"""Azure OpenAI chat wrapper."""

from __future__ import annotations

import logging
import os
from operator import itemgetter
from typing import (
    Any,
    Callable,
    Dict,
    List,
    Literal,
    Optional,
    Sequence,
    Type,
    TypedDict,
    TypeVar,
    Union,
    overload,
)

import openai
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import LangSmithParams
from langchain_core.messages import BaseMessage
from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
from langchain_core.output_parsers.base import OutputParserLike
from langchain_core.output_parsers.openai_tools import (
    JsonOutputKeyToolsParser,
    PydanticToolsParser,
)
from langchain_core.outputs import ChatResult
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
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
from langchain_core.utils.function_calling import convert_to_openai_tool

from langchain_openai.chat_models.base import BaseChatOpenAI

logger = logging.getLogger(__name__)


_BM = TypeVar("_BM", bound=BaseModel)
_DictOrPydanticClass = Union[Dict[str, Any], Type[_BM]]
_DictOrPydantic = Union[Dict, _BM]


class _AllReturnType(TypedDict):
    raw: BaseMessage
    parsed: Optional[_DictOrPydantic]
    parsing_error: Optional[BaseException]


def _is_pydantic_class(obj: Any) -> bool:
    return isinstance(obj, type) and issubclass(obj, BaseModel)


class AzureChatOpenAI(BaseChatOpenAI):
    """Azure OpenAI chat model integration.

    Setup:
        Head to the https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python
        to create your Azure OpenAI deployment.

        Then install ``langchain-openai`` and set environment variables
        ``AZURE_OPENAI_API_KEY`` and ``AZURE_OPENAI_ENDPOINT``:

        .. code-block:: bash

            pip install -U langchain-openai

            export AZURE_OPENAI_API_KEY="your-api-key"
            export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"

    Key init args — completion params:
        azure_deployment: str
            Name of Azure OpenAI deployment to use.
        temperature: float
            Sampling temperature.
        max_tokens: Optional[int]
            Max number of tokens to generate.
        logprobs: Optional[bool]
            Whether to return logprobs.

    Key init args — client params:
        api_version: str
            Azure OpenAI API version to use. See more on the different versions here:
            https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning
        timeout: Union[float, Tuple[float, float], Any, None]
            Timeout for requests.
        max_retries: int
            Max number of retries.
        organization: Optional[str]
            OpenAI organization ID. If not passed in will be read from env
            var OPENAI_ORG_ID.

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

    Instantiate:
        .. code-block:: python

            from langchain_openai import AzureChatOpenAI

            llm = AzureChatOpenAI(
                azure_deployment="your-deployment",
                api_version="2024-05-01-preview",
                temperature=0,
                max_tokens=None,
                timeout=None,
                max_retries=2,
                # organization="...",
                # other params...
            )

    **NOTE**: Any param which is not explicitly supported will be passed directly to the
    ``openai.AzureOpenAI.chat.completions.create(...)`` API every time to the model is
    invoked. For example:
        .. code-block:: python

            from langchain_openai import AzureChatOpenAI
            import openai

            AzureChatOpenAI(..., logprobs=True).invoke(...)

            # results in underlying API call of:

            openai.AzureOpenAI(..).chat.completions.create(..., logprobs=True)

            # which is also equivalent to:

            AzureChatOpenAI(...).invoke(..., logprobs=True)

    Invoke:
        .. code-block:: python

            messages = [
                (
                    "system",
                    "You are a helpful translator. Translate the user sentence to French.",
                ),
                ("human", "I love programming."),
            ]
            llm.invoke(messages)

        .. code-block:: python

            AIMessage(
                content="J'adore programmer.",
                usage_metadata={"input_tokens": 28, "output_tokens": 6, "total_tokens": 34},
                response_metadata={
                    "token_usage": {
                        "completion_tokens": 6,
                        "prompt_tokens": 28,
                        "total_tokens": 34,
                    },
                    "model_name": "gpt-4",
                    "system_fingerprint": "fp_7ec89fabc6",
                    "prompt_filter_results": [
                        {
                            "prompt_index": 0,
                            "content_filter_results": {
                                "hate": {"filtered": False, "severity": "safe"},
                                "self_harm": {"filtered": False, "severity": "safe"},
                                "sexual": {"filtered": False, "severity": "safe"},
                                "violence": {"filtered": False, "severity": "safe"},
                            },
                        }
                    ],
                    "finish_reason": "stop",
                    "logprobs": None,
                    "content_filter_results": {
                        "hate": {"filtered": False, "severity": "safe"},
                        "self_harm": {"filtered": False, "severity": "safe"},
                        "sexual": {"filtered": False, "severity": "safe"},
                        "violence": {"filtered": False, "severity": "safe"},
                    },
                },
                id="run-6d7a5282-0de0-4f27-9cc0-82a9db9a3ce9-0",
            )

    Stream:
        .. code-block:: python

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

        .. code-block:: python

            AIMessageChunk(content="", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
            AIMessageChunk(content="J", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
            AIMessageChunk(content="'", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
            AIMessageChunk(content="ad", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
            AIMessageChunk(content="ore", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
            AIMessageChunk(content=" la", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
            AIMessageChunk(content=" programm", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
            AIMessageChunk(content="ation", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
            AIMessageChunk(content=".", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
            AIMessageChunk(
                content="",
                response_metadata={
                    "finish_reason": "stop",
                    "model_name": "gpt-4",
                    "system_fingerprint": "fp_811936bd4f",
                },
                id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f",
            )

        .. code-block:: python

            stream = llm.stream(messages)
            full = next(stream)
            for chunk in stream:
                full += chunk
            full

        .. code-block:: python

            AIMessageChunk(
                content="J'adore la programmation.",
                response_metadata={
                    "finish_reason": "stop",
                    "model_name": "gpt-4",
                    "system_fingerprint": "fp_811936bd4f",
                },
                id="run-ba60e41c-9258-44b8-8f3a-2f10599643b3",
            )

    Async:
        .. code-block:: python

            await llm.ainvoke(messages)

            # stream:
            # async for chunk in (await llm.astream(messages))

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

    Tool calling:
        .. code-block:: python

            from langchain_core.pydantic_v1 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"
                )


            llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
            ai_msg = llm_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": "call_6XswGD5Pqk8Tt5atYr7tfenU",
                },
                {
                    "name": "GetWeather",
                    "args": {"location": "New York, NY"},
                    "id": "call_ZVL15vA8Y7kXqOy3dtmQgeCi",
                },
                {
                    "name": "GetPopulation",
                    "args": {"location": "Los Angeles, CA"},
                    "id": "call_49CFW8zqC9W7mh7hbMLSIrXw",
                },
                {
                    "name": "GetPopulation",
                    "args": {"location": "New York, NY"},
                    "id": "call_6ghfKxV264jEfe1mRIkS3PE7",
                },
            ]

    Structured output:
        .. code-block:: python

            from typing import Optional

            from langchain_core.pydantic_v1 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_llm = llm.with_structured_output(Joke)
            structured_llm.invoke("Tell me a joke about cats")

        .. code-block:: python

            Joke(
                setup="Why was the cat sitting on the computer?",
                punchline="To keep an eye on the mouse!",
                rating=None,
            )

        See ``AzureChatOpenAI.with_structured_output()`` for more.

    JSON mode:
        .. code-block:: python

            json_llm = llm.bind(response_format={"type": "json_object"})
            ai_msg = json_llm.invoke(
                "Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]"
            )
            ai_msg.content

        .. code-block:: python

            '\\n{\\n  "random_ints": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\\n}'

    Image input:
        .. code-block:: python

            import base64
            import httpx
            from langchain_core.messages import HumanMessage

            image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
            image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
            message = HumanMessage(
                content=[
                    {"type": "text", "text": "describe the weather in this image"},
                    {
                        "type": "image_url",
                        "image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
                    },
                ]
            )
            ai_msg = llm.invoke([message])
            ai_msg.content

        .. code-block:: python

            "The weather in the image appears to be quite pleasant. The sky is mostly clear"

    Token usage:
        .. code-block:: python

            ai_msg = llm.invoke(messages)
            ai_msg.usage_metadata

        .. code-block:: python

            {"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}

    Logprobs:
        .. code-block:: python

            logprobs_llm = llm.bind(logprobs=True)
            ai_msg = logprobs_llm.invoke(messages)
            ai_msg.response_metadata["logprobs"]

        .. code-block:: python

            {
                "content": [
                    {
                        "token": "J",
                        "bytes": [74],
                        "logprob": -4.9617593e-06,
                        "top_logprobs": [],
                    },
                    {
                        "token": "'adore",
                        "bytes": [39, 97, 100, 111, 114, 101],
                        "logprob": -0.25202933,
                        "top_logprobs": [],
                    },
                    {
                        "token": " la",
                        "bytes": [32, 108, 97],
                        "logprob": -0.20141791,
                        "top_logprobs": [],
                    },
                    {
                        "token": " programmation",
                        "bytes": [
                            32,
                            112,
                            114,
                            111,
                            103,
                            114,
                            97,
                            109,
                            109,
                            97,
                            116,
                            105,
                            111,
                            110,
                        ],
                        "logprob": -1.9361265e-07,
                        "top_logprobs": [],
                    },
                    {
                        "token": ".",
                        "bytes": [46],
                        "logprob": -1.2233183e-05,
                        "top_logprobs": [],
                    },
                ]
            }

    Response metadata
        .. code-block:: python

            ai_msg = llm.invoke(messages)
            ai_msg.response_metadata

        .. code-block:: python

            {
                "token_usage": {
                    "completion_tokens": 6,
                    "prompt_tokens": 28,
                    "total_tokens": 34,
                },
                "model_name": "gpt-35-turbo",
                "system_fingerprint": None,
                "prompt_filter_results": [
                    {
                        "prompt_index": 0,
                        "content_filter_results": {
                            "hate": {"filtered": False, "severity": "safe"},
                            "self_harm": {"filtered": False, "severity": "safe"},
                            "sexual": {"filtered": False, "severity": "safe"},
                            "violence": {"filtered": False, "severity": "safe"},
                        },
                    }
                ],
                "finish_reason": "stop",
                "logprobs": None,
                "content_filter_results": {
                    "hate": {"filtered": False, "severity": "safe"},
                    "self_harm": {"filtered": False, "severity": "safe"},
                    "sexual": {"filtered": False, "severity": "safe"},
                    "violence": {"filtered": False, "severity": "safe"},
                },
            }
    """  # noqa: E501

    azure_endpoint: Union[str, None] = None
    """Your Azure endpoint, including the resource.
    
        Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
        Example: `https://example-resource.azure.openai.com/`
    """
    deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
    """A model deployment. 
    
        If given sets the base client URL to include `/deployments/{azure_deployment}`.
        Note: this means you won't be able to use non-deployment endpoints.
    """
    openai_api_version: str = Field(default="", alias="api_version")
    """Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
    openai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
    """Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
    azure_ad_token: Optional[SecretStr] = None
    """Your Azure Active Directory token.
    
        Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
        For more: 
        https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
    """
    azure_ad_token_provider: Union[Callable[[], str], None] = None
    """A function that returns an Azure Active Directory token.
        
        Will be invoked on every request.
    """

    model_version: str = ""
    """The version of the model (e.g. "0125" for gpt-3.5-0125).

    Azure OpenAI doesn't return model version with the response by default so it must 
    be manually specified if you want to use this information downstream, e.g. when
    calculating costs.

    When you specify the version, it will be appended to the model name in the 
    response. Setting correct version will help you to calculate the cost properly. 
    Model version is not validated, so make sure you set it correctly to get the 
    correct cost.
    """

    openai_api_type: str = ""
    """Legacy, for openai<1.0.0 support."""
    validate_base_url: bool = True
    """If legacy arg openai_api_base is passed in, try to infer if it is a base_url or 
        azure_endpoint and update client params accordingly.
    """

    @classmethod
    def get_lc_namespace(cls) -> List[str]:
        """Get the namespace of the langchain object."""
        return ["langchain", "chat_models", "azure_openai"]

    @property
    def lc_secrets(self) -> Dict[str, str]:
        return {
            "openai_api_key": "AZURE_OPENAI_API_KEY",
            "azure_ad_token": "AZURE_OPENAI_AD_TOKEN",
        }

    @classmethod
    def is_lc_serializable(cls) -> bool:
        return True

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        if values["n"] < 1:
            raise ValueError("n must be at least 1.")
        if values["n"] > 1 and values["streaming"]:
            raise ValueError("n must be 1 when streaming.")

        # Check OPENAI_KEY for backwards compatibility.
        # TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
        # other forms of azure credentials.
        openai_api_key = (
            values["openai_api_key"]
            or os.getenv("AZURE_OPENAI_API_KEY")
            or os.getenv("OPENAI_API_KEY")
        )
        values["openai_api_key"] = (
            convert_to_secret_str(openai_api_key) if openai_api_key else None
        )
        values["openai_api_base"] = (
            values["openai_api_base"]
            if "openai_api_base" in values
            else os.getenv("OPENAI_API_BASE")
        )
        values["openai_api_version"] = values["openai_api_version"] or os.getenv(
            "OPENAI_API_VERSION"
        )
        # Check OPENAI_ORGANIZATION for backwards compatibility.
        values["openai_organization"] = (
            values["openai_organization"]
            or os.getenv("OPENAI_ORG_ID")
            or os.getenv("OPENAI_ORGANIZATION")
        )
        values["azure_endpoint"] = values["azure_endpoint"] or os.getenv(
            "AZURE_OPENAI_ENDPOINT"
        )
        azure_ad_token = values["azure_ad_token"] or os.getenv("AZURE_OPENAI_AD_TOKEN")
        values["azure_ad_token"] = (
            convert_to_secret_str(azure_ad_token) if azure_ad_token else None
        )

        values["openai_api_type"] = get_from_dict_or_env(
            values, "openai_api_type", "OPENAI_API_TYPE", default="azure"
        )
        values["openai_proxy"] = get_from_dict_or_env(
            values, "openai_proxy", "OPENAI_PROXY", default=""
        )
        # For backwards compatibility. Before openai v1, no distinction was made
        # between azure_endpoint and base_url (openai_api_base).
        openai_api_base = values["openai_api_base"]
        if openai_api_base and values["validate_base_url"]:
            if "/openai" not in openai_api_base:
                raise ValueError(
                    "As of openai>=1.0.0, Azure endpoints should be specified via "
                    "the `azure_endpoint` param not `openai_api_base` "
                    "(or alias `base_url`)."
                )
            if values["deployment_name"]:
                raise ValueError(
                    "As of openai>=1.0.0, if `azure_deployment` (or alias "
                    "`deployment_name`) is specified then "
                    "`base_url` (or alias `openai_api_base`) should not be. "
                    "If specifying `azure_deployment`/`deployment_name` then use "
                    "`azure_endpoint` instead of `base_url`.\n\n"
                    "For example, you could specify:\n\n"
                    'azure_endpoint="https://xxx.openai.azure.com/", '
                    'azure_deployment="my-deployment"\n\n'
                    "Or you can equivalently specify:\n\n"
                    'base_url="https://xxx.openai.azure.com/openai/deployments/my-deployment"'
                )
        client_params = {
            "api_version": values["openai_api_version"],
            "azure_endpoint": values["azure_endpoint"],
            "azure_deployment": values["deployment_name"],
            "api_key": (
                values["openai_api_key"].get_secret_value()
                if values["openai_api_key"]
                else None
            ),
            "azure_ad_token": (
                values["azure_ad_token"].get_secret_value()
                if values["azure_ad_token"]
                else None
            ),
            "azure_ad_token_provider": values["azure_ad_token_provider"],
            "organization": values["openai_organization"],
            "base_url": values["openai_api_base"],
            "timeout": values["request_timeout"],
            "max_retries": values["max_retries"],
            "default_headers": values["default_headers"],
            "default_query": values["default_query"],
        }
        if not values.get("client"):
            sync_specific = {"http_client": values["http_client"]}
            values["client"] = openai.AzureOpenAI(
                **client_params, **sync_specific
            ).chat.completions
        if not values.get("async_client"):
            async_specific = {"http_client": values["http_async_client"]}
            values["async_client"] = openai.AsyncAzureOpenAI(
                **client_params, **async_specific
            ).chat.completions
        return values

    def bind_tools(
        self,
        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
        *,
        tool_choice: Optional[
            Union[dict, str, Literal["auto", "none", "required", "any"], bool]
        ] = None,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, BaseMessage]:
        # As of 05/2024 Azure OpenAI doesn't support tool_choice="required".
        # TODO: Update this condition once tool_choice="required" is supported.
        if tool_choice in ("any", "required", True):
            if len(tools) > 1:
                raise ValueError(
                    f"Azure OpenAI does not currently support {tool_choice=}. Should "
                    f"be one of 'auto', 'none', or the name of the tool to call."
                )
            else:
                tool_choice = convert_to_openai_tool(tools[0])["function"]["name"]
        return super().bind_tools(tools, tool_choice=tool_choice, **kwargs)

    # TODO: Fix typing.
    @overload  # type: ignore[override]
    def with_structured_output(
        self,
        schema: Optional[_DictOrPydanticClass] = None,
        *,
        method: Literal["function_calling", "json_mode"] = "function_calling",
        include_raw: Literal[True] = True,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, _AllReturnType]:
        ...

    @overload
    def with_structured_output(
        self,
        schema: Optional[_DictOrPydanticClass] = None,
        *,
        method: Literal["function_calling", "json_mode"] = "function_calling",
        include_raw: Literal[False] = False,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, _DictOrPydantic]:
        ...

    def with_structured_output(
        self,
        schema: Optional[_DictOrPydanticClass] = None,
        *,
        method: Literal["function_calling", "json_mode"] = "function_calling",
        include_raw: bool = False,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, _DictOrPydantic]:
        """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 or be a valid JSON schema
                with top level 'title' and 'description' keys specified.
            method: The method for steering model generation, either "function_calling"
                or "json_mode". If "function_calling" then the schema will be converted
                to an OpenAI function and the returned model will make use of the
                function-calling API. If "json_mode" then OpenAI's JSON mode will be
                used. Note that if using "json_mode" then you must include instructions
                for formatting the output into the desired schema into the model call.
            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.

        Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
            .. code-block:: python

                from langchain_openai import AzureChatOpenAI
                from langchain_core.pydantic_v1 import BaseModel


                class AnswerWithJustification(BaseModel):
                    '''An answer to the user question along with justification for the answer.'''

                    answer: str
                    justification: str


                llm = AzureChatOpenAI(azure_deployment="gpt-35-turbo", temperature=0)
                structured_llm = llm.with_structured_output(AnswerWithJustification)

                structured_llm.invoke(
                    "What weighs more a pound of bricks or a pound of feathers"
                )

                # -> AnswerWithJustification(
                #     answer='They weigh the same',
                #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
                # )

        Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
            .. code-block:: python

                from langchain_openai import AzureChatOpenAI
                from langchain_core.pydantic_v1 import BaseModel


                class AnswerWithJustification(BaseModel):
                    '''An answer to the user question along with justification for the answer.'''

                    answer: str
                    justification: str


                llm = AzureChatOpenAI(azure_deployment="gpt-35-turbo", temperature=0)
                structured_llm = llm.with_structured_output(
                    AnswerWithJustification, include_raw=True
                )

                structured_llm.invoke(
                    "What weighs more a pound of bricks or a pound of feathers"
                )
                # -> {
                #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
                #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
                #     'parsing_error': None
                # }

        Example: Function-calling, dict schema (method="function_calling", include_raw=False):
            .. code-block:: python

                from langchain_openai import AzureChatOpenAI
                from langchain_core.pydantic_v1 import BaseModel
                from langchain_core.utils.function_calling import convert_to_openai_tool


                class AnswerWithJustification(BaseModel):
                    '''An answer to the user question along with justification for the answer.'''

                    answer: str
                    justification: str


                dict_schema = convert_to_openai_tool(AnswerWithJustification)
                llm = AzureChatOpenAI(azure_deployment="gpt-35-turbo", temperature=0)
                structured_llm = llm.with_structured_output(dict_schema)

                structured_llm.invoke(
                    "What weighs more a pound of bricks or a pound of feathers"
                )
                # -> {
                #     'answer': 'They weigh the same',
                #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
                # }

        Example: JSON mode, Pydantic schema (method="json_mode", include_raw=True):
            .. code-block::

                from langchain_openai import AzureChatOpenAI
                from langchain_core.pydantic_v1 import BaseModel

                class AnswerWithJustification(BaseModel):
                    answer: str
                    justification: str

                llm = AzureChatOpenAI(azure_deployment="gpt-35-turbo", temperature=0)
                structured_llm = llm.with_structured_output(
                    AnswerWithJustification,
                    method="json_mode",
                    include_raw=True
                )

                structured_llm.invoke(
                    "Answer the following question. "
                    "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
                    "What's heavier a pound of bricks or a pound of feathers?"
                )
                # -> {
                #     'raw': AIMessage(content='{\n    "answer": "They are both the same weight.",\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
                #     'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
                #     'parsing_error': None
                # }

        Example: JSON mode, no schema (schema=None, method="json_mode", include_raw=True):
            .. code-block::

                structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)

                structured_llm.invoke(
                    "Answer the following question. "
                    "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
                    "What's heavier a pound of bricks or a pound of feathers?"
                )
                # -> {
                #     'raw': AIMessage(content='{\n    "answer": "They are both the same weight.",\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
                #     'parsed': {
                #         'answer': 'They are both the same weight.',
                #         'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
                #     },
                #     'parsing_error': None
                # }


        """  # noqa: E501
        if kwargs:
            raise ValueError(f"Received unsupported arguments {kwargs}")
        is_pydantic_schema = _is_pydantic_class(schema)
        if method == "function_calling":
            if schema is None:
                raise ValueError(
                    "schema must be specified when method is 'function_calling'. "
                    "Received None."
                )
            tool_name = convert_to_openai_tool(schema)["function"]["name"]
            llm = self.bind_tools([schema], tool_choice=tool_name)
            if is_pydantic_schema:
                output_parser: OutputParserLike = PydanticToolsParser(
                    tools=[schema], first_tool_only=True
                )
            else:
                output_parser = JsonOutputKeyToolsParser(
                    key_name=tool_name, first_tool_only=True
                )
        elif method == "json_mode":
            llm = self.bind(response_format={"type": "json_object"})
            output_parser = (
                PydanticOutputParser(pydantic_object=schema)
                if is_pydantic_schema
                else JsonOutputParser()
            )
        else:
            raise ValueError(
                f"Unrecognized method argument. Expected one of 'function_calling' or "
                f"'json_mode'. Received: '{method}'"
            )

        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

    @property
    def _identifying_params(self) -> Dict[str, Any]:
        """Get the identifying parameters."""
        return {
            **{"azure_deployment": self.deployment_name},
            **super()._identifying_params,
        }

    @property
    def _llm_type(self) -> str:
        return "azure-openai-chat"

    @property
    def lc_attributes(self) -> Dict[str, Any]:
        return {
            "openai_api_type": self.openai_api_type,
            "openai_api_version": self.openai_api_version,
        }

    def _get_ls_params(
        self, stop: Optional[List[str]] = None, **kwargs: Any
    ) -> LangSmithParams:
        """Get the parameters used to invoke the model."""
        params = super()._get_ls_params(stop=stop, **kwargs)
        params["ls_provider"] = "azure"
        if self.deployment_name:
            params["ls_model_name"] = self.deployment_name
        return params

    def _create_chat_result(
        self, response: Union[dict, openai.BaseModel]
    ) -> ChatResult:
        if not isinstance(response, dict):
            response = response.model_dump()
        for res in response["choices"]:
            if res.get("finish_reason", None) == "content_filter":
                raise ValueError(
                    "Azure has not provided the response due to a content filter "
                    "being triggered"
                )
        chat_result = super()._create_chat_result(response)

        if "model" in response:
            model = response["model"]
            if self.model_version:
                model = f"{model}-{self.model_version}"

            chat_result.llm_output = chat_result.llm_output or {}
            chat_result.llm_output["model_name"] = model
        if "prompt_filter_results" in response:
            chat_result.llm_output = chat_result.llm_output or {}
            chat_result.llm_output["prompt_filter_results"] = response[
                "prompt_filter_results"
            ]
        for chat_gen, response_choice in zip(
            chat_result.generations, response["choices"]
        ):
            chat_gen.generation_info = chat_gen.generation_info or {}
            chat_gen.generation_info["content_filter_results"] = response_choice.get(
                "content_filter_results", {}
            )

        return chat_result
