SpeechT5_fine_tune.ipynb

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          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-3.0.0-py3-none-any.whl.metadata (19 kB)\n",
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            "Collecting speechbrain\n",
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            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-3.0.0 dill-0.3.8 hyperpyyaml-1.2.2 multiprocess-0.70.16 pyarrow-17.0.0 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8 speechbrain-1.0.1 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "pip install datasets soundfile speechbrain accelerate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, Audio\n",
        "\n",
        "dataset = load_dataset(\"facebook/voxpopuli\", \"it\", split=\"train[:2000]\")\n",
        "len(dataset)"
      ],
      "metadata": {
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        },
        "id": "4PFb_BC2aV9D",
        "outputId": "e639da26-e664-4aa2-a3cc-fef5209c506e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "voxpopuli.py:   0%|          | 0.00/8.84k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for facebook/voxpopuli contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/facebook/voxpopuli.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
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        {
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          "metadata": {}
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        {
          "output_type": "display_data",
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              "dev_part_0.tar.gz:   0%|          | 0.00/565M [00:00<?, ?B/s]"
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              "model_id": "6cb756bd0e4f445981405316373441f4"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75a9e6285b6d4437b778f5adff371a09"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5321c8023ee244789ff6ea42fa262705"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "460555180ffa43ea80c5db6a53a668f1"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
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              "2000"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=16000))"
      ],
      "metadata": {
        "id": "FOink5JPakQO"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import SpeechT5Processor\n",
        "\n",
        "checkpoint = \"microsoft/speecht5_tts\"\n",
        "processor = SpeechT5Processor.from_pretrained(checkpoint)"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "nTZPp9PYaoLK",
        "outputId": "ed1edbd3-b709-4e65-a4e2-ac5fb626fc5a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
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              "version_major": 2,
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            }
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/433 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/232 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spm_char.model:   0%|          | 0.00/238k [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/40.0 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/234 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fe32922224e143d1913440cb1fd0477e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = processor.tokenizer"
      ],
      "metadata": {
        "id": "PBuzkHctau7M"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_all_chars(batch):\n",
        "    all_text = \" \".join(batch[\"normalized_text\"])\n",
        "    vocab = list(set(all_text))\n",
        "    return {\"vocab\": [vocab], \"all_text\": [all_text]}\n",
        "\n",
        "\n",
        "vocabs = dataset.map(\n",
        "    extract_all_chars,\n",
        "    batched=True,\n",
        "    batch_size=-1,\n",
        "    keep_in_memory=True,\n",
        "    remove_columns=dataset.column_names,\n",
        ")\n",
        "\n",
        "dataset_vocab = set(vocabs[\"vocab\"][0])\n",
        "tokenizer_vocab = {k for k, _ in tokenizer.get_vocab().items()}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c73c9d3fa49d4b39bc19d76d3e0bba80",
            "26c6aa4bff6047bbaff944da62a29f23",
            "211bd9e2042c4e1ea9d7dea1545f446c",
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            "dc9f7d9eca45480d8edb73e276f63f7b"
          ]
        },
        "id": "VGaZNcxsay_V",
        "outputId": "6acd16b7-f2cc-4a9a-acf9-d81ddd865384"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c73c9d3fa49d4b39bc19d76d3e0bba80"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_vocab - tokenizer_vocab"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vp1Pv9-a2Qg",
        "outputId": "ea194051-ef5d-4029-f93b-41e29263b53f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{' ', 'à', 'è', 'ì', 'í', 'ò', 'ó', 'ù'}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "replacements = [\n",
        "    (\"à\", \"a\"),\n",
        "    (\"ç\", \"c\"),\n",
        "    (\"è\", \"e\"),\n",
        "    (\"ë\", \"e\"),\n",
        "    (\"í\", \"i\"),\n",
        "    (\"ï\", \"i\"),\n",
        "    (\"ö\", \"o\"),\n",
        "    (\"ü\", \"u\"),\n",
        "]\n",
        "\n",
        "\n",
        "def cleanup_text(inputs):\n",
        "    for src, dst in replacements:\n",
        "        inputs[\"normalized_text\"] = inputs[\"normalized_text\"].replace(src, dst)\n",
        "    return inputs\n",
        "\n",
        "\n",
        "dataset = dataset.map(cleanup_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "8a7b4866fcde4ddd8430aa2c2eda7493",
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            "b50c4351c6aa43d988994bb822ac750b",
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            "12a110984bad45df9195b1b535a416a7",
            "6441cc2041384421a8c168dbf23c1d01"
          ]
        },
        "id": "etiruEFPa8Hw",
        "outputId": "223deaaa-0da6-4da6-8065-b9d1fbb10e68"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a7b4866fcde4ddd8430aa2c2eda7493"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "speaker_counts = defaultdict(int)\n",
        "\n",
        "for speaker_id in dataset[\"speaker_id\"]:\n",
        "    speaker_counts[speaker_id] += 1"
      ],
      "metadata": {
        "id": "_NaANTZya-h7"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure()\n",
        "plt.hist(speaker_counts.values(), bins=20)\n",
        "plt.ylabel(\"Speakers\")\n",
        "plt.xlabel(\"Examples\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "eyk7177jbBun",
        "outputId": "ad44feaf-18da-4ca1-d8e7-c286aa65f316"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def select_speaker(speaker_id):\n",
        "    return 100 <= speaker_counts[speaker_id] <= 400\n",
        "\n",
        "\n",
        "dataset = dataset.filter(select_speaker, input_columns=[\"speaker_id\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "51d13d7b78704eef9246c7ac790463fa",
            "7d0712fefd6d4778bb8ad9245c01998f",
            "367b175b057e4072bec58ddfbc9fb266",
            "be935505a4b44967b40c5d15ae7dc06e",
            "5c4e864bac6240479bb45ceb8e278f3c",
            "3de5f7149c1b4bc5bdb5e9b978b98965",
            "2d3c83b8ac5a4a4e87425fa33b8b2ae6",
            "2dbf88fa43ad4f7bbdd32ce75eb36467",
            "9adf3c53c70b49b8b55de456b74de6a9",
            "7ef05e908d674029a75d1d8c25cae1dc",
            "c4d63cf7032d43bc9fb52a67722fe1e4"
          ]
        },
        "id": "nwjIve1ebG9P",
        "outputId": "1efd92d5-7101-4581-9af6-9f6364255b8b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51d13d7b78704eef9246c7ac790463fa"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygUkZdEXbztv",
        "outputId": "e80bd58b-82cf-4d74-aeb7-e40528b842fb"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "327"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from speechbrain.pretrained import EncoderClassifier\n",
        "\n",
        "spk_model_name = \"speechbrain/spkrec-xvect-voxceleb\"\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "speaker_model = EncoderClassifier.from_hparams(\n",
        "    source=spk_model_name,\n",
        "    run_opts={\"device\": device},\n",
        "    savedir=os.path.join(\"/tmp\", spk_model_name),\n",
        ")\n",
        "\n",
        "\n",
        "def create_speaker_embedding(waveform):\n",
        "    with torch.no_grad():\n",
        "        speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))\n",
        "        speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)\n",
        "        speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()\n",
        "    return speaker_embeddings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336,
          "referenced_widgets": [
            "f0a3f83a459f4f19824b335cf018987a",
            "bc5d85fb551e475a91fa5dafe1e68df5",
            "33b192a680b84ae396f79d71e3dc072b",
            "808614618e1948a48263be11ebbc384d",
            "3df25b320fb5403b84ce723e3c23a8b8",
            "db14404054944db1982033b49351eb2d",
            "31580d54ca794e8d9a385a71d5895013",
            "040e03f23988434893891ed2f77ded47",
            "55dd4f6a909f4db08dd8468cde05fddd",
            "f6da533610f642ddbd6b4168b81e873a",
            "d05f9d62f93f4795b4108b7f944006a4",
            "680b5ec86b964e4490cf3a084f0b2012",
            "5045cd7b373344ceb5021291cb798ccd",
            "4e1364940d234f29a58255c553b98f8e",
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          ]
        },
        "id": "46S56vZVb2BM",
        "outputId": "647fe93d-5473-4ccf-a21a-cbcd42cbc7b8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-f28baad2ea60>:3: UserWarning: Module 'speechbrain.pretrained' was deprecated, redirecting to 'speechbrain.inference'. Please update your script. This is a change from SpeechBrain 1.0. See: https://github.com/speechbrain/speechbrain/releases/tag/v1.0.0\n",
            "  from speechbrain.pretrained import EncoderClassifier\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "hyperparams.yaml:   0%|          | 0.00/2.04k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0a3f83a459f4f19824b335cf018987a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/speechbrain/utils/autocast.py:68: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.\n",
            "  wrapped_fwd = torch.cuda.amp.custom_fwd(fwd, cast_inputs=cast_inputs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "embedding_model.ckpt:   0%|          | 0.00/16.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "680b5ec86b964e4490cf3a084f0b2012"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "mean_var_norm_emb.ckpt:   0%|          | 0.00/3.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a1c0aaf08c04badb195b31ede788bf9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "classifier.ckpt:   0%|          | 0.00/15.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "10c4ba4f92364cd1811b4282d29ce34f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "label_encoder.txt:   0%|          | 0.00/129k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "68822e0809e5405b90db6e9e56cb4478"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/speechbrain/utils/checkpoints.py:194: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  state_dict = torch.load(path, map_location=device)\n",
            "/usr/local/lib/python3.10/dist-packages/speechbrain/processing/features.py:1311: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  stats = torch.load(path, map_location=device)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare_dataset(example):\n",
        "    audio = example[\"audio\"]\n",
        "\n",
        "    example = processor(\n",
        "        text=example[\"normalized_text\"],\n",
        "        audio_target=audio[\"array\"],\n",
        "        sampling_rate=audio[\"sampling_rate\"],\n",
        "        return_attention_mask=False,\n",
        "    )\n",
        "\n",
        "    # strip off the batch dimension\n",
        "    example[\"labels\"] = example[\"labels\"][0]\n",
        "\n",
        "    # use SpeechBrain to obtain x-vector\n",
        "    example[\"speaker_embeddings\"] = create_speaker_embedding(audio[\"array\"])\n",
        "\n",
        "    return example"
      ],
      "metadata": {
        "id": "Y_qMcip3cChJ"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_example = prepare_dataset(dataset[0])\n",
        "list(processed_example.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W964Y1ZOcE6y",
        "outputId": "a8a8220c-dab6-4252-f77a-1a8d2b3250e4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['input_ids', 'labels', 'speaker_embeddings']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure()\n",
        "plt.imshow(processed_example[\"labels\"].T)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        },
        "id": "RuvBi0HOcH44",
        "outputId": "b9e78084-82d6-4ca9-811a-63c191e324ed"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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QLEOyhGyo9cg3mhtC0nx2OGd3WjIsrKANSRODJgeN0plVOXBatnLYmyyIrc1El0lOYfqHrP/DkzuWTtboyg1snOwNRsna6pPjEEp8PgYDmnf9SuagG7ATMrhxPSkrDr5kCJbSBmrraexIbfx8/2szErKhixLM5iBIijaRTdmzLnpC0lROgkynEmk6eY77WEKhU2ZMljFZuuBovaOfAmRnI87EOTHQKuN0mlEOrTJWCTpSmsRq+vyh0XTBcdvW9LoQhHtUJJtJBeRS7iNaER6W3b91/JkHH0VR8Nf+2l/j937v9/hbf+tvAUIi/b3f+z3+zt/5O3/q91EJyBkdIRn5kw0Eq8gGxvMaMoRaM64UoVYSXCQwwxR4BIhx+r+FZJGgw0vgkbX83+7BHfJcpskKYqXIKrMuez6sb3nqtpyYlrhQtKnEZ8t9rNnFCqciK9PTJzfDjDkrLuo9IRn6aHla7zhxHVfDktYX6AnevDwsSEnzdLmnj46DL/DRsK4GFoWnD/KIfNaErHm62POs2rFyPYb0Cwf2uTvwSXXFQg8cUonPhjYVDMmhyTRmYEiO27FmU/aoDb9QkpFJF3jRbPlu855vlFc8MVsWemCXan4yPCckyUabasS7yKru5wncB8u2regVVLUERteDlIEKE1hYCTLu2pqYFbux5Em956I8oMkMyeKm0tPSDHxU3fDt5pIhW9pYzEHJMRC4GRcSMNgBTcYpgfMjCp+slBKmzSJlgZKdDvjpfRJqLotpMhvb8sTuiFlhpmAgTlivIeFUxKmAUYm3zQlv1ifch5ouFvMmVOrAIZS8L5bY8/2cqXgvz/Rbqys+qm7ZmA4ApwJ9duxjRcyaa7/AqYjPcjAsq4FxYTE2EaOiqjwfL275dnM5f9YhOYYgpbiY1YywHcspct2RiOLWL3DryJNix+W44me7c768PUHrTOUChZVs/Fmz47xscTpyNSyojOdZueNpseXMHFjoAa0SPltuwpIftC+4Ghb00+b5cXPLuTvwQXmLIdFnRxtL2lTQ6HGem++HFZ/vzrA2oVRgU/d8c33NyvYszcDSDLz3K3ZjSd8VhKhmGF4ZWZ9n03Ue583SDpTTc5L5In83emQfK678ktuxpjCRD+o7LtwerRL3oZFNXEdqIxtvY0Y2pqPRAysjiFTMivu44NP+CV10GJWpp8/0tl+jVaZ1BTuTISuy1zSrgW+eXPOdhZRPd7HiVX/C1lcM3qJsIlVTZqQgFhJ8uCpMwYAcyIdQ8Hq7Zl30FDpgdZoOY8chFoRsMC6hdCJFQ1OOPK32fFDe4VRkFyvufMMXuzOciXy4uOO7zXs2tmVIjj45CbhRuKnEWWnPLlbchoadr0hI0DQmi9FSQg3KkV0iO4W2Eqh+VN+iVWZYymEYp8Dx+O+lHVmYgcaMNFr+HNfDfWgYkuUuNFTao8myXpN5KA2QufPNvD+OyVI6KVuHYPAJstfgNWY9MgTLZbeUJMZImWJ6I5LL6FHOFgU8rXc8Kfbz+t/YjqXpSVMpPSrNNpTsfUkfBbG97WtyVhQ2YCb04N7VbMqO2nqW5cAQ7LSnL1DAwglqdUTM14WUvnKUAMm5SMia+0GSzpA1h7HA6MRp1VEZz5jsfE9C0uSsWE0l4dYVXKsGBTNCU01Bj6Alcp3HQKbQAfe1rw/JsvclMWlJtFUmLo9wktw3MigDqcgk86fXr/y5lF1+93d/l//0P/1P+a3f+i3+xt/4G/zn//l/zuFw4G//7b/9p36PrGFcaSYahQQfFqJTZAvtU0c2kJUiWQk2kpXgQ02BhR6ZA5IjpAdgRtBjJhUCHemYsUNGxSxwr1XEDGiojMcnw5AcXhue2C2/ot/Jxk5m5CHyvU4LLtyO/bqiT46NlQOgTQUb02FIfOXOAMl4D7Hgfqhw2vPh4g6AxhZYJRnwIRRc9wsKHXhZ3rM+6/mkuuIDd8uYDZdhTcqaj4prVloOsz47KuVnGH2XqikQkQV/ldeSDSfDshh4Wu94Vu5YmoFKezam5aW75WN7S6MDPmsu44L3YcV9rH+Bw3I8qKwWvkDKAnHrIlI5meSVCWg7srYDtRm5H6tf+LmQNEOUaXg9LBiTobEjp0WHUYlSBU7tgaduK59LewyJ67jk/bCi1IELu2djpOywSzVXfsW1X5CypjYjQ7J0sWBtO6xO7EI1BxhDtCSUQKG1IAN/tP+AhRmpjWR9jR5pzMCJadEkzu2eJ3YKRqdFm7JmzIZdqvliOEdPGd0YpVYqG8LAR5UEsj4b3vs1MWvufEMXHaUJdNFRG0kftMpUNlBWnpQUMRrZsOxAQvFqOKFLBcW0SFJW3AwL9r7kdbeeA8vGjvMzBvi15g2/Wr6mbxy/X3yT3firjMFSO89FvefD5m4+rD7vz7kdGlaun6+rNeUc2AASRPRLbgc5CKxKM0qQUCxNz0r3nBlBwZwKFCryPqy5GpbCJUoK5wLLYuBFec/TYssTu5vfv7Jh5gllPxX/s+KsbPmtzeecmPYX9g9Zn4lDKtmlaj40bsKCN/2Gm37BSdkxJMubcSOZtg6UJnA9NDMCcAgll3pFoQMXbs9Tt+UTd8kH7pYndjsHpAnNK39KFwtes0HrRF2PpKToU8FJ0/GdxSUfl9f02fF+XLHzUj4sXcC6SNR55gNQSBlRm8RuKPnp7gmNHdmNFd5brvsFIWmWbsBNzwHkQFY6Cf8jCM+li44v+7NpHTj2vuQwFgAMwbIwMr8v7JaE5p3f8GbcMCZLPaGSr/oTtmNFTJrFlBHPB96UpWMTeUJQjwjLEVE6c4c5CPbZzEHxM3f/sFfFil2qZK/Nhmu/4HZsiFlx2S8J2aDJFCawcT0LO2CznhAfKXPkrPDe4EdL6g2qE05KzoLcuGldmSKRnCSiRAUqS9JZJ5pSgodtqLkd5dDfuYrSBLa+ImTNxnXcDAu64B7OrKwIUWONovnaQX9RHubA6xhk+CPiagUJHOOxXJKEWzZxRqyRZ+tMpLHjjPRqlVm6gZUdvoZWJcZkJzTvQKkDl+NyToCWbmDjeta2m9dyFx2v+hPuBgmcFm6gMFIK6qOb0ZNxSoKVhlRFYhLeFRlUVKgIqcyCgPwpx59L8PEf/8f/MZeXl/yDf/APePv2Lb/xG7/Bf/1f/9d/goT6bxoqQ2gUKgj6QZ4QEMdcgslGAgsdMjpMxBs7vWYqsRzLLyqCmQI2FaZJpzJZSwASSiXvYaR8kxxgJdu4Cw1tKngzbliaC36zkc2uIE4LKuHJVMrz3eItAMW0KZ3ogX6C8UcMJ6ZlY1sMmV2suCgOnLiWX6ne4LNllyp8NtyHhi/SKaUNnBUtv7X4Gc/NlkYFhmz41D/hyq/w2fBRcT0FOSWv/BlDcqxMx0r33MWGd35DmwqcigzJolViPwr68ltnX/AfLH/CJ/aWRkWMgpihz5rLWPO5f8JrfwJAqT2nrsUqCRqOaMcxSu5Hx9ALdORMnEs2x4zxPspCXlcDv3bybt6QjErErLE6cjs2M+z6RXfGEC0r13PuDpy6Axs6CiVlhuthgU+Gu9CwMLIIu1RwMzZsxwqfDAs7Mk7B1q+u3+NzYh+KuQzRR8cYLZWV7OomLPj+9XMWxUiho2QsbmDtel6Wd9yHmqfFlkoF+mynbC1SqDBBwk7IxEkzBjPfkyO5LGbNfZQA6U2/ZpxQmEIHVjrwrJAyxz6WbJXA+1YnfDYcy6ghG+5DzY1foMmcWMn8l66hDZnGjtyNDWOQz92GAqMyyHmDVomnZs+Z8ax1j39muBwlkPuouuHD4ganAl+N59yMC+77iiFYUta8QQ5qqxKV9azswNp2nBbdfCDUxrMxHUO2XPklb4fNXKb5uLymUh6nAu/9WgLOiYdz3GAjmjfjCfehYWV69rGUTM1GtMnECdrVOrGwAx+5G4xK/Kh/Mc/zUntS1sJf6TdolfiwuiMkzbtuRR8sodAcQjmviW8211wUe+7Hitp4fNa869YPQYIJXFQHOIVfKd7+QvDpk6VNBfe+YteX9INwHFJWpEHIyk5H+uy4DQu6VExwfi8BcVeSM8TpUDoSsH22HIYCHzWFjYKylOO05jS18Twp9jPyejzUtM7YIuKj5npYcK+qmYC+dAPf2NxwOzTcHBq+f/ecO1/zorrHqcjluOLNFLx+2NyxDwXv2pUE1BNh2KqEjpmUG8JohQ+kAC3XftMv5ozc6jSXU5yO+GQodeDESQksZs02VFyOS9pQcFp01GYkZc1p0TImy/tWeF3LYmBddDwpdmxsN6+pNhX8eP90JrIeE005DDPJa5bFyHl14G6o0SYSq0iymqykBJOqTC4Tm6YTdGtsuB0a+cw6coiF/KzKrOzAs2qHz5rlhOh+qi7Y+4JN2fNr67c8L++pVCCi+LI/4w2KD5o7nhT7uZQHgsD2SYKYq2FJygpTSPmjsJHz6sDL+n6a1wG/kmfc6JGV6Sm1LIhjgN0nR0KxixXXw4LWF/TBsh9L2rJg50oKHaQ8Ew3vWiFJp6SpCikdhqQJ07rMWWGnkk2XgSn4116CDhXBDIqQQB/+9PYYf26E07/zd/7O/6Qyy/9wuDaTrDCck/naNzREBygpqxCBACpmdFDkKQlIhbzWDEjg4h6CEJXyFLRAslPgYiAaCWiOgQ1IlFzoQEiGK7/k1WHD9qziabGjTQUxazmAJjj/GNV/WNzwvfIVh2y5jCt8tmgSJ+bAR+4agPMpMHEqcaLhOirexSWfjk+58itS1jwp97wo7vmuu2alMl/GktfhlJ8Oz7j2Qiz84+7DGZbcxYpX3Qm18TwrtwzJcjUuGaKd67eFiTidOIxyUO5izbXu2SGb4/u44rU/5dVwyjbIRvzrzVdU2vOODcVErvLBsEsVWie8F4VBaC3KJYzKLI1A1RHF/RTA3Q4Nh1Gy9Q+LGxo9kNCM2XLhJNM9ljt+cHjJ9bDgsl/ypT6l0HI4vajuOXUtjR3po+Wyu2CIAgkeib9nZYtWiRPXkbLmvNjzwt3RaFGnABxSyU1cTHVsz4lpOaSS/+jDfz3XlksdqLTwMBo9cusbvuwFvXo/SI03ZTWTx0A2gcIGLu+WhN6RvcY0Age3qSChaPTIr69ecR8afqV6w7nd44i0ueTz8YJtqBiTYYgGrRNLF7gZG5bVwMIMvCju+cvNqxmNeRtO+Ko75bw88DdWn3Fu98SpFHQ8INtU8vPhgh8cXrLQA3+9+jnfsFv+9yf/HYdczAdBnx1f+nN+1D5jTIbTqiNkTWEmSHsqo62m8s5fbl5xZvbsUs0hlRQqoJGSzDu14bPugtfdmpt+we604pv1Je/HNX9095K7viYmPW1uaUamfDJ8Ol7wvNrN5ThnItpEklUonVk0wnnps+MuNPy/br7NECzPmi1Pij1dKrgba14fNlzUe07dgTaW7IaSdnToReZZueXUHVjpnpXp+XH/nL+yecWF2/FmPOFNt5kzzSFavtid8q/LlxgSb/wJP+/OASm3bUPJq/2G3X0t6IxmyqIkW70PtRwMWbGyPU+KHX1yvGo3eG9krvQGVBYlWVCkKhHKwLoWOL7zjsFbPlzc8Vvrz/mkuGShRg65kLJoFgWZUomTdUvOCk0WdZPbzaXX9+NDMPF2v+LH4xPelGtOy5ZCR55VOz6sb7mwe975NS+qLR8Wt9M8Kng1nPDF4YwQhdhqViNKZYZckr2m9U6eFxmfDAdfTBm0mcilil+9eM+7Yc3N0LAdKsZoWJUD31xc86K456WT3/fT4RlPy91c3j61h7m0fBsWbGyL94brfoEzkVUz0JqEt5YYNTnKfCmNIIT7sSSMFr0Iws3KijgYks5gM0s3spw4Gd9dXbIx3RwgpEbhdKRUgcYMtLHE6SD3tFhy29dzWR2YS/M3XlCSLjr2saT8Glo5JDsHwW/aNXddBSqjTWJd9bys79nYjlvfzDy04/s7FemTm8rrFqci114Cv0Mo+OzujJQ0bV+QM7xTK0GntRCynZuQa53JOeOjYRwNcVLlKCNllVFZlM6kvcPsNWZSNR5VWSqC2ylS/0sQfPz/OqJVuCHjGyX8Cy3Bgh6lvhRqCNXE6ygErVB5+v5Udgk1aAvlrSAcD0iIku+FLDKrNhOdIlbyc7YXtEV1hr0vZzLPkcG8DTVPp43jJ7sn3A7NTIS776SG+1dffsXlesWQHJ+2T9i4Dqcip7blP1z8hO+4LQbY6ESjHInEW+CQC27igl2o5izy1B64SwU/i0v+b/d/GZ8Nu1BhVeLEtbSxYB/LOWuttOd6XLKxLRvT8RuLLzgxBxZq5Dou+W/vf42v8gn96PiXV9/g+/cvaKxkU0dWdmU931pd8636SsimyfFZ94R3w5rbiXAbgsbaRM6a4A0pKggabKa0EqBchSUAL9wdbRJSYmEDb/v1XC8/klGF9NlT6sDH5TW/c/J9tquKL8YLPmsvOMSCk6Ljo+qGSnkWZuRZsxUJctZsbMtKC7nUZ8OZ2XNiWlmcuWQbKz52N6y0BERtKjlPDdsoCMMbf8rS9HxcXGNUotEDCz2wUA+16I05cBXWvBk3fFTf8qo7oY0FY7KsXc+L6p4hWT7jTJAKJVJbPUGZjR75zeZzPnE3GDLXqeaJFuXHz/wFX47n/Lh9zvteskCnEyMGpTJ15SmMzKHfan7GE90yZMOruOE2LLgcloLeNAvO7Z4P7O1cHgS4Tg13seH7uxf8X4df5/JkxTeKKyrlSWgOqeS1P+HL/oyv2hPetyvWZc92LOf68nKSmC/tMJdTTkzLLtX8q/03+LI7nYOSp8WOCyd/9rGaeR6GzM244P1+yRgs42AJ3jCUjpfLe05sy8Z2NMuRM7vnJix5060ZvCNFIzyKBLtDJaTkCdk7KVqeLB/4DRHF2WpPcRFp9EClPJ/7JxQmMijL0g58Ul3xibsUHk84IWZNRA7YI4FzWUgwWxvPkAz3vpagm8xPtyJFbpxnCJa7Qy3k0jpK2SOIHP2uqwjJ8O3qkkYP3MQFPz48595XvLrfQFbYMhBUJo9G+BNWygFD52iLQmSl1zXozP2Tat4r71LDj/oX/PPbT/j05oJ0VWKedbMSpzDChXE6cB8aXg8bfnz/lKv9gk3dc1p1bEp5vt9urvhO9W5aR4ndVGpdmp5CBV6Pp3w1nPK+X3IIBaUNnG4ODMEQghH5qUv8yukl3128F7Q1W+5Dzc5XwktJmjYUPC13nLsD18UCVszk6Wduy/eqr/DZ8oU/58v+dC4tHcu+e1WxixWfd+fzc7o5NPSjI3hDHCdJq8pSVikS7/YrXqe1HMSjcCPS8ftRgcsS+KnMN+tLntl72dPjgnd+zZ1vsDry1Ow4s3u+GM75g7sPeVFvOSsOc9nntq35/v0LfqKfcj9Kueak7FjYkT46frp7Qhfkmmvruaj2vGk3vLrdYG2ka0viYChWUgYMybAPErC8G1Z8/+o5TxYH/urplwD8vD/j3tfURvbEc3fgwu25jzX3Q81V29BUo4gf4jE4hWU9yHObUI7gJTCMnZF9PCG2FJsRMsS7AtNq7EHjdmA7OS9jJcm76SE+WHb9W8cvbfDRPVHixVA/cDn0hHAkN0VcSLCRimMZRW7C12gJaA/DmcIv5D2Ke4SBboBR4faZYa0IjcK2GRUhFpCtQgXFdSu14Y3rSFnxnfUlv7n8gl+vviSied2cCv/Ar/mj+5e8vllDVrw6bDgrhIdQTIz5lBW3oeHH43PGbPjSn/PZ8IRGj3ynekubSv7o8CF/fPeSm64R2M1Els+HOdO6HJf8pcU7frV5K0RSlfjI3bDQA99112gkGn5nNvzV+nO+5bY0E16/S5lDLng/LClt4Nefv8GqxN6XXHXLuVTyndUlf6l+M2cePxuf8ke7D/nqcMJN1zAEQ2EDZ4t25jREG9FGYZuREAy19Zzalm+V73nphHT4w+ElWmX2fYldJ54Uey6cZDT3oeGHh2d8vjvnVzfvGJxjGysq7fn36y/49+svAFjrnjPTchMb/tB8hM+GX6te8YG9o1KRXSr4/vABX4znuCqS0FyHJV+M5/hk+GK8kMzhawTcz9qLOZO4HWt2Y0VtPR82d5wX+5kM12fLfWj4D5Y/4T9a/jFGZe5S8Qu8Gp8NPx2eoYCiCCLlTZplM/BBc893qnd8bG8xZD4Pp/xkeM5ze88hlfzj+++ysv0MnXfBsR+KieUeKF3g+iAEPJ8Nr+OKnwzP+WH3gqthyRAsIWv+7+9+jX9efJPz8jCrNY6KiG2oZLPJhv/m3fe462ohkU1cg2NWWhWe0kRu+5r312v2i5JLt+S8ObAu+pmsdlYc+MqecTkuedetOS8PfFjfErPmp+1TFnbgry4+5zcWP2elPAnFXRLi6eVmyZvdWpj2TcZZUWX95eYVn7hLnpiOPhv+ZfpE1GE6YWxksehnn5FdkPf6TvmOv9F8SqEiYzZ4DIbME7PDkDlkx2VccxsELSyd3I/3Xr7ms+HtIOWZt/2aNgjxe+8LkX8PFY3zPKn21JNC6yN3g3sRuPULhmR5N6wJ6YK+lfpWjnriE0BpIyeundfUF+M5P9udMwTLMAVfJCW+IoV4ahAeHCIPXYFvC+x6pCg9X+1O+D3+Es/rDwB43y95365E7LcK+M4x3lTYzcjbw5rtWLEbS3Z9yThaxs6RR8OeBasne769vuLfX3zJJ8UVTgXu4oKfDhd8MZzxtl/PCF8bCi6qvWTjrufz/RnbvkQBQ+fIWcphR9XWyvRcaM+3S1E/mq/JZUHIu98soc8F97GmnVRTr/wpP+xe8sPdM3a+msvEhYkUEwrWWCG1348159WBi+WBn78/IwzixaEGLV4YOhNXkZu7BUUZ8KNF9UYURVlxvKTkMrkQDpoh06aSH3Yv6KLj3td8tTuhHR2/+fQVbVXweXvOSdHxK4t3lNpzMy6wRmTz31pe8436ikoFdrHis+6Cr3Yn/Mb5K75xIl8/EqH3UdDl8+rA6/2G/X2NMlOZUWVqM/Ld+h2FCvx6k/ibZ9+fg8NX/ox9LKXUaTtO7WFGIa+7J2zHkjFYFuWI1YnOW/ZtxdgX4sGjHnw/jBWPIdVkoheVEDaRvKiFVBBFUHIZvxLuZVYP5++xsvCnHb+0wUeyMDSTIgUJYEcLxk9SuwDFvQQiqYBYH5m3otc2HuwB/HKSuk2oh0piJqOSRG5iLCNBjkqCjoTp92YN3ei4G2pC0lz3C77cnfBRdYvPlo/slk/snkPS/DycsjEdHzZ3DEnq47+z+f58KGoyr+OKHw4vuQ0Lzs2ePjv+8P4DCh14O65xKvLz9owuOIwWLXhpA6/6E7ro+KS65j958k9Z654vwhn/fPdtfDb89fpnfM/1NLrk52Hks+EJfXJ84c+4Sw19cmxTzX2s2ceKlBWbosMqqZk/K7c8PdvxzN3zxG451wcqFXgfl/yL9tv8q7uPeF7v+Pb6Crjg7W5FTJqYFT5q+sFhTEKpJESvLMY0jRl4PmUPPxxe8o/vv8tdV1EXng/qO/7Xyx/zkb0TzkzW/M2VlHGcCrwKp/xfrn6TPlo+bO74bv2Oj5zwBdrkuI5L7saaT/sLlmbgS7cjZc1NWPB62HDZL/mj+5eEbDgpWn7n9F9zbvd8OZ7PhNA0lcy+2VxxYXdU2vNHhw/5Z4dPOPiCd+2SmPTkKdBT6Mi7dsk3P7nkREuJptKehRL56V1q+Hy84MovZ0MfrTNlOVIXXurdE1JxGdb8weFjuuh479Z8Ul3xf7j4lxgyn45P+YPdx7w5SJZmTOK+rckZqsLzeX/OO7+m0SMb2/K95jX7suJf8TF7X/K99RsimjtfiwRwXLD1AmvnrLgfKiorJZQhCIk1ZUV7KOdyRkwarzL96Mi3Bfudo115todKgpVgcEXgN1684oPyjt88+TmrM6nBJzTXURCvO9/w2fAUgIUe+cDeTmqGGqviNM8l4NEqs3HCU/qD+I2ZRPrZ8ITbvqG0UkAK05xzLrKwI7/VfMZ33TW75Pj9/hu88SdzgJlQvO5POAQpd2ny11QXhjYWNEZ4O7+5/Dk3UdRGtfFsQ8XVsOS6X0gQ1pfc9xXfOblirTue2zue2C13seFLf06XitmYiZwpFiMxGKI2kwIicRcb7mPD22GNVYkeRNEwWJRJwvUYDGp8MPBLKrPYjOhFz3bXMA6ORSVZdMyKE9dh60gfHVfbhRhf3RfoUREbzeV2OXuf1IVnXQ3cu0r4WSoTguEHd89YTITkPju+v/+AP755zrNGODAfLO75S8u3fFxcsZ74JX/YfsTn+zO8l2PE2EQ6OKrTVoL48AH9RMisrJ/Q30RtBGX9dvVeSPjjGZ9357w+bBiT4Vuraz6sb0XBV8ocOPporF3PRblnOXG8fDYMteVmXHB9aIiTMZnutBiIRUhWAhBjEuNgSa0VQm6V0QOYvca28rr+ReS+q/hR+2yaI5YzJ0F8SJp7V+OzZmV6/rdnP6JSI0ZlbsISnzX96MT/ZGx4UpQMyvHzTgJNCf4luNqYA30ueO/XvB02fHk4ZYhWSi5ZkVvDWFmeVPu5rHkZ1miVWOmONpVcxyVfDOf8aPuMQyiorWc9eQRddst5n940R26MmIo11chgLFpnChsmo0NLnDgeKapZJZRHjaojeTDYVs/kUh0eFKjkiQKRwfj/mQmnf2YjT2SWiWAWxd8E2wvEoyIU/aRa8cK6Pf5cVhBLCTz0CLaVACWWopAxQyY6mXCmB9NBuU24g5R6hhPNcAExStYzRCHnDN7y37z5NX68fsqH1R0b282KlrfDhkMUPsPadhQq8tKMbHRBmz2XKc5ER6MSlRK99+v9ZtaFt6HAmTgT1I4Z+f9m/WN+1b2nUIlXcckPug/4dP8EgP9H8Wu8qt6y1j3/uv+AH+6fc9Uv+EMjWdE41ayPpZW9L/npm6cUped7z97yjc0Nv1q94YnZYshsU8UfjE/57/cf8/2bF/zqyXtqPbKPx7JJZAiGbVeJ10PpGUaLHx1pcqq8XC34cft8lpBejit+fPeElMSk62pY8mn5lG2qpIQyweLbJHBym0p+a/M5r4ZTrsYl/237q4RksBPvw6jMu04W1z+5+tYsc1uWA8+aHd9dibLgid3ybXfJR9ZzN6FlL90tRiUMorwwCFP8Ji5Z257fefGjmRC2i9VcSy114Em15l+3L7kKK96PK94Na/a+5Em152UlhNQfb5+KJr53GJPZ3zYMg+Xz8gw7HWx3vubLwykv6i2fVFf8SvGWSnnuUsM+VuyCkBzt5F7pvWG4qxgWgZvzBd9bvuFb5XvOpiD2R/EFV/2C3ViybWq+VV/yl+uvWBwloGHDj9vn/Ovb53Sjoxsdz1c7mvXI/VDRDgVnJwdOapEFHnzB2/sVfrTkImFWHucim0XHqhwoTWDpBp5WO75RXLHSHZdxzY/75+xDSURPviUjt6Hh9fDduXy5MCNX44I3hzX7vmToRe1SlIHPi7PZf+Nn9+d8srlhjIabrmF7qKS8N4rrZazFsKlSnkO2fBFO+Wfbb/OuX83zHmBdSN38k+pKyg6HDb0XovQH5S0fuWvWumfMhqsgpdKjZDpkzXl14Em9F6ffvmEfSv7J7jvAd+jipBpJhu1Y03tLDsIzGCd3Tr23bLuK729f8Jk9Z+9L3hzW3GwX8nkGI26oo6LYPfDNVIZQZdIaCQIzkpF2lq2NnNVCxrwZF+xCyV1fM3YO7oqpBi1S06rwk1PwyJgsV+2CriuIB8fqyZ51NXAYC/7g9kO+rE7ZuI617fjfffB9AL7sT/lGdcO3yvdolXgf1rwZT3g3rMVjJ4rKzRUBTjJNIeTiWR01qfY+vz/DR826GshZ8eKDOyrl+cnhKZ9vzwhJz3PrhbujKj0/Hy4YkiGkwJgMY5KD/YaGMVnaUPB2v8IH8fpRCtSoMINCTdl4rDLqYPFBQ5FmN1Y9KNxeYcZJzFBmTCvZ7vWw4NfXr3lR3AHwxXDOjVmgy5alHYW0Hyv+sPuIr1oJbvdjQTc4YjD86OYJP7l9Ijy0cpjLQvuh4GZY8KTai+/R2HDwBVf7BUYn4agERfXW0ldS+nvnN1yGFW+HDZfjkj46+uikfOWLmfx/VB81dmThBl6sM60v2PUlu0NF8hptM9YJ36RvCw6hFsdcJtXKFPAqm2f33dwZ9KDRHur3QleQ5FyhknAmVYDikOH+Tw99/NIGH24vUrOsJHAwPs8yWymLQI4QJzdA8QWZEIspIjNJ3gdkLR49PmwvyEe2oDyU9wntodhHYUkrTagyplOMgyU2mkJHFm6kcR4zlSr+eHzJMEmQnInsx5LXN2vJyCph7b9dfDFLyS7Din2suLA7Xtpbvusu+eDZLV+cnc/Sy1vfiIvpWM8S1JgVmsRNqvjcP+H77Qf8rL0gIVDoP7v9Jp+WT6ZsreRurIlZ83FzxfnXmNV9cvzscMFN17BYSIT82d0ZY7K8Hk6ozUgXC67GBW0oqIznL5+9YWkG3g1r7saavS/oRsc4ilNj7KzIwhQSJY9iQLM7VLxuN2gyT4odCzvwpD7wk0PNMFR81ZxQmoBVz8VMykl54yYsZgnef9j8hGoRuYkNn/sLLoMcKhvTceVFqng063lSC/R4zKoMief2nt8oX/PMWPoMb+OCz8cntKngmb3nub1nl2p+1L+Y788X3dl0XRJspCx+B0eDoZfVHaWSUsba9lyOS/TE4gchy66LnjdqTQyGohjRRSRFw3ao2NUTxOoOsJBN7g/3H/G5vWAbag5RTObeH5bs2oqhnUiIxeRXclfQBmGz38WGy7DmnV/zRXdK6wv2fcn/89W3+MnqCd9dX85lo5g1VglcPXqLc4FV0bNyIrc+BsUhGe7GmptDwzCIbw2KWW1S2cDK9VQTee9qWPL76pOZ6NZFR6kjQzKMSWScz8otT90OpwNf9meSpfYLtq3Yw+esSF5Ix0LGFCIpG5Gkb8c1u7bE9/bBmtqKq+ztWPMH/cdyb7LmO817vlFfi1FcMly4PReTLBrgB+kDShM4qII+ON57QRzv1Mgbf8p/v/2In9xciDswgnyWLnBetyzdwIfLOwC+ak+4HRr6YEUNMMnGu6FA6UweNHqQQ870isOu4qvyZHYpPfrqJK9hKg+4vaK4nzyIphKyWinS1jIcDNlNVv2DZjgU/Cxe8Jk+nxUeMRiy1yjA7jUqgXeSMN31NTe5ofeWti2J2wI1KnZXC5qXnrNaCNwfVHd8VAkv6mgq+PnunDFZ3o5rdkESsdIEDkFUFH5fQFaYxVF1ofj3Vq/5leqNqPpSxU1YchMWbIME8x9Vt3y3fEufCkEz6gNaJZpJdfbObxiS5YvujLux5raXwO7QlaSkWDYDp43cy2PJMCUt3Cp4EBm4yccoSBksD0LkPZbxs3k4T2IFsZF1JiRP8T65Ckt+enjCl7sTutFxuxBp/LFdxpNqTzGVhgCsiywKz7rsqYwEYq/3Qipe1fK1hRlZ244X1ZY7X88qoG1f0ZcFYSF76uv9RkoqriMkw7NSVD6l9rSx5Kv+lOFrioxyckxNuaQPjqvdghA08eBQoyYBg7VwlMQGhe40qUpkm8mT+RrTPdSdxnVaPLKmEsuR9iDusYqsMyZOHMru34Hgw/YZJgm1DhkVwIZMqBXagJkCCDOKYiU5RXZyc7J+MBpLWcorKmaKrSzqo4spmjmCy1pYvbHUJCtwndsr/F3BtqgIUdMUskhf1Pc8Kfazq+RxImqVeatXDIOjKT1v+zX/NH57zqCOFrex0fjK0Ew8jedmy/sJpvbJcKfqyX1uChqi44874TdcjnIAryZjrYSaMpWeC7efJVsATybC30IPxKy5iQusjvReXFm1yoRoeLtfcdM1c6lg4UZOio4P6jsq7fmqO+Vdt2I/CuntSFpKQYtdsoJsMvpg5qDO2jSZUGnejWuBFIcaP1piZxmi/CmckOFO7YEP3C3fKcWQ6qvxHEPmRAfO9JZvuclpE+FB/XA85ef9GbUR34+17Wcp8eW45HXcTHB64JVpee1P+dnwlLfjmrf9mu8sLimayDbVfNmfoSdvijedXKtWed5clBIbZR+Fy/KbJ1/y75VfcmJafntRElEslGRDX3oJJN81qxneX6+6WWvfRTerOW7HmqtOHG8vqsPXDNHErjxnsGUgu8mxVGcycNUt+QP14SxjLHSkn1xJfTCULrAdS/776w/ET2Wyc/ZJeooULmBN5NV+w7oYxAJaR8Zk2Q4V14eG9lCSo1yHGjTeOPzB0bUl78vlDOF/uLrjEEu+WQvSdPRxeOc3fNpesAslC1vy7eo9z90d3yiu+Go855/Eb7FvCrH3jobRCgx8Wra8rIQweuJa7nzDVbuQA76I8/3JSZGi4e1hzfeLD/iN1Rd8VLynUp5dqnntT7mPNY0eJgl7LWTb/QvCVMt9e1ix899m4UasioRsuO6EtGj0Q2+LfV+y68R6/MV6y0W1p0IMovrwICc9EvpybySDzBJ4xEpMoyorxMMxGvaqlHJLGYnDEeqQMvHxYERNfkWDEOpzmSSLUtJTRJtEXXqaUqSe265i30pvk2MSRoZuVzJ0D/sCCnCJrKQXyvXdkuJcEMWjzP8qr3jVn/DF/pRtL5/d1mlWnQzJ4tNG3s4lcm+IOwcmE5aiHlvrHk3CqUClPM/c/eyv89zeca5b3rKWvlhTD6W7seZurPmqPSFkPbuMxqSpXaBy4sGxLIYZyTUqcXlY4PuCGAwqTUZ0E5rBJDIgSR8SlCgjj46mooSczpNWWjJcDUtRAvqKu7Ge0cEjurCyPZ9U1/P8+rw/57KTdRGjZj8ULIuBPjr2XSmWBDozeDHt6lyP1ZGQDIdQSuIwFBPCpYgLKcHth4KdL7EqsQslG6fZWFHwDZOiZR9KDr6YE+F2dBKkRS1I2US8zTZNUa2UBVUhYgEdFHpnHuTJU8CmvJrnoQ5TgFaC2wldAabpeFSbjhnb/uktTn9pg49sBO3ID5wrQRJjJgchCSU7BRl6CtUmmOj4Pe2lH1E28jqR1j4s7hwegpVYKPxyqnkd/UNGWfh+tLRANxSMUTgEG9tyZg58s7zEZ9H4/7w/5+1iRe8t582BbzQ3OCUTLMSCMVr66PhJfopTv0ZjBla6x6hEnxyf9xd8djjnul/M/hkgzZd+0j5l50tOi45npZhUdRP09nXiZpystMdk+Ko/5YtOZKHHiX7VL/HeMrSOsvHUk7Xx0WJ35QZeNvc8KXaUKvBp+4SvDiezkU3KarIuFiY/E8xJluxNj+BPJIjpJ6LWsZ/LfVdJYyyvuWlrnDmVw6YWXsiZ2XOie/psuIsLPvVP+OFoOTEHPrG3vDSZUlnaLBN8nDaHMZk5E2+Dow1CTns9nACitOmzbL618ZwW7SQD7nhit6zXHX0WeHPnK0F9rKcPblY4hSQWwwdf8K5Zc1mseWp2fOCuaRQYpTikTJ/EJKzQkVU1iEP2xK8A6INjqyr6aAnJ4EzkpOxmj4V9LLnql9IALyuKIlIXPSFqgU4n+FYrcfcUR8+RlGvpOzNanq73XNT7SSYe5z4O1/2C3lux3Q+SRYbGYE2kHx1tX0jN/Ahf20zySjajiYMQg6brjdibr8Xo6NnkelppT58cfXaTTHniIQxLPrNPcCpwbvec2T2FjoQoTHvvDWE0GCu9KfrkuE81N6NA6z6Y+br0VJ7MUaGctA9Y246P3DVPzY5tqnjtT/lJ95SbcUFtzucgHuCqX9J6RzvIfDh6llgXRc3jRljApuznXkMqZsZgafuCz/0Z1VPP83rHSSFW6cfE4n27EhTCJUFmswJlSGVC2zRLT0M2+MmJUoGQ+gpNDKCcJEcpif/Q0bQpG4QHEgQaT0lRl56zRUttvdhfR406WPFcWGRilVBeoe4cqYmoWnqq5KRmr4ZsM+HguCoWxKwYk+UrfTKvL6sSi2IU6W11y5k90GfLZ90TLrullK9cApsliBpFVvxqOCVN/httFBn3mCzPiu3sSXJtOu7igvfDinftijFOPUhsYFP0M6q5HavZndPqJM9rmtd7Lxl+SpowTiUsNfk0TQnlsRx/TFazlkDk63xCoiJPrqdtW/Kz+3OeL3YUJtBMPh7H3iubouPUtZRaenEdlTCdl8QsBsP+UPEqacKUqB2VJCkpXifNbixp3NToLkmwMnixhQfEe2QUiez9WDMmO6uEfj61Rzj2+emDZQyG+LXyizFpMtacyLfHpqpqMgKLitxamUt2agp6RNwm52/F5Is1VQ1UlDOxvkrYPs3VBpUyekzYLsLuTy93+eUNPrRMlFg+kD+PiIWaLNdRxw6QGR3V5Pfx8Hozgm0nme1MNj0ST8WLngRmzIRSESqF7RIqf02rnCEHTVAWVGZ7qPiyEOnXt5tLzuyelelwKrAwSxbFiDNi5PNhccuZ3fOiuOM2LHg7bPiqPeGyW/IH6UP2vqTQkXUhhKC7seGr+w2Dlyj5yERunEcr8UB4VsrifTWccDfKwX4z+X1sQ8WPt0/ZjyW18/TRSR04GnGINJHWixQtd5ZYRGyTeNrsWDmR9a5sP1t/fzWc8qO7p3TeUljpRuknSVYajTQT4oGoe8z0QhB74/1YzlyTowdGnjbP/bYWPk1VzT0lAM6N1MnaJBvWlV/hVOSVO+O5vZt5EZ8NT3l92IhMEen3oqYAqjSRs+pAocX4y6jEc3M/Z+VNOXLhdqx0z3Nz4KXZcZkaYtaUJsw9JQY7ldQmSPTaNNyPNe/7Jf8kfoev6jNeFHcs9CAul1nz2p/yflhx1UoGvaoH7NTFc4yG+/FoHCa+IPVExDv2QXnbr7k8LOkH6eoZVKZX4lcQe5mDMcp1HnvjHC38B28JB0c4kzLhi+qepZXN4D7U9NHxNqzBTodbVvTekgZH3xak1nLslqptIh+JZ2aSI2rAJnQRMS7OWajPhh/3Yr2/DfUsfQRB18Zk+Nnhgn0s2dhO4GVfMQRD15aE3sKoSS7xZieZcB8dN10zd2JNoyEHRdTSIZmgYDNOagAvBnvZ8tPhGT88POddJz2BrEqcVtLPY2HFLGrXVXSHkrKWHkHPmi1LO068LsuYDIWO7FOJn0h45dS3ZRgs79qVlAemcpvOUoZtvdT7pXmGAptJRZJMHCZYXlRJ7VCIxHgw4CVwV5Obs4rq4WCMgEEkoEFhWnmv2FnaqsCaxEFLua3bldhODpMjfG4GJV2cayVOn0mRe4PdGrLJpKmB29A7tqaapMUjCyvop9ORy36J1RIYHy3W33RrbrqGvnfCJXBJjPRGxTBYXnebX/CwaIMTFKGqZd4bP3NCrvsFNwfxsKgLCTJOSzEzvB4abjpxzt32wjlzRpp5Gp3Y9qLmGAZLGg1qMJJ0BgDZa45Gkygw3UOimidTtHmrV5Ldp87SNk6C0InAmbJiP5az8+dX3SlfdaeCtHZL9r7g/lAz9k4CYwWHCWXQOskcj4pcMSvgemdxk8/IsfNvCIY8anQvFzY2bi7ngCjgOu+wkxhhmHiIcQpkRSEpSGeXC3xQc7fx47xKWUGS+QQSgKoEGSWlFyvfU0z3xz/0SCt2mera4/YeNURUnAJ7Ldcb9Ndu6L9l/NIGH9J/JZOs8Dw4yo7NFDioSWbrBNVQKc/9EFQ6ckXENv0YjBwDGB3la8kKmVXIpw/Rsdi2y3WoqMhBrqGqpD3xfVfxo/iUN91aGsIVPQszsg0lYxQiZmEi7/yajTnw3N7Ph+rVuOAQJpJatFweltwV4vyZsqIbRAJlbSQES4qatnacVwe+1VyxsS37WPG2X4tZTtL84O6ZmCAFy5urDUUZeH6yxapI40aMlmz3aIedkkLVYWp2lalM4Gm5mxu83YaGN/2GL3an3E0qi0NXohSiZpk2THXMFmwmK4UdmTc8P0gZYFlIqamP0m302HY69wbvLL1JXHcNIT3lalj+QrOl/9X6U75TveMuNrz3az7tn9JFaejURcdtX3O4qVFFItaawgVRlZhIYeLsAFgp4ekMyfFVf0qYnAAXepg9QH42POXVcDKjPNKNMol9tY44HalMILiRkAyv2g2v2s0voFOFmRjxY839riHuHH5jsPbB/npRjbOlch/EvvhNt+Fdt+YQhHi229fEUTL9kCxDkHttWk08ka69i6kx4ft+xXW/4KptGPoCfTC8u1kzBMt+Vc7NwY5dKlNSDL2jKAPORYbeEYMc7sfgQuksh30nrb5TmclVFKJ2HambgWIKZq+HBfdjRR/d3MgQmDOwxo5sCpEPv+42/GB4jtFJpORMSO+R6OYNt/dCvFMq045uJmLnzOw9oILwimJl8FHzeXs+N+Q6EoCPhnRWJz6o7oDJKTYZht6Rg57uo5CX96Hgql+yH0uu9w2HSrhN/dSFt6w8deEpbODuUPNpPmecGgoe0cChL4itRfUac5D7pr1IE5M33O4a+lICs35w+M5BrzEHQ3kjrd2Pe998c7TYBKRSTYeEbExmrxlTxdVh4phEJSVQPSVkQWFHhW0V43oqPfRG5lGvMJ0ilVO/q0rQkDyhDs/qHWeFuA/f+AX7seRH4zMhPv/C8xWeCXtHVHJtbqfxayFEnrqWU3tgSI6bsBBuw1TedJMVeBdFHZJ5aCdQmcAQLffR8uaw5m5fT0oVR4oKbTJF6SlspJsQrOjNjMLqUT7jEQFXGem8bSfEW8n9VTyUXI5lKhVkPjojycG9r9iOFe/3S/aHipwVh0XBdqjwE9Kw70vGCU3OnZV90SWSztgikJKWpHnUJGUwTrhXhRWzR2kvMB3eUwB1LAn5QRATo9IserhoJLHqo5uN22LUs2FaNxhZ00nPcu2skH42M+1gohpMZT3thaQbM5CEhGu6yT9rUoYu3iZ0ADMkdOfRu07azVtDWtSkxhHdQ6D0bxu/tMGHazPKCfcj2al/y9QYzowyUXSEOJVITA/kTKzkoRW7TLFPZD1lAhpCrcBCnqJhPbmdxlLNJR4xLJsWeC8BDiYLLFt4VqU4+RU68up+M0v+jh0ab7YLxtuKlBR/XLzkTb/5BZ/9PjgWVjoxPq+2/OHNB4SkqSeToqYaJJLvpcZOUoxry9NyR6kDn3VPZsSjcSOtL7jvKg5dyWbZ8eRsJ1GxCZwWHd9ciJvq0Xa8Dw6ts2jfg2HbVnymz7gZmtnFsfWihhi8xZpEPzG4tYlTkQ+pn/Yic84mYzqN6aS+rQdF7gwhimb+XbtmO5bcbxuJ/ouEKqT3xRE2FmKWLPajIoc1/KXiDSvtuUsFPxxe8OnwTJQIUYyv8nPpp3CxPLBy0vipCyKP/kqfcBcaboL4MNz5hh/cPuO+q/hscc4Xq9Oph07JF7tTYlZ0o5PrBMrKS7AwwaNjNBid+PfO3vJkcrh936/oo50PyZANQ5iIkSaTk2bojWjlTZ79JcSfoOK+q4RjYNJsVMcEuepW4FKVHzoup0YzjparTnqpbPuSfVvhBysZ7aCI1yXXQbM9VKSoKUpPNbU3z0nhD44wGrHf7qUMhs6oMmKdZDJ+kiuaTjKmoDMqixPkaC0hGNq+xE9dk797csmL8p6IWJZfjQte7zf0wbF0Ax/XN0Q0P4rPuB8rDkPBohzJwCEpcl/IvJnitCfNgU3Zo1WWDLssCMfAVQOjgk6u4dVhw9ZXswzzk+Z6bkZ2bC7ok+XaL+auw7YSmOHNbs3b/WpGgWLUdPdC+stBo6xYOHat+GM4J14Ih6Gg7x3hshZORjFljFn8gXSQbNGMiuEsg1eMvZvcJbP4egwaPerZZ8J2UN0kip1YlYdK9iJfK8JSCfg09dPIFui0yCLLhCrFG+RodW1b0AOkcio3x+mwSXLYZJ2J5WSqtbfkRUDrJImUHRiS5av+hKtuyfVe1oMxibOmY1lMktxgJcv3CjNqshLkc4xChD+1B369+pJzLWTfESkJx6lp3U1c8o/3v8Kn9xfzYZyy4qpbMIQNKUPtAqtmkH4nC+HL+CCt3g9tSQwaVwbW607+f2enZ8HE9UDm9rSOyDAJ6mb7BbKSniXT84tRArE3hzXbrqLvCuFOeEkIDjpx0nRc1HtS1lQ2cNcJOVxFBRFUntbaXoKR4zNWvcEbxzARpg+x4LCvZvQoeZG+H5Po4xiiZT+U9N7idCJl2Tu6fgpig0Y5QSXjEU2rA6oO5INFaakM6GkeZCWItWmlVH706HCT2kp7Of9SMZ25vZzFZsiS/DOpXYwmOwtWkwqDbr920f+W8UsbfKgILiSJRBXEUhFKJX1dSvALhekztp84G6V0oo0lEmUGUFFL1uAU2strjwZjOmZsJ+WW6BT1TaQ7M+QCil0iWYVfTlBzlEx+lysWheflYkuhw0zUs1pg1y/vThgPBZjMuukZk+GPrl8Qk6JyQVjoOvF8seVpsWWlpWZ/7RfUxnM1LHnXrbjaLxgQIhfAEAxftSd8xQlvDmuAqRQjZYbzRcvHmztOy5YhWn6+OwXgMMkAtcpzXfowFoSdE7az13SDobupeZ2R8Fhn3GrkZN1SF52YEu0LCIqo7ZylHpsK2YMmlmLOpo4M81JY+d1QMAZLCOKol5NCD5IFJKArC/aTkU5l/NwJ8ogmfDGec273fGKveaIHVtUXPLU7/rv2m2xDRRcchQ2MoaS2cj9u+pohiiX4mCxXg+NurAXmj5YhWAob8EnzvlsJmpE1Hy7vpDwwnrNY9gwzJ0Akm9u2orupcauR60XDh9UtL4p7/lL9BhBvhCu/4vP2nJuuIW0d1BFjIypqUmfRRZi7bt4PNZ13c5txP1qqWgLbmWyuEXVDkAwEN2W8uuLVaCgqMTETPgRyMNtMriN1M6J1pg+G7lDSHUp5bzMRzXpBDcrlMJf4lIJxNPhtidkZsgG/SdLau9doL5nRmAuUyZgi0jjP02Y3lT4KroYlWy+GVn2whKj5Up1y4jqeFjt+6/TnvB02/Mye826/ZBytrMkqEUuE4zIF4gnFfnTsOjlk0IKcqTihoS7T31b41YFPFtd8UN7NHXOv/JJ3w5p7Xwk3aCzpvGPfl2Qg9pY2KkIl9vU5K9yUSZtaDuKYJ8Vdhtgb4uAYbaY+ExddpYScF22GckJoeo1Osg/ZVmT89qDwRkMVSUlQEq0zqUyovZEgYSofV7cRdz+iQyI2luHEEQsz1eIzsci4ncKvMvEkCPdmKqegMrHKkvUHKLaZ7tlEvpwmlT1o6kvFuAG3VXOJon1haeuKV3bD3SBraN+X5CyN7z7e3M1oHSCcpYmHk3Um1pJFh6hRveGz6zP+39VHgEjbT3TLWvc02lOpKB5Bupv7Pg1eEBCFIMAhGJwLrKthJsL7aIhJekiNwzRvkuzNIKUMjgHaVGZXE8fDDA8u2ebuF8+ao+JFu+NzUNxeL9kWYmqndEI7uf85atJkXdBHN5G4K7SSDsSjfyDhk0DlKdkatZTC6ogpoyidkiiTijIwYjF2UltuLSqI+ka5RDcKalxa6Tp9tV/QtQVVPWJMJjpp6GdcomkG9GK6V1HT31US4HpBwdwBkpkCYi37triTylxI9oHzYYaM7WBcyb3rTzTrLzz23T0ohQoRhlH+dlb2/yP/8k8xfmmDj1Ar3KBwfSLUmnGhiaVMqDSxuCU7YEY7VBS/DjjKgKbXFxDrqRFdFA5ILKSZnJoI5LFQ6CDqkWTl3yrJKak6gQUDlrc3a3zSrIuB764v+UZ9NbfqfrPacPOsEb1/tHyyvOGk6PhqfzJ7hMSsCEm6In5U3fK0kEDk6Oy39eJAmZLGVUG4AqPlD372Mcomzk4OfPPkmoUduR0adr6kNIF1IbX0r/Yn3OwWdKVjN0pmenSw672VzX4KPABMFShLL90gR+mPcrY5cNEcOPiCviswdZD6/2Svm8osBLbeUL9XhFoRatn4pAQjzyAlReESq1UvG0ZvSXXE7AUiDaNhe5Da/86VvNGJ+0PNZtFxXrf80f1LfrB7zvNqy0fVzWx3rMnsfMW7uxW+cyw2He8PYqTUj25u6JZWig+ae05cy/dWb3g/rvgBz9mPDxyTZ/WWEyfdTT/fnxOTprSRphAr89pKGWdT9nAqAcq7dsV/2/8KF/Weby6uObXtTAAutZQjVFCwdQxJYaqIrqaurEmLS6yX9t9WJ8pFz83tgv2bpWROSU2o3MSnSWBaya79KqMWgeWqp3RBsvUjz4BpXQSxvl82A6qR0po1QvDsekf0FrMIxFEzvpesNhvZjIgKczBinaxAJU0qJEuOTSLXUdCA6ffddxW3bc1PVKYqvJjPJcWhKzEm0ZTytR/cP+cLe8bSDbM6xJlE8JZ0cKhBmG2jLXhvl4yDI96WgqqtvJBRvcXeWNxWCJVZa9g8BD8/78+5GRuu+8Xcv6XvJACv65FmMnsbB0scNVnLNW/qXhpIdpKBZq8FflYItyR/rdSzTPT7ktE5itJz8mKLPhJ472uIaoL3M+6gZsdH7RWxN4yAmZIKRk2xlfev32eWryNu5zGHAeUjUKGX9qF1xDChJBN3IZWGbDSmFYTq+Ez0CLbLcnhFBAmYfmcysi8uvxDlYKzAryRQCtcFV71huxxZ1APVpIrSKvOuXc7rSus8d47NSUGZUH5q0DbNwb4t+P71cz7fnc17+mpSqBx7IYEQxO+7avZ6OUqpV+uOwkZeXZ0QLsV4a24e6ibSZCkE2tRahlYSIzcoiltBfiQhypgBxpUcsrbL1FcJlY/zW86aw3NNLCDVx+ZgimbRs64GQtLc7hrG/iillQBATwGR99L+wB8VRYWsD1NJRhZ3TtQ0XkMT2axani33s1Pw3pe8PawYvOVAga8S0RtSLUhG6QKlifTBsioHlpuRsNb0wU4dsyFFITDv9xVKT+Tfzgh3ZOoxpJKa/EweFC3CeVRUl9JqpH+iiFPpzi/FARzEeNP2oEIGY1CHDmIkdx3ZB3ThMFaT9L8DyIfxUm4ZNmZGKnT74KSWTaY712QrstvkhB1uhkyxz5T3iXGpCY2iuM+ERgIK04OOgmyEWt7LHWRxVzeRVMrkL689yZV0zySKdcuR1aInZ8W2Fdg4ZM02lJwVLY0eObUtT90O30hzuQu7gwX0J9LJ8kf7Z3x+f8auL/kXb77BP8ufsKqGOdPvg2XXVvS7ErwiuolcZ6VF9pOzHS+X93N5pLJiV33TNfzknRiOGSNwntGZmPRMbDsetgBqIhNiMmXp+ej0Tto150l9kAxX7UKY/jrjdGIYi5mYpLyCBeQyMZwo7EEyC5G1Ta6CQc8E0HYo6LoC9bbCBVnwcZlZbToulgecjnOAdF633PY1n9+c8b2nb7koD7wfVvzg/jmazKYU2WofHetFz9Wu4LCraPelQJZaekt4nfgqGs7Klmdl4Mov+fH2KW/u13hvsDYyeMmICyNk2lfvTwBIEzJUnfasmn5uj210okDcFm+6hiuWvD2sxc3VeRo3EpPmrq0l+NDA3hKn3hH9KGTdsvKUzjMGw24v9uY5KdQiiIJIZ3EUvDcimzQCyZs7yfi9s3SumJn0Dw82k58NVFWQXineMg6SNSmdp01qMr7SCV1HQmtZfD51r/wf4YolJ5LzsFAM51Ger5VeNTlp7u8a8mBwqxFrEtZEQFMUAe8t7eBIGTrveB+W7PcVOSnMxIOJrUV3Gtsqslb4OtHtSzFDOuuJQQvycC+Br9sqpPGvIhtNOsscfMmn+wvetyt80jiduGgOnJ0eWNthJjuHZPj8cMauLaFIKJvoBjc33TImYytPwKEmW2k57eSgShMPI4+adO/oaotfernGUUutvtOyx3hBH8wAxVYR/FS+yorgE3iN3RmyFi+i+jpR3I3o3pMLSy4d46ZgXBqSmfhttbxvsRVzRHEwzcR1JC5FEl3caZKDw8sJAUDkpfrWTmoF2SdjAeMa/CpLIFckcQGtA8tGDl0Qkmfbl9TlOPM9FuVIKhR7XdINBt2Zae+dJL5l4sOnd3x7czUhugvet6vZa6QdHf3oWNbD5NOhMTZilSQswRt223rmHplRCbozdYbONqPqICqbrNCbAX8oMDuDHkUK2lxGbCcl4mQV49rgV5AKRX0FxTai/YQsnzrxf+oERYx1Ru8sbVUJoTYa0l0hZeWlBMFNJfy4cRSCYExm5iWpqMgqy7qffFn0OBGFB82+rRi8rEtnZB/q9iV5MBAUdkLDslGkICT/QQsCsuvK2Zl0t6/x2+IXE49ClFV4jfJa+D1TA7hj6TZr2cNVhuJeUV9mXCtn4pBkHz+W77SH8i7jF18j6tYFxIjqBtRiAXVJWtbE2pHCvwPBB0kitCN5NFk1K1+OpCGVhPMRTh4gNVAMWhxKk+PB80MJ3KSsBDVHwqrxYIfMuNBYKzfYdolx4/C1uOTpYWItKzhrDiQUnRcSKMDn+3Oxb86KIVhOpiZcG9fzrNzSGDF50khvAgqonee+q3h/t2SzFCmlj3I4KZvISaM6OSjscuRbz654Wu/mJkMnRTt3XkxZ0Y6OwkaeL3bztZQmcFJ1M5dgiJbbtma3rWdGvp5Ip4WJEKVUs+0r9lPmam0URMQksjLiFphA72UjUlmQJRXB7iavFZNRoyJ4i3eRGDWLZqD7RmK8bDA7jRo1o7ds+6kGbyIvFlvOSzFz2pQ9CzuyMAOrpueiOEy29Ur6eUxZCAqKyrOe+n2EaNi1JcO2xN+W/L7/iC82p9RO7M03TUc7FJQusCrFOCxnhbGeF0/uOQwFd4NBFYmUFNe3S66Bqh5nGd3Tsy2NE3bg7EzrHbetyF2HvpDSE5NkziXxrImK6DWjsrPaJCWpN794dkfjPK133B1q2tua5DSxntA7d4STJAP29yKTbZZyfw59QdsuyElq1kXpsSZhmkEUJd2UpWZg1MRSY2wiqzwT7ZJlQq8e1h3IJmhbIBvGjZReojVoF6mXA3qd594Rw5SNhaBFrZMNxiQ+Ob1Fk7lrJNh6d7+i25VSxgtCeAtVZvVkz8v1lsoE+ijmWLe7Bh8LVFKEpTgaJzeRxneOn6SnuDJQlZ7TqRZ/Xh54Uuw5s0KcfDNu+Fl/wXaoCF46dAKEIBB6XY+cNh2HseBgZd4Pg5sh/RyFJI3NmCYQp/UTb0vMTrLLfITak6K4k7l5LAlnDXavSE6TFlKWTEWGVs3PN5UGM0TM5T3ZGqxdY9bi7qSCuDLbdgoSFRINmYyug/TkwRJLhe2Fa+J2UxlUq4mXpSjvhA+XNajrLOTwUeGXEBeJNBrudw33u2aW0+dRc/bxgefljkJHcQPuF7SDlN8A6rcav5qy6UnaGrPCJ8Np0fJRfcupa6ceOmt2vmLlet51az4digdr76Skc+rOCBJ3GsjPB1wpKLAf7RQFyjNJoyFmg71xFLdClDyKD2yfsYcoyeNeEys1l4f1mHC3HShFLDSu1cRaymR5rxg3gljEcorIyyjIe5FmBDOmiRQ+TGsrSHlSeUEp8UylDQmetId4ciT3gtEi4R8HIUCrKgo/I018oR78wbIzNQddUlaeVT1wGKTvkCsC7kIIrcdSnpmULq0pid0xcEPQu4kDEyeFkx7A7TNmzFQ3gd1HhQTMkyJIT+rR0ExVgwR+adBjiQ0Jru/Iw4BWp1AWqMqi/b8DJmOLtwO6NMRSEyvhbsTyIYLLStAPldWk3Z5YvHrqzTLBi0cegvaQpxILWcimOgjnwzdShjkS+2KpSGbqpjsFMGE03G6l2dvTxZ5Pljd8WEn/gR+HYiZp3m8bbqqGZT3wfLljF0rGKA2/Dl4OzKeLPSdFS2lW7A8Vu7ZkPTmOUk119+jIKqNQWCfS3T6Kh8XSDaLHbwUyPysPnD058KyUlvR/ePsB90NFdBp/XCRILbWwUSDsqObT5e1uxTslJmchGsapV0PpPIeuRJuEsYl+so1meJCvJQO4LIFcp0gmz3XTnEQCa3RiDEaIrtPIVkh3e0qck1rm7SBS1k3RcVHt8cnwpt+wdj0vyztO7RTs9Re82m+4vV9IZpOUyEzjtJCBcj2I02w5snAj59VhRphOq46z8sCLasuZPRCz5j7WvOpOuBkaVtUw29tvB0G5RNGgqeqR+7bG6MQ3T284Lw+TY6UEIe+6NT+5uqAtRPVgD4YY5F7nIqEqIXXmLJ4ZzWKYlEhybd3oGPpCWOpaIHN5eKALOVT8WcSsPItFz2rKUNtjVu41PhSo5ci66VkXA13dc9vWtIeS1IuqJR0cuQ7Cm3iS5PCvs5AXo8LsNEaSOwnyj0ZME7fEVp7zkz0XzUHMoSZJ9xiMIG2T2+RR6bMbS07Llm+uhQA9RkN/KB7W5NFYC2ZjqRwcm7IXNYHLpJDRUxnKtorxVN67qDzFxKcxOrEbK+7Hmp8rKfNsXM/tWPPl9lSUATsnPJpFoFyM1OVD6eV+V4sUfZyUAioLSqIn+WoUhM4tR/x9iTlo3F6JmsI97C+xkqTHHTLDqWSTs3/CoEVR0Ak6ooKUgJPTJKtFWRsi2kfMkHCtxveQ/KSImThtpgeVLNzZ+f6Z/kESWewy3YWW/lZG9kayEAeL+0A2isMzKxm2kd+fywnxQQzudBHwxkp5L4kctplkx4UNdDaRqoRfTeinzZid4e3tis471lXPpugYCyFlN1oSpmNL+ZA1hQ30CKmTrKBIpCcRZcT2IEc1c5vCzok9+sS3mZN+k/FrSMdEMYDr1CQo0JhBen3ZfiJb5kxclsTSECv5vp6sHeZRJKql8LCCNzO3KgbNfqhE9RcEcVJqQpgmhCM7SBuPq4IkLXcFuZekt65Hnq721NYzRsOlWXCwwq/xO4fyD2TT4z6qnfh6LIqRk6qjMFGk7UMl6FRb4jsJ2JTOpJ3DTsRjO7t8SwVApWMgK0m7HdI0B5Ck3B/nUp73eR1EPYoC0wfU6FFVCYWDnFHdQLpoCP8uqF3sXY9eWPRoMKNBJYMOepbM+oXAtLbN4l6XJZhIRpGncoryU5Axyo3U4evBR55ktxIpq6iwXRbzlJAJCyO8j4lcmXpD7Cw3Eyt+6QZpXqUyKzdwbSLbWJE6Sz9tUHopke1Vt5jdLp1OWBVZ2hHX3NOfO3aD9HPRk9Y7JYVPCj0xyP1oeXtYE5Lmw9UdT8sdQ7Jc9wt8NHzc3M7ck591TyZzJCW1VD91zFQZ56IY8BxleVmyb6OFD9J3k8zXRZpqFBMoPxGhjm6fk4xN4Ex5VkfIVWWm+uJ0UAE+GLrkOOwqyVgmscxRMli4wKoaZtvpgxdTnZANT8q9kA5DwZWW4EiTOUThsjCVE3wnjG9TyEmmVaYoAi9WO14u7llZCezufD2reY7W9amU5lSvuw23Q8N+LOameUsnTor118yAjE7ctjWDt9z0DYUJPC33nLgWM8kH5UbJB1Vy0WSTBIJVSIaiI6YUIt2hK+lHh7VxymAS0TzUz/Wo0FFQOL8WJMUVgQxc7xbCpZmkhngNLjEeCu50Ji9EPrmuRTlyUBXpyqJHRQoK6khcRdLXSXL+mC09OP9KUJ+JC+nzslz08305Ep9rE1mVA3cm0g1iWHb0NLhra/pgKUxkjIbr7UKswL30jFBZ1nXfFbxvV3M9e1GOM7nweNKodOR3KVIlhkqLchQXVJWoraePUlK762vuy4En1Z7vnl6yDyXf31Xku2IKJJBnMBa0g8P30nRMDeJSmo3IWLPJs99J2DkJSOpAAJJ9aLh15HjM/VmSlFWyUQ+cqGltMHWbPgYtKiRUTOTCoUJE+YgeEipOngxfe08zAPpojCX7mG3VVD7IlHcJ22W6My3736DkgI3CZdM+E60c0tVVxu2hHzVdqdFrkWFXhbiODruS29slzXLgbNFyUnaiIIqGvUmk6TMckZZUZBaVZ1lKWWXnK3a+4vPd2Vy6Ka0EH4exoBsK8cHwEnjoIkqjSp3FOKy3DP3U9XecpKNmKhUlIZdmpR7m7zQ/VMrC+RgTxSERGi1ChjZhrztyZUlOz75PZhR0PSyFDGyqhx4oefr9FElI2zqzOO3IWfbnGDTpXhRbR+oLcUJFoprRjDiK8dh+LLjvZX/2wYg/SG+m6572yQhEhOSaFF3vuGSB1YllOXIYHbt9Lb9j1BIERUVayCTU40OTVBWkDCeKpIkIq6XfWVbi7K3jpBqtmE02XZtA6a9VFkCNAVKCqkR1PenuHlVXqHHzPymi+KUNPvShx4wKXRbk2mF6QywNfm0Yl3pa1BnXZWGGOyE+5lqINXIgMnewFVfTPAcfxssCnDyYxKeijagMppO/XSOKlxCYs4HYG0YnB//RUv0otdQqg03SShshMy7NQGUCCTWZTImldsiGxo48X2xZFiVdcOyGkhDFDY8wbSxOLLV3fUldeE6Lbm5M1npH7y03Y8PzUshON37BbStN1rw3MxHpeAjkpMQFsDciBc1SAhL2tWT3buprcThUIq8Fxt49OOVNsrRsZBLryGTCIgtGAykoctSMQRwHrYvSC2ZiWKtRMv/jQR+S5qxsWbueNrgZpbAqAWJ3/FV7wm6s5v4yCiQ7UsJ6L0qPm+R6KQnS5Cf74i469kEUGPu+ZJxg3pthQRfc5HpZMIyW4C1aJ+6KMJelrIk4I0Zv4i9QcNdVxMl0qCvF2bSLQshTE9kra/msODk/c0IO6kIO7m50+FECvMJJ0KQU+J10JZWDRg4OyaiFGBq8lOiiFzvvI9SNBl1GctT0u5Kxt7gyUBThQcqrs0joopKmUnoKFr3CTIZX9nD0SXhodHYcOUujszsgVpqLek+zECLhwZc4k/BWym1KCfq1KOT727GSZx7MTIQTGFOuPWexlN63JTlpKWtNgVzWkmgkl9FMZPEp06xsYIyGhRs4L1t2QZwvFeJI+UlzzdIM/Lw/4yfVE3rnIEvvpuAN1kWCN2ibMC5KUNEbOcwmEnDWQiRUgygaciUBZXayr6j4YGZ4hLrhuA/JRj+bWqWHGroELVn8E8aIGr1kk2NAhwkiD8xKvqPUNtSZ2IgrJ70WtDdKoKMDmFHS5+OedzyUAUJjGDZSmk5WCPfiASFIQ8DQTgRQZTJl7Xmx2fKiuceozP0oc//IJcsmy0FmIVeR51PgD1LuHZOhDVIuGILsl+I9I+RopTO5yLMDa9IKw6TO8lr2wyKRmyif9wh5DKKgs91RZsxsJCn3GXRI2DahJs8L0yXUMEpgotVUWtEzGTcreU6xs2Ls59WMyLhmpCgiWifW1cAYDa2CEUtQEmDIs5I9gEn0oMJEPraSgPSTlYEf7Twf1PiwFtFqnivHVgLHqCYmzX0nhnA5K7RLZJNI2kA/vc58zcNETfuHYr5vxyQ+ORg2k8dIJaq65B7mifGZOCX1dki4XUR1AolmZ1HBoYwhDyP2citR8J9y/NIGH6muMJiHWmrOaJ9QURzsXMzYfgog2kisNckJ+edo1qOiTMTkBAbNWgnHo88i0+0ipo+E2pAKjRlkxuohoELCrs282FU46uQVzkVpLhdKgeVHIf8d3RDJ0vl1aQa+W7/jW/UlQ3K8Gk74UXrGzaFh15ds6p6z6iCmQ0EcKmMSSewRVqSQoCYj5Zq17TiEkjfthrtDTQiGn+oLQtZsXC+ujpM0DaQfyHHEKDwMRj3LqpSCddlzUQcOlfQI6Lxl31akqKibwDhKp041SkAUygcXvByFEzOXw8xUHyxEpmpMonKBZTWw60ranZuzkzQYejt165wOmmfVjroU98hx4ngUWppYvT2s2U7yvzgFeChoFgPOxLmbZh8sh6HgMBT89O4CpTLrQnxYpDGgEL3qqYkVgJkQkxTFO0EpxTA4MdtKCu2kXGVdpC5H6qmfRu8t1zR0weF0pAvSm8Ee9CSXO7oHThBqFsfJMRgOXUmMGq0zZSkGaXGS3hIflBJzSRFkY/GaaDLaCLk2KcQqOSqySSgNSj/A5yFIEBqjJh0ks89VRAVRSiSHbIrhf+AIOUH5YlrFZHykSb6gd46hEWTivGxZ2IGf7S+4PCwmAqciRY1SkcIGni+2WJUIWSyijY3C3Si0BFQKkhN0rnQBtZi4BC4Iv+cIjWjx8klFnrkPYbKWPtpyr61Ynh/NylZOGtUlFIfwNdnuhOpkBP0oSo/3BmMyqZAGZTko2dCPrqpTkKSCQt8L2nSE2481cnUMxpGyZCqY0UCYfnYqn+TprNAB7G5AtQMMI2gNPmD6gPGFHKwjk8+C7GWzt8ixHHM8cDOYPsl+mfK8JtPRIXrimIRKCMzjWhEaOTCJUpJLIER3lygXI984v+EbyxucStyM4nJ6GAopwyL3JE6eItjMqug5c8eSZKbSHp8M+1hyMy7QKnE3NvikKY49e6L44eSo0EWYPFUgT07KWWdUEVFm4k34Bwnp0a1TTLDkbDCdIEmpMPN9zgqyVeRFJcjSKKUtCdaEJGoGuT/ZCopIkdAuUZSBevJ5AiHjjqMV1MI/tJcwXtaKioo4qWfmMooTToY1CZDkJk57ToaHJGJGb9Q8P10hKLFReTbzO46cFb1xc5M9kiBtbq8eArGpNHvcU46I43Cq5q8d/bT0hDSKT5aaTDsTxXUnShdnyU6RS4dar+DQwvUd/Lugduk+WhJ0hR6TkLEKLX/bqY3vJKMKtSIVdm4QpwPkJHdO5Qn6zkwcDtkApM+L+NGbw0hyFZBQIWE6j+4D2RnIlUTyx404KmItJMOzspVGcvsV9zuxChc+hJng9czluORXm7ecmS13cTFn80pl2l6cE/tg2XXlJNcCO/leRDUFPGiooSk85+WBfSx50214v1uKJXbQ3EzoxrPljrPywLPVjiFKw65dX4rnv044B13SD7XVLNezdANPyx27UPHF/pSbfYMfLEXl54P+SNDLLj/ItLKayVQgk/m4CR1LIke30ZAkC2Zy1cta7mmOoulvh4LXcT0jICvXo8kMU38JqxInlRgc5ay4PCwEqRkszsRZEtcHCeCqqf6/nyyZT6uOxo5UJkwuqolSB0od5k6+X3EixmpTgOGj4RAr8mhJ+dibQSDQj87vKCcnRkDu9SCIyrE/w3GkcoKIkc2k7yXr9vsC5RLVUgKjw1AwDJbQuymQmH7eyQEiB5sSJnsTKEqPmSyZh8k1Fp2lrJahWg7UUzmi7Qv8vsDeSwkjFRJI6glhSyqjs1gqJwOpkTVmpoxSB6CfApNeMqRgMrXzrK3o27dDJd4uvZs3OZAALSTNB80dpQ68G9b8mCcyp2wi1XIIZifr5klzEC4NmXf9irc367mOr6LwK+Z5ZKWR1xgN61LmTJekGVfrJbv8NF1wPSzEwKpdTKZMQtZ0pZhYWSM8nOthydCLlTtmOq2mcfS2yWUiW4W7MQ+mXp3A1EfEI1s5PLIVO4B8pFgdAY9jcDnX1DNq8CgfwFowGrRGhYQ7RLjQmA6qW3Fj1utJzdJ9rRvuKHtisc0U9yOxsnMimqxkryCZv/aJUDv8Qs3lU8nSFUxOxdmCMpmmEvT22EdptpKf9h2pu0wJdysoURsKbrygwws7sDEdp8WBD1Qk1ZqbsOCPwktKE4nlSEol/Wil/YLNVJVI3IfezeUvfTDkQZPc9Pyn9XRs3keWM8C1YgFe3A2QM+NpwbA2s3N1qDVmU2K247QXKVQUaXSsJPAwPaRNoliOsw+O1nn2LQIY9uVDiwmvBaH0QuQ0R75JdQyKpoQyixdKYePcEHDflwyt49ieQnt5H5VFUq5smh123XS4r8phRk5D1JJgBOESESc/panUMxlXPyTSGYgPJmKxEBntPNXzQxAdnXBBjhUBfX8gjx6VksQyzpKrQv7ddSjzPyKZ+/8yfmmDj+HEoLzGpSyE00a6zcZCNk4QadSRxCVw0fFBCyKSjMhpbZup75JkeFnqezOT30n2YvqI3Y+SeVgzQZcZM20qyTHXF7vB0UdLZQKVDSwXvRx8XUHYi6a77Qs+356LIkWP3PgFl/2S276eGoYFjM7cH2qR1o4aVQeiNcSDxWyNWDMXGXOeRB0zVtwODdcH6akAU9l4kqkebcwvD4tZytV3BaEThrgqHyAxYYVr+l7cQK1K3AwN77Yr+n2JLQPWJrrD13p+fG3jPE5iycKm7GpyoJ25IKNh31b0Ns4ZpaoiWQujG430CJmskoG5j4NWmbPiQAqKLkpJ42m1Y2kG9rEk5JfiDGrTDGH2XSEoQiWdPlflwMvlPZUJWB1pg7Sj33sJSAoTWExNo+6Gmsv7pcDMOs+uo1Uz4l0kRdHyGxsZ9iX3i4qXyy0vljcA3I41u7FisBL89E3CHvScnaug5yw7aFFbmIUED0Pv6PpKNn+T5oMvlmIYZTv1C5B51sIZCEFKdPFIBGaa31patYeg8dZQ2ohzkdElQY+3Qg4cN5MT5DFZKmRdmFFh9w8lhFgcD0gmkz/x/FAucd9XXI5ii39R71mXPW/3K27vF4TJGjpEw6v9hsoEnpVbhjShL1GheoMa1VTagHGQfkQAVgnh97gnHl9z7EOhAuRaGP7H7r3Xw4I+2v8Pd/8OK9uWZYeBY67f3jsizjn33vfLzKrMIrspFiUCcmSQBVCOQIKgQYe0aNCiKxqi5MgiadGkRXqCZAmEZMuT2hNER0YLApoSySKrMivf737OJyL2Z31mG2OuHec1P5XVDTYSCuAh37t5bpyIvddea84xxwcf5wM+PR1Ri8Mjjvh5fXcrfk1ho46FZLDR4ss8oKwe7ikCkd4YndzYRkpRYR02QkN+Y8/SlbD9sJBvoQ5AD7K0jb223gTpXnj4maZPLnMfq3cjfG3ASNJeGxPqMaIFgd84rjx+vSIfA5Z3cUcxXCaxNb0o4qUhnjk2bZHPqV9vaIvPQHrcEB6vUP+APEWEK9AKUCb7nkNFGGij35rDZR7we+3tTuh9GBac4oZrSLhmh/BsBFC7T+7s8e35hLWG3dPj98M7BFfxNs04hhUvedyl0dc1kXy8mOTbc9+6qqA+JqQV8KvbD9HOyyh3dT8k/VV2BEkq93M3Zyb3ZsZs5JNd96XBP29wa0a9H5HvPPLB0fPkQG4TH1RBLR7VpPB71lEVuKMRCJ3ATQVt83AfPOIL71MzQ8z0ZIVmAY3YKm3415UjQWcBcLp4uO7CKtYARKBODeNEW/+mRFvOlxExFaRQUaojJ6UaOdor5OrhFtlVoV1CvxeZ9meFWy3SM8PitntDwxyM/6j75yFyqxy1pAjUCpTKZ1OVY8JpArYrftXXr23xkZ4ajh8uHH8cE9ybiPXeM3lvMUWKebow2ZYHlopBigfsXv7Te8X9716M5QyT6SrENMnb/UTd/OcJLh/ZsQg32nhR1BfO38pEzfZ6saTBkDFYImxutL2WlQqZvAU8ziP+H//i36PZVqKfv4gihoqvpithyHngJpJNKgfAzXST7KTO1pjL8d31DsXGE94rYVRrOa5rwv/54QsE13CZBxLnXiVYyiY2p67AWIGXALcB22PCz4c3+Dge9iwLF1lpL3NCWz38C2Oq1etth9Ebwa77CfQ5otsA7wUteuQUcHiz4TBkPijnZJW1AFmQZ1q3x1Twozcv+Hw640tTuvwoPeNtvOL353eYa0RwdWfJTyFj/jQh3a84jhuuK/NKDuOK6BvWHPDdywn+vu1R8bmRg/JyGVE2Xp9uJ16yB74baDh3V7E1gRwKnFfmrKweiI2mXani+28f8Pgy4SfvJtwPC4Iw6GrdaP8Mh32c4FZ2gy0pJbcgB8Y5xXpJwHOAe7uxCPYN1Sl9UgxxGD7Bujozg1IHbQk5NBrGAWTdbyRhp6FARsoSz48HXDx5DD5VlDvaL2swo65kpOpMpYN6hdv8rdARbk7hShOifGdBVZtDXRI++BN+P2b8B2+/wZ88fYdfLG/w8/KGyyQ7aGw4DBuia/hnj5/j63Rv7osWcubUeCaEw/NzxDfuHt/gfifSde+DcOWaayvXolTBOjnoJHhZeWDfmyW7vkIsQio4HUjumteEZTaZeBXka8IHlZ24DBW0U4E/lD3fxhV7Pgelk2lmV4mxASu9hsqRhSJU9k09nnUvDtBYbKiQm9EGRVU+OyzwBP6ykcinHWV0yEeqUcKsiFeFf95Qo+P4pRIVqyM71PRccPjnnwDv0KaIqMD00SO9yI1gn0nCxPtHxHdHxGtAnBXnn9gJ5RTpkHEYN1RDPdfzgBAa3h2veDtcUZrHL69HPD9PkDPRlWpqnjbQ0XUrAXdxxSmuuJaIb693+HQ+oGSmF6vauMyScP2LN7WMyb2/S0gvtybTL1yD3RitTlYMmqW8q3R0jVeFK4r184TtTYLfKCA4/HKGuonoj3VRcl3gIqWjfutKEFhukMKfPdrioQO5PV1K61ZBOxSMp41Ga4YouA27Y2hNVsQ4AKDM2RWBrB7VCKsAlTMAIAvXN0B1ijT7+173/X9KGe+mC4ZI/tYUM3L12MYNtcluIpjrCP9ohPpkUwBD4faxiwfEAemJlv7DY8HwRGhGO9+kI6TOowXB+acjTk0RRIjQXWfo5QotBbqscMcJGm/P3R/2+rUtPvxa4b/+BAwJdYqQohieKnkbW0O4FJx/OmK744JtkQUJYB15BdK5w56Cpz9xRFgVw6eM9ItH4PkMGQe0N6aiKAq/EE7dJXGNXvZ+FsiJ8Co2QfwNqifeX487cbFaRLa+y0wEbYK7ccXJRiCdHBpdo1T3+JHkRFE8HUb6NDyPTPccGjTdDvvtGvFejvitzz7hGFd8XI5YMxe9Voe2OFw/DUAFq9RTQZwyUiLpT6e8Z4fMLwNgNsS9pVyXRHKqpa9OtlG3JpDYUB8q4vuA8Xu3Q3W0rWeasNu46awHyre6u6yOFcNEYuVlSchbgPsU4TdBsZAsLbQdrsXju+cTBs9gqWuJeJcmfB5f8LPpI34xv8X3ywk/v7wlvJ8Dyb0Kys+mGYMFu324HnG5MoHyd5fPAAC/9cUnvAsbki9w7zgzvZa0hzU9XiYsXwD4bthhyyYebapGaAXcU0BW7Phk/jji9x5HyFiRDhsGU6Bsa4Q/Oxy+sSiAO+uGYWO7xAJqnZmAiYlcku4ZgOeA9OLgr4Tzw8U6tyPTKfWe/hzuWDBOHKsslwT3HIBNsB0CC7HDijYxSK4Ym96/EFFTR4i3OOyx7V3ZJTBr54Y93iBejXR24WZWJo4118z1fy4J55Lw+y/v8PjNHZn3DpAjowB+dvqEqoLff3mH755OqNnDxYYaFfXU0LJlhFwdyoG5M+mwocSA+kQ0sQ70yohnYHtjnaQoaqFHjncNnw2X3V9iLR7Z1F5dZdEaCwRa1lNZ4XzDYdyQ7q5EIt9PqNcAFGdkasu/KJ7XSgCIQC58n26jHi+0o3aVRN2WuixYURMwfBTElw7tcx5/+kVDWBXpsaCNAeKF0PZ1gY8B8m7cQy+3k0P97Tu4QqSxN13SxIpdh+0n9wjnDNkKwvML7t6/AClCRSCtAcKOXA4j/HnF6ZeC+fO0f24UNgQvluV0d1zgXEMyZOnTesDHywHXhSNf8YpytLXTFMV8acZY8NX0jJ+OnzC4jPVNxEsd8ZgnCyOc8HvfvWMa61TRxnrjKxSHEmki54pge6O7068USpo1KlODh4Y6sEDc3siObrDoU6QXh+PXTGC9+2cvVMUEsx44sUqM54JyIGm1F475XtEmys4RrPgww7l4EWzfjFjTzZxNNjo/l8mKyYE+Lu3QLE2W4xQ3C2rg2NSHBmdj0xZuEEOLQHqx8YiySI82Fky+4o/fE23dmsfXl/tdqZgXrlkptz24c438QmFAR0JkY1EcFhqMDd/NkFyx/uSEfDBi7qpwGws6NuxAnQJ8DDQZ857jwQIqr7YM3fKvfMb/2hYfogCCbRijx/IuoHQbdfWIV0L348dmBmRy0yPPCjmTQEROCLAlgTwBNTmSjVShQ0K5H9nlCR/eMvHCxwtD6cp4Q1GksMMZzD103iKePx0gV8sZsAAjVIG+yXj/dMLDl+/xs8NHbC3gn3/6nOqG6vFcBnxcjzS+GumcOsvIxZONeW6WznpHE62vDs/wopQizjYO6YdhBfRUEcZCTgeoqlAFcvZQ9YixIh0yydvOur9ClchvvH3CFDI+zgd88+EB7f0AHSv8qaBuQH5TAXjE5y7R6iiTAhOd8vpib8Eq7mBVe/EYYkEpJGWpI2Tq7zOGkc6Y3lxZt+axNY+PyxH/z5LwZpjxW4eP+Nn0EZO/wzfLHQ5h44jjIWK+JqzV4xDJ0TjnhOAr/viXH3CM9ND4fLjg6Fc8ZguMcwVOGpo6fNwO+MX5DeWadwsuAORTurkpFreTwNpdRY+WP352NYKo4HxhsFteA7Q66OxxfBS8+ycZT38soE6CfDRLaMVuqKQLzcwUQL4mjt5WIh4tAJgUEM60e0giZ/EKxMaAMku07KOBcHHQX45Yjw313YKYCoaRG0LOjryKJhg+YedQtXgjlnYDIr+yK0rPijhTTTb/xJ4F83KphwYsDr/3u1/i67f3UAWGoeD+qzM7sEtCXT2+frxHbQ5vhplxAXOEblSSuIsZIZmMr06Arh7D/YKfvnnEWgP+pX4GXIkQlpOiGq+ivCmQoaJtLJzvzB/kWhLt1S8j6lNEfPRo5wPKkW6eDuxu24QdLQOAx/NEA7Iq8M/xli7LJ4WdfaIZF8m4DuMHy2+ZySXzm2J4YiNz/Spw32jYx5It2ZjkzIImHzlSIadHaateKtAq5Lpg+DBA/Yin34rI9ywYj1+3XULpF/oclRF4/qnHcOeQLhGHP1jgUsD81YQ6Wi6WE8Rrw/TtCp8CtjccP4Zrg7NnU2yt+1BxOiw0wioe88uITx9OcPGWeiyXAH92t/HcwIJIquDx+YD/zf8EfzC9wVfjC74annHnF3weX/AnDsB32z3u4kKlWIn4/uWE68vA5y00hGOFPghKfxYFcKvb01mlCLBRfZfvG9Jj//9uZ4g6AaBsZD+9MIFVBHoYIVuGpoh2N6IcA/LRoZjstA3G7wmNaNd8Q37b1LB40LOnCMKzRz0RMSxHfq70AkzfmVLydPP+aFGBRJRDs0O2/cCfPYYXOteGmaOzFoRE0NhwOjJK4bIm/O6Hz2i2ZkrGsnleM+OU+FNG9bSoOHxz856R2vdrRo2U0c60bv9/XdFOA8rBYXnrUJNxZz5VjB/o/O2ywm38C9KUnKQYITFCT18Qsfvml7/yGf/rW3xU3VEJyQ3Th2JVm6AcuNnWKBzhJpttboDbuAkUIw7FM93ueq5Liw7zj4+AO6KMvMiiQDw3hEuFX5oZkwnK6LiADtgzYNrYsK4Bz36g/HIsaIms+rIE+PfRNhuSlH7v41t8nA5UYVhqZlEHL4ogDb/7+A6Xl5GH1uIRn+zw6V4tChxOC/7Uu2/xJs743fPn+O7xxAUXKTMV3+AfuCnkhemmLShKrczI2G4yrWRdt4JSThfJFzmGDae44pJJhNV7IjgAbv4Gxwb1bi/WyJUR040Tbm2Rf85uTHGcNhyHDWsxp0iTs/mLQ0XEXAVpzBhSwZenZ/zGgfbxP69vcIgbSnP4br3D5+mMz+yf3Dz+5fUz/MHTAx7uOWP8+eMbKlhSxuArtuYRW8BPpo+Y/Iafz2/J67gcUarHu+MVX0xnBrsps0YOKSPngCVFjql6ue9AqLZyMxBPX4sUCpKv+OJ4gQgt7x+vEx7XO/qzHB3CQv+DOgA1kPvRVo8wFYjlqyBzw01vF7Lfi0M+Jziz3/aroLy4fW7MYDXZi8wQKir48WpStFOFOxCaXeaENBAhCbFilhH6KSGeWZiXN0QH86Q3opn5DNSB/0wfGnAg6qUCOCuC2uKR7xrqO0q1t2tCa7RWD6GhxAYtDM37/uWI52WAN/v7OgeEJ4/0JDt3qyauIXxwmE8J613AMW54uL/i8SntB2OXp6IKdA7w9xvupgUvecDH5YBvH++wfRpNFq7InxWUO8LlKrgVFQJsa8C23txfxSvC5zPcl1YkXQL8zIagu4r6C4l9HEMp8onLxHfy57PidKm4/5csal5+OiCfgO1BbxEEZi4Wz2IZMAoUIhMIHnI4kMznBfngUE68Pj27ijJJxsTnuxv3o06C+slh+BSApeL6hcf8FblyLgPTdw7jBwfZCob3DRoc5h8d9pGppoY00bStNYd5DQzLVCCdSLoomcTPTorupm+ucP35FVjngOeB7sXP64h/ol+iWnr3XTKyZGEjNmc64opXiO/KF7veCqSPnqM+c+ElAZujF2lAfKZJ2PCoGD82hIWFeh14v/NdhHt7B//+iRevNei8QGz84nJDvDbko9sJwy4D7RIM2WBj2UmuOlXE+5Wj28uA9N6zsAB2L4x8AtbPGkcdR91VZJIFTm0sWMVk70SPXOai7PlldeCI1zul+aPJclMq+/1pzaGauaYkcruajXLOP6UsPVwF97/HtOT13mEeTIq92nhncnj505+hJsF6T1Szc2cAoBwcyigYnhh2KEp+mvv+EbptkNORa3bN5IL8iq9f2+IjnwLqF9PO+O8kUlgXUUbLKBhunVt1tILNp9v7lAO7qnhRK1zE+BzcSMOqlvMiqGNAuBpacHDIZitLeFMsvVV24uVWApxvBv16wBALOHZvLVZs14Trt0dIZihUuyfsf/QbMADL8hXwfkBYZCcGqTnNqedGdTeuOIUNawv45fM9avGE6zNNnDQKnLdrk2ldDpt7+0BYeWdsC709/Mrr0gZW0Y/rhKJUizTT9qsKBDaf3Iyk17sLI3dZKCVldpuRgD2wveXseEwZVQVP5xHlw4RQXxUpQZHGjOO0IoWKl23AH+CB72fcli/HM5IrWFvAXBOcNMw14eN6oPkUgPtxhRrB9Bg2XErChwvh4fM20MAN/fsTLEqu4suB0uWfTM+Ya8RLGfDxfACcETG70ZczItfm2Ak5xcvzhNoc3h2vuFSPOQdsJWBdA+CA9Z3i05/0GD/ofjCpB8qhMnAKQMtUEbn7jZLEvrl1cpIdsnQc5Pr1V0E58mOFoeA4ETm6LAklJJIkDwUP91fcjyt5KCXgukVctwFyJTlwfSvY7oFyZCrqvnluArcpJAjURi4vvxmoMqvA9D1HkS0I1nd8nsTm0qiC+pwwJ9tW9lwirqXzdUDZAnT1BkUbwtN9MZLNpQHUx4Sfuzf47OHCcaaQsxDmvm5hMmF6o2zF45Q2TIF8hXwIlD8mSqTzS4IWFuG9I4Yn58e/ej5WW1MxVpINmxgiwIagDa/0rCJ7dDsLIxpV0QTRQyaHeK6Ic0OYPVoUVJu9SyOx15X+rAvgBXJegC0DQwJSRLMRQc+J8ZvS6dmeo9fySaCTS9lo5buIlm41dJiB4UURLpkISytADOj5J9ygukSab7ptlOZjqLtnUFs83JW5Q+QysPHLDmhHoAg9LvRB8DAs+PHheVeuFaUyadOwNyRroQW/GzO2LaBeItxEGWo5VJTc5a/YSe67X0ovJsFmdHlL8q8rHCmE2VCErw7wbwaExxVtCnBvTzu3xl8L/ORRh1uER50Ueqh87h2N5mBOxbsKSoFyavBgOi1jOIygHWxNGA+xu293smdXT3ZvmOGRSJjLdg8D9+jtEvGcRgxDRmskwMZYUQqzZFp2fKaNjpdjg/OK608L/MVbHpcgT/zFOzWhYS/E47nh/Bse+SQ7Z68Hq7qs2E5uV5mmjwtkKZAuBwegz2fgugApwr19A/yKnNNf2+KjjAI3EhYGjJNhMNV2ZLHgs+5BOc1zvNLzXLpDG0PpYJ1ofy+DjWDQnIIbyaun2G9KJKUAuvHhKpNg+3HFu/sLBl/x/nxkd2Q+AG51e3gPCuf3PlU0z8wEzQ4uVkRX8Vk64406fPHmjF8+D2gH7Au6d3hq8OJ1TfiX53cAaE7TCw+3sBtuiYFVbmLEtjaqaR6OdA3dSkA1j4lcPfLVMhk8f1dZA55NktpUcDiuKIVpjXWlcY1rr66d+QX0wKKOdqgj4gTjEYioSU8dtDm4WRCfbSMZFGJJm9G3XQqbXMXjNjFEaRswnTb8dPwID8X7csL32x2KVWnFDop18syCMQv680bY6GFacEp0twzS8Gmb8FEO+z0eXMbn8YyD3/A+nzBXdmCIvfUQ0OzHutVVUA8C5xumw4rPjlfKAQvtxLeN/+uPGVWBVTzUud03Qz1HUc7kci7V/exozcFpMxdaJVoFEhJpBCQ/4OnIwC4HAGqjy6eMTJx9uOdISESJHKQZ15RQmsNLSiROFyJZ9dC4juqrzyiyH2jrW9mfJyl8BrjjW2aJAHoNKADCoTCzpso+VkEjadGZGZyI0rfBUAk4PrcdWVSQ2I2xIQQLJVwiuvSzJoWH+SlcBe0e+xp6SDOCazjnhGc3QbPjRygO7uI5ahlYYInSA6eHqKkKrqYQa81xxj5mXIaItpKPAhAVaoORHbOHv8oPx0aJ3IM6cC84WdHYQt+LBE3YuXcJtYqgHDzK/Yg4b8C8cHMPHn5NiOeG49ddzaHIB8eMl0DuFT2MKJtuprBxW0VLjkjAlUhxvNjI5cMLTPMJiQEu3yG9sKgpBwe9FxxSxhgKjeHmADkH1BcW1i7fnntp/L27wWAx9Y6/kX7vwoIv0wsGl+GhyOqR1eO77Q6/nB/g5G4ng7cmwEEQYkGrRJh0UFSHm09KJnnTrY5I9GeNJNHEP+9unN4ybpon4TY9ewyjx/KOakbeTzpfN0NwdmMuS3l2qRIVmz3Ci9/HfRDFcMgosaEMEUyDdmDUvIVsvgJP+9pukTwVUQHWXqC8UqLo7ZqKEZ3zGujTY2fIVYGWma2kChZDwj2hI9vz4KGLFa4K5Ht5VfQqsHWUU3ciqlQ+4/TBojIKAMbHipocpwJbgSwr9DCgPRzoynuegZcLUY8x4ld9/doWH2FReDXPeTM5aV72vBZp1LtL0NssvMsazRxJ1DTXKxdtd26TShJNmBslWdsNKnJbBURQB48yjbtBT69eJTVE1xgItHkLAhLaJgciI+lFIOqxfg64KWOc6O+/zhHiFbl5RKkYpOCL6YLzlyRHznNCXQI3506+csBlTvgDfcBpXBGCmexYZ6Ie0KS7dTdcDy4SeDMnK+Y0qsrESGyUlQG4EejAgyE6ung2J4gRqGYz3YmH/Es3aH53TzSPjxaw26x3meVe0tnDWAd2kCFVDKns4WyMWq8IrmHbAuYc8e16j7fxis/DCx78jBw8JhcwDxGwEcXTZcKyRQxxRK4Oa+YDEA8z3qQZD5HGbOc8INt1eN4G/Hx+CwCo5qC6NW8dMDcKtzjg6m5dloKM94kjl/thwcGkutcx7TkLuXpcikPLDrm+KpATC640MMsm+Eb7+TVCBBhThnMNWSxUS0xF4I1MrbcNWIsz+XLD4Bu8K1jHjLIF3I8r3gzE54s6FHgkX/FmWnC9H9Dmcc+wcBuVVbfALSO5XgE/K7Y3sntUtME6Qnse1GNf85odWncirQ4wBU3n/WxN4AK7shaUoVu2MLoqrXdj6nidS/aWgcNNv4fJaTRpZePDrio4JgbbXQvdb5uRa+Ec2qjmwGmHlxX5qLY+Tap+N1imjCkJLqK4OI42XGMTIwVEhYKiReXnKK/Wt0kkYQZD6l5xoZKabFn7A8cadwRyEwxBgBggzpnXhyfy4awZWhXpKSMNDlNyuH4Z6NORbnklLVjDpkB4yfBr3Rsst9WbombdoK1B7PPVxM/Rkv7ApBDgKErHdjPAAmsX8QDUCsGeA1TAcZdQyfFxPuCfyRf4OBzxJs74LJ1x8gtOLgMJ+LgRxuuE/T5SUGXCandO3ved2k3wjMRtBOIeoNbdTfcxuXGaGriu23DjB7I26siNIlxJ6u6FCRQspKu7me/ZnjyOGUOouEiCOC7c7BKkBrhNXxUxuHm69ObBATDPGgHXdTkSyeqNHVEHqmtq8ijeuGdqn6crGdsN/dLibuaLtRdiuqs3uy1/PCsb90aaQi9K+udrXuw55X+nj3SD7WtWDwPqcUAbPNWopcFlKl4wr/hVX7+2xUe8FKSn1R5opnDWyWO9d7tZTxuwH9LdTKdf5O4e6LZXh2vjQ9KDleKnBW7ZWLk1g1NbA0KAuz/CfzlYoi6gvWFuwvTSYghCkd2JVOF2GDBcBXl12F4SykgDsVYcpCke5wmf8gGDK9gayZhLvkHVYs59/b3LGlDNBdBLLzJeFQ5DJT9DFFrpm7CtAZdEBGDNN2OcLu3aLastsAiASZWVtsnNdVRy31SBfk1fHVAGwavj7HlfUQ107lMmt7YqtPxN2AlkyQK9DnEzm/qI4BpetgHaBFvxeL8c8S/c53hKEw5+w8FvN7M287NYLgmLJELooe0mX8/LiCAN5zzgJQ94XkaU5nYp5vM24mM4IjmGyDUVhNCwvXK/dFvv8pUjl2KHigouOeEUVrxJM74cXnAZBnwd7/H18z0luo4HlAQ7VKO5FE4LTmY3vvqAaB4n3rUfGNGpp4yyNcAlYHc9rMJANqHhXfCVrq02KgCAMWQEaXjO4x5oCADDkDH7AfVwu099Q5TVyJNXEk2Hp4p05vixjsD2IHvR6ZTEU3W2/hUM2vIKrA7+Qll8i0AVgarfVRvYlQti8DN2rwtRO+QXh+wjaqomO5Yd/WiiwMGKkbHB+4ZoPi7fXU44X4c9FI6eNAzMgwpl7ObUi3azBz/GDaPPuGwJ2dZPqR4wNBOi2GPMmv330FCON8m/qLnAdlRe2I33vUc9zC8Er5CUV997M5mt93SQHCPa4MlNG2RvutLjClkKWrxHHUjY3Rsk62ylNsAJqneok6PapXqEwSMEB//t436g1MER1X3ToKcKcZTuXwDMVz6L8bSZa62gXciJEmvwWnztBWOFbOEeMm8R73HE8zpiCAXH+BbJFSRX6ceyMA6iGXrnbf/p1vxcT7BsklvYYZeJu1mQngTjB/qc+JUHa3fm7NbxNfFMaIHcBTrF2s/FG3elvgg22/Mk2AEPPrs12X4rtwLN+4YYKkceS+D4UrsDL0ce3c2W0Acgq9v9e9TZurLCoMuFc5f9FqBtbneUhoCfidAd96PGZlA3hzr4W+FoTWJ3Mw1XhbfnDGAT7nJDHjmK3cmn9RZJAgCaHLA1e/4c1IzE3FrhlkLZrXNArdA841d9/doWH3QfXalKEYGPHvntiPXBkddxx5+LL1YcuA4b2793KKkQbvKrWuVMHTgEaBO/vivMU9BlBZqy8ygVLnP0UgfsG4wuHhchQRSL2+d42Eyl4oHtASgWIuVeAnAOLFAdN6zLkvD18oDJZ7y/HvHxiVHobfNcmBdnTnc0gsn3bnfDm9dI22wzXAL4u5tSSqcbH1p23Z4ddqj7THtpDs3iteGAehS07DGvaXfQ86IooljnRF5JD8MygqPbgOGTYnikTAsAtjuHltwOJ7tNUJeA1TXaJq8swKRiR116/HNwDaPP+4b0eKXJ1JoDw9Bqwst1QHIV93HBXCO+udyjbcazCUQUeA3kxjGYB1zMSXY3SDPeS/IVoy9wUJz8ChcWvN9O/FnHYrI1Ejv3vAW1Q0OZDnzNEY+eXJlj2Pasi36gdXddEkQViA3DUHCIxDMf5xFjLHgYF1wzyXdj4n0W6+y6LfT+srcOh4L744Ih3JAjEWAYM3Jz+OX5AdFzxFfN+XXryhgPVGPrA0CPjdiRQ2UR4rLi4feeUR4GXL9MqOkmJaCbJ3kj2bE4EVNC6BZhpqfsnvozUhyc+XW4LJAMK2LVvCI4c9+7xM0x/M5GXv3z8n0V9b7CjcVUXbKn6rbG0Vm91705kezgZh5eYSahHE5xPy340fEZn6cLnsuAeYuYl4g1R6xLtNGmIZsdeQm3922HBojbu3LnXm3avo+pcJvzr9YsFaqYunmcy1YwVKsqGx2XpZBU2YIgT4B8nuC2iPH7GempGBdFUCPTuTWYv4gC233C5UcR8xeyu1imx4DD+4DDWuGuJGyV8VbYiefYdt0ClVRnjhRqsvl+dXBXJvl23g5RJbmFljXALRZcCCD6iiEwhv7DfECuHqU6a4q4Jr2N5UoTxjlUcokkNRp7ZTHkic8jD0oe9H6l8+vhmw0uMx9HBbRVDw519MhHh+3kkCeHu1+sSN9dIPMKnQbkdwcsnydyIDalpYICEujiKU7RvEPTAGcE8cs84DBumBJdoNdiY1q9jVbgFXL28IulGouNrNZbCGAd+fn9AqRHxfG7inCpyIe485+6s66z80U3dytm+rPVwRoFfGiQoUIDUeuWOEr1mT/QORxhAaYPXFt+perJb7rnnnXE9vrVYH4/FekR8NeNnKHNHHk5T+VzkF7HAv/bX7+2xYc6Qf5iMpMdEMZLZN12EpXf8APolomKtz/j+/CgdNkg/yRY3nhsdw5SI6Vxj7Rx9+cNbiuoKaBNYVfRiBqTfLQNxvwB8jbAXQViUkd1dKXMDxUYGiRQRqnZsROzLtM5htFtjYZULTv42IBUoWe/E486i7wmauDnbWRnK8pKeeYirB5QVSiMgT7yQU6h4DRs2Kqnbbjnrng+R4SZ360mj3oE1hBxTQkP04IYKs6XEbp664r1Jok04t36hv8eL3RM3E4j1RAO3BA80M4eud+sLHtB1WI/3B2JoTjgMGz4bCJTKQZq2vNGh0QHFosf1wN+/+UtcnNYtsiCbxNoohlY2zwaPCSwG3ahIviG2uj22a3inTBtODeHp23EFEgcOG8DrucB7RzJ+1AeFp0jAKUUdRgzppjxxXQmYlPinu6qvfjoFYN1Pn3M0ZpgLYGdUvVIodJ0qzk8jMtumdyKAwwR6I6Hrlmh/W7D4bD+YPRzzgMD364JZ0NSLtcBo9kyd/vwkgNHdvZ93Co7qkBvAkO5VBCvHtMvgfj+CvkisbC0or5zNp3lWLTk2e0XQbySiAhYVwzAjcUCFz3G95SLuswUaTQy6q9fOI45e2eWBdJY1Kdn+nsAdKGsB6DNDq1G1OOGu7Rg9IX+Hjlgzm5HAaUyKC890/G4diDIzJs2k75/e73H5TpAq2DOTFOVpGj90NhHjw5aiKr4VRBeZL/drtwIeypGjLeGiEWGkbbt+rcArO8EfnO0Qx/jDYVdN8SPCnhBmQaUUXCdPMKsyHckTLZIToMovWCcGZuVYwSaooxEJOvAAyieWeSVzybI3QBNVPQtn+nu9+JDQwiVsmMzKGxiz0QVpLNg+vbWaO2GbmKEW7ED8xxxURsBH6+YQsbn04LkKhoEH5YjPl4nWvJv9GPpnjqtOI4mHIt2dRaCZ/wGtdEz0UGqNPJhoCnfc8XwfkP8cIGsGfXhiHBKKONgwgGPNgY4VdRjwvYQsd05LG9NSp64z7eNvIquJiuuk0od8hoQjgu8a3i6MF4DxRlHS4GhwcWKpkAxZKpH1fdzqRyUHkBeEZ+dBdIJyhhuiFnpVu2G4hwqXaIb99R97ALwPj1HlHs+v2q9sbN70oKhKy+8hq4owoXFjgYBmiJ9WCC1oo0R5ZSwPQRUI/qq5+9yH56hlytv1DhCTwfAOchxAiQC3/1hpztfv77FhzdjlqJowjldGW7yqV5MADA3uBs60YsPl2GHnUCuinTlhuey7sUMBCiTR30TIF8ks14nsxdCxKRDqy1xoYRg8HZqaAM7uNedTGmc92tqdER04INrFey2BlxLwufjBX/87Qd8OrBN/P7lhMvikQU79AsAeg14kiNCLPAG8VXgB4tODw2IClxZgasXLBsJdP3hHkdyCiS1PSwMoux0Q8OYMgZfcM0MR3NTQbuE/UL3DVSDctPxnMe0OCC9NPisWN5yM1NvhLD9S9weEk0kcokw+6U1h5d5xKeXA5wjR6Dnz3x3PuG8DTjGDdFXnNKKudBOHanBnQPwMfLAtLyP5hXNK3KwpyaTcKiJltk6FeTsccaAxUXMkQvn+TpCHN9HVg9/cQgXQbhwFLC+pSFW9BXPy4DPxgt+On3C23iFg+KpTvi5vMV35xND6qzz2z6rNBXyJDOuJTD0DMBlTfji7Sf85mnB4zbh549vcLmMVC0J9mRbWh4LalKUwtC0z6Yr3qQrxw3nE64fDoDXvegYR5JOO/oDgKF5ReAXmru1SJSFkmnZJZdlAs6/6bC8eYPhmT4fYdadbFgHYHtrcsBNcPy5B5pHvtfdWM1v1rFdHeRlRFhJUvaL4vj1SrjY8pTy2xHLu8HgX8Hwwe/BfH6lL0l64nvvaEJQIDT87O0n/Gh8wVzjD1CnvtZUFdk55Hs+i84IpP7i8P3P3+Lj/RFv769ETUyW7iLt47UKnyenQBBUG5vooUJXB7d5lDsl78EKru2eqFCYgdM3DWUUbA/mzHlf6EpbBSU7uEWQPtlIdHBwxwTnHNxWINcFeLkiOIdTYxJtnRzK6PDymw7LF2x2RHkPwlUwfatEOF4cwqVQxTfcULQ6ULqLLxLOP/GoI7tuvwCbV8RUEWNh0KKvqGMFTg0+KOol0G5e2QC6le673I8V24MZPpqsV54c2jXhcvW4ngYcTiuGeNiLFe8U14VBf3uAY6rwsSKNRGzXNaA8T7eiLdzQJ1GBJsX6rgGNTrNSgPUlYDo4TKPH8P0V/vEMtw44BSI44VpvnXtLJDIPhlQd7f1tj5oOG5xruObAYtgQQt0cahP8+O6Crw5nfFwO+MUSoBeT61ZBU+7FnXzaBAgzycIusxEWc/kNV0ELipff8Duy4zZg+p7fuxyB7V7hLw7uk9/VM83fzPa6UKFtnkWbWiFswoky8NlNLxXxuSCfAuYvI7aTJcI7wOWBfMuVIxpXWNSGuSE9ZoTHGbpugHeQ8cD/LRV6TKjHEZgvv/IZ/2tbfFx/nIBD2jfDPv+KZwVgTnRq0rNR9vyE/rN9hicNOytcncP4WJE+Mf5XSkV7c8L8G8fdldDPiviywS8F27sRy1tHz5Chj23EIuptU0qN6X4bK+IwC6bvBOsbGpG5Fw8/sxquE+eUeQlYa8AxrHt6Ky2nCelJkT2LoZwa4v2KlJgOer4OqM+JM/UNe7hUtoAwVwTlVIHNIYaKn94/4T7NaOqwNY9PK3Nh1s+Z9wLHDqJVQW0OQ7AxwJQY2KVAO8cdSm4GhqAfQG8EgENYFNu9IN8JqhVqLRlRDQZ7ny1f4uJQXMR5JbFSPCW3b040oXo8H6AzoxVfzhOWFHGJabfpLo2QLSyyvo1WeHW+RGxwgd8pDgWHkX4h1/NANAdACIy19+42Zyb0S0jdzbye+WQdwya0O44NX57O+OnxEQDw7XqP7zdqu7cWsJSI4BvKdsup4L9wFFa9oCmA5lCqw5gyXrYBc4kojeO163Ugyz8wMj09EjVTAepBcXwz40+8e4/PhwvOJeG76x2eXw4surPD+JZFx7rQcn5MtGG+Xvj93bsNEirKOSF8jHAbML6nP0VNVLjkO+Vo8wvgupk74/p6xq976KJ6bp7pWXGVW+Ls9tBumShV0AZBaeRBbPcT4pld2OG7Qry4E069Ip+U/jlNoOf+TAA7uc6RENymikPYcKkJ//zpc3z/fGJWUgOhZ5uLSwMLLqFDpQpQTgAiFUbzRqWTi0wDrtcAZIf46PeignN3h3rQ3V23fFn5HK0Ow3uP8EngZ+xI2XpPdHJ5p6ifZYx3K7xvWJaIeo6oIigrEZl88mjRIb046OzggoMsGbJuiF+v8HcHXP7YCcsbwfpWUe4a4xIA1Oyg3iO+CPkMRaGBz5sUoI0s5LY7jg39Jrsy0G3cN9zi4MMtKl4bvTe0OjTlczF+T08NNCLBHdGTxuLKGRotNqL0GwDnoPf0xpliQVXyuT5+OkIfzb8lEA1FAo7ThnfHK9YS8M18v6tP9mfd5M7yEhBsb813HDURKeZB2waHekxoU0SdAmpyRNueF8bCm3s2pdfYzSRbUuih4LM3F6RQGM65OUMf+OXiMSP4hg8zYymuS4LOgV4dCrSFhn4YG1pq8I8B6ZPD8ARM39PO3G+metsaG+DJI5+J7pcJu6RahPeuE6a5DnEzhFuEhZgDysDRZxgz2lcNmx+QAKyOzXyZBJcfOzRruDoiIsa9YpMtCFcAFzbi+cCxHhRwS4T78h2dYlWZcLusQIrAIaGlX72k+LUtPtyqGOaK7c7ts0xV7GoVt7GT7vLZTkLVALqHV7BjzbBNkrtWfKlwuaK9PRF+GzwlREWgRx4Y5RhI9Jq4cbjCjWrPu1ByB5AF43cB4cIbWE6KfNdQDoJ6bNDQgCsNaLq7JZRQ4vvzEZftN00R4hC8ESW97qxkzvgcyhowjRnzymRSSoONzGTk4vZWoRNhPlj33lTQrAUs6vC8jbhmWm130yR1/HlttED/4A+Ijt199BW1CloIqHcWwX5xEOtgw5WOfB0uVOHnUZsy7SFeTeAWAbosF/ydLBKMKb4GvM93RIyMmAVLOWWiZMNWAp7NSrjMZv5UBOFiTrjR1BipodqZXwOLlZw98H7geH71uKrAGd/kfB1Qbfwl14BwvpkN9RfHDQK5eDytI94MA96lK5oKXsqA523EUiKWEvB8ISdI6m1cAwAu8ruer+POQ3l6OeB8HfHjt88YfMGCQHTCMiR6mFp6YeG3fqUYYsb38wlP60TSm2sYpw2XlwgZKw9Ru//LnLB5c7cdCkM4X5FImnWSL39cd8JkeGHHReTBoGIbGbZgY+ae5xOIfpx/elu3w0eqONogqIulplon1klzLRIhKJOg+YCwMlujTrrHGzjjVKn9fLFnvQ0syvVQcXxglstLHowoze65ZQe8RAwfPNQp8ruGbBJHUTPKa4B7CViqwB8LnGtUd109xu890hO/cxkJkbfEjb8dKkJotAN/pk+GRsX2rqGODuHKZyM+c85fDoI2NgynFYeRnjbeNbwUBzxFFnaZEtoyKpa3I8LScPzFgvh0gQZPz4+RLs8kSJocVAWydZ4Y9tHY9hDIbev3OSnaQVETD1G/mknaETj/MaAcK+TNhofjjGPcUHXgdlUZsRBSQa6C9bO2K9r8zABCvwLjh67sAAPKDrLnaLlRkDfyPAAmTDflyOv8lJA+OdRBUe6A5jzWMeBlHXCIGe8eLvh+DsDFE8WeAD9UjmZGQT5YkVkcwveRY4qtH6SC7U3aXTvLKAhXxYMA6XtFG6MVaPpKak7U4/7tFcGzKK3VitJT4T0vgvyS8P05Ec0ejfCvZvI3NsS3K8Qptmv3IWBezKYCFYftng668aoYPmYM312g3iMdIzQ4nH8zYelE2ArEF+4FXf3GPdfuv/I57q7S5CwTH69mP+8X7M0CSbgsaH7AjzTvGL8Cw7Ni+j5DvWC7i1jfCCAebhsx/vwJ7umFmS4A4D1k3eAeFW6Xfv3hr1/b4iOfHLwxwp0lLuaD2Jjk1UIxpnjvirr7nb+SoNRMQuZnPgjPP0uQliitWmiDnJ42pPOK5Sd3u726y7rbthNKtuCoCuSnAbI5hIvjYjNDGb/YrFsAP3t467LXdw1qagXKt2ig9W66Ilgnfwgbvgt3+IOXZF0vUDrEWB2u1wHiGsKhoDwn5gi8qlp7xgCykZGK4Pw44ffkLcqDw5s0491w3Q2s/GrFx0pYsBmhrBMmXQ+oe7HYaOtIy32FFEFducH4zaR2y41I2kc6bhNUSwfVze/Fgdu6w2lATV2/BvipkCezeLiNBlLi1EYxHOGIkHMhoiizR9j4GXqWwh60ZvB9nQOycUjafWGKaha0lbkfPuWbSsTuVxvUuB63Tr8MtFrW0HBeBjyPI855QHIFD2nB58MFc434+nqPy5ps08EOFVMaRwWFmJy3zh4oDhXAt04xpozSmInSggKL23OKho9UvpxDw3UZEH2DS4pcPB7nEfOVRAbdHNaNeTPODL5EYHbMfrdAF1Fkz+wJd+KIphSHfEnI9w5LY85KuAhJZhv5BXRpZMebT3Ts7CnH1eDlMvKQ7oUtQAJiWDjCyneKcjJ+wSwo5o7pihXoxrFgYWRS45FFR0/61EROlTN57SUndqjFv1IoNOQH2SPZqVICuoqpFxLTuxkxVPqJANCpYv2M3gi7qyqwNw9QEnsxZmxD2E38ti2gygC/ONQo8InW6cvnQJu4Br0ZyTWT+2pqEPVY3plhGfjn6dlB3QT/oxH5wI61Q+M7L2do8GMBDsLO+SVwjwKsSHH2PRR6Khw1O8XaPNQRLc53ivpQ4E8ZD3dXJF/xcT7g6WUi78KpOSZzf2nRRm89PySQorLd00Olq0ogQD6o2R0I0tcR88Xjepzwy9C4xxnymk8k9sdHh3ZxmKsgflWRfKUK0IjuHF8Q0eykcP4fLDjr5KFnj3IgkXP5LBDJvhiZeTIztDsPv4ymdGFi+vrWEDtbc7U5RNdwOl5x3hLWJUFepe5KasweygGtGD9kplBAg2AYM94eZszHiJfrgC024H2CNENQwUJ/eBSoRJTjPebP/K5Yasn2sHbznFLP4k+HBtncjo7/YG3a3rItgSPk0FBPgtU5uqhusiOLmnqHZjEezgISNzGELCI9Vxy/rWhR4Gdz/76f4LyzGIBGqoJ3JEzr/wWKjzoAy52zsYv8UMniWcmTVEMvi7DdjHwAG7WcsPNCeq5C/++eaxE94HKwDanBmamZerqcdr//mzMi4A4FLXo+BFYxw3TZ3eCmDortDRGC4SM76XxS5AcetssWkXzF/+30HoMruJQBn9YD+RhJIcurrjmz2xE41CUAnpuompKn9kO3e280YS5Lart8c/IZThQfcIRmdhrONmWqHG6mQPfDgilYYuJQIZPuUfNdUtngd08GZ6gALZ9xi2H2YCiTafOdyUQpNYTFmhva41/9Dq9oU4U/cO57HDZ41/A8j7g8j7upG4mOinXgJtd5lDCUiQgKURXpRjxGHOzpxLXQorhtJhMt7OZc4eHXte6usMBBYNx3vve4iwuKenxYj3DQPcq9X0cdyPNxZw+drYX3Nk4YKpn8nvyCaSBz/nkeUa8B/uwJbyvXbpmAeAWVB8Xhut1Irt4kfzu/xkY93pQL/bo649fEUDHEgnVJaOeAdo6YNxJ1JTbe1yyoRwAg0S893eb5nVy9X0/lJhlfLBpgIErTk6DbXUU7AvrMB5idPgvWcKF9u9/YlYdZaDImoP21rSMNQA0W1DUV8mG8Yr4O+KUjYbgUbwZuDk1B3pKRnF1m8esykJ4NGo8cB25rtOulKEbKxgNREHelORnfQ7A93ArcajHmIiSRe99QY0NLvA41Ae4Vgdc5RW2CXKikUeM1tcDCzpn6B7ghCHVw2O5YJLShsblRHsTiqNyCo4snAjk86zvB8oU5wUY1noLCDxVVPIm6gzAPLPO6CujY3JqzAljhQkNrflfiAdxbhg8Ow0feo3zC/tx3o6wwc4TRx8w9qwoPGeOUyZmrjrlPkaRy9TdfCjjgeh0YDvlhYF5RIDohC/cdDGZe0bA3VGrjvk4v4wiPz23tYZhe0GLAeucxfSxEPRobonDm+9XisS4Rc4p4Wfg56ku8ET+jQotgLUSFdOV+1P083EbPpmhW8c48OcRCONXOGL8C8YWk6/XeY7uX/XzrBZxfsCPG+cgzzz8HKm3HhtoRdfDnZfXQolC1/czdPKFatObcmXLLRkii5Muomd5t9yzW1reCcHbwmX8/XAXx6hAuGRo9MXWz2IcTtOOI5v4vYDJ2k6sRwu0bHM1b+g/pHkndxyNw/dC4keMAbgQyCeLltfIFABykBmhwgJK81SJHCN0wqCW7WeWmtXaJDoJuNZld10X3gkHYeVUn2JrbzWT2pNo14Hc/fIa1BPIclgnvP91BrwF+owZcMkc5sIffpUopXPE34xqDNnvuAKrAPzvgxaHcOzwLZavnbYB3DZ+uE1Dc7spYPa+jeHben01XfDGe6YsxDJB7YMsBtZoHxkauiBTZTXI0EJbuahbpqgxnH04plfTLzRsAoJJBvWNQ113e4+0rdgcglOJx3SKOdji3k2DxCW1lvkTXp/cxgFtAqaVJG9VbPL1aQdLTI6tAF49qyhgMYM7K6lAOenNutHGDOuW/Z4fqPT5eJ7iDYgpsPRoEVcnjKMVMgMQKu+5totxY++FFCaEDQsNigYPbRq6LRuvwGqC2Jsuo8BePHBNiKtBAid9lTsiXuK87NJp+FbEHRQAvXW5N9Y93DT5U1KOF5dl4CwDgGxDATWw20yWlaoxZELLHjzdv4z+z/y6ToTaG2EgD4Eneq5XFolsF8dkhPSvuf68gPm0odxHzO3dDMjO5E20AdhlrAIo4NAQgcJauxeNZD+yIi9s3YjWIv06KeuQz5AoQLjw4WxDke6IczvMwrNXtxk3S1U6b7OZPdbw9vwxCbJhrRIoFKVSOTfso0657nQxBW90ejb4sEXmO5HdVebXXYUc2tgfZjapqwh5e5jbm/bSpYjqteDiyWnm+jrhew17IlAMRqRbIkwhDhQ82jhIeZG5k4+JiRYjklHmnqNlQCSUygdCgFpgpZnXOvfi1WRdfXZ3VuUCvJertGrBsvDhdlt1szOlKRwk51qjZQYtDNNJ3HbkO/AzULaCeeK2ldBl2sMOc50AviPIJu214HbuaSwDwIE1r2VHwOrAx0qCoq8fVitLWeJ/8LMYxMU+UwPUii0N6dPs62cAQRxrXbfBOMT+P3ANwI0+PTxXpqYDcIA+/KvLJcnjKbU30xHZX+f375fYzR3xS2ezW0QoIawxem8J1m/e9yBgq3Fi5J64JLgtdZIOiTvY7C8ybh9d6fA/EWZlDBEA7v6M0SGuoh4gcXv3OP+T1a1t8xDP9OLrHBnMgeKH9ChJeQBitj5luahe5VYOwEYACflGkZ0U6V24+B0tkNGfA/TA3R8DWTbF65LhBiHnm5rcbfeH2d7rZmVsFbiVzmXM2RY+/VmCHwr87n0gGfBmAl4h4dpyjWlibRkG70O+gCSAGWYqRAAEbu9hG5i8O03cmR44O7eiwlYDLpgieng/sGjqECHQSX38lV3AXgVMiwfMJI2Bn2634wM1KXoCuN39tTgMj5fYuUzfwAG8w75BXv9tksN06XqqgLh4yKd1hczDuh/lwyKu/2xEfgEhDM5jdsSuSV4UZrAvsL62C5mjaBTNga2OzkZS7ydTsIODYjLyNrQSMsey+IqVa19ifV/v7EFtHURlM50jik9nDLUIHQzNHU+U9bmODZM/gsTO5NT0qoPaxjRmx7cWWcWzczHVd72QPwoMKQqi3lONROG4a+fA416DN0YROgBArij1TYkQ0vzEjaXgicXH5TFAf2IW3Lgm0QmFH7RS30VloQOZp1Z0Wh/cz3JxRD8Fm0rZJJkUP2OoSRW0CL4A0hzoBSHaAVV5w7cXH6nY+UHdFdZmFR7ywM89H4yhYeGKXsLvY0M4RWgRi79fzdfTVxjrEgrthxUskN6Gp8LCy9cXDV9BgWR3Kz59zQDWXYck9N0b2EyWfrGAabiNkTQ0yVSBzFNkiIGPFNGy7Z8wSIq5DRRs81Gy7Neg+9oLtOeJu/LMaudZjrEixwAmQi0fpqcuNa51/GTuiVhOgb0gsV2/r4sxRdb675aP4xUE7Jy3pvha1G8apFXoNewigOhYqunCd1ETEg/wn+yiqJLF2bt9VMHwk4fmmZOTohQGjdugGIpv9QL+Zbdm+7w2xE+4FXepfV0O/qu0FyXw8DNX1i8PwyWwEgu3xmXYATphdI073sTOeBeNjxfTtAjSFRo/D9wX56rBsbi+Q9pyZnqxuKFbPxmrd9Az8ncDN2Gy3ojefGqn2LERy4iTRjqFadIY6Q8iCoWqeC0aa7j5afhWUJzYjAmCXhFuj5y8b4j4H+sNfv8bFR4PsbQxvBMSg+43nWph1d7yrA1AMrXCGRPSu2G8sPOJVEdYGtzaEizLWuuxgNcrof7DB7PkLFroFI2q1Z7LSu9vjPraIQHP8OeD2WVtk09vtowFAhIFYYnNore6GXigoo9ofDsGuBrCD2WV+FnWs5nsx4mfZ+RcdBu2On14UKVQjPuJ2gDte4zUHXHLC1gImn/HZeMHzNu7X4+PlsCcmdkOq/fMBezHQ55YuA00E8Dx4WwScyo461WNF9xHpahBdPWSzBNItoBQqi1qjEVHrs+JMVKKPjmAFBAuiLmED6iqoqyPvoruDdvRBCIe24njPGnbYF5GddVsdzdWsmOlzVecYyFfKK0dBu68i3GCDmTG5Yumnd3TB1Wyd/dWKhGZojTH01D5Ld+SNL5S75aN5KNiBWWyBeN+AsaBqANbb/BYCiCd8HmKFcw2leJSNviJDzLsds3MN1ToaEQau1erQBibTtoFzYHV0CO5roCVFm5QeA5FFqbeMj51o6pQW1IBthPyqNQmWLyeEOVHpYaRw6YV/Lxxs03/NIekW6S6S9wFRVGekYXs+0MBN1OTFrtw6yZZss84OVThaCAOlBc3Wowo5DnXA3tD05gEAgpCYvVaPUj3yFnaCNZSHapgBqVQndBM8+wKA3swE+/XsCpJOUtekkKkgjgUleNTKQzpa2mmufpcXu9D2cbRfgRztWdwEZQlU6Rk6Q1SkAV4RQiNyo9hVd6piSqFevegNFZjMNSDY/royPfz0dcanfy+hjNaEFO6BGoE6NLih0j/N0Wpfm3Rg9NX41w5SQ0goD1bjKvD3t4Qdyeo+M33f6WTYHsboZ37O5Z3sMQJ+gZnkgfvGzTuPW6JlDzGTCrcGyvKw+kgR4P/nVp4vUACRKCKKw7qkPQBOfEOLDS165BMwv/NoYbJAP3OlVQadaiZXDpC9AS4HZSZTR8qE6pdybPALi+SO0Hd0HoBJ9W8+NP0z6+pRwQZJBXRvtvvQv5tWYM/AsQayjIIWHHvNeeO5lHjAuZcZTjf8qq9f2+IDsM4hWGBct8h12HMWwsyblo+U7+3yWutUXKVPh9uwh9CVke5u6bkifZjhXhYmOwLA5wfkYzD2s8Jv3dZZ7fChg6fbbhvRXny422YM4cZRQcZ+h8KkCedvFrpUikcwd0aIAlGR7ytacLf54kBvCRLsSP5isJIhPs46gsiNshwUyxfCg+7I2XjJHmvwGAKLnbZ5DLMdZIOijboHf+Xq8ZIHHP2GL4cXBGm4TwuetxHfPZ3YXYKdaVPAiQDFCKxdrtXR/tbHBq86F3k1fgqKnmEi3g5twa2oACAloGWH9eSQDhuCjUl6yehsdLQT4WpX4cDST7mG6mhz81509N8P7PPtLg4QpwiJJ2QJAXqhs2G/h3CKh+O8Ix4AdpfRtQS8XAeiN0oTtnimZK0Fh1a5SbmN3cNeIBYBYBdu9fs4rxzs2jVTFDkemKU4xKgIdt96CBc6KfhYGFLnWOR2YvO2BbTVY8kBD9OCKRY8Xic41zAYimPUJpTszUKdyN32INjeslvVwM2wS9CBPiKkLNm/AO1k96XnDgnQpgZNgq3SMnz5LGJ4ImR+e/ANvi8cG0jh5lmjcvRhzYDOAW2siMdiVteK4jyqAHl0RNlUOKIZHeqJqNAeJqn8HRo6n4l8GgSOidrCoqQIiXhd1QIBrmtCqQ7zmlgQqCDPkYVzxu6vkJ7VUABBLo7ZTK+JuH00bM3ArlgxkUR/xVgxDhlX47ilgYjVVm8np+qNf8Pnj+8hRehLoQCyYyBktUKqisUggBmKrtEKPzuOBXtDtPG6aLRU3VcjZhaIiuG7K473Huo9tnse+nXUPXuqzYFNgwOJ5kXgXzzSc0cUAG1q+4bcPG5shFNHIxtHW2eXGx9n/pHiEiijDmc+c/ECHB4r3MqWvjckwyOt2OO5wuUGSW5P5nUbgAS0LLtbqA4VrQhq9eYEKoCzBtSuYzlg52jtvkh26UKgoWJXMpaj4vlPAOrc3rC5zSGeYQ2SFfgNuzPzznmMDcuX5rh74Mi0qu2twc6pdnMD7o0xizU2aMwru5Fr+x58a35fNeDC51l62q7aP1uBzCtQK1Dopqq+E1Z+tdevbfGh3bG052JYh84qGSgR2O5uzm9tsNnzZkSeq2L60BCf+ZC25NCSGZdtLC7aGFGnCIjA5YrwYYa/kP8hpaEOR8SLRzl71KmhDYotAHqfMd2tKMXROrwY5NvnospNro3mczAQL9TKwyFOGd4r1mvEeknQ2cOfzfNir6RlrzbVZL0YFHIo0BpZmPVMCTus26CodxXtIMApYzpuCIGS2bthwymteFym/bDYxyPgQzaljOgrSvNoEBx9xo/HJ7zfTnjUCSE0ZHPtU2N2qdgDYu/Zdf91BP1GHDsviJGpjBXaxmYkWfAwqwKJyvnyQZGnhnS/Mhn3mqBVsF4SNnu46FFipkCBipqmJOi1QdBSd3ME5q/afmC52dk6snGEfR5xDcPxlgoZXEOudFzcio2DjCDKkYfDFPO++QfXMPgCJ4qrj4AFmW0P3ET3oCkzqmtGHtu1+rHt6ECbBM15qPPmkyB7fkYbjBRqG0QnFGfv98PW2b3nvaSja3e3dcICbF0DdAK+mC742d0nTD5jrhGP24TzNmApXU+p+8HIbtyewcCiVWOzXCM+jwAs4tu/4j7QmK/YJt6DzKTSH2SvJOW2yTYzjevku969dX6Py6ZwcooViWiUFab+wMjxakTA/TAIijY1rO84K29J4Y4Fw3hDgFTFLPtvf0ftmotvTBE3tHKIBQdTKL3MA4sPOyzLieQ9v8LCzYiGFZN0wxCgMvHipieOtADg6oxIbsWOCzTjuxtXDLGgVIcpZdTmUJvbEYuOuNZJEV8E6VFQTqZC6geMYkcAsJns85JwBnYiaM2eY8FU4YKSC5Vujs3FK/y5e4rcLNXrFHH8xYz1/mhkWcr/YenVWD0Rq7FAq8A/BRy+Ftz/HvfHfHQc6Rhpf3iqyAeH5TOatKmj0sNNfEZrULTJoT2xexfFztGCsOFMTwXpuwvS84TzT0eUgQqk8VPF8H4mf0EZ29ET1FtU6ERlR+0hbmNlokVy8LOhJ6ugnBTrFwXrV4A/u50cC4BompG7RRTXa7ihhVbEdtI2BCj1Riuo5pvymuOoqUGOJM2J2L5Z/A3V6LWDV3qhZJK7w8USuUdFOd3Gyt3FeeeGOHBdFrGiTnbHXqkWqfHU4NbCPLRSoKpMYQ4BGLqk81d7/doWH+u9h4yCOhHx6NyPTmYECOdJwX7Rm1e0CbtBy3Z3k7umZ0Wc6TaoRwd35yBqLPdVMX7c4M6A/+YTS9bg4X58QB2AemwI9xvv0zVABLti4HwZARH4kcQ1fjAmcsZUULbADqIJZKgIqezM+DdvLwieseHyI36JZU7InwbIe793QRg4W8Qzo8Vp8MVDRAPQpgp3KJgOm40mGAmeK/kAzgqQ0WeckoMbKp0RbdFJJcQ4hIKfnj7hPqzISv7C3BKqCoJruJsWbCs7595FyM7h4P+2qHDmTSEq0FiBqaHeC+olwL9YUFdQ+JHeClDZk0XFrk2xxNb7w4J6uLXFW/GY50RC19WKziJAM4RprMiJhMjlKz6wbiKbTatAc4RbBFJ5CPTcGucVcdhorLZGDCMNyGKs0OMGnRy82ZbHyKyK6Otub+5Ed8lnrQ4uVUAD3MrskhRpod0JhfmOxaw6hcwecqTktxRj8NUbH6AFGn9BiDiJsitrTZANLXFO4U8FLTtMx+0H4XVTyEiu4CWPO0w/jhlLDvim3eHL4xnBVTjRPaU3d+5KFZqcAaYAa9BTgZ+IpjQrvlRBkmp10KFh+ZKHXAdzUqiQw4ZrE4RUUI4B5dsBfgZ5AhZf0Ausvvn16O9y4H3qYxsS8gTNMp5CrCTh2loKoe6ZRgCwrZHXbPOottnXqSFFPsfZeczXxNwM85dBbJCxYhgzjuOG4CtK9ahN9sTmS6FkezknyNXvbrQddaCqDrtZlHMNTWw01OWq7rZnoQOtV0MDRhKhr0uyEMaMK7hvBfOpKc0h1wafKsrBoZSA8ftXh1ZHGO3VgjLNVgAdGuKUcRg3muNVesSU0OCsYG0lGjoLuImFQzPVFVw/JB3Sy4D0aUO8KP0zCg9Rhl6CAWke6OaG7dBQDh4tCsb3GWFxuHwVcP2RYPmq8tm9Up69fVZ3Pw1xhqZOBXJS5HsHfEpWfRDZSS+K4x+sSL/4CD1fEZ8S3v6BXZDK76XTgHY3sjG1wkOKIL5YHsrI5yoNGXJgIVILfZdK5Z6fEvewdY1o67i/h7+Y9f+Y4QfFEAvm2FAeKs9nG+1KYCORM0PhwsVGc2JjjsnCJQ+N0mq7L33Um0167BYifV0CXItHeSEHqQUWEOFK3kpZBW2k6s4NlSaSVaCHAngWpK1/j8UMx2DvUxSyFBYe60bkIwbgeIAOEVj/L8D5WB8EcjJzMXcbbZBJT9goXLjAyxH7vLD/7BYV0b8iF3nBdu8NhqSSwW/A+Kli/H6Bf/+C9nBA+/wBUEU9JOSj3VB70HxocGPdCYzBN0yHlXP04lho2FhCnKIsJK6hd0s2X07HDUPMWLaIy8wHYhjyTl48F4d6ZndWj8yRURVgpJuiNoH2yHKAB3mg18CbaUFuDlvxGBxnuYOvJgWNOG8D2hx+QApV+x5fHl7wJnY31IDfW494P5/wNI8IvmHtwWQ2buoeHy0AZVDKwqzRrJNC7jdMh9umdt0OcJXSTcRmD67uibvTkHczsbxyRp2rI09FlE6C1wHlEuGfPOILLbPrkTN/tia3Q7onSGpOcDM3lDY2tAmEQIeKccqI8RbOdl4HlJUy6vGw4ThuGKLgugwQoSJoMJfGa6apWI68h0/riI/PB+SnAfFjgBRg+aJh/hHJeB3urgMPPkRDFjw5Ga05pFTgfcOsA3Qh+axtt1wTNCAMBcNQkAJJgl1WmgFIaDiNK+7Tiodhxl1g4fZpm8w2nx2ut/HKvEX8n+cvcH9c8PnhguQqztuATy+H3aW1HLne60iotwduhdAgsSJvgeqNPsqICg2VML/wwH9uk31+4XOxeGhUrO+AXMGIgq7uSUwQrcaBcTMPFJhaRhOVRFI7SkHk7jBkJAswW3PAvEasc4TzlBmPY8Z8HVBnjmbkVPDu/oL7tGJrHufEIELviQK+XEcsLwNWJa/maAnCTT1Ow4ZD3FDV4XGeULJHAWjitdE23TnC8RyPAmo8Ah8b2sOGMnrGA1xpIjV8UsCRn6DjjXyJGpAFeBppTleawyFmHCILxed1ZBPdr5cRhLuRWn/OYcT8emgQG79AgTxHPGWPwWSwMVQ2AMUzbTY2NKcQB8RUUCtJ950Q6zYAInj5DY/2xw4ccR6Va9xRheRigz/Z2LfRBwN3K5bjgG/fRfgl7XsHnAJ3BXHMyClimzzkUDDYsyog8ritlAYDHEeIuTzXQTiKHxzQGuR0QP7RG5x/NtGaX4F0aQhzg4pgfUN0JZ+wu/PqoSKmisNIe/XWHJxriBHwhxUxVJTq8HKeUEuEzh5hfYWwmb+RczQBvCzjbZzhAAy0QlAFE6pTRbszBFMc/JWoSn7DokuM5xN61kw1Qmz3JpqsUD6yAVUVuKOZoqmnt4jYJMEarj5yhCjcTBWfjCz6UPx+RvhFMT4qxo8F4WIptkMikDbPQC4sQlq4URh+hdevbfFRR6BLhneL9Uw/jzJhT/zrpjauJ152aLYyGtxVYDPv/nAh1Lv7UAi9PNybAXKKKCea70gj1JwPAo2ABoPgXhUWuXjMS+TYpQqcV4RUOC+0B6LOAXIJSI8O5dhQH1hEXM8DZklolwgEMo+dUxyHDfcji5n5LkCqh6aGYSxIkW5yOQd2vAvDyeAV6bThOK2YEscA1y1iXSMD7BwzW1oSnM2ICY6ptnU0joqnd8Uvzw8IruEYNrzkAf/i8R3GWHAYNpyXAS/fnyBXRqW7Ins13e9XC2qdnLIgihUpVFSl0yZMrhpWoiDtuGEaNpzGG1RXm9sLHBHFV6czTnGlPXpKCL7hE44oxphvn2X4VG9eBJY6Cwv/iwMP83VJuz8IzFnVG3FzWeLOJYmRpDi62PJQj77h7sAE0B4elzy/1yluP5DbXoaEshzgMrA9mLlTE9Qi0HBTz3AhEekJZoEOMO9i1kioNyqaZ4E9PCqWdxzbOU+i45tpwV1c0SD4OB/wSYExZSRfsTWP523Eh+UIJ4q1BJyXATl7PJwYiHVeBpTi8Nn9ZVdsXIohXaGhZABOUd4WyGqhhaJIU8Y0mMrC/Eb8UI04zesvVgwrCD87U6Xo7MmpOGa06CHnAFmoUAKAcl/hTpnqABt3ydmaAFM3yEIlSnlgZk6IFadppWvoMuB6HeiHI4ow1L2D96I4HRe8qEC/p9V8qR7RV0wh4+1wxcd03LONKJ0lR+ryPOLqBm7qXoH7C5bCArk2oWx5o4rFX90uKWcUPFAODf5QcDzwc24lYAbXpCgblPUzHox9v9PuTVMEbWFir6pgOQ8I5kI8hYz7YaHIZwvkQsyyB0DWURk0maoFXNoBOTRUMWL26jA9zLizazhvEXmLu5OzOJMdidpB7EnIHkgwVhGUI7B8AUBtD4gspGTkgVk2z5woARAUs2XoIDsipeBj2wYbo24OuaT9oIYC6zUih7AjkE1lbxIWAP6bgZ8n8FlpccDLz35zd7+evxCUI7lhYfYYv3eYPlUMT832fY5rrz/mmM29QotEFN5+51a8qdqIihTvoakgB0W9UOGI+/wq2sBhOQ+QjxFINsazka8TkIyqNHfkM2+AoV2rOBEVbc3tIxxvyEetjoVMaEhDwZRowLi4gGHIuApQAfoMOZiXB/d8Nxa01cM/BxasqzMuHDmG5c64lqNgfedw/TLh+LXH3VrgWoOIcNwCQA8j2pjosP0rvn59i4+BBKhOUuyz4G4LC1iBkm8PeIe0W2JXVBugmUQiLmyDgovaAUoZUT653ZOfhjO6K1PUKcTgchmIPAwDYdg1Byjyng2yrpHOlpZZQr+GRj8BI6LuyAUApIZ42DCN3MhH69q8bxZaR5JS78S24pFBWaBunl29QcQpVGwWWNYXZic8bSXwH+tQw/cR6QWYLSugb0gdynuIMx7ijNEXnPOArXkeaDlgCeRfNBWULKjPtD2m5I7EJDV+g/fs0ubrgHa1hEwr/CD8HiRi0TMBALJV9JodSvY4bwPHRXHlaCMnTIcViygyBvhUjYDq4A/bTr6sxXIpVJjwO2Rs9v2GIbPQ8BVTLNiqx8t1xHwd4HzdE6Kddbpj4M/MmzlgGiLzG/fPOIUVxcgNThrOY8L5VLA5z03fIGcMQBscmtDsKbrG2boKTocVbw/zrp54XCYsc6KSzZF0On9hiq/U8OY04yenJzwkFkSXkjDFjEtImGLZzY34OZnamis9SNRQsc9PC5KveJw5ilmFniUOuqcANwtCvPm6AFBBq3wPJr9z7TjXUNZg/hCA5oBmnTgAKkhsnkxzI3uuYwNm5hTB8fudTjxMr5cREhrKyQMXK378q5m1I+LXD/TLSnUBeS1U6dTs0KogpGoGZFznzTb/ap+/j86WuGHto6QmJEJ3TwpTRalTbCXg7WHGEAgnHFLGR6dM010E0v2GEot8DYpxzLgbVwTXcM0N6xJRrdhY39JFWbt80g5wORT4VBCkw/78bI8fT1AVfHk6YwgMguv3ok6K6wSULzIL8Fj3orpWPle1OGAUeOum3xxnJCtmDjHjAw54ejrA+Rup2hk5vjuTdvVXS4p1bFRimPy/HSot6/soGrzf7pThzc9HBNiukfu1kZg7F8wfCkIsqMXDB/vsxRRh/d4AGA9sYEpxyA8espCL0iIwf8E9v1jwGgxJChcSSssk2Aw93u4F2wN/ViMbyTExv2UrVIf1a9DDL/0rM7/a1YqezerpbtnXx1oCHn2jGGIRNJC8Ds/CTpwiSEMTRa7MpFETBMApx7yxkL9l96BnUcVUmFRtxfJWPIKvOzrkXAPuM6oLlI7316t92GXu3QqgROHBG8zXqiPVoDq0jA7rZyPajw8cE4LnqNsU8aUAT6+Z4//2169t8cHqGbt9+M71cNgNWFzBXiQQTrKxjBUt3WjmZkTFv6fOVCfWgXakRAMhJrHyTbvPhyekr2e6i+YhYLWo9nWJJCsOBcdppc1yrwaVRFRpgLs41Iqbpa1XuIkP1/nMDWXrqMYcjbQHiFcchg1vxhkv24Dn87QT6cQkhOvjiO+3gBArjtPt5uctoHkbSfTKOjuTr8EkiaYgUMF5GfAhHvH5cMFdWPAuXeCkoZh8ZRo2GkWZTXcLDkVghlAOzSucktgEf8tl2RlRavfDCFmtUt4L2EMtJM61KpDVoU4OSwlYKt1g7+KK8a7gzTjiw3jAL68RzjUcJxLxAGDNAcsW4YPsKo8hFgyvyJfeHuDeOTb79w/PR5QcmO8xe2xTQr4LmAbjQViX3FTw+WHGKayYfMbkM4KrmGvEUiMe7w9Y2rhD3sOUMaa8c2cOKSO4hqUEbMXjiyOTIDt5tBqno6NzLSjyvRWIF/6dpUZgA5o6LJVZGGsOCN4cbUM25YruChbvOS9uNjLyohhjwbvpikPYEFzDVgO25pEnhxAarueBxUG2qHBDflSF8PsaoNeA2n0FIrkHCnCdNo6CXDRp5SgIRq4VAeorh9ROoM7W4b99uMC7hvc4Qa8jn/cux5xoFy6i+Px0QXRMSX7/6Y6JyMLfuxOhTT5am0NZAuW7hWqVT8uEtXIdvqwDXuYBrbk9R0m7P0VxwOag8Fh9w2bOu1PIQCQPbDZPFb/c0IcyKuRUcBpXHG1Ucs2RxYLe9iDuOQpYOJgIuUjO8ZrXwmdDzVlzXiO+wwljylzPsSIPDeUo0KkiHri2U6o42GddtohLFXatiYfXEAsOkYnWfZ0XC5XzZp5WjYvUeQ9YPaRnD8GIwVYw1aHtUvbOBI1jgQ4FKdW90dgynYd1oBljLyglNqSBz8yaA0rx8L5iPHBNbyVgXSLKyiIqb2Fv5rQJM7tM/demBndnSFp20Oe4O2ZvD0A5UOnSIvcnAdfX6bDiYVp4qGvaiw8BdiNBVcE0bBhjIVq00CkbjnwcJ4qnZcTzhWnZ9djgL24fD6uNS7XKTTlnSBdg/LlUEXyDd4rgy14s98/SM6lIQqXsPxfKvstqUQ5OIVOFztgLEBnJh9LQzCKfDbfbHC0eHLllLdw8PxgWKdge0r6fu0y7C5dZaIft39HY5e/8nb+Dv/t3/+4P/uy3f/u38U/+yT8BACzLgv/8P//P8Y/+0T/Cuq74i3/xL+If/sN/iK+++uqP8msAYDd+2V+2Qe32udYxSAG1192gyO6/NOUiMyKk7yxoOxA0YJcolQnwzrTWVni02BeCLQYPOLPzLtlTVdFYbYp61EoPis26Py2OkOLsdmKnWx2Q+aC2oe3fS82meXkagM0hnP0esay+MeOgRDxeJtQXivclyy1m2ux7S/Y46wAoxzLYnC0esvNhhzpwG1dpwG4eBBBWfNwmTG7D4ApOYUO1zuc6JibKrq9Y4J7Qeu0sKjG9fhVsa0TeeJjvhlPtNotWc5XcxEOV1fu2BODDgDAL8sHj+TIienI+uq9FU3Y9EmiMlQKRhI769A0q+oovjhfcG0LwaT3gvCVcDMGIvmEtwQyi0p7TQMMhvZmTAYYGeCxzYpbHHf+/uUasLSC6itz8rdvwClQH3TxKbKjGKzmkjFNaCZ+XQGSoRCpTqsM1O1xWokvo0LvjvF4qIJtgXhM+hsPOU9kqpbOqghQKO2H7/6LnoVxdQ1NgUVp7A0AKJGg+rSOe1tF4IIrcaDnfD7/WLMCwCTR4FBXUjQUoCs2ywqNnztEJkKEgWO6KNgfnG0IgbLwtAWU2+N0C0XZfosY/21Z69A93Bce4YT0GPJ4YuKWRI5vDiUV2a4LSHKIjQpWGjCxhP7xbte8CMEAwe8jZ00cm8Hl98hOWGHA0BQlgqKHjWACr/8FWBDs0xsBcn0PY8JxHWp232xoXc+0UALp6PF/HXZ0y2z3WyEwiEg35O/1QkYa8c3OasiudrwPJ3iuLsxobZNyIvAL74YXXUl5T5jC11yPnVyogAJvK3lU3jbjmiMuasCzcZ/p9bK3uB+5uFW6E2WYqids+TWlvCw5+YESC9+QJ9WI4eKKKIoYW2d4Bz0TW47hhihneKZ62gG3l5++oZncHVq9UiTjdjdu4v+uOsKmp6XTxiBeTGhta3s+R/89XbY6eJyrkoGXz6xDjvanQZAws1pYtQudAHyg4nC/jPkopOaDOHv56Q9hrFDTvgVg5xoMhz/pKsuv42Zc17vtKf+a7Gq8Wx1Gm8BaQOyYol8iG2c4GpAYZOE5xoSGZuWDt6dsOu8kfzP+FZ4Yz5Iv8rH729uLab/yD3b/mjzB3+SMjH3/6T/9p/I//4/94e4Nwe4v/7D/7z/A//A//A/77//6/x8PDA/7T//Q/xV/5K38F//P//D//UX/NbrOMemuc95fgxigHbk6AvEbmrtifevsZD7herAA0NFKFr32+SrJSi1Z1BkGXpTGECvth31aP2SUuapthbx1duHQtIhGB/SArJIKR4IndshoCMuyrILx4xCfB8ARIUSyfE96/rIkJpdfEQKFsIXYri6HuTdCqsAo2kx5pAlWFgsSvm1ES/9m9QooDwAM+V8/iw2e8iVckg5maOowhQzDtHXWze9EJnt3NUBoPyTp7HsIbjbrcJtycDX5vlRB+gW32KmjZIy63jaw1FgYCIPrE36uCtbDIqxs5LkhAz7Qq1WNd2THlaWHy7Dbi/fmI60I1Sox0iKzNYS20vK7XcCt4RSGePJA+jqrFocwBEhvWGvBs6hERxWhd41pZBOzyNUOAmBJMqeI1J+TG+7ouES9GZO3jnJx5eLbUAEvH3R1vPR1Kr1vcDyaAPKNmUHR/9Y0KsOKpepKiq8D5G/rT0aeetlptNFOKR90cfOmcKoVugiaORGrh9+NzozeJoSE3zinguq07bt1dJofDLSTW7bb89sw085f48HLEdrAufKgcgRqXqH+3AoeteiSzFk520PVrU6olGqtwLDQHfhcFC+BidvgSkMJtHBBCQ04VbSOBtCMT3YipVcFaPeYSmRi9jrjMCbJ6oqhyM1tTTwvubY0sBKsjYtQdWW3j5miXCcUPhxknQ0kuOWHOAZcqfFaLUJVgSM5W/d7tYnOQTaCHW/Hcv38tDm0Jt0LBK1oRvFy4jr1rWLdAGL9zbIxfIK7BpbYjMLUXWgUQZ2ujL77KDlsjCxA1zs0Qy446iihq8/uo6OY1YdffxjVLDigbx8z7QdBItnezSdgH8wTKN+l6/yxSBHoNkJW5QfFJGDSXbs+JKCAbG1ANgKyC83ncD/x1ifwMr+z7JTZkCfvnX88D3NVZhISgxIjZNY5mbOSjDnAbYwP86lEODs1QKroeC+Tid6dggMVHH7UBHtmRd+M9oy40O1P7CRSBVgR2b3gvxLJ1BBgbQiK5PcbCv29uqLtRpHJd+KHS48cO1eaJdALY40nEzuHmBdKamcr9O5TahhDwox/96F/586enJ/xX/9V/hf/2v/1v8Z/8J/8JAOC//q//a/z7//6/j3/8j/8x/uyf/bP/2vdb1xXrehsVPD8/A8CecwBgN63qRcdeZXTwoKthbLzienhYw22x2EEvTa0I6LOu/t+GlDge/OFS4W0jkR541OdkVWiY4xSSHbTRXVGED510h0mbi2rbJzw3LbpyYTmzOnazQ3wRTN+TWVwtIErrzUJbbS7q51s4F1Tgzw61seNANedTIQMaFhYmjsXC7lhnm6lfhL4YRbDlgKvBhZ/8hODqzme5lISlRGw5cJNTsPvY2bu94+O1EoO1ucPfkBq/vvr9PcPBNqMutW3xtpGJEKGac9hJXqV6XJZECNyRG1Cr20cvm1lY1wK8Px+xlIDHy8QgNYM8a5+dNlrGl9WMFZxyc6t0eGwmq1QYkdg2/o5abNVjNARB7MAT4ZgOoggjfSRG+2xLDrsEOmfKXtcc9i6p9sMSYOEmvK/dFbRnmJTi0Zy+Isbyn9Ic5hxRvNtHSk0FW/VYt0DSrUHTvL58QKKvO3LUgYha2fXDNqi+ge6FNXAjfr+xMchrxOEVV2AYCrwnR6PExnThXYlBYngDN+e6keu05OF2LQDsFvIg0XWfv6tga373XBFg/959DZVumtVuz6EUFjolB7R6W4e1uptrrdqBYZJfdZTE1s2zeMwB3inv6xx3QzhYo0OfD75H64Ze1ZE/kh3c5iwrhWvPJWasdCLzZptfrvTeeA3BaLO1bhyd2g8ixS5Zb43mWPx5uTmwwpDiJqjXgNlIwapihnd8jx4AV4pHjFwjzldUF2/+QvHV3ghbr1lsRCsci9jI8XVxO88J+RohM8dz3fa9bB4vM2Wr15cBOpNICwsaBHCzpm+4EWdtbUilckrz7ee7LwddUS2/y7YnEucV7pN5R1XBMkXMShUV0YVgKLaRYgGUJjuKJzaalGp11OJRpsCxhiEzaoR80gX4wYrzFA8GSu7jk9tdu6l0kp3426rDco27a3GdA2X61Yi/1aFKYNpvoj2DmE19/0dcs5gFK2qMzuAyDLWTPRXahYZqAX7a3D6NeG2Mpx1pU1hMyb8GRvo3vP7Ixcc//af/FD/5yU8wjiN+53d+B3/v7/09/OxnP8P/+r/+r8g548//+T+//+yf+lN/Cj/72c/wv/wv/8u/sfj4e3/v7/0roxzADqeIPYkPuEFkuwfAKwRkh8465NluEJEoF1gvSPxKbw+fdX+YmROg8GszOVFF+Czu4Wn8AAZLNdxcEA167tBsDzpqg2VHmFGLyquC6hWkBjE4LLv9Ac7TzddfzPq4akOOHm2qKEq+RYti1sZuD0GLZ0ag53tg/kqgRy7UfQEalN+LEKqDyKZfBbvT5VIjLmXAUsm5uOSEp3mkvG0OO+dEgxHNNtk5OD3npV+z/dWoQNrtqqtAN4+m5AQAvCZtVIjJADuxzNshk01Cuc3RuquGOnvMecSWGJ6lKntK7rpxc16uRKp8bERc2o00WXLgxm5ZIbp4ytA2zpO7dG2fYTvd4flsXYk3SJTFB/YuzQd26UsO++/s2Rm10vGylFuyajN4uHfDaMIOJAhQ7XDu8LfqjnioAq16nBcGCCqAo3FVVAXXLaLkcHtfEFVal8j3GDdDaPhdeyIuhDkWJNRhL0JksyIX2E2TuhEX81YsgbkI2lhxd1z2925NyKwPpibrcnihUqNFT0XAWPeNkF18s89N22sfAJGGUj1mFcxb3EMQtbn9nG7FQc+BqhKYA+vM/aNlHr7VK8rm2QyAB3vbqERx1Sy8Gzkcoiwucyanyru2m5Tt630fv1Bi7VZmhBSzFb9xtrAfolL4WZct4qM7IPhKfs8WuT46OmDPbefdtMZxBNTIvJXcro60aDM0afbw8+1waIUOrn18sBNLg/L72+i0Fb5fNuWFNrd/v47s7mnWip1DByvcWzP0BQnzmlh0FwddmBr8Wp1TThVt9bhsE59hG133MbVUPgc96VXNemH3zlBzKu2yV6td+2iPqjyxPBq6oMZrQzw3hLlgfRPRUsD6BdVz2tHiYtEVm6AUpQFc919yrxphQ4KlAu0aUDrRugriE7lI24PuaINbBeroN+TPbj/Ye+pz3RzNxXrRdQ1A5b6YzDwMDrZHgBSAE/jsZCLqroD5VYH7Xuc/kXP0al8zJ1kUy7MxVWDLntLsWW4jxU5j6Mupb1fp35HD6Z/5M38G/81/89/gt3/7t/H111/j7/7dv4v/+D/+j/G//+//O7755huklPDmzZsf/J2vvvoK33zzzb/xPf/L//K/xN/6W39r/+/n52f89Kc/hc+A87ihHcAtwdYWfjmQDPM6CbMTU9uke+4ArMt3Fg1cI/jGctsgpAHhUs1V1EEHv5NdX1d3fuGcUVfLIVFuxLuVcb55FWjinH34mghKHW9sag0WWW0dWQ+JW95ZtW4QuxZ3g8VTQQYr7gZCd2J5FTthTejuWg4s0NpieRCG2OwvwR6RrgLOWo3h7UXxvI5IrqJB9g5fRBFTwdZ4QO65B7YYm6nMpI9zGtAtv9vU4NYful7yA2NX5ex/rrfP6ANnw6pMcM1GQNON15RMSkrz6iWgFvvvRNJbEUCVf18Xj2Ldo4SGDZHjh46Q2ay6Q7TFc76cs789/BZ6dV0j7g8L7sZ1j7bvx10pjg9/oTqhpoJa+dk7dKnNoW0e6+oNKnf7fe9jQzFJsxhKRz8FXqPWBM4Z2pE92kKm/+UpsoiKDdsULAvHCpQqu1Pl4cDPvW0BKeYdNaJrpmBZ4m4+BH9bY3QUbvShCJZWvAnw4tEmgY5gt6dC063MrJRliyieEHY7RyqfMv9+uHKj7TEKbWE36+7paZANHhZTfgEsQLZrQg4N2+aprMq3nB1VCyY04nZY3L5xhjPvQY4Kd5cRh8LxX/YWptY3FPwAXd2509YkbEuA8/oDh9S+p+yhi/3PFHAvfl+fHQ4HeG1dZuFVLwHz5jBjRCdg9gdbfD+AwS5/J4LyM+vGg3r46KAfEz1wosKtDqM1JemFnKz1nWB5p0QNlkAkxkyv+u/TJqgLnUhFgfKmoEbjPmj3UNJ9TSqsAam9mCLK2LOFRJRNwOb2BqWD2DTVUpJtjQMRhoKsgYm11d3OAWvoSHBl+BwPfNmJyx2p7ghx5zRAAdeAcFEcv2uYvl7gX1bUuwHLVwNefiMgn+y+VRpu8eaZ/4dTHuZFoMcGSbfIizCTG0XuBCDV70gHx+TkF3LMD8uZEcSzvzXMr9BFVzhe0iqGYBF5DRfB6feB+98n2ri89Zg/d9gerJZYjafReP6lRxrd5XvKwteD7ARc2Nmw55iZRXu/B81CEPvWPHyyHBs7PwA282FWuLXRgOxXfP2Rio+/9Jf+0v7v/+F/+B/iz/yZP4Pf+q3fwn/33/13mKbpj/JW+2sYBgzD8K/8eR0AHfiFuxlOh2nLwZCQLs/ytv6rjU687tBb5yQArIjxagNh2qVy9LI1DN9fIZcFUirq2zvIV3FHK9TxvXnq9w/Jf9dBKSsE4BsLEjeTz+BWB78A7/5Jxnbn8fR/d1i+VMhYd5+JIlzMdTAuh1nE1wxCswZl73PuJXAUJLRxbomSYQhhr4syD2e/RjZq8FeLfS7kEKBnxvlbR+WEpLCtenxcDsjNIbpbZ9TZ2VDQCXIhl8MvZslbjBsTCH1roJlO3Rxactje8H60gYscgXyAVgQQuSXWAvCPAYuQa+GMf5EiHQXn4iBnT7jSK016ggdWS3HMDloouWSHbLLbF3OhDIpiJl/S7Z/BDUxTQwHD7eo1oAaqAgTYC6OOhjzNI/0VcJPcbdfEgnIkAXKZE5UTltyJqPtBgsVg07cbUZtGXgnMk6H7ZnSXwjayS9muERsiUBzci8fpG4fTzxu2e4ftPmB7o1jfGgTbOShWIGsWlKND8LeOfdl6fDgh9nol1C1ZfhCjDuugdKDVdZ1AuH/jddfiLC6ewXpQ6+avCT7WfVNkvg4PMb8C4yPdh68/Acq7bLbuhOzbS2QRkxTNogqotnBosUGPAqS6j0zaZuvAckQ0NVQAh1963P1+g8sN1y881s+suA1EpuRjYprwWM2HoUEjv0OYWSDkoxUhs0NTRtgD2A2+AP5PmOmq7Kpi/pIKsB7ehSp7eKIYUqABqAc+D+FQjJPDN2sqqOcIzZ4jXQ+ifqkx28cp489N5VMOuo9C/PyKrwIrilQxmwGjDs2CFrlO9tFOf8Ztj3WLwJ0ZRtbRjY4qSBWqTFdDlAzZbQM9IpBgIxhymcKJ3gf5TO+f8mAZI8LNUJtAUkW+RMjqMb4nyrZ8pshvGsrh1fNqiG9v3viH/IzOSNt14r1hkjJw+Lbh8N0GyQ35IeHlj01Y3wiWd3afrOmFKUXU9hMiPa/GO9cAtwjSR7ebALbE6A+Sr81a3s4hWTzCi7u5QltuUrgIwpWjIJ53zCvrOVRdZdSyg89AmAVhVoxfX7F+OUHlFVrihEno1jCrU5QJtxRcBVp2cKZAQ2hoL5Ey77Fyb7JzqV0D/Ivf13QdFctnJBq7Aro3W2Dr9H4jT/EU8au+/n+S2r558wZ/8k/+Sfyzf/bP8Bf+wl/Atm14fHz8Afrx7bff/ms5In/Yy88mwrDquCf7qXUWGgDZ1IJvCLPtPxOYgihLD58D3KY7ychlFt61B0wJF/H6xQH44gBXWGjkgyDfKep9gUwVJQa8/d880jMwf+mwfKa7c5yrfPDGD4LxgyLOagVTY77Axxntt06AMofAp4rTcaF5lg6oQ0O54/jEXfl5oYLw7LHcJTjXkH1DLR7iG3Sk1ttlLiodGjshaVgGsRyRBnfgrF2V0e0tca7Y/Qc6ACGWWVKqw+bYQW5GQutmSvMakdfAMZPK7pgo5VUX5MSSQBU6VMSxsMgSYuvxLMhH44SoMF+iQ3ipAdlh/NZj/MCH8PrjhPnHjnK5sQAWpAZlB9s2T3TDkydAno1JqQ2qzTMdCKdf0nV0fadodwVurDt8HUwe2pIDCjGMeqSjrapgfhrhXmjGU+/s4TTEo7tBrksiXyETPWktoI0F07RhGFmENPveWox/0I24rgEt8nfv3ba/MfalCcLCcZQOwPiwIUZyTa7TgEtIuPyGQMcCyQ7Ddx7x2aMcuQGjp7o6oB2qsfdlj3nPAh40vRt6hUbtXJ7Oyt8IR/ao936Y1OK41gDUFmjh379Lo+RTteJ6DTdejbAbXCsPqnJfEE/bHkGQt0DibfOWNhpuUHLiPe6mZ7l6XD4cED8EpE98NvK9WdMHxfWnFfNXlIWH2bq9154moT8MfHa1q3E2Sxe18VAdmf0RjpnF2mJjyNkIh60HGVr2yQro2WELbR+d6NBQIqiAa3Q4VQfmqbxSB1UjqcP4EFqV6Juzotu9krR7HuLlqNBjRZhoTFiLQ149trd+z7UBDJnZ77V1+H0sF1ggk+jZD0jBCkNqDN10xShspjjEpLtUlMQIR0WFcwwYdHqTh3bi+dBuzy74mfQaEJ88/JX7+vI5myySTbEbsEF5DeOL7E685NrYKGfg+oLJgdUL6uiw3Q04fsNgOb+pja+x3zvJfM5YDfB+1daDRYHxWxu73im2tw3lThCfrUjvhZnA9ko+W6i2Rzax9d+J3GDh8YqjuBd2jdLqOpNM7Fc2jq4oypsBdXS3PKReYADwV4fhk9iEACh3jb5DZvomxherhSGWUoxI7hqrAvscLRCR89WQuYGpwvHMJOPjd+YQ/vECeIdR//9kr34+n/HP//k/x1//638d/9F/9B8hxoj/6X/6n/BX/+pfBQD8H//H/4Hf//3fx+/8zu/8kd97/UzhJrVFfqtqNYBEu4A9nbK5V+MMU3WoVS7lRATBVVsYhTfOZUVYFMOngvi0QHKFDpEHAIA2eG6Gpwp/KhjGDYtTvPxxb8qahm5akx4Fh28Vh+8rpAKXH3lcv/IoJwANGB49/JpQJj4gOnEB1OaYCbB5YOFMTR2w3QF+4Hcr93QJ7axn6XCwmdR0trI7kNi4XBP0KUKniuO7GYeB5km1OdQ7yjTXdkR65kMsJq8KZiV8N644JULyayVfoorSNnwxCeNQ2ZEXjqBqM9QDQGcrarghCoSzBe3UsFleQBsr/B11+9XMm1yqcAfFEhpaivCLWCgW6JYaGdxWFHSZfdvYPW6OAVb5JrWDVfn9fRuA+cd8KN0mwGNAPTnIVJCmbMmTgiIeeQ62wbNIUINd21RpaV9pKCfAzv0YhgwMGVsM5q/i4ceKd3cXfDZdMZeIb3D3yjzM4Np+qNu6daPlk2wOsnAePr4XTN83tChITw4bAhYAek9L+vu7GddYaXBXBTD0TL3ARbklLXfCXtS9IG1zQA+PE/OeAcDvvRgRsHM9sDf3nDWvnN2XEyFycYCYvBbF7UnHGtlRbybr44HBDA2Xb4gmAPrfmDpm5yAMFdrHAXbg1Cbs3Dpxz+6DP2bkIpDqmSR7tMI8GXonDm7zuzS/5/ekVJAPGdsS0M7RiJksujqpmpwzoqpiSItzQDgyE6jWBJ2xX2upyrRth1tA3Vh3/5F2DTtBHgDaSAnkYey+MqAfh1TUUJHXsJMT3aHsDprONaw5Gpk6oI109wXIW9GFSpE2VWxOUUe3e6ukT9zP8psKdyxEp9prOSsLnToo0rPg9HPHeIDEA69biYs5Hk/fCdGwZIjI4iAvfjd6LKcGPVZ0rpk63AoP48IA/P/qwDU3fORayfdM+N7RBNv76rECzt2ySGbsY2CplnBtnip9JNwCUEeBenLn1MQ0HYlym0BXfzNUA2j7HxXlTrF9Abirw/ieI1qXOcqBAPoZ14w/FOOgwdDAYAh129OR60mwvZEf8H7ipRckYGFmaJnrNgkjcP2RQxkHOBvXhKsiHwR1PwcbG7I+HrHCUgv3c2eKrXaJiCtHOSoe2xuBpoo0ZWTj3LQR3FP682f7QB0Ely89Ll8e4bcj0rlBni7A/+tfPc//da8/UvHxX/wX/wX+8l/+y/it3/ot/PKXv8Tf/tt/G957/LW/9tfw8PCAv/E3/gb+1t/6W3j37h3u7+/xN//m38Tv/M7v/BvJpn/Yq8/G9JVUaY81L0Q9elppn886c9FUd4v7DuUGW8dFST6yi3f9KiK8CaZuaQjnDVK4OewyWVFzvFTULzdC5vbfZQ5YQ0A5Cl7+mN85J+WuAg8ZDw/X3SVvnhMPcCFMexg2nMYV52HAJQ0obYC/ckwDU8z4+w1v7667EdZ1TcwT6AdtHyspuQaaSZyEkORVo2BMBd4SWLcSoENDC26vtP3VoUjCk5Hd6h1tw5Or2AAcfcU8RUr1+gHSeOjLeisM+yHiXhHscpNb9d+remHRVlfPnBBRSNQ9ZE5SQ35o2N5Z1z/SobEHe21r5CwyKKO5exdU7VfssLHAHTakVFBEUURRI4PFxMiULnKcQ+dAU2iEBlTKhF/bhDevQHGIx4xxyGZb7szpk5yBYv4qiA0hFjwMC06Wr5JCQQ4eTgXqG3IRYCFRxo0V48RDZ10Y076TlR1w/dJBA7C9bWgPGYf7Bcdx29UDw5BpYb1GaGKejGSxjed27bvHSi32Xd2t8JDQbt4mvhG+nbDbTvuVP98PnZ2AnR0KAlwfoZn7LpTjNTUiL1SYDTLRLEmyICy3AqR34tUyRYYxUx2zeVOGcV0zQdrtuRZrFWzGWYqxwr9bUE4emyl13FSQRvpNXC8DcPYcoybFNG10ovQFaw34Zb1HczStcoUeJH08osJOtVmBnYabm+xlTqgmOe7FRB15IL9uoJw58tZCRIFGiAb1W+EVAz1Lmor5bwhy8TsSVDJdP3sOS/IVToBtDciHSpTL04ZfBkU1xYVuHFPWU+PI2NaFmt09XWAVFQ0ucN9txbyBhIceZoutyEAZ+ZhwfbBAu/yGMjumCdqhQoz3Vjui19El2Bqa6m19xgY3tZ2fBXGoAmz3/TqB6j1vaji9oSUVQHz2iC9MX41XZQha47mbD4Lrlw75HnvW15IdxqeG9FyRnmg4dv3SESkDdmVbV3f52e1FTTs0tFPF9ciGJJwd4hP34/UtkbFWBXTFAxsjI5junhg7qs+zys+3ooOjOAVSgx8tumBzcNnd1DlG7G0B+8jGbYJqz2bt4ynbI7tKJoRKxdUlIX5kkR4u5G2IOiwxYrO9QYfKIqY5lCP2kVEbgPUt4HO3AejNye08+MNef6Ti4xe/+AX+2l/7a/jw4QO++OIL/Lk/9+fwj//xP8YXX3wBAPj7f//vwzmHv/pX/+oPTMb+v3n1uXwNN4mUCBeNGgwNcP31WVyv5jvTujOHXxO/yiSQCBJaN6IgKoAGQRUysl1u0CCU5a50RFSlBFCEXbgz+LBX3+VIaK5v5giK+/sZP75/RnA0s/pOTniujq6npp/vj6L3DTU16MKOu5m7K1T2+PYunVyHgNmHXXnQI6t7wmg/jMvmscaI6BuCvU/XpasAceZFIVnXo10dljHg2y3gfFwwpYwp8pB9mBYiNZ4dllZnRFFKi7siwGXsoyjKkF8dcJnqCPXKMZnz0CTMJjA/iNYJmeb06EcaLt0fFoyh4LIlzJLQfRqQqEnvEtRdiy+AWFZLtI06h4BsTH6AXg7jtGEILGyWLeL68bDPYyUoxmnDYcg06NoirguNfZY1Yhzy3qWqCjZH9KR1gpbQ96NB4EA30a5cqCa1hJk16UiSWAwVLRXogWOdmj1N8IxzoZGKg7tptUOHRdW8RWYFKe+/BvMJcda9d6WTAjJ75G61bAFXvUPbVSJ9D7HCSxx2Anb3rtjTUk1JpUrFiC6eVvt9caudEzYy64iGRoXu8kfbdDNlrJ1U25VLPjYLJeufz9a+ZwHpnGIYCsbIwmZ2CZsQIUupIAaO1XZfA2Xx09VK0fJwWjPir9lLd6PC1omAcrs0DL8mIbqq+Yh4v5Mbmxkctgh28UPFMJhlt+PB3orAbX53bQ7mtdKUY9WmJAnmSnOrsnngJSKbVDq4htpohAWAqGQLGK2oAoCPlwN9bIwIoE15qNq96cGU45D33JltFVSx++qwE+/r1AmdoFLJyJEwRIajZiH6Yp1/H8l1Im8vHjl2k1vxIZTm8kQWHnxRkIUIoCaOQUO0LKdmoyIY78jI99nQhD5mp4jAeBiRY/ia+DMtOgQrKvKRo7I+vtCpsljazCjSGihXAFwd6kODOzLlt0iEGF+mJTUODXZLfjQWHulJrOCzosHQHbcKwvmHz0I/szra3ay4lSzY7vk5w4VTgDIaT1KsQR+p+VUr0F1ocOZYC7BZ6rzHfo1qsrO12B4WSVxXr5YIbu7VwrPTL734UKSzIl4q2vaKYPSHvP5Ixcc/+kf/6N/6/4/jiH/wD/4B/sE/+Ad/lLf9177cKtDuCdH6PwJkdgqNuVG7J/0PJJ3gz+7GReZk1y92L0YBwFUgzA1+bVS5OAFUOTfceKBq9KgryaOSBdVHNg5VsJPDA41YIMY3UME8JzyPI4ZQ6Bw4J8LcoPX5NUYMkWFACiCnCIi/5cp4Rd0ozzwmdrnRdNqdkAdVmsIElvgtsMNB634QDrneuqdqplW7iscKubAAcha44gAEzG9GPH9WkO5XPJxmHGLGlGzGnT0/m80N/UvY55z6igciGTsrv7eOtwNJgKokmIGJoMF8PlxoqA6Q2DCMTJYdA0cuTpRBVZ7dqRgZ1PuGnD2K8zTRCgx9Oo4bknWRi1AR4aLZ4Q90URw83Ue/a6cfKoKA3fshOsXWR11KaWsKFUPgXJ0Oo+FGxi2UQV5zxLMf7RBx5vMgtw17aDszPxtxtRZHZKevA2eM+VUZ1f2GWS3d7G0P47N130cj+zNRSVKTzI6lwRBD4+Skoezy3C7By1sgsbRahx51l1R3Vn4bcZNsL4EFRhYkk6XuChkjRwL4gaU4muzGaWViYORO9LPiAyq70qgHm6kHtHrIUHcL8HHMuB9XRF8x54jZXCL7X+0eKrq+kjEr5c7XzJ1+KXb/7DPsa+A16tofu9UjDx7FNvOuwPJGuu4EeWmwFOPbKInXGSZlviGGEH6e7h3Cz02Pj24nLs8R4SIo90AZAq6GwHar7l122m+93U/tssr+elW8ib+FLObqf+hk+oorJA6oQaETbtehqw3NA4WqQcF2x6CzPfdEKE/t8nkAN97GK4dlNg2VBWUnECugxe/keTWDNVWgp1j3pO1eGHVuTkcVutGjvzpDzLn9qLC4dNvN54mJtBxB+tBQgkdrgcWFEbfb0PZ17GNDORSULSKYaV4ZPHOLbIQI8BmMZ1sPgetJgZubtz3n4aqoExESca8s7j3FFc4U8wXgfu/4nj21GyMjDnwgcq3XwCUxVn4H0Z1nJ/b7/cJxYp34+dX20ddE4E40blbESaMlBDNyACDA1VeH6x/y+vXNdqlWgHjdH/4O+bgsu9+GVMDbZq6eRQkln/SykIzbgQfYBbTiIpP7wb9rD0S/do2ckHDmL5cKJCMUqZO9+Gkmq2rxh2Y7dQBKHvEHL8lGFFz4sbHazXnAp2tEPG4Yx7z7SOyVaGVxI0L1CcOuEiPcM/0TqF0X1OhRbG5/IwzycC+DN1MrtdwUt2+MuwZ+41w0vijSiyK9VJTJYXkT8PgfeHz8kcd6pEW5Kh8IJ7eAox90uHY91FvnHe0EtWAyt4lJRu3+NIE22xwGdn4iyqwQe99SmfHSD1rv6SehJjsKgcZM3jWUUFGMGzOmjMHsxnsaZR+hBfPlcKYi6m6YvCB84NQO4au5Cm4bn3pnCgkAZr/sseZIx8COKJjEtZgLJsDcmVbZWbvYoGY+IJ6FYt28udT6W7clSgKtPQMuA9vs8eImhFh31c26xJsyxTxj+v0N8y19eAtKIl1siFPGODJnprubAgwivIKoQVYjocI6211zjJ31z1m6WFQ3n78uz+2fAeAB7ZzB5s6cUT12dIEqDsrDkRqaV9YBPSivdX8TgcSGOBQWganikDKirySdroncjewB1N3volWBu3jCxOa9UIvHdU07uqDV7QcynLcCXXfOwE6sLoKyBVzs85QlwL0w1KwrY8KsNz8EALo6bClYAc/xnFudqcO43rtdvOrNobUUC4Lb3CtkF/ShAXlYe6SDfa9tC3iUkWGXlwQsN5dhqSwmWzQ+jLNQshxY+Daimmr7j9p+pkF3/o+UV8+98Qn4XbkvI7s9BRxggQTLFRJLoe68DQD7yE8cECJzslpjITZnKuUAMK6gI2dViB4C5Jb05tLGER2h61r+/3d77xZjW3aVB39jzrku+1K76txPn763bewYXwJ23OkgSKJugR2UkIQHQvxgEmQEMRJIBHETMeTFSJEiBYR4QcALigWRDVFiojg2dkLUNti48Q0at2m7u8259LlU1b6ty5xz/A9jzLl2tbvdjeE/XedkD+nI7VN1qvZaa645x/jG932DgrT55PkjzyRJvk+pfdFP5P6CBK0zLAe/rGUWtWOygdBkP6n0yBNsYISOwDCCPjBArZERBUh8E84eVmxkDwiJa2K0XdcD3qfk0QxJuxYkbIGgKFtQrx2uI2wl6JDRoXUcIW3KfiOhDiafn5kUbpF5J6YHopKQKVAmHIda+Fbp3c/UCKc8H/McFOBrxLFNPgC9AXqjM2eAFMnoFd3wCv+0kgX6kUrWoAfhRkaeGdodlOEsfcFYSpZvetb5EQak/ULbAgChWAGTv4w6BVdksWwAq7CqtMgow6dghu0M7MrCNdjYuAAfANNaEFv0OxbznQK2FGZ5qiiSt0N6w5dtiQMvfIewcHD7Dm6prP+5QagHEl1aRFzIOPDWyubrvRXORpJ+RcAt5V4UC6BcRriVmKyVNyLGF4H12QlWowILRnZA5KgvXa9uriQIkun0npNCj3WErb0w27tCzJ1a6RkDlBduBMC1/Hxj5CUECSFzjQqNLUX9kKZbBkL0RrwGgkhDnU6gNZR+hsn+JNClk6BpZuhY7KQ1lk1/vaykNVRyJmH61mrvFjpXwwATaZN1XmaqtK2TScPeHEFOWBOTzlmhpCQCpdF+vBuqIg/Arx1YE0tBjig7VsowQEXl1hZ9J6ZPqeWGtYVbGriGUBymCgWZnS+qKOTxAtDnCUCttfmIHbvRdkXw6k2hB0lKgmLJOjZdDh4uRarHDMTaHPnsCAAasWoPioBQL8ou28qaMB3DWjVNYivS887I8LNRVL8PygeacTFX1cyS2LVqQNceVqClg4kAWytVWxB0r74uMLEfA2FM6FcOjVbYwRtwawAmsPbdCYq4RkjbKiXsjQHaEtHL5x+vSPgG+4xyEWF6BgVGt2vFxbQBIluxxSmsSKTX0k4wXuSZ1JvsBiptXZMN5TZbG+x00aiZGOt/w1M2UgveYh1Etm2vFygPKHM1QLKH9VNG0DEHXbLL3yzUvMkeSdB3H4pumU7uSywYkSjvz+wEVXAHFj2ViFOv762seQOAiiBDCVclktszgGznnhCiwASf2pMETa5tNuUiJtil3Fu7Ftt007N8trTfWiBUFn4ie69tZc8r5/Kc3DLCrQPIR/hpAZBDGEk7xtcOnXJlqB8OfyZd07XsEUG9elLbma16LFnIM+lFCWmCKG/CSBMPx4NzsB1QDBggjaFAb+CbQhKYzhzxj8kO4EVKZKAtd84TnOXZSeLF/eA6nEzuUjfAjyj/jKTUIS0qbEN5ncZIcjaSui77jXM1mUy+xDi+yQdhqJR7ZHfSdMNtp+6AIVXuKsMNJNa6BkdQRruWQzZZqqc/FLQCDzJUjo1aT1fSA0sKNNMB9bUeiztL9BMdk60bccpi5XMNGxT1GFjeE/m+bEWr8CSTQYgFQhXFE6SlbNMLhhzAbSGHwI0KxQ0xDCrm0ORJric6wuocoZ8OFag3goT0pKPOW/EJcEpsMr2gHa6Ve9fODNYnDUAuZ/6cCGW9wpWBpGJrDExErnjSc4rQnmjKoBIUrFIt28i12x7gVg2C9N753ophllY0pjHAWsiBvmBBd5L2vR8Qhq51OuJa0QgdWOd7MfaSwVgGXat9ccNoGznguLVCnO0IrhOkzO/QwDJXMx7qDNxSfqcfO8SqB1mWxEMdG6H93WQxH3uDdVMIl06rd1JeSwhGq3rhK1DaLHpSbxh94Xv1mNCR8xSE+IZ49GWnKAZH5aG8C8WK0ezJeoglsg26UYJYsBZ95bKCynuLxrnsh5JMjVLileys5YdI+wG9VAbJ5p9bylW+0UQz2Y1zqvaS0VMviUAxF4QRDHRIhyOBvBgv+VlAUJJmai0m07S+0ynQrSQQsZNpxMVC/GwSkdU2ss7HVwPK/R6xMDh4oJQiYilzSmIhLRm373Swl8ttk5R4RD0U3FruRXUDKA8Y9X6AWwa4pYfbX4H258O+87fuxPJcBRChZ+gLJYPt3FJVSak4aggxlOjSVNgse41HeAM59L060nJOJnusHBsre159jVFflz2umxqEmvTeiMlVdFYVhAqzR0Fw7JoG2ajej2Ihe06oxI4cY87tivR9xZLAziKUSijXKcZpVhIn1zaL3OKKyhHzywIN10LkPDAYrRQ1VRQ7VBDlS8kwLVA/S5hcjigWAaaLOuI9wHQB1HnAGKzunuLgPifuyppkFYuI+tIStFgDRHCHJZh2wOSUOOkAcpK86zvNVv6tFLpREIRWJNnFQp5nKBSdT22ohZHErwP6KTZM5QZkiC3DNoTyEKj2I5pTJgsmeK0mZiv9HWrZn9AGQM8oD8S1mEqulVcYO5vN/Li3gyIUyCToNOU8Ow0nfhMUfQ+SULY7G8qmHoPpJ4Y1uHnmvlgc2+TDNoDb/HSadaabg+RZACCOAT/eyBr1QdgeebFQGhnsxVbdeM7Opkn/7UsS3gbkYN/8vTkTjJromCFThFb+2Dhr9Z9kqDxlxLHkI46p5SHBe8nOE9phO6mgTA80Zy26qgR7QnXV4uTnI0ZXO/ixRT+RbNz24mGS3fyYhBvjWKymN9QmphVWtm1E/93tiCmNr4UFz+OgsJ1IPrtVIQeqws+mjGAn/Bj0aqK20APIyEUzaaW9dAiNEKSKQ4PyUHqL/UR9EwohS4EgGX4o8tRdk4inBYOLCDPyKCpRMxhiNG2Bbr8CFC2RaZeS9QkhlsBFzK2JzIkgyEGZnmtyQ4UBLcWnpSGRbgqhUip4tyCUh4RuJrByjCaTHIMTFUIggFmcGE1nBH1m+d6iCFlXHwPlWQ2+V5KoJi1uLs8nJRHp+RZLxvq0yffXBmDyDDC9FEAe6GYGUUnS3Q5hfq9BP4uII0l60hA34zFU0J30peW++jyMLUQS/kwaXFVEcCXTOu2K8oBA08umWs6l2mRD8GN5L4wXMlx9EODWEd2OxeqsyZu/bdIal+SXIoOtTnPu5PP5MWeJbB5Apq8kB4JXy3A4hbYbi+qGqAGiS/4O8t+2Abqp+DvYjo+iqb0QlY36dIyuAOMrAaEktLsiJfYKkhEjqwPq61FQi9oglIR+x8HtlihOTmDnDajpUV5bYXK5wJKM7DG63IsDkeezER8S4wl+sz+s7xs02c+OsPqOm9YglGJiZQolN7ZWrMidJLl13cO7iPU5wv5Upqra1oh0diSKlOoaoTxgdLsikZXZVrL+pl8JmD1+gPVdO1idcYilHKDjZz0oqIkdDPpIiBUyOpNMBikQsJY2SfKVAJDHvAOQCjzZuzcWdmFQLAzKffn9O19aADGC2gAKAe2FGQ7uE1tmGoufB2urfXGHy8ir6YFqzhhfblFcWaCYe9Q35DmBZW3sv6IAXrEHCrsolsDkco/xU4cgnqGfFpIER2B0mVEuI6IFfC3JfDcjdMHJ/h8hiONC9jczouz+69ZAuT/43YDVCVXt3cslw62iJiSMYu5BIaKfjIdzxxPc0qA4JFQ3GOOrYgzXTQy6mRq5kXyz7KsWQJGTrDQrJqgPVqzlDBJ3YfmMIrpI592wT6T2GnnkxJeSi21AVtyw/q5Y3QZtF9sBrmW4tRzw3Q7BjwU6TmzbRIRKLHTwACuzkZudKgtAEopiLRuekHQMiOXhy2Q+wK6j9Nz6iGJJqK+JiUux4IyOGE8o55x7iwMaI3MC0gFRLCPYElZnLPqpwlYFZUtz24rzaT8xWJ216HfSy4+BZBsgxloWaM96XN4xKOYV3Cp3ZDLZs58gDz0yAWIxXwdY9SToGwesZBMsDxm+J/RToB8pKctxJnCOqg7jssfotJg3XT7YEUJk0ohbHfUdGP1UjGfS/ItstOQF6TGdoDzRAajkRfETcSvkakNqZwC2QSDM5I4JgGvpBcdg0OohFINFcu70c+17OWllsJcNO7YWLQA3DdgZt1gaRrMsc29ce2VaVQocurybsy8DgDyrhwKh22F0pwKoiBkZsOq8ilINnRgAJUWB/OxwWMrfl8OQP46UoVS2DDMV/kc/NvA7Du3KZGTDNgbVNUlCyMsmV10HZl/uMf6LG6CmRdyZIOxU6E6W6CdOJenIXIxYR1CQwwcsv68ofXbP7bpBAu69EeVGIga2diP550wyhGH4qSQciTOUyG/spE3UnJGqOpaceVrJpKqfEMo5odqPyqXSlz+BWqUgXBwIAUP7DCweH7GTloo7tOhnAVwHNGdl1lCaQ5OeX7erXjRBUENSw6eYFWpaqbIQ/sZXOizvqJD764omJkVFc5axuBfgiZfP2xm4fYvywMK2Bcr5COWcMb7UYfaFObqdGfoJgVSREEaM1blhow6lSH+5iiD16GAvlvlpLonxopZgEhSJlKwdtbjILrNWh5lBJsl2I4/QSjIdTIL7Aa4CQk0II5Usn9CR643M3QmVRXPiRN6Doxsgf7dK/RiXVRKhlkQqSUWr64T6SoLc9JlaeffDSOT1XEgSnNRrsQC6vYhuBnQzi/ndO9m+e3JZHM1cy5p0DORnikB1GDUB11byMsC0AbEu4UdWEocCmVgqhydne4Tl+QKLCyeR1SjaGvH3De1B0tZJ1GIPnHiGUpDaTlw/xRlWkl4TgFAQTJCCkjrOLer5XQbsDNwC2P1yj+LqAjAGthuLqVdpclFnO/mzPmngx5RRlFxYBkGlhLcX0Zww6HdE9RNGA/JOHnLymyFRIy/S3ZRcsRl8R5LMup/KuknTmt1KJb992rsx0A5eQhzb5KOfAFwSujBcULr5GWrSRZMhH10EYMqDeaJTMqVyG4QRLRWiW0cUhx3sqgd8zC8IALC1CKOJcDTGBD8i9JMKxYoRCsn0+7HwS7JP/5pgekEwygNGeRgw+cxFTL0HrAXPJgizWnXTUlXHwqDdEzll3Hga0aWKjdB5IyTPVghjshGqeqEA+pPqL6ByrYTmhJVBMBY+6CGrm5PxMv/l9B8foD09wup8gXZm4CcG/Y5Fc9ohnFR1hsI4zqkrZhD5X+Y3KBokL6I+k41hgKaFHDDXWCaDslTm3QwCGSdSmhLVqDdwc4PRZYHkQYR+UspAKJXLgQAqAMyC9M4bqQqAYXaM8dKW63cs5nsOc7UZp7VFuS/fn3gR4ETKVPi3oGxJbBTujRZoTyL3TrtY5Ip0QC6U/GxUvl0zinEv0kAW9YvvnBAqAw3ukmUcBqgRwEVEHEGSKJbfXSwI5VzaKoBsxs1JCz85hWIRUV1eoXjqWRRfiihvnEN9YwRfE7qZkXdJNymjBku+saDKZ6WEMZynXXady1M8WWFWMPKYA0n2B8lyLBkeJLMl1IOhWMohb3SQYK9jD0RZQHlTZoN8YIRy2PAF0pW2QmwtokpqB96PQTHqEYqIfiwKFkpJ6wbJMxcoVtZEsQDqq7KGhCRuxX6bOE/2lEGQNCRDrJVeag84hh9H8CjCjkRVEGoDTwXYOnErdYRQEnxVgU2FbiYGgyJDZ8AMNtvZul55G+zdsKdVEdEIIlYeEiZfkc/QzeWzi4xXpLwJ+aVe5Mk+CHk08aPAg3EauyiJYMFoznpJlHXvjKOIzokTqAyvlOKrOmCUhxGmiwiVFDGukdO/m0mLjzzgtOArFoy9JzrUTx+Aeg+EALQdwt1nsbx7jMUFi+YU0O1G4Vnp2k+oVFwTeD208pgIhMG7Axt/bM+Y/fFFIET0d53C8u4RDu4vECqR5bsVY/alDuV+i9WdY/TjYVZMKvTE0Vp5gzXQq5Ox0Va6Hw9njnAjNGF0LITSXhCj0bWA8RXG6qxDu6utcCOtUT9WvxQeEPGEMkh7z4CJUM4jirkdilXoQDqSwhcYCu/EcQH0XQuEYkWo9xWhVlGAaTX5yuiQcBndSqSybhVhmwC2hG5mc/LSzaAFTJLwSyHJJMqjzPcIQO1fOunj2CYfpgdYKygAslkokdI1yBUWgKHfmKFUHianQmVIDtKmgNwk10S4RYBpPfq9OrOp7TrA3VjD9B3Ij3VzBOCg0CrlxIBYLL5TO0Y+jG7wlnBwf4HVubuR2j7lUqzWTRsQK4v12RLXX2PQnorgwsOuBda2zTAJFwyZmBuB+prB3p9LT7OdGXR7igZZAJHU8haor0W0ewLDszVZ/mla+dnFXNpPV96ym/Xig6GbmPXwYoJ5HbE8nhIDPAAASNtJREFU3aIoAjqthK0T86wYCb4Um+QYCmBFanMPdCfkZ9kWSnQT6ViogHIBjK4x/Fh3Sn1m6ffbhjC+JBtptzto9W0nh0Z1yOhHhMU9BL8DRcEI1Q3CyT/tUV1ewXQe6HrAWey/8RQWFwp54SGfafQsY/eJJYq/vAFhSDJgDTL7s2llExhVgDEIJyc4fGAMkIFtLPodky38bUMYPQuMno0gBlZnCe0JseWPAFwRMNGJsau2RNAELsHobkGgazYTp6MTy+aoh0li7Ucn92N5dxwsj3VuUHlosWvHmFy8Cg4RxV/ewO6VQ5APADO4aUGTMZr7T+Hg/lLaI3OHxmupX2zA4EBeLxT1dSoUtYBwKcoD4Ssk++puRxA3KI/JdmL0VB3oQTkhJRlLkje9KDhwLEgh64B+6rA+5bKrZX0VmD5FOLyf0J1hmKpXvoDwBsrSiyrKBTQsEnZSIqRdk/j4qOvk5HLA+KmlrAsfgBAQTk5R3TlGcy21q0hbqhHTZ1oUVxegMEWxKtDOrOwh6iERRgAODUJHCJ4QnHCkqDOSrGYSvMx2ObzHyDMtpYVIfiDrJWVcdvokwEx7SWi8RWytcFz0HW1OUZ4xkxJn20hy6kfi9AyWZ5j8VKyL8CNx+WVVCnIdB4mrJs8JcUy8M9NK0Ta+EjG+1KG8eAg4i/b8FM1Jh3bXoN0j8TgyAwKb1HOuAbo9h9W501ifNcPANkbmyLFluBWBFpSh/1TI7TzNOPXxK0AIoLYHmNHfcxrrM+OsMkzocz8mXPmHd+ZDuJ/IoSncPCkQl3dVIF8BRvaB4lD2k2o/oFh49DsOTWfR7RBaI9PCqZfryHYNkCS529NZLI20UchDioM5o362hXt2jnJ/B+2pSgq7WkUKdgO990KSLQ8YO0/3GP/5s7L37O3khIuCEKTdSsZ27P5Fi+KgFUfuQhAr6rwaYzIQImitshpmzMY1YA268ztYXCjRnBLUhEmua/pMwN4nLsne18k95nENd24GNrUUsaUk02DkIlBQQ4A7QrGWd11Myp7vNH/+OL7JR4B6+A+HYiJA+hGO9KiypI822hWeMhkV6XtD4lQIxBRqi/bMGOQjyDNs42EXLcyqATsL1wQUS3dE6stG+VH6AuXKuREYioIsSj8V2HmdW0JyWLilTIhsTwD9RKyBs0trhrbl35qeYdTpMo3fXp82guAAedJpSlTkgAKiMxhfiWIvPTP5frlGiGfjK17QlskwDydUQBxF1aZDNtPWwD0xQrsXswIkEgOFTNgFkM2qyMvmHHYAX8vnk0RHEA8ZyCVcm2IRMLpCKpnWf1fJAWbUFrzdlaF5yV4dEVidT86kQCyiqF30pvkaOLjfYbQzxfTpNdxleYY7f7HE9CmDWFrxcCH5+VwYhBM7MG2POKnQnK7BllAsJTkUq06GXXWw8xazJxj19VoUJwSYllEe9jCtB/UB7ZkxDu8t0e0oJK7Vd132cDbAEqNVmalIX6TfWiwoI15SFQkkHUu5PrM0eU5FPwXCRA6N4AxgjA7zkxagwCYRvG5AROC+B5UliAhYN6guLTCe7oKikDJNT5heFM5IqGUy5voMoT0ZEevBIZgNi5FcJ9wEmRbNGF0Xd8hoCWEkGywbIZDWV3uUl+egdQse1+jPTLA+XaKbGizPF1ltViwDTBNgqkGTK60mxuh6wOLuodVn3eBGSwAMMTaoIGDLuTUbeklK+wnJAXhmhp2ne4y+tA9UJZpzI6xPGYRKZlTMvtyivHgoFXoryVHZ9nCHIxSnx2j3HPqJEamh/j7bCnLJmqXlPrgmX+UiopuawZK+U0OxLs1K0c9aDnsWj2R0gY8uS0lNI7NLZH+RA0AszDWh0aIsoW8JpvfeCBm6cUIO1hYcZj3KyuchfBxEusxsBrJ86ud7aR9XX7gEXq2BskB9uIR54Az8qM6HjahwoLNaGN4Q1k4q6CTVzWioeqGI3FWRFVWfGJ/2Nkb9bANaroHCQWYSSBEn7W8aEBBIsedHup+NtLVTAdkanZANxlKLKBQEP5E1Ul83KA88TnxuCb9T4cZravgRwbaM8ZWI+moHigw/cQiVQT8xYmeeEkeWdotroxBdmw7FV67DXXGY+oD5G89jfpfLiAd4kNOKh0+Ua7QWXLrhGXgAoOxqahspKMx8DawbvfmCEKMsAGOkkAoR8B40X4JjRGkMxpbgWmmTSREfMf7KGrxag5wDe9nTadWg/EqAbXYATNFPCb7UtTrWfZcEsUzyZK9ojju8DTgf0QFWk4nsFcADsTO5BwKSpEAZoRSHqiJnqyrnykzczfujB6LpZQPhwoIrScuL62uMSwNfF+h3KENcUOTUNQCpLW3S9Pda4UeFuZP3iKAj0qeLFdCeCuCJkDs5QszHWGcTbCQxpJsoW6A7weh25R5YRRqS0iTUnA898oTmpFRL/URbQwC4SBmcQ7FmkRwqWSkRO6lSRUEwYBvQFpyHWkGd9uS2iRzQlAFhh9A3hRySaTNSZMpPBqOaBC83J60cpKrCoKh9USMsdj/RNVAy4lgrtIiNB6gVwTJZDcu98CPC4i6L5fkpbDvRnyEV4WaVlOF0jHICGQtBMsq5VrFpvdAkQ6IUNfN3CWYscj8+k48B5RMAaA2W60qcZdWLI6wczEpaP/VVbZvtcIZxYyFQOyyDNakcDi89oCyDXUQYQ4jPpUE/LbA8f7/AtUtpw9iLV4eNyVrAB1T7vRxMlSSxo0trgBl+p0QoS/Rj2ZDjSH4HWOy1BWmRatjoukufbXXOYX1aKmDxw5GNqTl9UtDCMeUeddBJ1eST7NqiOnDZbyCtn26X0M0c+imDqpiHpyU1Dkh9a5KBFil/oGREw0iTY1N0M4Ifl2hPnBay5J5Bc4rgx4z1OWB9rkZ1vRLyrFclnBfkwo8M+rFBtzNMi46ltjoSZ0T9HpK7qR8BVpPrVCSkd7VYSnIlE7iRlW+mJzS1ERKpi4AlRMOIxqJDahcPcv5kpsWF8FzsShJatgZhZDUX1ZarISHmqlLMk5Ut00ZwtHnydUaYXbIZALodg+Ub7wQY6KdC2vQjUssB3VISamGH1lQ3032Fhr1DVBtyvWkNJUSp21W7ACXpT3Ys6t0S7Ay6HXkG0mKizDti0v10Tz97mVoiyFwnQFCp9Huzn4Vmkf1YVIdsHMoDi/IrN7BnT+L6ayppEe9YmHtH2soXkmi3I201t2ZM/7JHuS9oBIxBv1vh8JV3oR9JAVxpgi4KS0EREk8xFdYyMZ0BZlDTw60j6hsG5XzYX5qTBt1sBNON4JodQfc8a0LDcKuAYt7B3FiAmMEnd9GfHANW5LHlQSeT233MCCD1ah1eOGA2QX92iuak7AOxkOebxnCk0SbJaDDdv4RCGc9wq5dO+ji2yUeCfVPQRuKRZLjJTTNvaCpRTTcmucVttkVSlmqbiGLuYVcdzLoHiBB2KsTKga0BRUaoHfxE+25JZQOdbbBO8wOQnf58Jd8n/XH5TMEPjpPpQItWNkpbe5k7kmamGO2nVdCLRCbyyHAn+TkxJ1KUkR7TyyGc4G0/0l7wmAWKddr6KJNrISP78LMezL3JG3v+0GqxC/1IZeUxrmVXalGImqYKiLVMfGVB5+XHOtZqeMjyASE19ZNNdr1+f5L06SaeOTCGh5kQkfIAuYT2hJp1Q+LhZ+QkSA3MlFRqgr4sTqvGOoqUsbWwc5sPl3S9oZQkyHg5OMRFUJIhtqxGYBsv5uZETwOx0TdxsL73OvnUD9wlgTN5QJ3UGREwuaKMhSalvXhQoGBwIYqMWMmwMD8muJWFbSzqEzuYTgp5lI6Uw0DwY4tQSsXva0J4YCLPYyowdb/Dkqy6KEoTJcaiVdKo0UpnLFWtW4lss58Jn4E8AUSaMBuBvkfCSQraM86yxBZZ3i6LEJmv0U/03+lQvCxJJs6PN5F+M/xR6n1LfBrNZGRSqYwDj05aVUmm2O/IAdWdJLR7MijMdtoy0V66qAQo87IGJJb1YGcx/esGOXIstGXIaT+Sv88GYaRFSjnsS2kiKZHwbwAgkIWPJH4lpDLXVlAwjAHYYd1lb4tSiONipW0RMldH73EvJGerk5zh9X0nQdzE9E2eIxuCnxisTxmYnmXY5mgoCHmDwJoSeNvqPSoYcSyKnLTVxN6gLw1iYTKh0aocOxbyzNM05H5m4FYVbKuFTPq9aSvQ39sX8nOSkV4mRG8Um1zEPAE2eUWZND9Lf56cL4zmvlNYnynE/0n5HYGTakQk081J5T2YgRvEhUU/KzG/s8DyLhlqaDxQX3UoDxnFguFWLInwaFgPoQbm95ToZxdg17LXhtLkdZeCLdAlVScGB++0f9vOwnQlitUkP5t+KgpO24qqTOzbld/RRpgQYRcdYunQzwo0pxyaEzLHJdrh9+Z9Naj0WmmLYSRrx7aE6Aby8kuJY5t8FCu5WcBR5CO5IRIDpMQmsVtG9sVPyQcldvEGQcn2jHIRUB70ME0PClIZspWNiZ0BWSNyp90CzZ7JnvnZ6McCUF4HmHMlkPgpSY4b3dHEA9CqWdsoonjQsiGS6q0527Ynm+b0e/NGpl9L15V9AjqgWMrnkZ6wOEiKcyoDBQ8ktVLY167R0z5tDhFSJaVbXiA7D6aI0cAp/A2onXnQqtxsysqQf27Kkn2VoLohuSAMCRRpde2njMiQxW6EQJedR3UeDyyLTfJIf8BzET+SJAMRMN2GesbJIQgnE3qhvyeRE6EcUhjZQGEZEQRO1XUttt7Gis4/Vd4gyGh2L8RelMOmK/MZdD0b3WRr3QALiNw3JXnJgjwl2n6AmNN9NEUQBZCV5CAGkmKmlvXVzQzavZFs6pa05SU/MxZSKUZF6GIFhDocMVcDyXNPDo3RGRgmoABCwcIVDkA/I5XZQg8wOYT8mNTbA8qeH9aCCcIlGF2NqPZloGO757A+LestHeziGyHKIBnsGGEV/QAAOGkt5EWU5syogRJFNXhqhjUo00vVLM8JUoJSrOV9IHj1wCEPuVYtBqRtgHzIylRpZJ5GSriztc1G8pzUMtHpvXDDPQs19OBlGQmvw/OS8igS53YKBRpQJ1Xz5ISN088TNKYoZVJvy0CAg9jup2QJmbzrfSq99Vr050YYRE/wetJLG1gOPlk3g9W8kBh5WLMsvkmmITBZcCrAtIjgOsA7beWpCicNbAtjGf0OApqaYdbCvYDeo9Rqzwgr657pZI1TUOMzVdLkPcEAsdahd5ooCk9FUNNyISrI5swIi7scmlOUJ/cCA3E5FoAH5STUT4D53Q6rMw4miBihOUVoT0TEcRQiqldiZhyQCkATYCsJRrsHNCcdiiULX6iTRK+bIfNospBAW1hpdllq3cRSzpA1UZZFJ1QqLaqUvLm1FYPJllEfSAbcj4ScngtXJQFLx4GG9V0o2TwCbCm3+URhunFQvEgc3+RjATg3+HBEq/yAjcFgAAArmvuEhiQ/j9RyYSdJSjIUsx3DLQNMI45YYVwglsJGTKx7UsZugkWBDdQFyoSeyMOw6vCZ/D5yNt9pXzExm5MPSNqkOkJsLXqjRjCNyY6KORJiw7rRpCFHaqFsgh4e6h1iRkDvFZJMFVoaPsc40idlp/yXtSAgsdJKzXCWmQIAFTGP2QYgzoTB5goUqnxJKppYAByHhCInbUY4DbEYnE3zhqUndG6ZsSZoPmV74k1AJQ8bWCHSUbAe3DRc4/DhkRMWkZrqBpy+BgymaS1lAmBOQIzKqVV5wVaqag5iEW0sI1IEvNpRD7dNUBE7tAtsPjAjwoQQgn5WFrknjYLcex1jTmqnnPrjAwGZACscCAAg5SOxSd4zang3BvpdTVZY7nPyiACA5mxEnGhVajmPh2eW6hS9EQmkixl9g6pB0v0Te2fW0eXC9E/crGRaBGwkTJ180fSU21jFYQ+77hHqsSjMlMOAdqiwoKZnAPL9NARYE9AZmSlDaqwFTwMSwuoXtBo2zgwXJ2hek9qsMnIGyfcnQt6dRDDfdBBmK+su2YJHALGSgVx5Cm5qDVhF6KYypCvUwuFItuoM/R5tt4nDJ8TsTQsTaALTT1UZpIZtGc1gvey0BrU9RQRJanszrGG7kcDlnp4mmxRFydbrz9RCLyO/ivIBKalCVjgldCeRsV2TEiWbC4FYq6+OTo5mq7Jzvb4hWZAkJQab1U8m6DPBkHyQJ1g7vLe5dVkyuBJZPCtamkm9TEeS4ZS8dTsGzZ5Btwv0u4oYQ/fJRtDDJHpIa7OfSnuJAuBWgpqFaihauIhyVkTdQwxp60j3CG0nJ2Q/cf2OoDZGktZ8FmoSFiGS40TwpaiF8EQTZNbWPA+cOnmOjDAWfxC3JJAaBcq8FqETpLUxqAchBXLas9WXiJIBaBiS1Jcaxzb5IBY56SZ9NpviqdrF1yKZTSSiQZIFQKXjCcYXV0152H5kQVzCLnuYLiCWVjw/gjjj2VaNmboItzKiTR8PyEZSJAipS3/nBtQnBD6xL5asXolg6QVJyoJW3E2pMygOh7kLyc1Psll5kTAK4g9RGoTeSF83eWbsyOEbS1lQpiXEfLCnl1orqDhcA6wsln4mJFFOdr964JAVky7nIrrWwtiQB7JZEzFvpA9EViB10+tmBWRuhbQTCH2Uw9bqwK9QKZmUkBnuodSXlpOsk4AoJmXsUi+cEaMmW2leTGuH1lpE/pnRsYysjyTEXmLYlSRKsWTEkZaQ2jpJuvcjzyh5XWRXQIAaAy4NyHm4IsJUqrzQqaNpeBoZGY9+cryWgV1RpY+FIiY6qhylGkWRmMLl0duessxPNk5kcjLrQk8SXehmSD1ldQynVyJXWoLgpIRKEBaSEQTR5EM4zdwgTbSSEVuMQ8sIZiORjoOEVXwaNJHV944TGqAFApNUjOvTBqEcoZxXwmfQlikMcuWa1m5GCTciMrJpHGzUzX1jc/RCkAUJAVGUaLIhZJOxKOuXDIPJ5vdUJgnTRvKPAWnQf5f3KssgjjKaQfco6NLaRLDYRUmcPak3Buf3JCVoCfHICYiiarGK8Ckh9ZS5HqkdybSRvHcyDbnTL5DV1mPytok82LZDUbSsfCFEivIZK20jMWRtMImVgRLA8/3T9Z6l1MrnCOmwS66a6d/1BNTa2guixhvcrAmhl6m4xOLm65aSyEiRtyGBhtxXM6dsuRALbaeWEVRGuNIjblgGyHsQRbYbpM3az4AwMsOcrlKqfq6lGozO5EIBhazPNLQuFWpptgwUdQAByeo8egKvTW7FhWpoD7PRBLnR/1X+oO0YppMkLjl5s7Y6DBE4CvJRLISsmygA0TGaKBuZCRCOlnYFgidNWinvlcWCUd+IcKugXBR9lk48hzr18QmVtqxTKzOJN1pJcMDqEbLpV/EicWyTD9MxihgRShJfDTdUEWmTsi0PUqCNaxb+h242yd2tgDwQAmxgRGtgiWCWLQof4UhgO/gIajvAGBSlRT+1krnScDCl0eLR6uaXXsT0+xO/pBig48SEB4Zig7Q37ZYmSxfTQy3nsgi7XTUO6gxQSaVl6oBYG/i5kwMiVc3Worcmo/c5u4+AWFZTVk6AJVnpp6p2qSPSsCSBriF9aS9W5WlgWoxBhkMx5eScI4EcI4yQ5xik6YehArxyGcTKeeOgT7C0olSk5waXnGfjcSnVINxROShYyGOFytsmFwNsE9DPHNYn1flPOThChrLIwwHXieBGyinY2Pz1sMy/xg6HoFybkFwDQyzSmUF6YuXJoikhIOTx6ESMwga0Jg4vKEGNoowoKEiSpSSJNj1li/5izkP70RuElUOy3KZAMDpXwq7SjAupRmwLdcAVnk1aEyYQwtLJua1E4OSjwmn8eCL7JsWVJnZftb8o/u7HG0TAhB7R8P9ZDcoSTBsaQijkQAGLi6tbmswtCVagea5ELmqIcwKXx84HgimHAXsBVtZQqsRGSQGjKKpOqk7VrF3avNbMWirA+qps5raV+9btkHo0kJp7AdXKIKwIfmoRdMKp2Rj8RszqarxxvzhlFJoUJrpKKpB6ApIra+LaHLnPw/ss7rsYCAtJHq1cF2PV4dRb+M5KAplQkoSUeMotLbIB0RvZZxTJlD124JWRB5wm/KkFEUZiXJYGCcJIOzFV6GEcgZEQ6wHIumXSZC+hLjzsTR5wJARQ8RcBJpcCimVEPzFoZ0bUF2NkgnfymIiVmB0ioVnqfmxMABeEGC2S8R8IiKOo77AkBZsKn7Q3JwQ0FvLvTObsyPPLSCvL800tcYR0jXoO6PllO0ke8zthEqqATKDNZndrebcoqtKlY/hahAR+JIW3WwHVXGZykZcEGAAoyPfZVuTEgLS8/ZgGheaKMboWUd3oUV5ZiERXCag8ruFPTtDtleh3LBYXjNwDbduIZ46gnXbNmQRN/XMX7QvHsU0+XMOwhRiByRRZ2dh8LfAXMWdXxG6GXDm5Rg7tNEApzZKgdADHwfufCQjTCuwM7LIDmMGVBVydT6BN+DQlFaJyoSyNzCZjHR2BJKlX2DNXWQrHOs4QK0iGipE32SPB9FodjhRybzRLVyfDUMaNKg7ZNVNmohBsZzJ8zQQxJ2t1ouNaFns5l00ZIOV92CwTNEHY3OtzgzcBpO0K7yx8kM2/Kjy63sHbCNOIz4JVCDC3ecLQ83cLRRH0sE92vUJG1R2RSZKRSOqmOSQerJs3K4QpiUOafxLQzRwWd1g0Z9RG2A2JRXnDYPoVRnUQ0O4m8yQaiKfq6GdahVjtsIEKyiAbkluLxbpUqMN6ZQZimjypdtdgoHQBhQlog4xJN+kACJSt5CkCaJ2obQ6U+1MzfM1wINhDYHIlYH6nA1tBSGht4eYGbjV4wsgmJc925+kO1Zevg0IEW4O4N0FzdiTJtFqg2xbYeaoF+YjmbIXVGVGAdBsVHRVRoHW2gNqq5/ahonx2nSa5Mry6XYKUFxApH/ZhpOiBvkeZR0FAsWbwnFEeKirgADZCjGSdkMoF4EzERgcUzkXxw9CMnpPxWyL+RlGdFAvK/8itxBpd3i+CXzkhwjZD/3/y9BpcGrR7BaAHoZhPie/C5IqHaRntnkU/HojKgpQkOF0/goMk94AOXEIWj6U9Kil1irpHWcqUZh8NmjUhGqvEUk2YGoCige9IzL422qSpCIuBdDotDS0pNcCjShABIghS11mEeQHqjXgN6fuYPC6Sc20sZG3aVvZeXxNaS/Ajzq2W7C6bWnVGkCFj9MQeybTV4A04Wp0TImukPBArANdEVfaIHHR0qYFZdHB7NWxXYgWLMJIEIg1as1G5KMrnIuW0pGGYRlsgAKSQSrwqTTxtL+vCdjL+wZUAaECy0t5mm6E1HDrKfjxG284UIKcqsSRyQUnouhyL+YAQh5FIVNPvML1wHculDiYcU0aLR9cCRk/eAHX98KwXK1BVgnen8HsjdLMC/dQIwT+InYRbiet2MQ8oDloRV6hsGb3P/incdmCOIOeAogB1Pdyzh7DrEWAmkP4uFMFi+N0IPyOsezHAq6+Jn5Nb4iXH8U0+VgFl7GC8ZHTUByAwuHZoT9XopzY/tPJihK/EZyA6yfAcpJIq52K5azzDriOKwxZm2QIhgstCjFoMgUun/V4lS/WSqSfSqlsL0iG2vaJ9ty3Dj0gGNVVyaJsucUKkamAlb6bEhfQQYyO7RIZpAR3UBNgSkJkinDcw0xLqK0bmYXRG5gvsELo9Rl8WMr4+QjaPVn536DZ4LHFAbQT6G8iwue0bgOogYny5h20jVudLcSE8S+L2pz3Qzjsd+03wvfRzwyzAzC1MkKSMlNyYoLk0HLC+wfC1bOKsJM1kXQxNjqYXI9oZYXGXQWsg01JJ0adk0W1YiZPCHj94ZambeMx8kqRyYQBNGdCd0CFahThUxnEQ2FpNv9CoCdsqJUPSbut2OBMD84YVSaf7GrStHW6k1z+lcCna3qEvLQyxTN1Ndu5eDgLTKrS8FgjV9NLXB2nyRXJoz+9S7b8n8IiBHS8S1pWFW2jLTnvCxITV2QKrc+dEi2/l2bq1MNyjM/AjQrtLWNxZH2kZpoQtwfOCasWcUEhvm/KwO9tLRd3NVNXkJamxuvZSAkwKz0cnSXpq04yuedhGHDOTPD3U8qLYVg68GAYEzhKLvwcTnA2IUbhTZu7EkyK1LnXTLg+kwqsPBEWNTtZ4sfAIlUWxsGh3ZW2l6tJ4Rnu6QnnYY/zMEmbVgRotTkYV4qRCv1uBImP8Jweg5RpxZ4Lu7ATtCQe2BNtFnWNksLzDyqGcUASvqCHJvaAgSB+IURQBo+QNEyxCmfw4ChRzmXlCiujaThVLI85orCh0DPiUqqxSZZ44B0ooTu0d68Sm3u07jC8RJhcjJhdbuGtrwBL6EyPM767Q7hG6PW03Q3k0yj1JXhogRqz1MF+TjlkwCNGhL9I7AoQi5raYzMmRd842rAWmVO2mB9pdi4N7xZ0s2YaXS2nvJJuAhNqJ4id9FmTXXgI02ZFETHggAjOw4zwklKIkGPW15CMixaTpRGkmZFBF2w8AcQKVPTjtrZZ1/a9l+KHVPQX6bKoDxuiqR7QiLfaVrIliFVFf71FcX4PWHfpzM/QTC1+Jh9H8LofV2TOoDgKqax2KK3PQdAx/bhftiQrtnqzjMBKxQWozujVh1ThQcKAklyToeyqIcXWtRfHMNfB6LV4fzEAH0HiE5uwYi/NOZod5kjERRg8y9RYSoinglsgI8ks641/6t97cCKWBd8WRv4tuGGUPShavgN8zmRiVNsneib+A1erS9gyaWvixRXlQwC46UB9g2h7o5IaTM2LwomVtLI1siko2k82ZML8H2RHVtGJ77dbCdO5mSZrHA+kzVfqJvKhGQBSghk5R3S5FgphVF07hwYpBZ1u0xFgelnCHFuX+AB2HNSFYEjJjxXk4Xqg5VyUJ+nULGbveTxRJGiefCWkNtScN1mcr2IaxPkPo9oS1jTLCFmLyVDqP0gXxr4gELgm+sPBtLVCuYdCGayqA7L0SRiqrcyIDzmS+grKh2PqcGGv5KavHCGUuxybZKZYqC03oiEK7aVItACGdaWUYlw49lHhaytwbsozYWqCTTaNYDYOg/BiKsiETNdkKamWrjQoumKNERyVJhpVDV1ksuxJEjKYrBOa1ERjLdYXKIEwIHQtCBWh1oZ4MpjEwnYGxUhWZlhBrA1b4nquIvoqAJ7EWJ6A9BRw+gEwOS/10t7a5gu9OBlEVMIbDI93nzCPh3NLonckJcyyA3iXoWfhFQQm9CQFMklIYaeGEsSBMpEldGQjVPGD8xHVQ79FfOIH1aYdQy7uTJ506AJVMtE1SW2s4t10WK5WojwJI2wVpcJdR0iQAhIJQHgYYLz3vw3sqrM4T2pPaGgCk/Tk3eViWWzuYvs4JZ3RDGy+USiivxDGTvGz0yTLbNkaVZ5oUtIQwjfIuewLWBlZ79IAku7E3aNZlnhkkk5kdok6j9iOGmepAy5KzyiSp4xGSwijCAvn9FPSVM5eH0x9CliqHacTiHpmMff21NUw3yu3tI+RMfQdCKb8ztcjS7JtcyUDbF0a5Q8m+XVG/JJcHZH/rCgLvyfcl3x27JiHm6lrqZoT2hM0qlNSill+GnKwDyC2XfHboF4hYEMrUeikZZOUHJHNHP07SXlZkRPgV0RJQyxA+P5Zn1+/FbK+OFQ33CxstFI/cHm5nhOX5Ms/3SolAMTeIrkQxdXDLMBSKKm1OKE83dZjf5WD8KHOKNsUQgHJtStYiF7lFFktFhjJ3Mo0DKeH2R6ANrxEwgwuXhR45qUiqw1SEOVYiq7YlT24slBeJY5t8+LFBmNi86DerzvT/Zega9CZoj1S/h4JIwYzXkfGKQESdPsmOYNceZt2LuqX3IDgAXjT1+mCF0JpOPXloxZJEe68LB5AHbALDLABbUEYA0guR0Achj6UDlxH3etgyInQGIRQD+9nLZ7eNZOcmSQwLqRL9GMO4a+jH8zqMjIeFL6x8zhJKPxbCbdnjSN8xtVf8LtCck+qZLUsFb8Q3wBhJPpyNsEb++GDQKezNTqrgNAtEjMZksSZeQnpumRjmZLprlnCZoVUmhm5mkBVq/1Wer3BLpLrWBKsVmNMkcu9I/g05OWRZE5hYsCQLcxl3b3TktG0U7ZiQ2vEn3wtpO5lWYHq7tIjjAFNFkIEkE4m/Ekig9QhA89jMT2A6Qpxkhtisd0MFh0hwHWB6kxO2YilwrchWedgAkjEUI8PXwAahTderAkxy7UkdpUPujkwaTUqLCCHEqhw8KHkjs/z7VHnL54uFJOvkNWFLfI/EISIgDe9LCEgogdVpC/ytUygPPNgKVAwlxDIIYeblvhcBTqcZA7I/Ihr4NKnYcF4j8qzEy8OuBQou1iKvL5YeCAwqra6DtO7l54aSUcRhOmmxknfX10LkjqVUoclgrN8LwsOyDG4N4sKiONQEkiCtPNaDMm1sjHwfkmQ0FTbsREEWg5GJx50Fr52YOqmPR6yQW5lhkhyJFb3TIXRcJl+UCOegAx75CInat07UWAagKgj5cwL43iC0OuG3Q1buZD6aTi82vWQ8csAlYjCUUCytMtYDPnFTqIhyna3Niao4nVJuQWWOGktSEnqCmSNbC6SDM6/bVHAyFK0YEtcQhuSLIO+iqMTUap7EPdZsDI3b3H9Su7C6oWjZUuS43dxgeYdBewLDPQWy4iw9Z64i/CjZGxhgQ+mS7lW+r0nKqwZksTDZN8p2EJ+QpSTJvjZHuIfGS3vKdoxQyYbeN5TFGYIgAiECVAzJhG2GpIJLB5gx+tNj9FNxcU18p1BTtnTIz5Flj4D62kiLGnC3A/JRLCMMxwzdAtAsj2FaBjtxYIvaBxXq+/DvE6xUHQaBdRNS0UeZZNhHSVCS25sxuoFGhZJMrrST1JGCLC4myfRS1e7VabJYipFMfV3aFmwJ/dSiOTH02dMgKFZzo6RySB4J0lrQDYtlE3RLg74oRYUCAE5+f5Zf6mEMIJtvDTdCd7iEGKReJzC87GmDcGmD0M+TBl1FAy5IZ2tIIpTgb2flYO2JEXvKvU8h9zK4U5WQ/o6UrbPKM5PmPn8+KMy5FnJf3qCtvMzpsyVDJ/KipglAbmkBG0mP3rPY28zRECMkhVIXkuR1MzkQ/QggdfXjjV48AB30JlK40NgjI9CNyhejIaE0eJOn3+4vhUUolawFr22u0pMpFYAsg4Yy0bP6A4BreRidHaVNRDptNyl97FpUAckBMlWGxUJIYRSFANz3JAqvtFDK9Is3Eg9N5FjJcYN3hm52a7EQr28ExJKwuMMq9yoR7pC7N1aVAqZP/X25t90uAWSlTbGOSNwsYc/LZ+RKpJIx6kBDlXwnvw9rGT6t8w3Dt8ThkTkzHqO/EP4LAMRpDXOhgPEyWyd2VtYaUnUom2hqwdjeoA9Ap9L7WIoFfp5LxNCCYRhomSzoZc+RxCUZ0CVZulG3WorDK8nqa2NshCsDPBMYUgxFyauASGqwxcNNZhK0bKPCZx48YOA3pLasvydoVkQbZG6SJIVVkurWyE6nYORxALntUfKR/ZmJsxUAgCOfMyuzsiQX6nWjfJsegKo7cjFPonrMyUbQz5OcTI0kREmKStrSZG/ALsBalrUTCdwbePXhkQ+nvyQVa5os20ZQCrCsAauFWjcVFNyPBOGOpbRXsghiA33IXkcExCoiVBuoPSMjZLlV5oFyEVEe9rArj/ZkpTOChMxZLiLGF1u460vAGKELMGdbCAAiBZ6WsG0NPzLKpYoyYDPZwdfK3VG0LJSiarGnZI7Z6nwhTr5usESQ56gFYBTyPAxkcnf63VnqfxsgH9I3DCh7JQklLwhmUBfR75aiZkhWtUDevJKZS5p5YdoAu/bD9FoAsJJgiITTZhIrdEAPBR6qd92QEku5n1IeIy9VJzTjTQe7U+hWPsPoWkClRLp+JH1CdmJjnYZ45RcgPTv9b3ME7oLI1DYRIB6uPRNaEzlW/y6XBhtJR2awJ3g8/RitKmVi62ZCJG6SIRiBc01EYIJP/z9tekEeXjJFIp1NAAwvmrxUw89O6hsTZFNMUxK5l9ZKZv2TVv1a5bu1HLjJYCek+QOV3KAYLbg12YNAeCdKKjyQgVmjZz0osLx0U8nykxGP6ZDn6uRN28hGiRsWYaWeEHVArDSRTKGoRILQAUj/PW4+ZGwgQcitL5u8TrwiDI0oL2yj5kg6kE3uAWU5XWbP94A91P7ytYByvwdFluFtp4WTUMzlvsSxIlsKpXKkbLVNCR7XVkpxSHmUtm1Zyd46mC1wRl6strBsw0fs5zOCtVG12lYOH4rqRKwEVkB5SzDgQomTmvQGVv8LJjgX0FkHThNpW0HEykMZUDh7cg233wCHCzm5jQFVRb63zhC4lUOVvBIsl6zE9QjTRjgTYXoLJpG3J4VDbC2MtyjmpA6WQLEO+d/BEA7vKZBbX62QqU1H+RrFfl3ehdAYRGsRDYOI8juVZPKm20hwWpJDSE30clGhKJjvHZwTBRAHkxPaI0qltD8EI1tKT5kknmSvSf4ZysGReNOnJylcciQicgSKubRN/Fhm/6RpqlzEI5/bdKJaq28wipVkYmn+CDFgu4Dk4RQqUby0extFKZDPgCNusoRMOM33MV13Krr0f0xCYtRCvb4RMb64hrj0Fmj3rLQER6T+TUMb0bbScisP5N+GWpPLlRHkrqNMjDWdgPW2E5mr7WRQJhvAjwxsa/N+bHsGVgTXMKrrXkQRPoD6Vj5674WjEVmMMasS1HaYNIIkCs/EAzEiTmt0uyW6XSdW9YruwsjeYUIEOka5sKCovkpR2pV9Giug6yV6kyX5uYBMiPPGUnixOLbJRywJ1AD2sINpugGNAABLCGMHiiYjBekQM6ld0euG3QYYHwUZ8REUgqzKlPrZdDDGPBmQIcRTsc5VhEPd46KDur8hjxsnfRljCZWBDZJG27BKHzl7jZgki/PS52WtZhK8uenUCij02UifspjLxmW0Ko4WMOVGVdoNEGaqiDkC2bU0JR2eFd6XDafvDcKYECtFfJS0x0YrfwbYG/S9RacISIhiOOaVzLW5seXERqWiyaHPdrLpCAl4eAGiZdWz66THfTm4/EQY4X6qPBVVJZT7hPo6o9qXWSZ+bNBNjc7wUDMgpoxcpR616XTy5PWA0bM9iusy68CtRuh2SzSnLLopqb20bPKxJGmhpJfMA1DWOwgyvKs3QqTTl9K0BtEwuBIZcvKjYE95SBt5mcVheplpE5G0+boZXxNkwTbiAtpP6qOoFaC20pIM9FN1NuzlAK1vBIyfXoAWMpyL3RQgNWLZSOiYIYZivaBdpMRZSodZSiL5aLIaaqDdM6qAEGjYK6fHrRjVYYRbRlBkxFIJ4VmVJoRtt/awKyF/9js2zytKFajIvTf8TPTSI2/4fjCAzsAdWrh1mhkDlMsIt9/ALFY4Ik2iIYFPVvvkpd1l15zl+WDIwC/NfmUfkBkv1pLM8NLrLueshMQI08tocnmfC/FH8KK4QK/vcJPUP5QluaTD32Ivpyprm06SeVWEzLUgUodODrJ3JF4EFLCMvRGLbkWvqJeWbExeKHlcgfxe04hyanM8hRyq6kWiPJdsNLYhrd1cj/KSybvjVnJvYgF0uya7GvsJIYwiTCtTvMtDYPxsxORih+LaKqPP6L96Tog/NQWbGv3EDi7Pm20sLXpiahlq64W97GtkGZy0slpUyOgBljMmFWbMMI0c3HUXYPoSJhRajOozU4J1MZe9aHRNSMbdjgHTcD/Tu1Ys5CxIZ5NbCQepPeFkbo3Kzt2Sc+EKALaTF647NQKfHsH2Uc7FxRq0avJAOIQI6j3oYClrVqdaA4Bte5R+AuJa9gDdrIulSG3djbUIOwKjGDst6gn91MgEcpb9OkD2N06IG44WwJnw+xLi2CYfpmfhZDSdbJ7MoMKBnQWPKxivciSVZKVDxvZpUBzrga+La2TBhQX1ARRUQdMpvtd7wFogSPuFChLDID2AxMJW4VGjB79WiZm9vokg6KbtRzK0qDkpcj+3kipPNrFNyHTIyI0O3LINC4ReqHT1uvRzy0PALQeIuJ/IphQL1ipCE47nQb8oqOmSwn6uYWDJmZnedzKPIIxlii2DtVqSyou1BROZ0CeeB2Ng1EMrb95InHrAroUsabuh6o2FJAeZzFuQWgYLv6G+EVEdBPQTi3Zm0On4eiZBPMpDqU7T87Yto+SIYjUgYRQ0CW1YJGgkrPLyRgd72ILaDuQD4AOK+Qpufwzb7cCeKtBNh83FdKJsSRwMYCBu2U4Gekmbxg7GaRYyOTcYkLcwRhQa8AZuaVBdJ1QblV67Jwez6TknDqOLK9hLN4AQwNMx4qvqbBltWklcykOg2pef0y83tf1REI91J3LbQq4n6LCo6JSHs7aghc2D4xJPJ/lyZLmzJq9ZkpwQOTNA3tWByrttQr70PTGk83zkGbhGJhuXl5cw86VUbqdmoFDJv9Gq1K5lTSSkIwQjmkpgaClEg+hFIlrdGAyPstvjXo2i0UpR/gHAMjBOVBKqZAqs794g4Q6VQXEoBwHp9yeUKpTqMllK27U5bQT5WJhcQBTLOBQShRYW/cApiQWB0mj5VCx48biICRGNJMlBNygx2t2BO0VKEk7rVN4tMbbqe5Xn90Zhfm1pOGlJ5BaeTzJSZM+X5OuREsHNAZCsQMpz/Y0SuYiVgwMC6gPxoPBjg6B8g3ZG6GeSYNm1rJvyMMJ0UVSHNUHUfoV4VxBJ0aifJT3fxHcynoGGhjEYkOuKUWZ0xWCkZRpJ5SgMNdZBspNnm4i8QOgJ7cwC901RX2nhri5QH65h2x2EotJpt5TRgTQ93baMct6L/8XcKaqVfjbBthHFoYdtZBK2uBs7GB/hpnLgFwsPd30JPjvN6yINj5QZYwQfGRUA0sFwR7b6ZLWc7IrLAnFnhDgu4ScF+h07qD87WaPCkzEI0wJh5BBqEVrkGVz1Jqot9zZPCzZiaRCtFJSpmH8pcWyTj/Kwh7vWytS9JP+xBnClkGMiMi8iq0dUX2+89J+kv1XAroOiDl65HgHJDhYhYVwGcFpxWNIkRb4nb6QRAHO2kGUnI73FpyHJDJNEULJYP1W2v1b7eZqjBeIowNReZnR4ygYxAqkLShAKHWilkHw3E/mnbSBohCYyiR9RsOjzSX+OZKMk6gi9VFbp5SaDP0OJNYNrcQfkSEBn8ku+mdFEFgmrtRHe20xUTPcrUVgA2SiilffBNaokaZVMpcQnme6qCpwRwY8MfC3waoJ7AQw29VYcMldkFCIHRtc86r9cwV49AHdSvpJzQ7M8IWepUnBueO4xgg4WqFcNius7WN81wbwUqVJSEhCQNz+rw7rcWg6E0bUAdsDynBX53Uwg2dhZ7cBw9mkxnbQETn3sMmjVCCoxrsHGgLpetPw6dVIuWj57SAz5IAZr44uM6cWA+tkG9qqMr0fhBI7teiAGwFjAWRARTBNQrBm+JZRzAuZQ4i4wuh7BxGj3CO0JUVb4EhlG57CBMHrlNTDy8KnkyFssZEOq5hHFUrx0+plDuyvkNRDg1wTbOxTXrSKSUmGK+gBZGpxJuHrbxfWTcuIBQNxAtWVkPFAecOZnuSYO948I7APAEdR0cMuIYiEto9DoZ58jJ8i5eFHZfYATJUSa4WTEWTjJuamj3Ne3LdSrx+jPo5xYyIHOWUGVR7Lre2O61PZCdvBNapliIYeFr0UV55Z6eG7wCMQim0CtrLsEjadWaCqM8k3V/xV792F9p/+NBaGcSwJvG9kb8zqE7gm6LyK1nVMCYuXAHX15H3S4zM8inpiiPT9FN7OazDL82GB9apTRu3RNQgQVZKlcRpiOMyqdOTdGLclZE4gqgsoA65L5HECOwY2aqAHARsG0eW+SlblwwCyYakxaESakd0F4H5wLyNYIQhdLi7om1Nc8RpfXMItG2iTzpSxep8etlfeZjcjs7cLC3TBga2FWrbz/KdnVtVhda2CvzcEb51RGNjSh/ip0r3AIe1Os7p1gdcbKFOJyWG9S5Bq4tUOxriTZUCfvNH8rtfAzLTEm1CPduwH5yCaELzGObfJBfdDslgWVYPlvansFjGrYtZUXxBrNMgUmtU0cbkhg2EZmuZiDlZiqWAMunGzUVplcISJOK4i/gpDgwEB5EGA7kzfZbipSPLuWjZQJufJI5CAKyFJoQKsS9eBnB3QToN8LMGMvUs9Ohi8RoFIz5YNkV1epssKY1W5bKtR8yMeNRZGCMbRBGNoTHirSWAgJKY2ID/XQSkIkcGOln7kyYqQ2CoCV/mmMBOdiTkCcC+hbXUokyVaaI5PkmOJ/IkQ5MdIRFVL6Hj8aSGrFUtCh1TlCt6sJ0eaMmo5QHsqIbPJAtIRQyOjzOHIwVQGKEdlMx8oBzPrSUk4yTa4QqAn55TVtj2LhMbpGMF6mxQY9iE2rA9EOIuqrDey8BZjBpYPfrdRXw2qPnMVCXzc86gnFUirkNDQxR9eDykISkRjlHU6bTIiSQGwcGqEGVhcIoXYI1QiTyHBXhs2IygJAMej2ew93sMb0KWD0rIMfWelfq/Pp8pxBGGkyYaT9ZXogqN19ek6hZoSO4JMB1YqBlSSV9XWP+plD0HINtB1gLXg2QXFygmJZoNmT9yr1sE07GCblRDlsLFs9FDNHREmUfW9FIaV+H9zZzMOYfqXH6OlDScSitluZwboWaDQCF6J2A8SboLrBqPcjioUQ3G0TUO63sl90MvHarGqYdoxiWaLdlwRz7W2eq0Qb98h0EORuP0jF25Bc91raOvW+IHVG560ITwmIKk0VIqHKdtWgSpIXmUY6CYxwQ02tWIl+VojGy/NGEpyeZH0zcqs18TXIa4KTulZG5PlxwjDl4OMiDphiuGbXPcKowPqOGqszRk3ASFEhbBBCB/8Ok2YBhTgUAADMwRKj+Rr93z6P5XmDbicVa4NqRt4X4eJYXW8mGBgrk7ITMdhw2iOVA5LQF2255Bk2Srqn3gwKoTi0DSiqWeFqk/PDqK920n7pPdxBi1FtwaYAjLw7QkodUKNQEg7vK9HNKgA7GF+J2Pv8oSCQupZkGl4AtUHui7WC7BiTEwm77FHdEOTG3VjDXJ/LuggBqKsB2Uj722a7Q4sWHtfwexV8LWRTKWKRFUNpL0kkUdurG6omdEkgIRYXgvLGtSaHKiJIXCOjYx1wK6tdWG+iDy186KR6s2k7CmCOQB/ACw/DLeAMqBIoybYRbtHBrDqRy1oS6NsacBfAvgEQgJ6BaMDB6cHTyuFEY8SRA8PAFwW6CaObGtFZQzwgYgf4jsALwB1ApZAClQPSBomVPmgGcJiuCwg+vwOg6xG+ieKGqVPwqHUic2sJZs1wc5HeucLA15rgdJBkRzfrWADsAQ7CmcCKgJVm8IVMxeQonhHUGwSF63HIKDyDRsqlUDtudPI7jRJbGQA8gzuR4nIVgbYHaoH7fDDoWoew6mGWEbwi4Q5EudXkAbNg1NeVVBgA6nSccxtgu4DoDLq9EjSzMD2jnAesTzlQLXI/SlMzsVE9toBdsG6SEdW+R3mjAR0uEZILIBF4NEJ/ZorlndWgOLJDYgcGbJ/mI8gB5FYe7soNTL7cYWIM/KkJlhdquU8ErEaE/XNA/2YLdiOA1dl1QSgPO5gF4DqCuSGHuVNegIyxFh5Sud8iHO4DhtCdn2F9rkK3Y3SE9kSesZrWlQsxIDI31jBXVTmxJtAKiGtgVTH8HQaTWMPur4FRDS5sPmCZ5CCKtUU3Y3Q7EatzjG5P5uscmf4Z5MAzvawjs8JwGKh/BpYAtyyvkROUIjJg4OFCC+rXyGMvFweggxuoAFTWAJERzu6iPTnC+v4a5GuUh0KI9X2D2FiVAwsgEloGLyI69oirCDIRHIPaj4s6Kq4DejZAZXDtXkK5N0E5r1HOvUD2keEOO5j5SmBh34CXK5h9JwXiMqK41gGWpGhoA8K6R2QPgvoArRcw14DRkxYjZvBsiut/+wSaE7RhUgWYJVBcjxg9tUbxzDV095+BqWtEoxNKp4zFnrZW10Cxz6gbedH6sbSniIUfMLnco/7LxZBEp0O8cPC7I/SzUlCiTnw9+lMF6MDAtbKx204SCNdKcuHHQLtL4BmDW/WnYAI1BFoaLaCA6pBR3YgYXetQXF6A2hZ+NkFzymBddmhJDQwB2VsCgzvIKARWBcjCgBcMOmwQlofgZi3X4Bzi3Wcwv3eC9ZkezaTPa1CI4ZLAmA6gVtYgrQQ5XldA2JHr8JUW+wbAWNduC9ABg1ptHY892EZxe+0jEHshxCROD+R3sScgSCulmDNowegtoasBHgn7nXwDLHrY2GG8KIDDEu2eyFFjCXgnbXajnMBuKgVj4whNUaE8LDG+0sKuelAfgWBArEUuZO+lpgVAYGOB9RLUrEC9R2xbRN3TeDICTxy4MIiFFRPO1oNaQQ8pamLFPdB40JWI0o/guxK0K8hdLHTfW0uXgCJAc8b0LxawNxZA00kBVJcIZ3ZxeN8E4bTRAZIMckCsWNRVEUBrEBuC94Bdr4+c418riF/Kd93EeOaZZ3D33Xe/3B9jG9vYxja2sY1tfB3x9NNP46677vqa33Psko8YIx5//HG89rWvxdNPP43ZbPZyf6S/0Tg8PMTdd999W14bsL2+Wzlu52sDbu/ru52vDdhe360SzIz5fI4LFy7AJI7dC8Sxa7sYY3DnnXcCAGaz2S39IL5W3M7XBmyv71aO2/nagNv7+m7nawO213crxO7u7kv6vq+dmmxjG9vYxja2sY1t/A3HNvnYxja2sY1tbGMbNzWOZfJRVRXe/e53o6qql/uj/I3H7XxtwPb6buW4na8NuL2v73a+NmB7fbdjHDvC6Ta2sY1tbGMb27i941giH9vYxja2sY1tbOP2jW3ysY1tbGMb29jGNm5qbJOPbWxjG9vYxja2cVNjm3xsYxvb2MY2trGNmxrb5GMb29jGNraxjW3c1Dh2yccv//Iv47777kNd13jwwQfxh3/4hy/3R/q64ud+7udAREf+vOY1r8lfb5oG73rXu3Dq1ClMp1N893d/Ny5fvvwyfuIXjv/9v/83/vE//se4cOECiAi/8zu/c+TrzIx/9+/+He644w6MRiM88sgj+MIXvnDke65fv463v/3tmM1m2Nvbw/d///djsVjcxKt44Xix6/u+7/u+r3qWb33rW498z3G9vve85z34O3/n72BnZwdnz57FP/2n/xSPP/74ke95KWvxqaeewnd+53diPB7j7Nmz+PEf/3F472/mpTxvvJTr+wf/4B981fP7wR/8wSPfcxyv71d+5Vfwhje8IbtePvTQQ/i93/u9/PVb+bkBL359t+pze774hV/4BRARfvRHfzT/3a3+/P7awcco3vve93JZlvxrv/Zr/LnPfY7f+c538t7eHl++fPnl/mh/5Xj3u9/N3/iN38gXL17Mf5599tn89R/8wR/ku+++mz/0oQ/xJz7xCf67f/fv8t/7e3/vZfzELxwf+MAH+Gd+5mf4fe97HwPg97///Ue+/gu/8Au8u7vLv/M7v8N/8id/wv/kn/wTvv/++3m9Xufveetb38pvfOMb+WMf+xj/n//zf/iVr3wlf+/3fu9NvpLnjxe7vne84x381re+9cizvH79+pHvOa7X9x3f8R3867/+6/zZz36WH3vsMf5H/+gf8T333MOLxSJ/z4utRe89v+51r+NHHnmEP/WpT/EHPvABPn36NP/UT/3Uy3FJR+KlXN/f//t/n9/5znceeX4HBwf568f1+v7rf/2v/N//+3/nP//zP+fHH3+cf/qnf5qLouDPfvazzHxrPzfmF7++W/W5PTf+8A//kO+77z5+wxvewD/yIz+S//5Wf35/3ThWycdb3vIWfte73pX/fwiBL1y4wO95z3texk/19cW73/1ufuMb3/i8X9vf3+eiKPi3f/u389/96Z/+KQPgRx999CZ9wq8vnns4xxj5/Pnz/B/+w3/If7e/v89VVfF//s//mZmZP//5zzMA/qM/+qP8Pb/3e7/HRMRf+cpXbtpnfynxQsnHd33Xd73gv7mVru/KlSsMgD/60Y8y80tbix/4wAfYGMOXLl3K3/Mrv/IrPJvNuG3bm3sBLxLPvT5mOcQ2N/3nxq10fSdOnOBf/dVfve2eW4p0fcy3x3Obz+f8qle9ij/4wQ8euZ7b9fn9VeLYtF26rsMnP/lJPPLII/nvjDF45JFH8Oijj76Mn+zrjy984Qu4cOECHnjgAbz97W/HU089BQD45Cc/ib7vj1zra17zGtxzzz233LU++eSTuHTp0pFr2d3dxYMPPpiv5dFHH8Xe3h7e/OY35+955JFHYIzBxz/+8Zv+mb+e+MhHPoKzZ8/i1a9+NX7oh34I165dy1+7la7v4OAAAHDy5EkAL20tPvroo3j961+Pc+fO5e/5ju/4DhweHuJzn/vcTfz0Lx7Pvb4Uv/mbv4nTp0/jda97HX7qp34Kq9Uqf+1WuL4QAt773vdiuVzioYceuu2e23OvL8Wt/tze9a534Tu/8zuPPCfg9nvvvp44NlNtr169ihDCkRsNAOfOncOf/dmfvUyf6uuPBx98EL/xG7+BV7/61bh48SJ+/ud/Ht/6rd+Kz372s7h06RLKssTe3t6Rf3Pu3DlcunTp5fnAX2ekz/t8zy197dKlSzh79uyRrzvncPLkyVviet/61rfin//zf477778fX/ziF/HTP/3TeNvb3oZHH30U1tpb5vpijPjRH/1RfMu3fAte97rXAcBLWouXLl163uebvnZc4vmuDwD+5b/8l7j33ntx4cIFfPrTn8ZP/MRP4PHHH8f73vc+AMf7+j7zmc/goYceQtM0mE6neP/734/Xvva1eOyxx26L5/ZC1wfc2s8NAN773vfij//4j/FHf/RHX/W12+m9+3rj2CQft1u87W1vy//9hje8AQ8++CDuvfde/NZv/RZGo9HL+Mm28VeNf/Ev/kX+79e//vV4wxvegFe84hX4yEc+gocffvhl/GR/tXjXu96Fz372s/iDP/iDl/uj/P8SL3R9P/ADP5D/+/Wvfz3uuOMOPPzww/jiF7+IV7ziFTf7Y/6V4tWvfjUee+wxHBwc4L/8l/+Cd7zjHfjoRz/6cn+sv7F4oet77Wtfe0s/t6effho/8iM/gg9+8IOo6/rl/jjHMo5N2+X06dOw1n4V2/fy5cs4f/78y/Sp/uZib28P3/AN34AnnngC58+fR9d12N/fP/I9t+K1ps/7tZ7b+fPnceXKlSNf997j+vXrt9z1AsADDzyA06dP44knngBwa1zfD//wD+O//bf/ht///d/HXXfdlf/+pazF8+fPP+/zTV87DvFC1/d88eCDDwLAked3XK+vLEu88pWvxJve9Ca85z3vwRvf+Eb8p//0n26b5/ZC1/d8cSs9t09+8pO4cuUKvvmbvxnOOTjn8NGPfhS/+Iu/COcczp07d1s8v79OHJvkoyxLvOlNb8KHPvSh/HcxRnzoQx860gO8VWOxWOCLX/wi7rjjDrzpTW9CURRHrvXxxx/HU089dctd6/3334/z588fuZbDw0N8/OMfz9fy0EMPYX9/H5/85Cfz93z4wx9GjDFvKLdSPPPMM7h27RruuOMOAMf7+pgZP/zDP4z3v//9+PCHP4z777//yNdfylp86KGH8JnPfOZIgvXBD34Qs9ksQ+QvV7zY9T1fPPbYYwBw5Pkd1+t7bsQY0bbtLf/cXijS9T1f3ErP7eGHH8ZnPvMZPPbYY/nPm9/8Zrz97W/P/307Pr+/UrzcjNfNeO9738tVVfFv/MZv8Oc//3n+gR/4Ad7b2zvC9r1V4sd+7Mf4Ix/5CD/55JP8f//v/+VHHnmET58+zVeuXGFmkVndc889/OEPf5g/8YlP8EMPPcQPPfTQy/ypnz/m8zl/6lOf4k996lMMgP/jf/yP/KlPfYq//OUvM7NIbff29vh3f/d3+dOf/jR/13d91/NKbb/pm76JP/7xj/Mf/MEf8Kte9apjIUVl/trXN5/P+d/+23/Ljz76KD/55JP8v/7X/+Jv/uZv5le96lXcNE3+Gcf1+n7oh36Id3d3+SMf+cgRyeJqtcrf82JrMUn+vv3bv50fe+wx/h//43/wmTNnjoXk78Wu74knnuB//+//PX/iE5/gJ598kn/3d3+XH3jgAf62b/u2/DOO6/X95E/+JH/0ox/lJ598kj/96U/zT/7kTzIR8f/8n/+TmW/t58b8ta/vVn5uLxTPVe/c6s/vrxvHKvlgZv6lX/olvueee7gsS37LW97CH/vYx17uj/R1xfd8z/fwHXfcwWVZ8p133snf8z3fw0888UT++nq95n/zb/4NnzhxgsfjMf+zf/bP+OLFiy/jJ37h+P3f/30G8FV/3vGOdzCzyG1/9md/ls+dO8dVVfHDDz/Mjz/++JGfce3aNf7e7/1enk6nPJvN+F/9q3/F8/n8Zbiar46vdX2r1Yq//du/nc+cOcNFUfC9997L73znO78qIT6u1/d81wWAf/3Xfz1/z0tZi1/60pf4bW97G49GIz59+jT/2I/9GPd9f5Ov5qvjxa7vqaee4m/7tm/jkydPclVV/MpXvpJ//Md//IhfBPPxvL5//a//Nd97771cliWfOXOGH3744Zx4MN/az435a1/frfzcXiiem3zc6s/vrxvEzHzzcJZtbGMb29jGNrbx/3ocG87HNraxjW1sYxvb+H8jtsnHNraxjW1sYxvbuKmxTT62sY1tbGMb29jGTY1t8rGNbWxjG9vYxjZuamyTj21sYxvb2MY2tnFTY5t8bGMb29jGNraxjZsa2+RjG9vYxja2sY1t3NTYJh/b2MY2trGNbWzjpsY2+djGNraxjW1sYxs3NbbJxza2sY1tbGMb27ipsU0+trGNbWxjG9vYxk2N/w/UE3BDUlX/FgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.map(prepare_dataset, remove_columns=dataset.column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b278a7d1cd1a4f25ae71acc7efe0d99f",
            "66dd736228c94b1e911c5bbf7aec8088",
            "75317e8b1c3e4ad2951e0fee909d838b",
            "74ce0d197f0a42ba9c0e42fcec65d492",
            "7cc35f1b258a4f47b501ffdb99b83906",
            "155c55dd3b5941b3a7570f950e173fda",
            "6b860a52b83a4f2eaa3db6b242b55b3f",
            "c07fcd77fc46463495b9dcb8a67167e0",
            "db196dc85c48474ab3c220eaf09d9920",
            "e6691dbfcd09428da6f360ee1c3084c2",
            "e4c9cdebb083428990283b812bd18f08"
          ]
        },
        "id": "q2AKa9VwcKa2",
        "outputId": "9f2a913f-7f7d-4615-fb93-23ca75275cff"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/327 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b278a7d1cd1a4f25ae71acc7efe0d99f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def is_not_too_long(input_ids):\n",
        "    input_length = len(input_ids)\n",
        "    return input_length < 200\n",
        "\n",
        "\n",
        "dataset = dataset.filter(is_not_too_long, input_columns=[\"input_ids\"])\n",
        "len(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "7ccc5e59bd364f6993cd90d1f9dd2297",
            "6eae11d2e1fd414da18eb4b8afeb959b",
            "cf8b2898f76c431e82266cb2de48e7b9",
            "1fa11b44bd2e427da8dc525ece633d94",
            "eb11924adfea49349d8ab5ca4bb86846",
            "58aacab065da4e3592649ab480f18414",
            "8cc6fc24c0e344ae8e1cd81d4979a9bf",
            "c715c188bcf04a4db7818518385b3dfb",
            "a590f27fdc7b43c787b18399cbacf72a",
            "140ad872001049b28cc7d89ba24e93c9",
            "62b2c0ad5fd2402bb27c0b8bed7fc68d"
          ]
        },
        "id": "AbFZX4sGcN9Z",
        "outputId": "2ba52cfd-1ea8-4e7f-a4ef-8ed612f7008e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/327 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ccc5e59bd364f6993cd90d1f9dd2297"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "176"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.train_test_split(test_size=0.1)"
      ],
      "metadata": {
        "id": "ffl5HqgZcP1t"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from dataclasses import dataclass\n",
        "from typing import Any, Dict, List, Union\n",
        "\n",
        "\n",
        "@dataclass\n",
        "class TTSDataCollatorWithPadding:\n",
        "    processor: Any\n",
        "\n",
        "    def __call__(\n",
        "        self, features: List[Dict[str, Union[List[int], torch.Tensor]]]\n",
        "    ) -> Dict[str, torch.Tensor]:\n",
        "        input_ids = [{\"input_ids\": feature[\"input_ids\"]} for feature in features]\n",
        "        label_features = [{\"input_values\": feature[\"labels\"]} for feature in features]\n",
        "        speaker_features = [feature[\"speaker_embeddings\"] for feature in features]\n",
        "\n",
        "        # collate the inputs and targets into a batch\n",
        "        batch = processor.pad(\n",
        "            input_ids=input_ids, labels=label_features, return_tensors=\"pt\"\n",
        "        )\n",
        "\n",
        "        # replace padding with -100 to ignore loss correctly\n",
        "        batch[\"labels\"] = batch[\"labels\"].masked_fill(\n",
        "            batch.decoder_attention_mask.unsqueeze(-1).ne(1), -100\n",
        "        )\n",
        "\n",
        "        # not used during fine-tuning\n",
        "        del batch[\"decoder_attention_mask\"]\n",
        "\n",
        "        # round down target lengths to multiple of reduction factor\n",
        "        if model.config.reduction_factor > 1:\n",
        "            target_lengths = torch.tensor(\n",
        "                [len(feature[\"input_values\"]) for feature in label_features]\n",
        "            )\n",
        "            target_lengths = target_lengths.new(\n",
        "                [\n",
        "                    length - length % model.config.reduction_factor\n",
        "                    for length in target_lengths\n",
        "                ]\n",
        "            )\n",
        "            max_length = max(target_lengths)\n",
        "            batch[\"labels\"] = batch[\"labels\"][:, :max_length]\n",
        "\n",
        "        # also add in the speaker embeddings\n",
        "        batch[\"speaker_embeddings\"] = torch.tensor(speaker_features)\n",
        "\n",
        "        return batch"
      ],
      "metadata": {
        "id": "OpfynwO2cR1X"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = TTSDataCollatorWithPadding(processor=processor)"
      ],
      "metadata": {
        "id": "kq4fPpSCcXHV"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import SpeechT5ForTextToSpeech\n",
        "\n",
        "model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "132f527c935348928225eb2ef3bbd4ab",
            "7b9e8af285454fb4bff29c8c7b999bf6",
            "e24a5a1e1e1b436bb8e380fa4a8ccbf9",
            "e67c6cfc0960432ebebf3c41dde8b659",
            "14ab7f79e49c4ca6b37591ec153ad1a7",
            "7c8d8ab25b494ac09e569e09ef2865bf",
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            "cce9db080b3d41d2bd1fd6f67421af55",
            "d33a394c8cf54e78952c0a2695342b55"
          ]
        },
        "id": "QJg5cWH3cY56",
        "outputId": "a5e84bf4-ab0f-4ba4-c8d6-a90b0d172f18"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/2.06k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "132f527c935348928225eb2ef3bbd4ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/585M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a9c14b2749cc44978a4ce0b49952eb1e"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import SpeechT5ForTextToSpeech\n",
        "\n",
        "model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)"
      ],
      "metadata": {
        "id": "raQ03QuTcboH"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainingArguments\n",
        "\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"speecht5_finetuned_voxpopuli_nl\",  # change to a repo name of your choice\n",
        "    per_device_train_batch_size=4,\n",
        "    gradient_accumulation_steps=8,\n",
        "    learning_rate=1e-5,\n",
        "    warmup_steps=500,\n",
        "    max_steps=1500,\n",
        "    gradient_checkpointing=True,\n",
        "    fp16=True,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    per_device_eval_batch_size=2,\n",
        "    save_steps=1000,\n",
        "    eval_steps=1000,\n",
        "    logging_steps=25,\n",
        "    report_to=[\"tensorboard\"],\n",
        "    load_best_model_at_end=True,\n",
        "    greater_is_better=False,\n",
        "    label_names=[\"labels\"],\n",
        "    push_to_hub=True,\n",
        ")"
      ],
      "metadata": {
        "id": "-BjmWQgWcdg-"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Seq2SeqTrainer\n",
        "\n",
        "trainer = Seq2SeqTrainer(\n",
        "    args=training_args,\n",
        "    model=model,\n",
        "    train_dataset=dataset[\"train\"],\n",
        "    eval_dataset=dataset[\"test\"],\n",
        "    data_collator=data_collator,\n",
        "    tokenizer=processor,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BSpNXUldgNNs",
        "outputId": "6ae45472-c408-48cd-d5ef-b0ff8bddabcd"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "h-ARJOUGgPmM",
        "outputId": "1de2f754-1842-4a69-f057-2136062b9a1e"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1000/1000 33:03, Epoch 200/200]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.486500</td>\n",
              "      <td>0.525955</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1000, training_loss=0.560443021774292, metrics={'train_runtime': 1989.719, 'train_samples_per_second': 16.083, 'train_steps_per_second': 0.503, 'total_flos': 4734625705148592.0, 'train_loss': 0.560443021774292, 'epoch': 200.0})"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "kwargs = {\n",
        "     \"dataset_tags\": \"facebook/voxpopuli\",\n",
        "    \"finetuned_from\": \"microsoft/speecht5_tts\",\n",
        "    \"tasks\": \"text-to-speech\",\n",
        "}\n",
        ""
      ],
      "metadata": {
        "id": "_AVmF2FdgwSt"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(**kwargs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "iqfuB0TKr0W-",
        "outputId": "43eddef9-23bf-45ea-da92-5640658e0fe2"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 1876}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/umarigan/speecht5_finetuned_voxpopuli_nl/commit/4835e24d1a9d812175e0967581ba69f0f7f90182', commit_message='End of training', commit_description='', oid='4835e24d1a9d812175e0967581ba69f0f7f90182', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    }
  ]
}