/mUznBlQSNUdxlaWAhtCQxYaaUnkqnE47zBY/crIzYTCk4x33a3BLpJAQgiPhRtyoXgZKkco1UzIjd4CQG0T2A+fCrQIYwXU1yh9qWJ6qSgNsEDK5BgY6BkNKEUjEDX9UiqDbjH6zB5IYZ2yWoWNAWAZg7Px9evmIkAAAljusMNHhm8eYlzcBEuJGXLxOyANAAiAoHh65w46SOzTSOoANg5WmwDTQjVBYgoLCPMikcgAUd9+CmQ/4jaxhBaBfOupzQaDsz5s3D7FYTGR3NDQ0gGVZUZuelpYGhUIx7OdNJhNaWlpQUlKC9PT0SWvTJ4PZYHBs2rRp3AidPXv2jPozQp5rEp9+dHR0oK2tDatWrRKH0gD3vi+Tyc7pPoUa6/V6UVNTA51Oh7KyMkj8ZwBnHU6zLjhiDsQkNKr8J3Gp4Yq4pioESNQgrKCr9IOVKLitJCUDRbHckIrln7cUBYqw0KhYFCxkYO+RgiYsNCla5C/SwNrngVojgS6VW0Jwxossp2Uk4DeghGs242PmhGWBTI+8hWG4rLkYPGNFOCyB3kBh9cYFMKSrEQoF4fcH4LAOIBIjkIKBMS2LGwpKaTF2iTOHFLxGDPzmUQ0x95LE+MEc4RmBiEsL4K4RUm57m1uch85GJ2TSKMJROVTyEHIWFsNl64XNFUJhjhFKjY6ryyD83yvKmwC5h6i5qeWAYXidVCgUYpwcy7JwuVyw2Wxob29HQ0ODyO5IT08/q/4IjJ3CwkLMnz9/0tr0yWKiDeVUc8zPFwbHrDaUDMNwkx9+pToyh3LsH2aAgVd4Bz65aLYgZOuwhOIOCyQa5/LH28BTKn7KQoNlojhuasRx+iNUFnwJGlUKTFYPGEoKVqIHSAysLAU0OIdYwRgHiIGVp4GNhdBv9oMwEeQVr4ZUQkBoLdeQyYwcdVT4KOW45qDjMiR5sxuxqZMahrZ+/BRKyJKkmCDnwMpz0qUSKfRaLRgiR8DfC316IRB1w2QPgDbXQa5Jh07lglqbCikCAAgoiuboaXyRgKgp5RpfURMKitc0RfjberjGNubkriXqEItbJBJC36AdOiWFtJxC0IwLRL8USFs3peeHUqlEfn4+8vPzwbIsnE4nrFYr2trahlG/0tLS0NXVBafTiYqKimHr+XM1rohHPE133rx5iEajaGlpgdfrRUNDAwghw7aXMplsxhvKJ554Ag8++CBMJhNKSkrw2GOPYd26qf29kzj/kXB9HAWxWAz19fXw+XxISzUiLXyM64EoKZd5TQhPX4qrlbRgc0+JOkcpQiBEBkQ9YMHRPilaCioW4n/ez3/0gVBS2K1meHwBFOVkQKWWwxPuQUegCRq+AwtGTOgNtqJQWQzEYoCU4bST4Oz0Oc0QzTe8/AEGYYDi3WWlGn5angYgCsjSACoKSpEOrYIBTcvg9PdAZSyAVELB7vUjbO2CUqWEVhODVqOCXKrkXBEpOUDCXEQIE+AOQ8LmMern3V4FaUCMp+IqRbMdRD282ZuLowhHHGAhxeBgBwgTQ2H+PEhJEICUO5zJ9UNmPvxkH1mXjfbPdxakUimysrKQlZUFQgg8Hg9sNttZ1K/09HRx8l5aWorU1FQAk9emC/+fCCbaECUHbklMB1JTU3HBBReIesl4Hdy5QogN+eSTT1BUVISFCxcCITPQ9zIIJUOt+zAoACqWQXWwHuXqUmikafzZUw2KDiFKaQDQYGVGTiYlNfD1RcIPjyhwCwUKHo8TZrMF+YtzsHJtCthYBN3NTnSfsoBhAJmcQsHSDGQWavmfI0MfWf4cKQ7XhIaT8MsSCpREhhWXzMe8VVmIRSlo9VJIFZzzqlKng0QRhDvAQqUG1FoDAj4nrHYPVHIblNp06JUOKLTpoPmNI+f4L5juKHgmoIT7fYTlaz9v+sgEOEYeG+AinZgAVm2aD7miA71n/NAqgIVlRZBoI7C6YygoLIZSIcXQwoXfhEp1/JY2FWBDIOp8IOuKUf/9BNA0LcbJLV68GIFAQFxQnD59GkqlUqTGUhSFuro6zJ8/H0VFReLPT0abPpkBnHAfM0l5PV8YHLPWUMpkMpSWlp5l+5zQBH7gVcDTGkeN4twFIdNDznrAkrRhroIcLWC442ssYEGPyY1ueTUIRdDi/wBFmhuggBeEMQJ8Rg8d8/AZky5uexhzgUjViAWs6Bu0QyEHsnMLIKFCIAxXMCiWgEjAZ67RoFgGRKIQaaNgQyC0EhQJiZmSXIZkhPuc1zByVFglR7uV8EYPJArCH/isrig8bgdyC5dDrZQBVAbSCItwOAaf3wuHz4sB6wCUShnUOgN0GhkUylTQYECkgqGOEB5uACEAJZHx9x/j+O+Ici6zJCoGjHO0XAlCwQB6B9xI0euRlpnHPT5VHkjOtml9rtA0LTZsAvXLZrPBYrGgtbUVFEUhNzcXwWAQCoUiYWMfQsg5bS/VajXkcjkWL14Mr9cLm80mRt3cf//9SE9Px+nTp7F06dJpt+h/7rnncMcdd+DJJ5/E+vXr8cgjj2Dz5s1obW1FZmbmtP6uJM4vJFwfRyAQCKC6uhoKhQLz5s0DO/A2EPICtIZ/s1eJByJuGyiPayj5rR3LWdwTiRYMAVhaBULRoCSEozbyBxguj4wGQ6QYGDQjFokir3ABFFKuflV7GxCiACVLEKJpqFmCY8F6FMryAPlIHWK8uY5rqAGT6Yaor0JUSIwfcrE+7oADAp/PA7PFgexUPfSpRiAWQEZ6PmJBF3xhGiGvGd12GkpJGEptBrQqCiotp2sHpNzfAjT32Cgp1zwyNB8zFeZzL7n8OU6vb+Sn9AaAohGjgN5+E6QSJXIK5kFKxdHFFKlc4yzTg5vka4G8bdx9TxIURcFgMMBgMIjUL7vdDqvViq6uLrAsi/T0dESjUcRisQmpX1OJJQEmNi1LSgKSmA6kpKSc1Tyea40EOAbH4OAgotEoSktLua1n1Af0vAhQEnQFW2Fl3FARgjBNQ8swqArU4lJVmViTKIqCNOYEQ3KAiI2TCTBebiAvbPZIGIRSwG4ZhN3tR35OATR6I8DG4HSyGOgJQaHUQa2k4ffE0NPmhSE7DQoFnzbAhkVGG6RKbtgl0wJRHy8L8PG0U25ZQEed0Bp5J1aZUtRhRrwm9A56odNQyMgpBkXCSDUawbIR+Pxh+H0e9LpCoCgX1GottDoJtGo9aJriKbsAIB1qlCmpKI1A1BtHy+WvRaKGhPFg+QU5WL6eBWEZWGxOhDxuFBUshIIOgoCPxYv7GURcPMOQ4ZiIudt405/EoVarUVhYiMLCQjAMM4zdEY1GodVqIZFIEAqFEk4eOJfcdOHn4+9vJHw+35Tr4/nC4JjT2JCEKF2W9wFfO+8uyL+Z81mQYIJgZAbImSggz+T0LYKgV/goS0HIZ0XPoBMBjRmDdBAalkUzBrHGfwYxWouAxwypJAdK+ACZhjPOkahAxfyARI6Qz4FBkwU6nRbp6ekclYGNgZLQ3FZPogQV9YnbPYHfDqkgqtaDjjr4bZ8zLnJDCiAM8DpF8BpMzriH3yLyFvWDNj9iQQeK8udDLglzBz7CAmChlMuhpCVISy0AG3bBF5Yg5DGhz0FBToWg0qZAo5JBpVKDlshH2Vo6hjaREq7x5XSWKq5plOoR9FrQa3IhI0WLlPRszqiIloLNuYbbJswQBOqXSqWCy+WCRqNBUVERXC7XMOqXMJ0fSf2ajuIQrxHS6/XQ6/WYP38+AoEAtm/fjoceegi33HILNBoNurq6zjrATQUPP/wwbr31Vtxyyy0AuEDagwcP4qmnnsJPfvKTafs9Scw9RmrQBH0PISRhBzi73c5Z3efmYsmSJTC3HQEbqAMylgJRu+g+yNUV4XOhzoz4esQFCgRMwAq/XwmNPAZKboyLX+IMImIBO3pNHsglERQWLYSEhESXQIMiB/NYD6K0FDLCIAIaOZI0XrMY5fSIYLhDCcvGaRmNXLMmNG9iE2cAQLgpNgGn66EkcDqssNh9yMvOhVafxh3m+PcAqSoNRrkf0C9AZiyAQDCCQMAHs8UH1myDViWDSpcOjQKQKI1DTa6Qexlx8HRZB0+3DZ9l0BML2NFr9kAtjSAzdz6omJPftrLc45IoOIqcoDNNLQe086fleaNQKJCbmys2lkuXLkUwGByV+qVWqydtXDHydmPVx7GQNC1LYqZwrrIAhmHQ2NgIh8MBmqa5ZpKNAf2vcnVHosapaA8iFI0wRUCDgoem0Rpsw6X6L/DMhhTQAQuCjAI+5yDUhkzOOFEqNEncR0aig62/Bb6IBPNz9ZBpePMdqR4I2QEG0KjCiLIaaDRR+LxSxMIhKNQGiBFEgr5bGE4Rhv8Y4z6yUc54kuF1l3xOOvizZNBjRr/ZhTSDAilpubysQQYwPtAUDb2ahl5tAJCKUNALXzAGt6UL5pgEWiWg1GVCq6IhU6aIVF+QMMfAY6P8MoLw5pjgKLOgefMiGiwbQ7/JDCYaQ968lZDRMRCaY4Zw8SO8vIJEwUkRaG7jmbOD0/ZP8TmSmZkJiUSCwcFBzJ8/X4wsbGlpgVarFeujwWCYVH1MRJs+kWlZIBCAwWCY0mM8XzDnDeW4dIVAP+A7AyhzeI0Kr9OhFDztKRNwEISkKkCZyU0xBAcuwk3aHXYbekxS5OSuwgGmHtaYEgwFUKCxDz34csFXYLc70G/yQSpRQG80wmicB53OCJoGbA4XeixdyJtXjvTMNBDw2k1Kym38IAFFEbCgOWIDxT1puBxKPo+HonnCFw1C8R+HfZ//yP8nfI+AQizGoKm5GYyUxYoNKyGVK8FSGLq94NTK/wwFCjpQ0FEU0lgCl8sNq92GVpsdYWcEaekZSEtNR0ZmJhQKJf/ziLvG4SDghPCNPY1YsnoJjHl5IMCot50pMAyD+vp6hMNhrF27FjKZDHl5eSCEwOv1wmq1oq+vD01NTcOoX3q9flqKA8MwozaJarUa119/PX72s5/B5/OJ2s/pQiQSQVVVFe6+++5h13/FFVfg2LFj0/Z7kjg/IRzSx3r+xYMQgp6eHrS1tWHZsmUcdTxsh8pzHA5wlCwu+oIdisCQp3GNkSKdo5fKeSoXTzVipSnQ69xwsBmwmPsQY+XQq11Q69OgIV5IZAaEvBb0md0wqCikZy/gHKGlGs49VarGGmkh1qQs5YZ9EiW3BaRlXDNKScHpEfl8NMICLM+WoLnBl9i0SbVxzqxe3hCC09g4TO1w+hgU5aRAqdFy36dkHH2VUEOaRVoCWqqG1pgCrT4FhJYjFPDCG4zA4XJhIByFWumFRqeHVh2GXGHkPHZkem4qL5GD01Ay3P2RCCBPRTjsR++gB1q1AplZhdzPSFT8BjUMyJS8mY+RuyZFOpD1hWl7nhBC0NHRgd7eXqxdu1akBI6kfp05cwYKhUKkfqWkpIzK7gCGm5VMtL2cyLQsGauUxHRgtKHauTSUoVAI1dXVoGkaa9aswccff8xtdwYOAt7TotP15dpLsEa+CKyUN1yk5NBRaoCnsxKWgVxtgN7AwuoOIGbrgFKlhU7LQKs1QkYIIpQeg71dIFCjKD8NEoWGX3QYgZgXMl0q1Go7PF41tGo3XB4pjIYQFGql2HRyJjw6jnUn+nRoucGeVMO7qiq4Oink79Jy7jqlGrg9HphNHmRl5cBg0PMSLN50h4qjmvKGOEpdJpTqIJCZh2jABl+IRshrgtVGQS3th0KbBp3KA6VaxzWVAHefLMMNzqJBXovJpRKwIQf6zB7QJISCgvmQULxJkVCXKQlAq7kGlzCARAIgBmIoB4yrzv0JEwer1YqGhgYsX75czCstLi5GJBI5K3kg3rtjpDb3XHLTJ0ogCAQCyMvLm5bHOdeY1YZypOnEhHQFdR4w/5Zx79PjawYA5BYuG/Z1wbil16JG6fodOKM4g309EdBIAQUKLFh8CAu+UFSGBUsWiLo9i8WCrkErYr0xKJVKBIM6rFzzVWRmZYlN1EQfpwuhUAg19TVQKnOwevXqSdMpaQCpWQakZhViMf/GbrVaMWC2ouV0B3Q6neiqOprlMgAMDAygubkZK1euPCsIdTbAMAxqa2vBMAzKy8uHvcDjN4YLFixAJBIRD089PT2gaVosDqmpqedcHKLRKJRK5aghuH6/HwqFAiqVCitXrpzWx26z2cAwzKgBtC0tLdP6u5I4/yA8L0ejLcaDZVk0NTXBYrGgoqICKSkpIGwMVM9e0Kybc7COeYcGbSJlFUN6STEPkgYQAgEFwkYhl8mQk2EESTciFI3A6/XBYbdiMByBXGFDOBxBaloq0lPTQNESgNJzWzy5gbsvmYw7bFAycMY0HEXWR7xoDbdgjaqEd0IUIkNiPDU3wm0pmRDPRhE0jgHRZZClVTD3tiMcjaEoL4t7fbP8hJsC1ySLjWg8nZaLR6EiTqgUaqikDGDIQiwWgd/vg8/vhMfaBch0fN5lNtTSKCg5v82VqjlbfUgQijgx2N8HozET6UYV9/eMp/FKlHz0CM3Rw0ADeVv55nTqIITg9OnTGBwcREVFxVnUqZHUL4fDAavVilOnTiEWiw1jd4xF/ZpImx6JREBR1Jgh4UkNZRIzhcnqzIXIuoyMDCxfvhzRaBQAQCwfgfK0cayGqBeQ6qCKupGnXsBRSxXqoaFY1A+WTwOQUBJkpqiRmapBNBKD3++Fz+WA3dIPWqYCG4tAqdYgNzsTtJSXEtBKcNs8LXSpQObCeehvs8PlVUCllSJv5TxIZTQIZeTNdoycTErKGZYRiRDhxA+sQPGNHW9qQ7gEBLARON0BuOwm5OcVQy3nvi/koYsDOibAGf+wfP6lIIcgDGSqNKSoAaSmIYuNwef3w+/zodfkBuCBVqOCVmuARi0FLVMPsQh5Yx8m5ES/yQ4ZzSA7J4/LaWe5PE2KCQ5RXUV5Gv+5IgPIunxaniNmsxmNjY2jnmHlcjlycnKQk5MDlmXh8XhgtVrR2dmJxsZGGAwGsT5OlDwwFrsjFAoNG9CNrJFCDuVnAXO+oZyO4G6hKAiIN6S44IILoNVqEYlEsDNlJxgypKVT0kqkSodMCwTd3qJFi1BXVwePxwOlUomGhgb09fWJOYZTyWlJFILrWHp6OpYuXTpl++L4qAxhMmOz2UTdjZDTKDRfEokE3d3daG9vx5o1a0Rzh9lELBZDTU0NKIpCWVnZhFsauVyO3Nxc5Obmjur6ZTQaxel8otQvh8MBp9OJrKws8Y0r/nYCneuzEEqbxNxirOfjeDUyHA6LA5d4wzMy+Bb3pi41gCU+jiLKhHldIK/FiQV5Y5rgkJuqRAY2GuRikEgEFK/5pmgFVBIWqvQ0EKMKNqcfXqcZKoUOPns/gj4vdCoaakMmFHQUlEzHWeuLESBxm8WoEx8GP8aAvwl6WotFkswhcxshG5JWcT8nZkHSvMaRAggLhqXRPzgIsBTyipZARjN8JAdvOS8Y9/BbRM6JltePy1MwlFUMbjJPSSCVMDAo02BIiYCh5Aj6nPD6wnAN9oMhFHQqDzRaLdQaAimthC8UhaXfBGN6IVL1cu5QJuS4CfmV4A94QmObsWFSbtjjgRCC1tZWcZAw0aFEIpEMy+L1+Xyw2WwYHBxES0sLNBqN+B6QKPUrEAhgcHAQGRkZo24vKYpKaiiTmDFMRkPZ39+PpqYmLF68GIWFhaAoChKJBKrYAFibCxKpeohaGh+nJNQPqYEzbpRqwTJhUFItKBKBwFaQKdQwSikYU9Phczthttohl1IIB3zo7ToDtVYLjVoFlVoDiiIAaFBgkTdPhoysNETDBAoVDamS5uoWZBCdrSmePirhDRRpQcvIgtN9x/jPGYDiBnImhxsBrwu585ZDKWV5524vn2vp5redbhCpho/7EMx3pJxeE4gzlJSDjoWg12pgULJgs7IQDtjhCQAOex8GByhoNHKoNQZoNQQyuRLRSBS9A04olVpkZ2eCEjaQUm47SWjOSJPIUwAmyjW5LMM1zvk7ecnA1DA4OIjm5masXr2aj9cbGzRNi8kDixYtEpMHrFYrOjo6IJfLhy0oRi54RltQRKNR9Pb2QqPRjKlN/yzVx89EQ2kNWXFPzz34bvZ3kcakobq6GjKZDBdccIEYfpsjy8GdWXcOW0mP1gSEw2HU1dWBoihcdNFFkMvlCAaDsFqtouuo8MabkZExKq1yqnA4HKirq0NRURGKi4tnpFkZ2XwJrqotLS2IRCJQKpUIh8NYtWrVnDST0WgUNTU1kEqlKCkpmfx2doTrVzAYFItDe3u7SP1KT09HSkrKqMXB5XKhsbERixcvRnZ29qjUL5fLJT7Hphvp6emQSCSjBtBmZ2fPyO9M4vzCeDVSsD43Go1YtWrV0BuZsw5w1ABSPSSsCzGW5qfram4KLUyjpTxliZ+AQ2YAy4TByoygEQMl0QAQLOK5DEeWjcJkjyAYCCC3eBVUciliLAW/zwWvPwR7bz9Ay6HXBqHRpUAjYUDJ0zhaFr9xdNAsOgMtiEgkqPZ+gIVpN4KKBfjmMcgfYoQhIcu7egt6RS1iQSd6zV6oJBFkFi3ijNQk+uHGPRIV50BLCTEfvCMhI+TIjXRqdQ7lXtJKSEgAWiUFrUoDkipDiJEg5LXB4fTAauoBJdODjfpgTM9Gil4BEIE2JuMaW8Gplg0Dcj77Ul0AZGyclucFIQTNzc1wOBxYu3btpIec8W7WY1G/0tLSxBo5GrsjHA6jvr5ejH6KZ3YIz1mWZZMayiRmDImcIYXBS39/P9asWYP09HTxe3TEhuzYR2CZRZAQvkaGeQdmwegx6uJqZcwPhlYBTBCUVAOKhIdqI6Xl5AMyI5xOGyxWH3KyCqA3GEHYKHyBEAI+LwYtHoC1cnmQKhk0OiMkiEKuVkOuCPKGYwHuo2C6E/XwEXYuvgl08RTYIZ0mt3H0ABIdSNSLPpMLbDSA/KKFkEsJQPE1SKoDwHIGOkBc3Aev4aMl4NgUvCEOG+XeL5gAV79jXhCpHnTMA6U6BUqFH0grRjTih98fhN/ngNPSDVquAWIBKDRpyE1XcppIYQMZsXMmlLHQUHMsRJGABTIv4yRsU0R/fz9aW1tRUlKCtLTJ6zDjkwcYhoHT6YTNZkNra6uYPCAM4Maqv8J5es2aNeKWcqQ23WazncUO+bRi1imvw345P12ajOnESNA0jTfwBo54j4BECa7svhJZWVlYtmyZODGId/kUfmY0eL1e1NbWwmg0Yvny5eIBTaVSibShaDQqNibV1dXDpr6pqalT3iQODg6iqakJy5YtQ25u7pTuK1HEb2cXL16MhoYGOBwOqNVq1NXVQafTiZu9saix04lIJCK6VJ4L1Xc0qFQqFBQUoKCgQKR+2Ww2NDc3IxKJiJluAvXL7XajpqYGCxcuRH4+t1EYSf2KxWL4/e9/D7fbPaXn8FiQy+UoLy/HoUOHsHPnTvF3Hzp0CLt3757W35XE+YmxKF0mkwkNDQ2YP38+5s+fLz73SMgCauB1EAln8U5LFJCyPm66HXXx+kYntzETPobtgFQLNmwHpDpIYryTdtQed7DSgg1Z0G/xgIr5UViwADL4AaKBlPHDoFXDoKZAMo0I+p3wBWKwDZyGiaGg1aqgVuuh0aohoWlUeY9xPmSEwMG4cTrShcXyIo5uRUU4jTwiXDQU4Q0fSBSQpyAU8qNnwAODToPMzAWgBLdVEECRwhngCIcjip/igxEds7n7Cg+ZvMmMHDVWouFjSxT8QUrCu+D6QMmNUEU9UGUUIiXmg9OtgMNuhkKhgMtuQcBlhlKXAZ2SQKHLBh11882pc0gLCgCFX+YPbFODQHF2u92oqKiYlsNIPPVLyC+12Wzo6urCqVOnzqJ+Cfpug8GAFStWiNse4fqE99ynnnoKAKcTSiKJqWAsDeV4lNdoNIq6ujoEg0FccMEFwwYbJBYANfAKGMi5s6FUy5vgaLkBnJR3UpVqQCIusFJ+WCU3cCaLIk2T+8hKdXAMtsHpZzEvRw+FlmNpUBI9dCoJdNpcZDEhBKOA3+uEzRWEyeqEUqWHTquEWmeEgqLASIwwt9vgdcegVEaQvSADCjCcyQ4b5g0nA7wO08c3k15AqgYTdqDf5IQEEeQWzIOECvK69IjwV+R1ijK+7g0N6sQhW8zPvU8wYX4oxt+OxDina4rwjSgAmpM2yJRyGFXpMBIGvmAEg/1dkMpTEQ4G0NYTgV7tgUarh4YEOWNIwoKj/SqHIvOibhDDCiBlzZSfK729vWdFJ00FEolErH+CLtxms8FsNqO1tRVqtVr8vtFoBAA0NDQgEAigvLx82NIhXpteVVWFxsZGdHV1TfkazwfM+YYSSMx0Yiz4KB+O0kcBAEeDR7GjeAdWFK8Qvx/PaRbWzKNBsBSeaCsok8mGca6FzV5TUxNisZjYeI021R0PhBB0dXWhs7NTDKOebbAsi8bGRvj9fmzYsAFKpXIYNba7u3tUaux0IhwOo7q6Gmq1GqtWrZpygz4aJqJ+KZVKhEIhcTo1EsI13XXXXWhvb0dzc/OMXCfA5QnddNNNqKiowLp16/DII4/A7/eLrq9JfHYwWs0ZSekihODMmTPo7u5GSUnJsOgYwkRA+l/j6KK0FEJmb5iouf8XKETCYYBvxFiZEYQwgCwFNMUANJe9C5mBe+OX6REJhdA/6IJcJkVO4RLQFAGg4b4vUYHTLcpAAVBr9FBrJSBpeoRjFAIeGxxuN2yWPoQ1MvRJ2hClaWhZBj6JFI2e97EwcyHoqHOMqBA7IFEh4DVj0GJDRooRRqMaFOMfvr0U9ZKCfT2/XRA2j8LWEjRAhfitZYy/Dy03MZdxmwgIOiVRU6QEQMHhCcPmCiAvfz402hSwTBi+QBRBnwt9Vj9gPgO1Rg2tloJWrYZEqgIQBVIqANXUWQVCjfb5fKioqDjL1Xo6QFGUSP1auHChSP2y2Wzo6OiATCYDwzDQarVYunTpqNRYQgj+7//+D/fddx/eeecdrFixYozflkQS547xNpR+v188S1xwwQXDzmOEsEDfASDsAk1LwJ3xaY4uT0sAqDktuMwAQlgwQrakPB0UFeUzEoWsxCBiEj2n52akmJeXCqnSOMzxX3R+leqhlnigSs9EhtGDCFQIuk1w+6JwWbpAyY3w93tg7qUgl0QQYeRwmuxYcdECyOQhMQ2AY0Gw3CCMogCpHpFYDL0DLqhUauRkFYOiaUDIsYzfONLqYXEfkBu4LegwCqybq5dRL/dzsQiG505y9XdIA2kAFXXCF6Zh6e9AenoOUgw6gFAIRhn4fW5Y7S6YIkEoVAbotEpodEbIKZr7GyLG5U3mbpnyc0I4R5eVlYnN3XQiXj4mZJULCwoheUA4F5eVlZ3FYBPOivX19fjiF7+I+++/Hz/60Y+m/TrnAp/6hvLV6KtgwBUUCSXBG7I3sAEbACTeTPb09ODMmTNYvnz5pKiE8Zu9JUuWiI6j3d3dOHXqFIxGIzIzMyfUXRJC0NLSImph9Hr9JP4C04NYLIa6ujrEYjFUVFSIL4KJqLECLSojI2PKh5tQKCROvZcvXz5jTVo8RlK/HA4HampqoNPpYDKZYDKZzqJ+sSyLu+66C2+99RaOHDkihuPOBL785S/DarXi3nvvhclkQmlpKd544405MUhKYvYRf2AaTRs+DP2vAoFerikKc02ZhPFwmznedZVrvFQctVSiBBH0kmwIlFTNOfSJ31cBTAiBCGDu64AmNQsZRi0oiYT7vlTDU2lV3LZPquWNHbjPKXkKlLIIlKpFSEUE4RhQ7foYLlYCKQicNPexGz54Y24YZPHaxriPMgNcHjcsZjeysotg0Gv4g1WUaxYZYeMYb+Cj5yfsav4QxZsCUTR3faJRj9B4erjbRlz8VtHH/W5CAUwIRKqF09IFh49BUbYBCjW3raBpOfTKGPRKLTIzUxAJuOEN0/Dae2EyyaBTxCBPWQBlzgpopshiYFkW9fX1CIVCw2r0TCOe+hUKhXDixAnIZDKEw2EcPXpUjCUR3ucIIXjhhRdwxx134MUXX8Tll0+PuUYSSYzEWBpKq9WKuro6FBQUYPHixWe/7gZe5x1d9ZAjAALCU0aVXK6jVAFEI2BBg7AMKIpz8Qcd5WoCuC0TSBSRGMHAQBdoiQqFhdn8uZYS6aRiwyRP4TSCgmZQngIZE4YsrQB6JgSGZMNpsaCzj4VEEgaRSaFSROGxxuAcsCKz0Aggxv1uSsIxKiQyIBZFKEpg6uuE3pCBjBQVQJEhEzLGz8U4sUG+NkeG4j6kGrHGAgRE0JVLDQAoQCob0lFS/KZToh5qphl+Qxr1wxuIwWLqR0ZmJnR6HX//aqgRhiozBxlRD8IkByGPGV6/B05rNyQyFbQaNTQaNRTLrgdFT62mdXR0oKenB+Xl5bN2jpbJZMjKykJWVpZoIun1eqFUKvHxxx9Dr9cjLS1tGMOvsbER27dvxw9/+EP86Ec/+sx4cFBkvDTMaQbDMGfRE958801cfPHF56SxsIasuO7MdWCooYJCgcI/F/4ThbJCkZo4VjPJsiza2trEg/p0TjPidZdOp3NM3SXDMOJqfM2aNbNi+DMSkUhkmF4xkeZeWPsLj9Hj8UyJGhsMBlFVVYXU1FQsW7ZsTl5gHo8HVVVVmD9/PoqKioZRv2w2G3w+H/785z+L+s4PPvgAixcvnvXrTOKzCUHEH4/jx48jNzcXqampIg28pKTkrGaCtVcB1g+5zSTD28aTMKIxoON0C5YsW8VRPSkF3/QpwMb8IJQSFBvkm0k+8iPm4+lebrgDBA5zJ1KyimFUUyO2f54ROkRPnPmOELyt4YK4JZxLIpHI4YpawbAEoWAQwWAA4SADLRRQa418JmQqKCYo2vfbPTF4HX3IyFsEjSwW97v5+6aV3EGHUHxOJMM1xWyY1//w2iQmKDa7oGW8bT0fuUQIr+Vhhr5HcR8JJYV5sB9efxBFBXmQKxS8pTdvuQ/+sCXGiUQBSoJoNAS/P4Ru6kLYPAwUCoXI7hgtsmM8MAyDuro6RKNRlJWVTYr9Ml0QaK4ajQYrV64UDXesVitsNhtcLhcOHjyIwcFBvPXWW3j22WdFqn4SSUwHwuHwsM9Pnz6NcDgsOqwTQtDd3Y3Tp09jxYoVo8qGiP0kYP2Ij8mI4nR7F/Ky06DWGvhYIznABMBCztFiJUqAcDWTG1LJuaaMkiEU9GHQZIFWo0Z6RqZotjPMTVv8KAWEzHEI8XZCveE++pwBnHy7G3I5BUgAJhaF1xlB1iI5suenQqdRQaPVQ0IPZZd7A1FYBjqRkpGLVJ2M00lG3dzGMebht4he3h02GGdeJuHdvlnRFRYSFZf/KNVxeeiyoe0l2AC/3Qzzrt38Y6NouJxOWGxO5OVkQqM1Ahiqn0NmQXxtpjhjNZaJwh+MIOB1oje6CD7ZIvH8mJaWNqklEyEE7e3t6O/vR1lZ2ZzEFBFC0NjYCK/XKw78wuGweH602+2oqanBhx9+iNraWtx88834zW9+85lpJoHzoKE8dOgQ1q5dO+lpQiAQwGu1r+GPuj8iSkWHHJYgwQP5D6BUVQpgbPMdYdofCoVmvJGL113a7XaRcpmSkoLu7m7QNI3S0tI5OSQImUzCIeFcKazx1Fi73Q6pVCoWh4mosQI1JSMjA0uWLJmTF5jX60VVVRXmzZuHefPmjXqbQCCA7373u3jvvffg9XqRlpYmmlIkkcRUMVpDWVVVBZVKhcHBQeTm5mLJkiVnNSIkMAB0PANCK3gLeDX3UaoBG/agrWMAixfkg5brxKaKZUIgkIFCFJREmFrzmkVKBkKisNjc8LrsyM4rhFbNZzAKjoPgD04gfIYkIP5P/EEKJO57cZN1Sgpu2i4FYcPwh2Lwe53w+MKg2BCUmhToNVL4gjGE/E7k5hdDKSU85SrK3x8gZlayIV4fOpLyOkrjKWwq2Vic2Y/QCPM/wxv3sJDCYh5EJBJGTl4R5yYr3FZsooX7l/FbTYbf7gaAnM1AWsWwyA6r1QqWZYcxH8bbNsZiMdTW1oIQgjVr1kxr1m2iiEaj4nNxLClCNBrFQw89hH/+859wOp0ghGD//v3YuHF6jIiSSGJkQ9nR0QGPx4PS0lKwLItTp07BZrOhrKxs1LB44m0Hup/nt2xBQKJFV0cL0rPyuRrHZ+UykIGwUVASGSgxhiM6lJ9IS+F1OTBotiAjPRXGlFT+dhK+lsp4yjxfk+PNdkZ+jPrFARxLadD0QQusJkCrjcAfUECriWD+2vkAnHAHCJiIF3JVCvQaGWKQw2k3ISunEHqtKq5JpLgtJSQQt6lcUDr3dcJiqCbH0WgZf5xbtYbf2qr4940411l6qAF12fph80SRn6mDUp8FKurhaLMxP9+8xviBnSxOoxrg9JhsENDOB8m/Fm63W6yPgUBgmOmNWq0e8zkhRASazWaUl5fPiQEYIQSnTp2Cx+NBeXn5qGw9lmWxf/9+PPjgg+jt7YXX68UDDzyAO+64Y9avd6Yw5w3lkSNHUFJSgpSUlITvx+l0orq6Gjk5OcjMzERTUxMuvfTShM13gsEgampqoFQqsWrVqllt5ATa6MDAgOjemZGRgczMzEnrLqcKoZFLTU3F8uXLp62Ri6fGWq3WcamxPp8PVVVVyMnJwaJFi+a0mRT0s6OBEIIHHngATz75JA4fPoxFixbhk08+waWXXjrLV5vEZxWEEEQikWGfHzt2DF6vFytWrBhVz0uYEEj709ybt1DKxek4d5A4fboN8xcshISfJrNMlDerYUEJpjUY+siyMZjNVkTCIeTk5kIuF2oSf5+UJG6DFx0ynhGoslI1b/AgbClHNGtyAx8lIjR8/PelapBYAOEYDZ/XBZfbB5YASoUCOmM6tGolZCojT68VTCM0/CZSxTWLgrYIEr7xBXd9YPnDjZADxzeihD9QAfztJfzUXQqGZdB6ugOEZbFoyXLIZbzJD0WB01sJ/y/8jPA7+YaVogFFJn+b4f/OHo9HHMD5fD4YDAbx8BQfQySwISQSCUpLS6dds54IhGZSqVRi9erVY76vvvHGG/i3f/s3PPXUU7j++utRXV2NhQsXJgduSUwbIpHIsCzznp4eWK1WrFy5EjU1NeLQZTSjKhK2A+1PgdMU8rroWBBd/VakG5TQpuSARNxgpXzUkdwAOubldZAeblsX4fSCTmsPbM4wcnNSodWlc+wPCa+3FnTXtGz48ExoRiEDEIXIZhAGYhT3MRCQoLO6E047C602isKVhUhNZ7ns3KgXEaJAwG2BwxMCG/VDIlNDr1NBo9FBoeBrtUiJlYvUf4rxg0h1vIGPgdOT03H5kyQKrpYR/v0B4KaB8e8RwmY1CkJJYTMPwOkJoCg/CwoVH00lbCLFASIvPYhxjJZhDafcCMz/d16jPoRAICDWR6fTCbVaLdZHo9E4ZELHy8VsNhvKy8vHbTxnCoQQNDU1weVyjatr7+jowNVXX40bbrgBDz30EDo7OwEACxYsmM3LnVHMakM52gT+/fffx9KlSyfMiBHQ19eH5uZmLFmyBIWFhXA6nairq8PGjRtFO97x9JIulwt1dXXIysrC4sWLZ0WnNxJutxu1tbXIyspCTk7OsINForrLqUKIHMjLy8PChQtnrJEbjxqr0WjQ3NyMwsLCYU6Vswmfz4eTJ0+K1zAaCCF4+OGH8cgjj+DQoUMoLS2d3YtM4nOB+IZScPIcGBhAdnY2Vq9ePfrPmN4FvO0gUjUolgu9ptgIiEQFio2CSJSor63CkuUlkEkpEIo7OFASJSjwU2Ow3GEGLCIxzvQHtAyLFi6ATK6GeKjgGy1u8i3l2aJCMyblGye+sQLFm1vENVegx/9/0AAfR1FbWwuJRILFixfD5XKdJR3IzMycUcfpc5EBnCvi884cDodIjU1JSUFHR8e0ul1PFtFodJjj9ljvl++++y6+8pWv4Mknn8TXv/71zxSNK4nzByMbyv7+fnR3d4tO7StWrBj1dUKYEND9HBDxiuQGjkVBo6u7A0ZjGgxaFVjwDRCtAEWCXKMl6s59IBINbAPt8IYo5GfpIddm8M2m0CjpOZqp8FFsRkd8lGg5CqlE0J0rhppNigEoCViGBUXToIQLjmuiTCYTAoEgcnOyEY1F4PP64Pf7IaEpaHR66DQKqDQGUPHNnDQ+gsQLIlVzA0CRAkvz+lAud5IzKdMOd2Hlm1pWooFtsAu+YAz5+bmQyZVcU0gYPo6KNzoTdZrMkOERxRvGUQD0ywFVzrj/5rFYDHa7XaTWAxCZHVarFW63G+Xl5XMiF4tvJsvLy8d03O7u7sbVV1+N7du34/e///2c9B2zgTlvKD/66CPMnz9/QjOc+Cyh0tJSMVfG4/HgxIkTYkM5XjNpMpnQ1NSEhQsXorCwcHoe1CRhtVrR0NCABQsWnGXmkqjucqoQci6Li4vHpHfOFARq7MDAAJxOJyQSCbKzsxOixk43hGayoKBgzCkRIQSPPfYYfvvb3+LNN9/E2rVrZ+36kvh8QWgohYaKYRjodDpIpVIsW7bsnO/37bffxrp166BSqcatj16vFyc+qULMrUBebgHyF6XCkD67E1+/34+amhoxjiL+jTdeOmCz2UTH6emKbBIgmIPpdDqsXLlyVt/8BWqs2WyGyWQCAPExTkSNnW4IzaRcLkdJScmYf4f33nsP119/PR599FHccsstyWYyiRnDyIayubkZ3d3dWLJkCebNmzfqc48QFuj6P8DfJVJaBQds0Ap093ZCr02BXq8BaCk3LxPjfYRmjiAWYzEw0A+WpZCfnwOpjGc8gDetoaVDm0kmzDVQJDTUnEnkPA1WxusQ5XwjF681ZId+5zDzHTnARMBSMpj7uxAhMuRnGSFRpXBNoTwFJOaHPyKF32OHy8cCJAKVNg16vQ46fSokMjnfzEm55pFWcL+LlvPDPRmnQafiPxcaQP4jJQFDaDScakEoFEHpmgooZ7GRE3wtLBYL+vv7EYvFYDQakZWVNSE1diauRcgCHi++qb+/H5s3b8aVV16JP/7xj5/ZZhKYY5dXYGyXrngIDqSBQOCsLCGaphGLxdDR0YHMzMyznQ/B/cN3dnaiq6sLq1atSngbOt3o6+tDa2srVq5cOapL58i8S7vdDovFMq15lxaLBY2NjViyZAny8vKm+pAmDblcDrVaDa/Xi4ULF0Kv18+Ya+x4EKi2EzWTf/rTn/DAAw/g9ddfTzaTScwoKIoSmQNGoxGrVq1CR0fHWbqhyYAQAolEgq6uLuTm5o4pLbBaraj6pA5d7zNwDnhwkrXAmKlB5W3lyFuYuBxhKnC5XKitrR2TNTFaZJPFYplyZFM8BBlAWlranJiDSSQS6HQ6nD59GhkZGSgqKoLdbkdPTw+amprEPMiMjIxh1NjpRiwWQ01NDWQy2bjN5EcffYQbbrgBDz74YLKZTGLGQVEUCCEghOD06dPo6+uDQqEYU6oCABh8Bwj2c5uzmI/f2AkGZAFIJXL4PFZIJBQ0Kinn8MqEhiiqlASRSBiDAyYo5DJk5WWBQgSIRritIS2FqCenFVzDKFHyVHsjT7EnXKYjKP4jhmietJz7eZp3VKVlPH1eOqyZC4VjaDjVDJnxAqxcXQqJXBVHTeXaUC3/XyYh8Hq9sFgsaLZa4bf6RU1iRuq5s98EPTfLUihfe8Gs+35QFAW9Xo/u7m4olUosX75c1F62tbWNSY2dbghU24maSZPJhK1bt2Ljxo34wx/+8JluJoFZ3lCO1AgBnOlERkbGmBvDQCCA6upqKJVKlJSUiE9goagwDAOTyQSLxQK73Q6lUilSRg0Gg7iSdjqdKC0tnTP3p/b2dvT29qK0tHRSelHgbE1iNBo9p8PTwMAAmpubsWrVqmH5dbMJh8OB2tpaLFq0CAUFBeLXp9s1djz4/X6cPHkSeXl5WLBgwRhTTYKnn34a99xzD1599dWkVjKJGUc0GsXbb7+NefPmiRTwjo4OeL1elJSUTPr+BJdrp9OJwcFBWK1WAEOabWEw1dPTg/b2dgR7dKh7awApWRrQUhr2AS8KlqTh3+69eLof6lkQBl0j60IiIPzhyWq1wmKxwO+POzxNQjrgdrtRU1OD/Pz8MevCTENwu05JSTlL1z4WNfZcXGPHQywWQ3V1tUj3HYs1cuLECezYsQO//OUvcfvttyebySRmHNFoFJFIRIxPWrhwIdra2rBp06ZRb08C/YD1A15jLWTmEp6eScBAilAoDIvdBafThVCEhdGYipT0TKSmZUImU8Lp9uBUUyty8goxf8FCULSM395Jh3TaMwyfz4eamhrRBX+yr/WR7DetVivWx0TPVpFIZBhjYS4o+AzDoL6+HuFw+KyMx7GoscISZrqa33jdZkVFxZjvLxaLBddccw3KysrwzDPPzImZ2mxjzhvK2tpaGAyGUSdMQibgSHfDkeY7gpMrwzDiG67VahVvL5VKUVZWNicca0EL5XQ6sWbNmlE3qJNB/OFpMrrL7u5udHR0oKSkBKmpqVO6hnOFzWZDfX09li5dOqqddzym4ho7HoRmMjc3d0ztKCEE//jHP/DDH/4QBw4cwGWXXXZOvyuJJCYLt9s9bNrZ3d0Nu92OsrKyhO8jftgGDNVHlmVFPaLFYkE0GoVcLkckEkFpaSk+fL4LjR/0IT2PG7p5nSFI5TRuf+yqGW0Went7cfr0aaxcuXJaBl1jSQfG010KMgAhMmguEAgEUFVVhfT0dCxdunTcv/lUXGPHg7CZFJzHx6qzNTU12LZtG37605/izjvvTDaTScwK3G43Tp48CYVCgdLSUgSDQZw4cWLSWaejZZQLQ22LxQKLxQKfzweVSoVgMIhFixahqKgIXmcIH796Gi5LAOl5OlywbSFU2pmloTudTtTW1k6b18RY0oHMzMwxB1PBYBDV1dVzIgMQIGQ8MgyDNWvWjNsgCtTYc3GNHQ+C9M5qtY7bTNpsNmzduhVLly7Fv/71rzlJcJgLzHlD2djYCIVCgUWLFg37umC+s3Tp0rM2WYmY73g8HtEdTygc6enpopvqbExXBKpuJBIZ03lsqphIdwkAZ86cEfN5ZivsdSQsFgsaGhqwYsWKCfWyIzEZ19jxEAgEcPLkSWRnZ4/pKEsIwfPPP4/bb78de/fuxebNmyd1rUkkMRWM1Aj19fVhYGAA69atS+jn4+uj0EiO9jwXTGeCwSBkMhmCwSBsLRTa3nfBmK6BVC6Fy+zH/JJMfOXHG6bt8Y28VqE2TXcOsICxIpvipQNmsxmnTp1KaNA1U/D7/aiqqhLN4iZzaBzLNXay1FiGYVBdXT1hM9nQ0IAtW7bgzjvvxN13351sJpOYNXzyySdQKBTigsHv9+ODDz5I+H1aGLaxLAtCyJj1UdDHDQ4OQq1Ww+/3QynToOZlJ5x9IUhkErAMwcLSTNzwowsgkc5MgyXUpsWLF4/q8j1VsCw7bDDFMAzS0tKGsd98Pp8Y6zbRoGumIAy6KIpCaWnppLd9ibrGjgchnsRisYzbTDqdTmzbtg2FhYV44YUXZlX3PteY84ayubkZFEVh6dKl4m1aWlowMDAwzHxH+N7IqdJosNvtqK+vH6aP83g84uQpFAqJL5qMjIwZ+QcPh8PDNCizse6O110KhyepVIpIJILy8vI5ofsCXFFsbGycFqrtuVJjE2kmAWDfvn341re+heeeew7btm2b0rUmkcRkMbKhNJlM6OzsxIYNEzd18fWRoqgxp8iBQAC1tbVipqBUKkUgEEB/7yDe3tME0xk/aIpGWq4WO3dXoGDh9NPjhcw4t9uNNWvWzEp2WPxgymKxIBaLQaPRwOv1Yvny5XPWTAp67vFYE5PBuVBjGYZBTU0NAGDNmjVjNpNNTU245pprcNttt+HnP/95splMYlYxsj6GQiEcOXIEV1111YRbM6GRZFkuTmmsZpJhGDQ0NMDv96O0tBQajQaRSAQnDrXizb80QaYBpDIJKCIBE6Gw695LULAk7az7mSq6u7vR3t4+a74fAvvNYrHAarXC7/dDp9PB5/MhLy9vzjLCheikiSj4iWI819i0tLRRt4mCZtdkMqGiomLMDafb7caOHTuQkZGBl156aUZ9QM5HzGpDCZwdTNvW1oZoNIoVK1YgGo2irq4OwWDwrEwZoRBM1EwKxjfLli0b84Dg8/nEQ4XX6532qA6B7y7oYOaCHiBMdHw+HyQSCWKxGNLS0mY973JgYAAtLS0zVhQTocYGg0GcPHkSmZmZ407/X3nlFXzjG9/AP/7xD1x77bXTfq1JJDERotGoeOABOLOc1tZWXHzx+DrGRIdtgvFNdnb2qLFJTIxF3xkbrBYbGKkf3oAbGo0GmZmZounZdFCu6urqwDAMSktL5+RNV6Au9fX1QalUIhQKnZPucqoQTJgKCwtRXFw87Qe2RKixApWMZVmUlZWNeWBrbW3FNddcg1tuuQX3339/splMYtYRi8WGmTjGYjG88847uPzyyyekQCaSUR4KhVBbWys2L/H32XSsH/sePQFjlhoME0PQH4LPFUHF9WlYWlYgRv1M9bwnbMJMJhNKS0thMBimdH/nioGBATQ1NYm0X+F9YLo9LcaDoNucqeikRKixApNmcHBw3GbS6/Vi586d0Gq1eOWVV2aEkXi+Y9YbypETpvb2dvh8PixatAhVVVVQqVRjmu8AY0+VhBfh4OAgSkpKEja+CYVC4kRGECsLh6dzcdET+O7CdnSu6AF1dXWIxWIi19zn84mPU9BdCrz5mTo89fX1oa2tDSUlJcM2zTOF0aixRqMRHo8HmZmZZ5lcxEMI5X766adxww03zPi1JpHEaBjZUDocDtTX149pOgEk3kwODg6iqakJixcvTtj4ZqTeRiaTifXxXFz0QqEQampqoFQq5yxbUZg2Dw4OYs2aNdDr9bMW2RQPt9uN6urqWYtvGo0aq9frEYlEIJVKUV5ePuahvL29HVdffTW+8pWv4MEHH/zMuxUmcX6CYRjEYjHxc0II3nzzTWzatGnMA3yi9dHj8aC2tlZ0eB75HPc6gvjbLz6A0+SHXCVFOBhFdrER23Yvh8vrgMViAcuy4rkqLS1t0vWNYRg0NjbC5/NhzZo1sxqDEQ+BVbZ8+XLk5OSck+5yqgiHw6iuroZarcaqVatmpeaMRo2VSCQIBAJYu3btmB4ofr8fX/ziF0HTNA4ePDgrjJvzEXPeUHZ1dcFkMsHv95+1Vh/LfGckYrEYGhsb4ff7p/QijEaj4uZScIwVXjQGg2HCQ4XwIlyyZMmM8N0TQSKB3MFgEDabDRaLZcYOT4Jz5Lm42k4HCCGw2+1oaGgATdOIRqNjUmMPHTqEr371q/jTn/6Er33ta8nJexJzhpENpWBCMZrpxGSGbR0dHejp6cGqVauQnp5+TtcmbLuEwRQw3DF2osOT1+tFTU2NaDozF00Jy7JidlhZWdmob/yJ6C6nCmHwuGDBgjnLRBaoz+FwGCzLjkmN7erqwjXXXPOZD+VO4vzHyIYSAN566y1ceOGFY0bGJdJMCi7TginXmFnmXS4c+lcTXCY/Mgp1uOLGlUjN1oq/S9h2xUurhK3eRKywaDSK2tpaEEJQWlo6Z9o7YREwFqssPrLJarVOW2RTPIQsYIPBMGcsv2g0iqamJthsNvG5Mxo1NhgM4vrrr0ckEsHrr78+Z9Ky8wFz3lDW19djYGAAK1euHNaEJaoHEigKMpkMq1evnjYqJ8Mwoh5RcIwVJvOjTWRmm+8+GkKhEKqrq6HRaBKe6Ai6S2HyNB2Hp66uLnR2dqKsrGzO6BqhUAgnT55EWloali5detYhUSqV4uOPPwYAPPTQQ3jsscdw8803J5vJJOYUIyldfr8fH374Ia666qpht5uMHiheqzhVl+n43+9yuURd+kRRRoKLalFR0YxQOxOBMP33+/0oKytLiJI0XZFN8bDb7airq5sxo41EwLIsamtrEYvFUFZWBoqizqLGdnZ2wm63Y8+ePbjqqqs+86HcSZz/GK2hfPfdd1FeXj7srBFvvjNeM0kIER3wp8tlWrjfkY6xApUyMzPzrNoTDAZRU1MjbuPmirnR1dWFrq6uhBcB0xXZFA8hOkmISJmrM1l7ezv6+vpQXl4OjUZzFjVWuM7q6mr4fD68+eabc3bePV8w6w2lMIFnWXaYhuWSSy4RbzMZikJNTY3oPjVTb3YjzRwYhhEdY1NTU9HR0SHSp+bqCTUdgdyjHZ4mo7sUNiG9vb1z6igrNJNjFSThcf74xz/Gq6++ilAohKuvvhq/+93vRBOnJJKYC4xsKEcznUhUDxQOh1FXVwcAMzrxJoSIlHrhUJGamipO5h0OB5qamsbVtc80BBlAIpbzY+FcI5viYbVa0dDQgGXLliEnJ+dcHsqUwbKs6D5eVlZ21t9CeJx//vOf8ec//xkDAwPYsGED/vu//xtXXnnlnFxzEkkA3HM3Go0O+9rRo0exatUqMQ4t0WGbwFaw2Wwi9X2mIFDqLRYLXC4XdDqdWDdYlkVNTQ2ysrLmzPgmXrdZVlZ2zlu20aQDk9FdCtFJGRkZc/a3ACAyeioqKkYdwgYCAbz77ru499570dbWhkWLFmH37t24/fbb5+Bqzx/MSdKmYMoQCoWwdOlSdHd3i99LtJkU7JQnoihMB2iaRlpaGtLS0rBkyRLRMfbMmTMIBAKgaRrz58+fk5xLYMjYIS8vb0ougSMfp3BI7O7uxqlTp8bVXQrC5YGBgTFfhLMBgSqRkpIyZmNN0zTa29tx8OBB/PrXv8amTZvw6quvzlk+ZxJJjAVhUi3EJCVaHwVjMKPRiOXLl8/oxJuiKOh0Ouh0OixYsACBQAAWiwUDAwNobm4GAOTm5s5ILEgiEGQAMpkM5eXl5/y3oCgKer0eer0eCxYsGHZ4amtrm1A6IEQnrVy5EllZWdPx0CaNiZpJgHucwWAQ//rXv7Bp0ybcf//9eOONN+bs3y+JJMaDYDoIJD5sE86gsVgM69evn3EDFZVKhcLCQhQWFopGghaLBe3t7SCEICUlZdJxatMFISvd5XJh7dq1U9Jtxj/OeFZYd3f3hLpLwe06JydnXCf+mUZnZ+e4zSQAyGQy/Otf/4JCoUBHRwdOnjw5y1d5fmLWN5QulwsnT56EWq1GSUkJPB4PGhoacOmllyasB5oJisJkIfDdY7EYMjIyYLfbZ8QxdiIIVLKZNnYIhULihG2k7lKn0+H06dMwm80iPWAuEA6HcfLkSfEQPVZBqq6uxvbt2/Gzn/0MP/jBD5I01yTOG4ykdLEsi7feegubNm2CQqFIqJm02WxoaGgYFoTtdYbgdQShNSqhT5v5uiS4qJpMJuTn58Pj8cDhcIh1IzMzc1acAmcrkHsi3aXZbEZzc/OcvmexLIv6+nqEQqFxDXhsNhu2bNmC5cuX41//+tesRF4lkUQiGG1DeezYMcybNw/Z2dkJ1cdAIICamhpoNBqsXLkSUqkUIX8EDpMfcqUUablTd7JOBIKLan5+PmKxGKxWq1g3ZtLsJh5CREowGJyxrHRgYt2lsAjIz8+fMzNLgGsmu7u7x43Zi8Vi+Pd//3c0Nzfj3XffnbN6fj5i1hvKjz76CBqNRlxnu91uVFVVYePGjQlTFOx2O0pLS+eMUjkW331k0zVVx9iJIAjJlyxZgry8vGm97/EwUncp6FyXLFmCnJycOdHZhMNhVFVVQa/XY8WKFWP+revr67Flyxbcdddd+PGPf5xsJpM4rzCW6cSGDRugUqkmPCz19vaira1NdOcDgNYTg3hvbwtCvggUahkurFyMlRfNnHZPOKQEAgGsWbNGHKzFYjFxMh/vGCvY7U/3a3GuArlHc5smhIjRILMV2TTymoSD43jNpMPhwLZt21BcXIznnnvucxXKncT5j9GyzE+cOIHs7Gzk5ORMWB+dTifq6uqQk5MjRoiZu9x4/ak6OM1+SGQ0VmzIx2VfWQZaMjPnGEKI2LiUlJSIzKj4piveMVZouqabZSJsaVmWPWcZwLlgNN0lANHvYq6YfoJ+dLxmkmEYfOc730FVVRUOHz48Z1vl8xVznkPp9Xpx7NgxXHjhhVAoFAlRFEpLS+cs40XQbWZmZo57SBEcY4WmS6FQiM1lIo6xE6G/v1/Md5yrCQkhBI2NjXA4HEhPT4fD4RB1l0IhnI0iFYlEcPLkSXELMdbfVgjl3r17N+69995kM5nEeYfRbPHfffddLF++HOnp6eMyN4SNYGlpqUhPdNsCeP7BTxAORKFPV8HrCEEqo/HFH6xDet70u9FFIhHU1taCoiiUlpaO+fqPz0e0WCwAJucYOxHcbjdqamrmfOLd09OD06dPIysrCz6f75x0l1MFy7KiGVF5efmYTaLL5cL27duRnZ2Nffv2fe5CuZM4/zFaQ1lVVQWNRoPi4mJIJJIxX+sCBT/ehZ9lCf756w9h6nQhNVuLSCiGoDeCzd9YjRUbpn/oxrIsWlpaRN3mWI2LEPEjNJfxjrFCfuxUIOQ7yuVylJSUzIkJEMDVnOrqaqSmpoJhmGHst9lisQAQWY/l5eVjLqoYhsH3vvc9fPDBBzhy5MisLnE+LZj1hjLedEIoDrW1tXC5XDAYDMjKyjrLBcvv96O2tnYYRWEuILjzCfTSRJ/o8Y6xNpsNFEUNOzxNdqMnuKjGT7dmG8Ihxefzoby8HAqFYpg5x2zlXUYiEfENZTxKmxDK/Y1vfAO//vWvk81kEucl4ildggSgtbUV/f39UKlUyMzMRFZWFrTaIVpWLBYTt0+lpaXDNDDdTTa8/PhJpOVoIZFyGkxztwc7vlOGBaXTq+MTqGRarRYrV65M+JAS7xgrbPQmYwY2EkKdXrBgAYqKis7loUwLhEPKmjVrxAZ/tvMuE20mPR4Pdu7cCb1ejwMHDnwuQ7mTOP8R31AK5jv9/f04ffo0KIoa1YmfEIL29nb09vZi9erVwzKxg94Invqvo5BIKaj13ADF0uPBhu2LcNHOxdN67UKdDoVCk6KXCo6xwvBtqtIqQQYgsLnmyrnZ4XCgtrYWixYtEnORZyOyaSSEiLvxmkmWZXHHHXfg7bffxpEjR+b0feV8xpx5gAuHJYqiUF5ejksuuQTZ2dmwWq344IMP8Mknn6CrqwuDg4M4ceIEMjIyxsxVnA0MDAygtrYWy5Ytm7TtvUQiQWZmJlauXIlLL71UjPRoamrC0aNH0dDQALPZfBbVbSSEQG5hLT+XzWR9fT38fj8qKirESbZgzrFgwQJccMEFuPjii5GVlQW73Y4PP/wQx44dw5kzZ+B2uzEdc4xEm8kzZ85g27ZtuPHGG/GrX/1qWg9t7733HrZv347c3FxQFIWXX355wp85cuQIysrKoFAosHDhQuzZs+es2zzxxBOYN28elEol1q9fj+PHj0/bNSdx/iPefGfJkiXYuHEj5s+fj0AggBMnTuDDDz9EW1sbLBYLjh8/DpZlRzVU0BgVkCul8Ls5ZojPFYZCJRUPT9MFt9uNEydOID09HatXr57UxJuiKKSkpGDJkiW46KKLxADprq4uHD16FNXV1ejt7T2L3TIazGYz6urqsHTp0jl90+/s7BSjk+LNbATTivLycmzcuBHFxcUIBAKorq7G+++/L7pOxmeRnisIITh16pQ49BurmfT7/bj++uuhUqnw8ssvT3szmayRSUw34utjTk4OLr30UqxcuRIA0NjYiPfeew+nTp0S64HJZMLatWuHNZMAIFdJodLJEfBwtPRIiDuDaQzTWx8FSQ7DMKioqJjUa4yiKGi1WhQXF2P9+vW4+OKLkZmZCYvFgg8//BAff/wxOjo64PP5JjxX+Xw+nDhxAmlpaTOqKZ8INpsNtbW1WLp0qdhMApzZTU5ODlavXo2NGzeK8qWmpiYcOXIE9fX1GBwcPEtHe64QmsnxUglYlsVPfvITvPHGG3jnnXem/X3ls1Qf52RDGR8dMhrfPRKJwGq1oqenBz6fDwqFAvn5+cjMzJx199B4vvvI6dZ03LfH4xEnT8FgcJjdfvwBgBAi6kfHCuSeDTAMg7q6OkSj0TFdAkfDaHmX8dErky1s0WgUVVVVUKlU42ZudnV14eqrr0ZlZSUeffTRaS+gr7/+Oj788EOUl5fjuuuuw0svvYSdO3eOefvOzk6sXLkS3/72t/HNb34Thw4dwve//30cPHgQmzdvBgA899xz2LVrF5588kmsX78ejzzyCF544QW0trYmBeCfcbAsi0gkIjoVjkZxFRgPfX194hQ3JycH2dnZMBqNZ93+xJsdOPF6O8LBGGRKKdZcVoQLK6fPRU+IwpiJjWAgEBDro9vthl6vF+vjyBo4USD3bEDYhgj5ZYna70933qXQTHo8HpFBMhoCgQCuv/56xGIxvP766zPy/pqskUlMJ0KhEFiWFZ2vR9YxgfEwODiIgYEBEEKQmZmJnJwcpKWlnTXs6qi34K09DfC5QqAkFOavysDW/1gDuXJ6lhd+vx81NTUwGAzTvhGMd4y12+1QKpVifRwprRJkAAUFBaJZ21xAcLtesWJFwhrE0SKbppp32dvbizNnzoybl86yLO69914899xzOHz4MBYvnt6tNfDZqo+z3lD+7W9/w8qVK7Fo0aJxw2bPnDmDvr4+LF++HCzLwmw2w263i7Sv2eBXJ8p3ny4IQbhWqxUejwcGg0GkfZ05c2ZSgdwzAYZhUFtbC5Zlx9VHTYSx8i4T1V0m2kz29vZi8+bNuPrqq/GHP/xhxqdxFEVNWAx+/OMf4+DBg2hsbBS/9pWvfAUulwtvvPEGAGD9+vVYu3YtHn/8cQDc36ugoAC33347fvKTn8zoY0hibvHmm29CIpGgoqICMpksodgkjUYjNl0CnT4rK0ukfRFCMNjhgsfOubzmLZo+A5y+vj60trbOShRGOBwWa4bdbodarRbfC+x2+6QCuWcCAoNkcHAQ5eXl59ycTVU6IDSTbrd7GINkJEKhEL785S/D5/PhjTfemJUM5WSNTGIqOHXqFE6dOoVNmzaNa3To9XpRW1sLo9GIgoICsT6GQiGkp6cjKysL6enpIuPNMeiDudsNuVKKouXpkMqnR1PocrlQW1s75Ui3RMAwjEgXHekYK5hyLVy4EIWFhTN2DRPBbDajsbFxyt4fY0kHEu0LhOHjSAZJPAgh+NWvfoWnn35a9DGYaXza6+Os8kcJIXjppZdw6623YtGiRaisrMS11147LC+QYRg0NjbC6/Vi3bp14hQ6JycHsVgMdrsdZrMZJ0+ehFwun1ajm3gwDIP6+noEg0GsW7duVpo4QVheXFwsOsaazWa0tbWBpmkUFBQgGo1CoVDM+nQpFouhpqYGFEVhzZo1U6Iej5V32dPTg6amJvHwlJGRcRaFT2gmlUrluM3k4OAgtm7dii984Qt44okn5ozaMRLHjh3DFVdcMexrmzdvxve//30AQzTeu+++W/w+TdO44oorcOzYsdm81CTmAMeOHcOjjz4KtVqN7du3Y+fOnbjwwgvF1xshRNRQx2/iBCdTQYvY2NgIQoj4JptdnIbcBdPXaBFCxPDnsrKyWWniBKaKYLMvTOaPHz8OQgiys7NFXdVsv94FUySr1TrlLLeRuZ7BYFB8rKdPnx5Xd0kIQVNTE9xu97ibyXA4jBtvvBFOpxNvv/32rDSTiSJZI5MYC21tbbjrrrvgcDhE5tFVV101jK0gMCbmzZsnypOMRiMWLlwonjU6Oztx6tQppKamIisri9Po5UyvyYpQh+M1gjMJiUSCrKwsZGVlDXOMra+vRywWg8FgEKOn5sKEZ3BwEM3NzVi9evWUGSRj5V1WVVVNqLsUmsl4bftIEELw29/+Fn/9619nrZlMFOdzfZzVhpKiKOzbtw9utxsHDhzAvn378PDDD6OwsBCVlZXYsGEDnn/+edx6661Yt27dWZoPqVQqvmAEl0Cz2YyamhpRpygIsqfScIXDYdTW1kIikWDt2rVzYvOuVCqRlZWFgYEBGI1G5OTkwG634/jx49PuGDsRotEoqqurIZPJpt0RbOThSWikrVbrWYcnlUqFmpoaKBQKrF69esxDo9lsxtatW7Fhwwb85S9/mTMHs9FgMpnO2uRkZWXB4/EgGAzC6XSCYZhRb9PS0jKbl5rEHOC///u/cc899+Cdd97B3r17ceONN0IikWDbtm3YunUrXnrpJWzduhWXXXbZWYwJmqaRmpqK1NRULFmyBG63G2azGS0tLYhGo2JzOVULeiG+yeFwiHrH2YZUKhU3k3K5HAsWLIDb7UZDQ4PYSGdkZIxKcZtuCE2c0+lERUXFtJuPqVQqFBQUiANFQTpQXV097PCUkpKC1tZW8TrGGoJGo1HcfPPNGBgYwKFDh+ZsozsWkjUyibFw7bXXorKyEidPnsSLL76I//7v/8Z//Md/4KqrrsKOHTvQ0tKCjIwMXHfddWfRKUeeNQRGmDDITklJEZvLqToc9/b24vTp03OWOysM7QOBAFiWxaJFixCNRnHmzBk0NjaOKa2aKQhNXElJybTKxoAh3aUQGyOw35qbm8+SDlgsFrGZHKvuEULwyCOP4LHHHsM777yDVatWTev1ThXnc32cdYcbYVq0a9cu7Nq1Cx6PBwcPHsRTTz2FRx55BIsXL8b+/fshkUhQXl4+ZtMQ/0YqPInMZjPq6+sBQGy4JqvPE/juer1+TkXLoVAI1dXV0Gg04iYuPz9f1E9ZrVbRnn8qjrETQbCXViqV4zZx0wWlUjnm4YlhGCgUChQXF48pPrdardi+fTtKSkrw9NNPn1fNZBJJJAK5XI4tW7Zgy5YtePLJJ3H06FH8/e9/x9e//nXxTVAul+Oyyy4b8+Aj1Fmj0YjFixfD6/XCbDaLBwpBv5yRkTEptkEsFkN9fT3C4TDWrl07p/R7IetSYJDk5eWJ+imr1Yq2trYpO8ZOBJZlRa3iZM02zgUymQzZ2dnIzs4+6/AUDodBURQWLVo0Zt0TQrnb29tx+PDhaT/cJZHETIOmaaxbtw7r1q3DAw88gLq6Ojz//PO48847EQ6H8YUvfAEGgwFbt24dd+AezwgLBoOwWCwYGBhAS0vLmIkDE0GQa/X3949Lp5xpxHt/xDNIFi1aBJ/PB6vVir6+PjQ3N894jJFgfDNeEzddGMl+E3SX3d3dIkU0Pz9/zH9TQgieeOIJPPTQQ3jzzTdRVlY2o9f7WcPcWKbGQa/Xo7KyEnfeeSfuuecerFy5Evv27cOOHTtgNBqxY8cOVFZWYv369WO+ScY/iZYtWyau+puamsAwjNhwTTStnk2++3jw+/2orq4WH0/8dcRvYlmWFSluzc3NiMViww5PU3XEFZzJhBiA2W6uhcNTeno6qqqqAHDPl9bW1lF1lw6HA9u3b8eiRYvwj3/8Y84cgcdDdnY2zGbzsK+ZzWbo9XqoVCpIJBJIJJJRb5MM0f38QSaT4YorrsDvfvc7bNmyBbfeeisOHjyI733ve/D5fNiyZQt27tyJyy+/fMzDAEVR0Ov10Ov1WLhwIfx+P8xmM7q6unDq1CmxZkw0rQ6Hw6ipqYFMJsPatWvn7PUVi8VELfdIBongGJuSkiIeniwWi/hYU1JSxMc61eZP0CUFAoFxtYozBeF9LzU1FSzLwmaziayWtra2s3SXDMPg29/+Nk6dOoXDhw/PmXHRREjWyCQSBU3TWLNmDd544w3k5eXh4YcfxkcffYTHH38cu3fvxqZNm7Bz505s27YNqampY57pVCoVioqKUFRUdJbcSDACy8zMHJfKLgyX3G431q5dO2fGiYQQtLW1wWQyoaKi4iwmi1arFV1jQ6GQqNVua2uDVqsVH+t4GtVEIcgzxjO+mSnEv++p1Wo0NTUhLy8Pfr8fH3744VnSAQD4y1/+gl//+td4/fXXsW7dulm93kRxPtfHWTflGQsmk2nYgw0Gg3j77bexd+9evPLKK1Aqldi+fTuuvfbaYZqi8UAIgdvtFsNhI5GIKMhOS0sbdh8C332uRcuCE9dkm1rBBUt4rIFAQGy4MjMzJ01rCIVCqKqqEp3J5qq5FrSbNE2jtLQUEolENK0QhPbd3d149NFHwTAM0tPT8eabb84KjWMkEhVUv/baa2hoaBC/9rWvfQ0Oh2OYoHrdunV47LHHAHBvVIWFhdi9e3fScOJzCovFgvT0dHGowzAMPv74Y+zduxcvvfQSbDabqCnavHlzwocZgfYl5JsJDVdmZuawBsnn86GmpgYpKSlYvnz5nDE3phLILWwh4h1jhfo42cOfoLEPh8MoKyubk3oDDNduxtNthUOxxWKBw+HAb37zG8hkMnR1deGjjz6aFU3XaEjWyCRmAoFAALFYTGwMBIOsF198Efv27UNdXR0uueQS7Ny5E9u3b0dmZmZCZ5pIJCLWDIfDMazhiqf6R6NR1NXVgWEYlJaWzvpwSQDLsmhqaoLL5UJZWdmktNzRaFSsGYJjrFAfJyutEjakgsZ+rEiO2YCg3Yyn247Mu9yzZw8cDgeOHTuGAwcOnKVRnC182uvjedNQjodIJIJ3330XL774Ivbv3w+KorBt2zZce+21uOSSSxJ6Mx/ZcAWDQaSlpSErKwvhcBgdHR1zxncX4HA4UFdXh+LiYsybN29K9xUfhBvvGJuIS2AwGMTJkydH3ZDOJhiGQXV19bBmcjScOXMGt912G86cOQO73Y6LL74Y77777qxco8/nw5kzZwAAa9aswcMPP4zLLrsMqampKCwsxN13343+/n787W9/AzBk+XzbbbfhG9/4Bt59911873vfO8vy+aabbsKf/vQnrFu3Do888gief/55tLS0zLiTZhKfPrAsi6qqKrz44ot46aWX0N/fjyuvvBKVlZW45pprEn4zH9lwCTVDoVCgubkZBQUFWLBgwZzVAyGQW6fTTZkxIURTCYeneMfYiVwCBbdrhmGwZs2aOdHYA0ObCIvFMq52MxQK4dZbb8XHH38Mr9cLnU6H2traWdtQJmtkEnMJobnZu3cv9u3bh5MnT2LDhg2orKzEjh07xPy/iTCy4RISB4xGI9ra2qBSqSadwTudEIZcoVBIzCicyn3Z7XZxe0nTtMjsmEhaJdB+BwYGpuR2PR0wmUxoamoaV7vJMAzuv/9+PPvsswgEAggEAnjppZdmran8LNXHT0VDGY9YLIajR4/ihRdewP79+xEOh7Ft2zZUVlbiC1/4QsIvIp/PB7PZjN7eXkSjURgMBuTl5c2aSHkkhA3p0qVLkZubO633HT+tFiyW46ds8cXU7/ejqqoKmZmZWLJkyZw2kzU1NQC4F9lYRdrn8+G6666DXC7HwYMHEY1G0drairVr187KdR45cgSXXXbZWV+/6aabsGfPHtx8883o6urCkSNHhv3MD37wAzQ1NSE/Px8/+9nPcPPNNw/7+ccffxwPPvggTCYTSktL8fvf/x7r16+f4UeTxKcdLMuivr5enMx3dHTg8ssvR2VlJbZu3TpqTuVoCIfDsFgs6Ovrg8/ng1KpRH5+PrKysqbkYHqu8Pl8qK6uFt1sp7MuxTvG2mw2yGQycTJvNBqHHZ4Eui0hZMpu11OB0EyazWZUVFSM+W/Csix+/OMf45VXXsGRI0eQn5+Pjz/+GJdccsms1fZkjUzifAEhBL29vdi3bx/27duHY8eOoaKiAjt27MDOnTtRWFiY0OtCqBkDAwNiFnBeXh6ys7PPcl6eDUSjUdTW1gLAlCLdRkO8VttisYhMMEFGFl8D4+tSeXn5nNF+gaGIkpKSEqSnp496G0IIXnzxRdx222148cUXsXnzZtTW1qKoqAipqamzcp2fpfr4qWso48EwDD744AOR9uX1enHNNdegsrISV1xxRcJ892XLlommFV6vF0ajUXT7mg3Tif7+fjHLbaY3pMKqXzg8KRQK8fAklUpRXV2N3NzcOdWQChsA4dA2VjMZCATwpS99CSzL4rXXXpvTSVgSSZxvENxHhc1lU1PTME1RWlrauK/x7u5utLe3Y9myZWBZVpzMCwOprKysadHZTITZDORmWRYOh0OczBNCxMOTXq9HfX09JBLJuIyJmYZA5xM0UuM1k//1X/+FF154AUeOHMGiRYtm+UqTSOL8BSEEg4ODeOmll7Bv3z689957WL16NXbu3InKysoJ2RgCo6ygoAB6vV6sGdOZOJAIBG37ucgAJgtCCDwej/hYg8Gg6Bibnp6O9vZ22O12lJeXz8ngUYDQTE4UUfLyyy/j1ltvxbPPPovt27fP4hV+NvGpbijjwbLsME2R1WrF5s2bRU3RSL57fX09otEo1qxZM2yrKYiUzWazqLMR3L5mwgFLEC2XlJTM2kREQLxjrMViQSwWEy2109LS5kQnJTSTLMuOuwEQQrn9fj/eeOONOeXoJ5HE+Y54TdFLL72E2tpaXHzxxSLtKysrSzz4CFPmwcFBrFmzZpiZwsiBlBBvlGig9GRht9tRV1c3J9r2eA2+2WxGKBSCXC7HwoULkZmZOSdU13g6WUVFxZgbAEII7rvvPjzzzDM4fPgwli1bNstXmkQSnx4QQmCz2cTm8t1338XSpUvF5nIkK2JwcBBNTU1YtmzZMEZZ/EDKYrGILvxZWVlISUmZ9jNVMBgc5nUx22e2eA2+x+MBTdOYN28ecnNzZ+S8nAgsFgsaGhombCZfffVV3HLLLfj73/+O6667bhav8LOLz0xDGQ+WZVFdXS3Svvr6+kRN0YoVK7B3717s2LEDq1evHpeyFA6HRbcvp9MJrVYrHp6musofaS89lw2R2+1GVVUVsrOzQdM0rFarmN8zXY6xiYBhGFHYPl4zGQ6H8fWvfx1WqxVvv/32nFlzJ5HEpxGEEHR1dYmaouPHj2PDhg3YsWMHrrrqKjz11FO4+uqrsXbt2nGnzAzDwGazwWw2i1RRoT5ORz6u2WzGqVOnsGzZMuTk5EzpvqYCwe1aoVDAYDDAZrPB5/NNq2NsIiCEoL29Hf39/RM2kw888ACefPJJvPvuu+ddjloSSZzPIITA6XRi//792LdvH95++23Mnz8flZWVqKysxOuvv47FixfjkksuGZNKCWCYC79AFRWay9TU1ClvEgUZwFzLk1iWRWNjIzweD/Ly8uBwOMTz8nQ6xiYCq9WK+vp6rFq1aly235tvvokbb7wRTz31FL785S/P+HV9XvCZbCjjIVi7v/jii/i///s/9Pf3Y9WqVfjGN76Bbdu2JUxJEATZZrN5GO1rNB1iItckBIOXlZXNKc/c6XSitrYWCxYsEDcA4znGTkfo72hgWRa1tbWIxWIoKysbs5mMRCLYtWsXent78c4778xIjtoTTzwhcs9LSkrw2GOPjWkhvWnTJhw9evSsr2/ZsgUHDx4EANx888145plnhn1/8+bNoiNXEknMFQgh6Ovrw759+/Dcc8+htrYWmZmZuOWWW3D99dejqKgoodrGMAwcDgfMZvNZtK+ROsREIARhr1q1ak7jLYQ8YJ1ON2wDMJ2OsYmivb0dfX194xpdEELwu9/9Dr/73e9w6NAhlJaWTvt1JOtjEp8nuN1uvPLKK9i7dy/eeOMNyOVyfPWrX8WNN96I0tLShGrbSLaDMLDPyspCenr6pJtLl8uFmpoaFBYWzrgMYDzERyeVl5eL/iPCedlqtYrSKuH9YDqGjaNBaCZXrlw5rvHM4cOH8eUvfxl//OMfceONN87ItXxea+RnvqEUMDAwgBUrVuDrX/86MjIy8PLLL+PUqVPYuHGjqClKT09PWJAt0EQF2pfwYplIkM0wDBobG+H3+1FWVjZnweAApwGora3F4sWLkZ+fP+btxnKMzcjImBaePMuyqKurQyQSQVlZ2ZhUsmg0in//939Ha2sr3n333Rk5aD733HPYtWsXnnzySaxfvx6PPPIIXnjhBbS2to468XI4HIhEIuLndrsdJSUl+Otf/yqKpG+++WaYzWY8/fTT4u0UCsWMh/wmkUSiYFkW69atQ1paGq655hq88soroqZImMwnqqsWTByE5pIQItbHRBwCu7q60NXVhdLS0jl9jQh0MiEqZazHHu8Y63A4RPdHId9sOg4s7e3t6O3tRUVFxbjN5OOPP44HHngAb7311owYkyXrYxKfV3z/+9/H66+/jttvvx3vv/8+XnvtNaSnp4uGPmvXrk24uRQG9gKVPp4NNhGV3mazob6+fs4j7gRGWTQaHffcFu8Ya7PZRBpwIu8HicJms6Gurm7CZvL999/Hl770JTz66KO45ZZbZqSZ/DzXyM9NQwkAJ0+eREVFBYAhyqmgKaqpqcFFF10kaoqys7MTnswLmiKr1QqZTCYaVoycxMRisWFZRXOVXQYMFaXJusoK7o9WqxUOh2NKm1og8WYyFovhW9/6Furq6vDuu+/OWEDr+vXrsXbtWjz++OPi9RUUFOD2229PKL/nkUcewb333ovBwUFxU3HzzTfD5XLh5ZdfnpFrTiKJ6UBNTY1oey9oivbv348XX3wRhw8fxpIlS8TmMtE4IYE+NpL2JTgExk/mBZ3n4OAgysrKzgrknk0EAgFUVVUhPT19Uq6ysVhs2OFpqptaAOjo6EBPT8+EzeSf//xn/OIXv8Drr7+ODRs2TPr3JIJkfUzi84qOjg4YjUbR6yIQCODNN9/E3r17cfDgQWi1Wmzfvh07d+7Ehg0bEto6EkLg9/thNpthsVjg9/uRlpYmDqRGnhFNJhNOnTqF5cuXz6kM4Fyjk0ajAY/lGJsohLPs8uXLxz0XHjt2DNdeey3+53/+B9/+9rdnbKv7ea6Rn6uGciwQQtDd3S1qij755BNccMEF2LFjByorK5Gfn5/wZD4+u4eiqGFuiLW1tZBKpSgpKZkzu3lgKKJkohfgRIg36LDb7ZDL5cPs9if6mwkRB6FQCOXl5eNOuHbv3o1jx47hyJEj0x6rIiASiUCtVuPFF18cFix70003weVyYf/+/RPex6pVq7Bhwwb8+c9/Fr9288034+WXX4ZcLkdKSgq+8IUv4Fe/+tWM0HWTSGK6ITSFBw4cEDVF8+bNQ2VlJXbu3JlwJqTgECgcniKRiHiYSE1NxenTp+FwOObcIVCITsrKysLixYvP+eAxnmPsyGZ6LHR2dqK7uxvl5eVjNtiEEOzZswd33303Xn31VVx66aXndL0TIVkfk0hidIRCIbzzzjvYt28f9u/fD5lMhu3bt+Paa6/FRRddlHDDFQgExPro9XpFnXZmZiYsFgtOnz6N1atXj6vdnGnEYjHU1NSAoiiUlpae81lWeD8Q2B3xjrGJxvcJpm0T6exPnDiByspK3Hfffbj99ttnrJn8vNfIZEM5AoQQ9Pf3Y9++fdi7dy8++ugjlJWViZP5efPmTYr2Fc+ZVyqVWLJkCdLT0+fEQRUYmnBNJFqeLAQNVXwzLWguR3OMFbj3wWBw3GaSZVl8//vfx+HDh3H48OEZpXgMDAwgLy8PH3300bAJ/1133YWjR4/ik08+Gffnjx8/jvXr1+OTTz4Zxpd/9tlnoVarUVxcjPb2dtxzzz3QarU4duzYnEUPJJHEucLtduPVV1/Fvn378MYbbyAnJwc7duzAtddeizVr1iTcXApZwGazGYFAABKJBAsWLEBubu6cOKgCgNfrRXV1NfLy8iaMDZgM4jVUFosF4XAY6enpYo0c7fEK1N+Jmsl//OMf+OEPf4gDBw6Mmmc2XUjWxySSmBjRaBSHDx/G3r178fLLL4NhGGzbtg07d+7Epk2bEmamBYNB0bfD5XIBAAoKClBUVDRnDqrRaBTV1dWQyWTTHlEiOMZardZh0qqxEhYEydZEzWRNTQ22bduGn/70p7jzzjtnVG/6ea+RyYZyHBBCYDab8dJLL2Hv3r147733sHLlSrG5XLRo0YRPTmHardPpoFKpYLVaEYvFxqR9zSQGBgbQ0tIy4xMugdYgTJ5GOsbSNC3qSOOF3KPdz1133YWDBw/iyJEjKC4unrFrBqZeDL71rW/h2LFjqK+vH/d2HR0dWLBgAd555x1cfvnl03LtSSQxF/D5fHj99dexd+9evPbaa0hNTRUn82vXrp2wtsViMdTW1oo1wm63w+fziZPqzMzMWZMGeDweVFdXi0YXMwWB5iY0l6M5xnZ3d6OjowPl5eVjOoATQvDCCy9g9+7d2Lt3LzZv3jxj1wwk62MSSUwWsVgMH3zwAV544QW8/PLLCAQC2Lp1K3bs2IErrrhiQg+N+Ain/Px8uN1uOJ1O6HQ6kf02W2yOSCSC6upqKJVKrF69ekaXIqFQSDw/Co6xwplZq9WKZpITSbYaGhqwZcsW3Hnnnbj77rtn3Lzo814j5453+SkARVHIzs7Gd77zHXz729+G3W7H/v37sXfvXtx///1YvHixSPsaTVMkBHLn5+eL0+4lS5aIwbBtbW3DaF8zGc8huCaWlpbOeN4lTdNITU1FamoqFi9eLArQOzs70djYCJlMBoqiUFZWNm4z+dOf/hQHDhzA4cOHZ7yZBCC6rZnN5mFfN5vNE1KD/X4/nn32Wdx3330T/p758+cjPT0dZ86cOa+KQRJJTBZarRbXX389rr/+egQCAbz11lvYu3cvvvjFL0KtVouGFRs2bDirtgkHFLlcjnXr1kEikWDRokUIBAKwWCziAMxoNCIrK2tG4zkE18T58+ejqKhoRn6HAIqioNVqodVqMX/+/GGbiNbWVigUCkQiEaxcuXLcOKmXX34Zu3fvxrPPPjvjzSSQrI9JJDFZSKVSbNq0CZs2bcLvf/97fPTRR9i7dy9+9KMfweVy4eqrr0ZlZSWuuuqqsxpDlmXR1NQEl8uFdevWid+PNwFrb28XfSwEadVMNE1CdJJWq01Y4jAVKJVKFBQUoKCgYJi0qqurCzKZDJFIBEVFReNuJpubm7F9+3bcfvvts9JMAskamdxQngMIIXC5XKKV9FtvvYWioiKxuVy1ahVOnDgBn8+HhQsXjnlAEWhfAi1W4JALh6fpon319PSgvb0da9asmdPMRkIIamtr4fF4oFQq4fV6YTAYxMmTUDAJIfjFL36Bv//97zh8+DCWLl06a9e4fv16rFu3Do899hgArqgXFhZi9+7d4wqq9+zZg29/+9vo7++fkNfe19eHwsJCvPzyy9ixY8e0Xn8SSZwPCIVCOHTokKgpkkgk4uby4osvRnd3Nzo7O5GdnT1uIHcoFBLroxDPIWRdThfty+l0oqamBosWLUJBQcG03Oe5oqOjA52dndDr9fB4PFCpVGJ9jHeMFUK5//GPf+Daa6+dtetL1sckkpg6WJbFiRMnRFNIk8mEK6+8Ejt37sTVV18NiqJw9OhRpKamoqysbMyotvhmKz5xICsrCzqdblqaqFAohKqqKhgMBixfvnzO5FoAp5msra2FTqdDIBAY0zG2ra0N11xzDW6++Wbcf//9sxqr8nmukcmGchrg8Xhw8OBBMacoPT0dJpMJ//u//4ubbrop4RegQIMym83TRvvq7OxEV1cXysrKYDAYzuk+pgOEEJw6dQoejwfl5eVQKBQIh8PD7PYVCgUOHDggUrkOHz6MlStXzup1Pvfcc7jpppvwpz/9CevWrcMjjzyC559/Hi0tLcjKysKuXbuQl5eH3/zmN8N+7pJLLkFeXh6effbZYV/3+Xz4xS9+gS9+8YvIzs5Ge3s77rrrLni9XjQ0NMxIpmcSSZxPiEajOHLkiKgpCoVCoGkaX/rSl/DAAw8kvHUU6oXZbBZpUEJzea7Zj4Kpw5IlS5CXl3dO9zFd6O3txZkzZ8RaPZpj7JEjR6BUKvHb3/4We/bswQ033DCr15isj0kkMb0QMriF5rKzsxM5OTnIy8vDv/71L6SlpU174kCiEKKTUlNTE3b2nim4XC5UV1eLMXfxjrGClOzUqVOIRCL44x//iC9/+ct46KGHZr0B/jzXyGRDOc344x//iB/84Ae4+OKLcfz4caSkpIi0L4HWlQiCwaDo9uXxeGA0GsXmMpEDGCEEHR0d6O3tHdfUYTYwWjM5EtFoFK2trdi9ezeqq6uRlZWFG264AQ8++OCsO+I+/vjjYihtaWkpfv/732P9+vUAuBDaefPmYc+ePeLtW1tbsXTpUrz11lu48sorh91XMBjEzp07UVNTA5fLhdzcXFx11VX45S9/OW5eUhJJfBZx7NgxXH311Vi+fDl6enrg9/uxdetWVFZW4vLLL0946ygEZ5vNZtjt9nOKLxKCsOfagh8YkiSUlZWNyiIRTN5uu+02vPPOO6BpGtdddx1+9atfzXoWXbI+JpHEzMBkMmHTpk0ghECtVqOpqQmXXXYZdu7cia1btyI1NTXh5lIwSbRYLOcUXyT4f2RmZmLJkiVz3kzW1NRg4cKFo7JIhGzPhx9+GM888wzsdjs2b96Mu+++GxdffPGsX+/ntUYmG8ppxj/+8Q8UFBRg48aNCAaDoqbo1VdfhUqlEnOKLrzwwoQbJYH2ZbFY4HK5oNfrxcnTaAcwIWNzYGAA5eXlY2aXzQYIIaIOoKKiYsxpCiEEjz32GH7729/iwIEDcLlcOH78eEJ88iSSSOLTgdraWnz00Uf47ne/C4ZhcOzYMezduxcvvfQSHA4HNm/ejJ07d+Kqq65KeOsYi8Vgs9lgNpuH0b5G0kTjYTab0djYOGEQ9mxAaCbXrFkzblD1e++9h+uvvx6PPPIIli1bhpdffhk/+clPZlwTn0QSScwOHA4HHn74Yfz85z+HVCpFW1sb9u7di71796K+vh6XXnopKisrsX37dmRmZk4qccBsNovxRUJ9jKeJxsPn86Gqqgq5ublYuHDhnDaTbrcb1dXVYzaTAvr7+3HVVVfhqquuwg9+8AMcOHAAl1xyyYxl8iZxNpIN5SwhEongnXfewd69e3HgwAHQNI1t27bh2muvxSWXXJKwXjISiYjNpcPhgFarHSbIJoSgtbUVVqsVZWVl50wFmw4QQtDc3AyHw4GKiooxN6uEEPzpT3/CfffdN6Oh3EkkkcT5CZZlcfLkSZH2NTAwMExTNJ45TTwYhoHdbhebS6lUOmwyT1EUBgcH0dzcjFWrViEjI2OGH9n46O/vR2tr64TN5EcffYTrrrsOv/3tb/Gtb31rTg94SSSRxOxCYJwJWelVVVXYsGEDKisrsWPHDuTm5iZUEwT/D+EMOVrigNfrRVVVFQoKCjB//vzzoplcsGDBuEwMk8mEzZs34+KLL8Zf//rX8ypK4/OEZEM5B4hGozh69ChefPFFvPzyy4hGo9i2bRsqKytx2WWXJcyJFmhfFosFdrsdKpUKNE0jEomgoqJiTsPBJ9NMPv3007jnnntw8OBBXHLJJbN8pUkkkcT5BJZlUVdXJzaXHR0duOKKK1BZWYmtW7cmrAdiWVbUIArZuBqNBm63GyUlJXMaDg4MxThN5LwthHL/8pe/xO7du5PNZBJJfI5BCEFPTw/27duHffv24dixY1i7dq1oCllQUJBwcykkDpjNZkQiERgMBrhcLsybNw8LFiyYhUczNjweD6qqqiZ03rZYLLjmmmtQVlaGv/3tb8lmcg6RbCjnGAzD4IMPPhCbS6/Xiy1btqCyshJXXHFFwpqiSCSCuro6eL1eEEKgUChEw4qxaF8zBUIIWlpaYLfbJ2wmZyuUO4kkkvj0QaDMv/jii9i3bx+am5uHaYoSNaxgWRZtbW3o6+uDRCIZ5g6YlpY268YNiTaTQij3f/3Xf+GOO+5INpNJJJGECEIIBgYG8NJLL2Hfvn14//33UVJSgp07d6KysjLhDaNwP83NzZDJZIjFYkhLSxOzcacrcSBRCFvSefPmYd68eWPezmazYevWrVi2bBn+9a9/zbrfRhLDMXf+v0kAACQSCTZu3IjHHnsM3d3deO2115CdnY2f/OQnmDdvHnbt2oV9+/bB7/ePeR8sy6KlpQXRaBQXXXQRNm3ahMWLFyMcDqO6uhrvv/8+Wlpa4HQ6MdPzA4Fya7PZUF5ePm4z+fzzz+POO+/E3r17Z6yZfOKJJzBv3jwolUqsX78ex48fH/O2e/bsAUVRw/4bef2EENx7773IycmBSqXCFVdcgdOnT8/ItSeRxOcdFEVhxYoV+PnPf47a2lo0NjZi48aN+H//7/9hwYIF2L59O/7yl7/AbDaPW9t6enowODiIiooKbNq0CSUlJZBKpWhpacHRo0fR0NAAi8UChmFm/DENDg6ipaUFJSUl4zaTDQ0N2LFjB+66664ZayaT9TGJJD69oCgKeXl52L17Nw4dOoT+/n7ceuuteP/991FeXo4LL7wQ//M//4OWlpZx66PT6RSNYTZu3IgLLrgAer0ePT09OHr0KKqrq9HX14dIJDLjj0loJouKisZtJp1OJyorK7FgwQL84x//mLFmMlkjE0dyQ3megmVZVFVVibSv/v5+XHHFFdi5cyeuueYaUVPEsizq6+sRCoVQVlZ2VrwIy7LD3L6EyXxWVhZSUlKmdTJPCEFbWxssFgsqKirG3a7u27cP3/72t/Hcc89h69at03YN8Xjuueewa9cuPPnkk1i/fj0eeeQRvPDCC2htbUVmZuZZt9+zZw/+8z//E62treLXKIoaZtrxP//zP/jNb36DZ555BsXFxfjZz36GhoYGNDU1zVjoehJJJDEchBB0dnaKmqITJ07gwgsvxI4dO1BZWTlMU9TR0YGenh6UlZWdpcWMp31ZLBaEQiGkp6cjKysL6enp035IMZlMOHXqFEpLS8fNGmtqasI111yD3bt34957752RZjJZH5NI4rMJQgicTif279+PvXv34p133sGCBQtEWmx8nqTNZkN9fT2WLl2K3Nzcs+4rEAiI9fFcEgcmA5/Ph5MnT6KoqAjFxcVj3s7tdmP79u3IysrCvn37Ziw6I1kjJwlyDnj88cdJUVERUSgUZN26deSTTz4Z9/bPP/88WbJkCVEoFGTlypXk4MGDw77Psiz52c9+RrKzs4lSqSSXX345aWtrO5dL+0yCYRhSU1NDfvrTn5Lly5cThUJBtmzZQh577DFyxRVXkD179hCXy0X8fv+4/3m9XtLb20uqqqrIa6+9Rg4ePEiOHz9Ourq6iNfrnfDnx/vP5/OR6upq8sYbbxCr1TrubZ999lmiVqvJSy+9NKN/t3Xr1pHbbrtt2N8xNzeX/OY3vxn19k8//TQxGAxj3h/LsiQ7O5s8+OCD4tdcLhdRKBTk//7v/6btupP4dCNZH2cXLMuS7u5u8rvf/Y5ccsklRCqVkvXr15Nf//rXZNeuXeSnP/0pMZlMCdUwk8lE6uvryTvvvEMOHDhAPvjgA3L69OmE6utE/3V0dJADBw6Q7u7ucW9XXV1NsrKyyN13301Ylp2xv1uyPiYxF0jWx9mHy+Uif//738nOnTuJSqUiixYtIj/84Q/JL37xC3L99deT9vb2hGqY3W4nzc3N5OjRo2T//v3k8OHD5NSpU8Rms025PprNZnLw4EHS0NAw7u1MJhNZv349ufLKK0kwGJzRv1uyRk4Ok24on332WSKXy8lTTz1FTp06RW699VZiNBqJ2Wwe9fYffvghkUgk5Le//S1pamoi//Vf/0VkMhlpaGgQb/PAAw8Qg8FAXn75ZVJXV0d27NhBiouLZ/zJ8mkEy7Lk1KlT5J577iE6nY5kZ2eTK6+8kjzxxBOku7ub+Hy+hBvA/v5+sQl85ZVXyMcff0w6OjqIx+OZdDNZU1NDXn/99QmbyRdffJFoNBry/PPPz+jfKRwOE4lEclbTumvXLrJjx45Rf+bpp58mEomEFBYWkvz8fLJjxw7S2Ngofr+9vZ0AIDU1NcN+7tJLLyXf+973pvshJPEpRLI+zi1YliX9/f3kscceIwUFBUSlUpELL7yQ3HfffaSuri7h+uj3+4nFYiGNjY3k0KFDZP/+/eT9998nra2txOl0nnMz2dXVNe7t6urqSG5uLrnzzjsJwzAz9ndK1sck5gLJ+jj38Hg85NlnnyUXXHABkUgkpKKignzve98jhw8fntRiwel0ktbWVvL++++T/fv3k0OHDpHGxkZisVjOqZl87bXXSH19/YQ1+eKLLyabNm0iPp9vRv9OyRo5eUy6oZxsx37DDTeQrVu3Dvva+vXrybe+9S1CyGe/Y58p7N69m1x++eWktraW3H///aSiooJIpVKyadMm8rvf/Y60t7dPqrkcHBwkdXV15K233iIHDhwgH330ETlz5gxxu90T/mxtbS15/fXXJywkBw4cIBqNhvzzn/+c8b9Pf38/AUA++uijYV//0Y9+RNatWzfqz3z00UfkmWeeITU1NeTIkSNk27ZtRK/Xk97eXkII9+YGgAwMDAz7ueuvv57ccMMNM/NAkvhUIVkfzw8899xzpLCwkHzyySfkT3/6E9m8eTORyWRk1apV5Gc/+xk5efLkpJpLm81GmpqayOHDh8n+/fvJ0aNHSXNzM7Hb7RP+bGdnZ0LN5KlTp0hBQQHZvXv3jDaThCTrYxJzg2R9PD/Q2dlJdDodeemll8jevXvJ1772NWIwGEh+fj757ne/S956661JLRZcLhc5ffo0+fDDD8mBAwfI22+/Terr64nJZJqwzlosFvLaa69NOPCz2Wxk06ZN5OKLLyZer3fG/0bJGjl5TEpAF4lEUFVVhSuuuEL8Gk3TuOKKK3Ds2LFRf+bYsWPDbg8AmzdvFm/f2dkJk8k07DYGgwHr168f8z6TAH71q1/hlVdeQUlJCe6++24cP34cbW1t2Lp1K55//nksWbIEV199NZ544gn09fWNK8imKAp6vR4LFy7EhRdeiPXr10Or1aKrqwtHjhxBTU0N+vv7zxJkE0LQ3t6OgYEBlJeXj5t5+d577+FrX/saHn/8cXz1q1+dtr/DdGLDhg3YtWsXSktLsXHjRuzbtw8ZGRn405/+NNeXlsSnAMn6eP7gS1/6Ek6cOIF169bhP/7jP/D666/DZDLhjjvuQE1NDS666CKsXbsWv/zlL9HQ0ACWZce9P5VKhaKiIqxbtw4XX3wxsrKyYLFY8MEHH+D48ePo6upCIBA46+csFgsaGhomzLzs6+vD1q1bsWXLFjz66KOz7jqbCJL1MYmpIFkfzx/MmzcPp0+fxs6dO3Hdddfhn//8J0wmE/7whz8gEAjgy1/+MhYvXozvf//7OHr0KGKx2Lj3J5PJkJubK9aG+fPnw+/348SJE/jwww9x+vRpuN3us86hfr8fJ0+eRG5uLhYsWDCmVjwUCuGrX/0qQqEQXn31VWi12mn7W0wnPu81clKOAzabDQzDDBOYAkBWVhZaWlpG/RmTyTTq7U0mk/h94Wtj3SaJs2EwGIZ9TlEUiouL8cMf/hB33nkn+vr6xJyie+65B+Xl5aisrERlZSWKiorGfOFSFAWtVgutVosFCxbA7/fDYrGgr68Pzc3NSElJEQXZfX196O/vR0VFxbjN5IcffogbbrgB//u//4ubbrppVqzv09PTIZFIYDabh33dbDYjOzs7ofuQyWRYs2YNzpw5AwDiz5nNZuTk5Ay7z9LS0um58CQ+tUjWx/MHNE0PM02gKAqpqam4+eabcfPNN8PtduPVV1/F3r178YUvfAG5ubnYsWMHrr32WpSWlo7b0CmVShQUFKCgoACRSEQ0rDhz5gy0Wq1YH4PBIBoaGrBy5cpRDRwEDA4OYsuWLbjsssvwxBNPzEozmayPScw2kvXx/MLIv5lSqcT27duxfft2RCIRHD58GHv37sVNN90EQgi2bduGnTt3YuPGjWeZP8ZDKpUiOzsb2dnZYBhGzAKurq6GVCoV66NMJkN1dTVyc3OxcOHCMc+F4XAY//Zv/waHw4G33377rLPvTCFZIyeP828MmsSUQVEUCgoK8J//+Z84cuQIenp6sGvXLhw6dAglJSW49NJL8dBDD+H06dMTxohoNBoUFxdj/fr1uOiii5Ceng6TyYT33nsPnZ2dyM3NHfcAdPz4cXzpS1/C/fffj1tvvXXWctTkcjnKy8tx6NAh8Wssy+LQoUPYsGFDQvfBMAwaGhrEF35xcTGys7OH3afH48Enn3yS8H0mkUQScw+DwYCvf/3r2LdvH8xmM37961+jr68PW7ZswapVq/CTn/wEn3zyyYSbS7lcjvz8fJSVlWHjxo0oLCyEx+PBxx9/jNraWqSnp0OtVo9ZZ81mM7Zu3YoLLrgAf/nLX2YtlDtZH5NIIomxIJfLsXnzZvz5z3/GwMAAnn/+eahUKnznO99BcXExvvWtb+G1115DKBQa934kEgkyMzOxcuVKbNy4EcuWLUMsFkNtbS2OHTsGmUyG1NTUMetjNBrFzTffjP7+frz55ptISUmZiYc7KpI1cvKYVEN5Lh17dnb2uLeP79gTvc8kEgdFUcjJycF3v/tdvP322xgYGMB3vvMdHDt2DOvWrcOGDRvwm9/8Bk1NTRM2lwLtKy0tDVKpFPPmzYPH48GHH36ITz75BJ2dncNoX9XV1bj22mvx85//HLfddtush3Lfcccd+Mtf/oJnnnkGzc3N+M53vgO/349bbrkFALBr1y7cfffd4u3vu+8+vPXWW+jo6EB1dTVuvPFGdHd345vf/CYA7m/5/e9/H7/61a9w4MABNDQ0YNeuXcjNzcXOnTtn9bElcf4hWR8/ndBqtbjhhhvw3HPPwWQy4eGHH4bD4cB1112HpUuX4oc//CE++OCDCTMqBdpXfn4+AKCgoAAURYm0r7a2tmG0L5vNhu3bt6OkpAR79uyZtWZSQLI+JjGbSNbHTyekUikuu+wy/OEPf0Bvby8OHDiA1NRU3HHHHSguLsYtt9yC/fv3j0r5jwdN00hPT0dxcbHYaBqNRjQ2NuK9997DqVOnYLVaxSFeLBbDN7/5TbS3t+Ott94aN2ZpppCskZPEZEWX69atI7t37xY/ZxiG5OXljSuq3rZt27Cvbdiw4SxR9UMPPSR+3+12J0XVMwyWZYnD4SB79uwh27dvJ0qlkixdupT8+Mc/JseOHRvT7auxsZEcPHhwmA2/0+kkbW1t5IMPPiD79+8nd955J9m1axdJSUkh999//4xa30+Exx57jBQWFhK5XE7WrVtHPv74Y/F7GzduJDfddJP4+fe//33xtllZWWTLli2kurp62P0JFuVZWVlEoVCQyy+/nLS2ts7Ww0niPEeyPn52EAwGySuvvEJuueUWkpqaSrKyssg3v/lN8uqrr44ZI9LT00NeeeUVcubMGfFrHo+HdHZ2kk8++YS8+uqr5OGHHybXX389WbVqFamsrCSRSGTOHmOyPiYxm0jWx88OGIYhx44dI3feeSeZP38+0Wg05NprryXPPPPMmDFNNpuNvPHGG6Sqqko04BmZOPD888+Tq6++mlxzzTVk8eLFZHBwcE4fZ7JGJo5zig1RKBRkz5495P+z995hcp3l3f/ntOltu3ovluUmF9mSA4bXYIPhtf2SEFPiQsAQMCGEKyaGN4EEiI2x8wslAQIBm4QaQ4x5geCAsQEjd6tXq65W0tbZnZ0+pzy/P845o9nV9qaV9HyuS9dKs+ecObM7uuf5Pvd9f+9du3aJ9773vSKVSon29nYhhBC33nqruOeee6rH//73vxe6rosHH3xQ7N69W3zyk58c0vY5lUqJxx57TGzbtk3cdNNNoqGhQSxatGhMs4q+9rWviT/4gz8QqVRKpFIpce21155y/O233y6AAX+uv/768b78s5ZMJiO+853viLe85S0iEomI5cuXi7/8y78Uv/3tb6vi8rnnnjtFTA7l9vW5z31OrFixQui6LlavXj3tI0IkktmCjI9nJ5VKRTz++OPive99r2hubhYNDQ3i9ttvF48++mh1jMjOnTtPEZOD/2SzWfHYY4+JdevWiWAwKJqbm8UnP/nJ0/3yJJIZQcbHsxPbtsWLL74o7rnnHrFq1SoRCoXEm9/8ZvFv//Zv4vjx4yKXy4lDhw6dIiaHmhqwfft28ZrXvEZEo1ERDofFH/3RH53WTTfJ2Bm3oBRifIpdCHcw7apVq0QgEBBr164ddjCtr9gvuOACYRjGmGcVveMd7xD/8i//IjZv3ix2794t7rjjDpFMJkVbW1v1mNtvv1284Q1vECdOnKj+SafTE3n5Zz3ZbFb853/+p7jllltEPB4XixcvFm984xvFkiVLxNGjR0e0gPaHcn/84x8X2WxWPPLIIwPeHxLJ2Y6Mj2c3pmmKJ598UnzgAx8Q8+bNE6lUStx4440ikUiI3/zmNyPGxxMnTogrr7xSXHfddSKbzYpf//rX4kc/+tHpfkkSyYwh4+PZjeM4Ytu2beITn/iEuOCCC0QgEBDXXnutaGlpEQ888MCIsy6z2ax4z3veI5YuXSoOHToktm3bJr785S+f7pckGSMTEpTTzXhnFQ3GsiwRj8fFt771repjt99+u7jpppum+lbPegqFgvjTP/1TEQgExIoVK8S8efPEn/3Zn4lf/OIXp8yo9Idy/9Vf/dW0z1GTSM5VZHycPViWJb74xS8KwzDEmjVrRCKREG9961vFd77zHdHV1TUgPnZ0dIirr75avPa1rxX5fP5037pEclYi4+PswXEc8eSTT4r6+nqxZMkSYRiGuPbaa8UXv/hFcejQoQGZymw2Kz7wgQ+IhQsXigMHDpzuW5dMgFnn8jqRWUWDKRQKmKZJfX39gMefeuopmpubWb16Ne9///vp6emZ0ns/G1FVlf379/P000+zfft2vva1r1GpVHjnO9/JypUr+dCHPsSTTz7J/v37efOb38wf/uEfcv/998/KOWoSyZmOjI+zC03T2L59O1/96lfZsWMH//M//8PixYv5xCc+wZIlS3jnO9/JI488QkdHB3/8x3+Mqqr85Cc/IRKJnO5bl0jOOmR8nF0oisLhw4d5y1vewv79+9m1axevf/3r+fa3v83KlSu54YYb+OpXv8qxY8f427/9W3784x/zxBNPsGzZstN965IJoAgxirXnDHP8+HHmz5/Ppk2bBtjofvSjH+U3v/kNzz333KjX+MAHPsDjjz/Ozp07CYVCAHz/+98nEomwdOlSDhw4wMc//nFisRjPPPPMjLvrnWkIIU5xaDVNk6eeeoof/ehHPProo3R3d/PWt76V7373u1JMSiTThIyPs4+h4qPjOGzZsoUf/vCHPProo+zbt4/ly5fz4osvkkgkTtOdSiRnNzI+zk4Gx0ghBK2trfzoRz/iv/7rv9i0aRPhcJgXX3yRNWvWnMY7lUyK05keHYpjx44JQGzatGnA43fffbdYv379qOffd999oq6uTmzdunXE4w4cOCAA8atf/WpS9ytxe4r++Z//WZRKpWl9nn/+538Wixcvlo32knMWGR/PPBzHET/60Y/Erl27pvV5ZHyUnOvI+Hjm4fdcPvbYY9P6PDI+Tj+zLpU0kVlFPg8++CCf/exn+Z//+R8uuuiiEY9dtmwZjY2N7N+/f9L3fK6j6zp33XUXwWBw2p7jBz/4AR/5yEf45Cc/ycsvv8zFF1/M9ddfT2dn55DHP/XUU7z97W/nySef5JlnnmHhwoVcd911HDt2bMBxb3jDGzhx4kT1z/e+970J3+O///u/09DQQLlcHvD4zTffzK233jrh60okPjI+nnkoisJb3vKWad15l/FRIpHx8UxEURQuvPBCbrzxxml7DhkfZ4jTrWiHYryzioQQ4v777xeJREI888wzY3qOo0ePCkVRpn1XRDI1nAmN9oVCQSSTyQFjUjo6OoSu6+LXv/71lD2P5NxGxkfJYGR8lEhcZHyUDEbGx5lh1mUoAT7ykY/w9a9/nW9961vs3r2b97///eTzed71rncBcNttt/Gxj32sevz999/P3/7t3/LNb36TJUuW0N7eTnt7O7lcDoBcLsfdd9/Ns88+y+HDh3niiSe46aabWLFiBddff/1peY2SsXOmNNqHw2He8Y538NBDD1Uf+/a3v82iRYt4zWteM+HrSiS1yPgoqUXGR4nkJDI+SmqR8XEGOd2KdjjGM6to8eLFp9QyA9WB0YVCQVx33XWiqalJGIYhFi9eLO68805x7733jrmm+qGHHjrl+sFgcMAx/jykOXPmiFAoJK699lqxb9++Kf25nItMti9CCCHe//73i2XLlolisVh97Hvf+151GPKjjz4q1qxZI6644gphWdaE7/Xll18WmqZVZ1hdeOGF4lOf+tSEryeRDIWMjxIfGR8lkoHMRHxsb28fV1+ejJGnBxkfZ45ZKyinm+9///siEAiMefjtQw89JBKJxIDBtu3t7QOO+exnPyuSyaT48Y9/LLZu3SpuvPFGsXTp0gFvQsn4OdMa7S+99FJx7733ihdffFGoqipaW1sndT2JZKaR8fHMQcZHiWTmkTHyzEDGx5njnBWU462pfuihh0QymRz2eo7jiDlz5ogHHnig+lhfX58IBoPie9/73pTd97lIuVwWmqaJRx99dMDjt912m7jxxhtHPPeBBx4QyWRSvPDCC2N6rsbGRvHVr351orcqhBDiy1/+sli1apW46667xHXXXTepa0kkpwMZH88cZHyUSGYeGSPPDGR8nDlmZQ/ldDPRmupcLsfixYtZuHAhN910Ezt37qx+79ChQ7S3tw+4ZjKZ5MorrxxznbZkaAKBAJdddhlPPPFE9THHcXjiiScGzJoazOc+9zk+/elP84tf/ILLL7981Odpa2ujp6eHuXPnTup+3/GOd9DW1sbXv/51/vRP/3RS15JIZhoZH88sZHyUSGYWGSPPHGR8nDnOSUHZ3d2Nbdu0tLQMeLylpYX29vYhz1m9ejXf/OY3eeyxx/j2t7+N4zhs3LiRtrY2gOp547mmZOycSY32yWSSP/zDPyQWi3HzzTdP6loSyUwj4+OZh4yPEsnMIWPkmYWMjzODfrpv4Exhw4YNA3YzNm7cyJo1a/jXf/1XPv3pT5/GOzs3uOWWW+jq6uITn/gE7e3tXHLJJfziF7+oBt/W1lZU9eT+yFe+8hUqlQp/9Ed/NOA6n/zkJ/m7v/s7NE1j27ZtfOtb36Kvr4958+Zx3XXX8elPf3pK5mkeO3aMd77zndM6m1MimS3I+Hh6kfFRIpndyBh5+pDxcWY4JwXlZIbf+hiGwbp166qDbf3zOjo6BqS8Ozo6uOSSS6bmxs9xPvjBD/LBD35wyO899dRTA/59+PDhEa8VDod5/PHHp+jOTtLb28tTTz3FU089xZe//OUpv75EMt3I+HhmIuOjRDIzyBh55iHj4/RzTpa8TrSmuhbbttm+fXv1P/7SpUuZM2fOgGv29/fz3HPPjfmakjOfdevWcccdd3D//fezevXq0307Esm4kfFRMl3I+Cg5G5AxUjIdnPHx8XS7Ap0uvv/974tgMCgefvhhsWvXLvHe975XpFKpqo3zrbfeKu65557q8X//938vHn/8cXHgwAHx0ksvibe97W0iFAqJnTt3Vo/57Gc/K1KpVHU2zU033SQtnyUSyRmHjI8SiUQyPDJGSiQDOWcFpRDjG3774Q9/uHpsS0uLuOGGG8TLL7884Hr+UNqWlhYRDAbFtddeK/bu3Tuu4bfXXHPNkEN2b7jhhuoxt99++ynfv/7666fuByORSM55Zio+CiFkjJRIJGcccg0pkZxEEUKIGUqGnpP84Ac/4LbbbuOrX/0qV155JZ///Od55JFH2Lt3L83Nzaccn06nqVQq1X/39PRw8cUX82//9m/ccccdANxxxx10dHTw0EMPVY8LBoPU1dVN++uRSCSSqUTGSIlEIhkaGR8lZwrnZA/lTPL//X//H3feeSfvete7OP/88/nqV79KJBLhm9/85pDH19fXM2fOnOqfX/7yl0QiEd761rcOOC4YDA447kwJBP/yL//CkiVLCIVCXHnllTz//PMjHv/II49w3nnnEQqFuPDCC/n5z38+4PtCCD7xiU8wd+5cwuEwr3vd63jllVem8yVIJJIpRMbIgcgYKZFIfGR8HIiMj7MXKSinkYkOv63lG9/4Bm9729uIRqMDHn/qqadobm5m9erVvP/976enp2dK7306+MEPfsBHPvIRPvnJT/Lyyy9z8cUXc/3119PZ2Tnk8Zs2beLtb3877373u9m8eTM333wzN998Mzt27Kge87nPfY4vfvGLfPWrX+W5554jGo1y/fXXUyqVZuplSSSSCSJj5EBkjJRIJD4yPg5ExsdZzmktuD3LOXbsmADEpk2bBjx+9913i/Xr1496/nPPPSeAU+rlv/e971Wbth999FGxZs0accUVVwjLsqb0/qea9evXi7vuuqv6b9u2xbx588R999035PF//Md/LN70pjcNeOzKK68U73vf+4QQbr/BnDlzxAMPPFD9fl9fnwgGg+J73/veNLwCiUQylcgYORAZIyUSiY+MjwOR8XF2IzOUs5hvfOMbXHjhhaxfv37A429729u48cYbufDCC7n55pv56U9/ygsvvHDKLJ3ZxER22p555pkBxwNcf/311eMPHTpEe3v7gGOSySRXXnnlmHfvJBLJmYuMkTJGSiSSoZHxUcbHmUQKymlkMsNv8/k83//+93n3u9896vMsW7aMxsbG6oDc2Uh3dze2bdPS0jLg8ZaWFtrb24c8p729fcTj/a/juaZEIpk9yBh5EhkjJRJJLTI+nkTGx9mPFJTTyGSG3z7yyCOUy2X+5E/+ZNTnaWtro6enpzogVyKRSM4EZIyUSCSSoZHxUXImIQXlNPORj3yEr3/963zrW99i9+7dvP/97yefz/Oud70LgNtuu42Pfexjp5z3jW98g5tvvpmGhoYBj+dyOe6++26effZZDh8+zBNPPMFNN93EihUruP7662fkNU2Eiey0zZkzZ8Tj/a8T2b2TSCSzAxkjXWSMlEgkg5Hx0UXGx9mPFJTTzC233MKDDz7IJz7xCS655BK2bNnCL37xi2qKvbW1lRMnTgw4Z+/evTz99NNDlipomsa2bdu48cYbWbVqFe9+97u57LLL+N3vfsdzzz3H//7f/5t58+ahKAo//vGPR72/p556iksvvZRgMMiKFSt4+OGHTzlmvDbNQzGRnbYNGzYMOB7gl7/8ZfX4pUuXMmfOnAHH9Pf389xzz426eyeRSGYHMxUjP/e5z/FHf/RHszI+goyREonkVGR8dJHx8QzgdLsCSaaOn//85+L//t//K/7rv/5LAOLRRx8d8fiDBw+KSCQiPvKRj4hdu3aJL33pS0LTNPGLX/yiesz3v/99EQgExDe/+U2xc+dOceedd4pUKiU6OjrGfX/f//73RTAYFA8//LDYtWuXeO973ytSqZRob28XQghx6623invuuad6/O9//3uh67p48MEHxe7du8UnP/lJYRiG2L59e/WYz372syKVSlUdy2666SaxdOlSUSwWx31/Eonk7GW2x0f/ejJGSiSSmUbGRxkfJ4sUlGcpYwkIH/3oR8XatWsHPHbLLbeI66+/vvrv8do0j8aXvvQlsWjRIhEIBMT69evFs88+W/3eNddcI26//fYBx//nf/6nWLVqlQgEAmLt2rXiZz/72YDvO44j/vZv/1a0tLSIYDAorr32WrF3794J3ZtEIjk3mK3xUQgZIyUSyelFxkcZHyeCIoQQpzNDKpkeFEXh0Ucf5eabbx72mFe/+tVceumlfP7zn68+9tBDD/HhD3+YTCZDpVIhEonwwx/+cMB1br/9dvr6+njsscem7wVIJBLJNCHjo0QikQyNjI+SiXBO9FB2dXUxZ84c7r333upjmzZtIhAInFJffS4xnKVyf38/xWJxQjbNEonkzELGx6GR8VEikcj4ODQyPkoGo5/uG5gJmpqa+OY3v8nNN9/Mddddx+rVq7n11lv54Ac/yLXXXnu6b08ikUhOGzI+SiQSydDI+CiRjI1zQlAC3HDDDdx55528853v5PLLLycajXLfffed7ts6rQxnqZxIJAiHw2iaNuGhuhKJ5MxBxsdTkfFRIpGAjI9DIeOjZDDnRMmrz4MPPohlWTzyyCN85zvfIRgMnu5bOq2MZqk8maG6EonkzELGx4HI+CiRSHxkfByIjI+SwZxTgvLAgQMcP34cx3E4fPjw6b6dKSeXy7Flyxa2bNkCwKFDh9iyZQutra0AfOxjH+O2226rHv9nf/ZnHDx4kI9+9KPs2bOHL3/5y/znf/4nf/mXf1k9ZrShuhKJ5OxAxkcZHyUSydDI+Cjjo2QUTrfN7ExRLpfFxRdfLG6//XZx7733iubm5gnPwpmtPPnkkwI45Y9vo3z77beLa6655pRzLrnkEhEIBMSyZcvEQw89dMp1R7JplkgkZz4yPsr4KJFIhkbGRxkfJaNzzowNufvuu/nhD3/I1q1bicViXHPNNSSTSX7605+e7luTSCSS04qMjxKJRDI0Mj5KJKNzTpS8PvXUU3z+85/nP/7jP0gkEqiqyn/8x3/wu9/9jq985Sun+/amjSVLlnDHHXec7tuQSCSzGBkfJRKJZGjO1fgokYyXc0JQvuY1r8E0Tf7gD/6g+tiSJUvIZDK8//3vn5F7OHDgAO973/tYtmwZoVCIRCLB1VdfzRe+8AWKxeKM3MNk+frXv84111xDS0sLwWCQpUuX8q53veus7CeQSM4VZHycekzT5Pzzz0dRFB588MHTfTsSiWSCzIb4KJGcCZwzY0NOJz/72c9461vfSjAY5LbbbuOCCy6gUqnw9NNPc/fdd7Nz506+9rWvne7bHJXNmzezdOlSbrzxRurq6jh06BBf//rX+elPf8rWrVuZN2/e6b5FiURyhnG2xMdavvSlL1XNLCQSiUQiOduRgnKaOXToEG9729tYvHgxv/71r5k7d271e3fddRf79+/nZz/72Wm8w7Hz5S9/+ZTHbr75Zi6//HL+/d//nXvuuec03JVEIjlTOZvio09nZyef+tSn+Ou//ms+8YlPnO7bkUgkEolk2jknSl5PJ5/73OfI5XJ84xvfGLBY8lmxYgV/8Rd/AcA111zDxRdfPOR1Vq9ezfXXX1/9t+M4fOELX+DCCy8kFArR1NTEG97wBl588cUR76evr48Pf/jDLFy4kGAwyIoVK7j//vtxHGdCr2/JkiXV60okEsl4OBvj4z333MPq1av5kz/5kzGfI5FIJBLJmYzMUE4z/+///T+WLVvGxo0bRz321ltv5c4772THjh1ccMEF1cdfeOEF9u3bx9/8zd9UH3v3u9/Nww8/zBvf+Ebe8573YFkWv/vd73j22We5/PLLh7x+oVDgmmuu4dixY7zvfe9j0aJFbNq0iY997GOcOHGCz3/+82N6TT09Pdi2TWtrK5/61KcAuPbaa8d0rkQikficbfHx+eef51vf+hZPP/00iqKM/gOQSCQSieRs4HTPLTmbyWQyAhA33XTTmI7v6+sToVBI/PVf//WAxz/0oQ+JaDQqcrmcEEKIX//61wIQH/rQh065huM41b8vXry4OkNICCE+/elPi2g0Kvbt2zfgnHvuuUdomiZaW1vHdJ/BYLA6o6ihoUF88YtfHNN5EolE4nO2xUfHccT69evF29/+diGEEIcOHRKAeOCBB8b0+iQSiUQiOVORJa/TSH9/PwDxeHxMxyeTSW666Sa+973vIbzxoLZt84Mf/ICbb76ZaDQKwI9+9CMUReGTn/zkKdcYaVf8kUce4VWvehV1dXV0d3dX/7zuda/Dtm1++9vfjuk+//u//5uf//zn/OM//iOLFi0in8+P6TyJRCLxOdvi48MPP8z27du5//77x/R6JBKJRCI5W5Alr9NIIpEAIJvNjvmc2267jR/84Af87ne/49WvfjW/+tWv6Ojo4NZbb60ec+DAAebNm0d9ff247ueVV15h27ZtNDU1Dfn9zs7OMV3nta99LQBvfOMbuemmm7jggguIxWJ88IMfHNf9SCSSc5ezKT729/fzsY99jLvvvpuFCxeO63klEolEIjnTkYJyGkkkEsybN48dO3aM+Zzrr7+elpYWvv3tb/PqV7+ab3/728yZM4fXve51k74fx3F4/etfz0c/+tEhv79q1apxX3P58uWsW7eO73znO1JQSiSSMXM2xccHH3yQSqXCLbfcUp3L29bWBkBvby+HDx9m3rx5BAKBSd+nRCKRSCSzDSkop5k3v/nNfO1rX+OZZ55hw4YNox6vaRrveMc7ePjhh7n//vv58Y9/zJ133ommadVjli9fzuOPP046nR7XLvzy5cvJ5XJTsviqpVgsUi6Xp/SaEonk7OdsiY+tra309vaydu3aU7537733cu+997J582YuueSScV9bIpFIJJLZjuyhnGY++tGPEo1Gec973kNHR8cp3z9w4ABf+MIXBjx266230tvby/ve9z5yudwp9vN/+Id/iBCCv//7vz/len5v0VD88R//Mc888wyPP/74Kd/r6+vDsqxhz7Usi97e3lMef/7559m+ffuwzokSiUQyHGdLfPzQhz7Eo48+OuDPv/7rvwJwxx138Oijj7J06dJhz5dIJBKJ5ExGESN9wkqmhJ/85CfccssthMNhbrvtNi644AIqlQqbNm3ikUce4Y477qguPnwuvPBCduzYwZo1a9i1a9cp17ztttv4j//4D974xjfyhje8Acdx+N3vfsdrX/vaaunpkiVLeM1rXsPDDz8MuLb4r3rVq9i2bRt33HEHl112Gfl8nu3bt/PDH/6Qw4cP09jYOORr6OvrY8GCBdxyyy2sXbuWaDTK9u3beeihhwiFQjz77LOsXLlyan9wEonkrOdsiI9DcfjwYZYuXcoDDzzAX/3VX038BySRSCQSyWzn9BnMnlvs27dP3HnnnWLJkiUiEAiIeDwurr76avGlL31JlEqlU47/3Oc+JwBx7733Dnk9y7LEAw88IM477zwRCAREU1OTeOMb3yheeuml6jGDbfGFECKbzYqPfexjYsWKFSIQCIjGxkaxceNG8eCDD4pKpTLs/ZfLZfEXf/EX4qKLLhKJREIYhiEWL14s3v3ud4tDhw5N6GcikUgkQpz58XEo5NgQiUQikZwryAzlLOULX/gCf/mXf8nhw4dZtGjR6b4diUQimTXI+CiRSCQSyexBCspZiBCCiy++mIaGBp588snTfTsSiUQya5DxUSKRSCSS2YV0eZ1F5PN5fvKTn/Dkk0+yfft2HnvssdN9SxKJRDIrkPFRIpFIJJLZicxQziJ8E4dUKsUHPvAB/uEf/uF035JEIpHMCmR8lEgkEolkdiIFpUQikUgkEolEIpFIJoScQymRSCQSiUQikUgkkgkhBaVEIpFIJBKJRCKRSCaEFJQSiUQikUgkEolEIpkQUlBKJBKJRCKRSCQSiWRCSEEpkUgkEolEIpFIJJIJIQWlRCKRSCQSiUQikUgmhBSUEolEIpFIJBKJRCKZEFJQSiQSiUQikUgkEolkQkhBKZFIJBKJRCKRSCSSCSEFpUQikUgkEolEIpFIJoQUlBKJRCKRSCQSiUQimRBSUEokEolEIpFIJBKJZEJIQSmRSCQSiUQikUgkkgkhBaVEIpFIJBKJRCKRSCaEFJQSiUQikUgkEolEIpkQUlBKJBKJRCKRSCQSiWRCSEEpkUgkEolEIpFIJJIJIQWlRCKRSCQSiUQikUgmhBSUpxnHcTBNE8dxEEKc7tuRSCSSWYMQAtM0sW1bxkeJRCKRSGYp+um+gXMVIQS2bVOpVCgWi2iahqZp6LqOrutomoaiKCiKcrpvVSKRSGYc27YxTZNCoYCiKKfER1VVZXyUSCQSiWQWoAi57TvjDN51r1QqKIpSzVL6QlJVVQzDGLCAkkgkkrMZIQSWZWFZ1oD4KISo/vHFpK7rGIYhBaZEIpFIJKcRmaGcYfxdd8dxqgLRF4/+v/1F0+HDh8nlcqxZs6a6ePLFpRSYEonkbKO2BQBOxsZaoejHx76+Pnbt2sVVV11VPU5mMCUSiUQimXmkoJwh/F33Xbt20djYSENDQ3XXfTB+htIve9U0rZrVrFQqAKcsnqTAlEgkZypCCBzH4fDhw1iWxeLFiwdUbQBVcVgbH4FqfLRtG9u2KZfLAyo8/FJZ2UIgkUgkEsn0IAXlDFC7697X10c8Hh/zwmZwCaz/mH9N0zQBZAZTIpGckdS2AOTzeUzTHFN8HCwwB1d42LaNZVkDBKjsUZdIJBKJZOqRgnIaqRV+fonreETecIud2t35wc8znMD0d+glEolktuDHLdu2xx0fgRErPAYLTMuyqmLVj6F+BlNuwEkkEolEMnGkoJwmanfdgWo/z3BlriNdZzRGEpi+oYWqqkPu0EskEslMU5tB9Dfbxhsfxxq/pMCUSCQSiWR6kYJyGhi861678JmOBdNQ541FYA4ukZUCUyKRTDfDbbbB+OKjf63xMprA9O9JthBIJBKJRDI2pKCcQobbda9lJhZMg6kVmP71HMehUqnwwgsvsGrVKqLRqBSYEolkWhnscj1afBwpBk1VfBpOYJqmye7du0kmkzQ3N0uBKZFIJBLJMEhBOUWMtOtey0xkKMdyTV9g5nK56j1VKpUBDolSYEokkqmgdrYkTE189K871dQKzHK5XI3pskddIpFIJJKhkYJyChht172W2bBgGsxwGcxyuTzimBK5gJJIJKMx1GzJkQzHBse8kY6dbnyX7dFaCGoFpuxRl0gkEsm5hhSUk6B2110IMaZB2qc7QznW5/RFo1/+JYQ4RWDWzniTQ8QlEkktQ7lcT2V89J9jphmPCVqtyY+MjxKJRCI5W5GCcoI4joNlWaOWuA5mti2YRrt+bTZhsMAslUrVY3yB6e/OS4EpkZy7jLUFYDCzfcNtKKQJmkQikUjOdaSgHCe1iwW/HGo8C4MzccFUixSYEolkJEZyuR6N2bjhNt64NZIJmuxRl0gkEsnZiBSU48Dfdd+1axepVIo5c+ZMaLExmxZMMDnhOhaB2d7eTlNTU9VJVgpMieTsw3e5PnLkCPl8nlWrVk1rfDwT4sdgE7ShBGZvby+BQID6+nopMCUSiURyRiIF5Rip3XUvFotEIpEJfeCPd8F0OnqEJsNQAvPIkSNVMVkqlVBV9ZQdeikwJZIzl9oS10qlQqFQmPb46D/vmcRQArOjo4N4PE40GpUmaBKJRCI5I5GCchSGmy3pOxaOl8ELpnQ6TXt7O6lUilQqRSAQmKpbHxPTvSDzX6+/OPKzl7ZtY9v2sCVgUmBKJGcGg12uVVWdcFwZHB+LxSIHDx4kGo1SX19POBwecCxMrCx1tuDft98iMNgErTY+ShM0iUQikcxWpKAcgeGMJaZiwSSE4ODBgxw4cICmpiYOHTpEPp8nFotRV1dHfX09juOccTvwQ1G74BtuiPhggekvnGpnvMkFlEQyexjO5XqqBGVHRwfbt2+nrq6ObDbLvn37CAaD1NXVUVdXRzQancqXMyQzIVb9nx3IHnWJRCKRnJlIQTkMfo/LUHb3k81QWpbFiy++SLFYZP369YRCIVRVpVKp0NvbSzqdZu/evZRKJTRN4+DBg9TV1ZFMJqsLj6lkJhZMI82SG0pgWpaFaZrV7/vCsnbGm1xASSSnh5FcricbHx3HYffu3Rw7dozzzz+fhoYGwM2EZjIZ0uk0ra2t5HI5APbt20d9fT11dXXo+pn3kTaW+AgjC8zB8VEKTIlEIpHMJGfep+8042fLfBfXoT6YJ7MDXy6X6erqoqmpiXXr1lWFJEAgEKClpYWWlhYA2traOHLkCMVikWPHjmHbNqlUqrpDH4/Hz4hFw3h2+ccjMGtnvE2H0JZIJAMZi8v1ZOKjaZrk83mEEGzYsIFIJFKNj7qu09DQUBWY+Xye5557DiEEBw4coFgsEo/Hq/ExmUwOGOcxW5lIfISBAtNxnKrAlD3qEolEIplppKCsYayzJVVVHfcOvF/i2tHRQSqV4uKLLx51Jz8YDBIIBFi7di1CCPL5PL29vfT29nL48GEURakunurq6iZkFDQTJbWTKRuTAlMimR3UlrgCw1YJTDRD2dnZyZ49e1BVlauuuqoqmIYz6jEMA4CVK1eiaRqlUqkaH3ft2oVlWSSTyQEbcLMxLkxFfISBAtNvIZAmaBKJRCKZCaSgZPyzJcfrRFipVNi2bRv5fJ558+aN68Pcfx5FUYjFYsRiMRYuXIjjOGSzWXp7e+nq6mL//v3oul4t/aqrqyMUCo35HqeTqexDGk5gdnV1cejQoWrWd7BD4mxcSEokZwq1Lte1//+GYrwZSsdx2LdvH0ePHmXRokV0dXWNKbM4OKaEQiHmzp3L3LlzEUJQKBSqArO1tRUhxIANuGg0OmpcmqkeyumOj6Zp8tvf/pb169cTCAROMfmRLQQSiUQimQznvKAc6657LaqqVrOYo5FOp9m6dSupVIqNGzdy+PBhyuXygGNG6p8Z6R6SySTJZJIlS5ZU+4t6e3s5duwYe/bsIRQKDRCY/o7+eJ5nstQK4unA/3351/edZE3TpFKpVL8nBaZEMn6Gc7keifFUcBSLRbZs2YLjOGzcuJFCoUBnZ+e473EwiqIQjUaJRqMsWLAAIUR1A66np4cDBw6gaVrVAK2urm6Ag+xMMp2itTY+2rZdFY/+71T2qEskEolkKjinBeV4dt1rGUuGUgjBoUOHOHDgAKtWrWLRokXVD+mhzh1uUTHWnX5N06ivr6e+vh4Ay7Lo6+sjnU5z6NAhduzYMcBBNplMzoiBhX//0y3gHMcZsHiq3aH3f8+maQKcIjD9RZZEIjnJcC7XozHWCo7Ozk62b99OS0sLa9asqZatjmdO71hRFIVEIkEikWDx4sU4jlPdgDtx4gR79+4d4CBbX18/YyOcZioLClQ308ZrgiY34CQSiUQyEuekoJzIrnsto/UI1Za4rl+/nmQyOeDcwQum4RYUk1lk6LpOY2MjjY2N1XtKp9P09vayd+9eyuUyiUQCgEwmUy2DmmqmO0Pp4/8eB+MvjmrvxxeYQ2Uwa3foJZJzlZFcrkdjtPhYW+K6du1a5s2bN+D74+3rnkgfuKqqVfEI7gZcrYPsrl27iEajmKZJNpulsbFx2jbgZkJQ+r+Pwc8je9QlEolEMhWcc4LSL306ceIEixYtmpA5wUg9Qr29vWzZsqVa4jq4zHS8/ZdTZZoTCASYM2cOc+bMAdxSs3Q6TSaTYd++fezevXtaHGRPt6AczFgEpqqqpyygpMCUnAv4m2379u1j/vz5BIPBKY2PxWKRrVu3Yts2GzZsIBaLjfncwUzl/8nBDrKmaVY33zo6Omhra5s2B9mZEpRj+awbTWACskddIpFIJKdwTglKf9c9n89z8OBBlixZMqHrDNUjNFyJ62DGIyinc5ERDoeZN28ee/fu5YorrsCyrCl3kIXZJygHUysw/XsdSmAOXkBJgSk526gtcd2/fz8tLS0TMvYaLkM5VInrcPcx3vueagzDoLm5mdbWVhYuXEgymZw2B9mZFJTjZSSTH9mjLpFIJBKfc0JQDp4tqev6hAdvw6misFKpsH37dnK53CklrsPdz3jufbqYCQfZmRCUQghsy570IqbWft+/LpzciCiXywgh6Ovro7m5mUAgIAWm5KygNj76AmKisWfwuY7j8Morr9Da2jpkiWsts2XDbTDT4SDrM92CUgiBZU4+PsLQAnNwj3pfXx/JZJJIJCJ71CUSieQc4awXlEMZS0xkjmQttef39vaydetWksnkkCWug5mtC6ZaptJBdjoFpeMIdj7dxo6n2+jPZGlcGuK81TZGYGpK0YYSmJVKhR07dnD11VdXnYFlBlNypjKcy7WmaROOkbXx0S9xtSxryBLXwYy3JcB/DdPJUH2HU+kg6xuKTQdHdnXzwn8fpK8nhwgVuXhtiXjd1I2TGqqF4JVXXmHFihXV78sedYlEIjn7OasFpb/rPrh/xF/wTHRn2C/pOnjwIAcOHGDlypUsXrx4Qg6IY5mDNt2Mdg9DOcj6u/OjOchORriPxisvtbPpsVdQdZVK2ebgS328vPAQV75pxbQ8n/97VxSl6gDpl4CVy2UqlQq//vWv+Zd/+Rd+/etfT8s9SCRThZ9Z8v+P1mawJrPp5se4rq4utm3bNmqJ61DnjvV5ppux3MtkHWSnK0PZfSzLk9/bTSFbQQsIeg6UeeoHu7nhzovRtOkpSfVHkgQCAQzDOKVHPZvNctVVV7F3796qKZxEIpFIznzOSkE5eNd9sBlBbcZpIh/kjuPQ399PoVDgiiuuIJVKjfncoRZMw93DRHbrZwJd12lqaqKpqQkY3kHW772E6Vn8HXulF9t2aFwQJ5OxcWyHwzu6p01QgrtJUft+qs1i+uWw+Xx+2p5fIpkstYv84cxaJlvFYds2W7ZsGbXEdTDjHavkf282MVYHWf+YifY3jkb7oQy5vhItS5KUyyWKZZ2OwxlyvSWSjZEpfz4fx3Gqn7GDM5iFQoH29vbTNvNTIpFIJNPDWScoB++6DzWg2f/wnsgHeW9vL/v37wcYU4nrYGbTDvx0Osj29vaSTqdpa2sDYMuWLVPuIKsHVBxLeK9DYFsCIzg15a7DYdv2sJkWRVHI5/NEo9FpvQeJZKKMdbbkRAVlqVRi27ZtAFx11VXE4/FxnT+R+DjbBOVgBjvIVioV+vr6qp8lpmmye/duGhsbp9RBVtNVFAUcWyAECEugaiq6cfpiZD6fJxwOT5lDrkQikUhmB2eNoBzLrrtPraAcz/UPHz7M/v37mTNnDtlsdtxiEk7f2JCZJBwOV11ks9ksL730Eg0NDVPuILv6irkc3tHNiYMZSqUiwYjOha9eOA2v6CQjLZbAXTCN1icmkZwO/Pg4OMs+FBMRlH6Ja0NDA/39/RPaWBlvfJyJstepfo5AIEBzczPNzc0A/OY3v6G5uZlisTilDrJLLmhkzpIUxw/2YVkmpik4b/1cIonA6CdPEL+VZLj7zeVy4zIskkgkEsmZwVkhKMe66+4zXkHpu7hms1muuOIKyuUy2Wx2Qvc6mzKUM/U8qqqycOHCYR1kDcMYIDDH6iDbsjjJG999EQe2dnL82HHmLk+x8tI50/paasu5hqJQKEhBKZlV+C7XlmWNeR7heARlrYvr+eefT1NTE+3t7RPaDJttpjwztaHX3NxMNBqdUgfZcCzAG959EbufPU7H8S5EoMyVb14+rfHe/wweKUMpKzgkEonk7OOMF5Tj2XX38ctgx7Jg6uvrY8uWLSQSCa6++moMw6Crq2vShhU+pmnS1tZGLBYjmUyesrN7JmYoaxnc+zSVDrIATQsTNC1MsHNneUYWKv77bDjkgkkymxjvZpvPWAVlqVRi69atmKZZdXH1e9dH23wZitoyVj9WHj9+vDq2yDf7Gnz8mUxtjByLg6yu6wME5kj9iNFkkMuvX8qxYwG6u7unzYzHx3/PjCYoz4bfm0QikUhOcsYKyonsutcy2oKptsR1sIvrZMxyas/t7+9n8+bNaJpGuVzGcRxSqdQAR9XpZCbE6mjGR5NxkK1luowtBjNayWsul5MZSsmsYDiX67EwFkHpl7g2NzezZs2a6v/LyfQ21p5rmiZbt24ln8+jKAo7d+4kkUhU44Xfn3m2bbrVMlkHWZ+ZjI8jvdfkhptEIpGcnZyRgnKiu+61qKpaPX8wg0tcB7u4ToWl/rFjx9i1axdLly5lwYIFOI6g63iadE8fHce7qrPMLMuivb192IXCbGe8TrrjcZD1DSz838dsEJT5fJ6WlpZpvw+JZDhGc7keCyPFR8dx2L9/P0eOHOH8889n/vz5p5zrHzde/Pvs6+tj27ZtJJNJ1q9fj6qqpDv76e5Mk+7qp62tDcdxcByH9vZ25s6dSzgcnpbM13Rm0/yxQ2N9jvE4yNbX15NKpdB1fcYF5XDIDTeJRCI5OznjBKVt23R0dNDV1cXq1asn/GE/nCj0S1zj8TgbN24cUsRNJkMphKBYLLJnzx7WrVtHQ0MDxWKJQ9u66DicwbYd4nX1XHLJavLlPvbt28fRo0fZtWtXNVNniAiiYqAbGg1zY0QSwQndS+3rmS4mO2NtJAfZY8eOYds2qVSKYrFIPB6ftpluPmPpoZQ78JLTheM45PN5duzYwSWXXDKky/VYUFV1yBg3VInrYPznm8zYkRdffJFVq1axePFiTNPk6N5uju5OUy7ZhKJRVl20hHCdwksvvURvby9Hjx7FMAzq6+uJBOLYBR1N00k2hkk1T/z/40xlPycas0ZykH3llVcolUpVV21N00bdEJsso11fxkeJRCI5OzljBGXtrnu5XKa3t3dSwmGwoBRCcOTIEV555RVWrFjBkiVLRnSJnchiqVAo8Morr2DbNq961asIh8MIIeg5luPY/l4SDRGMoEb6eJbWXWmWrkuhqipXXHEFlUrFdUnde4K9zx+gmKsQDIZomp/ksv+1nJb5DbOyL2Ukx7+JUOsgK4Qgn89Xy2NbW1tpa2ubEgfZ4RhLD6XcgZfMNLUu15Zl0dnZCUxcqAwV47q7u9m2bRuNjY1cdtllQ5ae154/XjFmWRa7d+8GYN26ddUqhVxfmSO7etANjcZ5Yfp7ihza3sUlr12Mpmmcd955hMNh+vr6ONHWxTO/2k1fV5FAwCBZH+eiVy9m2Zp5s3JUhf8zmqoYNdhBtlQqVc19stksv/vd76bEQXY4xlLBIQWlRCKRnH2cEYLScRwsy6qWYOm6Pmw51lipXTCZpsn27dvp7+/n8ssvr5YTDcdEMpR+v1EqlUJRlAFGCpWSBUIQDLu/jkgiSClfwal5iYFAgJaWFo7vLDJvjk6iKUg2m+X4gTTPPPkyDYtPGtnU19ePaXD0bOihnAyKohCLxYjFYnR0dLBw4UJCodCUOMgOh1wwSWYbg1sAfBOr0TY/RqK25LW2xHXNmjUsWLBg1PPHanrmk8/n2bx5c1WkJpPJ6vcqJYtKySLV5P6/itWFyHQX3LiJ+/o1TaOhoYHsCYdUtMKylREKxTwnDqd58andHOs+QDKZrPZfxmKxWbEBN9WCcjChUIi5c+eSy+VwHIcFCxZMiYPscIxWwSE33CQSieTsZFYLytpdd1+Y+KU7kymngpOCciwlrsOdO9bXcODAAQ4dOsT5559PMBis7sL7BMM6iqJQypsYQY18pkzj/BiafqpwNcs2gZBOMBgkGAwiigEWrqlnzsoo6XSa9vZ29u3bNy6n1OlkuktQffyFzGgOsuFweMACarw/F9u2RzxHLpgkM8lILteTiZF+jPNLXCuVCldddVXVCGcs5491w6q9vZ0dO3awcOFCli1bxhNPPDHg+8GwTiBkkE0XiSaDZNNF9zEvbtZimg6qqhAIGAQCKdQFQYyQxoWXz60KqcOHD1d7EX2BOdJG03S3BEz3c8DJ+DiVDrJDIXsoJRKJ5Nxk1grKwbvutb1Afi/IZFAUhY6ODjo6OkYtcR3q3LEs1iqVCtu2baNQKFQXYz09PacstBrnx5m/qp6OwxnyGYdkY4QlFzShqqc+R+O8GPu3dKJqJRzbQdEUUk3RqpBaunQplmXR19dHOp3m0KFD7Ny5k3g8PsAptfZDfzb3UI6VoUwnxuog6wvv4RxkBz/PcDvw/gw5maGUTDcjuVz7f59MjFRVlXw+z6ZNm8ZU4jqYsVRx+PMrjx49ygUXXMCcOXOqcbU2vsZSIZZc0EDr7jS97QWCUZ1lF7cQirgbO7XPk2wIoxkqfV0FdF2lmKswd0VztZLBn4Xb399POp2uOqX6G01+LPBf63RXccykoBy8ETYeB1n/5zLahutYeij9fk+JRCKRnD3MSkE52mzJkRwIx4JpmhSLRYrF4phKXAczlt33TCbD5s2bSSQSbNiwofphPtRCS9VUll7YxJwlSWzLIRIPYAR1CoXCKccuOr8R2xF0tWbRdJ1lFzXTvCgx4Bhd12lsbKSxsRGAcrlMOp0mnU6zc+fOqpGNX1Y23YPBT5egHMxEHWRrGcvYkLFmcSSSiTAWl+vJxEghBJlMhr6+PtauXcv8+fMn5BI70qZbuVxmy5YtVXMffxNmqOdRFIV5y+uonxOnUrIIRQxCUWPI45sWxjlv/Vxa96RxLIelFzWxdG3TKfeWSqWq7t3+RlM6nebAgQMUi8VqHLBte1rjo/8zmglBOVp8HMlB9siRI+zcuXNIB9laxtISIDOUEolEcvYxqwTlWGdLTqbkNZPJsGXLFgCWLVs2bjEJpw7frkUIQVtbG3v27Bky8znczr2qKsRSA8uuhnrtuqGxct0cll3UjIIrRkcjGAwyd+5c5s6dWzWySafT9PT0APDss88O6L8MBifnGluL4zizRlAOZqwOsv7PJRaLSVMeyWllrLMlJxojS6US27ZtI5fL0djYOKZ+yaEYqYqjt7eXLVu2UF9ff0rmc6gZlv7fI/EAkfipGbLaYxVFYf7KeuYur/N6K0ePCYM3mkqlUnUDrlwus2PHjgHxcSqNvmYyQzne+DhWB9naDbixCEq54SaRSCRnH7NGUI5ntqS/WBpP9muwi2s6nZ6UYQWcWv5o2zY7d+6ku7ubSy+9dMjSnvEa+gx37FgWSkNRa2QzZ84cnn76ac477zwymQxtbW3s3r2baDRaLRVNpVKTckecyQzluJ9HOGAXwcqDXSBs5QmHSsxvziEaypRzneSKRzjWdwmHDx+uXj8QCJBKpU5ZWPolr1JQSqaaWpdr3zl5pPf7RNoCal1cGxoayGazE77foao4amPw6tWrWbhw4bAZybHGyOGduBVgYnEnFAoxb9485s2bxzPPPMPChQuxbZvu7m4OHDhQHU/iC8zJzAeu9QaYTqbCbXs4B9ne3l527dqFZVkEAgECgQCZTGZIB1nZEiCRSCRnJ7NCUDqOQ6VSGXXX3cf/kLJte0x9PaZpsmPHDjKZTLXENZPJTDjLOdQuej6fZ8uWLWiaxsaNG4c1eZjIYmmqRVnGzvBU9imuD18PQH19PY2NjSxfvhzTNKtZOr8MtNYd0Z9pNlZmtORVUcAqgO39sYooVg5hl1CsLDgVMLMgyihmDhwTcGovAloAhA1qiJCqEFz1VhpCzTiOQzabZfv27WSzWV544YVTHGT9Rf9kBeV9993Hf/3Xf1VNhDZu3Mj999/P6tWrhz3n4Ycf5l3veteAx4LBIKVSaVL3Ijn9DHa5HmuMHKugFEKwf/9+Dh8+zJo1a5g/fz6tra2TMvUZnKG0LIvt27eTyWS44oorquWmw507FZtuE6XjSIZcb5lgRMexBZFIhPr6ehYvXlw1+kqn07S2tlbnA/sCc7wbcDMVHyfj+DscvoOsX/lSKBSqnxlbt24d0kF2Kkx5ZHyUSCSS2cdpFZR+iavv4jqWhRJQ/cAey4LHL3GNxWIDXFwnOkvSP7f2+Ts7O9m2bRvz589n9erVI35wT8Xu+2T5t+5/4//1/z+ijVFUBt6rYRgDdqELhUJVYLa2tgIMMGmIRCIjPtekF0x2Gey8JxSLYOVQqlnFEtg5sMoszL9M6MgLqKorBnFK7le7DEbcFY9aEIQFig6KitDCIExQVBSriNCjKFY/wkigmP04c2+AkPtzUFW1atqzcuVKksnkAAfZl19+mXvuuYc5c+bwxBNPcMMNN1TNgMbLb37zG+666y6uuOIKLMvi4x//ONdddx27du0acXc/kUiwd+/e6r9nw1gEycQZzuV6LIy15NUvcS2XywNcXCcTH/3z/TiXy+XYvHkzoVBoTE7a442RUyko9zx/nB2/a8Os2KiqghPNsXr1yZ/DYKMvfz5wOp1mz549mKZZnfM4lg24mRKUUz0PeDCKohCNRgmHw6RSKZYuXXqKg+w//MM/UCgU+P3vf8/69etZsmTJhJ5LxkeJRCKZfZw2QTmRXXef2gzlcAghaG1tZd++fSxfvpylS5cOuP5kFkz+dSzL4vDhwxw5coQLLriAuXPnjunc8S6ApnLR0W628/P+nwPwcN/DvIt3jXh8JBIhEokwf/78qs18Op2mo6ODffv2EQwGqwusocZwnHLvtlkjEF1RqNh5hF1GMfsRwkatpBGKilLuBC0KZgaMpPc1BXYRR4ugCAuhBsCp4KCjqAbCiKPYRYSeQLH6wYihmH1gJKCSdq9j5xBq1BWTagiEhWOkUEQFJ1CP4pQQqUug7pJTfh5+j9DghWU+n+euu+7iM5/5DPfffz933XUXPT0943LG9PnFL34x4N8PP/wwzc3NvPTSS7z61a8e9jxFUar9oJIzm9oSV2BcYhLGVvLa09PD1q1baWho4NJLLx3wXp2soPTj3IkTJ9ixYweLFy9m5cqVY3oNUy0Sx0o+U2bv8yfQgxoN8+OU8ib7d3fSfSxPY1PjkOf484FbWloQQlAsFqv9l0eOHBkwnmSoMRyzybRsKvDj41AOspVKhbvvvpsnn3ySL3/5yzz//POsW7du3M8h46NEIpHMPmZcUE5m191HUZQRS7r8Ete+vr5hXVynQlBu27at6lI41jKe052h/E76Owjc52+z2tgd3s1ree2Y78dfJPhzHvt6e+jrPk7bKy9xIJ8mEdVIxYMkozqxkEqkuw0l14Ny4Fk3s+iYuL1NjpspdCyEGnS7nVQdBYFAR8HC0ZMoThkC9W7pqp5AMfsQRgLV7K1+tZQYmlJBKDoIC6GFUYSNY9ShiDIY9Sh2zss8ZhCeyBR6BKXSi9BCqFQQioEiLAg0IOa8bsifwXClY9FolFe/+tUIIdi6dSvZbHZCYnIoMpkMwKgZz1wuV128XXrppdx7772sXbt2Su5BMnPUulz7sW68jBQfa2fjnnfeeSxYsGBIl9jJCsqjR4+SyWS4+OKLqxUPYz33dGQoKyULs2wTb3BFXyhq4Nhglcf2c1AUpboBt2DBgmqZfO14En8+sN+ffrYJyuHGKqmqyutf/3p6e3t55JFHWL169bhnXA6HjI8SiURy+plRQenv4B49epRFixZNyoxguJIuv8Q1Go1y9dVXD1teNZkFU19fH+C64F1++eVTPp9tMFO1YPKzk47XN6ig8GTySd7P+2ufzOs/9PsQ82C7vYg4ZbCy4NgoZi8KCk2VHhr1OEpdP1Z9lGK2k2yfRs+JDCdsg6AOjqpRLoYIBEIomgZCoDi2+1wKKMJ0/y4q7i2oAfdbKoAGwkHoMRA2IlAHThmhx1GsAqhBFLMfEGh2FkUJgigjVAMFB4QGqoPQo+51jBQ4plve6njlrsICYaMoDuDgzLkF1KHfNyPNoczlclWjHn8ky2RxHIcPf/jDXH311VxwwQXDHrd69Wq++c1vctFFF5HJZHjwwQfZuHEjO3funLBTp2Rm8VsADh06RGNjI+FweMrjo9/fViqVBpS4DmYy8bFUKpHL5dB1nY0bN45aFj+Y07XpFk0GiTeESbfnSDVFyWfKBMIq8fqJuV77ZfJDzQf2x5NEIhF3Y66vj0QiMW2ib6bctsfqgj1VxmUyPkokEsnsYMYEpb/rXiwW2bNnD4sXL57UB9zgkq7RSlwHo6oqlUplXM9Z+xyKorB69epxZ6GGWywNtVM9ZQsAqwh2ga29P2VBKUfAcYg6DoZwiDlpeo/8G82OerLMVI+D2e+Wj1YKiEAUbMs1rHEc0FQEqptdNOrAKSGMFLqVJVY3l7jZjzCaMfPddPaZWOV+Wo8eQ1cdQpEk0XCQSCyOrgJKyDXLUTW3VFULuJlMNQhWyRV2joXQIq5ZDgqgg6ohlCggcFSBEAqK6v0uhIUiFLAr7nUqFYQR8/ooQ6BobnZUVRGKBpiA+/zOnDdAqGnIH6OfXR9OUObz+Sl3MLzrrrvYsWMHTz/99IjHbdiwgQ0bNlT/vXHjRtasWcO//uu/8ulPf3pK70ky9dS6XB88eJBYLDZuIVbLUCWvI5W4DmaigtJ/Dk3TWLp06YRew+AYOVocnKoNt0BI5/LrlvLyrw6T6y0RjhksnB8l0Tg1mbTB84FLpRLHjh3j6NGjbN++HcdxSKVS1QzmVI4nmemS16GoVCqYpjmlY0NkfJRIJJLZwbQLysGzJf0eu7E6tA5HbUlXbYnrZZddNiYzlPEumCzLYufOnaTTaS6//HJefvnlCd33lGQo7fLALKJdRLHynptpv5ttq/Qi8AViFMx+rtMjXO68ClsPIhDYjqCzrYumpOUKK6eMY9S7gs6ocw1qAlG3t9GIuf9Wwyh2GaGEagReABAIPeGWnAbqUKw8RqSRWOEoJS3J4vowRTtAKdtFX6ZMV+cxlECMeCRIOJogEtJQjSQ4FYSeRHEKbhbRF7Zmxs1Kmn0II+mKTzUMWAjFQKgGGHGEY4EeQqlmMbNeiWvmZB+lngQrizDibtZVCbrXSV4AqQuH/T3U9vsORS6XIxqNTtki8IMf/CA//elP+e1vfzvuXXTDMFi3bh379++fknuRTB+DXa4nMvJjMLXxcSwlrkOdP574KITg0KFDHDhwgDVr1tDR0THhez+dbQEN82K89u1rKOZMgmGdF19+fkqvX0soFKKhoYGOjg42bNhALpcbYGKj63pVXE52PMlsEJS5XA5gyrKTMj5KJBLJ7GFaBeVQsyWnSlD6JV1jLXEdzHgWTLlcji1bthAIBNi4cSPBYHDEwd0jMXgUSKVS4ZVXXsEwDBobG4mpfSiVXhSniFbO0lR5AfVYD4qTB+GgVHpcUWf1ewLJFUZK1cVUgKIgFNUzm/H6EI06NKufxsgStx9RC2GXCxSdjJshVHRQNLf8VAujOL5RTRlh1LnGOdUexpgr9LQwipl1s4eO7ZWJuosWv0TV0uIodh6MFBE1R7hpAfVWHlttoZTtJlcRpDsO027phMOGl8EMEQhH0bA8kxzvHqwswkidFJVmL8JIQqUHRaius6sSdEtjdf811KGIivcaCm4fpdWP0CLu+WoYxe4Dox6n5doRf3f++3i4BdNUzVgTQvDnf/7nPProozz11FMsXbp03NewbZvt27dzww03TPp+JNOHZVmnuFxPhaD042O5XGbbtm0Ui0WuvPJKEonEmM4fz9gR0zSr43TWr19PMpmkq6trwpnDWkHpOA6HDh2iUqnQ0NBAKpU6RRhNtYGPbmjE67TqtaezVLTWRyAejxOPx1m0aFF1PElvby9Hjx6tOphOaD6wEAi7jOoUoOK4lRrCcmO2UwbHcg3KHKvme6b7uYBw3bRRXBM1RXfbHlQDKhmILYV5J2PMSIIyn88DTCrz7v/MZHyUSCSS2cW0Ckr/g3hwr6SiKJNeMCmKQkdHB+3t7Sxbtoxly5aN64N/rIKyvb2d7du3s2jRIlauXFldzEyFoMzlcrz88svVXqnuQ79nTvn3GNFGYmGNYLSesN0OpTioGuAgtLjbI2jUodiemLSyrsGMlQPFQCi4H/yoKFhun6GwXfElvAygXUCoQTRsFIS7sHB/MqBYCDSvx1AHBI4eRxEmItDgZgeNpCsq9ahnmBPxMpghFMtys4fC7ZN0MEBVEIqXwTQSaHaZSKKJqFNENNVjlbIUyg6lfDfHe2wU1SEYShKNhglH4gQMB6HHARsRqAe75N1DDkcNEVCPgwDFzrii0i66fZSKg9uo6SD0MKAg9KQrOrUoinCFsLPwLe4iaQRGM0nxM5ST5a677uK73/0ujz32GPF4nPb2dgCSyWTVyOK2225j/vz53HfffQB86lOf4qqrrmLFihX09fXxwAMPcOTIEd7znvdM+n4k04cvhmpdrnVdnxJBmc/n2bRpE3V1daxbt25cG3hjjY/9/f3VDb0NGzZUN/QmGh/9c4UQVCqVar9nPB5n165dWJZVdU2d6Fie2YSwTTRRdh2sq4LORHMs6nWT+kab5fUGphknl+miP3OIjqNdHDUrxCM68ViceEQnFAqh2CW3YMQqAc5JsQgsyO0n2LoYdMVtXbDLoIXdKhcj5m7GaVFXPOrhk+0CogJKALDdpnbHAVX1zkvCoE24sbQETDZTKuOjRCKRzD6mveS1dh4ZMCU78JZlUSqVKBQKYy5xHeq+RlrwOI7Dvn37aGtr46KLLqKlpeWU8yeyM+4vGk+cOMGuXbtYsmQJixYtQhTb0YKdFMtLKeW66O1TKBw8SrEcotU8ROOCJmKJiGtUo7gC0dFTKMLtX1TsvJsxtAsoigGiDI6DUAIoiolQg16vYsDL4EURdgFLCbvZTEVDcSqgCLcHERPwXFiVIIpieue6YlHBRARSbomq//y6V5pqxKtiUzWzaGrQLZNVQ94PQUNo7t+FaoCw0EMJkkGLZCxCM1CulCkWiuSzPfR0HkfTdELRBOFwmGgsgaZ5mWgjjmMVsBVv11s1AOFmc3HcBZIaBLPkZlYd0xWWQrgjR4SNaL4GgkOPBRj8nhgpK+AbTkyWr3zlKwC85jWvGfD4Qw89xB133AFAa2vrgIVZb28vd955J+3t7dTV1XHZZZexadMmzj///Enfj2T6UFX1lAX2ZOOjEKKa3Tr//PPHVOI61H2NJgiPHTvGrl27WLp0KcuXLz9lLNNkMpT5fJ4dO3aQSCRYv359NZOXz+dJp9N0dXWx9bm9FPpMelstVl9cZOGyuVPmrIxwM3mqXXTHDpW8jJ1juSLLsb2vtVk97+/4GT4vAyjsmmMq1WsjbEL5HHOzvfDKC6Ao1QoThMDdCHPjjaFo1AlBXVxDRAWmrZHP5yj0d3O8PYdAIxwJEY1EiYbDGIEAoIHmOl+bBFEUFfSIKx71mJtpDLj98u5Ypj6v6qXX66PvBTUCIuuKSn9zzlHdjcb5N7hVMTWMZMozVS0BMj5KJBLJ7OO0zKHUNK06X228+DviQgiWLVs24V3qkRZMpVKJrVu3YlkWGzZsGDLrNNEdeH+RtWvXLi6++GKampowi70oJ/4b9AhhtUIosgy7tZ1suQk7d4Kegkqu7wCRhiDhaJhILOZm7QJOjUCMee6lXl8gOopiV4WhYmURWphCupv2NgfHLGFEwjhCgBJ2y0L1mLvoEb7zqruAUETZdUu1+90RH6LkikNhVnevHT3mzoWsZg9TKFY/th5Fs/oRaotbYqrHUSoZRCDuzqRUg4AKqg6OhtA1ECbBcJhQMEyqoRlh5ilWoJRLk+krke44ghpKEQvphGIN7utWdFANr7fT8hZCJTASbubWiFfLhJWKWyqrVnoRqYsgObw7YC2jORgWCoUpEZRjWYg/9dRTA/79T//0T/zTP/3TpJ9bMrMMtbiejKD0S1yz2SwNDQ0sXLhwQtcZKT7ats2ePXtob29n3bp1VZOZWiaTobQsiz179rB8+XKWLVtWNXRTFIVYNEosEkQvqvTli6RPdOF09fPy8UMcXQr1TVFSiSjJeIRYJIiC7WbbhDVI0JU8R+sSoIKdA9XAzKZpbytjZtOo4RQtHCdwYiV0VjzR1e/2YNs5V3TZRbdH3S66hl9OBRQDcHAdqr3MHr5Q9H4mXouBQwBH0d3456YXXREpKu7mmF3xMopF96tVQlEDBBSTQDJBXTyIo7ZQLuXIl2xy/b10dnQSMDRCkQTRSJBQJOY+vxFzRaERd68bqHPv16h3r2+k3J58Peb+W3Uds1EUwATb9u6lAItugcDAz17fL2EmWgJGQ8ZHiUQimVlOi6CcSEmXEIKjR4+yd+9eli1bRn9//6R2OodbMKXTabZs2UJjYyNr164d9sNxIjvwfgkXwBVXXOHOIbNMlLafIMysu5usGlilCululXAsQKESZ05jgnwuSkMqAAEo5vrp7T6BooeJhoOEovVEIwFUPeTukmthV9wRwC19skANUclnadufxy5mEXqcUv8JCgLEyhwYETf7qATc0lY1DNi4IysdFMedG6kIb9SGnfcWPEWEFvKym24GEy3kZlADdSCOYWlJFNvLZFoZt4+x0ufuhptpd6Fm5RBa1C3JVUMowsTRY6jChGADEaNEJFZHg53DYhGl/nZyFY38iVcoizCGyNObThAJqwRCSVdIe8ZBQksAlrtwEmUwkihWHoKNiDnXjfn3N9JiCaau5FVybqPr+oQ23Hp6eti2bRt1dXUsWbKkOp9vImiahhDilB7CYrHI5s2bURSFjRs3DjtLcCLx0a8KKZfLLF++nOXLlwNU7wO7DEe+C+UerD1pFhoOzYv6CEZSOMUSqXATYaVMthO6j/TSTpBYxCAcTRINBzCCES8W6tXNMhQ8czEdu5yn9UCJbFcGPRjFyfaRrwRonpuHuiYvk5dwM3l6ws3gGQmo9HhiM+OKTCvvxlMr74nNkic2TTdmCstrJbBxVFdYuuLt5AYhesA9Xo96GcWIW2bqGaxhJMDbIFPNfsKRBGGjH+oW4Zj9FMo6pUIPPT15rBOtCAJk0icIR5OEQw6q6lafuPfhuPeHAC3iCm3NddBG6F6pqwlGyH0tLa9zeycH4f++xzJWSSKRSCRnF9MuKKdiB96yLHbs2EFvb2+1xHXbtm2TKgsbPKdNCMHhw4fZv38/q1evZuHChaOOHRnPDnx/fz+bN2+uZrAikYj7AXz8JyjFowjFQLEqCM3AcUARFSCIqggsJQSUUANJEnU6qVQ9QtiUCjnyRZNM9xE6LJ1oQBCINROL6ATCCa8/0B2PAZDPBynme4k3NKBSpliIo/S2Y5oaQdGL0BOoVsbLVFa8bKTbg+n2VQbdxRCAaqF4wyIVpwyoIPK4ZbIlt3/RthFCQVUctxwXv0zXPGmSo3smOXqsmsFUKz3uVyeLUKOeE6srjoUeR3MsIvWL3f7Llrn09xwnnYFKoYvetEBXWglGkkSCOuFIAiMQRCiKu9OuuAskYaQQC/7PqH2TtYwmKPP5/JTNn5Scu4w3PgohOHjwIAcPHqzGrra2tknFRz8TX1vm3dXVxbZt25gzZw5r1qwZMVs/3gxlpVJhy5YtVCoVYrHYqeZBjgXHfwxmDqEEsCwNVXWwMdCoYIkQhlIgUTeXRLwfR1uNme8iV9HI9R6ns1MjpNmE4o1EQxrhaAoVT1wCKA6lgkO+r0Q4EccwwCZF3+F28oUAsZTtZvCcspuZs/Jg1Lllo0bKFZO+2DQS4G+YVdJe+WifK0Kt3EmRqXobcaqfyfTEpupvzIXdKhA97mYsvR54gm4VyMn7SLn3oSfB6kc14sSUHLHYfBqtPGWrgWNH92OZAbqPH8RyFCKRMMFQmEg0QdBQUfSw1xIRAk62N7hC13IFrbAgdRE0XD7k73A007KpagmQSCQSyezjtJW8jnWx45e4hsPhqsOqf42JllTBQEFoWRbbt28nk8lUM4ejMR5r++PHj7Nz506WLVvGkiVL+OUvf+me2/k05A6DHkUxC6AZKI5FULeJJoLk0n2US4Jyf5ZANEY4olZ3shUE4XiIcMyE5jnYpQy5kqCc7+ZYn4ouDmFEm4iGFMLxFgzNQVFUFDQcW0FoYYRTpGRHUbA8gxt/xEafVxKaRugp1/xH80pq1YCXpQwjsEEooHhlsg64KU3HNfQRAsUpoioqilNyx5ioeH1BAqFHQDgII+XNtEy4xhJeLyiq4Zapqrq7gFJUt7RVUV2hi4ECKIE4umHRsmAhwrEolYoUCgWy2T46ezKEdEEwWkc0CMFYI5oo48x705j6JmsZTVAWCgXmz58/rmtKzm2G23AzTXNM5w/n4joV8RFOjpvwx46cf/75Y3qPjydDmclk2Lx5M8lkkksvvZQXXnhhwLlCOHDiF644Q0dRBfGGKJ1Hc1SKgn7bIBpWCCSbPMGXQjWzBONNBM1+aFiFXeqjYGqUst10Zh3ECW/TKRQkHEsQ1DVUVbhxxVFx0MCxEYCqOtWMImrQFbd61P1qJL3nrHNjlFHvuqEaKVc8GqmTIrO2R9FIgplGoGE4OUA9KTatvCsm7ZI3+7fsldHaXhWI7Qo/x3YzicK/D9MTnebJ+9KjaE4RB52W5iYQ9ZiWoJjPkC9X6E/vx9EjxEMQiDYRC2TRwg2eAPb6LLWwO8s41ARzhnfCHutYJYlEIpGcfZy2ktfRSroGl7gOdnEdj639UPiCMpvNsnnz5qpgncqxI7XGPhdffDHNzc3VhZLo2wV9W1xHPbOMo0VRFTfjh2LQMt9BM1IUioeIpJppmSswwkF33qQePdlb49ZsoYUbSAZNqGug2cxRsjRK/SfI5DTSXVtRgynCqk0gHCCf7gMtjGNZRJIaRiTqCrsBJjt+9rDPK1FN14hMd06ko0dRqCAIoGAjtABgApq32625i0HdXRQpashdNKlBV5xq3q63FnRfh6IjtLD3d8N9aYoKQnHdahXNdZjVwyhm3nOWLYFw3GwDGooqCEVTRCJxRNN8HKtEoWRSyvXQ3mujdO7Gil+AGopST4Z4PD5m18HRZrn5LoYSyWTQNI1isTjqcel0mq1btw7p4jpZYx//fV4ul9m6dSuFQoGrrrpqzEPpx+rk7W+2LV++nKVLl1YdwQeI0fYnUHKH3JJLLECnYV4MRdE4ciBHKKLTvLiJWEL3hFaNoAq4/YFauJ64niceW0qjlcN0dIq5NIVihb7egyhagEgwQCQpyKWzaCUNy3KIJh3CUa+nEQ2ouBtijgWoXgltsNpW4Jar1ohN2+tRrN5LAQIpMHOgJ1CsEzhqBKzMyUymEfMEXRTKvV5GM+/Ge+GNeBKWFxsdrzfTxh/Z5JbV4vWnKziKjaMEUbQAKDoB3SQQTpJ0ijjKIsr5NPmSINd3gs6ySsRoJxCrJxrIEYqmUH2hu/CmqknQUPg95sNV9kxVj7lEIpFIZh+zsuS1tsT10ksvpaGhYchrVCqVCd+XqqqYpsmzzz7LkiVLWLFixbh6O0bLUNaWcNUa+yiKguF0wYnnIVQHdsHtW6TsZR/d0kwtkqJlkUmmkmTeogB6KOn1HSZdceeb2xgxbzGjeAsM13whpNsEw8tJ2SVssZBSroNcCUKpTkpCI6QWCcVSWIpnWOE43nrEm7/mGfSIQL1bwhpIoZg5tzTV8jOYPe5Xq88Vn07Rc3K1EVoEVZhYShRNUdwMqF1y79tyM55uqatvkpNwHWL1BDhlt0TW6+VUhYXQvJ13tc59HiPpZi/1OKrVhqIGUMw0wkigmn0ILY7iFFC1ILGISiy6iEYcTKJ0Rv4X6d5+2tracBxnwBgCf4TLUIyl5FUKSslkGa3HfKgS18Hv2fGW5A/Gv94LL7xAMplkw4YN1RnCY2G0DKXjOOzdu5fjx49zySWX0NTUVP2ecMAsW27vZMeT0P08aDGoZL3exByqHqJxboCCEyaViJJIehuBju0JKrs6kxYt4vUjxsGpoBhJAk6ZQP18kqKCwzxK+X7yxTJ6rB+lXEFxFBL1YRxNQ9esk+6uiuYZ1bjjlFD8XkRf3Kk1//ZLah0vttueWZjlZiodC1tLoCg9bobTKnmGOaUao5+wl6HUqlUavnFa1eRH0b3XGXKvoXuvVwtWDcs0hepGnit8LdAiqMImHG8iHLWgqRnbLFMoFSnkcnSkC9idGUKhEGLha0kWHOLx4edyjsUFW8ZHiUQiOTuZdSWvw5W4DnWNiS6YHMfh8OHDmKbJZZddNmAxM1ZG6hEaXMJVmzkQlQxd+pd5LCf4Y+3NrqlCuRdbi3uCzS1hcoSGqmgoiurNnnRwjHp3OHXV3MYfzxFxRZoacKd+qDooitdDGUIXNpHUfKJOBdEyh/mlfvJFi2J/O1YlyNEDuwjG6okGFUKxOnRFuGM9hHDHiOBd10i4C5RAnSsOjYRbJqtHvHEhXh+kkUA1exFaDFWUEUrULR/zzIIcox5FlL2ZlvmqI2y1j9JIeGK1VhwW3dEn+GNLHBwjheKYWGoSBc9B1sqDGkCxc15GMwco3o69jr7kbcwLNjBvPtVZoP4Ygv3792MYRlVc1tXVDchYj0VQyh14yWQZKT5WKhW2bdtGoVBg/fr1w/bsTiZDKYSgra0NgLlz57J69epxG6mMFB/9rKe/2VY76L7zaD9HtvTTe0DFPLSZ+YktaKEklHohkKiWYDqVPIpmoNklECE3E4jwhJdnfuN4TqnC/7cnsjzjMFd8hlCxicQbicRtaJqLubBCsVggn8+Ry1U4fryDcCRMJBIiEo5gGH6GUHHFpagRk26g8V6N6jmk4glBvHgKrgusAtje+CLh3VutMU7MM+rxxKvQ3HOECZrhzZIMudlOI+71cyZO9nP6pbXlLtdbzcy417bynqu2dVKUCkA4aJpOPF5HPBYHNEyzRNq4mM5ymENbtgBUN+Dq6uoG/O5kfJRIJJJzl1kzNsRfxOzZs2fIuWZDXWMiC6ZisciWLVuwLAtN0yYkJmH4HfjafsnBZbrCrnD80Fc5FuhEMwUHRC/LbcPNtpV7IZCk1NvJ0QNlKoU8RjiKFSojhOtYqniZP0VYCL0eRMnL1BW8GZRFT0yVvIUCoCiu0PT6cBQcjFAddSGTcDRJ4cgr1M9dQSnXTU/Gwuncix5KEgsphGL1hAy8fh4LdE/cCxDe4kcYCcD2MpDlqqgTWhDFzgIC3ckBcbdMVQujOMXqAs8Xh8JIuo6svljV4+64DzWIYve7r8vKervxBdzyVtdqXwjbnWep6KDHEQpeyaziusuqBoqVw5nzJgiezHYrikI8Hicej7N48WJs2yaTyZBOpzly5Ag7d+4kFotVBaZpmnLBJJlShqvgGKoloLbEdbSM4UTjo23b7Nq1i66uLlRVZf78+RNy5RwuPvqbbalU6pTNtv6eIvteOoFZEsSTr2Ae305XMU7LQqtqiGNaIY7taqe/18QIOGhRm3jS8USjawjmzp3Nupk6M+tl+7Juv6FdANXL+qlBr3XAy9wpGmBjBEIYgQCJZD37979CY3MzplUhkyvQ2d2HYQSq4jISDtaUwSu4QlF4AtMBp3aupOqN3jBcMasEEHbFNTertjDYgDd2xOs1d19XTebV21jD2+Aj0ACi5PVxFt3SWjPr9WlmEWoYQ+nwXLnznuguUxXgjukK03LWFe1mv1e2W8BoWEfLnNfSgvsZnc1mSafTdHR0sG/fPoLBYDU+CiFGdcEea8m0RCKRSM4sTkvJq67rA0wnLMti586d9PT0DFviOpiJ9FB2d3ezdetWWlpaWLx4MZs2bRrX+YOfv3YH3i/hOnbs2CklXOBZqh//KS/2PI6CikDwTP/vWd54q1viGajHKhc4sNvEzPWiBaMUevsoWSb24gKK5riGNJgIJQBUqqVLwoi74sxIVBdIil2u7pgrTsldOFgFd8QHZW80RwFHMYhGQkSiS2gQJpYtKOb6yBYqZE4cxSJAPCgIxRuIBArooYRr2qNHUZyK2/MoAFVxeygVFaHEXXGoRRGi1+3fcUzAc4QVuOVjAu8eOdn3o6nujj2qKw6hWl7mbvKr3vMG3OymHkW1MmhKoFpCi112HWqxcfQoqrBw6q+A5MhDrDVNqy6OwM0GpdNpent72b17N5VKhUAgwJEjR6ivrycWi1Xf30KIs6ZH6PDhwyxdeupYgGuuueaU+W6SyTO4fH5wyWttieuqVatYtGjRqCJvIvGxUCiwefNmNE1j48aNbNq0acJVIENlKI8dO8auXbtYsWIFS5YsOeU15DIlykWL5vpOmoyd6HqYcq6E5cRRMRF6mNYdJ+hsMwlHVPJZKHebxFOCRNJ3Jw26mUk97M1UjLjjNar9iXF3VJGerBn/ka0pMY14mb+gG5cVjUBAJ5Gso6HewnYUioUs+UKRzu5eLMsiHA4SicSIRUIEgkEUcFsXsFyHbX+kkj8+xC9ltfMoioou8qC2eM6w8ZMZxqpJT8F7PX5bRMV1YXUsz6zHG5HkWK5YFrbbhylcZ1qn0o+jRgFvPIni97h78y19852gP/rEMxCKLYeW1wz4nSYSCRKJBEuWLMG2bfr6+kin0xw6dIh8Pl81cKqvryeZTA7oOS8UCsyZM2dC76fZhIyPEolEcipjcyOZYmp3z7PZLJs2baJSqXD11VePSUz61xjrYkcIwYEDB9i8eTOrV6/mggsuQNf1k/PNJkDtIrBSqfDiiy/S09PDhg0bhs56djxBV9dvOFI5QsSqoOHQa3ZwqPwKOK5raSGvUMyWMBJNBCIGgUQdZtGhkHNfpyIsFOF4mT8Hxex3r+2UvNlqticYdYQeQ6hBhBbyBJoCqjdHElCsrJutFHkQNqqdBwG6BvFkE/PmzmHR8ouYP38BaqSZbH8/h46maTu8n86+EsX+ThwlgGJmXA+dci8g3JJTxd2pF4qOI3R38aSFEHrUvSc9BEJxs5xOBaHqbjZSwe0TFQ6KlUN4/UhCMUDRcLQoimq4fZ2q5o4eETamEkcThWrJLKqGava6Zj2VtCt6m18z7t9xIBCojkjYuHEjTU1NRCIRMpkML7/8Mk8//TQ7duzg+PHjtLe3k8vlJi0o77vvPq644gri8TjNzc3cfPPN7N27d9TzHnnkEc477zxCoRAXXnghP//5zyd8DwsXLuTEiRPVP5s3b6ahoYFXv/rVE76mZOzUxsdKpcJLL71EW1sb69evZ/HixWPKGI63JaCzs5NNmzZRX1/P+vXrCYVCk+rDrM1QOo7Drl272LNnD+vWraua75x6zypB5wTN4hm3p9ty45qmVBCKhlUqk+mpEImpBCJhoqkQlYpKIeu4oze0sCuW9Jgr6IyEJ6qSroAyEp44S3oi0ze/CbszJbWgKzY1zXWUVXU3Piq6m7VDRaNMLJqgpbGOZUtXsGTRAmKxFOVigSNtJzh08AjHO9L0Z3OYIgBobukqqvv8iuLeD6LqzGprcQ4WdpLXDM8Z1r+viPtVDbjlq4ru3ie44lNY7vG2DU4BsLx+eNvNkDqiWu2Pormbd2rI/apHAcX9WYCb1RS2OwpFWBCeBwtuOlmyOwSaptHQ0MDKlSu58sorWblyJaFQiHK5zM6dO/ntb3/Lli1baG1t5dixY1Pi8irjo0QikcxOTmvJ69GjR8dc4jrUNcayA2+aJtu2bSOXyw2w1fd3TgcP7h4r/mJrpBIuH9G7FXq38py5h4BjU9E0QgJMRWFz/7MsSjYiCKDhZRXtMrYaQthlLBFA1QOe+6njlUqF3PJSLej2TupRz9gmjur92zVjCLrzLDXXhVAQdK8hHFBVhFlEKLqXwTRQ7JxXOtvnmepUCAVDhEIBaGjAsSvki2VK+X5OpE1E1ysEQgkS4RyhWCMhs98zDXJ7dxSzF6GAJooIUiD8sSMWjhFzzXYC9V6Gts4tlTVS1bElvlmPanoOs2YfQouCKFbt84UaQpBF6Cn3Z22kXJGqRd2sqBpALLj55Ly5CeK7T/pD4x3Hob+/n97eXo4fP87/+T//B0VR+NKXvkR3dzdveMMbhu3/HYnf/OY33HXXXVxxxRVYlsXHP/5xrrvuOnbt2jXsYmzTpk28/e1v57777uPNb34z3/3ud7n55pt5+eWXueCCC8Z9D5qmVTMJpVKJm2++mQ0bNvB3f/d3476WZPz4sa23t5ctW7aQSqXYuHHjuExxNE1DCDGqM7HjOOzfv58jR45wwQUXMHfu3Or3JiMo/QxluVyuthgM7pccTH1djqWpJ2k74qDZvWjxZpoaK6h6HaLch6rHCeglypUQoXARywmiKTaKquLGNbd3HMUrExV4cxUdry+xpkzUixPuLMeCZ4rjj/nIQMDN1tlKENXsg2CTK+aMpPc1AWYPgUCSgO5Ql1qIsPIUKgqlfB/pvn6szhPowSixSJBwNEk4CKoW9MpzXfMyW43QoxzhV8XfssZu4w3JG2scYUuuwLMLbobVLnplu37LQAW3zNZ0e9QV3Y15WggqXl9lpQ/hKOh4VSpOwctmlrxZkxXPSMhz50a4Qnb+/3avMw5UVSUcDnP++edXKzbS6TTpdJqPfvSj7Ny5k+PHjzN37lze9KY30dg4vrFNIOOjRCKRzFYUMdEU3Tgol8sD/n3kyBEOHDgAwMUXXzzmrGQtnZ2d7Nu3jz/4gz8Y9pj+/n42b95MLBbjoosuGrAgM02TJ554gmuvvXZcCzWf7du3Y5omPT09AyzvByPyrdD2E0BlZ2Ebh802Cn19hKMxAqqgSW/motBahO2KrbZdx+g45qArJcpOFC3Yz9qN5xEO6t4iSVA1fhCmZzxTqhGXnlGPkXSzh0YKnIIrxJyKKzK9ERzlUpHWo22sXLHccw705rB5ZhGge4ufgJdJDKIgvJJbm4oFhXyWXKFIueAaPcQiAcLRJNGgg2bE6GjbRyjeQiqqnHR41eMoTtlzhPVdCgWoGopj4yiaN+tSc+dXKrrXH+obSWictMtX6OruQmgBmhoaPPFsu9cSDk7LtZA4b9y/36HYtm0bdXV1LFy48JTv9fT0cMkll3DjjTfy8ssvs2nTplOHs0+Arq4umpub+c1vfjPsDvgtt9xCPp/npz/9afWxq666iksuuYSvfvWrk3r+d7zjHWzdupVnn31W9j9NE6ZpDhBuftWGqqpjLnEd6ppPPPEEr3vd64bc5IKT5jjlcpl169adkl1/+umnWbVqFc3NzeN+TW1tbRw9epRyuUxdXR1r164d9j4ARDmNc+i7OBWTQ6/sxwjGaaoPEa6rR1QyWEoE1c7S3iboPNBG2QyjqwVMVWf52jgNc+a5fYRquMaEx40DrrmY6mbsVBV/1NLAOGedNO5RDc9ZNcDBA7uYt2AZId2u6cmMnSxLNTOeyKz96vYh2kqUYraTbEmhXOjFdALEIzrBSIpoJEAgGCXd08nj9q/poQsHwTvq30Gj7gst/z6pud8aUx/hPeaYtNqd/DbzOG+q+0PqMLwS3wwYcfJ9J0hnLRbOSXrlrXmvdLZGRDo26N7IlUVvcctdPSxh8X+P/19uSt7ExtjGYX+Hra2tZDIZLrzwwlO+VyqVuOmmm2hqaqKzs5PPfOYzXHvt8DMtx4qMjxKJRDI7mPEMZTab5eDBgziOw6te9aoJZXFg9JKutrY2du/ePaQ5Dgwc3D1e/MxkoVDg0ksvHXanVVR6ofWRqoPe2vAFrA2tZl/HERY2zSEccUuybNvyRodYzF+ziEiii1IlRThQIl2JoNsZFBqh0u/2SwrLzdApGqC58xxx3CydqLiloHbOE5Uns31u1jDtzZnMg2KgYiEUze37UVUQAa9UzVuw+G6xvshDQREFQCGoOATjIVKJKEI0Uiqb5AsFent7aC9XCAV7se0gmlnG0ZtQnYo7Q9IugKJ7X1WghDvTTfF8Ivw5agpCCbr3pWoIB9Acz2yn7GY77TyOEiAgSiiKftLZsNKLqLt0ysQkjOxiGAqFyGQyPPDAA7S0tEzZc2YyGYBqX+dQPPPMM3zkIx8Z8Nj111/Pj3/840k992c+8xkef/xxnn/+eblYmiEqlQq7d+9GCMEVV1xBKpWa0HX896lt20MKOT/7WVdXN2xlxWQylH19fWQyGVavXj1kv2QtwszhHP0xQiiohk4wmSQc0gnXNyNst7+cchaMeloWZgmEl1LuS0OgHlt0oIUjrtmMHnbFkub1BqKCZXp9jL4BjX8fvvuqBoo7w9Y15vE36lwDHFsEUHBqZkv6Rjh1XoazwTPCqXMznUbKy3Qm0Co5YqkWYlYeoTVRKfZRKAuK2Q56uhwCuqDPKFAIdKALga5qvJT9Pdcn3+iZ7wQ8M7SQJ5J98yBvA1R152EKLcIz/ZvoFiVeLLzA65M3uMcH6sGuYGkJFLrd3kjf4RbFdadVDe++vT7Klv81QEwCPN7/OM8VnuNw5TDro+vRlaGXDaPFx0KhwJ/8yZ/w1re+dczvo9GQ8VEikUhmBzMiKP3yp2PHjrF7926amprI5XITFpMwvOmEbdvs3r2bjo4O1q1bN6zYm6ig9Eu4yuUy8+bNG15M2iU48p9uL5Cw3MyZ1Qd6AoMsQpl30mzBdwBUAqg6NCyZX90pz7yyE1tPVi3hXUOcGmMcu+AZ43gugOigCITujho5WVJa7/Yl1ohM1exCUXS3z1BPofiZTOGa2hR6c3QcLWCbJtFkmOaFEfSAhuKYCEWtGv8oThlFNYgEHCKhOE2pMBY6hVyOznSGvkw/ff15IuEQkUiUSCRMIOA5FqK42UovC+mOQPHmXVa8UUrtHz0AAPFvSURBVCBm3hud4pvtWDi6WzLrGHUIkcM26hBY3kiVHCI0B9HyunH9bkdjpPLBfD4PMKWmPI7j8OEPf5irr756xNKs9vb2U0RsS0sL7e3tE37uH/3oR3zqU5/iv//7v1m+fPnoJ0gmTW9vL1u3bq0uTifzXvIHzA+OkUIIWltb2bdvHytXrhyxJ3MigtJxHPbs2cOJEyeIRqNDmpcMON4qIVr/C6ecQdEMHEe4rs9obkm7HnTHgOhhwELRE9S3mDAnAaLCseNlFOFUZ0y6MyfLXlVF2TP7snA3rKyqjqxWeTglqnMcVf2k8PSqOBRMhPBiuKKcrJBwbMolhc4jHeRzFuGITvPiFOGocEtNHdstmxWWu5HnmAQjDQRDJnWpOhzbpFAs8Xzu525xhqqiCofD5hE6ckdoji12+8r1qFtiqyfcvk4/8+gb6ehRjhb30m51ERSCPeV9XG5eTp1aB6o3D9NxXBds1XAFtaICtjfj0oRgnfu1bh00XjXg92MJi4fTDwPQYXXwy+wveWPijUP+LkcSlEKIKXfBlvFRIpFIZg8zIih9F9fu7m4uvfRSAHbs2DGpaw6VoSwUCmzZsgVFUdi4cSPhcHjY8/2euPEsmGr7JWOx2LDiQggHjv3MW+AE3Z4V2/TMILLYasw1nwnPgUoahxCikkUJxk8ubLxB2bYS9GYu1rn9kL4w1OOuq6kRPzkL0sq6CynLW0AIr5dIcW3o3RmXNk6gAcUp4egpNLoGZTLTYCSpZDo5uq+CU3LFXbG/B8sULDwvidBc51RheCVSesgVhXrMLWnVouhWlniyjnx/F5HUXEJqhVxFo5DtobNLENAhHE0RiwQIRRLoqoPQ6twMa6De7acMpFAq/rxNr0fU7HWdXc00Qg2hKmUcYaNi4r+dRaAOMf/mSfdNDmakBVMul0PTNEKh8fUdjcRdd93Fjh07ePrpp6fsmmNhx44d3Hbbbfz1X/81a9eurS68AoHAiJkAycTwXVwPHDjAqlWrWLBgAb/85S+xLGvEMtHRGCwI/TicTqe5/PLLqaurG9f5o1HbL7lq1SqOHTs24vHCMeHI93Hyx1G0EMIsev2QFogKjnBQbBshFOxKETUY8bKHunuMFkRRHGzV25jUvdmS3ixfd46j5cUBb+6jsD0HVq91QNHc7yne96C60YVjomK5ztS2N69RcUtmbcuhbV8v2Z4iRihAX3+eSqnCkrUNGMHAyQpV1ZsdqeqACpp7vqpq6JpGeymNQEVFUFRVAkLwUv9+1nTZBKMp4sEioXgjhp07paQWLYIwM7xQeIG442ChEHQEW7PP8prE/4KK53Zr5VAV5eQoFafk9nB6o6RcUdkAc15/yu/o8f7H6bK6AFBQeLjnYV4ff/2QWUrHcWZ0bIiMjxKJRDJ7mHZBKYTg2WefxTAMNm7cWC0NnOjQbZ/BpjxdXV1s27aNuXPnct55541oROEzngWTX0LrW97v27dv+NfQ/ivIHfSMILwFkOYtMgIpFE7g6Cmwy+TMAB1Hd2MSIhZoJxSvIxLUCATCKIqCppg4tlPtKXR37eMowvSyj5WaWZR+di9SnUmJncc181U8kQmK4t2TKFMRrugWxsBMZi4fRJQ7CNc1YJAnXwpRyXRgl2IYet7NgNolbxi3jVDdDIJj1Lvi0qgHp0RFiZMQZQKxZhrMDKJ+CaKcoWAalHOddHcpONYRd/ZlJEgomiQUVEALowiBCKTc6xsp19RHi6I4fvbABsdCcWw0pwjYKGY/zoK3QHDqP9hHEpSFQoFoNDqm991Y+OAHP8hPf/pTfvvb37JgwYIRj50zZw4dHR0DHuvo6JiwRf+LL75IoVDgM5/5DJ/5zGeqj0tb/Olh586ddHV1sX79epJJ13VzqOzieKmNkblcji1bthAIBNi4ceOYqkPG4xTb19fH5s2bqa+v54ILLqCnp2dEB20hHJwjP0Lkj6PqQYRdQVU1hLDRsD3n6QIVW+XE0QMUKwqhoEIw2kA8rBGM1KE6BRxUVKcMap0rkLSgW+aqGW42UQt4AtEA7OrmmisuhdeW6Pg35X5VhNeqaCGEiuKPNvL7F4VFuWBT7s8Rrwujqw52NECxr59SLoqhlDznWK8CxSl740hKrui13ZFPIUVwnfJmKuRJJhtRsXAUnRatAasiKOSzpLMlKt0HMYwQ0WiJSDROWLVRPYfYNqeXTqsLFMXNuQrBLvMgl1mXEA/OBbMfRw1jiG7Q5nrusZ6LbHWEiWfCM2gDrjY76b56QYfVwa+yv+INiTec8jsdrrzaZyrHKsn4KJFIJLOLGZlDeeGFFxKPx6ulVRMdul2Lv9hxHIcDBw5w+PBh1q5dy7x588Z9jZGoLeGqLaFVVXXALE0f0fMidD9X7ePDiHo7wyG3/BWv71FY9GQcurvaaZyznLChUCiWKeb66O6uEFAtwrEEimOhOCUUoYFlemNBSq6Ac0y3lxIbx4ijChMRqAO74mYL7aI7b9IuuqWiVXdAy92ld0w0xXQXXIrwRKbjijY1S9lKEHIEFTWB4uSwRBTFySG0OIqVcQWs2XdSyGphVCrumA88Mx9h42j1gKgKYCVYT0wvEI0uosEuYdqNFAs58vk8md4uFEUhHI4QDocJx+rQNcUTrgpoAYTQvXutADEEvW5fqFNB1F8xpX2TtYyWoZysJT64GzB//ud/zqOPPspTTz01askgwIYNG3jiiSf48Ic/XH3sl7/8JRs2bJjQPdxxxx3ccccdEzpXMn6WLFnCypUrB5iDDZ5FORH8toD29na2b9/OokWLWLly5Zg3Pca64eZvttWW0I5U/SGEwDn2M0SxHYwYwrFBDyMcxy3XVwPgCPIli/ZjR4glG2iJBSmaCqVsNyeyCprTRiDaiFrpww42Q6XHrQAp97klp1beFUpW4WQZrBY6uQnm+GZj/ixH2+shd3DbDxzAwEFHaCHQdLfnEAsIgVrAFiGE7WCrrjCzCaKKMhgNbsw3Eu54EsOb62gkTvZ4mz1gJGmwwFabaVaj3szJIqARCNhEQnU01glsR5AvFCjk85xobwfbJBSNEQ2HUYNBGow5nLA70VAI6PXM1RJubycCAvUIpwNLTbibm34PqOq1WqgGLHyL+zMbhC1sEmoCWzv5PlRQht0oGEvJ62RjpIyPEolEMjuZkZLXVCo1YHHhC8qJjuyAkz2QL774IqVSiauuumrc5TSjLZhGsrwfPIwcQGQPQdcmzwyh4PbQVLJe2ZEn5mwbjRLpni4q5RILFi4hHNCx0EgFQyTrGhF2hUKpQjGXwRIax050EgoFiUZCRMIWgVDYKy0Nu6WgehzVzladXFEDgIWjeQPBjQTYpicyXTGq2HlQNTRRcU13fLFpWQgtQCyuEk8a9PcUUHQD4Wi0LImjRVKu6NTdhYnQIt54Dt0tyUWpcYoVqHYRRZRRbA2hKe7jiuIKYkUFXUPXVeKBCPGUirBLlEyHcrabvmyJnq5dqKE64kGHYLyFiJ51s7xWFqFGgAqWo6EqDiK8GNHyv8b1HhgPI5V0TcViCdwyru9+97s89thjxOPxajlVMpmslnDfdtttzJ8/n/vuuw+Av/iLv+Caa67hH//xH3nTm97E97//fV588UW+9rWvTfp+JNNPPB4/RTxOxaabqqocOXKEnp4eLrroonGbRY0WHx3HYffu3bS3t3PppZcOcOuunUM5GNH+JKL/kJs9rGYRHU+0uf2ThbJFT287zXMWk4xHsYVKImiSSNTRZJcomVDM9tBbCFLqOUYulyQR6iUYbyEselECyZO9h2baE3M9Xi+i35PY54nPnNeTWPRitZfpdCpeL6fjjSmqeMY4NsFEnGRTie4TeXStjGVp1LUECdU1A5b7GeCUwah3R3UYdW61iG/ao3tOsFoYXRTcDKvptSw4BbcaxTZBCDRFIxFWScQaEGaJijAo5nrJ5QtUuvsox2xCKpRU0ITJGxM3oArfhMj2zG0V73PB/+qVDTdfA5H5Q/6egmqQbyz+xpjfLyMJylKphG3bky55lfFRIpFIZienbQ4ljF4iMxK5XK56rY0bN07oOiMtmAaXcA3+oBx8rih3w4n/xh3nYXvDrCueG2DZXbA4JpZtYVoOuiiyeNFiDM3GUQzUSga0OKrIIvQYsbAgGptPLlegsXkOtlUhn8/R3duFpilEwlGi4RKRaAzNzruZSCvjZhfNXkpmiEK6G0uJkEgpBGOuO6xvZiP0OrCyWKorgoQWRbHzrgurlccIR1i0KkBvt45TyROMJ0k1aJQLJj3H+rBsjWhCp35uEkXTESg1pWNeSapVwFEDqE4JocZRzIxbrlvp9cStZ7bjlNz7xwI9Tli3CEWSpEQFy1lMKd9LtmDSf+IIlqMSC3cSiqaIBIsYgRCaKKASQsx9g9cTNT3Ytj2iKU80Gp3wBonPV77yFQBe85rXDHj8oYcequ6Kt7a2DriPjRs38t3vfpe/+Zu/4eMf/zgrV67kxz/+8YRmrElmnqHeM/6s3olSKpUol8s4jsOGDRsmtNkxUnwslUps2bIFx3GG7Fcf7ly7cxOi5wXQo4hKHtWIIOwSiuqWvQpFp1QsUDEtFi5cSDQWc69j254AFShalLBmEQ4vpGwdRQ/UETRU8gWTvhNHsYRBPNJPOJoiGsqjB1OeqVmq2oNoF3rJZAwwjxFI1BOL+qLT++qJUEP0gzLfPU+LuWJQi6I5ReatrCccUygWFEJhSM2tQzgmx48UsUq96JEgLfMVtEC05jPBdq8vLFdkiiKOFvc216KeeNU8AyBvhIlddHsfrRxKME7Q7CfYOJeU2U+b42D3dmIrKgnbJisyvHzsNywPriQcCRMMBNxKFLvkZiQdf5ZlHuouhtTUxYiRBOVUmZbJ+CiRSCSzkxlzeR3wpJ74m4igFEJw9OhR9u7dC8CaNWsmLEqHW/S8+NtdPPPfu0nEUqQ2JFE4dcFXm6EUVhGO/MAtrxIWKEEQZW94dcmb+WVRNKHt6DEcJUh9czNGIESp4NBzvINyEWLxHMm5zeiVNMJIoJZ7EIrAoES8roFUMoqjGG55aK5Ed28Os7OPUChMLFIkHI0TUsoUSgbH97aSy6roahfpcJLFKwuEUvWoVp/nAFvEQXcdFav9j3UoouyWzToljFgDLeEcQmtAMTOU7SCtWw/Qm9YIGkU6RRS7mKF5xSIUu4SjRVBEBaGEUTBxjCSCbhzD7WcURn11nIkw+7wxJl6G1XRFpmqlEVoIlTJCaOgqxOIpYnEV0dJCpVKhkM+Rzefp7s6gahqKsOgKbmSeEmP8E0XHhuM4CCGmPUM5lrGwQ/XpvPWtb51SO37J6WUyJa/pdJotW7agaRorVqyY8PtyuPh4+JXjPP6d58EyOO+SpSgXnxp/h6zg6HkZ2p9A8doBFCOJKKdRAgmE2YejhDjR+gqWBalYiEgkhFUo0NVeIN9XIBgNUT8nQigS8ASXjUBDVQSJZB2JhIOgmXKxQK5Qor8/Q3unSSjQQziaIBbJEYrU4VRKHHrFptDTiyV0QsHjzF0xl/qmUs3YjySY/VhqFNXqh3BD1aUbsxeMBLqdoWlBsprhdMq9HNydp+9EH7Yw0BSbQibJsrUNqKrmGa3htrQ7AlSBIxxUz+jHLb21EIqOVamgGVFUxQLNHS/lbk7640DKYNSxo++35FWNgICK51Z7KNDKfHM5mePHsNUwQaUMgQRWKY0ernfvP7YUWl47offFcIxWweG2MgxvlDcWZHyUSCSS2clpyVAOZ2k/GrZtV91iL7vsMl588cUJz0nz76P2fMdxePqXL/Hb7x1AFyGscJknW3dRKVlcecOKIc8Vjg1HH3U/4FGqZjeoIbAzXmlVD/0ljY5jh0g1zSeX6QXFoFKBtoN9lPoVtIAg1w8Vs4/m5U1odgFhJAmIowhtjjdHMoFm9hENJ4gFFERLI2YpT6Fkkc/n6Ok7jqooFHsU8j0q9S1BFDVCPp2lu8NgYczPQPYjtDCa1YuCqPZBqmS93kzXNAJhIYwEimPiBBroO9xFT0+AukYFR2lEK/Rxok2jaX4agknUSo8rhM0e15HVziEcB1WUQEm519Oj7Cnt5ne53/OO1C1EAym3dFaP1pTOuqYcigpYBe+eC6DHCKkVgvUN1CVjOEqQYqGfA91B+jNhDj79NPF4nIaGBurr60kkEpPOGPr479WRFkxTaYkvObeZSMmrEILDhw+zf/9+Vq9ePamxCDC0oNy76wA//uJLmFmNaCLA8z8/SKazyJvftw5VU4c9V2R243T8BhFocEcdGXVu2X0ghbByVOwAJ1r3YERSpIImQjMQZp721jz93Vk0Q6O/q4hVzDNvRR26Bqg6qii6ozDsImCgiAqhUJBQyICGOmyrTL5QplDIcaK9D+GcwMoH6DtRIVEfIxpUyGZ0Oo70kmxegCa8DKJjgVGPItpdgzFhe0LOE512wR3hYZe97GGRfF4h25UhHA9jBMCsqGQ7MxTmh4jVRdyZkloAzLI7ssPsc0tanRwo9WBl6etR2PH0fvr7FKIxh/OuWkrLogiguz2QuGWsroGOYFX4AiJGPQiBQEFFUGc0Mje4AMFiSsUcHV1phFnmYFuFoJ4hlGhGb3oVKUcwginruBmtgiMWi01ZPJZIJBLJ7OK0CEoY/4Ipn8+zefPmAW6x43EhHIraRY9fwnVwWw8GEeatqENRFHo78mz/3VGueMNytw/Fo7oD3/4rKHe7PTYquA6CIXdBoifBytPZZ5JLH2TO/BXEQw75nIpq9lGsJKnk+4nXJ3BsC93QyGZs6ss2asgtf6ooCXeOmNGAYmdd8xnP4EGp9GEEEqTUfpKJFjAL5E2Vw53HMC2bnp4CuhHEsaBQsBFG3F146HFwTBw9giq6QQugOK4rrGL1uxlVs++koY8SQMHGdhRU1cYigobAJIbqOFhaEgPbnY9pl1yHVqsAqoYqyijYKLZbomzbDi9lfodJia2FF9kYfy2gukY+mu452fqz3kww3H5P93VnXIFb6UUYMVdcJ+eSK6xgzXlriEQipNNp0uk0R48eBdyB1/6fyYz08N8n023KIzn3GK7kdTzx0bIstm/fTiaT4YorriCVStHd3T2pPszaWb+O47Br1y52bGpFlAIsXFGPpqsUcxUO7+qi50SOpgWJAa/JzyY5ucOI9l/jqEG33F6Lui7VRhzFMekvKbS3HaK+cQH19Qm6utIIx6JiBsln0oRiUTTNQaCQzxQp5SxidQHAQVFUVFFxn9Tx+9QL1VJRTQ2SiCgkog0Ix6RswYmDHViVErn+PKgGuqJRLhrYZhEtEPDij2vA43gGam4PpePGRhy3bBTHNV1zHDB0bCePaYYIRA1soSJUB9t2sBUv7uieOZARd8d+6HE0pxOhN4DZR8WOsut3W8j26UTDZYo5g72/30MscR7RuIprFoT7fJoBVoVlgSUs0+a45bJO0RWq3uamIiqEw2FCAQ3DSJFMpSgWK7QHrqbzlSOYu/aTTCarG3CTLdkfi2mZFJQSiURydnLaBKWu62PuEero6GD79u0sWLCAVatWVXdBJ2tc4S+Yent72bJlCw0NDSxeFKdr96HqMW6VkvAs5ZUB54YLWyF93BWOdsFbMNjuYkbRsR2H4x0ZyqU8C5ZcRCggQA2i0YGjN6CWckAA1cmBGkCjjKMoKE4BHHcRoio2jjdoW+gxFGHhBBrd2WiB+pO7/VYGEYgRU/ppmd+CUj4GRhxhZ8lUFMqVTg4eLBGNBIjEUkRDQVAMbCWEUINeWa7wjDFsFMXwzHWEO7IEh0QCwmGbSn+/u54pqjQsjKIbuA6Iio7QIoCCohoIVGxheIswFaEG2J97iYxSIurYbCvt4tLAasLBFhTTLfNVrH6EHnMXRarrYCu8xZswUrhusW7plwikEPNvxjm+E1VVCYVCzJs3j3nz5iGEoL+/n3Q6zfHjx9m7dy+RSKQqLlOp1Igz0wbj774PtyCaSkt8iWQ8PZTZbJbNmzcTDofZuHEjgUAAGCgIJ4LvZF0qldi8eTMA5523hrbnd+L/N3CF48mJGz6+y6uTP4Y4+hgOCopj4agBFKeMoxgodpmudJbeng5a5i8mEYu4FQqKgq0aoCg4BN05lBjuXF6CCM2LV4BQdBzVwJ3xGHY38tSg51ztubkqbnZPUTVChk1TSwPFtIWDiqJY5PrLBKJFjraWPOOzKJFIGF030EQZxSl5rtjOSZdsLeCa5mhujEIJEI0HCCVC5PoKaMEgdrlCOBknEg+6Jj9ejzjCK1sVJhU1QUTYEGggezRNb2+AUNjBIkowbFEoCvo6CkQTCbck1i56brBZCHjzKA2/DDfuGg1pYff7ig5UUOwCqqGiO3niK/+IeHwlK4SgUChUN+AOHjyIruvU19fT0NBAXV1d9X00VsYyVkkikUgkZyenpYcSxiYGHcfhlVdeobW1lQsvvPCUuVHjHbw9GFVV6enpGWB5fzSQZvczx+hs7UcPaFimzbrXLhlQzgWgFw8RL7wMTas9S/ik5xoYBbOfiq1wrO0YqmGwZPESdF2lWIQjO45yvNWivqmLZZcsIZropjcNhlbCcjSa5mgYQXfRBRoaJqqTR7Hd5xeqjoL7PRx/t9/vf3RnUzbMLVCpzCPX1YOlpFixyqFhyTysUg/Zskq2+wgdlk5IsxEiQLnQTyCScs/XDG8MWwhFWAhF80qtVGKpMgvOn0/34eOUK0HmLXJoWdmMava7Zju+KLSLVcdZG81dXOoxHFHmN+ZOQKFX0wgLh+etA1yjJ73zs255q9nvOc5mObkrb3sjU/yZlyBaroWA6yA8uNRKURSSySTJZJKlS5dimia9vb2k02n27NmDaZrj2p0fqZwLpq6HUiKBsfdQHj9+nJ07d7JkyRJWrFgx4D08FRUcxWKRZ555hsbGRs4//3wK/Sab57XScbSfYEinXDRZfnELDXNjp5yr2xnE4e/juj1XQA+jmBl3VEi5h+PdeSqFNIsWrSJoOAhH4eiOw7QezhIIWIQvTZKqN+hqL6OqJSxbJ1FnEE5GcMd86IDmOrH6Dqaal8nD8VSugdu46JmGqRBrEDQvh67WDI6j07wgwbzlCYQiKBSyZPMFero70QJhECalsomhgaKFPVOeiJdhjJ50iq2kMYIJVl4Q4MgBFSvfj55KsGhFGCOguSY4WhCckisMvfjubtpFQAjUQABFUbAsDVXXsS0wHQ01EPIMfSzQUq6gDTS4rQnemCgCKferb+yjBnA/IyrYSghVmND8BoivBNz4GI1GiUajLFy4EMdx6OvrI51Oc+TIEXbu3Ek8Hq8KzEQiMeq4mZF6KHO5HJFIRGYoJRKJ5Cxl1pa8lstltm7dSqVSYcOGDUNmfyaToXQch1wuh2maXHbZZdTXu8Yxi85r4Pp3XczWp45QLlksu7CZy14/cNaVKHYQyvyeAhH8WV84ZfdD3cxRqMCJo/uJJRtobqxHUUzsisme3x+g64QFosKxTAWzcJDzX7WMUDxLqQSRmEayMQqK7c1iM905aA7u8yBcoamoJzOhVgGhBlGdSvUcRQ8zb0UQa1EKsNECQRRRRgvMJWQXEY3N2KUe+vICO91G2wkIcIRArIl40CEUb0HDG0MiKu5OP65DbF2LRWrO+e48SW8WpFDr3TEeXjmuMBLVrwGRBVpQK2n2mq1g9iOAoKJgAjsKW1kfuZyQoiGMJCAQug5CoKgqQtG9LGzMFZr+tRvWQ3xV9Xc52mLHMAyam5tpbm5GjLI7X19fP2AmoP8cI2U0c7ncgLEJEslYmciGW+183EsuuYSmpqZxX2MkhBBkMhnS6TRr1qxh4cKFKIpCvE7jze9dx3M/20+mp0jL4gQb/vcqNH3g/z/VztJU+v/Z+/NgydKzvBf9fWvMlXPmnueaq7qrx1IPqpYEEsgIGQlzdG0fcxwnIGSfA5cw99hAyIEd/gffcNxw+CDAEIF9CazA5hoLM1gWBoQmS0JqSd01dE1d87Cr9pQ753kN33f/+FZm7aquuaqrG5FPRPeqzJ251sqda7/re9/3eZ/na0h26KTJTkHYAztN2K1xda2GTZfFHU/qGUIrx/nvnObimz622aDS8zjTO8YT738C1+3R7nq4Vp/szBhW2AQnBX4LDIGI+nF4jO0+otg6SQVbOomWFsIRFhAxPlugMJVFRvpHhmECEYlkDlRIpASdVpOVtQ02NytsKIOUZ5NI5Uh7PWw3D1FLx/ywEYv41PEKY+x7toE0JzGiplb3DhpxEhrbgqherEatMGQXAxP8BvmxBPPbTa5eDDBkEz90mZyxmVjM6UTUGCSitk4mB11TYcZiPxZggYjQfscRmB5K1QgzT8LEK7f9vg3DGLI3QN9/B/Hx2LFjSCkpFArD+HizuI5S6p5mKEcYYYQRRvjexDuaUN6O0jWgoBaLRQ4cOHBbFdcHpXQNKFxhGLK4uDi8iQ6w89kpdj57a882Fbbhyn+FqIdQsRCPCvVCRkVU2hGltRXGp7Yzlk/pmz4R9Y0mtU2fbM6g51ukzJB6uUu/ss743BTSbyHstE7QhBt34hIgTJSZ0jQvBfp/EmEMbDoMhPT1NmwPZ4iUcLBFhDKdeBGiF1LK1IbWZmKClGpRq+fZsTRL14/oNTfZbESo0nFMt0DGK5FIT5Cw2rESawVlpTCinjb7loGutqtQdxhVhHKKunNq64VZgIeQIVg2RSPFdnsRZRgYKiIUJillYsoIIet6BlP1Y3/JvqbPqgBl52Kqb1EvTtM7UJPXFQrvJaHcipur81EUDRfPW6vzW8V97kTnAr1gWlpauudzGGGEO8E0TXzfv+XPut0uR44cQSn1Fn/crXjQ+BhFESdPnqRcLpPNZllcXLzh5xPzWT72Uwdu+34VdhDXPo8YdBGFo4W2rAydToOr1ypk0gmmpnch8FFWkajb5soFSSIREYg8KbdDrZagem2Dhae3ke1VEE7xOsXT1/YfVriCcrIQ1nXy5td0ly6It70KOBno1+OktqVHE6IOppnCFF2d7EUDNW4tSGZKn0y2gLG+xtziLpAB7XaPVqtJqdTHccp4ySzppCKRzGIib+gaGjLQ4j3S1zRXGWgqqpI6Zsa2IBGOFiNzUoigyVPfv4/C5GVqNYtM2mfuySUs1QAxSEhtnThD7FXp688TNLUAXFSPxYJasTBcm9Dw8Ivfd1/XgOu6zMzMMDMzg1KKZrNJpVJhfX2dM2fOkEgkhgW4fD4/LIqMZsxHGGGEEf564h2jvN6K0qWU4vLly5w9e5Y9e/awuLh4R4rMg1Tgt85LJpPJ+5qjUzLU9iBBE1NI3SUMm7oK3KuyVunQbpaZX9xJKhHPDsoQsFDCJwwdgshGqi79UCd6zaZButsAJ4kRd/WMoIKyc9riQwiE6oKIk03DQcgQZbgIQhRGnFhKULZeuAlLL+YEcUdTxFQrU88WGRYgUCrCECHCsEl6NklvkTGhCMKIdqtBp9OjfG0ZDJNUokIqmSHpKUzb0cmrMK4vbgbJrplAyEgnwEIgMcFyUXaSKXOJDyd26s6BsBGyG/tnNuKuZiy6E1T0vKhfiRVeWyBsDBGgDAc189Gh36RSCqXUfSWUN8M0zbtW51OpFFEU0e12byl9P5oRGuFR4naxbXNzk6NHjzI1NcUTTzxxx/j1IF6WW+cld+7cSblcvq/3y8hHXflDDH9Tx4QwAMtCKqg3yqyvrTM1OUEuX0QIiRI2QkmkkcAQkn6UwLYDeoGHUiGNpovf7WM6RYTytXej7INbgKhPaOWwlQ/2JIRtnWyGLZ1UhU1w4znDG+YNazd4TeKX4w5jJR5d2IztQWqaPRo2sb0criUpFheQQYd2L6LTrrO+UUGGEYlkknQqRTKZwrENwNRUfcPWcVIIGMRqY+BV7KGoIp2C/l3FiejC07tYkP24IxmAkdb3GtNFFzBV3IntXP+cdlZ7ZVqZ2DMzgQpbrKsGm8m/wZx5f/OQWyGEIJvNks1m2bZtG2EYDscHzp49S6/XI5vVgkydTgfbtt9y3x7NmI8wwggjfG/jwVfhD4mbF0xhGHL06FEuXrzICy+8wNLS0l3nLe5nRkgpxZUrV3jttdfYsWMHTz/99P3PGK3+2fUKt+EiEWB6REGH5bUyQWeTxaVdpKyYltov64VE2CI7liRXtOk0AvptSbPs0/NtVi60OH2kQ78Taspo1NcduaCJstI4sqU7ln5FKxr6FQRoxddBhVrECaLparl9M4kyE3pruCjh6k5nTLMCNGVVRVp0QvURYUuLTwRtLANyqSQzs7Ns37GbmdkFTCdDud7l/OV1Ll/dpFzv0A1AKaE7qSrQM45hC2XYWokWA0c1EULoZBETEXWHIhnKSDAQ2xGgxXZQKDurF16mi5ASFPrcZQ81/UN60Rdj8P09TEJ5MwbV+f379/P+97+f559/Htd1CcOQV199lW9961ucOXOGzc3N4YL9UcxQfu1rX+PjH/84s7OzCCH44z/+4zu+/qtf/ar+3d7038PaRYzweHEvBTelFOfPn+fw4cPs3buXp5566q7FsPstuFUqFb75zW+SyWR4+eWXcV33vuKjkhFc/n1U6zIIA5OeLoKFHcrrVyivLrM4O04un9bFJBkgoh5KRViWz9hcFoIu3VaE3+zg9xT1jRrHX92gsdkYinRhJuL4kMBAEgkPUDqpQunOIOj5QhWPJKgI7GI8f1jUnUO7oDuTTiFORnM6ORskaVYKW3R18jboDgYNDNMk48HUxBjbF6ZZmJsklTDpNMssXz7H8qVTbGys0qmuI2Wk34uhjyEMCLuABVEPqYRWqRWxHQhx0U/EtV5h6o6mYYHh6XOwM4AAK6+T1sHntIvx8xkQFldVlZ/lK/yF++1HGh8ty2JiYoK9e/dy8OBBXn755WEx7ujRo3zjG9/gxIkTrK6uDrvso/g4wggjjPC9jXcF5bXVanH48GFc1+WVV17Bdd172se9UroGFK5SqXTDvOT9iPqojb+E+psxbSnQKqbKoOdLlq9u4iVsZhaewiQEoxhXjWOhHieH7dd56vu2ceWNiyxf9LFFn+J8HicBjUqba+cVO59xtbQ+Ip4njAiEpkspuxjbZ+TiTl5u6E05qKwLvxx7pLXj+ccAZbraR9JMYiiJsmOqquVBv04ovFjwJhnPQcbiOHZG79/KkjR7eGNjTOS7BCTotms0OwG12lWksEknEyRTWVKewnCLiMjX3m1RC58kRtRGuVlEVIe4I6mpsj22KufqhWKoqVqGQgpX02jtNIb0kYVXhqISw+8llpd8lAumrRhU54vFIlEU8dRTTw2r82fOnKHf7/O5z32OTqfD5ubmfdNvt6LdbvPss8/yyU9+kk984hP3/L7Tp08POwQAk5OTD3T8Ed492JoMBkHAG2+8QavV4uWXX77hu74T7jU+DoptZ86cYe/evcN5yfuKj0ohlz+H6qxo6mnYQ2IRBj7rpQpR2GNp2xKWbWuGBUY8921jRB0wXJ44kCHhCdYvXqNRM5iZF3i5FN1GlSunAtLpCMNNY8hm7P3YQWFgDuJItGXOUMTdPRHPUgr3emyRERjJuEuYjj0nM3GSquMudkGrsIo4CbIzsRVUKn5fApSOXm4igevYFAqTSL9JJzDotUqsVQRGaRU7OU7GrZDITuEE1biLWAUriSVbCOXoQqUQDEcojAQE3dhipK7PM2rpuB22YvZLF10T9gEZv19bmyjp8x+Ny5Rsl6/Ir/C/if/tnr7HB0EymWRycpIrV67wgQ98YDg+cPXqVU6dOsWhQ4f4zne+QyqVwvf9+1aPHWAUH0cYYYQR3r14x1VeV1dXOX78OEtLS+zevfu+VODupcO4lcI18K8c4F4XTKp+Eja+omlQfgWcHEZYJ4pCrlx8k8L4NBPFor6hD/zKrIxeeDhjei7HKZI0O+x75Qn60SEalSzpdECoPDyvjd82UH4TJSwiJMJ0EAqMmBYGUiu6IpG2niVUdlFTWZ1CnAwOxHAyiKCqabP9ckyjraKsnO4gWlqBVZqu1kO0sxgqRDpjCBUng6ofm4+3tojt5LCDOlauQDbZQJrj+O0yrZ6iWbnKel+RSNh4yQzpVBrX1b9rZWXBsFBi0Hk0AIGIFMp04nPSliEDCXxl5zD8qp7d9CsobxYmPvCW7+bt6FDeCgNRnkF1fiCC0ul0OHnyJF/60pf4V//qX/Ebv/Eb/Nmf/RnPPffcfR/jox/9KB/96Efv+32Tk5Pk8/n7ft8I7x5s9W2E6wW3RqPB4cOHSafTvPLKK28Ri7oT7iU+Doptm5ubvPDCCxQKhft6/wBq9Yuo9rIuuMX+jBKLKytl3ITL4vwSwjAAhRKatqliWw+tCB1gehl2Pesi7BB1ZoNkvoAheliJFNJv4QfjuKJBaCUx+nWEncKMGvr9QT2endyyNT0Iq7qzp3TSekPSaVjxSAJa3Eap6wI3ItIPB88JE0Qs6iO2KMnGs/MYGSDCSIyRToSks0XGpE8/UHTbNeptn43KRUw7QTrZxktnSBIRCQcMU+/TcHT30kpvsQWpDwV/MFOxwE9Md4W4szmYo6wPKbznUjm+EOhOnC98/qz/Z/yf/J/3fO3cLwaCPIZhUCgUKBQK7Ny5E9/3abfb/Omf/imvvvoqxWKRX//1X+cnf/In7/sYo/g4wggjjPDuxTtKed3c3OTEiRM8++yz7Nmz574lxe9G6RpQuNLpNC+99NJbjO3vZcGkuiuw9mVNLxr4hwVt6h2FEbWZmV1gIp8AITWlKWzryrHs6kWK9AE7rmonQSiUnUdGik5Pz+V1Oi6pjIXhpDHQM42EXeSAEha2UUFHCwJFfiwGJONKdpxsWlkEAuno7qtyigjp605m2EGZaT2raDgIvwYIjKAKSJ24yRAjbKEr5PE5E+lZRhTK1rQqvdXHM1RIIpljvJBmfn6eHUuzFLJJIr/D+rXzXLp4Blt1aDVrRL06g0WYMlwQBtLOgDCRdhEESLuIUlInsrKPslKaImsmUHM/Fi/kbsTg+3u75ehvp2CYTCb5mZ/5GYQQ/OEf/iGf/exn2b179y328PbhueeeY2Zmhr/xN/4Gf/mXf/lYjz3C2wPLsuj1enz7299mfn6eAwcO3FcyCXePj91ul29/+9u0220OHjx4QzIJ170k7wa5/g1k9SjKMOPuoEWnWUMJk1TKY35hCWEaCEMnY0JYIEwEBhg2wjD17LWwNM004eBLj15X4Ycpup0I3AKJZALhanq8tDJEYUggkijpI80kamCbEflgJnij9R3Oh9f0nLdhxecm4pgstdetDHRcj3o6nkZtIIgti0Is1dZz6f0qILSiKyJ+naETO2HE7zf0MZQ+hsAk4RgU8mMszM+yfeceJiYnkJhsbFQ4f2mZnq9od3z60tXz8E4+prEW0fTdgj5XO6+PZ2ViCqwT308MnXDKUL827KJSO/g0xzDi27sSij/q/RHtqH1f18/94HaiZY7j8PGPf5xdu3bxcz/3c3zzm9/kgx/84Nt2HrfCKD6OMMIII7z9eEcor71ej9XVVcIw5JVXXrmtSuHdcDtK1+0oXLd6/50WTMpvwPLndDKoBAgXGQVc22jSbTeRZo5MOh1XvHuaBiV7aPGFSC9QsPSiw0zEczlpxqYh7CXwOx2CyCI3nmR614z2urTGtPqf6bKxukyAjes62oQ76hMaDkbUB9PGIIqPpXSVWwm9aEMBke4AoFBWWm+NvH7eziIivQgTqqoXdbEdiQjjBRKx2M7Av810EVEYi+0AWCgTwEQgNfXMjEi7OdLZHkpso9cucXW1Sqe+zuamTcq5hpuaIJ2QOKmxWDXWw4jasfJhFAtOKJRhg3BQwo7nJm9N8xtQTB9HQnk3ldd8Ps/Bgwff1vPYipmZGX7zN3+TF154gX6/z2/91m/xwQ9+kG9/+9scOHB7Fc4R3t2IoogrV67Q7/d54YUXGB8ff6D93InyWi6XOXr0KJOTkzz55JO3LJbcC4NDbn4Htf4VhJ1D+BWwslTXz7NR7eGqNmOFBVS/irDTyKCOsFLIqIUwEsh4dEBG/XgbIQyLdMGmMO3gt3x6nQA3abPtyXFMO6ZKmh5KSir1Hu1Wh6npaaQyQCRASoSRoBJV+Er/EF7fYX7sJ3GFBZajrUOMLb6UwogLc27ctUzqYqCpFWEjkUREHfAK1zuAQ1GfmL7q13RyFw66h62406mtnoi9fE3DI21HpKcmUEEHXzmsLp+n17e5eukUwkyRSdokUhlSXkJbmRjE5yziDql9nRJLFHdf42IlEhKTXJl8jpPXfvOG76mrunyt9TU+mrv/Dt+94G7xsdVqkU6neeaZZ96W498Ko/g4wggjjPD48Ngpr4OFTCKRIJvNPnAyCboCHwTBDc9tnZe8mcJ1M+60YFIygCv/RS8iAIRJ2OuwvFrFFCGzs/OsXLukZ1pUoGdcVKDpSshY+yZWgjUSuipuuBC0cBIOuakNlMqSTigKU2O4ThvIQtQhVDYrVy8jsdm2NBfTXwMilUREfSLDQUQ+kWFjyn6c7PXA9PScpZHQiyDDhbCn5yOlj7KS8fm4ccKmkCIR23NE6EwxBEyE7OluZtjWncKwpa1Bwka81QqJ0q9SLpsov0N6vEAiqT+vIMJJjgNVphafBBnS7vTotOtcrXcRqoyXTJJK9kkmLEzHiWdE01rQJ6ZuqfGDkNl12+9QSvlYzLLvJaF83CqGe/fuZe/evcPHr7zyCufPn+fTn/40//E//sfHei4jPBwGlNdOp8ORI0eQUmLb9gMnk3BrBsZWJe19+/axsLBwX++/YV/VY6j1r8Xz3S2kmWNj5QLNjmRpbozL1wQqbGKkx1FBE8NK68dWChW2MEwPFbQwzES8dVFBA9uCzHgHUfRwbIv8eJ50KtQxVAYgLDY2Vmh1AhZmJ0kkPVQUIA0HFYYow+a19rcRGATK52jvBC94z2AIG4wopomG2qNSynguPp6nJAIzG1sh5UGsIZ0i5kDcR/pbfIe10ix2NrYFSW5hjRj6fmAkaG5W6HRtUskK6YkJCBoIK4sbNhBWgom8TSK9i16rRrvrUymtUIoCEok0qYRFIjuOK1oxBbYSK9BWYo/L1lDxGmHC4t9k1s3zL6b/BV3ZBeD0mdNsX9rO+9Lve+Br6W64m0/vKD6OMMIII3xv47F1KJVSXLx4kfPnz7Nv3z6Ah1ZbM02TXq83fNztdjl8+DBCiLfMS94Kt0solVJw7fPXxQ9URKfTZmV1hUwqweT0NoJeQ3ct+5uxOmBVCyiEPT3Pony9sDDMmGZlaQdJ02ZszCWdzdNp1Wh1fK5ePoXt5ch4ZcxEnmrpNHZyjJmxJMI0Y2+xLJbsgeOhZEBkpHSSaWT1PKWR1d5nZlr7UloZnaANk8CbbDnsLGZY02OfW+YqlZnQIj5WZuj9aKhAz1fKPoHIYQRdDCeP6tc59mqNxtoGPd8hnb7Knpe3MTZpa+uS0MeUfW3eLSCbTZHNJFCGQ7/TotXxqTUbrGwEeIkGyWSWVKpLIlHAiDqo1DbU+Pff8Tt8GBGc+0EURbcVi5JSvmtk8V966SW+8Y1vvNOnMcIDoFQq8cYbbzAzM8P8/DyvvvrqQ+3vZsprFEWcOHGCcrl812Ib3LngJutnUev/Mxb7CglUgmvLl5DYbN8+jWl7GKKMtOJjOHlNSbVzKOmDndFFOzuDivpgp5FRF4RHKgmL23bTaVVodUI21i+zaXukPYtEqkCjukyIxdLcGJabgqiDMD3MsAV2kmrvKheDC2SiiK5hcrbxLZ629+CEdXCyGFET4WTi+J7WWzMVM0kGnpSO9sRF6OTRdOJ5y4EaqxUzQ+LkUVggLKJIICMTy1II2+PC0RXOHSnrRFPY7HmPYNv+aZ1s2gWUqiCtDIZhkcyMk8wIlAoJQui0azQ6IeUrFxFWkoxXI5EqknR18q39hdH3mKgH8z8KiQls4AczPzj8rtLtNC+lXyJpPnjx9m643UjAAKP4OMIII4zwvY3HklBGUcSRI0eo1+u89NJL5HI5VldXH8h0eyu2UrruhcJ1q/ffcsG08XVoXop9vwKqjR7rayUmJqcZK4xr9VQ7B/JaLJgwMNuuxcquZXByWsDHzkC/jbTTqLAHpothGnh2Gs9LMiYEYbBIu92k3mjSq60ghEdCBbT6ghQ1DCemWVlabAcrix3VUVYWFTRRZgYVdZHChchHCgcRhmBmMIh0B4EA5WgRn4HYTmRksNUmykpq0R0rqwVw7AyGX9bqqn4TZadQ/RZnj1WpXGsiMZjalsN2TFYvNXHcDG4Smo2AU99e55WP70AYJkppFUch+ygnh4h/T8KvkvByeFaAKs4j/SatnqLbabCy4gPreKksLH2cYhDcUfX3cSWUdzpOp9NBKfWuWDAdOXKEmZmZd/o0RrhPnD17lgsXLrB//35mZ2fp9XpIKVFKPXAHfmuHcVBsMwyDgwcP3rXYBrePj7K9jLz2eZQwENKn0w9ZuXoJL1VgZnoMDFczIoQNykcKPW+tTA+hAlpEpIWDMjwgBDuFCgMQKRQBws5imwG5sQVyxRDFNtrtBvVGh+rqGhEmmZRJs2+QMfpYTkYnVHYGZI9X/ZM4UtI0DdJSUjHhbOcQT2VeAb9BZGURPa2MPYx3N6ivxuI3hoOluhgyhDBEJ47EIjw2hH6sutpFWWnWTl/mwhkfmy6JQoG5bS7nj5aw7RDDdon6PuePbDA2kyaTd0Ao1oxVkuRI48W2KCFCJHHMACcxQ74YEak5ep0WrU6PUrlCGAQkPI900sNLZ3GFgZj8IGT3vOW7Uko9lhh5LwyOd4NP7yg+jjDCCCO8PXgsCaVhGORyOfbv3z+UDL9fj7RbYbCPS5cu3ROF61bn9RZKWPUN2PifupLe32Sj1qdVWWd+YRfphKaJgoEhIgJcTW11ixAFWtFV9jQlKmxpSpRfJbIy0K+BnUaEseS9X49nLn0sM4GK+vS6HaamZrEcl3arzWa5xkoQkEw0SKazpJNdHDenPdxiJUBheYioCbaHGbZRlocKGkjh6WMbLkJ1NA1V9BCGpWcerQxhVGNjxaFbXcVNJZjZFmA5er5SmR4i7IFhIaIeV0+XKF2q4zoglcnGuau46RQJq4Np5zCNEMNxCfo+/cAmYQkiK4tkA+WOI1SAdIp6VtPOI6JOLLrTwbRscmnIpQoo4eD3GqwnPsTaRoO//PJX2TwbkkgkeOYDizz3fTtvWBw9zg7l7RZM7bYWu3jYhLLVanHu3Lnh44sXL3LkyBGKxSKLi4v84i/+IteuXeN3fud3APiVX/kVtm/fzv79++n1evzWb/0WX/7yl/nCF77wUOcxwuOH53m8973vJZPJAAyvtSiKsKwHC9ODglu5XObIkSNMT0/zxBNP3PPfyy3jY3cdden3EBiIsEu9E7C5eoni5AKFjKMFdgJtZ+TQRKlphF/RBbigyjXZ4vObv8f78x/hSXe7TiqDFghPdxotL55ldDXLw9TJqbBcet11MoVJ8rkkrVafZr3B+rpPwrFIprJk0govmaIW1WgZBhZQNwQ2sGqEPGua4I0jwj7SHYOoT2jl9Dy5kUFEAcJMYSgJVhIV+tQ3BeebZRzbYGKpQCqFnluMOmCn42JijuqVFc4db5OwO4SkaK+vcrGVwTba2K6HafSRJvTaffrNGpn8OOXuBV73DlHvXOUjqb8LQXlI9cdMgWyDsDCBlCtIJfNQ8OhLh26rTKvbZ235DMuXi1ypN9j+1BE+8GP7cdzr4k1vt63SAHeKj0op2u328Np+UIzi4wgjjDDCuxePbYZy586dt5TFf9j9NhoNms3mPVG4bsbNohWqfQXWvwxOgajf4Np6C/wq8zv24opYdCdoaEXCqINJHxX1EFhD4QVEbL5tZUAFWrk07CPcMYTsabW+sBVXwZtgpymvXabWDlicKZBIpSDqkRrLQNHDlyadZp1Wp015Yx3LSZBKJUmn03jJLIYgFgWKKJfAb1dJ5hLkilpN1ZARClCyg1KapQv6H8tvrFBd7tBN9ImkRXMzyd6Di5imC5gow9GiFVJSWgNh2uB6uEafWtXEDbv4kUdGtAhkGocawsmRMBpABiOoIVSEETZQwkEMRHeEQpkeCBPFQIZ/QCPzsec+zPzE+2l/d5WTf3KVfidAqRbnvrvJ2dPnefKVOcbGxhgbG3vXJJS2bd+zf+rt8Nprr/GhD31o+Pjnfu7nAPiJn/gJPvOZz7C6usqVK1eGP/d9n5//+Z/n2rVrJJNJnnnmGb74xS/esI8R/mpgYWHhhlg0uNbCMHyohDIMQw4dOsQTTzzB/Pz8fb9fKTXskqp+FXnlD1BGAhX12Kx0qFdLzMzvJpUwwfJQQTsWrGkRGbrTJ5LTsZBNjjdKn6dnCI43vs4TU3s1i2OLHRN+9cZtv0KjqyivXWRsajv5pADHI2GEUFxEBk2aXUWvVebq1QomPh9If4iMZ5JMFzAN3U00lKJR6dGqdrETNhMzJpg2SggkDhKFwkJJAwkIYbF8tsLGBYWX6BBJg821kCdfWSSZFmAW9NykMw7Sp1JNEIU1SBdxRERPZem2fSKVJOoqnIRFvyuxLXAzBVAh3/JP4UaSM+YGz/cuMenOQ9DWnUrp62KlYWgrETsTextncYMGbmECV1Q4eczm64efJIrqXDhU4+yJK7z8vyxQLBYZGxsbxqTHZat0OzyKGcpRfBxhhBFGePfisc1Q3uyzZlnWQ3Uou90uFy5cIIoiPvCBD9wThetmDBZMALJf4XPHf5pd7jZ2GjtYXi6RcC1mdzyLKSIQSYi6OlGMVVtNIhQKFfU40zvLjsROHMx4QdBGChclewgzgVD+cB4TW4s+RHaBtWuX6YcmiwvT2PE8kBZbaICdxlFtnOI4+bCNnJmk1yzT6gVsXjtPH4eUa5HMjnHtZJWVCx1NzbJt9h6YYmn/OEJJUAKlApQyUFEPJSzapTLVjZBU2ieRzmOqNrVSl/b6GtmZKURQiReGTZSVIZGQ1MsGiVREP/JAtnELU0wlA1YvtzDpY3k5nnl5Ehxd7VdY+nenIoSQQ2qYnuXMIPx6bGtSR5kZhGqjEnMw/goA3/rcOYJeSHbMA6BV61M+ZZL+oTSrq6ucPn0ax3FQSlGtVsnlcm/bwuluCWUqlXpocaAPfvCDN/yN3IzPfOYzNzz+1Kc+xac+9amHOuYI704MPP0eNEZGUcTp06cBHqjYNjgH0MnCyqlrNI/9J4Rsk5v06KsmQeCzuGMvrmMjMTCQmtquQpSTR4h1IitPq9ahXhE05DlOOSVsW3FV9HizfYy93jPaBskt6tjn5LX1kpOHoMlm3adZXWVqbjdJJ9KJlV9nVbUQ3RWmk7vIGW1yuQWmgja9wKDTKlOrhZTXV7C9DGnPobkJV46V8H2JaQgmljI88fI8ggjTcDBjqxHpN5BWCtmvsnKhTsL1Seaz2IZPddOnurJOcs+svhcIW28NE9uO4uQ70u4iASSSDjO7Clw+vk6zJnATJgtPz5MpJtgIK1zyLxCZBiaCb/aP8WOJbTr2i1glXFjXraoGW+lrexAVcOWi4Dsn3kMql0YYgl47YONURPZ/L1Cr1bh48eLQaqZcLjM2NvbAxYm74XGIlo3i4wgjjDDCuxfviG0IPBzldUDhyuVymKb5QMkkXO9QqqjPuTP/itXOKSqdCxiNgLFcnsnJOVA9TcdSAxW/CKwsIvS1B5qwOONf5outr3EgavC+zEGUDHQ3UHYQhgNRUyu9hjVqFehUakSGR2hWMNwUS/PTmFasEGhl9NYtxt5ieU39snMYUZdkfpZk0EBNzRJ2Nmn2DDYvXWb5dEjS6yOdDBZNzh9VTM6aePk8qEAntTLQCoaENPwEvX6E4WoVyZ7MgGzRj1KooI0y0xhBrOga1JjdVaTXvES7ZmIZdVIZj6WdFqlChoW9GcJ+SHoii5fUCbXCQFk2kXBRdj7+bB5EfT3TKduxQFAjVqltajuA2R8Z+k32uwHCEMNEzTAEUQjbt29n+/btBEHAhQsX2NjY4MSJE0RRRLFYHFbnH/S6uBXuVIFvtVoPpVY8wgi3woPGyE6nw+HDh4fX64NSDQcJ5ZWTy6z/z/8vpqwilcHqpatMbsuw9+klhCW0X64wdWdPSSQCoQIUgtpajSvHWwQ9n1JYZSy3jerTl7HciK93XmO3tx/TTKItMJK0610qaz7QJTB7IPrMLT2J65ixOmtEaGf4k9Lv0UfxfyQWcaxcnHDlSJghidQSRRkRhhGtVpN6tcnp1zYRUmJ5DkJJVi50GJ+pMbl9SjNG7AwETQwvj+E3aEkXwjLCzOBZbfzIw3NqhGEW2a8hTBdUHyEMUILpeYvysk2vVaUXeSQTfeb3bWdmQTA5v4OgWcHJ5MlkJciAo82vY0cKSygMpbjmX2ajd5lJbzuEHa04G3ViMaC+/uyRj7Yh0XYna/2D+EEDKw49hilQEibHpti5extRFFEqlTh58iQXL17k5MmT5HK5IbvjURTBBrgXyuu7YYZyhBFGGGGEtwfveEJ5P6ITWyXvn3jiCTzP48SJEw98DoZhIKMQtfzHvFb7CiECpfo0xio8UdwLMvaPDGp6XsavxIqudQwjg0lAqEy+3fwmPnCoe4xn0y/jiYQWbxDEM5cOyIDVK30uvXGNvq9QYRevkOU935/CHMwdWal4fiYDqhPP0fR1x1NF+udKJ5mCCDs9QzHp0y5b2PZ5pJnHMjp0ug6OqHLtWopp2cLLTmNFlVjptUKlGdLvLJMbSxC2u3SUi/RDvEySTNFBGjYoRWRkEFIhzAz5Kcmel3dTXWsgRERhJksmDcpyKY6HKKuo50OFpq+p2JdOoHRH0kwigg7KsIBQe2QKA2Vltf+l4SJn/qZe2MXY+9IM185W6TZ9bRunFHtfnB7+3LZtstksnU6H5557jlarRblcZm1tjTNnzpBMJhkbG6NYLJLP5x+qe3knFcNB9f1x2JeM8L2JW107D5JQbm5ucvToUWZmZtizZw9f/OIX72sOc9VfpRE12Ovt1f6uKqJ36j9jqxJ22qPdrOA6Lra0MAyFkj4GoFSIoSTKMDFUhMLCos/qmS4i8nGLFqq3QaKeYmwtT2NhEykbXOydZ5ezCIZNfbPGiW+t4Xf6+H6Ak7R47kM7cW2BpumHIATHO0fpyj5KwJHOMV7KvAzKAgyteIpW1rYch3zRwVQZDNFEOBaKiG6oiPyQ5ZUudqFBKp3FEULPvKuAdphgbe0SieIU7VKXejOBinwMK0t2zEOZHlIpwNKzpDLCTud48hWT9avjiKhNamwbxfEIrDTZbAsKE3FiaBFGPZb7l1GGwJWKrmmSiiLOhMtM+vnYFqQce1s2h58HGej5+6AOMz/M3LNzmJ/7Jp2Gj2kbhP2IbU9NkMonhtdPNptFCMF73/teut0u5XKZSqXCpUuXsCxrWHwrFosP1b2Momioj3AzBqJlDztDOcIII4wwwrsXj5XyesOB45vXvS52oiji+PHjVCoVXnzxRfL5PNVq9aFos4ZhkO0d5lz1AteiGqYQ9IXB4ehNnhev4MRzkNgZTW2yMxDWh2qrCpPL7depySZ5GdEWcKz+dV7K/6C27jDizqawiCLJheN1+j0brBArWaDbiNhYhbkdSieLUV97osl27CHZ0J3NoAFWAvxWLGvfjrt9PgiLdFYADjKQSCeLofpEZpFk1qXUDAhLZ3ESKTLJNv0goteqMb+0jbkZyeU3q3QbfRJjDov7iiQToZ65jHyUcHS31fRQUZ9kziOVdxCGixAR0rAwZIB0xxDS16I7UQ+cPCLsIE0PizUQ2tcSJXV+Hfa1sqzfiLuUVdTE+yG9/Ybv5wOf2EuvFXDoi5dAwQs/tJ0P/fiTN7xmMEMphCCTyZDJZNi2bRtBEFCtVimXy5w8eZIoiigUCsPFk+d593Wt3AvldYQRHiUsy7rnOXOlFJcuXeLcuXPDeckBPfBeY6RUkl+48gtsBBv80Z4/Iik8xv1XMaRPs2Nhhk2S6TGkoYgGXrZcF35RQoFSKGHp/E9ZhP02pu1gG1C0JwmtCJsFdiVnEcJg2szpwpvsce3NTaJeG9OVZDyToK8oXSiRzw+KSIpQSY41X8VBYUrF8c5rPO/swrbTEPRjJkgzFszRW8/rY7sOst/GsFKYVpee5TA+ZtBsdSiXVjFtj3QqAYZFtVJmanKKpcU0l06VqK71sByPxX2TFKc9wERGARJNSZXCARlhJrPM740Qxjh6EtNEq9hmARWruBpYRsSPjv/v9GSLqysbTE/kMZwk02Yhju++TiqV1PeQwXy+ndSib2MvQvEAu4rw8Z9+ni/97gn63ZDt75ngx372PRjG9Xvt1hlzz/OYn59nfn4eKSW1Wo1yuczFixc5ceIEuVxumGDeb4HsTgyORyVaNsIII4wwwrsX72iHEu4toRxQuCzL4pVXXhkKDdzNePtusJpvkO0f46u1I+RlRNMy8aKIrtHjROcIz6dfBhVTrcykrhI7xViMYQzFMv+zf5yEUjQNk7SMeD04zbP+UyTcKZ0oWRnwqwSBplBJbLIpC2G6tPttAl/qWRnD1FsA4QIKrEFncmCY7eoZGsPQMvlCgAopjFvsfjbDlVNloqCP5xnsfmGaqZkMmA6B36PV6VMplwkihWV61Jp9Uqkke17egUGojy197b8WdRBOBhE2kY6e55RWBoKa3vZLSCuNIWtIw0PQ1e8Tvj4nlBbuECGRkUKZKQQRSlhc61/keP8sP5R8n+60hk1Uagk1/oG3XiOWwQ9/8hl+6CeeAsAw39ohvJ0oj23bTE5OMjk5iVJq2L1cX1/nzJkzeJ43pH7dS/dylFCO8Lhxrx3KMAw5fvw4tVptaMsEuoh3PzHy682vc7F/EYDfL3+WnwhmSUSbhGaKSPYwozRhF8BmbDp3nTlhmPHMn6FFt4gTESuBkzZobPbwDAcvHCdAsnt8kplsAa06IwAJyqHRDOn6glQiie3Y+H2fXlddP44wOdF+nQYBtlL0hcCJ+hz1z/KCeOJ6Eulk4mQyC0EDy8vx9MtNjr2qMKI6gUqy5+kES/sWIOogGaPTqlKtNfH7XUxh0G1XMVSXHU/mMfcHuuAX6sSIoIZh5zCCOlhpbd1kuKhQMzBU2CNUaEVtJcFKYKi+juNRD4wE40Ya7CK9sMOMNY9rOXSaPY6+ep52tUOqkOapg9N42VRclEwCASTnYeoHht/Zgb+xjec/vISMFKZ17/HRMIzheABoTYJKpUK5XOby5cuYpjmMj4VCYTiLeTvcLT6apvnQomUjjDDCCCO8e/GOJZSDrtLdFkxbKVz79u274eb4MHOYqnWRaPXL9FSKrBLU7CzTMqRtWxTCEIGpqUVmQs+0GDZEUm8FoHwaZpuObBAJgYmiblpYKuKcrPGUmkDZBZAdlJ2jvnGO0PDwrB5RlMYMaphWglymD0jwmzHFqQd2Ss9NGol4zaUr25rOFel/E4Eyho+X9k8xtjRB2GqQyGVJuCGYFgRNTNOjW7+EbSfZNpehJ126jQ02Sh3UymXcVIF00iWVyeOYDEWDlFNEqBDccUzlgzWBEXaQdlZ7S2KhpD8U/hHKByuJ4WuPTCOoIoSIPS7TqKjJl5v/k1ZU46y9wB5zN8odQ839rTgRvc21cotEcgAp5V0r6Td3L8MwHC6eTp06RRiGFAqFYXX+5u7lwMttlFCO8HbhQSmvW4ttBw8efMui/V5jpFSSf7/x72NpHcmpi79Bx/oICkgmGjz98g5a62VCkaIwJijMpFF+E2EnUUEbYSZQUVdvYyEyA5+5JwtYb1ZpVENMQzK7Z4zpxVw8DxnHqCigUm+hLB+UDcqi0zOQkSBdzOvXCgeQnAqWkUDLMLCBlmly1r/AC5lXdNFtIGDjjmuKqTMGqk9xaZH3jrdpNSQJV5EqpHRh0ExjqIh2TxGEirnF7SgMus06lUbAxsY5rESObLKOl5kkIVrx2EMjZpW0EVYCoQKwLEAgowhl6gRTWmmEXyW0shi9Mjg6LmImwW8gZIih+gSdLt/6b5eQfptuL0HQqvGN9RY/8P+YxUxk9By+nYXZj+nfx03XjmndOgbeqwq253nMzc0xNzeHlJJ6vX5D9zKbzQ4TzFt1L+82EpBKpR6LGvcII4wwwgjvDN4xyivcmdJ1KwrXzRj4pN2v+bfqlam/+VlWVssIIv727D+gutGguuljCJ/cZJ5C0tCLmLAVm1e3Y9/IXkxBUhSlxydSH0OIAGm68QyRw4w9GSv0hYTSZWXlClK5PP/907z5WgW/1SYUGbY/lSG/MBuLQuR04uXkYhXULMKvxZTQwfxjVc8cRrHtiAr0OSm9MErnAsin42q+DbJPIDKsLF/AcPIsTOYQdopU0CA1s52JoEFfztBtrNNsB1RLyxh2moxn4mUKeK6NMCwQoe48mBbCtDENG5SNxETIkEgJTY0VCZB9IiOLEfUIRRpLbaIsDxG1ueBfwo+qJBQcbn+L3eaUXiBZD06FehDbEMuybuhettttyuUyGxsbnD17dti9HMxeDhSK7yTKM6JzjfCocTdrpUGxbXZ2lr179962E3UvHcqt3cmnWi12NZt8NTrMHjHFxOw+6LZpuRksv06kxlH9GkYihwoaGHZGb600KqjrrV9DCAvPbvHk++YImg0MN43r+Loe5td0DOtV2Kh16NZLPP3yEtdOrrKxqnDNFrmdkyzutnXnM2yCleZvZX6AqvIxZB9puJiqT8qaiDt/dhyfnZjBYYDqAwaoADfp4qYESKkLWIaFUgYrKyv4oWRxaRuWo9kgXipPUQYEEXTaDVqtLuWrV1HCIJNqk0pnSSUFhp3TRT+coTqr4WR0smqnULJPZIwjVB9pFSD0kSKJkAqEixQ2GCbry4pexycIkriOT6eXxAmbbKxFzCyFYLqw8LcZKvDcIx4kPhqGQaFQoFAosGvXLnq93nD2ctC93Dp7adv2HTuUo/g4wggjjPC9j3esQwm3r57fjsJ1q/fD3T2wtkIFHTZe+7c0KivMTY2zsrpBZXWDy282ESokkhb1chf15CzFcaVVVqUfq63GN3blowDTcumv+WSSHsl0kqSXjBmfCsImfghr1y5hJbLMTOQQtseLH5qk2zOwbYXtJhh6VhKhnOKwM4j0UU4Boi7KLkLYQFmxxYaV3ZJ0VuKks6yTTV+bihM16PUFK9eukMwWmJoY06bjSukKOwrlFHBkhDOxRE6GSDlDu92i026ytrqGkH0S6SLphMDLTmLLmj62X0XZWcw4wbWiljYCjwKkYaNUhMLGD1pEwiNSFgiPb/SO0hcmoVD0ZYND6SwHUtvu/8LZgof1oRRCkE6nSafTLC0tEYbhcPbyzTffJAgC8vk8AP1+/5bCE51OZ7RgGuGR43bxUSnFxYsXOX/+PE8++SRzc3P3vY+b8d+r/x2AnZ02+zptaqaBsq6xw99Ht+2zciYk6HZQhoMobRIEE8xul9reI46PKuqDndPWRFYG26iwXumT6a2QzBZJWYGOPVEb7AzSb7Cy0UD5dWa3PYFNh70H97K9UUbZOVwr9rT0a+DorecU8fw6uFN6ttyZgKClxwOCRlz8a8TWTcGw+AfxaEEUgOWC3yISLutXzxMZKZZmMhi2E8fPDPR1F9FWPXKZJLmUAypPp9ej3W6xWVpjze/gJvNkkjbJzDiu5esZ+ChOapWPMGws5FA0SAoTiYWKk1UURCRQsoof2JhC0fddhFD4gYNUji4aTv0geFP3fQ0ppR66M5hIJG7Zvbx8+TInT54km83S6/Xo9/u3LO52Op0Rg2OEEUYY4Xsc7ygH5VaLnU6nw7e//W36/T4HDx68bTI5eD/cu+hEGPhc/Mav0KqXWFjcRiqVwTAUtfUOluiQLnoUxyOiQNJev6aprUFFV56j9nDOMMJGSljYtofJmXmkmWS11ODMhRWurVepNds0OyFXlq+QzBSZnRpDmLpqLgxB0vOxHQFRQ9O+gpoWYggaICMtAKSMeEHkooUdtEKesrWfnHIKoKS25JA+yhrIzHsQtWh3uqxePUuxkGF6LKlFccKO/hxRRx8r8jUtTOm5R8NKkskWmZrbxfZd+5he2o/tJim3BBcuXODCSpPK5hqdKKE9Jc2U7pYaFkRdBCGmCLHo0e+1aGxeoVjMYwQ1zvbPEQYVpJIklGTTsvnl6FtI9eAzsPDwCeXNsCyLiYkJ9u3bxyuvvMILL7wwVCd87bXX+Na3vsWZM2col8vD667dbj+0bcjXvvY1Pv7xjzM7O4sQgj/+4z++63u++tWvcuDAAVzXZdeuXW/xYRvhrw5ux+C4ObaFYcjRo0e5cuUKL7300h2TSbj3hPL/mv6/+NfZf8jPtp/h6e5z/JDz/fzI2I9hi4hGJcRvt0kVs2QLFsL2qK7WkcpGKIkyEghk3D1TKDMJUjExs42FhXnMRJ5qpcq5yyWuLF9hsxHQ7nS4uFJHAHPbn8G2LU1XReHkZ3A9U9NVkeCO6a6iU9TxMS686SKf1EmqioXNQMdAIXRSJ+y4G+nFr02B9AlUgmtXzoKbZ2E6h+EW44Q0pbdmQqt8C0MnzERgQNI1mJicZvv8ONt27CHvKdq9gJVLx7h46SqbK2fptNsov6bfF3Xi2ctIq4ObJpZsIKVi8+qbZLMZbNmgOO2QzQS4Th/H7pNMdEikDCYmI8g/Dbl9d7+IboFHHR8H3ctdu3bx0ksv8corrzAzM0MURZw/f55vfOMbnDx5krW1NYIgAK7Hx4dRwR7FxxFGGGGEdzfeUcrrzZSuO81L3gqDn9/LgqnT6XDhL/8dGVVhdvteLFMQSUmoXMAmkCk8ICSNIeqEKqfnGu20nqV0tB1GZGSor1yl3UngGG2yM9NMjcHk5AR+r0mzG1Epb+JHCtt0wUjQC01cN4EQKqZH2dcV/Ig0VUuFaG8MPz7jMH7tFigVU2m1ZD1SghnPFw1FfQT1epe19QozU0tkc7nrlfqoH89ptsFKa0GcAdXWzsXU2gzCL4OVwTM6mKk05XMNmjULL9/Hnneo1K6AEKTSWdLJBMmUh2kOzi2g1o7YXFtncmYX2aShqbDN00jDIqsUfcPg27lJXOXT9bskrARCiAda+NxPd/p+MeheCiFYXl7m/e9/P9VqlUqlwunTp/F9n2PHjnHx4kVmZ2cf6ljtdptnn32WT37yk3ziE5+46+svXrzIj/zIj/DTP/3T/O7v/i5f+tKX+If/8B8yMzPDRz7ykYc6lxHeHbg5PrbbbQ4fPozjOLzyyiu3tWnYioHX7t0w3e3DqcP0/DxPT+/ASRYgbHHRMLBkDTBwqBGSxrPqRKQhqKCcvN7aOW0XZCRpVUrUKhaWaJMeH2Mi78LYNkK/RbMT0aqXKPUiHENiJcfodaokk9nY89fWBbWBSNjAf3GgdCriOXJhaUsmNUgYY2EzJdEU1xBMW8c+I63fY6QBSSd0Wbl6mUx2iqmJiZiqKm9MVAV6nxDH3fi4pk5IcYvYkY81to3NN65SWc/huBJ7m0djfYMoikilUqQ8l2R2DHsw3xm16EmX1SvnSOcnmSho26S0Ay98ZBtHvr5J1GlgJsd47gM5zOIeouIriAdMDB91QnkzXNdldnaW8+fP8/TTTwPaJ/rKlSucOnWKzc1NPv/5z+O67kOdyyg+jjDCCCO8u/GuoLzeD4VrKwZJyJ1mhC73L/OLl36Rn7z8BM/bm4xN70BEDTCyGGEbIaAwbtOq+jQrfRQBwkiQn8zorqAYVMb7RFae0qV1rpz1sagRRC6F8gUWntpOwq7jJDIY9UtIpViYSBIaKXqNda5UwDEivPQ46aSNlylgoPSCJ1Yv1AshoR9jAHGCOUgGZV+rvEZd3QkIO3rr13XS22+Alaa2uUyp7rM4U8TL5GPl1oROVO14vtIuADG1VgWaUqv6sYjQoPPYJYxMvvnHx+k1+khlYIiQTnmCFz48RTe06DVLlMstNlfPY3rjZDyDQFk0amWmZhfJpLQyrkDwZPYVnsy/H5RETv8In/Tmh2I3Wxe8hmEM/7sXSCnvqkD4sBgkrYPu5cTEBEopOp0O58+f59y5c/z5n/85X/nKV/iN3/iNB1qwfPSjH+WjH/3oPb/+N3/zN9m+fTv/9//9fwPwxBNP8I1vfINPf/rTowXT9whM08T3dYGpVCpx9OhR5ubmbjsvebt93C4+dts+X/+DN1k5eYrF7J8zvi3NvienMdwMKuyAncVUK7j5CZKVMpWyhevU6PkuC9tAODlE2AA7g/K18FZzY4OLp3oov4EfJUmvXmDuySWy2TqWk8Fur9L3I2aKCSwvT7dRYm1NIuQlvFSBtGfiZYqYsXI0sou234jjI3ADfdV0tV2I6ekYaGtVauyMpsraWc0AsTIQNWh1FOurlyiOz1DMxXPoSoEUYISxaq2jk1jTu+4DLH1duJP9mJHhAwavf+Esy2cqGMIAQ7FyscsP/K+7kaZFp1Wj1u6zvnkey/HIphMYlku5tMHY2AzFsbE47ltARH52jg/+3WnAQKmIyMogZ38EJaUuHvJg8fFx+ONGUYRt26RSKfL5PDt37qTf7/P1r3+da9eucfjwYaampvin//Sf8gu/8Av3vf9RfBxhhBFGeHfjHU0oLcsiCAKOHj1613nJ2+FOlC6lFL968VcJO2/Sc1cYn/7bsUdZDvwaws5iqza5yW3YRkBl08LEJzeZIV9UWuABhdLpH1HYZ325i2mCHyTptnpUSworWWdh/xTrK5fp9U2W5saxvTyEdXLZvUwFNdqBRa+xznoJxOolnPQE2YTCy01jq0YstqNni2prFTbXIlxXMrVjAsdBL25UAGYxTg7zeuvkQfaQdp7y6nkaHcnSTB4nPRZ3HjNx5zENfiX2OWvo5FRFgAlG/PsTEWBr5UXDYvnNTaqbEEUelimJIpf+xQa1xnYKRUUyvZNi0CSQM3QaJar1FlHQw7Iseq0KZtQkkSpiql7c+ayjJr8PI7t9yLUeiCoNCgtSyuECeKAEfKfu5dtdgYdbKxgKIUilUvyDf/AP+OIXv8hP/uRP8vTTT7Nnz5639VwG+Na3vsWHP/zhG577yEc+wj/+x//4sRx/hEeL21FeB0WLCxcusH///vvuhN9pDvMLn3mD068eZ8/0X9KuCdS5kPkFm5wrkHYKQymkkcJN2szv387m1Rqhn2F83GFivoAQMp73DlBWHiV9NtYs/F6A6RbptxpUSxDIDZ754A7KpTXKtR6z01OkMwWQPunMbiZCn24Q0mnWKNd7+KULJBJJUimPVCqF65ialm9YIAO6HYPSpQ0kLlNzPl6hGKug5mI2SU4X2uK5ywHLpN7yqZRWmJqeI5300HYlIUShnqsMu5oSGzTi91avz3Da2jYJM6ktSYRJu9ajdGkV27SwrS69wMMIaiyfrbPziQSJYpFipklEkW6rSr1Ro9vrY4kIv5egVengZWcwZVULk4XNOC4HCMPFXvpbCCc7LLoNYuWgaz2Ii3eLj28Xg2PrtXSr47iuy4c//GGOHz/O7Owsv/ALv/DYlF5H8XGEEUYY4fHiHU0olVJcuHCBdDp9zxSum3E7SpeUki+e/CJvBl/jB5oNzqo6m0GJcXsSiOK5HB/fyKKkJD83Q346roqHbTAdCPsow0JG/XiOUmLSo9UKaJeb+IGmSp15vUdPlknmkiwtzWKajq48W3mdtLljpJyAVGoPY7JPP4R2o0SlHRJunsRyc+SSVbzcNNXzFznylzUcs0237zFx+grP/fAzuEZde1qGtZh21Y19KQMiZbO6sowfGCwuLWLbLhDp+UoVaTqaDPT7VOw5qVPk4fwjVhIRNOLErzqkwgog7bXp9D1cp48f2kS9Nhh6hhMzjWVCN3QIVcj84iKBVHTadVY2e7B+HjeZJ5fs4I3vwx5//1u+P7hRYGnr4mmQXN5u8fQoRCfuhjspGIJWMSwWi3zsYx97W89jK9bW1piaulGkY2pqikajQbfbfYv1yQh/NVGtVqnVarz88stks9n7fv/tGBzdps+5w6d5Yu7rZLNgOTatapfKaplMcUK7QwqBKUKkDMjmk6RyRc0yFQKhAqQ0gD4oUCpACBMlA3xfUb9aIgwkKFg+08dInscbUywtzuE6sbWJETMLTBvPdrW6MgZB4NPudGk165TK69iWQSqTJZPy8NsGr/3ZebrtCMNUvOk4HPwRj9xkQcdapwAM5iyjoW/wZj2kVqsxu7CbZCIRNztFTHX19GymW9BjATEjRVNUe7poF/VjhofUcVeY+P0ufT+BbQd0uikc16fTT6L8NtjjQ89JE4VU0O1HzEyNYyTydOtrbNYFauMNLC9HxqvhpTK4rqltmBZ+DOEUht/hIMYNCm6D/+7G7ngcHcrB9XUnW6VMJsP73ve+t/U8tmIUH0cYYYQRHi/esRnKUqlEuVwmm83ywgsvPHBScCtKV7/f58iRI3ze+i/8cKuOKSW2ErzW/CY/nPmB2GC6DYaHLULUoFtnptECOAWQAdJ2UVEApo1QEZZrkC6kuHJmBQO0sA4CGfbobFrsfaKAEFFcaXb0IkS4erFiOPGMkInrgDs2Q3FcEUZztFtNmq0OG5eucObrHS3UIzK4iZDSusHVE9fY8fxcnOi6sRCODVGLUMLatasI02Tb3DSG0ddUMBmBldD/tpPxTFAipsAOZjbjz2smtbCPXQTZ09TXqI2TLZBwVgkjG8/toZRBwvHJ5NBy/KGPMj1KKxfpBhbbZrNYSY9E2CaTnmVqvEfPj2h3OlTaARebBRKlVxkfH2diYoJsNvuW7/3mxdOge3m7xdOd/M8eFe6WUA4WTCOM8DAY2NOAvqYuX76MlJIPfOADD1Rsg1t3KKWUnHnzKHvGv0omGeG6CVARplCYQmKoEIXAUAohJEL6ICMMFaGEqbcYCBWhlEDJKFaQ9skXTa6daoIM8DwDJUGqgMqlTd737JOYZqjZqzK4Ti21XPD7Oj5FPWwrSd5T5LNzKL9J2xd0miVWVhusvrFJp22STkcEkY6Fb35nhZc/Mh/Htbj4N5xBD1ldW6HbC1iYn8H10rEKq6fjsaGtjhCuPhfD0swPYV4vvin01rB0bDYsUBFeoYgwLtLtu1impO87GEKRyOX1LKhlACaVSolSucfi/BJeKgsqwPN2UVQRYbhAq92k3WqxUStjmQIx+T6y3RRF761x51bxcZBc3qp7+bji4+DcboV2uz1SwR5hhBFG+B7HY+9QDrqSFy5coFgskslkHuqGd/OCqdFocOjQIfq5NuOtb+CUXcy2B3bA5fFzVFIvUgyVVvEL6nrh4JfBndRbKwthlcjIgGyizBSG6g8pp9O7Zznx3U38PphCoWKhHcfNI6yUnscxYpEHYiEHpeLkEk1bHUJgKUkuZZFLF+h30lw0zhAphZQBvpREkUGlErIQmdh2BhBxcirxI1i+do2Um2Rqehah4oVRqKmzWnQnE/tZ5hB+Obb92Go1ktavFwkQ3Vh8IgSRoF1v0PEToAxEZKGkwDQknW4CN2MRmQnWr13Aly6Ls3lMO6npYoajk2oUCcciYbuMPfNJ5pwZyuUym5ubHDlyBICxsTHGx8cZGxt7y6L5bt3LIAgIgmD4/IMK+9wNd6ONDYy7Hyemp6dZX1+/4bn19XWy2eyo+v5XHBsbG7zxxhsUi0V6vd4DJ5Pw1vjo+z5HDr9GtvYFtu/Lc+XNGu1mgFSKdD7J2OIEyrB1804JMG2kcLRnIzYohcS6Ht+EQpjxbUTA+JyNmy7RakiUEkihMEyBMJKYphEnkT0dT8PBPHg7pqU2Y2ppc0hbFU6OtKiTTm9nMqhz7VCPlFfFDxI4Voueb+O3K/j+JI4dXhfYUSESi7VrlwmVzdJMHjORvE5jDSoxnbUS002r8T2hE8dA4qKcC0H3upWIkxvuo19dRYiIdKJHpAwEIAyJ3+4M7Ucqa+eotCTbZrK4nqufH4j9yAjLTpBPW+TzCyi/SctaYlU8xenTp+n3+xQKhWEB7ua/69vFx62z6YMZ3DAM72v28n5wt4Sy1WoxPj7+yI97J4zi4wgjjDDC48VjTSjDMOTYsWPU63VefvnlG6TFHxRbKV1ra2scO3aMHTt24ImvUjk5DyeLmAoiBGq6Szhp6VkbGYKTwxArREY2np/JQdAgMlN6oeHkMcMtCqh2FtdosLCrwMaFq3Qjj4QVEgqP8ZlEbKzt6+o3YVzljkV2BvOKA7EdFcTCEjF1NerhJF2yeahVBF4ioN+3cBJdrITH8rmjWF6ejGfi5aZQQZurqzUKuSRjk/MIAhBZXVV3Yz9LuxBvx0D19LxT2I6psx3dmY26WiVR9fUCKqb6YiaxRRPLELh2h27fI5Ho0u0nELJLGDmsXr2AMkwW5ucxDUMv5gyL6yIa2lCcsQ9AahEbfaOfnp5GKUW9Xmdzc5PLly9z4sQJstksExMTjI+PD9VVb/6utyr7nj17ll6vR6FQeChhn7vhTh3KgTjP467AHzx4kP/xP/7HDc/9xV/8BQcPHnys5zHCo4NSajgv+dRTT2FZFqdPn36ofW6Nj81mk0Ovf5c5DrE0m6bieFw7U6XTBcOKEKaFaYEw7GHXUQgTqXSnTRgmUkYMxcMMAy2QE8c5YYERMrtnlnr5Mr1AYduCIDRZWCjqxE1Fev6bUFNJVaTjlbwet4aPnTHdLYzHE3DGKE7XuXTCx0kI+kEaKUOMVIJLy6tYlkk6nSGdSmLZLqsryxhWhvnpcQzb00W9Ia21oBNbOxuPAyRjCr8DmPo5O+5m2vl4vj0PYUsnl2EHKRIYIkIqQRSZOJZPP0hg0gNrjs2V8zR7iqXZAnYi3pf+UiDsbUme9ZymkZonu+3vkjPMYVwplUpsbGxw5swZkskk4+PjjI+Pk8/n78ruWFtbY2VlhSeffPK+Z9PvB4P4eDtq7Sg+jjDCCCN87+OxJZS+7/Pqq6/eIHlfKpXo9XoPtd+BtP7Zs2e5dOkSzzzzDJPiLMGVK8xe3oudamF4OUTUolXOoa5FsD1CU6IMEBYKPQuIUkRWFiVDhDOGIE7GZFfPI0YdpJVjfHGDWj2D2+0SkWH3HhvTdqleWSYzNYkt4g6gX43Fdlrx4qQf+6H58QImjD3UIjATCBXyxPc9xZEvnqVVj3CckMkdizz9cpFIJOg2Vmn0DGqXThLgkXYlXnIC+pvgDGw/YlEJIwmqE3uxdbSohQjix0bsGbdlIUgYP44psrLH+I4lnNc36fU8Ek6PUNpMzUAyZ7Ny5SyOYzM1NY2IWmCk4hnMLCKMP3dYg9R2GHv5Ld+bEIJ8Pk8+n2fXrl30ej02NzfZ3Nzk4sWLWJY1XDyNjY3dkNAppThz5gybm5u8+OKLJBIJgAcWrrgb7kYbexSUrlarxblz54aPL168yJEjRygWiywuLvKLv/iLXLt2jd/5nd8B4Kd/+qf59V//dT71qU/xyU9+ki9/+ct89rOf5U/+5E8e6jxGeGeglOLIkSPDYls2m6VSqdyzx+7tYJom/X6f9fV13jh6lP3580y6PYSV4dqJMwgD5pYgiNL4rQrXziTZsb+PsFIov4kpFCKoI8ii/CYYCVTQ0myMoKdjhd8EJz3syuXG2uTnTfxqjyBMMbvNpjiToXx5leRYEc8bdChb17dGQitXG07spzsodhl6iwH47H1hinatT22jicJkfCbJ8z+wB8cOafUknWaFq6slkH0My2Mi7aFETPE3HZ2oYqETYVvv17C30FljqxE7cT2plKGeP0eBkdJMEytFbjKNl8tRL/cxhKTTT+O6UNyxjZXVFXq+zeLiHJZta9qsEavTqgicWGF2YFXizcDi39bUYa6LfqVSKbZt20YYhkN2x7Fjx5BSUiwWmZiYYGxsDNd1b/je19bWePPNN3n22WcZHx+/79n0+8HjYHCM4uMII4wwwrsbjy2hdByHbdu2MTs7ewNVZ6vP2oPAMAwuX75MGIa8973vJS2vwcp36UVZhLxKaGdJGT0CMjhmhSA0NB3T9CBsYooIghYqjOeXlMQQpr75I4B+LB6hCEmwenWZUDq89yPbMKwE7WqD04fK+BevEUmbsYnL7HxhCVe0485mPRa6qcU000GyeeMWvwZWmnw+4n2f2E+31sRwE6QzJhgWllJkiktE1U1azQxjY3kiqVhfXyeMFKlknXQyQyrVxbIcIIgFJKLrEvixr6YI6rH6a3XoH4ed1UqJRhJUF7BI5+Dljz/F8W9co9NymJjM8uTBCa6sbpBO5ZmamkUQan+3qKs7omFTJ7Vh7OE5/7d0B/QuSCQSzM/PMz8/j5SSarVKqVTizJkzN1C/xsbGuHTpEtVqlRdeeOEG+tKDClfcDfcyQ/mwCeVrr73Ghz70oeHjn/u5nwPgJ37iJ/jMZz7D6uoqV65cGf58+/bt/Mmf/An/5J/8E371V3+V+fl5fuu3fmskif9XFEIIZmdnefLJJ4cU10cVH2u1GqurqxyYLZOniTJzqLBNs2mRSHTxZQbb7NGKkgTdNthzqKAJVhpDlojsJDJo6yKY30A4WZ0EOoMOW7y1s5TXLlBrSZ59eYJEZoqgVeHM0TZX3jhHL0iSy5ZYem4H2Uw97tDVY8ppY0uS6ULQ2mKnRJxYhjgJh/d+ZJpGcwYj6pAaH9MFLTNHxqlj5gp0G2fwclM49KjXq5TXL+N4mdgzN43juFrZdUBxtTPQr+jiXr+sY2G/rItu/WYc/2UcTx3we2CnMWSL9//YHk795Sk2VhS5vGTPS0s0aheIIliaX8CUHVAZXVQ0M7rYZ3o61mKB6AMGYu6jOkm/DSzLYmpqiqmpKZRSNBoNNjc3WV5eHrI7BgW4RqPB2bNnee655ygWi8Pr4H5m0wf/vhc8joLbKD6OMMIII7y78VhFeRYWFoaiE6Bvkg9Tge90OtRqNWzb5uDBg9hhGa79BQiTRFIh3DT9ZgelkkjfJ5RZUlkvTp58vZBAEGFrKpdhI9SAqqp9xrSwgyIIQtZWrmHaLnOz4ximBNlm+XSFoNUiVUiCVGxu+KQuVll6chJQsddjeF1e3y6A7GtD8LCjZxiDlhbQiRNdW7Wwx2OxCGxNx8WgXC5RbzRZnJvDS7h6oTOWxQ8VzWaDarPDWmkTx02SzXgk0zkSlolwUrra7o7pxM8paOqrndMLQSt5XUhItuMvLIIgpDDh8oGPT4Odxm+XubK2SiFlUJwcdCaTmr41EPqxkvr3Zmd1Mmkl7/t7NQyDsbExxsbGhtSvzc1NNjY2OH369HDx3e12cV33noV9lFIP1L28UwU+iiK63e5DL5g++MEP3vC3cTM+85nP3PI9hw8ffqjjjvDuwfT09A0CYw8bH6MoYm1tjU6nw8HdimSvhDIyCBmg7Cy5sRQrlyQpU9IPHMAnkZ9AoFBOHqFCpJXVMdEuoGQfkRi7rp6qYloqAdLKs3rtCr3AYnFxGjuRAumzds2kttElWZggZ0TUyibGqRL7P7BdC4Y5WgANK6v3O6DFWkk9e466PhdpxV1DN0vWDsDI6+6lo7eNvkVp9RLjU9vIpfUIQkEGhHKWVqtFp9WgVGnhmAovUyTt9UmmxzSd1cnrOBzTWbUHZQCGqe8HUQBWKqa+xvYkdhaXBs/94BMge0RSsbq6holkZnEHhuyCG1uYDJJnM6E7sMSz9lEfsfBjCG/mnr9XIQS5XI5cLjf0eyyXy5RKJS5duoSUkvHxcYIgIAxDLOvG2/zdZtPvNz7ereD2KCivo/g4wggjjPDuxjtqG3InD8m7oVKpcPjwYRzHYXZ2Flv04cpn9SJABdimw9Mvpjn5Wojs11BGir1PZyhMZXSiZiZA+UjTo9nqYNrjeAlLLySGc456XqjTabGyuko2nWFyYlw/r3TFOux1cBMK2/RRhiDh+kT9tq60C0Mvigaqg4NG3WCBZCT0osLOAgoG1Cjb0u8zNTVLCVhd22DFv8xTC8+TMBWYDiKeh3SNJs7YBOO5BoGYoddYo9Ht0yyfIDJSZFMWifQYac9GmG68UHLjY2V1B9GwGcroDxQOY/VX3ALd5iZXVxtM5B3yYzNxN9MC2UFX71U8F5RGhA3U1IcgufCwl8iQ+uV5HrVajVQqxdLSErVa7Qbq16A6fzP1616EK7a+9lbdyzstmNptnYCPVAxHeFjcPIM2ULBWSt239UO32+Xw4cNEUcRsYhWvtYI00wi/prdhg13PTyP9FuVShGv1Gd8xweJOB4mBCFtIw8NQPdqdkHZjPU6+unFSFG9lhyA0WLl2DsPy2D43heEkYiVVi7DfBcA2Q5SERELRbcfej3C9A6l/A3GsjOmoqj/0ZWQQt6xBopeIY5RWqF2+Umf10iZjYwXS6axWWBU6GbQsk3whQX5sGhX5tDo+nVadq+stjPUqyVSGdEqSSqUxMXXiaBh6pnxAUbXiEQVzTB/fKaLtSfIAhFJwZeUaCSfFzOwiEOpzVZFWDSfSgj4K/dmEDSKEsZcQ+f0PcLVch+u6zM7ODhPLffv20e12OX/+PMeOHRuyO8bHx0kmk3ecTb+X7uX9xEd4Z0TLRhhhhBFGeLx4xxPKB6F0LS8v8+abb7J3716azab2iVz+Y/3DLfLuualxXvxBj27XwLF93Ew+rixnwK8QmRmmcga1tmJj+SSRkSabMvEyU6Q8A2EmadRKrG5UmZqYIF8cjy1A4nkbYeLlcqxfqiFsExlBFJokMmmdpAkzlrG3QPZiafq2XiSFHb0Y8rsxBWqgeNi7Pm9pJ4mCLldXylwTV3g1eQQryvBc4oUtYjv9uPvZR9lFLNkhXZwjHXZQYoJeu0qr06eyscxGGJFMpkklHZKZIhYB2ClE0I4psM149rGuZ0plF4RLq1FhZaXE5PQ0+VwW0LOnevYy0p9TBuAk9Xlkn4CxRyd+EEURb7zxBv1+nxdffBHbtpmbm0MpRbPZpFQqcfXqVU6ePHkD9Subzd7X4ul2whVRFL2lyj/AKKEc4e3CYJF+p+vvVqhWqxw+fJjJyUnG7A38C68iJvYh4q6aCGpg5/DcOk9/aC+9qp7B9twewklCEM9A+xWyuTEIr7K+UcVYu4qbHieTgGR2EsOv0gst1q+dJ5EZZ6qQAsuMZ7gTEDVJpk1sq6d1vkxF5PuMz2V0IUpYOg5aCQjaOv75zWF8ZkjHz2lKqpPV26Eqqx5bOHN8jQvf3UQpxaZqsn52lZc+th9L9ON91XUy168gnCwZxyczOc5UsUPPl3Q6DRqVKhtrEUnHJJEdJ+2CkxyHoHqdEmt6IFsME18VguER9BpcWauR8WByehGiemwX1dNFxEGH007GiXhK7ye1DTH5/rt8m3fHQDl9eXmZF198cWhhtGfPniG7Y3Nzk3PnzuG67lA1tlAo3JLdMbj2BrHwbt3Lu4mWjWyVRhhhhBG+9/FYE8qtPmtw/5QuKSVvvvkmq6urvOc976FYLHLq5Em8ylcgFydm0o+V+nSF2kraZJKgu29hTLHyiawchF0S2RlmUm3UzDS9xjqNnqCydo5VlcAzfTqhw/xEilShqEV2BnOPlp45XHxigqjfpbopMc2IsaUJprYVdEWdQIvwEA1tR3RV34/VBbt6G7bihV79+tylnSPqlFheb+AaPc565ygEEa93vsvT9iKmldPvszy9KBOxoA6m/qxWCiEMvOwkXtZEqQjfD2m36lRaPdY3L2Mn0mSTHbx0EU/E9NtwsBhsAQbN5jqljXVmp+dJe4CSugM5mAe1shDUrn8uKwUzP3xPc5P3giiKOHLkCFEU8Z73vAfbtoc/E0KQzWbJZrPs3LkT3/eHi6crV65gGMYwuSwWize8F+5O/RosqIIgIJFIIKV8ywKs3W7juu5b9j3CCA+LwXV5K9ri7XD16lVOnTrF3r17mcv5NE+/Shtd7BjQ7nHGNaXfGUPIAG98Nu76pVDS14WqqIuyCjgEjM/vZTzq0/MlrWaFzbpPsHEOy00R+m3yxVkmitnYzigEKxNvU0wsOrQaIeWVFkpCZizHwr7xWJxsIHoziIe9IX1Vq7C2dSIYdfU2jO07Iq1CraKAldUy516rapIEDpYZUFpTXH7jGjtf2BnPeg6o/WmduJqJWCDNJuEZJFyT4vgsYa9O2zfoNta5VDbwjDUSmXHSiQ5eMqt/Z0qzQ4j0ufitTZbXGxTTJoWJhZi5YccjE/K62JCd1Ym2nYGghkiMw/yP3nfn+WYopTh79iyrq6u88MILbylsJZNJFhcXWVxcJIoiKpUKpVKJEydOEIbhDeyOgbjZADfPUt5uNt33fYQQt4yPMPKhHGGEEUb464B3vEN5rwml7/scOXIE3/c5ePAgyaSezUt3X8ftnobirriaHfuFWclYPdCJZeFdkBFKSJSSKBTCdDUL1UwjhMLLzePlIqLJGVaXL9HpmziW4upGh3TzPF5mjGyihuUVEYEW2UlYDfa8tES/rsUcPLcLlqE9HreI8GjxiYxeFJmJ61L1MoqVXkOUrWlUyi7S77VYXmmSSbpU8n069TqRYeCGXY73L/Cs8URMywpiFcKBOqIXz+ykrwsChVWwc7hmD3dsjGK+R6im6bRqNNt9KlcvgzBIZ7KkUx7JpINhOFSqDcrlBrMLe0l5tl4oRbHibdhGmamY2hvboKgI5j7+QHOTt0IYhhw+fBghBAcOHLjronpAf56dnUVKSa1WY3Nzc0j9yufzw+r8vVK/KpUK1WqVqampYXV+6+sGdK6HXRiOMMLtrsd7iZFSSk6fPs3KygoHDhygkOihlv80/mEfqUCoAIVAyD4KA6F8vZU+SsRb9FyfwkBsZR8Ik4RrkHCmGR+XbGyUqNXq2HaCaqVMu9nQ8SOdIpGwY6aIgWEJdj6/wOzOFlLZeEmB4TjXlVMHcRCdgGql1Zgqaqbi0QBPU0UtDxBgJoik4NrKNfo9QRSYhJGDbSu6voMpQqp1LaSGFato21n9XsMETC1WNvAJNlNAiJWaIueF5PLjTMmAVrdPu9ngWqmFki0dGzM50kkD0ynSaTdZXmsxOVakMDYRs1fiEQKl9P5FeH3edEB9TUygFv82hune4pu8dyilOH36NBsbG7zwwgt3pZWapsnExAQTExMopWi1WmxubrK6usqbb75JKpUa2jblcrl7io+dTofV1VUmJiZu2b0UQrwjtiEjjDDCCCM8XvyVSCibzSaHDx8mk8nckFio6nESvbP0jKy+mTvF69XsoK0XCsMuXhdpOKiwDcLCUDL2UWPL3GBEGAlW11YAk53b5jEthyDwabQ6tFsNNjb6uG6ddDpHJtXF9YoYKsQbm44r52PXBW/CNspMXxe+Ceo64Rts/VqsNNjSdh6qBZh0ul1WV9YoFMcp5HP8Wf0LRAhCAcpQfKt3hP2p57CMeN7TiruTW2YeiXqx1Uk7TmhjdcGgpVVj6ZNNu2QzKVSUputDt7nJxnoDEZ6LDbwbzMzuJOVGoGw9F6XMLfOflv69DQR5ii9Acv6RXBtBEHD48GEsy+LZZ5+944zOrWAYBsVikWKxyJ49e+h2u2xublIqlTh//vyQ+jU+Pk6hUHjL/gfqmMePH2fPnj1MT0/fkvpVq9Ueynh+hBHuhHuJkUEQcOTIEfr9PgcPHsQz2sjz/xkFmKqLkiFG1EEZDobsowwXQ3bibVc/H3WRMTVTYepk0rB0XMVAi+MIkDqZbHe7LM1N4XpJZOjT7vp0mlWuVUtYIsDLjJNJGHjZcQgaeJlYCdaKaasDJVU7GxcC03prJXWcMZyYcTGI0VFsKdInUDbrV89juXnml1JcSNRJyCb9wCOdaNLzXcaKgZ6VV+3BbzKegfQ0C8NOX+8Y+pU4djZjGr9EKMh4NhnLg8lx+p0qzZ6gsbnMmi/w7Iier5gYL1DIaB9hol5cNGzG957GjfYnQvsRi7kfQbjFh7oulFKcOnWKSqXCiy++eIPa9b1ACEEmkyGTybB9+3Z83x/akhw5cgSAsbGxYYy8Fbuj3+/zxhtvDK2ftjI7BteslHI0QznCCCOM8NcAj53yesPBY8rrnUQnNjY2eOONN1haWmLXrl3D16n2Mqz+qR5RCTpgTGua0aC6bef0gsQtQtQnMrP6sZXRi6UBBRYDCABBv99ndW0dL+EyOTmJMCKQXWxTMJZ1GMtNIKOAdqdPu1nharmHYRqkUhnS6QzJZAqhQlot2Li8QRgq8pOSibmsZoDauVitMItOBNNxchZXqpVBs9WhtL7MxNQi2ZTNiioR+VUwTXJRRMs0ccMWl4PL7DImtliOxNL0hqsT24GYhOHpz2hn0JX52HtNRbpaLwOEmSZp9UhmdlMMWqysV/HbZUwnxcbKeWpuYjh76SbzWgxoaDuSRfhlVGbXLf0mHwS+73Po0CFc1+WZZ56572TyVvA8j4WFBRYWFobUr83NTU6dOoXv+0NPtwH1q16vc/jwYXbt2sX8vE6Sb6Z+hWHIr/3ar1Gv1x9IOGWEEe6Gu82Zt1otDh06RDqd5r3vfS+mbCEv/j5q8Pdvp5BqE2Ultcq0pVkRykyCilCmh5IBynDj7qCLQMWJ1aDLBsSJwsrKOpFULCwsYMVejYadJmP2yGSLTIVd2r6i09xkZTNCrJdwMxNkvS7J7CRm1CGQadZPXqXbs0inW0ztnMIIe3EyGcSCPApU7BEp+8M5837ksHr1LF52kqmCVux+8cNdvv2FENvs0vdtphfcWGWbmKLqQdiO6abNLfTT9PVCm+zFc+9AFOl5xyCm3AZ13NQYrtOAwk5qlTXK5RpewqNeLtFodsl4Ai87TVLW4uM0YnZMH019tSHsIGY+jMjseKhrQkrJyZMnqdfrvPDCC2+hqj4IHMdhZmaGmZkZlFLU63U2Nze5dOkSJ06cIJfLDZPLdDqN7/u8/vrr5HI59u/fjxDihtGBwWz6b//2bwNa6XWEEUYYYYTvXbzjHUq4teiEUoqLFy9y/vx5nn76aaanp6//zK/Bld8HBCZ9JCKmeaZiEYUs+Jtg56FfJjIzulpsZRGqp5Ms2YuppyGYaVrNBtdWqowXJyiOjSOEYqh6iohpXwLDgEw2QSbtoTDpdhq0Oz6l9WUiaWArg2tv+oS9PhKHymVJ1J1jZimhzy+MzcCjSNNUla/NwlVIudGlWq4wPb+PTCoBwmZKJvnA+P+CinpIw8KUfZRw2WZMXrccMV09uyOs2ARcxVS1MPZ0615XX7WzOhG0BjTctJ5VEg4q7LG6XsEPAha27cNyPKKwT6vTpd1qUFppYZsNvHSebLKJl85hyADlTsDsxx/J3GS/3+fQoUMkk0mefvrpBzbbvhPuRv1KJBL0er2hL+bNGJzTpz71Kc6fP8+pU6felvMc4a8XblWQuNOcealU4ujRoywuLrJ7926IOsgrf4ySIcKwUMrEMCFSJkKYKMNAIFCGqbeY15OogdCMETMQRBz7DM3c8MOIa8tXsV2PxfkJrRatQrBcIAQjnpt0sqTskFQ6z4QK6PYj2s0qpVqXcP0ctuuxfqpDsxJhGvq4tZrBEy/OaMaIiAteRGifxii2Cwlp+Q6rK1coFhcZK2aHRbPiwhwf+vEJ6qUOlmMzNu3p7iqxDYkMdWFRRvE2fItKK4YzZKnc8J6hRYp+bblSoVrtMbOwm1QygcKg027QaPWprawgMUknOyTTadJpF9M09OdREooHEOMPV3STUnL8+HFarRYvvPDCW1StHwWEEOTz+WHnsdfrDWfTL1y4gG3bRFFEOp1m3759t6TGKqX4z//5P/NLv/RLfPGLX2T//odTsh1hhBFGGOHdDaHuZO70iBGG4Q2LIyklX/jCF/jQhz50w40xiiKOHz9OtVrlwIEDZLPZ4c9UFNuD9GsA1OoNGrUyi9t2aG/JuBKM5UHQJDKTscpfHmOYUDVuEL9pbF5hvdpjdjxFqjAbzyKmNYXJ9HSV2fBiGft4/meo9GqBClDCpNxe47tHzmKdT2AmLFxH0uva5HKS93z0mfj4ubeI7+DXWK/06DXXmVrYg2f6KCujq+pmUiedg6q9YcSLvYHEfmxLIgcdx5iqpgLA1omz4erPFP9OMGOhCDP+LIBUgrWVZSJlMzddwHDziLB5/TwtXXXv9BStToNWs0ukFKlUErH4dynOP/PQi5terzesej/55JPvSJI2sKNJp9N0u9ry4Gbql5SST33qU3z+85/nq1/9Kjt2PFzHYYQRQMfDIAhueO5b3/oW27dvv7GgphSXLl3i3LlzPPXUU7qrFPWRF34H1S0hTFd7RhoOQa/NxSvX2L1zG8J0UJGvfx71wHRRYQ8lHITSQjdahTQW+rJSELbpBAYb186Tyk8zkXdjH8ZGTIuv3+DLqDt/8XZQrDI9VNDl8uk6G8tlaht93BRYjoGMLCwkz3xoN6k08TFbW/al913vSCprlxmLmRuaZh+h/RwtCAOwvVi4J31jJzL+HEPhMETsARlcp9jaqZiSm4ln72MfYBF7EauIUq1Nq7bBzOJuEkaojxPGrBAZgIKeH9FpVWl0FVG/juUVyCUFXnEH7t5PYpgPLt4lpeSNN96g1+tx4MCBd4Rq3+v1+O53vztMIvv9/tCWZGJiAs/zUErx+7//+/yjf/SP+K//9b/ywz/8w4/9PEcYYYQRRni8eEcpr4PB/TAMh8lIr9fj0KFDGIbBwYMHb0hSlFKw/EfQvByrA/YxRISMwut+jwKwkigU0kqDVFrNkGiocqicmAZr5SmvXabW8lmYKZBITcRJXnYouqNpnbnrIjt+RT+OtwQ1sNKIqMM3g0PUoyZjxh7y2SQ9XwE9NquCSxcvkc7lSSe7JLyiFshwxpBhl5VSh6jfZmbbEzgiRBkpvaAzBnYj6KRSRnrWMowpYEHnuviOndUqtFbuOgVWtmMxokEiLMGIPdYGFFgREUq4du0KwsgwPzuFYemZIOXkIexq8Z2oC4ZNMmWSTBhMTs4RdKuUrGdZLitOXPw6mUxmuLDIZDL3RQHtdru8/vrrFItFnnjiiXeEPtpoNDh69Ci7du1iaWnpBurX5cuXOXHiBP/+3//74XznN77xjVEyOcLbipspr1EUceLECcrlMi+99BK5XA4lQ+Tl30f1Sjp5k10w9FZYHkIGKNPTVh1mChV1NOU1aKMMDyE7sRVH6/oMYEwPrXcklfXzFKa2k08KneCFjetJpZ2NHw9mJAesiaQuYhkORD7H/3KVtUsNlAoJfIFtKIxEGoMWzabNypXz5GZmyCTaeNnpLYqodSr1HvXqGlNzSyQTcRIllO76DYpjTnz8YZKbiRkYg/Ow4hEHpWNi2LmRAus3YgZJM46VA0VXE8Iu6+Uu/XaJuaV9OCK2eAqacQc3jOnFSRK0SUwvUfTrhEzTaZRo9uCNlTmsyreHtPpbWXbcCVEUcfToUYIgeIva9eOC7/scPnyYXC7HU089NRTcKZVKbGxscObMGf7kT/6E1dVVvvCFL/B7v/d7o2RyhBFGGOGvCR5rhzKKorfMA33pS1/ixRdfJJvNUqvVOHz4MBMTE7fsUKnVv4DN72ypQKdp1zYoVVtsm5/S9KvIR2KgYssHMUgyhakrzYauOEcSVldX8MOIhfl5bMe9Ls4z7Pb5wwWR3sbekWHruuhCfC6b9Pnc5u8iNwvMnp4lzRwJx6fTsZjf4TK5d4xus0KrE2AKhZfOkPRSVGpVhLCYm53BtN3riTGSoQohMX0LEz3vaW9JEjtgJGMhiMG5ebEsvqU/k4pneKQPVlLPQFoZRNgkwGNt+QyGm2dmsoCwM/Hvy9YLLyFiFUZxXb3VdPW5WCkovgdgaNlRKpUol8tYljVMLovF4l2Nrw8dOsTExAR79+59R5LJZrPJ66+/zrZt29i2bdstX9PpdPiZn/kZvva1r9FsNhkbGxuKUowwwsPiVh3K119/nfHxcZaWluj1ehw+fBiAAwcO4LquVqy++t+RrctbGArXtyrwefPMefbt2YUwHSDUVNco0MquyJj9MGBbhHGsDNko12hUS8zOLZH0nOviW2xhQIjBHLrcsjWJfTxACOqbbb7xR2cxLYUwTPx2nyCA/IRLJC2KEza73jtDr9ug2eqCDPDSWTKpBO12n06nxfzCPK6buM7SiKIhHfdGZkYUMzbkdQaHUtcpvAPvXCwgvB7vMUDE4w0Kff7CQsmAa6sbhH6XuYUlbMsg5ggT31C4IVYP9jeI4cKE+R9FudNDy45SqYSU8gbmw526jWEYcuTIEZRSPP/88/flSfqoEAQBr7/+Op7n3XYUIQgC/s2/+Tf87u/+LtVqFaUU/+2//Te+//u//7Gf7wgjjDDCCI8X7+gMJVxXMbx27RonT55kz549LC4uviWpUNWj0Dil/clUCO5Y7KmWI5JxlVn6SMNDxYmgUEFMWdKiO6gIpCAIQlZXVjAtm22zExhGpBPUmL6KsHRnExtUF60Q2I/ne6ROpEBX6VFgF/hO7b/TMmzEZIPlvsWulXFslWJ2h8fSM5PYDmRTHpMKet0W9WaXjbVllGGT8SSdTpuU29ZU06C2RfQmF1uP5LTojp2PxYYKsWZGUS9axIxOKIXQCaUwNBVLWJraKszhY2VYYNi0uiFHjh4nP/+DPLn/mYdK5G627KhWq5RKJd5880183x8uniYmJm7oOrdaLV5//XVmZmbYvXv3O5pMLi0t3TaZVErxq7/6q3zlK1/hK1/5Crt37+bb3/72KJkc4ZHhTjOU9XqdQ4cOMT4+zv79+4cLerX+VVTzPGLYYUzpDuGw+5bCFD7SsLFkJ1adbmhxsrgTKcLmsMCkrDTKb7Ky0SDsVlncthvHCGJa6cDDcUCXH8TGcPAB4oTOiv2AtRhO6Cscq48UKTynh5QJLLNBJJPMLhhsf36RZDIkl51karxPL5C0m3VK6ytICV7CottpY8oeVrKACFrx+Tb0NmihzETcMdxSRDOdeGY9jQiaMeOkjrLTW7xz2zGzxYRwS8HNziL7FVY26oiww8LcIiZdCCSYLiLqxeewhclierF/L2DaEPYQCz+KSM4B3DC33Wg0hn65J0+eJJfLDbuXW22IBmwI0zR5/vnnH4lA2f1ikEwmEok7zrV/6Utf4pd/+Zf57d/+bf7O3/k7HDp0iF27dj3msx1hhBFGGOGdwGPtUN6qAv+1r32NTCZDpVLh2WefZXx8/C3vU61LcOn/F9OM4vmamJ7VaVe4ulJjz855pOEiwz7CdBBqIHsfV94JAUG30+HaygqZTJrJiYlY0ZD4dbGvYyzvPhRpuO1WJ6DlqMkfVj5LzzQQStExLMbMHD8x8Y+wLCP2OVO60yeg1ZWcO3eOfHGK8clpKrUW1VqDZscnmytSHJ9ibGIGL5mJxSKsWGTi0aHRaHDo0CHm5uZuUM991FBK0W63h5X5RqMxpMamUilOnTrF4uIiO3bseEeSyVarxWuvvTY8h1tBKcUv//Iv8yu/8it86Utf4rnnnnu8JznCXwsopfB9/4bnjh8/PrR02L17N0tLS/f1d6KU4s///M/5vu/7PhzHYRDuBx6BN6Pf73PkyBEMw+DZZ599JHN67Uaf3/h//QX1UgfXs/H7AY5n83/8fz7IzI7Cbc/BNE327NlDrVajVCpRrVaHXomTk5P3Tau/HwzonQPborezKzgQvSmVSlQqFVzXZWJigkKhwIULFx6p2vX9IgiCGxS3b5dMfvnLX+bv/b2/x2/+5m/y9//+3x8pXo8wwggj/DXDO9qhDIKAfr9PFEW8973vvaVXlepX4Op/iymmWwUXUhDUMcwEtqoTsQS9TYSTxxjaWVTi6nEF5RRo1Upc3WgzNTFFYXIpVilMAzJO+oi7e2gRHmHohM643t3DsK//J/T2q/X/zu/YX7/hvA0MfmTxw0zb0zc8XyqVOHbxGDv3fJylpSUA0guwiJ4jLJVKrJdKvHnp1HDxNDExQTabfWQ36UqlwtGjR9m+ffttO3KPCkII0uk06XR66He2ubnJysoKFy5cwDRN+v0+m5ubd6XGPmoMksmFhYU7JpP/9t/+Wz796U/z53/+56NkcoTHhkEnq9Vq8fzzzzMxMXHf+xjYOQRBgGVZw7n1W2Hg91ssFh+pKFYq6/K/fuq9/NdPf4dmpUs6n+Dj/88Dt0wm2+32cE5v0InNZrMsLi4SBMEw8XrttdewLGsYH4vF4iM734E4WCaT4amnnnrbxcESicRQUXpgabS+vs4bb7wBaMuj9fX1u1JjHzUGyaTjOHdMJr/2ta/x4z/+4/zar/3aKJkcYYQRRvhrinesQznwTwuCgN27d7O4uPiW1yulYO2LsVdYAjDiuT5z2Lnr9EMOHznG3MI2imNTpDJ5PStk2HEyaKOwuHjpEpcuXeLpp59+oIXZnaCUYj1cZ+uvMmEkKFg3LpiuXr3K6dOneeqpp5iamrrjPoMgoFwus7GxQblcvsHq4mEWTxsbGxw/fpy9e/cyNzf3QPt4WAxmZbdt20Y2mx12L+9EjX3UGFBt5+fn2blz5y1fo5Ti3/27f8cv/dIv8ad/+qccPHjwbTufEUYA3Z0DPTd39OhRarUaxWKR559//oH2p5TiK1/5CmNjY8zOzlIoFG654C+VShw7dmxYZHo7kgIZSTpNHy/tYFpvjV+1Wo0jR47cE2tiQKvf2NigVCoRhuEwbgzUmB8Eg3nusbGxd0wcbCBMl0qlWFpaolwuUyqVaLVaQz/IiYmJG6ixjxphGHLo0CEsy+K555677f3mm9/8Jp/4xCf41//6X/NTP/VTo2RyhBFGGOGvKR5rQjmgdA380xYWFmg2m0xOTt4yobzbvgbmyWtra8PEK5FIMDk5ycTEhFZAVIqTJ09SrVZ57rnnyGQyb9Onu/O5nj9/nuXlZZ577jkKhbdW5u+ErTOJpVKJIAgeaPG0srLCqVOnePrpp5mcnHyQj/LQqFQqHDlyhN27d7OwsDB8/k7U2AdRjb0T2u02r732GnNzc+zcufOW+1VK8R/+w3/gn/2zf8bnP/95vu/7vu+RHHuEEe4E3/eHxTbP88jlcnQ6HZ599tn73peUchg7VldXKZVKAEPK6KAwdeXKFc6fP8/+/fvvWuh6uzAodN0cF+4FSimazeZQbbTdblMoFIYFOM/z7mk/9Xqdw4cPD4tM70RyNFC7LhQKPPnkkzecw+2osQ+iGnsnbE0mn3322duyRr773e/yoz/6o/zLf/kv+dmf/dlRMjnCCCOM8NcYj71DeebMGc6dO8f+/fuZnZ3lyJEj5HI5tm/ffs/7GSSSN88DRVE0vOGWSqXhDdayLA4cOEAikeD0d1d58zsrCARPvjLH7gPTdzrUQ0NKOUxon3/+edLp9EPtb+viaVC1zufzwyT6douny5cvc+HCBZ599lmKxeJDncODYnNzkzfeeIN9+/YxOzt7x9c+jGrsnTBIJmdnZ2/bBVFK8Z/+03/iF37hF/jc5z7Hhz70oQc61ggj3C9WVlaGXbq9e/dy5coVyuUyBw4cuOd9bC22wfX4KKUcziNubGwQBAGO4+D7Ps899xzFYpHVCzVe/4uL9DsBi0+Oc+AHt92ym/gosby8zNmzZ3nqqaceSaFrMDpwP3OXgzGAHTt2DEcRHjc6nc5Q1Xffvn13TNAG1NgHUY29E8Iw5PDhwxiGwXPPPXfbOHv48GE+9rGP8c//+T/n53/+50fJ5AgjjDDCX3M81oSy0WjwrW99i+eff55cLgdo0QnXddm9e/c97UMphZSSKIruOA/UaDSG6nhRFCGlpLNqc/RPN5GxLlAiZfPxnz7A3hdnHsnnuxkD2prv+zz//PMkEolHfozbLZ4Gc5cA586d49q1axw4cGD43M0IVMBr7dd4OfUyhnj0C8iNjQ2OHTvG/v37bzBpvxfc3KF9UGpsp9PhtddeY3p6+raKskopPvvZz/KzP/uz/MEf/AEf+chH7utcRxjhQRFFEV/5ylfYsWPHkI5+9epVVlZWeOmll+5pH1vj4yCRvNV1PhCd6Xa72LZNt9vFDFK89gcluo0Q0zIRAl75W3v44N994pF+zq3nOohNzz333NuimLx17vJ2owPr6+ucOHHijoWu5dNlrrxZJpNPsP998488yW6327z++utMTU2xZ8+e+xZeGqjGPgw1NoqioQf0nZLJY8eO8Tf/5t/k53/+5/nFX/zFUTI5wggjjDDC46e89nq9G6g5p06dQgjBvn377un9g+TwTslkuVzmjTfeYGFhYTgf12g0+N3/9zdZPVfHy5vYtk2nGrHnhRl+/J++8mg+4Bb0+30OHz6Mbdtvu0rgALeau7QsC9/3ec973nNHuu/vlX+P3yj9Bv9i5l/wQ7kfeqTntb6+zvHjxx8J1fZBqbH3kkwC/OEf/iE/9VM/xX/5L/+Fj33sYw91riOMcL/o9Xo3XJtra2v8/9u787ioqv9/4K9hG/Z9EwXFDVGRHcQlNUlcGTS10gLMrCwrszK11MpcKj9lLmVWimblBohLuYumIi6sAoLsss2w7+vM+f3hb+6XUXZnGNT38/HgUd65c+fcYThz3/ec835nZGR0aP1u8/6Rx+O1OgWypqYGMTExXE1BNTU11NTU4PS+aNw+dR+aBjyoq6ujqRbQ1Objgx2ToaYh32RZEokECQkJKC8vh7Ozc4sJ2eSt+Y0pkUiEpqYm6OjooLKyEkOHDm01mLx27B6O/C/yQXlKBvR3NMfi/02U23siXc/d1qyJzujK1FixWMzVOG2rPEliYiKmTJmCd999F2vXrqVgkhBCCIBuDiiB/0s6IZWSkoLGxkYMGzaszedJ1wO1F0xKE9/Y29s/coEQtPoyCjLLoGeqiYaGBpTmV0O/txrGB/Rtd8poZ1RVVSE6OppbB6PoLIEtkU5dqqqqgqqqKpqammBiYgJzc/NH1l3WSmoxO3U2KiQVsFK3wl/9/4IqTz4XS3l5ebh7965CkiEBHZsaW1tbi1u3bsHc3LzNu//Hjx/H66+/jv3792PmzJlybysh7WlsbIREIuH+XVhYiOTkZIwZM6bN53X0Zps08Y2lpSUGDx4s0zdd+DsB18LuwchSG42NjagorYFELMbEd61h1dsS5ubm0NXVfewgorGxEbGxsRCLxXByclJo8q3WMMaQnJyMnJwcaGpqoq6ursV1lzWV9VgjCIa4SfIg+zce/GfWB+4YM8vusdshLd9kY2MDW1tbuQdoHZkaKxaLERMTA4lEAhcXl1aDyeTkZEyZMgULFizAhg0bKJgkhBDC6fayITweTyYbqvSCvzUPrwdq7WKJMYaUlBTk5+fDxcWlxcQ3QzytkJ9RhqqSeoAB2jpaGDN1CMzNNVFYWIiUlBTo6urC3Nwc5ubmXcqiV1paipiYGG50VBlfutKpthKJBKNHj4a6ujqqqqogEomQlZWFhIQEGBoacuuKwmrCUCmpBADkNebhfMV5uYxS5uTkICUlBY6OjjAxMXns47VEQ0MDVlZWsLKykhmBuHv3LhoaGmBoaIiKiop2g8lTp07h9ddfx549eyiYJD2G9GZQWzoaTObn5yMxMRGDBw9uMfFN/xHmiDqXiXJRLdQ0VKEiUYfjc7YYOKg3CgsLcfPmTairq3P9o6GhYaf7t7q6OkRHR0NTU7PNkTBFYozh3r17EAqF8PDwgL6+vszSgZSUFG7pgKSaD7H4QTApPVcVFR6K86oeux3l5eWIiopSaPmm5lN8m0+Nzc7ORmJiIvT19dHQ0AA1NTW4urq2+vtIS0vD9OnTMX/+fKxfv56CSUIIITK6fYSyoaFBJqDMzMxESUlJi0knWku+87CmpibcuXMH1dXVcHZ2hra2douvLW6S4PrJVNz57z54PB4cJ/SF++T+UFF5cMzGxkZuOpQ0Y6w06DIwMGj3S1Q6tdPOzg59+vTp8HsiTx0pyF1bW4uioiKIRCIIy4T4sdePqFGpAQDwwEMv9V6PPUopzRzZlay28sAYQ3FxMeLj46GiooLGxsZWp8aeP38er7zyCn755RfMmzePLpaI0jw8QlleXo5bt25h4sSJj+zbWvKdlvZLT09HdnY2HBwcYGpq2urrJ0Xm4frxe6itbkT/EWaY8NJQ8LUfzGaQjnZJS3UAshlj2wsOpXUupUlnlDFzQyKRICkpifvOaWmqbfN1l8K8Qpz+rgDiRsaNUALAy8tHwnPawC63Q3rjccCAAZ3OcC4v0qnP9fX1kEgkrU6NzczMxJQpUzBjxgxs3bpVKb83QgghPZvSA8qcnBzk5+fD3d1dZr+Orgeqq6tDTEwM1NXVMWLEiC7XH3uYWCzm1iNKM8ZK78y3tA4lKysLaWlpCpva2RHN65c5ODh06Iv/XOk5fCn88pHt6w3WY4zFmC5dPGRmZiIjIwMuLi5c8qXuVldXh1u3bsHExARDhgx5JDmHmpoarl+/DgDYvHkztm3bhsDAQAomiVI1NTVxASLwIFnL1atXMWmS7IwBafIdafDZWjApFotl1io+bpbp5q9fVlYGkUjEZYxtq5SRNItq3759FTK1syPEYjF341Ga9bs9EokEEf8kIeT7GEiaHnxv9XM2wOzlrjA3N+vS901xcTFiY2MxePBgpd14lEgkiImJQVNTE1xcXMDj8R6ZGpuRkYHi4mIEBQVh0qRJ+PnnnymYJIQQ0qJuDygfvgOfn5+PzMxMmaQTHZ3CJc3kamZmptA73g8ncxCLxTA1NeXuzKenpyM/P18me21362pB7npJPSKqIiDGg5Hg6qpq1FbWwrLQEpJGSavrLlsiHQm5f/9+mxllFU0aTBobG7f4Xkh/n59++ilOnDiBuro6TJ48GT/88AOXxIkQZXg4oKyrq0N4eDgmTZrE9W8Pz9xord+rr69HbGwsAMDJyanLpSTawxjjptRL60AaGxtz69JLSkqQmJjY4rr27iJdBiAWi+Hs7NzpQLBUWI2ceyVQUReDb9KEoqKiDpdsaq6wsBDx8fGwt7dHr16KyS7eHolEwmUfd3FxeeS9kJam2rVrF3bt2oW8vDx4eXnhiy++wAsvvKCUNhNCCOnZun0N5cOkZT2kOhpMSlO9S+uGKfKOt4qKCkxMTGBiYgI7OztUVFRAJBIhNTUVNTU1UFFRQf/+/eWS0KcrpIkdevfu3eksgXwVPsbrj/+/Df8/HmZ2rM11lw+fq7QEQF5eHtzc3OQ2EtJZdXV1XGHw1gJrFRUVpKWl4eTJk1i/fj3Gjx+PEydOKK0+JyGtkU4jlZZJ6mj/KE0MZmhoiKFDhyp0rSKPx4Oenh709PQwYMAA1NTUQCQSIS8vD0lJSQAAKysrhZQF6QjpMgB1dfU21wm2xchCB0YW/zc9duDAga2uu5SWbHr4dyMtnTR8+HBYWFg89nl1RXvBJPDg91lbW4u//voL48ePx4YNG3Dq1Cml/f4IIYT0fEofoSwpKUF8fDyee+65Dq8HysrKQnp6OlcImzGG0oJq1NU0wshcB1p6irkT//B5SKcMmZmZobi4GJWVlZ2+Y/24pFPJFJnYAXgQqElHaB+ud6mnp8cluXB1de2WEgAtqa+vx61bt7iL6NYutqOiojBjxgysXr0aH374IU1zJT2GWCyWScIjkUhw5swZjB8/Hnw+v0PBZFFREeLj42FjY4P+/fuDx+OhsqQWFcW10DXShIFpy2vM5UmaRbWgoAB9+vRBRUUFSkpKuH7D3Ny81RI/8lRbW4uoqCjo6elh+PDhCpvF0l69S6FQiKSkJO47SxkkEgni4uJQV1cHV1fXVkdpi4qKMHXqVAwdOhR//fVXt5S8IoQQ8mRT+jeFdISyI+uBpAkViouL4ebmBn19fUgkDFdCkhF3+T6aGsTQN9WCT4ADeg9S3GhTbW0toqOjoa2tzaVZHzhwoEzQJY+Mse0RiURcEiBpIXRF0dTUhLW1Naytrbl6l4WFhYiKiuLWudrZ2SltlLa+vh63b9+GgYFBm8FkXFwcfH198emnn1IwSXo8FRUVLqmUmppau8Hk/fv3kZKSgqFDh3JTKpMi83DpUBJqqxvA11LHmJmDMeI5xSWCEYvFiI+PR01NDTw9Pbk+oampiUsGduvWLS5jrJmZGYyMjOT+t1hVVYWoqChuSYQi/9bV1dXRq1cv9OrVS2aJRFJSEpc3wMbGRikJyoAH353x8fHtBpMlJSXw9fXFoEGDsH//fgomCSGEdEi3j1A+vEaosrISERERGDVqFPh8fqt3kKW1y5qamuDk5MQlVLgXVYCTu2LA11IDX0cdpQXVMO2th/mfj4KauvyneUnXbZqbm7d5kSLNGFtYWIiioiLw+XwuuOxIxtj25ObmcvUdlXXHmzGGO3fuoKSkBKampigpKUFjYyNMTEy4u/PySpLUloaGBty6dYsbhWjtvZUW5V6yZAnWrFlDwSTpcR4eoWSM4cKFCxg6dChMTU3bnLkhHRF0cnLipieWFdbg743XUF/bBAMTLVSW1kFVTQVzPxkJsz56cm9/Q0MDYmJiwOPx4OTk1Orff/P6iCKRCEDnMsa2p7y8HNHR0ejTp4/SyjcBD7Jd37t3DxYWFqiqqurSusvHJZFIuGRErq6ura6lLSsrw4wZM2BpaYmQkBCl1AclhBDyZFLq7UfGGDQ0NGBgYICrV6/CwMAAFhYWMDc3l8nAV11djZiYGOjo6MDJyUnmrmmZqAbiJgl0jR7sr2eiicrSWlSX18t9apc0O590emlbFynq6upcfcTmGWOlF1vNL546Ow1LmkXV2dlZaev+pBcpVVVVGDlyJPh8vkxyDmmds7bWXcpDQ0MDbt++DV1dXQwbNqzV30lycjKmT5+ORYsWUTBJeqzmn0vpEgBLS0vEx8dDS0sL5ubmsLCwgK6uLrdvU1MT4uPjUVtbCw8PD5mySWWiatRWNsC4ly5U1VRgaK4NUXYFykTVcg8oa2pqEB0dDV1dXQwfPrzNoLD5lFB7e3suY2xycjIaGho6lQzsYdJ+esCAAejbt+/jnlaXSZdmuLq6cgF+Z9ddPq6OBpMVFRWYNWsWTExMEBwcTMEkIYSQTlFaDnDpxRKPx4OrqyvGjh0LS0tLFBYW4sqVK4iMjERmZiby8/Nx8+ZNmJmZtVhXUceAD54KD/U1jWCMoaa8Hpra6tDSle86yry8PMTExMDe3r7Tae9VVVVhbm6O4cOH47nnnuNKeiQmJuLSpUuIj4+HUCjsUAHze/fuITMzE66urkoNJuPi4lBdXQ03Nzfu4kOanGPAgAEYOXIkxowZAwsLCxQXF+Pq1auIiIhAamoqysvLIY+BcWkwqaOj0+b6qNTUVEyfPh2vvvoqvv76a7letF2+fBkzZsyAlZUVeDwejh492u5zwsPD4eLiAj6fj4EDByIoKOiRfXbs2IF+/fpBU1MTnp6euHHjhtzaTHq+5sl37OzsMG7cOPTv3x81NTW4efMmrl69ipSUFIhEIty4cQMSiQTu7u6P1ODVNdSEhpYaqsvrAQDV5fXQ0FSDjoF8A4by8nLcvHkTpqamGDFiRKdGGHk8HoyMjGBnZ4fRo0fD3d0durq6yMzMxKVLlxAVFYX79++jvr6+3WMJhULExsZiyJAhSg0mMzIyuNJJzZPZaGlpwcbGBq6urhg3bhxsbW1RU1ODqKgo/Pfff0hKSkJRUZFMnoGuYowhISEBVVVVbQaT1dXVmDNnDrS0tHD06NEOlVPpDOojCSHk6aeUEUrpesnm64H4fD63Rq+hoQGFhYXIzs5GVVUV+Hw+1NTUUF1d/Uj20MFulkiPF+He7QKICyXQMeBj7Owh0NCUz6kxxpCRkYGsrCw4OTnBxMTksY6noqICY2NjGBsbcxljCwsLkZaWhjt37sik229+AcAY49aPuru7Ky3xjVgsRmxsLBobG+Hm5tbm6EFb6y5VVVVlSq90dpS2sbERUVFR0NbWbjOYzMzMxPTp0zFr1ix8++23ck/KUV1dDUdHR7z++uuYNWtWu/tnZGRg2rRpePvtt/Hnn3/i/PnzeOONN9CrVy/4+PgAAA4ePIhly5Zh586d8PT0xJYtW+Dj44Pk5GSlTW8m3ad5WRBp/6impgZLS0tYWlpyMx5ycnKQlZUFVVVVGBkZcdMpm98wMe2tB/fJ/RF5Mg3CrHKo89XgMrEvevU3lFt7paUw5DEi2FLG2MLCQhQUFCA5ORn6+vpc//hwH5iTk4OUlBSl1gJmjCEtLQ05OTlwdXWFnl7ro8Dq6urc7/ThdZft1fXsSDsSEhJQWVnZZjBZU1ODuXPnAgCOHz/+yA0JeaA+khBCnn7dvoZy3759GD58OAYNGtRqcglpCYqcnBwMHToUEokEQqEQxcXF3LSv5lkCxU0SZCUVob6mCaa99eQ2lUsikeDu3bsoKiqCs7NzmxcH8lBdXQ2RSITCwkJUVFTAwMCAm/aVmpraqYLciiAWixETEwOJRNLm+qj2NL94Kiws7PS6y8bGRty+fRtaWlrcaG9L7t+/Dx8fH0yePBk//fSTwoty83g8hIaGws/Pr9V9Pv30U5w8eRJ37tzhtr388ssoKyvDqVOnAACenp5wd3fH9u3bATx4v6ytrfHee+9hxYoVCj0HolynT5+Gqqoqd7OmI2WTdHR0uLWI0un0FhYWMDIy4kqN5KWVobywBnrGmugz2Fhuo/Q5OTlITk7ullIY9fX1XJ9RXFwMbW1t7ruguLgYmZmZcHJyUlriG+kMkvz8fLi6una5dFLzpQOFhYXcjYKOLh2QBpPl5eUyM0geVldXh5deeglVVVU4depUt9RQpj6SEEKeTt06QskYQ2hoKBYtWoRBgwZBIBBg5syZMvUCxWIx7ty5g8rKSnh4eHB3oXv16oWmpiYUFxdDKBTi1q1b0NDQ4C4obIebyXUqo1gsRlxcHLcuqTuCOB0dHdja2sLW1pbLGCsUCpGSkgIVFRVupI/P53f7GsCmpiZER0eDx+PB2dn5sbL/PVzXs7V1l2ZmZo/cMZcGk5qamm0Gk/n5+Zg2bRqef/557NixQ+HBZEdFRETA29tbZpuPjw+WLl0K4P+m8a5cuZJ7XEVFBd7e3oiIiOjOphIliIiIwI8//ghtbW3MmDEDfn5+GDVqFPf3xhjj1lA3H4mTZjKVrkW8c+cOGGNcENKrvwl6D5RfoMUYQ3p6OrKzs+Hi4tItQRyfz0efPn3Qp08fmYyxN27cAGMMlpaWYIxxM1+6kzQpUmFhYYtTjzvj4VHa2tpa7lzv3bvX5rpLxhgSExNRXl4OV1fXVoPJ+vp6vPrqqygtLcXZs2e7JZjsKOojCSHkydOtASWPx0NISAjKy8tx7NgxhISE4Pvvv4eNjQ0EAgG8vLxw6NAhLFq0CB4eHo9M01FTU4OFhQUsLCy4LIFCoRDR0dHcOkVzc/PHTkFfX1+PmJgYqKqqwt3dvVsylT5MU1MTFhYWyMvLg6GhIXr16oXi4mLcuHFD7hlj2yOdXqqurg5HR0e5Fkl/+OJJGkgXFhY+cvGkpaWF6Oho8Pl8jBgxotWLRqFQiGnTpsHLywu//vqrQou6d1ZBQcEjIzkWFhaoqKhAbW0tSktLIRaLW9zn7t273dlUogRffPEFVq1ahXPnziE4OBivvvoqVFVVMX36dEybNg2hoaGYNm0aJkyY8MiMiYen05eXl0MoFOLu3btobGzkgktTU9PH+puQlm8qKSnh1jt2NzU1NW5kUkNDAwMGDEB5eTni4+O5QNrMzAwmJiYK//uXBnGlpaVwc3OTe/IxLS2tNpcOSM/VyMgIycnJXDtauwna2NiIwMBA5OXl4fz580ob0W0N9ZGEEPLk6fY1lDweD4aGhvD394e/vz8qKipw8uRJ7N69G1u2bMHgwYMRFhYGVVVVuLq6tho0NP8ilU6hFAqFiIuLAwAu4Ors+rzq6mpER0dDX19foYWw21NXV4eoqCjo6OhwI3F9+vTh1k8VFhbKJWNsexoaGhAVFQVNTc02gzh5aWvdpVgsBp/Ph62tbatJfQoLCzFjxgw4Ojpiz549PSqYJKQjNDQ0MHXqVEydOhU7d+7EpUuX8Mcff2D+/Pncxb+GhgYmTJjQ6giUtJ81NDTE4MGDUVlZCaFQiNTUVNy5c4dbv2xmZtap2QZNTU2Ii4tDfX093N3dlTr9XlrrUjqDpHfv3mCMoaysjMui+rgZY9sjkUiQkJCAioqKNoM4eWlr3WV9fT14PB4GDRrUar/X1NSEhQsXIi0tDRcvXnzsnACEEEIIoOSyIQCgr68PgUCAjz76CKtWrcLw4cMREhICX19fGBoawtfXFwKBAJ6enq1+STafQmlvb4/S0lKIRCIkJiZCLBZzAVd7d6vLysoQExOD3r17Y+DAgUorLVFdXY2oqCjufJq3o/lIrEQi4aa4JSUloampSebi6XGLUtfX13MlOZQRXEsvnkxNTXH79m0ADz4vycnJLa67LCkpwYwZM3p0UW5LS0sIhUKZbUKhEPr6+tDS0oKqqipUVVVb3MfS0rI7m0p6AHV1dXh7e+OHH37A1KlTsWjRIpw8eRLvv/8+qqqqMHXqVPj5+WHixImtjozxeDzo6+tDX18fAwcORHV1NYRCITIzM5GQkMD1GQ8nAntYfX09oqOjoa6uDnd3d6X9fTU1NXFruR+eQSLNGGtkZIRBgwZx0+ml52pkZMSd6+MGfxKJhAtq21qrqCjS7z1jY2NIJBIUFRVxs1pSUlIeWXcpFovx9ttvIyEhARcvXlRa4qL2UB9JCCFPnm5PytOagoICmS+D2tpanD17FsHBwTh+/Dg0NTUxY8YMzJw5U2ZNUVsYYygvL4dIJIJIJEJDQwNMTU1hYWEBExMTmWNI1x0NHDgQNjY2CjnHjpAW5O5sUMsYQ2VlJXeuNTU1XMBlbm7e5oViS+rq6nD79m0YGBi0Wd9R0aRrN1VUVODk5ARVVVUuaYU0EUlWVhZ+/PFHiMVimJqa4vTp050+X3noaMKJf/75B/Hx8dy2efPmoaSkRCbhhIeHB7Zt2wbgwYWrjY0NlixZQgknnlEikQimpqbcTR2xWIzr168jODgYoaGhKCoqwuTJkyEQCODj49PhLNDSRGAikQiVlZVcwGVubi4TIFVVVSE6OhpGRkYYOnSo0mZuSGdMaGhodHr6fW1tLXeu5eXl0NfX5/rHzmbNlq6xr6+vh4uLi1L6G0B27Wbz6bbSpQMikQglJSXYuHEj1NXVkZmZiWvXrsHa2lop7aU+khBCnk49JqBsS0NDAy5cuIAjR44gLCwMPB4P06dPx8yZMzF27NgOfZk/HHDV1tbCxMQEFhYWqK+vR3p6OoYPH67UlOMlJSWIjY2Fra0t+vXr91jHqq6u5i4ommeM7UiWwNraWty6davFEdLuJBaLERUVJRNMtiQ1NRXvvvsuUlNTUVxcjDFjxuDChQvd0saqqiqkpqYCAJydnfH9999jwoQJMDY2ho2NDVauXInc3Fzs27cPwIOU+MOHD8e7776L119/HRcuXMD777+PkydPyqTEDwgIwC+//AIPDw9s2bIFhw4dwt27dxWeSZM8eSQSCW7fvo0jR44gNDQUubm5eOGFFyAQCDBlyhTo6+t36DgPB1zSPoPP5yMpKQnW1tYYMGCA0vqD2tpaREVFQU9P77FnTEhLU4lEokcyxkqzh7dGmu1aLBbD2dlZKWvsgQffadJapG2t3ayrq8OiRYtw/fp1VFZWQk9PDzExMd02Qkl9JCGEPP2eiICyuaamJly6dAmHDx9GWFgY6uvrMX36dAgEAjz//PMdnnZUVVUFoVCI+/fvo7GxEQYGBujdu3e7074URTpCOmTIEFhZWcn12M3vVpeWlkJHR4e7eNLV1ZW5eKqursbt27dhbm4OOzs7pQaT0dHRAB5chLQWTFZVVWHWrFnQ0NDAyZMn0djYiOTkZLi7u3dLO8PDwzFhwoRHtgcEBCAoKAiBgYHIzMxEeHi4zHM+/PBDJCYmok+fPli9ejUCAwNlnr99+3Z89913KCgogJOTE7Zu3QpPT08Fnw150kkkEsTFxeHIkSMICQlBeno6Jk6cCIFAgGnTpj1Sp7I19fX1EIlEyMnJQVVVFTQ1NdGnTx9YWFgopFZhe6qqqhAVFcVls5Vnv9Q8Y2xRURHU1dW5kUtDQ0OZwFU63ZYx9tjZrh+HNJgUCoVwc3Nr9XcikUjw6aef4vjx4wgPD0efPn1w/fp1jB07ttv6duojCSHk6ffEBZTNicViXLlyhZv2VVlZiSlTpkAgEMDb27vNCx9pMoXy8nLY29tzSSsqKythaGgICwsLuayz6Yjc3FyulpuiR0gbGxtlLp74fD538aSmpoaoqChYWVkpdQ2pdARAetHWWjBZU1OD2bNnQyKR4J9//lFKtklCeipp9lHpyGViYiLGjx8PPz8/TJ8+HSYmJm3+jWdlZSEtLQ329vaQSCTcaJ70hpSFhQV0dHQU3k9IlwFYW1ujf//+Cn09iUSCkpISrgYkY4xLYKSvr4+4uDioqqq2OWNC0aT1LgsKCtoNJj///HMcPnwY4eHhGDRoUDe3lBBCyLPiiQ4om5NIJDJrigoLC+Hj48OtKWoebDQ2NiIuLg6NjY1wdnaWGdWsq6uDSCSCUCjk1tlYWFh0aKpoV0hryjk6OsLY2Fjux29L84yxIpEITU1NXPkOExMTpayTkgaTEomkzREAaVHu6upqnDp1qsPT+gh5FkmDEGlwGRMTgzFjxkAgEMDX1xcWFhZcoCYd/crPz4ezs7NMjcKHb0hJyxt1ZKpoVxQXFyM2NlYpa9ubr8EXCoWoq6uDhoYGBg4cCHNzc6VMdWWMITU1FXl5eXBzc2t17SdjDF999RX27t2Lixcvwt7evptbSggh5Fny1ASUzUkkEkRFRXHTvnJycrg1RcOGDUNwcDB8fX0xYsSINqcs1dfXo7CwEEKhEKWlpdDV1eUunjqbxOFh0guD3NxcuLi4KDUgKi8vx+3bt2FpaQkVFRUUFhaisbGRuzMvj4yxHSEWixEbG8utTWrtNevr6zF//nwUFhbi7NmzMDQ0VHjbCHlaMMaQmZmJ4OBghISE4MaNG/Dy8oKvry8mTZqE3bt3Y/LkyXB3d29zlodYLEZRURGEQiE3VVTaP8qjPq5QKERCQgLs7e3Rq1evxzrW45Bmu+bz+TAwMEBRURGqqqrkmjG2IxhjSEtLQ25ubrvB5KZNm7Bz505cuHABDg4OCm8bIYSQZ9tTGVA2J03tfuTIEfz999/Izc2Fg4MDXn/9dUyfPh1GRkYduvBpbGzkgsvm075aWofYkTZJC4O7uLg8dnD6OEpLSxETE4MBAwZwIwBtZYw1MzNTSHp8iUSCmJgYNDU1wcXFpdVgsqGhAf7+/rh//z7OnTunkDpqO3bs4NbmODo6Ytu2bfDw8Ghx3/Hjx+PSpUuPbJ86dSpOnjwJAAgMDMTevXtlHvfx8eEyFhKiLIwx5OTkICQkBAcPHkRMTAzMzc2xYMECzJkzB3379u1Q3yYWi1FSUgKhUIjCwkKZ8kYPr0PsiJycHKSkpMDBwUGp5S2k9YD19PQwbNgw7jzkmTG2o9LS0pCTkwNXV9dWp/czxvDDDz/ghx9+wPnz5+Hk5CT3dlD/SAgh5GFPfUAplZeXh2HDhmH+/PkwMzPD0aNHkZCQgHHjxnFrikxNTTt08dTU1MRNE5VO+5JePOnr67ebIfDOnTuorq6Gi4uL0gqDAw+yysbExGDw4MHo06dPq/u1ljHWzMxMLgk6JBIJYmNj0dDQABcXl1ankjU2NmLhwoVITk7GhQsXFHKhefDgQfj7+2Pnzp3w9PTEli1bcPjwYSQnJ7e4vrWkpAQNDQ3cv4uLi+Ho6IjffvuNSyIRGBgIoVCIPXv2cPvx+XyuSD0hyiaRSODh4QETExNMmTIFx48fx+XLlzFixAgIBAIIBIIOr6uWSCQoLS3lgkvGGNc/GhsbtxlcSkdPMzMz4eTkpNS/kdraWty+fZsrldLauTfPGFtSUgItLS2uf2zv+6Cj0tLScP/+fbi5ubUZTG7fvh2bNm3CmTNnFJKYjPpHQgghLXlmAkoAuHXrFtzc3AD835RT6Zqi6OhojB49mltTZGlp2eE789I1RYWFhVBXV+cSVjw87aupqYmb0unk5KS02mUAUFRUhLi4uE5nlZVmfywsLERJScljjdQCHQ8mm5qa8NZbbyE2NhYXLlxQWAFrT09PuLu7Y/v27Vz7rK2t8d5773WovtmWLVuwZs0a5OfncyMVgYGBKCsrw9GjRxXSZkLkITo6GiNGjOBqvRYVFSEsLAxHjhzBxYsXYWdnxwWXHS0nxBhDaWkpN5onFou5kTwTExOZxDbSdZ75+flwcXGBnp6eIk+3TTU1Nbh9+zZMTU07lVW2qakJxcXF3M3Gxx2pBYD09HRkZ2e3G0zu2rULX375Jf799194eXl1+nU6gvpHQgghLXmmAsrWMMaQlZXFrSmKjIzEyJEj4evrC4FAgD59+nT4zrz0YqKwsBA8Hk8mG2JMTAzU1NTg6OiotHTzwP+VKBk6dOhjBWbNE3QUFxdDQ0NDJt1+e++ZtMRBXV0dXF1dWw0mxWIxlixZgoiICISHh8u9rIpUQ0MDtLW1ceTIEZnC2wEBASgrK0NYWFi7x3BwcICXlxd27drFbQsMDMTRo0ehoaEBIyMjPP/88/j6668VMl2XEHmTBoXHjh1DSEgIzp49i379+kEgEMDPz6/DNSEZY6ioqIBQKIRIJEJDQwO3TtvY2Bj37t1DSUkJXF1dlVKaREpaOsnCwgKDBw/u8ghjWxljHw6mW5ORkYGsrCy4urq2GmAzxhAUFISVK1fixIkTeO6557rU3vZQ/0gIIaQ1FFA+hDGG3NxchISEIDg4GNeuXYOLiwt3Z75fv36dmvYlzRDY2NgITU1N2NnZwdTUVCkZVAGgoKAACQkJcHBwkGuJEukaqubBtHTNZUsZY6VrW2tra9sMJiUSCZYuXYqLFy/i4sWLCs30mJeXh969e+PatWsyd/iXL1+OS5cuITIyss3n37hxA56enoiMjJRZU3TgwAFoa2vD1tYWaWlpWLVqFXR1dREREaG00gOEdFV5eTlOnDiBkJAQnDp1Cr169YKvry9mzpwJZ2fnDgeX0lrAQqEQNTU1UFVVxYABA2BlZaWUDKoAUFlZiaioKPTu3RsDBgyQW9ba5hljRSIR6uvrYWpqyvWRLZ2vdOpve8Hk/v378fHHH+PYsWMt1nuUF+ofCSGEtIYCyjYwxiAUChEaGorg4GBcvnwZw4cP54LLQYMGtXvBIb3braenBy0tLRQWFqKpqanVaV+KlJeXh7t372LEiBEwNTVV2OtIJBKUlZVx64oezhiroqLCrSN1dXVtdeqvRCLB8uXLcfLkSYSHh8PW1lZhbQYe/4LprbfeQkREBOLi4trcLz09HQMGDMC5c+cwceJEubSdEGWoqqrCv//+i+DgYPzzzz8wNjbGjBkzMHPmTLi7u7fbtzU1NSEmJobrI4qLi1FVVQVjY2Nuqmh3LQ2oqKhAVFQUbGxs0L9/f4W9DmMM1dXVXHDZUsbYrKwspKenw9XVtdUM4IwxHD58GEuWLEFwcDB8fHwU1maA+kdCCCGtU968yycAj8eDpaUlFi9ejLfffhvFxcUICwtDcHAwNmzYgMGDB3PTvlpaUyQtyN2nTx/ubrednR0qKiogEomQkpIiM+1LkeU5pFkTnZycFF7vUkVFBcbGxjA2NsbgwYO5jLEZGRm4c+cO1NXVwePx4OLi0mYw+dlnn+HYsWO4ePGiwoNJADA1NYWqqiqEQqHMdqFQ2O7U4Orqahw4cABfffVVu6/Tv39/mJqaIjU1lS6YyBNNV1cXc+bMwZw5c1BTU4MzZ84gODgYL774IrS1teHr6ws/Pz94eXk90rc1NDQgKioKGhoa8PDwgKqqKgYNGoSamhqIRCLuBpihoSEsLCwUWp6jrKwM0dHR6N+/P/r27auQ15Di8XjQ1dWFrq4u+vfvj9raWi6DeHJyMvh8PhoaGjB8+PA2y0kdPXoUS5YswYEDBxQeTALUPxJCCGkdjVB2AWMMZWVlOH78OIKDg3HmzBn07duXCy4dHBxw8+ZNVFVVYeDAga1eoEinfUmnxdbW1sLY2Ji7eJLXtK/s7GykpaXB2dlZqTUbGWOIiYlBRUUFNDU1UVlZCQMDA260VrpuijGGL7/8En/88QcuXryIIUOGdFsbPT094eHhgW3btgF4ENja2NhgyZIlbSadCAoKwttvv43c3Nx21/7k5OTAxsYGR48eha+vr1zbT0hPUFdXh/PnzyMkJARhYWFQVVXlRi7HjBmDrKwsZGRkwNLSUqYcR0vHkfaP0vIc0lqXWlpacmlraWkpoqOjMWjQIFhbW8vlmF2Vnp6OjIwM6Ovro6KiAlpaWlz/2Dxj7IkTJ7BgwQLs378fM2fO7Lb2Uf9ICCGkJRRQykFFRQVOnjyJ4OBgnDp1CqampigoKMD//vc/BAQEdHi9pHQalFAolNu0r4yMDGRmZsLFxQUGBgZdOoY8MMaQkJCAiooKuLq6gs/no76+XibdPp/Px7Fjx7ipXBcvXsTw4cO7tZ0HDx5EQEAAfvnlF3h4eGDLli04dOgQ7t69CwsLC/j7+6N3797YuHGjzPPGjh2L3r1748CBAzLbq6qq8OWXX+LFF1+EpaUl0tLSsHz5clRWViI+Pl4hNT0J6UkaGxsRHh6O4OBgHD16FHV1dVBRUcHs2bOxadOmDo86SvsLoVCI0tJS6OrqcsFlV2s/FhcXIzY2FnZ2dujdu3eXjiEv9+/fR2pqKtdXt5QxNjw8HJqamvj2228RFBSEuXPndmsbqX8khBDSEgoo5eznn3/Ghx9+iDFjxuDGjRswMjLipn1Jp3V1RG1tLZcNsaKiAoaGhlxw2ZELMMYY0tPTcf/+/TaTOnSHloLJhzU2NiI5ORlLlixBVFQULCwsMHfuXHz33XfdnhF3+/btXOFuJycnbN26FZ6engAeFOru168fgoKCuP2Tk5MxZMgQnDlzBi+88ILMsWpra+Hn54fo6GiUlZXBysoKkyZNwrp162BhYdGdp0WI0kVERGDy5MkYOnQosrOzUV1djWnTpkEgEGDixIkdHnVsbGzkgsvi4uIulS8qLCxEXFwchg4dil69ej3uqT0W6ZIEFxeXFmeRSJO8vfvuuzh37hxUVFQwa9YsfP311wpNVNYS6h8JIYQ8jAJKOdu/fz+sra0xbtw41NbWcmuKTpw4AS0tLcyYMQN+fn4YNWpUhwMl6bQvkUiEsrIy6Ovrc+VIWroAk9bYzMvLg6ura6u1y7oDYwyJiYkoKyuDm5tbq3ecGWPYtm0bvv32Wxw7dgxlZWW4ceNGh9bcEEKeDDExMbh27RreeecdiMViREREIDg4GKGhoSgpKYGPjw/8/PwwadKkDo86NjU1oaioCEKhEEVFRdDU1OSCy+bTRJsTCoW4c+cOhg8frvTARRpMOjs7w8jIqNX9Ll++jDlz5mDLli2wt7fH0aNHsWLFCoWviSeEEELaQwFlN2loaMC5c+cQHByMY8eOQUVFBdOnT8fMmTMxduzYDq+XbGho4ILLkpIS6OrqytS6ZIwhOTkZhYWFcHFx6fJUMHlgjCEpKQklJSVwc3NrdWSVMYZffvkFX331lUKLchNCeiaJRIJbt27hyJEjCA0NRV5eHl544QX4+flh8uTJbSanaU4sFqO4uJgLLtXU1LjgUlobNz8/H0lJSXBwcICZmZmCz6xtubm5SE5ObjeYvHbtGmbNmoVvv/0Wb731ltzKmRBCCCHyQAGlEjQ2NuLSpUs4cuQIjh49isbGRkyfPh0CgQATJkzo8LoR6bQvkUiE4uJiaGlpQUVFBQ0NDXBzc1NqcfDOBJN79uzBqlWrcPLkSYwdO7abW0oI6UkkEgliY2O54DI9PR3e3t4QCASYNm0aDAwMOlwLWLoGUVobV0dHB+Xl5XB0dFRo6aSOkGaxbS/z9s2bNyEQCLBu3TosWbKEgklCCCE9DgWUSiYWi3HlyhUuuKysrMTUqVMhEAjg7e3d4TVFDQ0NiI2NRWVlJRhj4PP5XMKK1qZ9KQpjDHfv3kVxcXG7wWR3FeUmhDx5pFPmjxw5gpCQECQlJWHChAnw8/PDtGnTYGJi0uHgMiUlBTk5OVBVVQWPx5OpBdzRxGny0tFgMjo6GtOnT8fnn3+OZcuWUTBJCCGkR+reb1HyCFVVVYwbNw7btm1DVlYW/vnnH1haWmLFihXo168f/P39ERISgurq6laPIZFIcPfuXTQ2NmL06NEYP348Bg8ejPr6ekRFReG///7D3bt3UVpaCkXfP5BOuS0qKoKrq2ubweShQ4fw0UcfITg4WGHB5I4dO9CvXz9oamrC09MTN27caHXfoKAg8Hg8mZ+H288Yw5o1a9CrVy9oaWnB29sb9+7dU0jbCXnW8Xg8DBs2DGvXrkVMTAzu3LmDcePG4ffff8eAAQMwY8YM/PrrrxAKhW32bdnZ2cjPz4ebmxvGjx8PR0dHqKmp4e7du7h06RLi4+MhEokgFosVfk75+fm4e/cuHB0d2wwm4+Pj4evri+XLlyssmKT+kRBCiDzQCGUPJZFIcPv2bW7aV25uLry9veHn54cpU6Zwa4okEgni4uJQV1cHFxeXR8qLSCQSlJSUcOsupXfmLSwsYGRkJNc784wxpKSkQCQSwc3Nrc3R1ZCQELz99ts4ePAgpk2bJrc2NHfw4EH4+/tj586d8PT0xJYtW3D48GEkJyfD3Nz8kf2DgoLwwQcfIDk5mdvG4/FkknZ888032LhxI/bu3QtbW1usXr0a8fHxSExMVFjRdUKILMYYMjIyEBwcjJCQENy8eROjRo2Cr68vBAIBrKysuAAsPT0d2dnZcHFxeWQtJmMMFRUVXP9YV1cHU1NTWFhYwNTUVO4ZpgsKCpCQkAAnJ6c26zEmJiZiypQpWLJkCdasWaOQYJL6R0IIIXLDumD79u2sb9++jM/nMw8PDxYZGdnm/ocOHWJ2dnaMz+ez4cOHs5MnT8o8LpFI2OrVq5mlpSXT1NRkEydOZCkpKV1p2lNJLBaz6Oho9tlnn7GhQ4cyPp/Ppk6dyrZt28a8vb1ZUFAQKysrY9XV1W3+VFZWsvv377Pbt2+zf/75h508eZLduHGDZWZmssrKynaf39ZPVVUVi4qKYqdOnWKFhYVt7nvgwAGmra3NQkNDFfq+eXh4sHfffVfmfbSysmIbN25scf89e/YwAwODVo8nkUiYpaUl++6777htZWVljM/ns7///ltu7SZPNuofu5dEImFZWVnshx9+YGPHjmVqamrM09OTrV+/nvn7+7PPPvuMFRQUdKgPKygoYHFxcezcuXPs2LFj7MqVK+zevXsd6l/b+0lPT2fHjh1jWVlZbe4XFRXFLCws2MqVK5lEIlHY+0b9IyGEEHnp9PDUwYMHsWzZMqxduxZRUVFwdHSEj48PRCJRi/tfu3YNr7zyChYuXIjo6Gj4+fnBz88Pd+7c4fb59ttvsXXrVuzcuRORkZHQ0dGBj48P6urquh4pP0VUVFTg5OSEr7/+Gnfu3EFUVBScnJywatUq3LlzB3/++Sf+/PNPFBUVtTntS0VFBcbGxhgyZAiee+45ODk5cdO+wsPDER8fD6FQ2OlpX4wx3Lt3D0KhEK6urm0mA/r333+xcOFCBAUFwc/Pr1Ov0xkNDQ24ffs2vL29uW0qKirw9vZGREREq8+rqqpC3759YW1tDYFAgISEBO6xjIwMFBQUyBzTwMAAnp6ebR6TPDuof+x+PB4PNjY2WLp0KS5duoSsrCy8+uqr2LlzJw4fPoyLFy9i165dSE1NbbN/5PF40NPTw4ABA+Dl5YWRI0fC0NAQ2dnZuHTpEqKiopCTk4OGhoZOt1FapmTEiBFtJgNKTU3F9OnT8eqrr+Lrr79W2JpJ6h8JIYTIU6cDyu+//x6LFi3CggULMHToUOzcuRPa2trYvXt3i/v/+OOPmDx5Mj755BPY29tj3bp1cHFxwfbt2wE8CEa2bNmCzz//HAKBACNGjMC+ffuQl5eHo0ePPtbJPY14PB6GDh2KiooKeHh44NSpU5gwYQL27NmDAQMGYPr06di1axcKCgravXgyNDSEnZ0dxowZw01RTU1NRXh4OGJjY5Gfn4+mpqY228P+f83LgoKCdoPJ8+fPIyAgALt27cKcOXO6/B50RFFREcRi8SM15iwsLFBQUNDic+zs7LB7926EhYVh//79kEgkGDVqFHJycgCAe15njkmeLdQ/KhePx4OVlRXMzc3B4/EQHh6OgIAAXL16FW5ubvDy8sKmTZuQlJTU7npyHR0d2NraYuTIkRg9ejRMTEyQl5eHy5cv49atW8jOzu5QUC8Sibhgsq0yJZmZmZg+fTpXHkSRiYKofySEECJPnfrG6spdzYiICJn9AcDHx4fbn+5qds3XX3+N48ePw9HREStXrsSNGzeQkpKCadOm4dChQ7Czs8PkyZOxY8cO5OTktBtc6uvrY+DAgRg1ahQ8PT2hq6uLzMxMhIeHIzo6Grm5uY/cmWeMIS0tDXl5eXB1dW2z5uXly5cxb948bN++Ha+88orc3gd58vLygr+/P5ycnDBu3DiEhITAzMwMv/zyi7KbRp4A1D/2HLNnz8bNmzfh4eGBN998E//++y8KCgqwbNkyREdHY/To0XB3d8e6desQHx8PiUTS5vG0tLTQt29feHh4YMyYMbCwsIBIJMKVK1dw48YNZGZmoqam5pHniUQixMfHt1vzMicnB9OmTcPUqVPx448/dnvW2Y6g/pEQQkhrOvWt1ZW7mgUFBW3uT3c1u8bAwEAm6Q2Px4OtrS0+/vhjXL16Fenp6Zg9ezZOnDiBYcOGYeLEifjxxx+RmZnZbnCpq6vLTfvy8vKCoaEhcnJycPnyZdy+fRv3799HfX090tPTkZubCzc3tzaDyatXr2Lu3Ln43//+h4CAgG5JfW9qagpVVVUIhUKZ7UKhEJaWlh06hrq6OpydnZGamgoA3PMe55jk6UX9Y8+hoqIik1iGx+PB2NgYgYGBOH78OIRCIT777DMkJyfj+eefh7OzM1avXo2oqKh2g0tNTU1YW1vDzc0Nzz33HKysrFBSUoJr167h+vXrSE9PR1VVFQoLCxEfH4/hw4e3mORGKj8/H1OnTsWECROwY8eObgkmqX8khBAiTz3vNih5bDweD9bW1vjggw8QHh6O7Oxs+Pv74/z583B0dMRzzz2HzZs34969ex2e9uXp6YnRo0fD1NQUBQUFuHz5MjIyMmBlZdXmBdCNGzcwe/ZsbNiwAYsWLeq2OmoaGhpwdXXF+fPnuW0SiQTnz5+Hl5dXh44hFosRHx+PXr16AQBsbW1haWkpc8yKigpERkZ2+JiEEOUzMDDA/PnzERISAqFQiPXr1yMnJwdTp06Fg4MDVqxYgcjIyHaDSw0NDfTp0wcuLi4YN24cbGxsUFFRgevXryMmJgampqbQ1tZutZ8VCoWYNm0aRo4ciV9//RWqqqqKON0W2039IyGEEHnpVEDZlbualpaWbe5PdzUVi8fjoVevXnjnnXdw9uxZ5OXlYfHixYiIiICHhwe8vLywceNGJCYmthtcSqd9mZiYQE1NDf369UNFRQWuXr2KyMhIZGRkyEz7ioqKwsyZM7F27Vq8++673V6Ue9myZfj111+xd+9eJCUlYfHixaiursaCBQsAAP7+/li5ciW3/1dffYUzZ84gPT0dUVFRePXVV5GVlYU33ngDwIP3cunSpfj6669x7NgxxMfHw9/fH1ZWVgpNMESeDNQ/Ppl0dXUxd+5cHDx4EAUFBfj+++9RUlKCWbNmYciQIfj4449x5cqVdpOVqaurw8rKCn369AEAWFtbg8fj4ebNm7h69SpSUlJQXl7O9bNFRUWYMWMGHB0dERQU1G3BpBT1j4QQQuSlU0W2mt/VlH5BSO9qLlmypMXneHl54fz581i6dCm37ezZs9wdy+Z3NZ2cnAD8313NxYsXd/6MSKukNSjfeOMNLFy4EGVlZTh27BiCg4OxefNm9OvXDwKBAH5+fhg+fHiLI48ZGRnIzs6Gm5sb9PT0ADxYO1ZYWAiRSIS0tDRcvnwZhYWFOH78OJYvX44PP/yw24NJAHjppZdQWFiINWvWoKCgAE5OTjh16hQ3fTA7O1vmHEtLS7Fo0SIUFBTAyMgIrq6uuHbtGoYOHcrts3z5clRXV+PNN99EWVkZxowZg1OnTlGNNUL941NAW1sbM2fOxMyZM1FXV4dz584hJCQEr7zyCtTV1TFjxgz4+flhzJgxUFdXf+T5xcXFiIuLw7Bhw7iRO7FYjOLiYohEIkRFRSElJQURERG4e/cuBg4ciP3798u93mVHUP9ICCFEbjpbZ+TAgQOMz+ezoKAglpiYyN58801maGjICgoKGGOMvfbaa2zFihXc/levXmVqamps8+bNLCkpia1du5apq6uz+Ph4bp9NmzYxQ0NDFhYWxuLi4phAIGAmJibMxsamQ7Xcdu3axcaMGcMMDQ2ZoaEhmzhx4iP7BwQEMAAyPz4+Pp09/adWeXk5+/PPP9msWbOYtrY2GzBgAPvwww/Z5cuXuRqVkZGR7OTJk23WdCsrK2PffvstGzhwIFNTU2N2dnbs0KFDyj49QroF9Y9Pp4aGBnb69Gn25ptvMnNzc2ZiYsICAgJYaGgoKy0tZdXV1SwhIYEdP36cpaamtlkLOCwsjDk7OzM+n8/Mzc3Z2rVrlX16hBBCyGPpdEDJGGPbtm1jNjY2TENDg3l4eLDr169zj40bN44FBATI7H/o0CE2ePBgpqGhwYYNG9Zq4W4LCwuuuLe6ujrbvXs3S0hIYIsWLWKGhoZMKBS22J558+axHTt2sOjoaJaUlMQCAwOZgYEBy8nJ4fYJCAhgkydPZvn5+dxPSUlJV07/qVdZWckOHTrEXnrpJaanp8f69u3LpkyZwvr168fu37/foaLcq1atYpWVlezw4cMynw9CnnbUPz7dGhsb2cWLF9k777zDrKysmKGhIfP19WX6+vrs0qVLbfaP+fn5zNPTk02aNIlVVlayCxcusODgYGWfEiGEEPJYeIy1s3BOCTw9PeHu7s7VYpNIJLC2tsZ7772HFStWtPt8sVgMIyMjbN++Hf7+/gCAwMBAlJWVUe22TqqtrcWSJUuwf/9+2NjYoKamBr6+vvDz84OXl5fMVK3U1FRMmTIF8+bNwzfffNMjU98T8qSj/rHnEIvF+Omnn/DRRx9h4MCByM3NhY+PD/z8/DBp0iSZurxVVVWYNWsWNDQ0cOLEiTZr9hJCCCFPkh53xd+VWm4Pq6mpQWNjI4yNjWW2h4eHw9zcHHZ2dli8eDGKi4vl2vankYqKClJTU3HlyhXEx8dj165daGhowPz58zFo0CC8//77uHjxIlJTUzF9+nS8+OKLFEwSoiDUP/YsqqqqiI+Px86dO3Hnzh2cOXMGffv2xZo1a9CvXz/Mnz8fhw8fhlAoxNy5c6GiooJjx45RMEkIIeSp0uNGKPPy8tC7d29cu3ZNJtX48uXLcenSJURGRrZ7jHfeeQenT59GQkIClwzgwIED0NbWhq2tLdLS0rBq1Sro6uoiIiKi27PrPWkYY48k1WlsbER4eDiCg4MRGhqKoqIizJkzB3/99RcFk4QoCPWPPU9L/aNEIkFMTAyOHDmC0NBQpKSkYMCAAbh16xb09fWV1FJCCCFEMZ66K/9NmzbhwIEDCA0Nlcks9/LLL8PX1xcODg7w8/PDiRMncPPmTYSHhyuvsU+IljK0qqur44UXXsDOnTuRm5uLrVu3Yu/evQoNJnfs2IF+/fpBU1MTnp6euHHjRqv7/vrrrxg7diyMjIxgZGQEb2/vR/YPDAwEj8eT+Zk8ebLC2k+IslH/KH8t9Y8qKipwcXHBhg0bkJiYiMOHDyMsLEyhwST1j4QQQpSlxwWUXanlJrV582Zs2rQJZ86cwYgRI9rct3///jA1NUVqaupjt/lZp6amhnfffRd8Pl9hr3Hw4EEsW7YMa9euRVRUFBwdHeHj4wORSNTi/uHh4XjllVdw8eJFREREwNraGpMmTUJubq7MfpMnT0Z+fj738/fff3e5jfv27YOJiQnq6+tltvv5+eG1117r8nEJkaL+8cnD4/Ewa9Ys2NvbK+w1qH8khBCiVMrMCNQaDw8PtmTJEu7fYrGY9e7dm23cuLHV53zzzTdMX1+fRUREdOg17t+/z3g8HgsLC3vs9hLF8/DwYO+++y73b7FYzKysrNr8TDTX1NTE9PT02N69e7ltAQEBTCAQyK2NNTU1zMDAQKZMilAoZGpqauzChQtyex3ybKP+kTyM+kdCCCHK1ONGKAFg2bJl+PXXX7F3714kJSVh8eLFqK6uxoIFCwAA/v7+WLlyJbf/N998g9WrV2P37t3o168fCgoKUFBQgKqqKgAPsut98sknuH79OjIzM3H+/HkIBAIMHDgQPj4+SjlH0nFPSiISLS0tzJs3D3v27OG2SbPjjh8/vsvHJaQ56h9Jc9Q/EkIIUTplR7St6Uwtt759+z5SlBsAVzC6pqaGTZo0iZmZmTF1dXXWt29ftmjRIrZhwwbWt2/fDhUH37NnzyPH5/P5MvtI68VZWloyTU1NNnHiRJaSkiLX9+VZlJubywCwa9euyWz/5JNPmIeHR4eOsXjxYta/f39WW1vLbfv777+5YvGhoaHM3t6eubu7s6ampi63NSoqiqmqqnI1/hwcHNhXX33V5eMR0hLqH4kU9Y+EEEKUrccGlIp24MABpqGh0eHi4Hv27GH6+voyhb8LCgpk9tm0aRMzMDBgR48eZbGxsczX15fZ2trKfEmTznvcC6aNGzcyIyMjFhsb2+Z+aWlpDAA7d+7cY7XXxcWFbdiwgd26dYupqKiw7OzsxzoeId2N+scnB/WPhBBClK1HTnntDt9//z0WLVqEBQsWYOjQodi5cye0tbWxe/fuVp/D4/FgaWnJ/VhYWHCPMcawZcsWfP755xAIBBgxYgT27duHvLw8Khb+mJ60RCRvvPEGgoKCsGfPHnh7e8Pa2vqxjkdId6P+8clB/SMhhBBleyYDyq6uOamqqkLfvn1hbW0NgUCAhIQE7rGMjAwUFBTIHNPAwACenp4dXsdCWqahoQFXV1ecP3+e2yaRSHD+/HmZWnwP+/bbb7Fu3TqcOnUKbm5u7b5OTk4OiouL0atXr8dq77x585CTk4Nff/0Vr7/++mMdi5DuRv3jk4X6R0IIIcr2TAaURUVFEIvFMnfQAcDCwgIFBQUtPsfOzg67d+9GWFgY9u/fD4lEglGjRiEnJwcAuOd15pik456kRCQGBgZ48cUXoaurCz8/v8c6FiHdjfrHJw/1j4QQQpRJTdkNeFJ4eXnJ3O0dNWoU7O3t8csvv2DdunVKbNmz4aWXXkJhYSHWrFmDgoICODk54dSpU9wFanZ2NlRU/u/+yM8//4yGhgbMnj1b5jhr167FF198AVVVVcTFxWHv3r0oKyuDlZUVJk2ahHXr1smlnmZubi7mz5+v0NqchPQU1D8qF/WPhBBClOmZDCgfZ82JlLq6Opydnbn1JNLnCYVCmSlBQqEQTk5O8mn4M27JkiVYsmRJi4+Fh4fL/DszM7PNY2lpaeH06dNyatn/KS0tRXh4OMLDw/HTTz/J/fiEKBr1j08m6h8JIYQoyzM55bWra06aE4vFiI+P5y6ObG1tYWlpKXPMiooKREZGdviY5Mnn7OyMwMBAfPPNN7Czs1N2cwjpNOofiaJQ/0gIIU+nZ3KEEniw5iQgIABubm7w8PDAli1bHllz0rt3b2zcuBEA8NVXX2HkyJEYOHAgysrK8N133yErKwtvvPEGgAcZDpcuXYqvv/4agwYNgq2tLVavXg0rKytaJ/IMae/OPyFPAuofiSJQ/0gIIU+nZ3KEEniw5mTz5s1Ys2YNnJycEBMT88iak/z8fG7/0tJSLFq0CPb29pg6dSoqKipw7do1DB06lNtn+fLleO+99/Dmm2/C3d0dVVVVOHXqFH7//Xf069cPmpqa8PT0xI0bN1pt1/jx48Hj8R75mTZtGrdPYGDgI49PnjxZAe8SIeRZ1J39o6amJnbs2EF9JCGEEPKE4jHGmLIb8TQ7ePAg/P39sXPnTnh6emLLli04fPgwkpOTYW5u/sj+JSUlaGho4P5dXFwMR0dH/PbbbwgMDATw4GJJKBRiz5493H58Ph9GRkYKPx9CCJEn6iMJIYSQJ9szO0LZXTpbINzY2FimOPjZs2ehra2NOXPmyOzH5/Nl9ntSLpQ6MxIBAIcPH8aQIUOgqakJBwcH/PPPPzKPM8awZs0a9OrVC1paWvD29sa9e/cUeQqEEDmiPlIW9ZGEEEKeNBRQKlBXC4Q39/vvv+Pll1+Gjo6OzPbw8HCYm5vDzs4OixcvRnFxsVzbrggHDx7EsmXLsHbtWkRFRcHR0RE+Pj4QiUQt7n/t2jW88sorWLhwIaKjo+Hn5wc/Pz/cuXOH2+fbb7/F1q1bsXPnTkRGRkJHRwc+Pj6oq6vrrtMihHQR9ZGyqI8khBDyRGJEYXJzcxkAdu3aNZntn3zyCfPw8Gj3+ZGRkQwAi4yMlNn+999/s7CwMBYXF8dCQ0OZvb09c3d3Z01NTXJtv7x5eHiwd999l/u3WCxmVlZWbOPGjS3uP3fuXDZt2jSZbZ6enuytt95ijDEmkUiYpaUl++6777jHy8rKGJ/PZ3///bcCzoAQIk/UR8qiPpIQQsiTiEYoe7Dff/8dDg4O8PDwkNn+8ssvw9fXFw4ODvDz88OJEydw8+bNR2qN9SRdGYmIiIiQ2R8AfHx8uP0zMjJQUFAgs4+BgQE8PT07PLpBCHlyUR9JfSQhhBDlo4BSgR6nQHh1dTUOHDiAhQsXtvs6/fv3h6mpKVdEvCcqKiqCWCzmskRKWVhYoKCgoMXnFBQUtLm/9L+dOSYhpOegPvL/UB9JCCHkSUUBpQI9ToHww4cPo76+Hq+++mq7r5OTk4Pi4mKuiDghhDwJqI8khBBCnnwUUCrYsmXL8Ouvv2Lv3r1ISkrC4sWLHykQvnLlykee9/vvv8PPzw8mJiYy26uqqvDJJ5/g+vXryMzMxPnz5yEQCDBw4ED4+Ph0yzl1RVdGIiwtLdvcX/rfroxuEEJ6BuojH6A+khBCyJOKAkoF62yBcABITk7GlStXWpzKpaqqiri4OPj6+mLw4MFYuHAhXF1d8d9//yEyMhIzZsyAlZUVeDwejh492m77wsPD4eLiAj6fj4EDByIoKOiRfTqbxr4lXRmJ8PLyktkfAM6ePcvtb2trC0tLS5l9KioqEBkZ2e7oBiGkZ+iuPvLbb7/F7Nmze2T/CFAfSQgh5Amm7KxARH7++ecf9tlnn7GQkBAGgIWGhra5f3p6OtPW1mbLli1jiYmJbNu2bUxVVZWdOnWK2+fAgQNMQ0OD7d69myUkJLBFixYxQ0NDJhQKO92+AwcOMD6fz4KCglhiYiJ78803maGhISsoKGCMMfbaa6+xFStWcPtfvXqVqampsc2bN7OkpCS2du1apq6uzuLj47l9Nm3axAwNDbmMjgKBgNna2rLa2tpOt48Q8vTq6f2j9HjURxJCCHnSUED5lOrIBdPy5cvZsGHDZLa99NJLzMfHh/t3Z9PYt2fbtm3MxsaGaWhoMA8PD3b9+nXusXHjxrGAgACZ/Q8dOsQGDx7MNDQ02LBhw9jJkydlHpdIJGz16tXMwsKC8fl8NnHiRJacnNylthFCng09tX9kjPpIQgghTx4eY4wpd4yUKAKPx0NoaCj8/Pxa3ee5556Di4sLtmzZwm3bs2cPli5divLycjQ0NEBbWxtHjhyROU5AQADKysoQFhamuBMghBAFof6REEIIkZ9nYg1lYWEhLC0tsWHDBm7btWvXoKGh8cj6k2dJaynnKyoqUFtb26U09oSQJwv1jy2j/pEQQgjpGDVlN6A7mJmZYffu3fDz88OkSZNgZ2eH1157DUuWLMHEiROV3TxCCFEa6h8JIYQQ8jieiYASAKZOnYpFixZh/vz5cHNzg46ODjZu3KjsZilVaynn9fX1oaWlBVVV1S4XHSeEPDmof3wU9Y+EEEJIxzwTU16lNm/ejKamJhw+fBh//vkn+Hy+spukVO2lnH+couOEkCcL9Y+yqH8khBBCOuaZCijT0tKQl5cHiUSCzMxMZTdH7qqqqhATE4OYmBgAQEZGBmJiYpCdnQ0AWLlyJfz9/bn93377baSnp2P58uW4e/cufvrpJxw6dAgffvght097RccJIU8H6h+pfySEEEK6RNlpZrtLfX09c3R0ZAEBAWzDhg3M3Ny8y7XCeqqLFy8yAI/8SNPMBwQEsHHjxj3yHCcnJ6ahocH69+/P9uzZ88hx20pjTwh58lH/SP0jIYQQ0lXPTNmQTz75BEeOHEFsbCx0dXUxbtw4GBgY4MSJE8puGiGEKBX1j4QQQgjpqmdiymt4eDi2bNmCP/74A/r6+lBRUcEff/yB//77Dz///LOym0cIIUpD/SMhhBBCHsczM0JJCCGEEEIIIUS+nokRSkIIIYQQQggh8kcBJSGEEEIIIYSQLqGAkhBCCCGEEEJIl1BASQghhBBCCCGkSyigJIQQQgghhBDSJRRQEkIIIYQQQgjpEgooCSGEEEIIIYR0CQWUhBBCCCGEEEK6hAJKQgghhBBCCCFdQgElIYQQQgghhJAuoYCSEEIIIYQQQkiXUEBJCCGEEEIIIaRLKKAkhBBCCCGEENIlFFASQgghhBBCCOkSCigJIYQQQgghhHQJBZSEEEIIIYQQQrqEAkpCCCGEEEIIIV1CASUhhBBCCCGEkC6hgJIQ8tQICgqCoaGhsptBCHkKhIeHg8fjoaysTNlNIYSQHo3HGGPKbgQh5OkSGBiIvXv3AgDU1dVhY2MDf39/rFq1Cmpqagp73draWlRWVsLc3Fxhr9Ge5ueupqYGY2NjjBgxAq+88goCAwOhotLx+3hBQUFYunQpXdA+JunvZOPGjVixYgW3/ejRo5g5cya682uQx+Nx/6+trQ0rKyuMHj0a7733HlxdXTt1rPHjx8PJyQlbtmyRcyvlQxz7Rbe+nqpjx1+v+e+hJWvXrsX48eMxYcIElJaW0o0qQghpA41QEkIUYvLkycjPz8e9e/fw0Ucf4YsvvsB3333X4r4NDQ1yeU0tLS2lBpNS0nPPzMzEv//+iwkTJuCDDz7A9OnT0dTUpOzmPZM0NTXxzTffoLS0VNlNwZ49e5Cfn4+EhATs2LEDVVVV8PT0xL59+5TdtGdGfn4+97Nlyxbo6+vLbPv444+V1jZ59YeEENJdKKAkhCgEn8+HpaUl+vbti8WLF8Pb2xvHjh0D8GDEyM/PD+vXr4eVlRXs7OwAAPfv38fcuXNhaGgIY2NjCAQCZGZmAgDOnDkDTU3NR0brPvjgAzz//PMAWp7y+vPPP2PAgAHQ0NCAnZ0d/vjjD+6xzMxM8Hg8xMTEcNvKysrA4/EQHh4OACgtLcX8+fNhZmYGLS0tDBo0CHv27OnQuffu3RsuLi5YtWoVwsLC8O+//yIoKIjb7/vvv4eDgwN0dHRgbW2Nd955B1VVVQAeTLdbsGABysvLwePxwOPx8MUXXwAA/vjjD7i5uUFPTw+WlpaYN28eRCJRe7+SZ5q3tzcsLS2xcePGNve7cuUKxo4dCy0tLVhbW+P9999HdXU1AGD79u0YPnw4t+/Ro0fB4/Gwc+dOmdf5/PPP23wNQ0NDWFpaol+/fpg0aRKOHDmC+fPnY8mSJVzAW1xcjFdeeQW9e/eGtrY2HBwc8Pfff3PHCAwMxKVLl/Djjz9yn4/MzEyIxWIsXLgQtra20NLSgp2dHX788cdOv19PO0tLS+7HwMAAPB5PZpuuri637+3bt+Hm5gZtbW2MGjUKycnJMscKCwuDi4sLNDU10b9/f3z55ZcyN46ys7MhEAigq6sLfX19zJ07F0KhkHv8iy++gJOTE3777TfY2tpCU1MT+/btg4mJCerr62Vey8/PD6+99pqC3hVCCOkaCigJeUZk3BHh+skUZNxRTuChpaUlc+f9/PnzSE5OxtmzZ3HixAk0NjbCx8cHenp6+O+//3D16lXo6upi8uTJaGhowMSJE2FoaIjg4GDuGGKxGAcPHsT8+fNbfM3Q0FB88MEH+Oijj3Dnzh289dZbWLBgAS5evNjhdq9evRqJiYn4999/kZSUhJ9//hmmpqadPv/nn38ejo6OCAkJ4bapqKhg69atSEhIwN69e3HhwgUsX74cADBq1KhHRk6koyaNjY1Yt24dYmNjcfToUWRmZiIwMLDTbVImCZMguioaEibpltdTVVXFhg0bsG3bNuTk5LS4T1paGiZPnowXX3wRcXFxOHjwIK5cuYIlS5YAAMaNG4fExEQUFhYCAC5dugRTU1Pu5kNjYyMiIiIwfvz4Trfvww8/RGVlJc6ePQsAqKurg6urK06ePIk7d+7gzTffxGuvvYYbN24AAH788Ud4eXlh0aJF3OfD2toaEokEffr0weHDh5GYmIg1a9Zg1apVOHToUKfbRB747LPP8L///Q+3bt2CmpoaXn/9de6x//77D/7+/vjggw+QmJiIX375BUFBQVi/fj0AQCKRQCAQoKSkBJcuXcLZs2eRnp6Ol156SeY1UlNTERwcjJCQEMTExGDOnDkQi8XcTTgAEIlEOHnypMzrE0JIT6C4xUyEkB4jZGskTu+L5f7t4++IWe97dstrM8Zw/vx5nD59Gu+99x63XUdHB7/99hs0NDQAAPv374dEIsFvv/3GrW/as2cPDA0NER4ejkmTJuHll1/GX3/9hYULFwJ4EJSWlZXhxRdfbPG1N2/ejMDAQLzzzjsAgGXLluH69evYvHkzJkyY0KH2Z2dnw9nZGW5ubgCAfv36del9AIAhQ4YgLi6O+/fSpUu5/+/Xrx++/vprvP322/jpp5+goaEhM3LSXPMLyv79+2Pr1q1wd3dHVVWVzMhKT3ai5AS+zPoSX/T9AjNMZnTLa86cORNOTk5Yu3Ytfv/990ce37hxI+bPn8/9XgYNGoStW7di3Lhx+PnnnzF8+HAYGxvj0qVLmD17NsLDw/HRRx9xI4A3btxAY2MjRo0a1em2DRkyBAC4EfnevXvLTLt87733cPr0aRw6dAgeHh4wMDCAhoYGtLW1ZT4fqqqq+PLLL7l/29raIiIiAocOHcLcuXM73S4CrF+/HuPGjQMArFixAtOmTUNdXR00NTXx5ZdfYsWKFQgICADw4O9x3bp1WL58OdauXYvz588jPj4eGRkZsLa2BgDs27cPw4YNw82bN+Hu7g7gwTTXffv2wczMjHvdefPmYc+ePZgzZw6AB32kjY1Nl25YEEKIItEIJSFPuYw7IplgEgBO74tV+EjliRMnoKurC01NTUyZMgUvvfQSN2UTABwcHLhgEgBiY2ORmpoKPT096OrqQldXF8bGxqirq0NaWhoAYP78+QgPD0deXh4A4M8//8S0adNaTZiRlJSE0aNHy2wbPXo0kpKSOnweixcvxoEDB+Dk5ITly5fj2rVrHX7uwxhjMslAzp07h4kTJ6J3797Q09PDa6+9huLiYtTU1LR5nNu3b2PGjBmwsbGBnp4ed7GbnZ3d5bZ1pybWhF35uwAAv+T/gibWfetKv/nmG+zdu7fFz0BsbCyCgoK4z5+uri58fHwgkUiQkZEBHo+H5557DuHh4SgrK0NiYiLeeecd1NfX4+7du7h06RLc3d2hra3d6XZJEwNJPx9isRjr1q2Dg4MDjI2Noauri9OnT3fod7xjxw64urrCzMwMurq62LVr1xPz2eiJRowYwf1/r169AICbYh4bG4uvvvpK5jMjHTWuqalBUlISrK2tuWASAIYOHQpDQ0OZz2Dfvn1lgkkAWLRoEc6cOYPc3FwAD6b0BwYGtptQiBBCuhsFlIQ85YRZZZ3aLi8TJkxATEwM7t27h9raWuzduxc6Ojrc483/HwCqqqrg6uqKmJgYmZ+UlBTMmzcPAODu7o4BAwbgwIEDqK2tRWhoaKvTXTtCmnG1eZbPxsZGmX2mTJmCrKwsfPjhh8jLy8PEiRO7nLAjKSkJtra2AB6MRE2fPh0jRoxAcHAwbt++jR07dgBoOylHdXU1fHx8oK+vjz///BM3b95EaGhou8/rSf4p+Qf5DfkAgPyGfPxb8m+3vfZzzz0HHx8frFy58pHHqqqq8NZbb8l8/mJjY3Hv3j0MGDAAwIPMquHh4fjvv//g7OwMfX19Lsi8dOkSF9x3ljS4kH4+vvvuO/z444/49NNPcfHiRcTExMDHx6fd3/GBAwfw8ccfY+HChThz5gxiYmKwYMGCJ+az0ROpq6tz/y8N5iSSB1O1q6qq8OWXX8p8ZuLj43Hv3j1oamp2+DUe7g8BwNnZGY6Ojti3bx9u376NhISEJ25qOyHk2UBTXgl5yln0NezUdnnR0dHBwIEDO7y/i4sLDh48CHNzc+jr67e63/z58/Hnn3+iT58+UFFRwbRp01rd197eHlevXuWmowHA1atXMXToUADgRgTy8/Ph7OwMADIJeqTMzMwQEBCAgIAAjB07Fp988gk2b97c4XMDgAsXLiA+Ph4ffvghgAejjBKJBP/73/+4wPbhdW4aGhoQi8Uy2+7evYvi4mJs2rSJG/W4detWp9qiTNLRSR54YGDggYdf8n/BFOMpUON1z1fSpk2b4OTkxCWDknJxcUFiYmKbn9tx48Zh6dKlOHz4MDf1cPz48Th37hyuXr2Kjz76qEttkq6X9fb2BvDgcyoQCPDqq68CeBDApKSkcJ9doOXPx9WrVzFq1ChumjcAboSfyJ+LiwuSk5Nb/czY29vj/v37uH//Pvf3mpiYiLKyMpnfZWveeOMNbNmyBbm5ufD29pYZ6SSEkJ6CRigJecrZDjeHj7+jzDafAEfYDld+eY3m5s+fD1NTUwgEAvz333/IyMhAeHg43n//fZkkKvPnz0dUVBTWr1+P2bNng8/nt3rMTz75BEFBQfj5559x7949fP/99wgJCeFGGLW0tDBy5Ehs2rQJSUlJuHTp0iMZOtesWYOwsDCkpqYiISEBJ06cgL29fZvnUl9fj4KCAuTm5iIqKgobNmyAQCDA9OnT4e/vDwAYOHAgGhsbsW3bNqSnp+OPP/6QyRYKPFhXWVVVhfPnz6OoqAg1NTWwsbGBhoYG97xjx45h3bp1nXqvlelm5U3kN+SD4cGoMANDfkM+blV2X1Ds4OCA+fPnY+vWrTLbP/30U1y7dg1LlizhRtfDwsK4pDzAg+mPRkZG+Ouvv2QCyqNHj6K+vv6RKdYtKSsrQ0FBAbKysnD27FnMnj0bf/31F37++Wdu+vagQYNw9uxZXLt2DUlJSXjrrbdkMoMCDz4fkZGRyMzMRFFRESQSCQYNGoRbt27h9OnTSElJwerVq3Hz5s3He8NIq9asWYN9+/bhyy+/REJCApKSknDgwAGuH/H29uY+b1FRUbhx4wb8/f0xbtw4bl12W+bNm4ecnBz8+uuvlIyHENJzMULIMyE9XsgiTiSz9Hihwl8rICCACQSCTj+en5/P/P39mampKePz+ax///5s0aJFrLy8XGY/Dw8PBoBduHBBZvuePXuYgYGBzLaffvqJ9e/fn6mrq7PBgwezffv2yTyemJjIvLy8mJaWFnNycmJnzpxhANjFixcZY4ytW7eO2dvbMy0tLWZsbMwEAgFLT09v89wAMABMTU2NmZmZMW9vb7Z7924mFotl9v3+++9Zr169mJaWFvPx8WH79u1jAFhpaSm3z9tvv81MTEwYALZ27VrGGGN//fUX69evH+Pz+czLy4sdO3aMAWDR0dGttqunqGmqYceLjrOQwhDu53jRcVbTVKOw12zp85aRkcE0NDTYw1+DN27cYC+88ALT1dVlOjo6bMSIEWz9+vUy+wgEAqampsYqKysZY4yJxWJmZGTERo4c2W5bpJ8NAExTU5MNGDCABQQEsNu3b8vsV1xczAQCAdPV1WXm5ubs888/Z/7+/jLnkZyczEaOHMm0tLQYAJaRkcHq6upYYGAgMzAwYIaGhmzx4sVsxYoVzNHRseNv2DOmpX6DMcYuXrz4yN9jdHQ0915LnTp1io0aNYppaWkxfX195uHhwXbt2sU9npWVxXx9fZmOjg7T09Njc+bMYQUFBdzja9eubfP389prrzFjY2NWV1f3OKdJCCEKw2Os2eIhQgghhBDSY0ycOBHDhg17ZESdEEJ6CgooCSGEEEJ6mNLSUoSHh2P27NlITEx8ZM0vIYT0FJSUhxBCCCGkh3F2dkZpaSm++eYbCiYJIT0ajVASQgghhBBCCOkSyvJKCCGEEEIIIaRLKKAkhBBCCCGEENIlFFASQgghhBBCCOkSCigJIYQQQgghhHQJBZSEEEIIIYQQQrqEAkpCCCGEEEIIIV1CASUhhBBCCCGEkC6hgJIQQgghhBBCSJdQQEkIIYQQQgghpEsooCSEEEIIIYQQ0iUUUBJCCCGEEEII6RIKKAkhhBBCCCGEdAkFlIQQQgghhBBCuoQCSkIIIYQQQgghXfL/AKozsb3d4KHmAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Change wrap to 3, Adjust dimensions, adjust scatter plot and line colors, shapes, and sizes\n", "fig = plot_results_panel_3d(cycle_mlr.state,\n", @@ -437,8 +586,17 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Change the viewing angle to 0 elevation, 0 azimuth.\n", "fig = plot_results_panel_3d(cycle_mlr.state,\n", @@ -456,8 +614,17 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 15, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Change the viewing angle to +20 elevation, +60 azimuth\n", "fig = plot_results_panel_3d(cycle_mlr.state,\n", From d99491fd4839b14cd6b314af2df62608116a124d Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Apr 2023 10:39:41 -0400 Subject: [PATCH 049/446] feat: add basic serializer --- .pre-commit-config.yaml | 2 +- autora/controller/serializer/__init__.py | 0 autora/controller/serializer/yaml_.py | 175 +++++++++++++++++++++++ 3 files changed, 176 insertions(+), 1 deletion(-) create mode 100644 autora/controller/serializer/__init__.py create mode 100644 autora/controller/serializer/yaml_.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d9a1837d..094dfbc9 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -23,7 +23,7 @@ repos: rev: "v0.991" hooks: - id: mypy - additional_dependencies: [types-requests] + additional_dependencies: [types-requests,types-PyYAML] language_version: python3.8 default_language_version: python: python3 diff --git a/autora/controller/serializer/__init__.py b/autora/controller/serializer/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/autora/controller/serializer/yaml_.py b/autora/controller/serializer/yaml_.py new file mode 100644 index 00000000..5e53b8ff --- /dev/null +++ b/autora/controller/serializer/yaml_.py @@ -0,0 +1,175 @@ +import pickle +from pathlib import Path +from typing import Any, Protocol, runtime_checkable + +import yaml + +from autora.controller.protocol import ResultKind, SupportsControllerStateHistory +from autora.controller.state import History + + +@runtime_checkable +class SupportsLoadDump(Protocol): + def dump(self, data, file) -> None: + ... + + def load(self, file) -> Any: + ... + + +class YAMLSerializer: + @staticmethod + def dump(data, file): + as_string = yaml.dump(data, Dumper=yaml.Dumper) + file.write(as_string) + return + + @staticmethod + def load(file): + result = yaml.load(file, Loader=yaml.Loader) + return result + + +class YAMLResultCollectionSerializer: + def __init__(self, path: Path): + self.path = path + self._check_path() + + def dump(self, data_collection: SupportsControllerStateHistory): + """ + + + Args: + data_collection: + path: a directory + + Returns: + + Examples: + First, we need to initialize a FilesystemCycleDataCollection. This is usually handled + by the cycle itself. We start with a data collection as it would be at the very start of + an experiment, with just a VariableCollection. + >>> from autora.controller.state.history import History + >>> c = History() + >>> c #doctest: +NORMALIZE_WHITESPACE + History([]) + + Now we can serialize the data collection using _dumper. We define a helper function for + demonstration purposes. + >>> import tempfile + >>> import os + >>> def dump_and_list(data, cat=False): + ... with tempfile.TemporaryDirectory() as d: + ... s = YAMLResultCollectionSerializer(d) + ... s.dump(c) + ... print(sorted(os.listdir(d))) + + >>> dump_and_list(c) + [] + + Each immutable part gets its own file. + >>> c = c.update(metadata=[VariableCollection()]) + >>> dump_and_list(c) + ['00000000-METADATA.yaml'] + + The next step is to plan the first observations by defining experimental conditions. + Thes are appended as a Result with the correct metadata. + >>> import numpy as np + >>> x = np.linspace(-2, 2, 10).reshape(-1, 1) * np.pi + >>> c = c.update(conditions=[x]) + + If we dump and list again, we see that the new data are included as a new file in + the same directory. + >>> dump_and_list(c) + ['00000000-METADATA.yaml', '00000001-CONDITION.yaml'] + + Then, once we've gathered real data, we dump these too: + >>> y = 3. * x + 0.1 * np.sin(x - 0.1) - 2. + >>> c = c.update(observations=[np.column_stack([x, y])]) + >>> dump_and_list(c) + ['00000000-METADATA.yaml', '00000001-CONDITION.yaml', '00000002-OBSERVATION.yaml'] + + We can also include a theory in the dump. + The theory is saved as a pickle file by default. + >>> from sklearn.linear_model import LinearRegression + >>> estimator = LinearRegression().fit(x, y) + >>> c = c.update(theories=[estimator]) + >>> dump_and_list(c) # doctest: +NORMALIZE_WHITESPACE + ['00000000-METADATA.yaml', '00000001-CONDITION.yaml', '00000002-OBSERVATION.yaml', + '00000003-THEORY.pickle'] + + """ + path = self.path + self._check_path() + + for i, container in enumerate(data_collection.history): + extension, serializer, mode = { + None: ("yaml", YAMLSerializer, "w+"), + ResultKind.METADATA: ("yaml", YAMLSerializer, "w+"), + ResultKind.PARAMS: ("yaml", YAMLSerializer, "w+"), + ResultKind.CONDITION: ("yaml", YAMLSerializer, "w+"), + ResultKind.OBSERVATION: ("yaml", YAMLSerializer, "w+"), + ResultKind.THEORY: ("pickle", pickle, "w+b"), + }[container.kind] + + assert isinstance(serializer, SupportsLoadDump) + filename = f"{str(i).rjust(8, '0')}-{container.kind}.{extension}" + with open(Path(path, filename), mode) as f: + serializer.dump(container, f) + + def load(self): + """ + + Examples: + First, we need to initialize a FilesystemCycleDataCollection. This is usually handled + by the cycle itself. We construct a full set of results: + >>> from sklearn.linear_model import LinearRegression + >>> import numpy as np + >>> from autora.controller.state.history import History + >>> import tempfile + >>> x = np.linspace(-2, 2, 10).reshape(-1, 1) * np.pi + >>> y = 3. * x + 0.1 * np.sin(x - 0.1) - 2. + >>> estimator = LinearRegression().fit(x, y) + >>> c = History(metadata=VariableCollection(), conditions=[x], + ... observations=[np.column_stack([x, y])], theories=[estimator]) + + Now we can serialize the data using _dumper, and reload the data using _loader: + >>> with tempfile.TemporaryDirectory() as d: + ... s = YAMLResultCollectionSerializer(d) + ... s.dump(c) + ... e = s.load() + + We can now compare the dumped object "c" with the reloaded object "e". The data arrays + should be equal, and the theories should + >>> from autora.controller.protocol import ResultKind + >>> for e_i, c_i in zip(e.history, c.history): + ... assert isinstance(e_i.data, type(c_i.data)) # Types match + ... if e_i.kind in (ResultKind.CONDITION, ResultKind.OBSERVATION): + ... np.testing.assert_array_equal(e_i.data, c_i.data) # two numpy arrays + ... if e_i.kind == ResultKind.THEORY: + ... np.testing.assert_array_equal(e_i.data.coef_, c_i.data.coef_) # 2 estimators + + """ + path = self.path + assert Path(path).is_dir(), f"{path=} must be a directory." + data = [] + + for file in sorted(Path(path).glob("*")): + serializer, mode = { + ".yaml": (YAMLSerializer, "r"), + ".pickle": (pickle, "rb"), + }[file.suffix] + with open(file, mode) as f: + loaded_object = serializer.load(f) + data.append(loaded_object) + + data_collection = History(history=data) + + return data_collection + + def _check_path(self): + """Ensure the path exists and is the right type.""" + if Path(self.path).exists(): + assert Path(self.path).is_dir(), "Can't support individual files now." + else: + Path(self.path).mkdir() From 90f1b80f35f5d06ed4cdc7d9d1cdb30700b732f3 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Fri, 7 Apr 2023 10:40:24 -0400 Subject: [PATCH 050/446] refactor: move SupportsLoadDump to protocol --- autora/controller/protocol.py | 22 +++++++++++++++++++++- autora/controller/serializer/yaml_.py | 16 +++++----------- 2 files changed, 26 insertions(+), 12 deletions(-) diff --git a/autora/controller/protocol.py b/autora/controller/protocol.py index 9cdacfe3..7234d940 100644 --- a/autora/controller/protocol.py +++ b/autora/controller/protocol.py @@ -1,5 +1,16 @@ from enum import Enum -from typing import Any, Dict, Mapping, Optional, Protocol, Sequence, Set, TypeVar, Union +from typing import ( + Any, + Dict, + Mapping, + Optional, + Protocol, + Sequence, + Set, + TypeVar, + Union, + runtime_checkable, +) from numpy.typing import ArrayLike from sklearn.base import BaseEstimator @@ -117,3 +128,12 @@ def __call__(self, __state: State, __params: Dict) -> State: ExecutorName = TypeVar("ExecutorName", bound=str) ExecutorCollection = Mapping[ExecutorName, Executor] + + +@runtime_checkable +class SupportsLoadDump(Protocol): + def dump(self, data, file) -> None: + ... + + def load(self, file) -> Any: + ... diff --git a/autora/controller/serializer/yaml_.py b/autora/controller/serializer/yaml_.py index 5e53b8ff..efde9b23 100644 --- a/autora/controller/serializer/yaml_.py +++ b/autora/controller/serializer/yaml_.py @@ -1,22 +1,16 @@ import pickle from pathlib import Path -from typing import Any, Protocol, runtime_checkable import yaml -from autora.controller.protocol import ResultKind, SupportsControllerStateHistory +from autora.controller.protocol import ( + ResultKind, + SupportsControllerStateHistory, + SupportsLoadDump, +) from autora.controller.state import History -@runtime_checkable -class SupportsLoadDump(Protocol): - def dump(self, data, file) -> None: - ... - - def load(self, file) -> Any: - ... - - class YAMLSerializer: @staticmethod def dump(data, file): From 9ea52c48ae8e05ce332abb6654ddc2759462d4f8 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 11 Apr 2023 09:59:59 -0400 Subject: [PATCH 051/446] refactor: move mapping between result types and serializer spec outside of serializer function --- autora/controller/serializer/yaml_.py | 48 ++++++++++++++++++++------- 1 file changed, 36 insertions(+), 12 deletions(-) diff --git a/autora/controller/serializer/yaml_.py b/autora/controller/serializer/yaml_.py index efde9b23..88f64cdf 100644 --- a/autora/controller/serializer/yaml_.py +++ b/autora/controller/serializer/yaml_.py @@ -1,5 +1,7 @@ import pickle from pathlib import Path +from types import MappingProxyType +from typing import Mapping, NamedTuple, Union import yaml @@ -24,10 +26,35 @@ def load(file): return result -class YAMLResultCollectionSerializer: - def __init__(self, path: Path): +class _SerializerSpec(NamedTuple): + extension: str + serializer: SupportsLoadDump + mode: str + + +default_result_kind_serializer_mapping: Mapping[ + Union[None, ResultKind], _SerializerSpec +] = MappingProxyType( + { + None: _SerializerSpec("yaml", YAMLSerializer, "w+"), + ResultKind.METADATA: _SerializerSpec("yaml", YAMLSerializer, "w+"), + ResultKind.PARAMS: _SerializerSpec("yaml", YAMLSerializer, "w+"), + ResultKind.CONDITION: _SerializerSpec("yaml", YAMLSerializer, "w+"), + ResultKind.OBSERVATION: _SerializerSpec("yaml", YAMLSerializer, "w+"), + ResultKind.THEORY: _SerializerSpec("pickle", pickle, "w+b"), + } +) + + +class HistorySerializer: + def __init__( + self, + path: Path, + result_kind_serializer_mapping: Mapping = default_result_kind_serializer_mapping, + ): self.path = path self._check_path() + self.result_kind_serializer_mapping = result_kind_serializer_mapping def dump(self, data_collection: SupportsControllerStateHistory): """ @@ -54,7 +81,7 @@ def dump(self, data_collection: SupportsControllerStateHistory): >>> import os >>> def dump_and_list(data, cat=False): ... with tempfile.TemporaryDirectory() as d: - ... s = YAMLResultCollectionSerializer(d) + ... s = HistorySerializer(d) ... s.dump(c) ... print(sorted(os.listdir(d))) @@ -62,6 +89,7 @@ def dump(self, data_collection: SupportsControllerStateHistory): [] Each immutable part gets its own file. + >>> from autora.variable import VariableCollection >>> c = c.update(metadata=[VariableCollection()]) >>> dump_and_list(c) ['00000000-METADATA.yaml'] @@ -97,14 +125,9 @@ def dump(self, data_collection: SupportsControllerStateHistory): self._check_path() for i, container in enumerate(data_collection.history): - extension, serializer, mode = { - None: ("yaml", YAMLSerializer, "w+"), - ResultKind.METADATA: ("yaml", YAMLSerializer, "w+"), - ResultKind.PARAMS: ("yaml", YAMLSerializer, "w+"), - ResultKind.CONDITION: ("yaml", YAMLSerializer, "w+"), - ResultKind.OBSERVATION: ("yaml", YAMLSerializer, "w+"), - ResultKind.THEORY: ("pickle", pickle, "w+b"), - }[container.kind] + extension, serializer, mode = self.result_kind_serializer_mapping[ + container.kind + ] assert isinstance(serializer, SupportsLoadDump) filename = f"{str(i).rjust(8, '0')}-{container.kind}.{extension}" @@ -118,6 +141,7 @@ def load(self): First, we need to initialize a FilesystemCycleDataCollection. This is usually handled by the cycle itself. We construct a full set of results: >>> from sklearn.linear_model import LinearRegression + >>> from autora.variable import VariableCollection >>> import numpy as np >>> from autora.controller.state.history import History >>> import tempfile @@ -129,7 +153,7 @@ def load(self): Now we can serialize the data using _dumper, and reload the data using _loader: >>> with tempfile.TemporaryDirectory() as d: - ... s = YAMLResultCollectionSerializer(d) + ... s = HistorySerializer(d) ... s.dump(c) ... e = s.load() From 713546a13d3e03e2a3d49a28be806a9f0edeaab6 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 11 Apr 2023 10:09:07 -0400 Subject: [PATCH 052/446] refactor: move yaml-specific code to separate file --- autora/controller/serializer/__init__.py | 181 +++++++++++++++++++++ autora/controller/serializer/yaml_.py | 194 +---------------------- 2 files changed, 187 insertions(+), 188 deletions(-) diff --git a/autora/controller/serializer/__init__.py b/autora/controller/serializer/__init__.py index e69de29b..89d40501 100644 --- a/autora/controller/serializer/__init__.py +++ b/autora/controller/serializer/__init__.py @@ -0,0 +1,181 @@ +import pickle +import tempfile +from pathlib import Path +from types import MappingProxyType +from typing import Mapping, NamedTuple, Optional, Union + +import numpy as np + +from autora.controller.protocol import ( + ResultKind, + SupportsControllerStateHistory, + SupportsLoadDump, +) +from autora.controller.serializer import yaml_ as YAMLSerializer +from autora.controller.state import History + + +class SerializerSpec(NamedTuple): + extension: str + serializer: SupportsLoadDump + mode: str + + +class HistorySerializer: + def __init__( + self, + path: Path, + result_kind_serializer_mapping: Optional[ + Mapping[Union[None, ResultKind], SerializerSpec] + ] = None, + ): + self.path = path + self._check_path() + if result_kind_serializer_mapping is None: + result_kind_serializer_mapping = { + None: SerializerSpec("yaml", YAMLSerializer, "w+"), + ResultKind.METADATA: SerializerSpec("yaml", YAMLSerializer, "w+"), + ResultKind.PARAMS: SerializerSpec("yaml", YAMLSerializer, "w+"), + ResultKind.CONDITION: SerializerSpec("yaml", YAMLSerializer, "w+"), + ResultKind.OBSERVATION: SerializerSpec("yaml", YAMLSerializer, "w+"), + ResultKind.THEORY: SerializerSpec("pickle", pickle, "w+b"), + } + + self.result_kind_serializer_mapping: Mapping[ + Union[None, ResultKind], SerializerSpec + ] = result_kind_serializer_mapping + + def dump(self, data_collection: SupportsControllerStateHistory): + """ + + Args: + data_collection: + path: a directory + + Returns: + + Examples: + First, we need to initialize a FilesystemCycleDataCollection. This is usually handled + by the cycle itself. We start with a data collection as it would be at the very start of + an experiment, with just a VariableCollection. + >>> from autora.controller.state.history import History + >>> c = History() + >>> c #doctest: +NORMALIZE_WHITESPACE + History([]) + + Now we can serialize the data collection using _dumper. We define a helper function for + demonstration purposes. + >>> import tempfile + >>> import os + >>> def dump_and_list(data, cat=False): + ... with tempfile.TemporaryDirectory() as d: + ... s = HistorySerializer(d) + ... s.dump(c) + ... print(sorted(os.listdir(d))) + + >>> dump_and_list(c) + [] + + Each immutable part gets its own file. + >>> from autora.variable import VariableCollection + >>> c = c.update(metadata=[VariableCollection()]) + >>> dump_and_list(c) + ['00000000-METADATA.yaml'] + + The next step is to plan the first observations by defining experimental conditions. + Thes are appended as a Result with the correct metadata. + >>> import numpy as np + >>> x = np.linspace(-2, 2, 10).reshape(-1, 1) * np.pi + >>> c = c.update(conditions=[x]) + + If we dump and list again, we see that the new data are included as a new file in + the same directory. + >>> dump_and_list(c) + ['00000000-METADATA.yaml', '00000001-CONDITION.yaml'] + + Then, once we've gathered real data, we dump these too: + >>> y = 3. * x + 0.1 * np.sin(x - 0.1) - 2. + >>> c = c.update(observations=[np.column_stack([x, y])]) + >>> dump_and_list(c) + ['00000000-METADATA.yaml', '00000001-CONDITION.yaml', '00000002-OBSERVATION.yaml'] + + We can also include a theory in the dump. + The theory is saved as a pickle file by default. + >>> from sklearn.linear_model import LinearRegression + >>> estimator = LinearRegression().fit(x, y) + >>> c = c.update(theories=[estimator]) + >>> dump_and_list(c) # doctest: +NORMALIZE_WHITESPACE + ['00000000-METADATA.yaml', '00000001-CONDITION.yaml', '00000002-OBSERVATION.yaml', + '00000003-THEORY.pickle'] + + """ + path = self.path + self._check_path() + + for i, container in enumerate(data_collection.history): + extension, serializer, mode = self.result_kind_serializer_mapping[ + container.kind + ] + + assert isinstance(serializer, SupportsLoadDump) + filename = f"{str(i).rjust(8, '0')}-{container.kind}.{extension}" + with open(Path(path, filename), mode) as f: + serializer.dump(container, f) + + def load(self): + """ + + Examples: + First, we need to initialize a FilesystemCycleDataCollection. This is usually handled + by the cycle itself. We construct a full set of results: + >>> from sklearn.linear_model import LinearRegression + >>> from autora.variable import VariableCollection + >>> import numpy as np + >>> from autora.controller.state.history import History + >>> import tempfile + >>> x = np.linspace(-2, 2, 10).reshape(-1, 1) * np.pi + >>> y = 3. * x + 0.1 * np.sin(x - 0.1) - 2. + >>> estimator = LinearRegression().fit(x, y) + >>> c = History(metadata=VariableCollection(), conditions=[x], + ... observations=[np.column_stack([x, y])], theories=[estimator]) + + Now we can serialize the data using _dumper, and reload the data using _loader: + >>> with tempfile.TemporaryDirectory() as d: + ... s = HistorySerializer(d) + ... s.dump(c) + ... e = s.load() + + We can now compare the dumped object "c" with the reloaded object "e". The data arrays + should be equal, and the theories should + >>> from autora.controller.protocol import ResultKind + >>> for e_i, c_i in zip(e.history, c.history): + ... assert isinstance(e_i.data, type(c_i.data)) # Types match + ... if e_i.kind in (ResultKind.CONDITION, ResultKind.OBSERVATION): + ... np.testing.assert_array_equal(e_i.data, c_i.data) # two numpy arrays + ... if e_i.kind == ResultKind.THEORY: + ... np.testing.assert_array_equal(e_i.data.coef_, c_i.data.coef_) # 2 estimators + + """ + path = self.path + assert Path(path).is_dir(), f"{path=} must be a directory." + data = [] + + for file in sorted(Path(path).glob("*")): + serializer, mode = { + ".yaml": (YAMLSerializer, "r"), + ".pickle": (pickle, "rb"), + }[file.suffix] + with open(file, mode) as f: + loaded_object = serializer.load(f) + data.append(loaded_object) + + data_collection = History(history=data) + + return data_collection + + def _check_path(self): + """Ensure the path exists and is the right type.""" + if Path(self.path).exists(): + assert Path(self.path).is_dir(), "Can't support individual files now." + else: + Path(self.path).mkdir() diff --git a/autora/controller/serializer/yaml_.py b/autora/controller/serializer/yaml_.py index 88f64cdf..569cf38e 100644 --- a/autora/controller/serializer/yaml_.py +++ b/autora/controller/serializer/yaml_.py @@ -1,193 +1,11 @@ -import pickle -from pathlib import Path -from types import MappingProxyType -from typing import Mapping, NamedTuple, Union - import yaml -from autora.controller.protocol import ( - ResultKind, - SupportsControllerStateHistory, - SupportsLoadDump, -) -from autora.controller.state import History - - -class YAMLSerializer: - @staticmethod - def dump(data, file): - as_string = yaml.dump(data, Dumper=yaml.Dumper) - file.write(as_string) - return - - @staticmethod - def load(file): - result = yaml.load(file, Loader=yaml.Loader) - return result - - -class _SerializerSpec(NamedTuple): - extension: str - serializer: SupportsLoadDump - mode: str - - -default_result_kind_serializer_mapping: Mapping[ - Union[None, ResultKind], _SerializerSpec -] = MappingProxyType( - { - None: _SerializerSpec("yaml", YAMLSerializer, "w+"), - ResultKind.METADATA: _SerializerSpec("yaml", YAMLSerializer, "w+"), - ResultKind.PARAMS: _SerializerSpec("yaml", YAMLSerializer, "w+"), - ResultKind.CONDITION: _SerializerSpec("yaml", YAMLSerializer, "w+"), - ResultKind.OBSERVATION: _SerializerSpec("yaml", YAMLSerializer, "w+"), - ResultKind.THEORY: _SerializerSpec("pickle", pickle, "w+b"), - } -) - - -class HistorySerializer: - def __init__( - self, - path: Path, - result_kind_serializer_mapping: Mapping = default_result_kind_serializer_mapping, - ): - self.path = path - self._check_path() - self.result_kind_serializer_mapping = result_kind_serializer_mapping - - def dump(self, data_collection: SupportsControllerStateHistory): - """ - - - Args: - data_collection: - path: a directory - - Returns: - - Examples: - First, we need to initialize a FilesystemCycleDataCollection. This is usually handled - by the cycle itself. We start with a data collection as it would be at the very start of - an experiment, with just a VariableCollection. - >>> from autora.controller.state.history import History - >>> c = History() - >>> c #doctest: +NORMALIZE_WHITESPACE - History([]) - - Now we can serialize the data collection using _dumper. We define a helper function for - demonstration purposes. - >>> import tempfile - >>> import os - >>> def dump_and_list(data, cat=False): - ... with tempfile.TemporaryDirectory() as d: - ... s = HistorySerializer(d) - ... s.dump(c) - ... print(sorted(os.listdir(d))) - - >>> dump_and_list(c) - [] - - Each immutable part gets its own file. - >>> from autora.variable import VariableCollection - >>> c = c.update(metadata=[VariableCollection()]) - >>> dump_and_list(c) - ['00000000-METADATA.yaml'] - - The next step is to plan the first observations by defining experimental conditions. - Thes are appended as a Result with the correct metadata. - >>> import numpy as np - >>> x = np.linspace(-2, 2, 10).reshape(-1, 1) * np.pi - >>> c = c.update(conditions=[x]) - - If we dump and list again, we see that the new data are included as a new file in - the same directory. - >>> dump_and_list(c) - ['00000000-METADATA.yaml', '00000001-CONDITION.yaml'] - - Then, once we've gathered real data, we dump these too: - >>> y = 3. * x + 0.1 * np.sin(x - 0.1) - 2. - >>> c = c.update(observations=[np.column_stack([x, y])]) - >>> dump_and_list(c) - ['00000000-METADATA.yaml', '00000001-CONDITION.yaml', '00000002-OBSERVATION.yaml'] - - We can also include a theory in the dump. - The theory is saved as a pickle file by default. - >>> from sklearn.linear_model import LinearRegression - >>> estimator = LinearRegression().fit(x, y) - >>> c = c.update(theories=[estimator]) - >>> dump_and_list(c) # doctest: +NORMALIZE_WHITESPACE - ['00000000-METADATA.yaml', '00000001-CONDITION.yaml', '00000002-OBSERVATION.yaml', - '00000003-THEORY.pickle'] - - """ - path = self.path - self._check_path() - - for i, container in enumerate(data_collection.history): - extension, serializer, mode = self.result_kind_serializer_mapping[ - container.kind - ] - - assert isinstance(serializer, SupportsLoadDump) - filename = f"{str(i).rjust(8, '0')}-{container.kind}.{extension}" - with open(Path(path, filename), mode) as f: - serializer.dump(container, f) - - def load(self): - """ - - Examples: - First, we need to initialize a FilesystemCycleDataCollection. This is usually handled - by the cycle itself. We construct a full set of results: - >>> from sklearn.linear_model import LinearRegression - >>> from autora.variable import VariableCollection - >>> import numpy as np - >>> from autora.controller.state.history import History - >>> import tempfile - >>> x = np.linspace(-2, 2, 10).reshape(-1, 1) * np.pi - >>> y = 3. * x + 0.1 * np.sin(x - 0.1) - 2. - >>> estimator = LinearRegression().fit(x, y) - >>> c = History(metadata=VariableCollection(), conditions=[x], - ... observations=[np.column_stack([x, y])], theories=[estimator]) - - Now we can serialize the data using _dumper, and reload the data using _loader: - >>> with tempfile.TemporaryDirectory() as d: - ... s = HistorySerializer(d) - ... s.dump(c) - ... e = s.load() - - We can now compare the dumped object "c" with the reloaded object "e". The data arrays - should be equal, and the theories should - >>> from autora.controller.protocol import ResultKind - >>> for e_i, c_i in zip(e.history, c.history): - ... assert isinstance(e_i.data, type(c_i.data)) # Types match - ... if e_i.kind in (ResultKind.CONDITION, ResultKind.OBSERVATION): - ... np.testing.assert_array_equal(e_i.data, c_i.data) # two numpy arrays - ... if e_i.kind == ResultKind.THEORY: - ... np.testing.assert_array_equal(e_i.data.coef_, c_i.data.coef_) # 2 estimators - - """ - path = self.path - assert Path(path).is_dir(), f"{path=} must be a directory." - data = [] - - for file in sorted(Path(path).glob("*")): - serializer, mode = { - ".yaml": (YAMLSerializer, "r"), - ".pickle": (pickle, "rb"), - }[file.suffix] - with open(file, mode) as f: - loaded_object = serializer.load(f) - data.append(loaded_object) - data_collection = History(history=data) +def dump(data, file): + yaml.dump(data, file, Dumper=yaml.Dumper) + return - return data_collection - def _check_path(self): - """Ensure the path exists and is the right type.""" - if Path(self.path).exists(): - assert Path(self.path).is_dir(), "Can't support individual files now." - else: - Path(self.path).mkdir() +def load(file): + result = yaml.load(file, Loader=yaml.Loader) + return result From a666afaab8434642c5174647cdad0f8f437b2a2c Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 11 Apr 2023 10:52:37 -0400 Subject: [PATCH 053/446] feat: add ability to change dumper and loader --- autora/controller/protocol.py | 3 + autora/controller/serializer/__init__.py | 110 +++++++++++++++++++---- 2 files changed, 96 insertions(+), 17 deletions(-) diff --git a/autora/controller/protocol.py b/autora/controller/protocol.py index 7234d940..1285f1f0 100644 --- a/autora/controller/protocol.py +++ b/autora/controller/protocol.py @@ -110,6 +110,9 @@ def theories(self) -> Sequence[BaseEstimator]: class SupportsControllerStateHistory(SupportsControllerStateProperties, Protocol): """Represents controller state as a linear sequence of entries.""" + def __init__(self, history: Sequence[SupportsDataKind]): + ... + def filter_by(self: State, kind: Optional[Set[Union[str, ResultKind]]]) -> State: ... diff --git a/autora/controller/serializer/__init__.py b/autora/controller/serializer/__init__.py index 89d40501..c550111c 100644 --- a/autora/controller/serializer/__init__.py +++ b/autora/controller/serializer/__init__.py @@ -2,7 +2,7 @@ import tempfile from pathlib import Path from types import MappingProxyType -from typing import Mapping, NamedTuple, Optional, Union +from typing import Mapping, NamedTuple, Optional, Tuple, Type, Union import numpy as np @@ -15,36 +15,52 @@ from autora.controller.state import History -class SerializerSpec(NamedTuple): +class DumpSpec(NamedTuple): extension: str serializer: SupportsLoadDump mode: str +class LoadSpec(NamedTuple): + serializer: SupportsLoadDump + mode: str + + class HistorySerializer: + """Serializes and deserializes History objects.""" + def __init__( self, path: Path, result_kind_serializer_mapping: Optional[ - Mapping[Union[None, ResultKind], SerializerSpec] + Mapping[Union[None, ResultKind], DumpSpec] ] = None, + extension_loader_mapping: Optional[Mapping[str, LoadSpec]] = None, ): self.path = path self._check_path() if result_kind_serializer_mapping is None: result_kind_serializer_mapping = { - None: SerializerSpec("yaml", YAMLSerializer, "w+"), - ResultKind.METADATA: SerializerSpec("yaml", YAMLSerializer, "w+"), - ResultKind.PARAMS: SerializerSpec("yaml", YAMLSerializer, "w+"), - ResultKind.CONDITION: SerializerSpec("yaml", YAMLSerializer, "w+"), - ResultKind.OBSERVATION: SerializerSpec("yaml", YAMLSerializer, "w+"), - ResultKind.THEORY: SerializerSpec("pickle", pickle, "w+b"), + None: DumpSpec("yaml", YAMLSerializer, "w+"), + ResultKind.METADATA: DumpSpec("yaml", YAMLSerializer, "w+"), + ResultKind.PARAMS: DumpSpec("yaml", YAMLSerializer, "w+"), + ResultKind.CONDITION: DumpSpec("yaml", YAMLSerializer, "w+"), + ResultKind.OBSERVATION: DumpSpec("yaml", YAMLSerializer, "w+"), + ResultKind.THEORY: DumpSpec("pickle", pickle, "w+b"), } self.result_kind_serializer_mapping: Mapping[ - Union[None, ResultKind], SerializerSpec + Union[None, ResultKind], DumpSpec ] = result_kind_serializer_mapping + if extension_loader_mapping is None: + extension_loader_mapping = { + ".yaml": LoadSpec(YAMLSerializer, "r"), + ".pickle": LoadSpec(pickle, "rb"), + } + + self.extension_loader_mapping: Mapping[str, LoadSpec] = extension_loader_mapping + def dump(self, data_collection: SupportsControllerStateHistory): """ @@ -70,7 +86,7 @@ def dump(self, data_collection: SupportsControllerStateHistory): >>> def dump_and_list(data, cat=False): ... with tempfile.TemporaryDirectory() as d: ... s = HistorySerializer(d) - ... s.dump(c) + ... s.dump(data) ... print(sorted(os.listdir(d))) >>> dump_and_list(c) @@ -108,6 +124,57 @@ def dump(self, data_collection: SupportsControllerStateHistory): ['00000000-METADATA.yaml', '00000001-CONDITION.yaml', '00000002-OBSERVATION.yaml', '00000003-THEORY.pickle'] + We can specify a different method for saving (and loading) different result types. + Here we specify a custom json encoder which works for dataclasses like the `Result` + object used by the `History` class. + >>> import dataclasses, json, pathlib + >>> class EnhancedJSONEncoder(json.JSONEncoder): + ... # From https://stackoverflow.com/a/51286749 + ... def default(self, o): + ... if dataclasses.is_dataclass(o): + ... return dataclasses.asdict(o) + ... return super().default(o) + + >>> class JSONSerializer: + ... @staticmethod + ... def dump(data, file): + ... json.dump(data, file, cls=EnhancedJSONEncoder) + ... @staticmethod + ... def load(file): + ... d: Dict = json.load(file) + ... return Result(**d) + + >>> custom_dump_mapping = { + ... None: DumpSpec("yaml", YAMLSerializer, "w+"), + ... ResultKind.METADATA: DumpSpec("json", JSONSerializer, "w+"), + ... ResultKind.PARAMS: DumpSpec("json", JSONSerializer, "w+"), + ... ResultKind.CONDITION: DumpSpec("yaml", YAMLSerializer, "w+"), + ... ResultKind.OBSERVATION: DumpSpec("yaml", YAMLSerializer, "w+"), + ... ResultKind.THEORY: DumpSpec("pickle", pickle, "w+b"), + ... } + >>> custom_load_mapping = { + ... ".yaml": (YAMLSerializer, "r"), + ... ".pickle": (pickle, "rb"), + ... ".json": (JSONSerializer, "r"), + ... } + >>> c = c.update(params={"a": "param", "nested": {"param": "dict"}}) + >>> with tempfile.TemporaryDirectory() as d: + ... sc = HistorySerializer(d, + ... result_kind_serializer_mapping=custom_dump_mapping, + ... extension_loader_mapping=custom_load_mapping) + ... sc.dump(c) + ... with open(pathlib.Path(d, "00000004-PARAMS.json")) as f: + ... print("PARAMS: ", '\\n'.join(f.readlines())) + ... with open(pathlib.Path(d, "00000000-METADATA.json")) as f: + ... print("METADATA: ", '\\n'.join(f.readlines())) + ... # doctest: +NORMALIZE_WHITESPACE + PARAMS: {"data": {"a": "param", "nested": {"param": "dict"}}, "kind": "PARAMS"} + METADATA: {"data": [{"independent_variables": [], + "dependent_variables": [], + "covariates": []}], + "kind": "METADATA"} + + """ path = self.path self._check_path() @@ -122,7 +189,7 @@ def dump(self, data_collection: SupportsControllerStateHistory): with open(Path(path, filename), mode) as f: serializer.dump(container, f) - def load(self): + def load(self, cls: Type[SupportsControllerStateHistory] = History): """ Examples: @@ -155,21 +222,30 @@ def load(self): ... if e_i.kind == ResultKind.THEORY: ... np.testing.assert_array_equal(e_i.data.coef_, c_i.data.coef_) # 2 estimators + + We can also have the function load a subclass of the History object, or something + else which supports its interface: + >>> class DerivedHistory(History): + ... pass + >>> with tempfile.TemporaryDirectory() as d: + ... s = HistorySerializer(d) + ... s.dump(c) + ... f = s.load(cls=DerivedHistory) + >>> f # doctest: +ELLIPSIS + DerivedHistory(...) + """ path = self.path assert Path(path).is_dir(), f"{path=} must be a directory." data = [] for file in sorted(Path(path).glob("*")): - serializer, mode = { - ".yaml": (YAMLSerializer, "r"), - ".pickle": (pickle, "rb"), - }[file.suffix] + serializer, mode = self.extension_loader_mapping[file.suffix] with open(file, mode) as f: loaded_object = serializer.load(f) data.append(loaded_object) - data_collection = History(history=data) + data_collection = cls(history=data) return data_collection From 8a0624cbdff69b0c7b739a3789a0e3dcf4709690 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 11 Apr 2023 10:53:29 -0400 Subject: [PATCH 054/446] chore: remove excess imports --- autora/controller/serializer/__init__.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/autora/controller/serializer/__init__.py b/autora/controller/serializer/__init__.py index c550111c..6c7606b8 100644 --- a/autora/controller/serializer/__init__.py +++ b/autora/controller/serializer/__init__.py @@ -1,8 +1,7 @@ import pickle import tempfile from pathlib import Path -from types import MappingProxyType -from typing import Mapping, NamedTuple, Optional, Tuple, Type, Union +from typing import Mapping, NamedTuple, Optional, Type, Union import numpy as np From 9e90744f0d1f46f06228084fdb3abc6c9eb6cd43 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 11 Apr 2023 11:32:17 -0400 Subject: [PATCH 055/446] feat: add broken example of custom serializer --- autora/controller/serializer/__init__.py | 118 +++++++++++++---------- 1 file changed, 67 insertions(+), 51 deletions(-) diff --git a/autora/controller/serializer/__init__.py b/autora/controller/serializer/__init__.py index 6c7606b8..6d55cd95 100644 --- a/autora/controller/serializer/__init__.py +++ b/autora/controller/serializer/__init__.py @@ -26,7 +26,73 @@ class LoadSpec(NamedTuple): class HistorySerializer: - """Serializes and deserializes History objects.""" + """Serializes and deserializes History objects. + + Examples: + Custom types + + We can specify a different method for saving (and loading) different result types. + Here we specify a custom json encoder which works for dataclasses like the `Result` + object used by the `History` class. + >>> import dataclasses, json, pathlib, importlib + >>> from autora.controller.state.history import Result + >>> class EnhancedJSONEncoder(json.JSONEncoder): + ... # Inspired by https://stackoverflow.com/a/51286749 + ... def default(self, o): + ... if dataclasses.is_dataclass(o) and not isinstance(o, type): + ... cls = ".".join([type(o).__module__, type(o).__name__]) + ... if cls == "autora.controller.state.history.Result": + ... return (cls, self.default(o.data)) + ... else: + ... return (cls, dataclasses.asdict(o)) + ... return super().default(o) + + >>> class JSONSerializer: + ... @staticmethod + ... def dump(data, file): + ... json.dump(data, file, cls=EnhancedJSONEncoder) + ... @staticmethod + ... def load(file): + ... d: Dict = json.load(file) + ... cls, data, kind = d["cls"], d["data"]["data"], d["data"]["kind"] + ... o = importlib.import_module(cls)(**data) + ... return Result(data=o, kind=kind) + + >>> custom_dump_mapping = { + ... None: DumpSpec("yaml", YAMLSerializer, "w+"), + ... ResultKind.METADATA: DumpSpec("json", JSONSerializer, "w+"), + ... ResultKind.PARAMS: DumpSpec("json", JSONSerializer, "w+"), + ... ResultKind.CONDITION: DumpSpec("yaml", YAMLSerializer, "w+"), + ... ResultKind.OBSERVATION: DumpSpec("yaml", YAMLSerializer, "w+"), + ... ResultKind.THEORY: DumpSpec("pickle", pickle, "w+b"), + ... } + >>> custom_load_mapping = { + ... ".yaml": (YAMLSerializer, "r"), + ... ".pickle": (pickle, "rb"), + ... ".json": (JSONSerializer, "r"), + ... } + + >>> from autora.variable import VariableCollection + >>> c = History(params={"a": "param", "nested": {"param": "dict"}}, + ... metadata=VariableCollection()) + >>> with tempfile.TemporaryDirectory() as d: + ... sc = HistorySerializer(d, + ... result_kind_serializer_mapping=custom_dump_mapping, + ... extension_loader_mapping=custom_load_mapping) + ... sc.dump(c) + ... with open(pathlib.Path(d, "00000000-METADATA.json")) as f: + ... print("METADATA: ", '\\n'.join(f.readlines())) + ... with open(pathlib.Path(d, "00000001-PARAMS.json")) as f: + ... print("PARAMS: ", '\\n'.join(f.readlines())) + ... # doctest: +NORMALIZE_WHITESPACE + METADATA: ["autora.controller.state.history.Result", {"data": {"independent_variables": [], "dependent_variables": [], "covariates": []}, "kind": "METADATA"}] + PARAMS: ["autora.controller.state.history.Result", {"data": {"a": "param", "nested": {"param": "dict"}}, "kind": "PARAMS"}] + + + + + + """ def __init__( self, @@ -123,56 +189,6 @@ def dump(self, data_collection: SupportsControllerStateHistory): ['00000000-METADATA.yaml', '00000001-CONDITION.yaml', '00000002-OBSERVATION.yaml', '00000003-THEORY.pickle'] - We can specify a different method for saving (and loading) different result types. - Here we specify a custom json encoder which works for dataclasses like the `Result` - object used by the `History` class. - >>> import dataclasses, json, pathlib - >>> class EnhancedJSONEncoder(json.JSONEncoder): - ... # From https://stackoverflow.com/a/51286749 - ... def default(self, o): - ... if dataclasses.is_dataclass(o): - ... return dataclasses.asdict(o) - ... return super().default(o) - - >>> class JSONSerializer: - ... @staticmethod - ... def dump(data, file): - ... json.dump(data, file, cls=EnhancedJSONEncoder) - ... @staticmethod - ... def load(file): - ... d: Dict = json.load(file) - ... return Result(**d) - - >>> custom_dump_mapping = { - ... None: DumpSpec("yaml", YAMLSerializer, "w+"), - ... ResultKind.METADATA: DumpSpec("json", JSONSerializer, "w+"), - ... ResultKind.PARAMS: DumpSpec("json", JSONSerializer, "w+"), - ... ResultKind.CONDITION: DumpSpec("yaml", YAMLSerializer, "w+"), - ... ResultKind.OBSERVATION: DumpSpec("yaml", YAMLSerializer, "w+"), - ... ResultKind.THEORY: DumpSpec("pickle", pickle, "w+b"), - ... } - >>> custom_load_mapping = { - ... ".yaml": (YAMLSerializer, "r"), - ... ".pickle": (pickle, "rb"), - ... ".json": (JSONSerializer, "r"), - ... } - >>> c = c.update(params={"a": "param", "nested": {"param": "dict"}}) - >>> with tempfile.TemporaryDirectory() as d: - ... sc = HistorySerializer(d, - ... result_kind_serializer_mapping=custom_dump_mapping, - ... extension_loader_mapping=custom_load_mapping) - ... sc.dump(c) - ... with open(pathlib.Path(d, "00000004-PARAMS.json")) as f: - ... print("PARAMS: ", '\\n'.join(f.readlines())) - ... with open(pathlib.Path(d, "00000000-METADATA.json")) as f: - ... print("METADATA: ", '\\n'.join(f.readlines())) - ... # doctest: +NORMALIZE_WHITESPACE - PARAMS: {"data": {"a": "param", "nested": {"param": "dict"}}, "kind": "PARAMS"} - METADATA: {"data": [{"independent_variables": [], - "dependent_variables": [], - "covariates": []}], - "kind": "METADATA"} - """ path = self.path From 48a4f8b690b2fed698a10ad90283c5c08022f1bd Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 11 Apr 2023 11:34:43 -0400 Subject: [PATCH 056/446] revert: make parametrizable dumper loader private again --- autora/controller/serializer/__init__.py | 117 ++++------------------- 1 file changed, 21 insertions(+), 96 deletions(-) diff --git a/autora/controller/serializer/__init__.py b/autora/controller/serializer/__init__.py index 6d55cd95..319d3432 100644 --- a/autora/controller/serializer/__init__.py +++ b/autora/controller/serializer/__init__.py @@ -14,117 +14,42 @@ from autora.controller.state import History -class DumpSpec(NamedTuple): +class _DumpSpec(NamedTuple): extension: str serializer: SupportsLoadDump mode: str -class LoadSpec(NamedTuple): +class _LoadSpec(NamedTuple): serializer: SupportsLoadDump mode: str class HistorySerializer: - """Serializes and deserializes History objects. - - Examples: - Custom types - - We can specify a different method for saving (and loading) different result types. - Here we specify a custom json encoder which works for dataclasses like the `Result` - object used by the `History` class. - >>> import dataclasses, json, pathlib, importlib - >>> from autora.controller.state.history import Result - >>> class EnhancedJSONEncoder(json.JSONEncoder): - ... # Inspired by https://stackoverflow.com/a/51286749 - ... def default(self, o): - ... if dataclasses.is_dataclass(o) and not isinstance(o, type): - ... cls = ".".join([type(o).__module__, type(o).__name__]) - ... if cls == "autora.controller.state.history.Result": - ... return (cls, self.default(o.data)) - ... else: - ... return (cls, dataclasses.asdict(o)) - ... return super().default(o) - - >>> class JSONSerializer: - ... @staticmethod - ... def dump(data, file): - ... json.dump(data, file, cls=EnhancedJSONEncoder) - ... @staticmethod - ... def load(file): - ... d: Dict = json.load(file) - ... cls, data, kind = d["cls"], d["data"]["data"], d["data"]["kind"] - ... o = importlib.import_module(cls)(**data) - ... return Result(data=o, kind=kind) - - >>> custom_dump_mapping = { - ... None: DumpSpec("yaml", YAMLSerializer, "w+"), - ... ResultKind.METADATA: DumpSpec("json", JSONSerializer, "w+"), - ... ResultKind.PARAMS: DumpSpec("json", JSONSerializer, "w+"), - ... ResultKind.CONDITION: DumpSpec("yaml", YAMLSerializer, "w+"), - ... ResultKind.OBSERVATION: DumpSpec("yaml", YAMLSerializer, "w+"), - ... ResultKind.THEORY: DumpSpec("pickle", pickle, "w+b"), - ... } - >>> custom_load_mapping = { - ... ".yaml": (YAMLSerializer, "r"), - ... ".pickle": (pickle, "rb"), - ... ".json": (JSONSerializer, "r"), - ... } - - >>> from autora.variable import VariableCollection - >>> c = History(params={"a": "param", "nested": {"param": "dict"}}, - ... metadata=VariableCollection()) - >>> with tempfile.TemporaryDirectory() as d: - ... sc = HistorySerializer(d, - ... result_kind_serializer_mapping=custom_dump_mapping, - ... extension_loader_mapping=custom_load_mapping) - ... sc.dump(c) - ... with open(pathlib.Path(d, "00000000-METADATA.json")) as f: - ... print("METADATA: ", '\\n'.join(f.readlines())) - ... with open(pathlib.Path(d, "00000001-PARAMS.json")) as f: - ... print("PARAMS: ", '\\n'.join(f.readlines())) - ... # doctest: +NORMALIZE_WHITESPACE - METADATA: ["autora.controller.state.history.Result", {"data": {"independent_variables": [], "dependent_variables": [], "covariates": []}, "kind": "METADATA"}] - PARAMS: ["autora.controller.state.history.Result", {"data": {"a": "param", "nested": {"param": "dict"}}, "kind": "PARAMS"}] - - - - - - """ + """Serializes and deserializes History objects.""" def __init__( self, path: Path, - result_kind_serializer_mapping: Optional[ - Mapping[Union[None, ResultKind], DumpSpec] - ] = None, - extension_loader_mapping: Optional[Mapping[str, LoadSpec]] = None, ): self.path = path self._check_path() - if result_kind_serializer_mapping is None: - result_kind_serializer_mapping = { - None: DumpSpec("yaml", YAMLSerializer, "w+"), - ResultKind.METADATA: DumpSpec("yaml", YAMLSerializer, "w+"), - ResultKind.PARAMS: DumpSpec("yaml", YAMLSerializer, "w+"), - ResultKind.CONDITION: DumpSpec("yaml", YAMLSerializer, "w+"), - ResultKind.OBSERVATION: DumpSpec("yaml", YAMLSerializer, "w+"), - ResultKind.THEORY: DumpSpec("pickle", pickle, "w+b"), - } - - self.result_kind_serializer_mapping: Mapping[ - Union[None, ResultKind], DumpSpec - ] = result_kind_serializer_mapping - - if extension_loader_mapping is None: - extension_loader_mapping = { - ".yaml": LoadSpec(YAMLSerializer, "r"), - ".pickle": LoadSpec(pickle, "rb"), - } - - self.extension_loader_mapping: Mapping[str, LoadSpec] = extension_loader_mapping + + self._result_kind_serializer_mapping: Mapping[ + Union[None, ResultKind], _DumpSpec + ] = { + None: _DumpSpec("yaml", YAMLSerializer, "w+"), + ResultKind.METADATA: _DumpSpec("yaml", YAMLSerializer, "w+"), + ResultKind.PARAMS: _DumpSpec("yaml", YAMLSerializer, "w+"), + ResultKind.CONDITION: _DumpSpec("yaml", YAMLSerializer, "w+"), + ResultKind.OBSERVATION: _DumpSpec("yaml", YAMLSerializer, "w+"), + ResultKind.THEORY: _DumpSpec("pickle", pickle, "w+b"), + } + + self._extension_loader_mapping: Mapping[str, _LoadSpec] = { + ".yaml": _LoadSpec(YAMLSerializer, "r"), + ".pickle": _LoadSpec(pickle, "rb"), + } def dump(self, data_collection: SupportsControllerStateHistory): """ @@ -195,7 +120,7 @@ def dump(self, data_collection: SupportsControllerStateHistory): self._check_path() for i, container in enumerate(data_collection.history): - extension, serializer, mode = self.result_kind_serializer_mapping[ + extension, serializer, mode = self._result_kind_serializer_mapping[ container.kind ] @@ -255,7 +180,7 @@ def load(self, cls: Type[SupportsControllerStateHistory] = History): data = [] for file in sorted(Path(path).glob("*")): - serializer, mode = self.extension_loader_mapping[file.suffix] + serializer, mode = self._extension_loader_mapping[file.suffix] with open(file, mode) as f: loaded_object = serializer.load(f) data.append(loaded_object) From fd66870e4f0807d02ca494ca80adf73429c86129 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Tue, 11 Apr 2023 11:42:58 -0400 Subject: [PATCH 057/446] feat: add typer --- autora/theorist/__main__.py | 9 ++++ poetry.lock | 98 +++++++++++++++++++++++++++++++++++-- pyproject.toml | 3 ++ 3 files changed, 106 insertions(+), 4 deletions(-) create mode 100644 autora/theorist/__main__.py diff --git a/autora/theorist/__main__.py b/autora/theorist/__main__.py new file mode 100644 index 00000000..bb1e5c7b --- /dev/null +++ b/autora/theorist/__main__.py @@ -0,0 +1,9 @@ +import typer + + +def main(name: str): + print(f"Hello {name}") + + +if __name__ == "__main__": + typer.run(main) diff --git a/poetry.lock b/poetry.lock index 25083ab1..f03e8a32 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. [[package]] name = "anyio" @@ -313,7 +313,7 @@ unicode-backport = ["unicodedata2"] name = "click" version = "8.1.3" description = "Composable command line interface toolkit" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -1312,6 +1312,31 @@ importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""} [package.extras] testing = ["coverage", "pyyaml"] +[[package]] +name = "markdown-it-py" +version = "2.2.0" +description = "Python port of markdown-it. Markdown parsing, done right!" +category = "main" +optional = false +python-versions = ">=3.7" +files = [ + {file = "markdown-it-py-2.2.0.tar.gz", hash = "sha256:7c9a5e412688bc771c67432cbfebcdd686c93ce6484913dccf06cb5a0bea35a1"}, + {file = "markdown_it_py-2.2.0-py3-none-any.whl", hash = "sha256:5a35f8d1870171d9acc47b99612dc146129b631baf04970128b568f190d0cc30"}, +] + +[package.dependencies] +mdurl = ">=0.1,<1.0" + +[package.extras] +benchmarking = ["psutil", "pytest", "pytest-benchmark"] +code-style = ["pre-commit (>=3.0,<4.0)"] +compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"] +linkify = ["linkify-it-py (>=1,<3)"] +plugins = ["mdit-py-plugins"] +profiling = ["gprof2dot"] +rtd = ["attrs", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"] +testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] + [[package]] name = "markupsafe" version = "2.1.1" @@ -1452,6 +1477,18 @@ files = [ {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"}, ] +[[package]] +name = "mdurl" +version = "0.1.2" +description = "Markdown URL utilities" +category = "main" +optional = false +python-versions = ">=3.7" +files = [ + {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"}, + {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"}, +] + [[package]] name = "mergedeep" version = "1.3.4" @@ -2349,7 +2386,7 @@ files = [ name = "pygments" version = "2.14.0" description = "Pygments is a syntax highlighting package written in Python." -category = "dev" +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -2870,6 +2907,26 @@ files = [ {file = "rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055"}, ] +[[package]] +name = "rich" +version = "13.3.3" +description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal" +category = "main" +optional = false +python-versions = ">=3.7.0" +files = [ + {file = "rich-13.3.3-py3-none-any.whl", hash = "sha256:540c7d6d26a1178e8e8b37e9ba44573a3cd1464ff6348b99ee7061b95d1c6333"}, + {file = "rich-13.3.3.tar.gz", hash = "sha256:dc84400a9d842b3a9c5ff74addd8eb798d155f36c1c91303888e0a66850d2a15"}, +] + +[package.dependencies] +markdown-it-py = ">=2.2.0,<3.0.0" +pygments = ">=2.13.0,<3.0.0" +typing-extensions = {version = ">=4.0.0,<5.0", markers = "python_version < \"3.9\""} + +[package.extras] +jupyter = ["ipywidgets (>=7.5.1,<9)"] + [[package]] name = "scikit-learn" version = "1.2.2" @@ -3008,6 +3065,18 @@ docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-g testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] +[[package]] +name = "shellingham" +version = "1.5.0.post1" +description = "Tool to Detect Surrounding Shell" +category = "main" +optional = false +python-versions = ">=3.7" +files = [ + {file = "shellingham-1.5.0.post1-py2.py3-none-any.whl", hash = "sha256:368bf8c00754fd4f55afb7bbb86e272df77e4dc76ac29dbcbb81a59e9fc15744"}, + {file = "shellingham-1.5.0.post1.tar.gz", hash = "sha256:823bc5fb5c34d60f285b624e7264f4dda254bc803a3774a147bf99c0e3004a28"}, +] + [[package]] name = "six" version = "1.16.0" @@ -3255,6 +3324,27 @@ lint = ["black (>=22.6.0)", "mdformat (>0.7)", "ruff (>=0.0.156)"] test = ["pre-commit", "pytest"] typing = ["mypy (>=0.990)"] +[[package]] +name = "typer" +version = "0.7.0" +description = "Typer, build great CLIs. Easy to code. Based on Python type hints." +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "typer-0.7.0-py3-none-any.whl", hash = "sha256:b5e704f4e48ec263de1c0b3a2387cd405a13767d2f907f44c1a08cbad96f606d"}, + {file = "typer-0.7.0.tar.gz", hash = "sha256:ff797846578a9f2a201b53442aedeb543319466870fbe1c701eab66dd7681165"}, +] + +[package.dependencies] +click = ">=7.1.1,<9.0.0" + +[package.extras] +all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<13.0.0)", "shellingham (>=1.3.0,<2.0.0)"] +dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"] +doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"] +test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<13.0.0)", "shellingham (>=1.3.0,<2.0.0)"] + [[package]] name = "typing-extensions" version = "4.4.0" @@ -3463,4 +3553,4 @@ tinkerforge = ["tinkerforge"] [metadata] lock-version = "2.0" python-versions = ">=3.8.10,<3.11" -content-hash = "462fb631f876e5145435416ab989b93a1b71cef94cb8d1afbf3e88762719824d" +content-hash = "be2c95a830dc249a8c33600793aa41c6f842fc6266db8b4363a53afd95fe8b30" diff --git a/pyproject.toml b/pyproject.toml index 90b4895c..43876b9f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,9 @@ sympy = "^1.10.1" tinkerforge = {version = "^2.1.25", optional=true} torch = "1.13.1" tqdm = "^4.64.0" +typer = "^0.7.0" +rich = "^13.3.3" +shellingham = "^1.5.0.post1" [tool.poetry.extras] tinkerforge = ["tinkerforge"] From 67c095aa2664b7476af9f40856a1c1d08116b075 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 13 Apr 2023 11:54:04 -0400 Subject: [PATCH 058/446] docs: add import for class plus debug flags --- autora/theorist/__main__.py | 38 +++++++++++++++++++++++++++++++++++-- 1 file changed, 36 insertions(+), 2 deletions(-) diff --git a/autora/theorist/__main__.py b/autora/theorist/__main__.py index bb1e5c7b..d67b7f2c 100644 --- a/autora/theorist/__main__.py +++ b/autora/theorist/__main__.py @@ -1,8 +1,42 @@ +import importlib +import logging +from typing import Type + import typer +from sklearn.base import BaseEstimator + +_logger = logging.getLogger(__name__) + + +def import_class(name: str) -> Type[BaseEstimator]: + """ + Load a class from a module by name. + + Args: + name: + + Examples: + >>> import_class("sklearn.linear_model.LinearRegressor") + + """ + components = name.split(".") + module_name, class_name = ".".join(components[:-1]), components[-1] + _logger.info(f"loading {module_name=}, {class_name=}") + module = importlib.import_module(module_name) + cls = getattr(module, class_name) + return cls + + +def main(regressor: str, verbose: bool = False, debug: bool = False): + + if verbose: + logging.basicConfig(level=logging.INFO) + if debug: + logging.basicConfig(level=logging.DEBUG) + regressor_class = import_class(regressor) -def main(name: str): - print(f"Hello {name}") + print(f"{regressor}: {regressor_class}") if __name__ == "__main__": From e0c8f36651fdf65f0cd317048e65bf7b9e8f98bb Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 13 Apr 2023 11:54:18 -0400 Subject: [PATCH 059/446] chore: remove broken plot_utils for darts --- autora/theorist/darts/plot_utils.py | 1129 --------------------------- 1 file changed, 1129 deletions(-) delete mode 100755 autora/theorist/darts/plot_utils.py diff --git a/autora/theorist/darts/plot_utils.py b/autora/theorist/darts/plot_utils.py deleted file mode 100755 index d256bf41..00000000 --- a/autora/theorist/darts/plot_utils.py +++ /dev/null @@ -1,1129 +0,0 @@ -import os -import typing -from typing import Optional - -import imageio -import matplotlib -import matplotlib.pyplot as plt -import numpy as np -import pandas -import seaborn as sns -import torch.nn -from matplotlib import pyplot -from matplotlib.gridspec import GridSpec - -import autora.config as aer_config -import autora.theorist.darts.darts_config as darts_config -from autora.theorist.object_of_study import Object_Of_Study - - -def generate_darts_summary_figures( - figure_names: typing.List[str], - titles: typing.List[str], - filters: typing.List[str], - title_suffix: str, - study_name: str, - y_name: str, - y_label: str, - y_sem_name: str, - x1_name: str, - x1_label: str, - x2_name: str, - x2_label: str, - x_limit: typing.List[float], - y_limit: typing.List[float], - best_model_name: str, - figure_size: typing.Tuple[int, int], - y_reference: Optional[typing.List[float]] = None, - y_reference_label: str = "", - arch_samp_filter: Optional[str] = None, -): - """ - Generates a summary figure for a given DARTS study. - The figure can be composed of different summary plots. - - Arguments: - figure_names: list of strings with the names of the figures to be generated - titles: list of strings with the titles of the figures to be generated - filters: list of strings with the theorist filters to be used to select the models to be - used in the figures - title_suffix: string with the suffix to be added to the titles of the figures - study_name: string with the name of the study (used to identify the study folder) - y_name: string with the name of the y-axis variable - y_label: string with the label of the y-axis variable - y_sem_name: string with the name of the y-axis coding the standard error of the mean - x1_name: string with the name of the (first) x-axis variable - x1_label: string with the label of the (first) x-axis variable - x2_name: string with the name of the second x-axis variable - x2_label: string with the label of the second x-axis variable - x_limit: list with the limits of the x-axis - y_limit: list with the limits of the y-axis - best_model_name: string with the name of the best model to be highlighted in the figure - figure_size: list with the size of the figure - y_reference: list with the values of the reference line - y_reference_label: string with the label of the reference line - arch_samp_filter: string with the name of the filter to be used to select the - samples of the architecture - - """ - - for idx, (figure_name, title, theorist_filter) in enumerate( - zip(figure_names, titles, filters) - ): - - print("##########################: " + figure_name) - title = title + title_suffix - if idx > 0: # after legend - show_legend = False - figure_dimensions = figure_size - else: - show_legend = True - figure_dimensions = (6, 6) - if idx > 1: # after original darts - y_label = " " - - plot_darts_summary( - study_name=study_name, - title=title, - y_name=y_name, - y_label=y_label, - y_sem_name=y_sem_name, - x1_name=x1_name, - x1_label=x1_label, - x2_name=x2_name, - x2_label=x2_label, - metric="mean_min", - x_limit=x_limit, - y_limit=y_limit, - best_model_name=best_model_name, - theorist_filter=theorist_filter, - arch_samp_filter=arch_samp_filter, - figure_name=figure_name, - figure_dimensions=figure_dimensions, - legend_loc=aer_config.legend_loc, - legend_font_size=aer_config.legend_font_size, - axis_font_size=aer_config.axis_font_size, - title_font_size=aer_config.title_font_size, - show_legend=show_legend, - y_reference=y_reference, - y_reference_label=y_reference_label, - save=True, - ) - - -def plot_darts_summary( - study_name: str, - y_name: str, - x1_name: str, - x2_name: str = "", - y_label: str = "", - x1_label: str = "", - x2_label: str = "", - y_sem_name: Optional[str] = None, - metric: str = "min", - y_reference: Optional[typing.List[float]] = None, - y_reference_label: str = "", - figure_dimensions: Optional[typing.Tuple[int, int]] = None, - title: str = "", - legend_loc: int = 0, - legend_font_size: int = 8, - axis_font_size: int = 10, - title_font_size: int = 10, - show_legend: bool = True, - y_limit: Optional[typing.List[float]] = None, - x_limit: Optional[typing.List[float]] = None, - theorist_filter: Optional[str] = None, - arch_samp_filter: Optional[str] = None, - best_model_name: Optional[str] = None, - save: bool = False, - figure_name: str = "figure", -): - """ - Generates a single summary plot for a given DARTS study. - - Arguments: - study_name: string with the name of the study (used to identify the study folder) - y_name: string with the name of the y-axis variable - x1_name: string with the name of the (first) x-axis variable - x2_name: string with the name of the second x-axis variable - y_label: string with the label of the y-axis variable - x1_label: string with the label of the (first) x-axis variable - x2_label: string with the label of the second x-axis variable - y_sem_name: string with the name of the y-axis coding the standard error of the mean - metric: string with the metric to be used to select the best model - y_reference: list with the values of the reference line - y_reference_label: string with the label of the reference line - figure_dimensions: list with the size of the figure - title: string with the title of the figure - legend_loc: integer with the location of the legend - legend_font_size: integer with the font size of the legend - axis_font_size: integer with the font size of the axis - title_font_size: integer with the font size of the title - show_legend: boolean with the flag to show the legend - y_limit: list with the limits of the y-axis - x_limit: list with the limits of the x-axis - theorist_filter: string with the name of the filter to be used to select the theorist - arch_samp_filter: string with the name of the filter to be used to select the architecture - best_model_name: string with the name of the best model to be highlighted in the figure - save: boolean with the flag to save the figure - figure_name: string with the name of the figure - """ - - palette = "PuBu" - - if figure_dimensions is None: - figure_dimensions = (4, 3) - - if y_label == "": - y_label = y_name - - if x1_label == "": - x1_label = x1_name - - if x2_label == "": - x2_label = x2_name - - if y_reference_label == "": - y_reference_label = "Data Generating Model" - - # determine directory for study results and figures - results_path = ( - aer_config.studies_folder - + study_name - + "/" - + aer_config.models_folder - + aer_config.models_results_folder - ) - - figures_path = ( - aer_config.studies_folder - + study_name - + "/" - + aer_config.models_folder - + aer_config.models_results_figures_folder - ) - - # read in all csv files - files = list() - for file in os.listdir(results_path): - if file.endswith(".csv"): - if "model_" not in file: - continue - - if theorist_filter is not None: - if theorist_filter not in file: - continue - files.append(os.path.join(results_path, file)) - - print("Found " + str(len(files)) + " files.") - - # generate a plot dictionary - plot_dict: typing.Dict[typing.Optional[str], typing.List] = dict() - plot_dict[darts_config.csv_arch_file_name] = list() - plot_dict[y_name] = list() - plot_dict[x1_name] = list() - if x2_name != "": - plot_dict[x2_name] = list() - if y_sem_name is not None: - plot_dict[y_sem_name] = list() - - # load csv files into a common dictionary - for file in files: - data = pandas.read_csv(file, header=0) - - valid_data = list() - - # filter for arch samp - if arch_samp_filter is not None: - for idx, arch_file_name in enumerate(data[darts_config.csv_arch_file_name]): - arch_samp = int( - float(arch_file_name.split("_sample", 1)[1].split("_", 1)[0]) - ) - if arch_samp == arch_samp_filter: - valid_data.append(idx) - else: - for idx in range(len(data[darts_config.csv_arch_file_name])): - valid_data.append(idx) - - plot_dict[darts_config.csv_arch_file_name].extend( - data[darts_config.csv_arch_file_name][valid_data] - ) - if y_name in data.keys(): - plot_dict[y_name].extend(data[y_name][valid_data]) - else: - raise Exception( - 'Could not find key "' + y_name + '" in the data file: ' + str(file) - ) - if x1_name in data.keys(): - plot_dict[x1_name].extend(data[x1_name][valid_data]) - else: - raise Exception( - 'Could not find key "' + x1_name + '" in the data file: ' + str(file) - ) - if x2_name != "": - if x2_name in data.keys(): - plot_dict[x2_name].extend(data[x2_name][valid_data]) - else: - raise Exception( - 'Could not find key "' - + x2_name - + '" in the data file: ' - + str(file) - ) - if y_sem_name is not None: - # extract seed number from model file name - - if y_sem_name in data.keys(): - plot_dict[y_sem_name].extend(data[y_sem_name]) - elif y_sem_name == "seed": - y_sem_list = list() - for file_name in data[darts_config.csv_arch_file_name][valid_data]: - y_sem_list.append( - int(float(file_name.split("_s_", 1)[1].split("_sample", 1)[0])) - ) - plot_dict[y_sem_name].extend(y_sem_list) - - else: - - raise Exception( - 'Could not find key "' - + y_sem_name - + '" in the data file: ' - + str(file) - ) - - model_name_list = plot_dict[darts_config.csv_arch_file_name] - x1_data = np.asarray(plot_dict[x1_name]) - y_data = np.asarray(plot_dict[y_name]) - if x2_name == "": # determine for each value of x1 the corresponding y - x1_data = np.asarray(plot_dict[x1_name]) - x1_unique = np.sort(np.unique(x1_data)) - - y_plot = np.empty(x1_unique.shape) - y_plot[:] = np.nan - y_sem_plot = np.empty(x1_unique.shape) - y_sem_plot[:] = np.nan - y2_plot = np.empty(x1_unique.shape) - y2_plot[:] = np.nan - x1_plot = np.empty(x1_unique.shape) - x1_plot[:] = np.nan - for idx_unique, x1_unique_val in enumerate(x1_unique): - y_match = list() - model_name_match = list() - for idx_data, x_data_val in enumerate(x1_data): - if x1_unique_val == x_data_val: - y_match.append(y_data[idx_data]) - model_name_match.append(model_name_list[idx_data]) - x1_plot[idx_unique] = x1_unique_val - - if metric == "min": - y_plot[idx_unique] = np.min(y_match) - idx_target = np.argmin(y_match) - legend_label_spec = " (min)" - elif metric == "max": - y_plot[idx_unique] = np.max(y_match) - idx_target = np.argmax(y_match) - legend_label_spec = " (max)" - elif metric == "mean": - y_plot[idx_unique] = np.mean(y_match) - idx_target = 0 - legend_label_spec = " (avg)" - elif metric == "mean_min": - y_plot[idx_unique] = np.mean(y_match) - y2_plot[idx_unique] = np.min(y_match) - idx_target = np.argmin(y_match) - legend_label_spec = " (avg)" - legend_label2_spec = " (min)" - elif metric == "mean_max": - y_plot[idx_unique] = np.mean(y_match) - y2_plot[idx_unique] = np.max(y_match) - idx_target = np.argmax(y_match) - legend_label_spec = " (avg)" - legend_label2_spec = " (max)" - else: - raise Exception( - 'Argument "metric" may either be "min", "max", "mean", "mean_min" or "min_max".' - ) - - # compute standard error along given dimension - if y_sem_name is not None: - y_sem_data = np.asarray(plot_dict[y_sem_name]) - y_sem_unique = np.sort(np.unique(y_sem_data)) - y_sem = np.empty(y_sem_unique.shape) - # first average y over all other variables - for idx_y_sem_unique, y_sem_unique_val in enumerate(y_sem_unique): - y_sem_match = list() - for idx_y_sem, ( - y_sem_data_val, - x1_data_val, - y_data_val, - ) in enumerate(zip(y_sem_data, x1_data, y_data)): - if ( - y_sem_unique_val == y_sem_data_val - and x1_unique_val == x1_data_val - ): - y_sem_match.append(y_data_val) - y_sem[idx_y_sem_unique] = np.mean(y_sem_match) - # now compute sem - y_sem_plot[idx_unique] = np.nanstd(y_sem) / np.sqrt(len(y_sem)) - - print( - x1_label - + " = " - + str(x1_unique_val) - + " (" - + str(y_plot[idx_unique]) - + "): " - + model_name_match[idx_target] - ) - - else: # determine for each combination of x1 and x2 (unique rows) the lowest y - x2_data = np.asarray(plot_dict[x2_name]) - x2_unique = np.sort(np.unique(x2_data)) - - y_plot = list() - y_sem_plot = list() - y2_plot = list() - x1_plot = list() - x2_plot = list() - for idx_x2_unique, x2_unique_val in enumerate(x2_unique): - - # collect all x1 and y values matching the current x2 value - model_name_x2_match = list() - y_x2_match = list() - x1_x2_match = list() - for idx_x2_data, x2_data_val in enumerate(x2_data): - if x2_unique_val == x2_data_val: - model_name_x2_match.append(model_name_list[idx_x2_data]) - y_x2_match.append(y_data[idx_x2_data]) - x1_x2_match.append(x1_data[idx_x2_data]) - - # now determine unique x1 values for current x2 value - x1_unique = np.sort(np.unique(x1_x2_match)) - x1_x2_plot = np.empty(x1_unique.shape) - x1_x2_plot[:] = np.nan - y_x2_plot = np.empty(x1_unique.shape) - y_x2_plot[:] = np.nan - y_sem_x2_plot = np.empty(x1_unique.shape) - y_sem_x2_plot[:] = np.nan - y2_x2_plot = np.empty(x1_unique.shape) - y2_x2_plot[:] = np.nan - for idx_x1_unique, x1_unique_val in enumerate(x1_unique): - y_x2_x1_match = list() - model_name_x2_x1_match = list() - for idx_x1_data, x1_data_val in enumerate(x1_x2_match): - if x1_unique_val == x1_data_val: - model_name_x2_x1_match.append(model_name_x2_match[idx_x1_data]) - y_x2_x1_match.append(y_x2_match[idx_x1_data]) - x1_x2_plot[idx_x1_unique] = x1_unique_val - - if metric == "min": - y_x2_plot[idx_x1_unique] = np.min(y_x2_x1_match) - idx_target = np.argmin(y_x2_x1_match) - legend_label_spec = " (min)" - elif metric == "max": - y_x2_plot[idx_x1_unique] = np.max(y_x2_x1_match) - idx_target = np.argmax(y_x2_x1_match) - legend_label_spec = " (max)" - elif metric == "mean": - y_x2_plot[idx_x1_unique] = np.mean(y_x2_x1_match) - idx_target = 0 - legend_label_spec = " (avg)" - elif metric == "mean_min": - y_x2_plot[idx_x1_unique] = np.mean(y_x2_x1_match) - y2_x2_plot[idx_x1_unique] = np.min(y_x2_x1_match) - idx_target = np.argmin(y_x2_x1_match) - legend_label_spec = " (avg)" - legend_label2_spec = " (min)" - elif metric == "mean_max": - y_x2_plot[idx_x1_unique] = np.mean(y_x2_x1_match) - y2_x2_plot[idx_x1_unique] = np.max(y_x2_x1_match) - idx_target = np.argmax(y_x2_x1_match) - legend_label_spec = " (avg)" - legend_label2_spec = " (max)" - else: - raise Exception( - 'Argument "metric" may either be "min", "max", "mean", ' - '"mean_min" or "min_max".' - ) - - # compute standard error along given dimension - if y_sem_name is not None: - y_sem_data = np.asarray(plot_dict[y_sem_name]) - y_sem_unique = np.sort(np.unique(y_sem_data)) - y_sem = np.empty(y_sem_unique.shape) - # first average y over all other variables - for idx_y_sem_unique, y_sem_unique_val in enumerate(y_sem_unique): - y_sem_match = list() - for idx_y_sem, ( - y_sem_data_val, - x1_data_val, - x2_data_val, - y_data_val, - ) in enumerate(zip(y_sem_data, x1_data, x2_data, y_data)): - if ( - y_sem_unique_val == y_sem_data_val - and x1_unique_val == x1_data_val - and x2_unique_val == x2_data_val - ): - y_sem_match.append(y_data_val) - y_sem[idx_y_sem_unique] = np.nanmean(y_sem_match) - # now compute sem - y_sem_x2_plot[idx_x1_unique] = np.nanstd(y_sem) / np.sqrt( - len(y_sem) - ) - - if metric == "mean_min" or metric == "mean_max": - best_val_str = str(y2_x2_plot[idx_x1_unique]) - else: - best_val_str = str(y_x2_plot[idx_x1_unique]) - - print( - x1_label - + " = " - + str(x1_unique_val) - + ", " - + x2_label - + " = " - + str(x2_unique_val) - + " (" - + best_val_str - + "): " - + model_name_x2_x1_match[idx_target] - ) - - y_plot.append(y_x2_plot) - y2_plot.append(y2_x2_plot) - y_sem_plot.append(y_sem_x2_plot) - x1_plot.append(x1_x2_plot) - x2_plot.append(x2_unique_val) - # plot - # plt.axhline - - # determine best model coordinates - best_model_x1 = None - best_model_x2 = None - best_model_y = None - if best_model_name is not None: - theorist = best_model_name.split("weights_", 1)[1].split("_v_", 1)[0] - if theorist_filter is not None: - if theorist_filter == theorist: - determine_best_model = True - else: - determine_best_model = False - else: - determine_best_model = True - - if determine_best_model: - idx = plot_dict[darts_config.csv_arch_file_name].index(best_model_name) - best_model_x1 = plot_dict[x1_name][idx] - best_model_x2 = plot_dict[x2_name][idx] - best_model_y = plot_dict[y_name][idx] - - fig, ax = pyplot.subplots(figsize=figure_dimensions) - - if x2_name == "": - - colors = sns.color_palette(palette, 10) - color = colors[-1] - full_label = "Reconstructed Model" + legend_label_spec - sns.lineplot( - x=x1_plot, - y=y_plot, - marker="o", - linewidth=2, - ax=ax, - label=full_label, - color=color, - ) - - # draw error bars - if y_sem_name is not None: - ax.errorbar(x=x1_plot, y=y_plot, yerr=y_sem_plot, color=color) - - # draw second y value - if metric == "mean_min" or metric == "mean_max": - full_label = "Reconstructed Model" + legend_label2_spec - ax.plot(x1_plot, y2_plot, "*", linewidth=2, label=full_label, color=color) - - if show_legend: - handles, _ = ax.get_legend_handles_labels() - ax.legend(handles=handles, loc=legend_loc) - plt.setp(ax.get_legend().get_texts(), fontsize=legend_font_size) - - # draw selected model - if best_model_x1 is not None and best_model_y is not None: - ax.plot( - best_model_x1, - best_model_y, - "o", - fillstyle="none", - color="black", - markersize=10, - ) - - ax.set_xlabel(x1_label, fontsize=axis_font_size) - ax.set_ylabel(y_label, fontsize=axis_font_size) - ax.set_title(title, fontsize=title_font_size) - - if y_limit is not None: - ax.set_ylim(y_limit) - - if x_limit is not None: - ax.set_xlim(x_limit) - - # generate legend - # ax.scatter(x1_plot, y_plot, marker='.', c='r') - # g = sns.relplot(data=data_plot, x=x1_label, y=y_label, ax=ax) - # g._legend.remove() - if y_reference is not None: - ax.axhline( - y_reference, c="black", linestyle="dashed", label=y_reference_label - ) - - if show_legend: - # generate legend - handles, _ = ax.get_legend_handles_labels() - ax.legend(handles=handles, loc=legend_loc) - plt.setp(ax.get_legend().get_texts(), fontsize=legend_font_size) - else: - - colors = sns.color_palette(palette, len(x2_plot)) - - for idx, x2 in enumerate(x2_plot): - - x1_plot_line = x1_plot[idx] - y_plot_line = y_plot[idx] - label = x2_label + "$ = " + str(x2) + "$" + legend_label_spec - color = colors[idx] - - sns.lineplot( - x=x1_plot_line, - y=y_plot_line, - marker="o", - linewidth=2, - ax=ax, - label=label, - color=color, - alpha=1, - ) - - # draw error bars - if y_sem_name is not None: - y_sem_plot_line = y_sem_plot[idx] - ax.errorbar( - x=x1_plot_line, - y=y_plot_line, - yerr=y_sem_plot_line, - color=color, - alpha=1, - ) - - # # draw second y value on top - # for idx, x2 in enumerate(x2_plot): - # x1_plot_line = x1_plot[idx] - # color = colors[idx] - # - # if metric == 'mean_min' or metric == 'mean_max': - # y2_plot_line = y2_plot[idx] - # label = x2_label + '$ = ' + str(x2) + "$" + legend_label2_spec - # ax.plot(x1_plot_line, y2_plot_line, '*', linewidth=2, label=label, color=color) - - # draw selected model - if best_model_x1 is not None and best_model_y is not None: - ax.plot( - best_model_x1, - best_model_y, - "o", - fillstyle="none", - color="black", - markersize=10, - ) - - for idx, x2 in enumerate(x2_plot): - if best_model_x2 == x2: - color = colors[idx] - ax.plot( - best_model_x1, - best_model_y, - "*", - linewidth=2, - label="Best Model", - color=color, - ) - - if y_reference is not None: - ax.axhline( - y_reference, c="black", linestyle="dashed", label=y_reference_label - ) - - handles, _ = ax.get_legend_handles_labels() - leg = ax.legend( - handles=handles, loc=legend_loc, bbox_to_anchor=(1.05, 1) - ) # , title='Legend' - plt.setp(ax.get_legend().get_texts(), fontsize=legend_font_size) - - if not show_legend: - leg.remove() - - if y_limit is not None: - ax.set_ylim(y_limit) - - if x_limit is not None: - ax.set_xlim(x_limit) - - sns.despine(trim=True) - ax.set_ylabel(y_label, fontsize=axis_font_size) - ax.set_xlabel(x1_label, fontsize=axis_font_size) - ax.set_title(title, fontsize=title_font_size) - plt.show() - - # save plot - if save: - if not os.path.exists(figures_path): - os.mkdir(figures_path) - fig.savefig(os.path.join(figures_path, figure_name)) - - -def plot_model_graph( - study_name: str, - arch_weights_name: str, - model_weights_name: str, - object_of_study: Object_Of_Study, - figure_name: str = "graph", -): - """ - Plot the graph of the DARTS model. - - Arguments: - study_name: name of the study (used to identify the relevant study folder) - arch_weights_name: name of the architecture weights file - model_weights_name: name of the model weights file (that contains the trained parameters) - object_of_study: name of the object of study - figure_name: name of the figure - """ - - import os - - import autora.theorist.darts.utils as utils - import autora.theorist.darts.visualize as viz - - figures_path = ( - aer_config.studies_folder - + study_name - + "/" - + aer_config.models_folder - + aer_config.models_results_figures_folder - ) - - model = load_model( - study_name, model_weights_name, arch_weights_name, object_of_study - ) - - (n_params_total, n_params_base, param_list) = model.countParameters( - print_parameters=True - ) - genotype = model.genotype() - filepath = os.path.join(figures_path, figure_name) - viz.plot( - genotype.normal, - filepath, - file_format="png", - view_file=True, - full_label=True, - param_list=param_list, - input_labels=object_of_study.__get_input_labels__(), - out_dim=object_of_study.__get_output_dim__(), - out_fnc=utils.get_output_str(object_of_study.__get_output_type__()), - ) - - -# old - - -def load_model( - study_name: str, - model_weights_name: str, - arch_weights_name: str, - object_of_study: Object_Of_Study, -) -> torch.nn.Module: - """ - Load the model. - - Arguments: - study_name: name of the study (used to identify the relevant study folder) - model_weights_name: name of the model weights file (that contains the trained parameters) - arch_weights_name: name of the architecture weights file - object_of_study: name of the object of study - - Returns: - model: DARTS model - """ - - import os - - import torch - - import autora.theorist.darts.utils as utils - from autora.theorist.darts.model_search import Network - - num_output = object_of_study.__get_output_dim__() - num_input = object_of_study.__get_input_dim__() - k = int(float(arch_weights_name.split("_k_", 1)[1].split("_s_", 1)[0])) - - results_weights_path = ( - aer_config.studies_folder - + study_name - + "/" - + aer_config.models_folder - + aer_config.models_results_weights_folder - ) - - model_path = os.path.join(results_weights_path, model_weights_name + ".pt") - arch_path = os.path.join(results_weights_path, arch_weights_name + ".pt") - criterion = utils.sigmid_mse - model = Network(num_output, criterion, steps=k, n_input_states=num_input) - utils.load(model, model_path) - alphas_normal = torch.load(arch_path) - model.fix_architecture(True, new_weights=alphas_normal) - - return model - - -class DebugWindow: - """ - A window with plots that are used for debugging. - """ - - def __init__( - self, - num_epochs: int, - numArchEdges: int = 1, - numArchOps: int = 1, - ArchOpsLabels: typing.Tuple = (), - fitPlot3D: bool = False, - show_arch_weights: bool = True, - ): - """ - Initializes the debug window. - - Arguments: - num_epochs: number of architecture training epochs - numArchEdges: number of architecture edges - numArchOps: number of architecture operations - ArchOpsLabels: list of architecture operation labels - fitPlot3D: if True, the 3D plot of the fit is shown - show_arch_weights: if True, the architecture weights are shown - """ - - # initialization - matplotlib.use("TkAgg") # need to add this for PyCharm environment - - plt.ion() - - # SETTINGS - self.show_arch_weights = show_arch_weights - self.fontSize = 10 - - self.performancePlot_limit = (0, 1) - self.modelFitPlot_limit = (0, 500) - self.mismatchPlot_limit = (0, 1) - self.architectureWeightsPlot_limit = (0.1, 0.2) - - self.numPatternsShown = 100 - - # FIGURE - self.fig = plt.figure() - self.fig.set_size_inches(13, 7) - - if self.show_arch_weights is False: - numArchEdges = 0 - - # set up grid - numRows = np.max((1 + np.ceil((numArchEdges + 1) / 4), 2)) - gs = GridSpec(numRows.astype(int), 4, figure=self.fig) - - self.fig.subplots_adjust( - left=0.1, bottom=0.1, right=0.90, top=0.9, wspace=0.4, hspace=0.5 - ) - self.modelGraph = self.fig.add_subplot(gs[1, 0]) - self.performancePlot = self.fig.add_subplot(gs[0, 0]) - self.modelFitPlot = self.fig.add_subplot(gs[0, 1]) - if fitPlot3D: - self.mismatchPlot = self.fig.add_subplot(gs[0, 2], projection="3d") - else: - self.mismatchPlot = self.fig.add_subplot(gs[0, 2]) - self.examplePatternsPlot = self.fig.add_subplot(gs[0, 3]) - - self.architecturePlot = [] - - for edge in range(numArchEdges): - row = np.ceil((edge + 2) / 4).astype(int) - col = (edge + 1) % 4 - self.architecturePlot.append(self.fig.add_subplot(gs[row, col])) - - self.colors = ( - "black", - "red", - "green", - "blue", - "purple", - "orange", - "brown", - "pink", - "grey", - "olive", - "cyan", - "yellow", - "skyblue", - "coral", - "magenta", - "seagreen", - "sandybrown", - ) - - # PERFORMANCE PLOT - x = 1 - y = 1 - (self.train_error,) = self.performancePlot.plot(x, y, "r-") - (self.valid_error,) = self.performancePlot.plot(x, y, "b", linestyle="dashed") - - # set labels - self.performancePlot.set_xlabel("Epoch", fontsize=self.fontSize) - self.performancePlot.set_ylabel("Cross-Entropy Loss", fontsize=self.fontSize) - self.performancePlot.set_title("Performance", fontsize=self.fontSize) - self.performancePlot.legend( - (self.train_error, self.valid_error), ("training error", "validation error") - ) - - # adjust axes - self.performancePlot.set_xlim(0, num_epochs) - self.performancePlot.set_ylim( - self.performancePlot_limit[0], self.performancePlot_limit[1] - ) - - # MODEL FIT PLOT - x = 1 - y = 1 - (self.BIC,) = self.modelFitPlot.plot(x, y, color="black") - (self.AIC,) = self.modelFitPlot.plot(x, y, color="grey") - - # set labels - self.modelFitPlot.set_xlabel("Epoch", fontsize=self.fontSize) - self.modelFitPlot.set_ylabel("Information Criterion", fontsize=self.fontSize) - self.modelFitPlot.set_title("Model Fit", fontsize=self.fontSize) - self.modelFitPlot.legend((self.BIC, self.AIC), ("BIC", "AIC")) - - # adjust axes - self.modelFitPlot.set_xlim(0, num_epochs) - self.modelFitPlot.set_ylim( - self.modelFitPlot_limit[0], self.modelFitPlot_limit[1] - ) - - # RANGE PREDICTION FIT PLOT - x = 1 - y = 1 - if fitPlot3D: - x = np.arange(0, 1, 0.1) - y = np.arange(0, 1, 0.1) - X, Y = np.meshgrid(x, y) - Z = X * np.exp(-X - Y) - - self.range_target = self.mismatchPlot.plot_surface(X, Y, Z) - self.range_prediction = self.mismatchPlot.plot_surface(X, Y, Z) - self.mismatchPlot.set_zlim( - self.mismatchPlot_limit[0], self.mismatchPlot_limit[1] - ) - - # set labels - self.mismatchPlot.set_xlabel("Stimulus 1", fontsize=self.fontSize) - self.mismatchPlot.set_ylabel("Stimulus 2", fontsize=self.fontSize) - self.mismatchPlot.set_zlabel("Outcome Value", fontsize=self.fontSize) - - else: - (self.range_target,) = self.mismatchPlot.plot(x, y, color="black") - (self.range_prediction,) = self.mismatchPlot.plot(x, y, "--", color="red") - - # set labels - self.mismatchPlot.set_xlabel("Stimulus Value", fontsize=self.fontSize) - self.mismatchPlot.set_ylabel("Outcome Value", fontsize=self.fontSize) - self.mismatchPlot.legend( - (self.range_target, self.range_prediction), ("target", "prediction") - ) - - self.mismatchPlot.set_title("Target vs. Prediction", fontsize=self.fontSize) - - # adjust axes - self.mismatchPlot.set_xlim(0, 1) - self.mismatchPlot.set_ylim(0, 1) - - # ARCHITECTURE WEIGHT PLOT - if self.show_arch_weights: - - self.architectureWeights = [] - for idx, architecturePlot in enumerate(self.architecturePlot): - plotWeights = [] - x = 1 - y = 1 - for op in range(numArchOps): - (plotWeight,) = architecturePlot.plot(x, y, color=self.colors[op]) - plotWeights.append(plotWeight) - - # set legend - if idx == 0: - architecturePlot.legend( - plotWeights, ArchOpsLabels, prop={"size": 6} - ) - - # add labels - architecturePlot.set_ylabel("Weight", fontsize=self.fontSize) - architecturePlot.set_title( - "(" + str(idx) + ") Edge Weight", fontsize=self.fontSize - ) - if idx == len(self.architecturePlot) - 1: - architecturePlot.set_xlabel("Epoch", fontsize=self.fontSize) - - # adjust axes - architecturePlot.set_xlim(0, num_epochs) - architecturePlot.set_ylim( - self.architectureWeightsPlot_limit[0], - self.architectureWeightsPlot_limit[1], - ) - - self.architectureWeights.append(plotWeights) - - # draw - plt.draw() - - def update( - self, - train_error: Optional[np.array] = None, - valid_error: Optional[np.array] = None, - weights: Optional[np.array] = None, - BIC: Optional[np.array] = None, - AIC: Optional[np.array] = None, - model_graph: Optional[str] = None, - range_input1: Optional[np.array] = None, - range_input2: Optional[np.array] = None, - range_target: Optional[np.array] = None, - range_prediction: Optional[np.array] = None, - target: Optional[np.array] = None, - prediction: Optional[np.array] = None, - ): - """ - Update the debug plot with new data. - - Arguments: - train_error: training error - valid_error: validation error - weights: weights of the model - BIC: Bayesian information criterion of the model - AIC: Akaike information criterion of the model - model_graph: the graph of the model - range_input1: the range of the first input - range_input2: the range of the second input - range_target: the range of the target - range_prediction: the range of the prediction - target: the target - prediction: the prediction - """ - - # update training error - if train_error is not None: - self.train_error.set_xdata( - np.linspace(1, len(train_error), len(train_error)) - ) - self.train_error.set_ydata(train_error) - - # update validation error - if valid_error is not None: - self.valid_error.set_xdata( - np.linspace(1, len(valid_error), len(valid_error)) - ) - self.valid_error.set_ydata(valid_error) - - # update BIC - if BIC is not None: - self.BIC.set_xdata(np.linspace(1, len(BIC), len(BIC))) - self.BIC.set_ydata(BIC) - - # update AIC - if AIC is not None: - self.AIC.set_xdata(np.linspace(1, len(AIC), len(AIC))) - self.AIC.set_ydata(AIC) - - # update target vs. prediction plot - if ( - range_input1 is not None - and range_target is not None - and range_prediction is not None - and range_input2 is None - ): - self.range_target.set_xdata(range_input1) - self.range_target.set_ydata(range_target) - self.range_prediction.set_xdata(range_input1) - self.range_prediction.set_ydata(range_prediction) - elif ( - range_input1 is not None - and range_target is not None - and range_prediction is not None - and range_input2 is not None - ): - - # update plot - self.mismatchPlot.cla() - self.range_target = self.mismatchPlot.plot_surface( - range_input1, range_input2, range_target, color=(0, 0, 0, 0.5) - ) - self.range_prediction = self.mismatchPlot.plot_surface( - range_input1, range_input2, range_prediction, color=(1, 0, 0, 0.5) - ) - - # set labels - self.mismatchPlot.set_xlabel("Stimulus 1", fontsize=self.fontSize) - self.mismatchPlot.set_ylabel("Stimulus 2", fontsize=self.fontSize) - self.mismatchPlot.set_zlabel("Outcome Value", fontsize=self.fontSize) - self.mismatchPlot.set_title("Target vs. Prediction", fontsize=self.fontSize) - - # update example pattern plot - if target is not None and prediction is not None: - - # select limited number of patterns - self.numPatternsShown = np.min((self.numPatternsShown, target.shape[0])) - target = target[0 : self.numPatternsShown, :] - prediction = prediction[0 : self.numPatternsShown, :] - - im = np.concatenate((target, prediction), axis=1) - self.examplePatternsPlot.cla() - self.examplePatternsPlot.imshow(im, interpolation="nearest", aspect="auto") - x = np.ones(target.shape[0]) * (target.shape[1] - 0.5) - y = np.linspace(1, target.shape[0], target.shape[0]) - self.examplePatternsPlot.plot(x, y, color="red") - - # set labels - self.examplePatternsPlot.set_xlabel("Output", fontsize=self.fontSize) - self.examplePatternsPlot.set_ylabel("Pattern", fontsize=self.fontSize) - self.examplePatternsPlot.set_title( - "Target vs. Prediction", fontsize=self.fontSize - ) - - if self.show_arch_weights: - # update weights - if weights is not None: - for plotIdx, architectureWeights in enumerate(self.architectureWeights): - for lineIdx, plotWeight in enumerate(architectureWeights): - plotWeight.set_xdata( - np.linspace(1, weights.shape[0], weights.shape[0]) - ) - plotWeight.set_ydata(weights[:, plotIdx, lineIdx]) - - # draw current graph - if model_graph is not None: - im = imageio.imread(model_graph) - self.modelGraph.cla() - self.modelGraph.imshow(im) - self.modelGraph.axis("off") - - # re-draw plot - plt.draw() - plt.pause(0.02) From b4509e9b37d86416a3b5455e71bed93afec9b96e Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 13 Apr 2023 16:48:28 -0400 Subject: [PATCH 060/446] feat: add example running theorist --- .../autora_theorist_basic_usage.xml | 25 +++++ autora/theorist/__main__.py | 96 ++++++++++++++++++- tests/cli/theorist/basic-usage/.gitignore | 1 + tests/cli/theorist/basic-usage/data.csv | 5 + tests/cli/theorist/basic-usage/parameters.yml | 0 tests/cli/theorist/basic-usage/variables.yml | 10 ++ 6 files changed, 132 insertions(+), 5 deletions(-) create mode 100644 .idea/runConfigurations/autora_theorist_basic_usage.xml create mode 100644 tests/cli/theorist/basic-usage/.gitignore create mode 100644 tests/cli/theorist/basic-usage/data.csv create mode 100644 tests/cli/theorist/basic-usage/parameters.yml create mode 100644 tests/cli/theorist/basic-usage/variables.yml diff --git a/.idea/runConfigurations/autora_theorist_basic_usage.xml b/.idea/runConfigurations/autora_theorist_basic_usage.xml new file mode 100644 index 00000000..a1198278 --- /dev/null +++ b/.idea/runConfigurations/autora_theorist_basic_usage.xml @@ -0,0 +1,25 @@ + + + + + \ No newline at end of file diff --git a/autora/theorist/__main__.py b/autora/theorist/__main__.py index d67b7f2c..83b530ea 100644 --- a/autora/theorist/__main__.py +++ b/autora/theorist/__main__.py @@ -1,10 +1,18 @@ import importlib import logging -from typing import Type +import pathlib +import pickle +import pprint +from typing import Dict, Optional, Type +import pandas as pd import typer +import yaml +from pandas import DataFrame from sklearn.base import BaseEstimator +from autora.variable import VariableCollection + _logger = logging.getLogger(__name__) @@ -27,16 +35,94 @@ def import_class(name: str) -> Type[BaseEstimator]: return cls -def main(regressor: str, verbose: bool = False, debug: bool = False): +def main( + variables: pathlib.Path, + parameters: pathlib.Path, + regressor: str, + data: pathlib.Path, + output: pathlib.Path, + verbose: bool = False, + debug: bool = False, + overwrite: bool = False, +): - if verbose: - logging.basicConfig(level=logging.INFO) + configure_logger(debug, verbose) + + regressor_class_ = load_regressor_class(regressor) + data_ = load_data(data) + variables_ = load_variables(variables) + parameters_ = load_parameters(parameters) + model = fit_model(data_, parameters_, regressor_class_, variables_) + dump_model(model, output, overwrite) + + return + + +def dump_model(model_, output, overwrite): + if overwrite: + mode = "wb" + else: + mode = "xb" + with open(output, mode) as o: + pickle.dump(model_, o) + + +def fit_model(data_, parameters_, regressor_class_, variables_): + model = regressor_class_(**parameters_) + X = data_[[v.name for v in variables_.independent_variables]] + y = data_[[v.name for v in variables_.dependent_variables]] + _logger.debug(f"fitting the regressor with X:\n{X}\nand y:\n{y}") + model.fit(X, y) + try: + _logger.info( + f"fitted {model=}\nmodel.__dict__:" f"\n{pprint.pformat(model.__dict__)}" + ) + except AttributeError: + _logger.warning( + f"fitted {model=} " + f"model has no __dict__ attribute, so no results are shown" + ) + return model + + +def configure_logger(debug, verbose): if debug: logging.basicConfig(level=logging.DEBUG) + _logger.debug("using DEBUG logging level") + if verbose: + logging.basicConfig(level=logging.INFO) + _logger.info("using INFO logging level") + +def load_regressor_class(regressor): regressor_class = import_class(regressor) + _logger.info(f"{regressor}: {regressor_class}") + return regressor_class + + +def load_data(data: pathlib.Path) -> DataFrame: + _logger.debug(f"load_data: loading from {data=}") + with open(data, "r") as fd: + data_: DataFrame = pd.read_csv(fd) + return data_ + + +def load_variables(path: pathlib.Path) -> VariableCollection: + _logger.debug(f"load_variables: loading from {path=}") + variables_: VariableCollection + with open(path, "r") as fv: + variables_ = yaml.load(fv, yaml.Loader) + assert isinstance(variables_, VariableCollection) + return variables_ + - print(f"{regressor}: {regressor_class}") +def load_parameters(path: pathlib.Path) -> Dict: + _logger.debug(f"load_parameters: loading from {path=}") + with open(path, "r") as fp: + parameters_: Optional[Dict] = yaml.load(fp, yaml.Loader) + if parameters_ is None: + parameters_ = dict() + return parameters_ if __name__ == "__main__": diff --git a/tests/cli/theorist/basic-usage/.gitignore b/tests/cli/theorist/basic-usage/.gitignore new file mode 100644 index 00000000..4c227386 --- /dev/null +++ b/tests/cli/theorist/basic-usage/.gitignore @@ -0,0 +1 @@ +out.pickle diff --git a/tests/cli/theorist/basic-usage/data.csv b/tests/cli/theorist/basic-usage/data.csv new file mode 100644 index 00000000..52c6adc0 --- /dev/null +++ b/tests/cli/theorist/basic-usage/data.csv @@ -0,0 +1,5 @@ +x1,x2,c1,y +1,1,7,2 +1,2,7,3 +2,2,7,4 +0,0,7,0 diff --git a/tests/cli/theorist/basic-usage/parameters.yml b/tests/cli/theorist/basic-usage/parameters.yml new file mode 100644 index 00000000..e69de29b diff --git a/tests/cli/theorist/basic-usage/variables.yml b/tests/cli/theorist/basic-usage/variables.yml new file mode 100644 index 00000000..c5e11e9e --- /dev/null +++ b/tests/cli/theorist/basic-usage/variables.yml @@ -0,0 +1,10 @@ +!!python/object:autora.variable.VariableCollection +covariates: [] +dependent_variables: + - !!python/object:autora.variable.Variable + name: y +independent_variables: + - !!python/object:autora.variable.Variable + name: x1 + - !!python/object:autora.variable.Variable + name: x2 From 93b8d2ed7507a9ba53edb9792a9217a07caa7982 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 13 Apr 2023 16:52:09 -0400 Subject: [PATCH 061/446] feat: add example using parameters --- tests/cli/theorist/basic-usage/parameters.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/cli/theorist/basic-usage/parameters.yml b/tests/cli/theorist/basic-usage/parameters.yml index e69de29b..8f78d83a 100644 --- a/tests/cli/theorist/basic-usage/parameters.yml +++ b/tests/cli/theorist/basic-usage/parameters.yml @@ -0,0 +1 @@ +fit_intercept: True From 48699b304ea2af3b9eb746527016e8887d2ecba0 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 13 Apr 2023 16:58:27 -0400 Subject: [PATCH 062/446] docs: updating docstrings --- autora/theorist/__main__.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/autora/theorist/__main__.py b/autora/theorist/__main__.py index 83b530ea..298017c7 100644 --- a/autora/theorist/__main__.py +++ b/autora/theorist/__main__.py @@ -45,14 +45,19 @@ def main( debug: bool = False, overwrite: bool = False, ): - + # Initialization configure_logger(debug, verbose) + # Data Loading regressor_class_ = load_regressor_class(regressor) data_ = load_data(data) variables_ = load_variables(variables) parameters_ = load_parameters(parameters) + + # Fitting model = fit_model(data_, parameters_, regressor_class_, variables_) + + # Writing results dump_model(model, output, overwrite) return @@ -61,8 +66,10 @@ def main( def dump_model(model_, output, overwrite): if overwrite: mode = "wb" + _logger.info(f"overwriting {output=} if it already exists") else: mode = "xb" + _logger.info(f"writing to new file {output=}") with open(output, mode) as o: pickle.dump(model_, o) From 6d3b641780d9d0273359c5ea49edbc9c8aa48cfc Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 13 Apr 2023 17:00:41 -0400 Subject: [PATCH 063/446] refactor: reorder file to match execution order --- autora/theorist/__main__.py | 88 ++++++++++++++++++------------------- 1 file changed, 44 insertions(+), 44 deletions(-) diff --git a/autora/theorist/__main__.py b/autora/theorist/__main__.py index 298017c7..7c6d8263 100644 --- a/autora/theorist/__main__.py +++ b/autora/theorist/__main__.py @@ -49,10 +49,10 @@ def main( configure_logger(debug, verbose) # Data Loading - regressor_class_ = load_regressor_class(regressor) - data_ = load_data(data) variables_ = load_variables(variables) parameters_ = load_parameters(parameters) + regressor_class_ = load_regressor_class(regressor) + data_ = load_data(data) # Fitting model = fit_model(data_, parameters_, regressor_class_, variables_) @@ -63,35 +63,6 @@ def main( return -def dump_model(model_, output, overwrite): - if overwrite: - mode = "wb" - _logger.info(f"overwriting {output=} if it already exists") - else: - mode = "xb" - _logger.info(f"writing to new file {output=}") - with open(output, mode) as o: - pickle.dump(model_, o) - - -def fit_model(data_, parameters_, regressor_class_, variables_): - model = regressor_class_(**parameters_) - X = data_[[v.name for v in variables_.independent_variables]] - y = data_[[v.name for v in variables_.dependent_variables]] - _logger.debug(f"fitting the regressor with X:\n{X}\nand y:\n{y}") - model.fit(X, y) - try: - _logger.info( - f"fitted {model=}\nmodel.__dict__:" f"\n{pprint.pformat(model.__dict__)}" - ) - except AttributeError: - _logger.warning( - f"fitted {model=} " - f"model has no __dict__ attribute, so no results are shown" - ) - return model - - def configure_logger(debug, verbose): if debug: logging.basicConfig(level=logging.DEBUG) @@ -101,19 +72,6 @@ def configure_logger(debug, verbose): _logger.info("using INFO logging level") -def load_regressor_class(regressor): - regressor_class = import_class(regressor) - _logger.info(f"{regressor}: {regressor_class}") - return regressor_class - - -def load_data(data: pathlib.Path) -> DataFrame: - _logger.debug(f"load_data: loading from {data=}") - with open(data, "r") as fd: - data_: DataFrame = pd.read_csv(fd) - return data_ - - def load_variables(path: pathlib.Path) -> VariableCollection: _logger.debug(f"load_variables: loading from {path=}") variables_: VariableCollection @@ -132,5 +90,47 @@ def load_parameters(path: pathlib.Path) -> Dict: return parameters_ +def load_regressor_class(regressor): + regressor_class = import_class(regressor) + _logger.info(f"{regressor}: {regressor_class}") + return regressor_class + + +def load_data(data: pathlib.Path) -> DataFrame: + _logger.debug(f"load_data: loading from {data=}") + with open(data, "r") as fd: + data_: DataFrame = pd.read_csv(fd) + return data_ + + +def fit_model(data_, parameters_, regressor_class_, variables_): + model = regressor_class_(**parameters_) + X = data_[[v.name for v in variables_.independent_variables]] + y = data_[[v.name for v in variables_.dependent_variables]] + _logger.debug(f"fitting the regressor with X:\n{X}\nand y:\n{y}") + model.fit(X, y) + try: + _logger.info( + f"fitted {model=}\nmodel.__dict__:" f"\n{pprint.pformat(model.__dict__)}" + ) + except AttributeError: + _logger.warning( + f"fitted {model=} " + f"model has no __dict__ attribute, so no results are shown" + ) + return model + + +def dump_model(model_, output, overwrite): + if overwrite: + mode = "wb" + _logger.info(f"overwriting {output=} if it already exists") + else: + mode = "xb" + _logger.info(f"writing to new file {output=}") + with open(output, mode) as o: + pickle.dump(model_, o) + + if __name__ == "__main__": typer.run(main) From a7be18b440b5e802e899a044a19dd81409e20560 Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 13 Apr 2023 17:01:18 -0400 Subject: [PATCH 064/446] refactor: reorder file to match execution order --- autora/theorist/__main__.py | 38 ++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/autora/theorist/__main__.py b/autora/theorist/__main__.py index 7c6d8263..1c4877a9 100644 --- a/autora/theorist/__main__.py +++ b/autora/theorist/__main__.py @@ -16,25 +16,6 @@ _logger = logging.getLogger(__name__) -def import_class(name: str) -> Type[BaseEstimator]: - """ - Load a class from a module by name. - - Args: - name: - - Examples: - >>> import_class("sklearn.linear_model.LinearRegressor") - - """ - components = name.split(".") - module_name, class_name = ".".join(components[:-1]), components[-1] - _logger.info(f"loading {module_name=}, {class_name=}") - module = importlib.import_module(module_name) - cls = getattr(module, class_name) - return cls - - def main( variables: pathlib.Path, parameters: pathlib.Path, @@ -96,6 +77,25 @@ def load_regressor_class(regressor): return regressor_class +def import_class(name: str) -> Type[BaseEstimator]: + """ + Load a class from a module by name. + + Args: + name: + + Examples: + >>> import_class("sklearn.linear_model.LinearRegressor") + + """ + components = name.split(".") + module_name, class_name = ".".join(components[:-1]), components[-1] + _logger.info(f"loading {module_name=}, {class_name=}") + module = importlib.import_module(module_name) + cls = getattr(module, class_name) + return cls + + def load_data(data: pathlib.Path) -> DataFrame: _logger.debug(f"load_data: loading from {data=}") with open(data, "r") as fd: From 3b23df366925c60867c864a28fc4e6c964b6c9cc Mon Sep 17 00:00:00 2001 From: John Gerrard Holland Date: Thu, 13 Apr 2023 17:12:34 -0400 Subject: [PATCH 065/446] feat: add ability to pre-set parameters of regressor --- .../autora_theorist_basic_usage.xml | 2 +- autora/theorist/__main__.py | 70 +++++++------------ tests/cli/theorist/basic-usage/regressor.yml | 6 ++ 3 files changed, 32 insertions(+), 46 deletions(-) create mode 100644 tests/cli/theorist/basic-usage/regressor.yml diff --git a/.idea/runConfigurations/autora_theorist_basic_usage.xml b/.idea/runConfigurations/autora_theorist_basic_usage.xml index a1198278..b70e88f2 100644 --- a/.idea/runConfigurations/autora_theorist_basic_usage.xml +++ b/.idea/runConfigurations/autora_theorist_basic_usage.xml @@ -14,7 +14,7 @@