llama_chat_format.py

from __future__ import annotations

import os
import json
import ctypes
import dataclasses
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union, Protocol

import llama_cpp.llama as llama
import llama_cpp.llama_types as llama_types
import llama_cpp.llama_grammar as llama_grammar

from ._utils import suppress_stdout_stderr


class LlamaChatCompletionHandler(Protocol):
    def __call__(
        self,
        *,
        llama: llama.Llama,
        messages: List[llama_types.ChatCompletionRequestMessage],
        functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
        function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
        tools: Optional[List[llama_types.ChatCompletionTool]] = None,
        tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
        temperature: float = 0.2,
        top_p: float = 0.95,
        top_k: int = 40,
        min_p: float = 0.05,
        typical_p: float = 1.0,
        stream: bool = False,
        stop: Optional[Union[str, List[str]]] = [],
        seed: Optional[int] = None,
        response_format: Optional[
            llama_types.ChatCompletionRequestResponseFormat
        ] = None,
        max_tokens: Optional[int] = None,
        presence_penalty: float = 0.0,
        frequency_penalty: float = 0.0,
        repeat_penalty: float = 1.1,
        tfs_z: float = 1.0,
        mirostat_mode: int = 0,
        mirostat_tau: float = 5.0,
        mirostat_eta: float = 0.1,
        model: Optional[str] = None,
        logits_processor: Optional[llama.LogitsProcessorList] = None,
        grammar: Optional[llama.LlamaGrammar] = None,
        logit_bias: Optional[Dict[str, float]] = None,
        **kwargs,  # type: ignore
    ) -> Union[
        llama_types.CreateChatCompletionResponse,
        Iterator[llama_types.CreateChatCompletionStreamResponse],
    ]:
        ...


CHAT_HANDLERS: Dict[str, LlamaChatCompletionHandler] = {}


def get_chat_completion_handler(name: str) -> LlamaChatCompletionHandler:
    return CHAT_HANDLERS[name]


def register_chat_completion_handler(name: str):
    def decorator(f: LlamaChatCompletionHandler):
        CHAT_HANDLERS[name] = f
        return f

    return decorator


def _get_system_message(
    messages: List[llama_types.ChatCompletionRequestMessage],
) -> str:
    """Get the first system message."""
    for message in messages:
        if message["role"] == "system":
            return message["content"] or ""
    return ""


def _map_roles(
    messages: List[llama_types.ChatCompletionRequestMessage], role_map: Dict[str, str]
) -> List[Tuple[str, Optional[str]]]:
    """Map the message roles."""
    output: List[Tuple[str, Optional[str]]] = []
    for message in messages:
        role = message["role"]
        if role in role_map:
            output.append((role_map[role], message["content"]))
    return output


def _format_llama2(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str, sep2: str
) -> str:
    """Format the prompt with the llama2 style."""
    seps = [sep, sep2]
    ret = system_message + sep
    for i, (role, message) in enumerate(messages):
        if system_message and i == 0:
            ret += message + seps[i % 2]
        elif message:
            ret += role + message + " " + seps[i % 2]
        else:
            ret += role + " "
    return ret


def _format_add_colon_single(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
) -> str:
    """Format the prompt with the add-colon-single style."""
    ret = system_message + sep
    for role, message in messages:
        if message:
            ret += role + ": " + message + sep
        else:
            ret += role + ":"
    return ret


def _format_add_colon_two(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str, sep2: str
) -> str:
    """Format the prompt with the add-colon-two style."""
    seps = [sep, sep2]
    ret = system_message + seps[0]
    for i, (role, message) in enumerate(messages):
        if message:
            ret += role + ": " + message + seps[i % 2]
        else:
            ret += role + ":"
    return ret


def _format_no_colon_single(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
) -> str:
    """Format the prompt with the no-colon-single style."""
    ret = system_message
    for role, message in messages:
        if message:
            ret += role + message + sep
        else:
            ret += role
    return ret


def _format_add_colon_space_single(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
) -> str:
    """Format the prompt with the add-colon-space-single style."""
    ret = system_message + sep
    for role, message in messages:
        if message:
            ret += role + ": " + message + sep
        else:
            ret += role + ": "  # must be end with a space
    return ret


def _format_chatml(
    system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
) -> str:
    """Format the prompt with the chatml style."""
    ret = "" if system_message == "" else system_message + sep + "\n"
    for role, message in messages:
        if message:
            ret += role + "\n" + message + sep + "\n"
        else:
            ret += role + "\n"
    return ret


@dataclasses.dataclass
class ChatFormatterResponse:
    prompt: str
    stop: Optional[Union[str, List[str]]] = None


class ChatFormatter(Protocol):
    def __call__(
        self,
        *,
        messages: List[llama_types.ChatCompletionRequestMessage],
        **kwargs: Any,
    ) -> ChatFormatterResponse:
        ...


class BasicChatHandler:
    def __init__(self, chat_format: str):
        self.chat_format = chat_format


def _convert_text_completion_to_chat(
    completion: llama_types.Completion,
) -> llama_types.ChatCompletion:
    return {
        "id": "chat" + completion["id"],
        "object": "chat.completion",
        "created": completion["created"],
        "model": completion["model"],
        "choices": [
            {
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": completion["choices"][0]["text"],
                },
                "finish_reason": completion["choices"][0]["finish_reason"],
            }
        ],
        "usage": completion["usage"],
    }


def _convert_text_completion_chunks_to_chat(
    chunks: Iterator[llama_types.CreateCompletionStreamResponse],
) -> Iterator[llama_types.ChatCompletionChunk]:
    for i, chunk in enumerate(chunks):
        if i == 0:
            yield {
                "id": "chat" + chunk["id"],
                "model": chunk["model"],
                "created": chunk["created"],
                "object": "chat.completion.chunk",
                "choices": [
                    {
                        "index": 0,
                        "delta": {
                            "role": "assistant",
                        },
                        "finish_reason": None,
                    }
                ],
            }
        yield {
            "id": "chat" + chunk["id"],
            "model": chunk["model"],
            "created": chunk["created"],
            "object": "chat.completion.chunk",
            "choices": [
                {
                    "index": 0,
                    "delta": {
                        "content": chunk["choices"][0]["text"],
                    }
                    if chunk["choices"][0]["finish_reason"] is None
                    else {},
                    "finish_reason": chunk["choices"][0]["finish_reason"],
                }
            ],
        }


def _convert_completion_to_chat(
    completion_or_chunks: Union[
        llama_types.CreateCompletionResponse,
        Iterator[llama_types.CreateCompletionStreamResponse],
    ],
    stream: bool = False,
) -> Union[
    llama_types.CreateChatCompletionResponse, Iterator[llama_types.ChatCompletionChunk]
]:
    if stream:
        chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks  # type: ignore
        return _convert_text_completion_chunks_to_chat(chunks)
    else:
        completion: llama_types.Completion = completion_or_chunks  # type: ignore
        return _convert_text_completion_to_chat(completion)


_CHAT_FORMATS: Dict[str, ChatFormatter] = {}


def register_chat_format(name: str):
    def decorator(f: ChatFormatter):
        def basic_create_chat_completion(
            *,
            llama: llama.Llama,
            messages: List[llama_types.ChatCompletionRequestMessage],
            functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
            function_call: Optional[
                llama_types.ChatCompletionRequestFunctionCall
            ] = None,
            tools: Optional[List[llama_types.ChatCompletionTool]] = None,
            tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
            temperature: float = 0.2,
            top_p: float = 0.95,
            top_k: int = 40,
            min_p: float = 0.05,
            typical_p: float = 1.0,
            stream: bool = False,
            stop: Optional[Union[str, List[str]]] = [],
            seed: Optional[int] = None,
            response_format: Optional[
                llama_types.ChatCompletionRequestResponseFormat
            ] = None,
            max_tokens: Optional[int] = None,
            presence_penalty: float = 0.0,
            frequency_penalty: float = 0.0,
            repeat_penalty: float = 1.1,
            tfs_z: float = 1.0,
            mirostat_mode: int = 0,
            mirostat_tau: float = 5.0,
            mirostat_eta: float = 0.1,
            model: Optional[str] = None,
            logits_processor: Optional[llama.LogitsProcessorList] = None,
            grammar: Optional[llama.LlamaGrammar] = None,
            logit_bias: Optional[Dict[str, float]] = None,
            **kwargs,  # type: ignore
        ) -> Union[
            llama_types.CreateChatCompletionResponse,
            Iterator[llama_types.CreateChatCompletionStreamResponse],
        ]:
            result = f(
                messages=messages,
                functions=functions,
                function_call=function_call,
            )
            prompt = result.prompt
            if result.stop is not None:
                stop = [] if stop is None else [stop] if isinstance(stop, str) else stop
                rstop = result.stop if isinstance(result.stop, list) else [result.stop]
                stop = stop + rstop

            if response_format is not None and response_format["type"] == "json_object":
                grammar = llama_grammar.LlamaGrammar.from_string(
                    llama_grammar.JSON_GBNF
                )

            completion_or_chunks = llama.create_completion(
                prompt=prompt,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                min_p=min_p,
                typical_p=typical_p,
                stream=stream,
                stop=stop,
                seed=seed,
                max_tokens=max_tokens,
                presence_penalty=presence_penalty,
                frequency_penalty=frequency_penalty,
                repeat_penalty=repeat_penalty,
                tfs_z=tfs_z,
                mirostat_mode=mirostat_mode,
                mirostat_tau=mirostat_tau,
                mirostat_eta=mirostat_eta,
                model=model,
                logits_processor=logits_processor,
                grammar=grammar,
                logit_bias=logit_bias,
            )
            return _convert_completion_to_chat(completion_or_chunks, stream=stream)

        register_chat_completion_handler(name)(basic_create_chat_completion)
        return f

    return decorator


def get_chat_format(name: str):
    try:
        return _CHAT_FORMATS[name]
    except KeyError:
        raise ValueError(
            f"Invalid chat format: {name} (valid formats: {list(_CHAT_FORMATS.keys())})"
        )


def hf_autotokenizer_to_chat_formatter(
    pretrained_model_name_or_path: Union[str, os.PathLike[str]]
) -> ChatFormatter:
    # https://huggingface.co/docs/transformers/main/chat_templating
    # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#instruction-format
    # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
    from transformers import AutoTokenizer

    tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)

    def format_autotokenizer(
        messages: List[llama_types.ChatCompletionRequestMessage],
        **kwargs: Any,
    ) -> ChatFormatterResponse:
        tokenizer.use_default_system_prompt = False
        _prompt = tokenizer.apply_chat_template(messages, tokenize=False)
        # Return formatted prompt and eos token by default
        return ChatFormatterResponse(prompt=_prompt, stop=tokenizer.eos_token)

    return format_autotokenizer


# see https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/tokenization_llama.py
# system prompt is "embedded" in the first message
@register_chat_format("llama-2")
def format_llama2(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_template = "<s>[INST] <<SYS>>\n{system_message}\n<</SYS>>"
    _roles = dict(user="<s>[INST]", assistant="[/INST]")
    _messages = _map_roles(messages, _roles)
    system_message = _get_system_message(messages)
    if system_message:
        system_message = _system_template.format(system_message=system_message)
    _prompt = _format_llama2(system_message, _messages, " ", "</s>") + "[/INST]"
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("alpaca")
def format_alpaca(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _roles = dict(user="### Instruction", assistant="### Response")
    _sep = "\n\n"
    _sep2 = "</s>"
    system_message = _get_system_message(messages)
    _messages = _map_roles(messages, _roles)
    _prompt = _format_add_colon_two(system_message, _messages, _sep, _sep2)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("vicuna")
def format(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_message = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."
    _roles = dict(user="USER", assistant="ASSISTANT")
    _sep = " "
    _sep2 = "</s>"
    system_message = _system_message
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_two(system_message, _messages, _sep, _sep2)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("oasst_llama")
def format_oasst_llama(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_template = "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n"
    _roles = dict(user="<|prompter|>", assistant="<|assistant|>")
    _sep = "</s>"
    system_message = _get_system_message(messages)
    system_message = _system_template.format(system_message=system_message)
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_no_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("baichuan-2")
def format_baichuan2(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_template = "{system_message}"
    _roles = dict(user="<reserved_106>", assistant="<reserved_107>")
    _sep = ""
    system_message = _get_system_message(messages)
    system_message = _system_template.format(system_message=system_message)
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_no_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("baichuan")
def format_baichuan(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_template = "{system_message}"
    _roles = dict(user="<reserved_102>", assistant="<reserved_103>")
    _sep = ""
    system_message = _get_system_message(messages)
    system_message = _system_template.format(system_message=system_message)
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_no_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("openbuddy")
def format_openbuddy(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_message = """Consider a conversation between User (a human) and Assistant (named Buddy).
Buddy is an INTP-T, a friendly, intelligent and multilingual AI assistant, by OpenBuddy team. GitHub: https://github.com/OpenBuddy/OpenBuddy
Buddy cannot access the Internet.
Buddy can fluently speak the user's language (e.g. English, Chinese).
Buddy can generate poems, stories, code, essays, songs, parodies, and more.
Buddy possesses vast knowledge about the world, history, and culture.
Buddy's responses are always safe, creative, high-quality, human-like, and interesting.
Buddy strictly refuses to discuss political, NSFW, or other unsafe topics.

User: Hi.
Assistant: Hi, I'm Buddy, your AI assistant. How can I help you today?"""
    _roles = dict(user="User", assistant="Assistant")
    _sep = "\n"
    system_message = _system_message
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("redpajama-incite")
def format_redpajama_incite(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _system_message = _get_system_message(messages)
    _roles = dict(user="<human>", assistant="<bot>")
    _sep = "\n"
    _stop = "<human>"
    system_message = _system_message
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_stop)


@register_chat_format("snoozy")
def format_snoozy(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    system_template = "### Instruction:\n{system_message}"
    default_system_message = "The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response."
    _system_message = _get_system_message(messages)
    _system_message = (
        _system_message if _system_message != "" else default_system_message
    )
    system_message = system_template.format(system_message=_system_message)
    _roles = dict(user="### Prompt", assistant="### Response")
    _sep = "\n"
    _stop = "###"
    system_message = _system_message
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_stop)


@register_chat_format("phind")
def format_phind(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _roles = dict(user="### User Message", assistant="### Assistant")
    _sep = "\n\n"
    _system_message = "### System Prompt\nYou are an intelligent programming assistant."
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_single(_system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("intel")
def format_intel(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _roles = dict(user="### User:", assistant="### Assistant:")
    _sep = "\n"
    _system_message = "### System:\n{system_message}"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_add_colon_single(_system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("open-orca")
def format_open_orca(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    system_template = "{system_message}"
    system_message = (
        "You are a helpful assistant. Please answer truthfully and write out your "
    )
    "thinking step by step to be sure you get the right answer. If you make a mistake or encounter "
    "an error in your thinking, say so out loud and attempt to correct it. If you don't know or "
    "aren't sure about something, say so clearly. You will act as a professional logician, mathematician, "
    "and physicist. You will also act as the most appropriate type of expert to answer any particular "
    "question or solve the relevant problem; state which expert type your are, if so. Also think of "
    "any particular named expert that would be ideal to answer the relevant question or solve the "
    "relevant problem; name and act as them, if appropriate."
    roles = ("User", "Assistant")
    sep = "<|end_of_turn|>\n"
    # stop_token_ids=[32000, 32001],  # "<|end_of_turn|>"
    stop_str = "User"
    system_message = system_template.format(system_message=system_message)
    _messages = _map_roles(messages, dict(zip(roles, roles)))
    _messages.append((roles[1], None))
    _prompt = _format_add_colon_space_single(system_message, _messages, sep)
    return ChatFormatterResponse(prompt=_prompt, stop=stop_str)


@register_chat_format("mistrallite")
def format_mistrallite(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    _roles = dict(user="<|prompter|>", assistant="</s>\n<|assistant|>")
    _sep = " "
    system_template = """<|system|>{system_message}</s>"""
    system_message = _get_system_message(messages)
    system_message = system_template.format(system_message=system_message)
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_no_colon_single(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt)


@register_chat_format("chatml")
def format_chatml(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    system_template = """<|im_start|>system
{system_message}"""
    system_message = _get_system_message(messages)
    system_message = system_template.format(system_message=system_message)
    _roles = dict(user="<|im_start|>user", assistant="<|im_start|>assistant")
    _sep = "<|im_end|>"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_chatml(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_sep)


@register_chat_format("openchat")
def format_openchat(
    messages: List[llama_types.ChatCompletionRequestMessage],
    **kwargs: Any,
) -> ChatFormatterResponse:
    system_template = "{system_message}<|end_of_turn|>"
    system_message = _get_system_message(messages)
    system_message = system_template.format(system_message=system_message)
    _roles = dict(
        user="GPT4 Correct User: ", assistant="<|end_of_turn|>GPT4 Correct Assistant: "
    )
    _sep = "<|end_of_turn|>"
    _messages = _map_roles(messages, _roles)
    _messages.append((_roles["assistant"], None))
    _prompt = _format_chatml(system_message, _messages, _sep)
    return ChatFormatterResponse(prompt=_prompt, stop=_sep)


@register_chat_completion_handler("functionary")
def functionary_chat_handler(
    llama: llama.Llama,
    messages: List[llama_types.ChatCompletionRequestMessage],
    functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
    function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
    tools: Optional[List[llama_types.ChatCompletionTool]] = None,
    tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
    temperature: float = 0.2,
    top_p: float = 0.95,
    top_k: int = 40,
    min_p: float = 0.05,
    typical_p: float = 1.0,
    stream: bool = False,
    stop: Optional[Union[str, List[str]]] = [],
    response_format: Optional[llama_types.ChatCompletionRequestResponseFormat] = None,
    max_tokens: Optional[int] = None,
    presence_penalty: float = 0.0,
    frequency_penalty: float = 0.0,
    repeat_penalty: float = 1.1,
    tfs_z: float = 1.0,
    mirostat_mode: int = 0,
    mirostat_tau: float = 5.0,
    mirostat_eta: float = 0.1,
    model: Optional[str] = None,
    logits_processor: Optional[llama.LogitsProcessorList] = None,
    grammar: Optional[llama.LlamaGrammar] = None,
    **kwargs,  # type: ignore
) -> Union[llama_types.ChatCompletion, Iterator[llama_types.ChatCompletionChunk]]:
    SYSTEM_MESSAGE = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant calls functions with appropriate input when necessary"""

    def generate_type_definition(
        param: Dict[str, llama_types.JsonType], indent_level: int, shared_defs
    ) -> str:
        indent = "  " * indent_level
        if "$ref" in param:
            # Reference to a shared definition
            ref_name = param["$ref"].split("/")[
                -1
            ]  # Extract the type name from the reference
            return ref_name
        elif param.get("type") == "array":
            items = param.get("items", {})
            item_type = generate_type_definition(items, indent_level + 1, shared_defs)
            return f"Array<{item_type}>"
        elif param.get("type") == "object":
            properties = param.get("properties", {})
            nested_schema = "{\n"
            for nested_param_name, nested_param in properties.items():
                nested_param_type = generate_type_definition(
                    nested_param, indent_level + 1, shared_defs
                )
                nested_schema += (
                    f"{indent}  {nested_param_name}: {nested_param_type},\n"
                )
            nested_schema += indent + "}"
            return nested_schema
        elif "enum" in param:
            # Enum type
            return " | ".join([f'"{enum_value}"' for enum_value in param["enum"]])
        else:
            # Simple type
            return param.get("type", "any")

    def generate_shared_definitions(shared_defs, indent_level: int) -> str:
        indent = "  " * indent_level
        shared_definitions = ""
        for def_name, def_properties in shared_defs.items():
            shared_definitions += f"{indent}type {def_name} = "
            if def_properties.get("type") == "object":
                shared_definitions += generate_type_definition(
                    def_properties, indent_level, shared_defs
                )
            elif "enum" in def_properties:
                # Enum type
                shared_definitions += " | ".join(
                    [f'"{enum_value}"' for enum_value in def_properties["enum"]]
                )
            shared_definitions += ";\n"
        return shared_definitions

    def generate_schema_from_functions(functions, namespace="functions") -> str:
        schema = (
            "// Supported function definitions that should be called when necessary.\n"
        )
        schema += f"namespace {namespace} {{\n\n"

        # Generate shared definitions
        shared_definitions = {}
        for function in functions:
            parameters = function.get("parameters", {})
            shared_definitions.update(parameters.get("$defs", {}))

        schema += generate_shared_definitions(shared_definitions, 1)

        for function in functions:
            function_name = function["name"]
            description = function.get("description", "")
            parameters = function.get("parameters", {})
            required_params = parameters.get("required", [])

            schema += f"  // {description}\n"
            schema += f"  type {function_name} = (_: {{\n"

            for param_name, param in parameters.get("properties", {}).items():
                param_description = param.get("description", "")
                param_type = generate_type_definition(param, 2, shared_definitions)
                optional_indicator = "" if param_name in required_params else "?"
                schema += f"    // {param_description}\n"
                schema += f"    {param_name}{optional_indicator}: {param_type},\n"
            schema += "  }) => any;\n\n"

        schema += "}} // namespace {}\n".format(namespace)
        return schema

    def prepare_messages_for_inference(
        messages: List[llama_types.ChatCompletionRequestMessage],
        functions: Optional[List[llama_types.ChatCompletionFunctions]] = None,
        tools: Optional[List[llama_types.ChatCompletionTool]] = None,
    ):
        all_messages: List[llama_types.ChatCompletionRequestMessage] = []
        if functions is not None:
            all_messages.append(
                llama_types.ChatCompletionRequestSystemMessage(
                    role="system", content=generate_schema_from_functions(functions)
                )
            )

        if tools is not None:
            all_messages.append(
                llama_types.ChatCompletionRequestSystemMessage(
                    role="system",
                    content=generate_schema_from_functions(
                        [
                            tool["function"]
                            for tool in tools
                            if tool["type"] == "function"
                        ]
                    ),
                )
            )

        all_messages.append(
            llama_types.ChatCompletionRequestSystemMessage(
                role="system", content=SYSTEM_MESSAGE
            )
        )

        for message in messages:
            # Function call responses
            if message["role"] == "function" and "name" in message:
                message["name"] = f"functions.{message['name']}"
            # Function call requests by assistant
            if "function_call" in message:
                message["function_call"][
                    "name"
                ] = f"functions.{message['function_call']['name']}"
            all_messages.append(message)

        all_messages.append(
            llama_types.ChatCompletionRequestAssistantMessage(
                role="assistant", content=None
            )
        )

        def message_to_str(msg: llama_types.ChatCompletionRequestMessage):
            if msg["role"] == "system":
                return f"system:\n{msg['content']}\n"

            elif msg["role"] == "function" and "name" in msg:
                return f"function name={msg['name']}:\n{msg['content']}\n"
            elif msg["role"] == "function" and "function_call" in msg:
                return f"function name={msg['function_call']['name']}:\n{msg['function_call']['arguments']}\n"
            elif msg["role"] == "tool":
                if msg["content"] is not None:
                    return f"function name={msg['tool_call_id']}:\n{msg['content']}\n"
                else:
                    return f"function name={msg['tool_call_id']}\n"
            elif msg["role"] == "user":
                if msg["content"] is None:
                    return "user:\n</s></s>\n"
                else:
                    return f"user:\n</s>{msg['content']}</s>\n"
            elif msg["role"] == "assistant":
                if msg["content"] is not None and "function_call" in msg:
                    return f"assistant:\n{msg['content']}\nassistant to={msg['function_call']['name']}:\n{msg['function_call']['arguments']}</s>\n"
                elif "function_call" in msg:
                    return f"assistant to={msg['function_call']['name']}:\n{msg['function_call']['arguments']}</s>\n"
                elif "tool_calls" in msg and len(msg["tool_calls"]) > 0:
                    for tool_call in msg[
                        "tool_calls"
                    ]:  # NOTE: probably doesn't work with the functionary model
                        return f"assistant to={tool_call['id']}:\n{tool_call['function']['arguments']}</s>\n"
                elif msg["content"] is None:
                    return "assistant"
                else:
                    return f"assistant:\n{msg['content']}\n"
            else:
                raise ValueError(f"Unsupported role: {msg['role']}")

        return "".join([message_to_str(msg) for msg in all_messages])

    if tools is not None:
        functions = [tool["function"] for tool in tools if tool["type"] == "function"]

    if tool_choice is not None:
        function_call = (
            tool_choice if isinstance(tool_choice, str) else tool_choice["function"]
        )

    prompt = prepare_messages_for_inference(messages, functions, tools)

    if function_call is None and (functions is None or len(functions) == 0):
        completion_or_completion_chunks = llama.create_completion(
            prompt=prompt + ":\n",
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            min_p=min_p,
            typical_p=typical_p,
            stream=stream,
            stop=["user:", "</s>"],
            max_tokens=max_tokens,
            presence_penalty=presence_penalty,
            frequency_penalty=frequency_penalty,
            repeat_penalty=repeat_penalty,
            tfs_z=tfs_z,
            mirostat_mode=mirostat_mode,
            mirostat_tau=mirostat_tau,
            mirostat_eta=mirostat_eta,
            model=model,
            logits_processor=logits_processor,
            grammar=grammar,
        )
        return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream)  # type: ignore

    if function_call is None or (
        isinstance(function_call, str) and function_call == "auto"
    ):
        stop = "\n"
        completion: llama_types.Completion = llama.create_completion(
            prompt=prompt, stop=stop, stream=False
        )  # type: ignore
        completion_text = completion["choices"][0]["text"]
        # strip " to=functions." and ending ":"
        function_call = completion_text.split(".")[-1][:-1]
        new_prompt = prompt + completion_text + stop
    elif isinstance(function_call, str) and function_call != "none":
        new_prompt = prompt + f":\n"
    elif isinstance(function_call, dict):
        new_prompt = prompt + f" to=functions.{function_call['name']}:\n"
        function_call = function_call["name"]
    else:
        new_prompt = prompt + f":\n"

    function_body = None
    for function in functions or []:
        if function["name"] == function_call:
            function_body = function["parameters"]
            break
    for tool in tools or []:
        if tool["type"] == "function" and tool["function"]["name"] == function_call:
            function_body = tool["function"]["parameters"]
            break

    if function_body is not None:
        try:
            with suppress_stdout_stderr(disable=llama.verbose):
                grammar_text = llama_grammar.json_schema_to_gbnf(
                    json.dumps(function_body)
                )
                grammar = llama_grammar.LlamaGrammar.from_string(
                    llama_grammar.json_schema_to_gbnf(json.dumps(function_body))
                )
                print(grammar_text)
        except Exception as e:
            if llama.verbose:
                print(
                    "Failed to parse function body as JSON schema, falling back to default grammar"
                )
                print(e)
            with suppress_stdout_stderr(disable=llama.verbose):
                grammar = llama_grammar.LlamaGrammar.from_string(
                    llama_grammar.JSON_GBNF
                )
    else:
        with suppress_stdout_stderr(disable=llama.verbose):
            grammar = llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF)

    completion: llama_types.Completion = llama.create_completion(
        prompt=new_prompt,
        stop=["user:", "</s>"],
        stream=False,
        grammar=grammar,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        min_p=min_p,
        typical_p=typical_p,
        presence_penalty=presence_penalty,
        frequency_penalty=frequency_penalty,
        repeat_penalty=repeat_penalty,
        tfs_z=tfs_z,
        mirostat_mode=mirostat_mode,
        mirostat_tau=mirostat_tau,
        mirostat_eta=mirostat_eta,
        model=model,
        logits_processor=logits_processor,
    )  # type: ignore

    assert "usage" in completion
    assert isinstance(function_call, str)
    assert stream is False  # TODO: support stream mode

    print(new_prompt)
    print(completion["choices"][0]["text"])

    return llama_types.CreateChatCompletionResponse(
        id="chat" + completion["id"],
        object="chat.completion",
        created=completion["created"],
        model=completion["model"],
        choices=[
            {
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": None,
                    "function_call": {
                        "name": function_call,
                        "arguments": completion["choices"][0]["text"],
                    },
                    "tool_calls": [
                        {
                            "id": function_call,
                            "type": "function",
                            "function": {
                                "name": function_call,
                                "arguments": completion["choices"][0]["text"],
                            },
                        }
                    ],
                },
                "finish_reason": "tool_calls",
            }
        ],
        usage=completion["usage"],
    )


class Llava15ChatHandler:
    _clip_free = None

    def __init__(self, clip_model_path: str, verbose: bool = False):
        import llama_cpp.llava_cpp as llava_cpp

        self._llava_cpp = llava_cpp
        self.clip_model_path = clip_model_path
        self.verbose = verbose
        self._clip_free = self._llava_cpp._libllava.clip_free  # type: ignore

        with suppress_stdout_stderr(disable=self.verbose):
            self.clip_ctx = self._llava_cpp.clip_model_load(
                self.clip_model_path.encode(), 0
            )

    def __del__(self):
        with suppress_stdout_stderr(disable=self.verbose):
            if self.clip_ctx is not None and self._clip_free is not None:
                self._clip_free(self.clip_ctx)
                self.clip_ctx = None

    def load_image(self, image_url: str) -> bytes:
        if image_url.startswith("data:"):
            import base64

            image_bytes = base64.b64decode(image_url.split(",")[1])
            return image_bytes
        else:
            import urllib.request

            with urllib.request.urlopen(image_url) as f:
                image_bytes = f.read()
                return image_bytes

    def __call__(
        self,
        *,
        llama: llama.Llama,
        messages: List[llama_types.ChatCompletionRequestMessage],
        functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
        function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
        tools: Optional[List[llama_types.ChatCompletionTool]] = None,
        tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
        temperature: float = 0.2,
        top_p: float = 0.95,
        top_k: int = 40,
        min_p: float = 0.05,
        typical_p: float = 1.0,
        stream: bool = False,
        stop: Optional[Union[str, List[str]]] = [],
        response_format: Optional[
            llama_types.ChatCompletionRequestResponseFormat
        ] = None,
        max_tokens: Optional[int] = None,
        presence_penalty: float = 0.0,
        frequency_penalty: float = 0.0,
        repeat_penalty: float = 1.1,
        tfs_z: float = 1.0,
        mirostat_mode: int = 0,
        mirostat_tau: float = 5.0,
        mirostat_eta: float = 0.1,
        model: Optional[str] = None,
        logits_processor: Optional[llama.LogitsProcessorList] = None,
        grammar: Optional[llama.LlamaGrammar] = None,
        **kwargs,  # type: ignore
    ) -> Union[
        llama_types.CreateChatCompletionResponse,
        Iterator[llama_types.CreateChatCompletionStreamResponse],
    ]:
        assert (
            llama.context_params.logits_all is True
        )  # BUG: logits_all=True is required for llava
        assert self.clip_ctx is not None
        system_prompt = _get_system_message(messages)
        system_prompt = (
            system_prompt
            if system_prompt != ""
            else "A chat between a curious human and an artificial intelligence assistant.  The assistant gives helpful, detailed, and polite answers to the human's questions."
        )
        user_role = "\nUSER:"
        assistant_role = "\nASSISTANT:"
        llama.reset()
        llama.eval(llama.tokenize(system_prompt.encode("utf8"), add_bos=True))
        for message in messages:
            if message["role"] == "user" and message["content"] is not None:
                if isinstance(message["content"], str):
                    llama.eval(
                        llama.tokenize(
                            f"{user_role} {message['content']}".encode("utf8"),
                            add_bos=False,
                        )
                    )
                else:
                    assert isinstance(message["content"], list)
                    llama.eval(
                        llama.tokenize(f"{user_role} ".encode("utf8"), add_bos=False)
                    )
                    for content in message["content"]:
                        if content["type"] == "text":
                            llama.eval(
                                llama.tokenize(
                                    f"{content['text']}".encode("utf8"), add_bos=False
                                )
                            )
                        if content["type"] == "image_url":
                            image_bytes = (
                                self.load_image(content["image_url"]["url"])
                                if isinstance(content["image_url"], dict)
                                else self.load_image(content["image_url"])
                            )
                            import array

                            data_array = array.array("B", image_bytes)
                            c_ubyte_ptr = (
                                ctypes.c_ubyte * len(data_array)
                            ).from_buffer(data_array)
                            with suppress_stdout_stderr(disable=self.verbose):
                                embed = (
                                    self._llava_cpp.llava_image_embed_make_with_bytes(
                                        ctx_clip=self.clip_ctx,
                                        n_threads=llama.context_params.n_threads,
                                        image_bytes=c_ubyte_ptr,
                                        image_bytes_length=len(image_bytes),
                                    )
                                )
                            try:
                                n_past = ctypes.c_int(llama.n_tokens)
                                n_past_p = ctypes.pointer(n_past)
                                with suppress_stdout_stderr(disable=self.verbose):
                                    self._llava_cpp.llava_eval_image_embed(
                                        ctx_llama=llama.ctx,
                                        embed=embed,
                                        n_batch=llama.n_batch,
                                        n_past=n_past_p,
                                    )
                                assert llama.n_ctx() >= n_past.value
                                llama.n_tokens = n_past.value
                            finally:
                                with suppress_stdout_stderr(disable=self.verbose):
                                    self._llava_cpp.llava_image_embed_free(embed)
            if message["role"] == "assistant" and message["content"] is not None:
                llama.eval(
                    llama.tokenize(
                        f"ASSISTANT: {message['content']}".encode("utf8"), add_bos=False
                    )
                )
                assert llama.n_ctx() >= llama.n_tokens
        llama.eval(llama.tokenize(f"{assistant_role}".encode("utf8"), add_bos=False))
        assert llama.n_ctx() >= llama.n_tokens

        prompt = llama.input_ids[: llama.n_tokens].tolist()

        if response_format is not None and response_format["type"] == "json_object":
            with suppress_stdout_stderr(disable=self.verbose):
                grammar = llama_grammar.LlamaGrammar.from_string(
                    llama_grammar.JSON_GBNF
                )

        return _convert_completion_to_chat(
            llama.create_completion(
                prompt=prompt,
                temperature=temperature,
                top_p=top_p,
                top_k=top_k,
                min_p=min_p,
                typical_p=typical_p,
                stream=stream,
                stop=stop,
                max_tokens=max_tokens,
                presence_penalty=presence_penalty,
                frequency_penalty=frequency_penalty,
                repeat_penalty=repeat_penalty,
                tfs_z=tfs_z,
                mirostat_mode=mirostat_mode,
                mirostat_tau=mirostat_tau,
                mirostat_eta=mirostat_eta,
                model=model,
                logits_processor=logits_processor,
                grammar=grammar,
            ),
            stream=stream,
        )