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#

# Source code for langchain.chat_models.azure_openai

    """Azure OpenAI chat wrapper."""
    from __future__ import annotations

    import logging
    from typing import Any, Dict

    from pydantic import root_validator

    from langchain.chat_models.openai import ChatOpenAI
    from langchain.utils import get_from_dict_or_env

    logger = logging.getLogger(__name__)

    class AzureChatOpenAI(ChatOpenAI):
        """Wrapper around Azure OpenAI Chat Completion API. To use this class you
        must have a deployed model on Azure OpenAI. Use `deployment_name` in the
        constructor to refer to the "Model deployment name" in the Azure portal.

        In addition, you should have the ``openai`` python package installed, and the
        following environment variables set or passed in constructor in lower case:
        - ``OPENAI_API_TYPE`` (default: ``azure``)
        - ``OPENAI_API_KEY``
        - ``OPENAI_API_BASE``
        - ``OPENAI_API_VERSION``

        For exmaple, if you have `gpt-35-turbo` deployed, with the deployment name
        `35-turbo-dev`, the constructor should look like:

        .. code-block:: python
            AzureChatOpenAI(
                deployment_name="35-turbo-dev",
                openai_api_version="2023-03-15-preview",
            )

        Be aware the API version may change.

        Any parameters that are valid to be passed to the openai.create call can be passed
        in, even if not explicitly saved on this class.
        """

        deployment_name: str = ""
        openai_api_type: str = "azure"
        openai_api_base: str = ""
        openai_api_version: str = ""
        openai_api_key: str = ""
        openai_organization: str = ""

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that api key and python package exists in environment."""
            openai_api_key = get_from_dict_or_env(
                values,
                "openai_api_key",
                "OPENAI_API_KEY",
            )
            openai_api_base = get_from_dict_or_env(
                values,
                "openai_api_base",
                "OPENAI_API_BASE",
            )
            openai_api_version = get_from_dict_or_env(
                values,
                "openai_api_version",
                "OPENAI_API_VERSION",
            )
            openai_api_type = get_from_dict_or_env(
                values,
                "openai_api_type",
                "OPENAI_API_TYPE",
            )
            openai_organization = get_from_dict_or_env(
                values,
                "openai_organization",
                "OPENAI_ORGANIZATION",
                default="",
            )
            try:
                import openai

                openai.api_type = openai_api_type
                openai.api_base = openai_api_base
                openai.api_version = openai_api_version
                openai.api_key = openai_api_key
                if openai_organization:
                    openai.organization = openai_organization
            except ImportError:
                raise ValueError(
                    "Could not import openai python package. "
                    "Please install it with `pip install openai`."
                )
            try:
                values["client"] = openai.ChatCompletion
            except AttributeError:
                raise ValueError(
                    "`openai` has no `ChatCompletion` attribute, this is likely "
                    "due to an old version of the openai package. Try upgrading it "
                    "with `pip install --upgrade openai`."
                )
            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.")
            return values

        @property
        def _default_params(self) -> Dict[str, Any]:
            """Get the default parameters for calling OpenAI API."""
            return {
                **super()._default_params,
                "engine": self.deployment_name,
            }