from __future__ import annotations

import asyncio
import functools
import json
import logging
from operator import itemgetter
from typing import (
    Any,
    AsyncIterator,
    Callable,
    Dict,
    Iterator,
    List,
    Mapping,
    Optional,
    Sequence,
    Type,
    Union,
    cast,
)

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
    AIMessage,
    AIMessageChunk,
    BaseMessage,
    BaseMessageChunk,
    ChatMessage,
    ChatMessageChunk,
    FunctionMessage,
    HumanMessage,
    HumanMessageChunk,
    SystemMessage,
    SystemMessageChunk,
    ToolMessage,
    ToolMessageChunk,
)
from langchain_core.output_parsers.base import OutputParserLike
from langchain_core.output_parsers.openai_tools import (
    JsonOutputKeyToolsParser,
    PydanticToolsParser,
    make_invalid_tool_call,
    parse_tool_call,
)
from langchain_core.outputs import (
    ChatGeneration,
    ChatGenerationChunk,
    ChatResult,
)
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_core.utils.pydantic import is_basemodel_subclass
from pydantic import (
    BaseModel,
    ConfigDict,
    Field,
    SecretStr,
)
from requests.exceptions import HTTPError
from tenacity import (
    before_sleep_log,
    retry,
    retry_if_exception_type,
    stop_after_attempt,
    wait_exponential,
)

from langchain_community.llms.tongyi import (
    agenerate_with_last_element_mark,
    check_response,
    generate_with_last_element_mark,
)

logger = logging.getLogger(__name__)


def convert_dict_to_message(
    _dict: Mapping[str, Any], is_chunk: bool = False
) -> Union[BaseMessage, BaseMessageChunk]:
    """Convert a dict to a message."""
    role = _dict["role"]
    content = _dict["content"]
    if role == "user":
        return (
            HumanMessageChunk(content=content)
            if is_chunk
            else HumanMessage(content=content)
        )
    elif role == "assistant":
        tool_calls = []
        invalid_tool_calls = []
        if "tool_calls" in _dict:
            additional_kwargs = {"tool_calls": _dict["tool_calls"]}

            for enum_index, tool_call_data in enumerate(_dict["tool_calls"]):
                if is_chunk:
                    try:
                        # For incremental streaming: newer Qwen models provide proper
                        # index to prevent parallel tool calls from being merged
                        # incorrectly. Fall back to enumeration index if not provided.
                        api_index = tool_call_data.get("index", enum_index)

                        tool_calls.append(
                            {
                                "name": tool_call_data["function"].get("name"),
                                "args": tool_call_data["function"].get("arguments"),
                                "id": tool_call_data.get("id"),
                                "index": api_index,
                            }
                        )
                    except KeyError:
                        pass
                else:
                    try:
                        parsed_tool = parse_tool_call(tool_call_data, return_id=True)
                        if parsed_tool:
                            tool_calls.append(parsed_tool)
                    except Exception as e:
                        invalid_tool_calls.append(
                            make_invalid_tool_call(tool_call_data, str(e))
                        )
        elif "reasoning_content" in _dict:
            additional_kwargs = {"reasoning_content": _dict["reasoning_content"]}
        elif "partial" in _dict and isinstance(_dict["partial"], bool):
            additional_kwargs = {"partial": _dict["partial"]}
        else:
            additional_kwargs = {}

        return (
            AIMessageChunk(
                content=content,
                additional_kwargs=additional_kwargs,
                tool_call_chunks=tool_calls,  # type: ignore[arg-type]
                id=_dict.get("id"),
            )
            if is_chunk
            else AIMessage(
                content=content,
                additional_kwargs=additional_kwargs,
                tool_calls=tool_calls,
                invalid_tool_calls=invalid_tool_calls,
            )
        )
    elif role == "system":
        return (
            SystemMessageChunk(content=content)
            if is_chunk
            else SystemMessage(content=content)
        )
    elif role == "tool":
        additional_kwargs = {}
        if "name" in _dict:
            additional_kwargs["name"] = _dict["name"]
        return (
            ToolMessageChunk(
                content=_dict.get("content", ""),
                tool_call_id=_dict.get("tool_call_id"),  # type: ignore[arg-type]
                additional_kwargs=additional_kwargs,
            )
            if is_chunk
            else ToolMessage(
                content=_dict.get("content", ""),
                tool_call_id=_dict.get("tool_call_id"),
                additional_kwargs=additional_kwargs,
            )
        )
    else:
        return (
            ChatMessageChunk(role=role, content=content)
            if is_chunk
            else ChatMessage(role=role, content=content)
        )


def convert_message_chunk_to_message(message_chunk: BaseMessageChunk) -> BaseMessage:
    """Convert a message chunk to a message.

    Args:
        chunk: Message chunk to convert.

    Returns:
        Message.
    """
    if not isinstance(message_chunk, BaseMessageChunk):
        return message_chunk
    # chunk classes always have the equivalent non-chunk class as their first parent
    ignore_keys = ["type"]
    if isinstance(message_chunk, AIMessageChunk):
        ignore_keys.append("tool_call_chunks")
    return message_chunk.__class__.__mro__[1](
        **{k: v for k, v in message_chunk.__dict__.items() if k not in ignore_keys}
    )


def convert_message_to_dict(message: BaseMessage) -> dict:
    """Convert a message to a dict."""

    message_dict: Dict[str, Any]
    if isinstance(message, ChatMessage):
        message_dict = {"role": message.role, "content": message.content}
    elif isinstance(message, HumanMessage):
        message_dict = {"role": "user", "content": message.content}
    elif isinstance(message, AIMessage):
        message_dict = {"role": "assistant", "content": message.content}
        if "tool_calls" in message.additional_kwargs:
            message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
        # support Partial Mode for text continuation
        if "partial" in message.additional_kwargs:
            message_dict["partial"] = message.additional_kwargs["partial"]
    elif isinstance(message, SystemMessage):
        message_dict = {"role": "system", "content": message.content}
    elif isinstance(message, ToolMessage):
        message_dict = {
            "role": "tool",
            "tool_call_id": message.tool_call_id,
            "content": message.content,
            "name": message.name or message.additional_kwargs.get("name"),
        }
    elif isinstance(message, FunctionMessage):
        message_dict = {
            "role": "tool",
            "tool_call_id": "",
            "content": message.content,
            "name": message.name,
        }
    else:
        raise TypeError(f"Got unknown type {message}")
    return message_dict


def _create_retry_decorator(llm: ChatTongyi) -> Callable[[Any], Any]:
    min_seconds = 1
    max_seconds = 4
    # Wait 2^x * 1 second between each retry starting with
    # 4 seconds, then up to 10 seconds, then 10 seconds afterward
    return retry(
        reraise=True,
        stop=stop_after_attempt(llm.max_retries),
        wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
        retry=(retry_if_exception_type(HTTPError)),
        before_sleep=before_sleep_log(logger, logging.WARNING),
    )


class ChatTongyi(BaseChatModel):
    """Alibaba Tongyi Qwen chat model integration.

    Setup:
        Install ``dashscope`` and set environment variables ``DASHSCOPE_API_KEY``.

        .. code-block:: bash

            pip install dashscope
            export DASHSCOPE_API_KEY="your-api-key"

    Key init args — completion params:
        model: str
            Name of Qianfan model to use.
        top_p: float
            Total probability mass of tokens to consider at each step.
        streaming: bool
            Whether to stream the results or not.

    Key init args — client params:
        api_key: Optional[str]
            Dashscope API KEY. If not passed in will be read from env var DASHSCOPE_API_KEY.
        max_retries: int
            Maximum number of retries to make when generating.

    See full list of supported init args and their descriptions in the params section.

    Instantiate:
        .. code-block:: python

            from langchain_community.chat_models import ChatTongyi

            tongyi_chat = ChatTongyi(
                model="qwen-max",
                # top_p="...",
                # api_key="...",
                # other params...
            )

    Invoke:
        .. code-block:: python

            messages = [
                ("system", "你是一名专业的翻译家，可以将用户的中文翻译为英文。"),
                ("human", "我喜欢编程。"),
            ]
            tongyi_chat.invoke(messages)

        .. code-block:: python

            AIMessage(
                content='I enjoy programming.',
                response_metadata={
                    'model_name': 'qwen-max',
                    'finish_reason': 'stop',
                    'request_id': '0bd14853-4abc-9593-8642-8dbb915bd4df',
                    'token_usage': {
                        'input_tokens': 30,
                        'output_tokens': 4,
                        'total_tokens': 34
                    }
                },
                id='run-533b3688-d12b-40c6-a2f7-52f291f8fa0a-0'
            )

    Stream:
        .. code-block:: python

            for chunk in tongyi_chat.stream(messages):
                print(chunk)

        .. code-block:: python

            content='I' id='run-8fbcce63-42fc-4208-9399-da46ac40c967'
            content=' enjoy' id='run-8fbcce63-42fc-4208-9399-da46ac40c967'
            content=' programming' id='run-8fbcce63-42fc-4208-9399-da46ac40c967'
            content='.' response_metadata={'finish_reason': 'stop', 'request_id': '67aec2b5-72bf-96a4-ae29-5bfebd2e7305', 'token_usage': {'input_tokens': 30, 'output_tokens': 4, 'total_tokens': 34}} id='run-8fbcce63-42fc-4208-9399-da46ac40c967'

    Async:
        .. code-block:: python

            await tongyi_chat.ainvoke(messages)

            # stream:
            # async for chunk in tongyi_chat.astream(messages):
            #    print(chunk)

            # batch:
            # await tongyi_chat.abatch([messages])

        .. code-block:: python

            AIMessage(
                content='I enjoy programming.',
                response_metadata={
                    'model_name': 'qwen-max',
                    'finish_reason': 'stop',
                    'request_id': 'a55a2d6c-a876-9789-9dd9-7b52bf8adde0',
                    'token_usage': {
                        'input_tokens': 30,
                        'output_tokens': 4,
                        'total_tokens': 34
                    }
                },
                id='run-3bffa3ec-e8d9-4043-b57d-348e047d64de-0'
            )

    Tool calling:
        .. code-block:: python

            from pydantic import BaseModel, Field


            class GetWeather(BaseModel):
                '''Get the current weather in a given location'''

                location: str = Field(
                    ..., description="The city and state, e.g. San Francisco, CA"
                )


            class GetPopulation(BaseModel):
                '''Get the current population in a given location'''

                location: str = Field(
                    ..., description="The city and state, e.g. San Francisco, CA"
                )

            chat_with_tools = tongyi_chat.bind_tools([GetWeather, GetPopulation])
            ai_msg = chat_with_tools.invoke(
                "Which city is hotter today and which is bigger: LA or NY?"
            )
            ai_msg.tool_calls

        .. code-block:: python
            [
                {
                    'name': 'GetWeather',
                    'args': {'location': 'Los Angeles, CA'},
                    'id': ''
                }
            ]

    Structured output:
        .. code-block:: python

            from typing import Optional

            from pydantic import BaseModel, Field


            class Joke(BaseModel):
                '''Joke to tell user.'''

                setup: str = Field(description="The setup of the joke")
                punchline: str = Field(description="The punchline to the joke")
                rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")


            structured_chat = tongyi_chat.with_structured_output(Joke)
            structured_chat.invoke("Tell me a joke about cats")

        .. code-block:: python

            Joke(
                setup='Why did the cat join the band?',
                punchline='Because it wanted to be a solo purr-sonality!',
                rating=None
            )

    Response metadata
        .. code-block:: python

            ai_msg = tongyi_chat.invoke(messages)
            ai_msg.response_metadata

        .. code-block:: python

            {
                'model_name': 'qwen-max',
                'finish_reason': 'stop',
                'request_id': '32a13e4c-370e-99cb-8f9b-4c999d98c57d',
                'token_usage': {
                    'input_tokens': 30,
                    'output_tokens': 4,
                    'total_tokens': 34
                }
            }

    """  # noqa: E501

    @property
    def lc_secrets(self) -> Dict[str, str]:
        return {"dashscope_api_key": "DASHSCOPE_API_KEY"}

    client: Any = None
    model_name: str = Field(default="qwen-turbo", alias="model")
    """Model name to use.
    callable multimodal model:
    - qwen-vl-v1
    - qwen-vl-chat-v1
    - qwen-audio-turbo
    - qwen-vl-plus
    - qwen-vl-max
    """
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)

    top_p: float = 0.8
    """Total probability mass of tokens to consider at each step."""

    dashscope_api_key: Optional[SecretStr] = Field(None, alias="api_key")
    """Dashscope api key provide by Alibaba Cloud."""

    streaming: bool = False
    """Whether to stream the results or not."""

    max_retries: int = 10
    """Maximum number of retries to make when generating."""

    model_config = ConfigDict(
        populate_by_name=True,
    )

    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "tongyi"

    @pre_init
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        values["dashscope_api_key"] = convert_to_secret_str(
            get_from_dict_or_env(values, "dashscope_api_key", "DASHSCOPE_API_KEY")
        )
        try:
            import dashscope
        except ImportError:
            raise ImportError(
                "Could not import dashscope python package. "
                "Please install it with `pip install dashscope --upgrade`."
            )
        dashscope_multimodal_models = [
            "qwen-audio-turbo",
            "qwen-audio-turbo-latest",
            "qwen-vl-plus",
            "qwen-vl-plus-latest",
            "qwen-vl-max",
            "qwen-vl-max-latest",
        ]
        if (
            values["model_name"] in dashscope_multimodal_models
            or "vl" in values["model_name"]
        ):
            try:
                values["client"] = dashscope.MultiModalConversation
            except AttributeError:
                raise ValueError(
                    "`dashscope` has no `MultiModalConversation` attribute, this is "
                    "likely due to an old version of the dashscope package. Try "
                    "upgrading it with `pip install --upgrade dashscope`."
                )
        else:
            try:
                values["client"] = dashscope.Generation
            except AttributeError:
                raise ValueError(
                    "`dashscope` has no `Generation` attribute, this is likely "
                    "due to an old version of the dashscope package. Try upgrading it "
                    "with `pip install --upgrade dashscope`."
                )
        return values

    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the default parameters for calling Tongyi Qwen API."""
        return {
            "model": self.model_name,
            "top_p": self.top_p,
            "api_key": cast(SecretStr, self.dashscope_api_key).get_secret_value(),
            "result_format": "message",
            **self.model_kwargs,
        }

    def completion_with_retry(self, **kwargs: Any) -> Any:
        """Use tenacity to retry the completion call."""
        retry_decorator = _create_retry_decorator(self)

        @retry_decorator
        def _completion_with_retry(**_kwargs: Any) -> Any:
            resp = self.client.call(**_kwargs)
            return check_response(resp)

        return _completion_with_retry(**kwargs)

    def stream_completion_with_retry(self, **kwargs: Any) -> Any:
        """Use tenacity to retry the completion call."""
        retry_decorator = _create_retry_decorator(self)

        @retry_decorator
        def _stream_completion_with_retry(**_kwargs: Any) -> Any:
            responses = self.client.call(**_kwargs)
            prev_resp = None

            for resp in responses:
                # If we are streaming without `incremental_output = True`,
                # we need to calculate the delta response manually
                if _kwargs.get("stream") and not _kwargs.get(
                    "incremental_output", False
                ):
                    # inline fix response text logic
                    resp_copy = json.loads(json.dumps(resp))
                    if resp_copy.get("output") and resp_copy["output"].get("choices"):
                        choice = resp_copy["output"]["choices"][0]
                        message = choice["message"]
                        if isinstance(message.get("content"), list):
                            content_text = "".join(
                                item.get("text", "")
                                for item in message["content"]
                                if isinstance(item, dict)
                            )
                            message["content"] = content_text
                        resp = resp_copy
                    if prev_resp is None:
                        delta_resp = resp
                    else:
                        delta_resp = self.subtract_client_response(resp, prev_resp)
                    prev_resp = resp
                    yield check_response(delta_resp)
                else:
                    yield check_response(resp)

        return _stream_completion_with_retry(**kwargs)

    def subtract_client_response(self, resp: Any, prev_resp: Any) -> Any:
        """Subtract prev response from curr response.

        Useful when streaming without `incremental_output = True`
        """

        resp_copy = json.loads(json.dumps(resp))
        choice = resp_copy["output"]["choices"][0]
        message = choice["message"]

        prev_resp_copy = json.loads(json.dumps(prev_resp))
        prev_choice = prev_resp_copy["output"]["choices"][0]
        prev_message = prev_choice["message"]

        message["content"] = message["content"].replace(prev_message["content"], "")

        if message.get("tool_calls"):
            for index, tool_call in enumerate(message["tool_calls"]):
                function = tool_call["function"]

                if prev_message.get("tool_calls"):
                    prev_function = prev_message["tool_calls"][index]["function"]

                    if "name" in function:
                        function["name"] = function["name"].replace(
                            prev_function["name"], ""
                        )
                    if "arguments" in function:
                        function["arguments"] = function["arguments"].replace(
                            prev_function["arguments"], ""
                        )

        return resp_copy

    async def astream_completion_with_retry(self, **kwargs: Any) -> Any:
        """Because the dashscope SDK doesn't provide an async API,
        we wrap `stream_generate_with_retry` with an async generator."""

        class _AioTongyiGenerator:
            def __init__(self, generator: Any):
                self.generator = generator

            def __aiter__(self) -> AsyncIterator[Any]:
                return self

            async def __anext__(self) -> Any:
                value = await asyncio.get_running_loop().run_in_executor(
                    None, self._safe_next
                )
                if value is not None:
                    return value
                else:
                    raise StopAsyncIteration

            def _safe_next(self) -> Any:
                try:
                    return next(self.generator)
                except StopIteration:
                    return None

        async for chunk in _AioTongyiGenerator(
            generator=self.stream_completion_with_retry(**kwargs)
        ):
            yield chunk

    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> ChatResult:
        generations = []
        if self.streaming:
            generation_chunk: Optional[ChatGenerationChunk] = None
            for chunk in self._stream(
                messages, stop=stop, run_manager=run_manager, **kwargs
            ):
                if generation_chunk is None:
                    generation_chunk = chunk
                else:
                    generation_chunk += chunk
            assert generation_chunk is not None
            generations.append(self._chunk_to_generation(generation_chunk))
        else:
            params: Dict[str, Any] = self._invocation_params(
                messages=messages, stop=stop, **kwargs
            )
            resp = self.completion_with_retry(**params)
            generations.append(
                ChatGeneration(**self._chat_generation_from_qwen_resp(resp))
            )
        return ChatResult(
            generations=generations,
            llm_output={
                "model_name": self.model_name,
            },
        )

    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> ChatResult:
        generations = []
        if self.streaming:
            generation: Optional[ChatGenerationChunk] = None
            async for chunk in self._astream(
                messages, stop=stop, run_manager=run_manager, **kwargs
            ):
                if generation is None:
                    generation = chunk
                else:
                    generation += chunk
            assert generation is not None
            generations.append(self._chunk_to_generation(generation))
        else:
            params: Dict[str, Any] = self._invocation_params(
                messages=messages, stop=stop, **kwargs
            )
            resp = await asyncio.get_running_loop().run_in_executor(
                None,
                functools.partial(self.completion_with_retry, **params),
            )
            generations.append(
                ChatGeneration(**self._chat_generation_from_qwen_resp(resp))
            )
        return ChatResult(
            generations=generations,
            llm_output={
                "model_name": self.model_name,
            },
        )

    def _stream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[ChatGenerationChunk]:
        params: Dict[str, Any] = self._invocation_params(
            messages=messages, stop=stop, stream=True, **kwargs
        )

        for stream_resp, is_last_chunk in generate_with_last_element_mark(
            self.stream_completion_with_retry(**params)
        ):
            choice = stream_resp["output"]["choices"][0]
            message = choice["message"]
            if (
                choice["finish_reason"] == "null"
                and message["content"] == ""
                and message.get("reasoning_content", "") == ""
                and "tool_calls" not in message
            ):
                continue

            chunk = ChatGenerationChunk(
                **self._chat_generation_from_qwen_resp(
                    stream_resp, is_chunk=True, is_last_chunk=is_last_chunk
                )
            )
            if run_manager:
                run_manager.on_llm_new_token(chunk.text, chunk=chunk)
            yield chunk

    async def _astream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> AsyncIterator[ChatGenerationChunk]:
        params: Dict[str, Any] = self._invocation_params(
            messages=messages, stop=stop, stream=True, **kwargs
        )
        async for stream_resp, is_last_chunk in agenerate_with_last_element_mark(
            self.astream_completion_with_retry(**params)
        ):
            chunk = ChatGenerationChunk(
                **self._chat_generation_from_qwen_resp(
                    stream_resp, is_chunk=True, is_last_chunk=is_last_chunk
                )
            )
            if run_manager:
                await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
            yield chunk

    def _invocation_params(
        self, messages: List[BaseMessage], stop: Any, **kwargs: Any
    ) -> Dict[str, Any]:
        params = {**self._default_params, **kwargs}
        if stop is not None:
            params["stop"] = stop
        # According to the Tongyi official docs,
        # `incremental_output` with `tools` is not supported yet
        if params.get("stream") and not params.get("tools"):
            params["incremental_output"] = True

        message_dicts = [convert_message_to_dict(m) for m in messages]

        # And the `system` message should be the first message if present
        system_message_indices = [
            i for i, m in enumerate(message_dicts) if m["role"] == "system"
        ]
        if len(system_message_indices) == 1 and system_message_indices[0] != 0:
            raise ValueError("System message can only be the first message.")

        params["messages"] = message_dicts

        return params

    def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
        if llm_outputs[0] is None:
            return {}
        return llm_outputs[0]

    @staticmethod
    def _chat_generation_from_qwen_resp(
        resp: Any, is_chunk: bool = False, is_last_chunk: bool = True
    ) -> Dict[str, Any]:
        # According to the response from dashscope,
        # each chunk's `generation_info` overwrites the previous one.
        # Besides, The `merge_dicts` method,
        # which is used to concatenate `generation_info` in `GenerationChunk`,
        # does not support merging of int type values.
        # Therefore, we adopt the `generation_info` of the last chunk
        # and discard the `generation_info` of the intermediate chunks.
        choice = resp["output"]["choices"][0]
        message = convert_dict_to_message(choice["message"], is_chunk=is_chunk)
        if is_last_chunk:
            return dict(
                message=message,
                generation_info=dict(
                    finish_reason=choice["finish_reason"],
                    request_id=resp["request_id"],
                    token_usage=dict(resp["usage"]),
                ),
            )
        else:
            return dict(message=message)

    @staticmethod
    def _chunk_to_generation(chunk: ChatGenerationChunk) -> ChatGeneration:
        return ChatGeneration(
            message=convert_message_chunk_to_message(chunk.message),
            generation_info=chunk.generation_info,
        )

    def bind_tools(
        self,
        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, AIMessage]:
        """Bind tool-like objects to this chat model.

        Args:
            tools: A list of tool definitions to bind to this chat model.
                Can be  a dictionary, pydantic model, callable, or BaseTool. Pydantic
                models, callables, and BaseTools will be automatically converted to
                their schema dictionary representation.
            **kwargs: Any additional parameters to pass to the
                :class:`~langchain.runnable.Runnable` constructor.
        """

        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
        return super().bind(tools=formatted_tools, **kwargs)

    def with_structured_output(
        self,
        schema: Union[Dict, Type[BaseModel]],
        *,
        include_raw: bool = False,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
        """Model wrapper that returns outputs formatted to match the given schema.

        Args:
            schema: The output schema as a dict or a Pydantic class. If a Pydantic class
                then the model output will be an object of that class. If a dict then
                the model output will be a dict. With a Pydantic class the returned
                attributes will be validated, whereas with a dict they will not be. If
                `method` is "function_calling" and `schema` is a dict, then the dict
                must match the OpenAI function-calling spec.
            include_raw:
                If `False` then only the parsed structured output is returned.

                If an error occurs during model output parsing it will be raised.

                If `True` then both the raw model response (a `BaseMessage`) and the
                parsed model response will be returned.

                If an error occurs during output parsing it will be caught and returned
                as well.

                The final output is always a `dict` with keys `'raw'`, `'parsed'`, and
                `'parsing_error'`.

        Returns:
            A Runnable that takes any ChatModel input and returns as output:

                If include_raw is True then a dict with keys:
                    raw: BaseMessage
                    parsed: Optional[_DictOrPydantic]
                    parsing_error: Optional[BaseException]

                If include_raw is False then just _DictOrPydantic is returned,
                where _DictOrPydantic depends on the schema:

                If schema is a Pydantic class then _DictOrPydantic is the Pydantic
                    class.

                If schema is a dict then _DictOrPydantic is a dict.

        """
        if kwargs:
            raise ValueError(f"Received unsupported arguments {kwargs}")
        is_pydantic_schema = isinstance(schema, type) and is_basemodel_subclass(schema)
        llm = self.bind_tools([schema])
        if is_pydantic_schema:
            output_parser: OutputParserLike = PydanticToolsParser(
                tools=[schema],  # type: ignore[list-item]
                first_tool_only=True,
            )
        else:
            key_name = convert_to_openai_tool(schema)["function"]["name"]
            output_parser = JsonOutputKeyToolsParser(
                key_name=key_name, first_tool_only=True
            )

        if include_raw:
            parser_assign = RunnablePassthrough.assign(
                parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
            )
            parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
            parser_with_fallback = parser_assign.with_fallbacks(
                [parser_none], exception_key="parsing_error"
            )
            return RunnableMap(raw=llm) | parser_with_fallback
        else:
            return llm | output_parser