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# Source code for langchain_community.chat_models.gigachat

    from __future__ import annotations

    import logging
    from typing import (
        TYPE_CHECKING,
        Any,
        AsyncIterator,
        Dict,
        Iterator,
        List,
        Mapping,
        Optional,
        Type,
    )

    from langchain_core.callbacks import (
        AsyncCallbackManagerForLLMRun,
        CallbackManagerForLLMRun,
    )
    from langchain_core.language_models.chat_models import (
        BaseChatModel,
        agenerate_from_stream,
        generate_from_stream,
    )
    from langchain_core.messages import (
        AIMessage,
        AIMessageChunk,
        BaseMessage,
        BaseMessageChunk,
        ChatMessage,
        ChatMessageChunk,
        FunctionMessage,
        FunctionMessageChunk,
        HumanMessage,
        HumanMessageChunk,
        SystemMessage,
        SystemMessageChunk,
    )
    from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult

    from langchain_community.llms.gigachat import _BaseGigaChat

    if TYPE_CHECKING:
        import gigachat.models as gm

    logger = logging.getLogger(__name__)

    def _convert_dict_to_message(message: gm.Messages) -> BaseMessage:
        from gigachat.models import FunctionCall, MessagesRole

        additional_kwargs: Dict = {}
        if function_call := message.function_call:
            if isinstance(function_call, FunctionCall):
                additional_kwargs["function_call"] = dict(function_call)
            elif isinstance(function_call, dict):
                additional_kwargs["function_call"] = function_call

        if message.role == MessagesRole.SYSTEM:
            return SystemMessage(content=message.content)
        elif message.role == MessagesRole.USER:
            return HumanMessage(content=message.content)
        elif message.role == MessagesRole.ASSISTANT:
            return AIMessage(content=message.content, additional_kwargs=additional_kwargs)
        else:
            raise TypeError(f"Got unknown role {message.role} {message}")

    def _convert_message_to_dict(message: gm.BaseMessage) -> gm.Messages:
        from gigachat.models import Messages, MessagesRole

        if isinstance(message, SystemMessage):
            return Messages(role=MessagesRole.SYSTEM, content=message.content)
        elif isinstance(message, HumanMessage):
            return Messages(role=MessagesRole.USER, content=message.content)
        elif isinstance(message, AIMessage):
            return Messages(
                role=MessagesRole.ASSISTANT,
                content=message.content,
                function_call=message.additional_kwargs.get("function_call", None),
            )
        elif isinstance(message, ChatMessage):
            return Messages(role=MessagesRole(message.role), content=message.content)
        elif isinstance(message, FunctionMessage):
            return Messages(role=MessagesRole.FUNCTION, content=message.content)
        else:
            raise TypeError(f"Got unknown type {message}")

    def _convert_delta_to_message_chunk(
        _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
    ) -> BaseMessageChunk:
        role = _dict.get("role")
        content = _dict.get("content") or ""
        additional_kwargs: Dict = {}
        if _dict.get("function_call"):
            function_call = dict(_dict["function_call"])
            if "name" in function_call and function_call["name"] is None:
                function_call["name"] = ""
            additional_kwargs["function_call"] = function_call

        if role == "user" or default_class == HumanMessageChunk:
            return HumanMessageChunk(content=content)
        elif role == "assistant" or default_class == AIMessageChunk:
            return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
        elif role == "system" or default_class == SystemMessageChunk:
            return SystemMessageChunk(content=content)
        elif role == "function" or default_class == FunctionMessageChunk:
            return FunctionMessageChunk(content=content, name=_dict["name"])
        elif role or default_class == ChatMessageChunk:
            return ChatMessageChunk(content=content, role=role)
        else:
            return default_class(content=content)

    class GigaChat(_BaseGigaChat, BaseChatModel):
        """`GigaChat` large language models API.

        To use, you should pass login and password to access GigaChat API or use token.

        Example:
            .. code-block:: python

                from langchain_community.chat_models import GigaChat
                giga = GigaChat(credentials=..., scope=..., verify_ssl_certs=False)
        """

        def _build_payload(self, messages: List[BaseMessage], **kwargs: Any) -> gm.Chat:
            from gigachat.models import Chat

            payload = Chat(
                messages=[_convert_message_to_dict(m) for m in messages],
            )

            payload.functions = kwargs.get("functions", None)
            payload.model = self.model

            if self.profanity_check is not None:
                payload.profanity_check = self.profanity_check
            if self.temperature is not None:
                payload.temperature = self.temperature
            if self.top_p is not None:
                payload.top_p = self.top_p
            if self.max_tokens is not None:
                payload.max_tokens = self.max_tokens
            if self.repetition_penalty is not None:
                payload.repetition_penalty = self.repetition_penalty
            if self.update_interval is not None:
                payload.update_interval = self.update_interval

            if self.verbose:
                logger.warning("Giga request: %s", payload.dict())

            return payload

        def _create_chat_result(self, response: Any) -> ChatResult:
            generations = []
            for res in response.choices:
                message = _convert_dict_to_message(res.message)
                finish_reason = res.finish_reason
                gen = ChatGeneration(
                    message=message,
                    generation_info={"finish_reason": finish_reason},
                )
                generations.append(gen)
                if finish_reason != "stop":
                    logger.warning(
                        "Giga generation stopped with reason: %s",
                        finish_reason,
                    )
                if self.verbose:
                    logger.warning("Giga response: %s", message.content)
            llm_output = {"token_usage": response.usage, "model_name": response.model}
            return ChatResult(generations=generations, llm_output=llm_output)

        def _generate(
            self,
            messages: List[BaseMessage],
            stop: Optional[List[str]] = None,
            run_manager: Optional[CallbackManagerForLLMRun] = None,
            stream: Optional[bool] = None,
            **kwargs: Any,
        ) -> ChatResult:
            should_stream = stream if stream is not None else self.streaming
            if should_stream:
                stream_iter = self._stream(
                    messages, stop=stop, run_manager=run_manager, **kwargs
                )
                return generate_from_stream(stream_iter)

            payload = self._build_payload(messages, **kwargs)
            response = self._client.chat(payload)

            return self._create_chat_result(response)

        async def _agenerate(
            self,
            messages: List[BaseMessage],
            stop: Optional[List[str]] = None,
            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
            stream: Optional[bool] = None,
            **kwargs: Any,
        ) -> ChatResult:
            should_stream = stream if stream is not None else self.streaming
            if should_stream:
                stream_iter = self._astream(
                    messages, stop=stop, run_manager=run_manager, **kwargs
                )
                return await agenerate_from_stream(stream_iter)

            payload = self._build_payload(messages, **kwargs)
            response = await self._client.achat(payload)

            return self._create_chat_result(response)

        def _stream(
            self,
            messages: List[BaseMessage],
            stop: Optional[List[str]] = None,
            run_manager: Optional[CallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> Iterator[ChatGenerationChunk]:
            payload = self._build_payload(messages, **kwargs)

            for chunk in self._client.stream(payload):
                if not isinstance(chunk, dict):
                    chunk = chunk.dict()
                if len(chunk["choices"]) == 0:
                    continue

                choice = chunk["choices"][0]
                content = choice.get("delta", {}).get("content", {})
                chunk = _convert_delta_to_message_chunk(choice["delta"], AIMessageChunk)

                finish_reason = choice.get("finish_reason")

                generation_info = (
                    dict(finish_reason=finish_reason) if finish_reason is not None else None
                )

                if run_manager:
                    run_manager.on_llm_new_token(content)

                yield ChatGenerationChunk(message=chunk, generation_info=generation_info)

        async def _astream(
            self,
            messages: List[BaseMessage],
            stop: Optional[List[str]] = None,
            run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
            **kwargs: Any,
        ) -> AsyncIterator[ChatGenerationChunk]:
            payload = self._build_payload(messages, **kwargs)

            async for chunk in self._client.astream(payload):
                if not isinstance(chunk, dict):
                    chunk = chunk.dict()
                if len(chunk["choices"]) == 0:
                    continue

                choice = chunk["choices"][0]
                content = choice.get("delta", {}).get("content", {})
                chunk = _convert_delta_to_message_chunk(choice["delta"], AIMessageChunk)

                finish_reason = choice.get("finish_reason")

                generation_info = (
                    dict(finish_reason=finish_reason) if finish_reason is not None else None
                )

                yield ChatGenerationChunk(message=chunk, generation_info=generation_info)
                if run_manager:
                    await run_manager.on_llm_new_token(content)