from __future__ import annotations

import logging
import os
from typing import Any, Callable, Mapping

import openai
from langchain_core.pydantic_v1 import (
    Field,
    SecretStr,
    root_validator,
)
from langchain_core.utils import (
    convert_to_secret_str,
    get_from_dict_or_env,
)
from langchain_openai.llms.base import BaseOpenAI

logger = logging.getLogger(__name__)


class AzureOpenAI(BaseOpenAI):
    """Azure-specific OpenAI large language models.

    To use, you should have the ``openai`` python package installed, and the
    environment variable ``OPENAI_API_KEY`` set with your API key.

    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.

    Example:
        .. code-block:: python

            from swarms import AzureOpenAI

            openai = AzureOpenAI(model_name="gpt-3.5-turbo-instruct")
    """

    azure_endpoint: 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: 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: SecretStr | None = Field(
        default=None, alias="api_key"
    )
    """Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
    azure_ad_token: SecretStr | None = 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.
    """  # noqa: E501
    azure_ad_token_provider: Callable[[], str] | None = None
    """A function that returns an Azure Active Directory token.

        Will be invoked on every request.
    """
    openai_api_type: str = ""
    """Legacy, for openai<1.0.0 support."""
    validate_base_url: bool = True
    """For backwards compatibility. If legacy val openai_api_base is passed in, try to 
        infer if it is a base_url or azure_endpoint and update accordingly.
    """

    @classmethod
    def get_lc_namespace(cls) -> list[str]:
        """Get the namespace of the langchain object."""
        return ["langchain", "llms", "openai"]

    @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["streaming"] and values["n"] > 1:
            raise ValueError("Cannot stream results when n > 1.")
        if values["streaming"] and values["best_of"] > 1:
            raise ValueError(
                "Cannot stream results when best_of > 1."
            )

        # 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["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_base"] = values[
            "openai_api_base"
        ] or os.getenv("OPENAI_API_BASE")
        values["openai_proxy"] = get_from_dict_or_env(
            values,
            "openai_proxy",
            "OPENAI_PROXY",
            default="",
        )
        values["openai_organization"] = (
            values["openai_organization"]
            or os.getenv("OPENAI_ORG_ID")
            or os.getenv("OPENAI_ORGANIZATION")
        )
        values["openai_api_version"] = values[
            "openai_api_version"
        ] or os.getenv("OPENAI_API_VERSION")
        values["openai_api_type"] = get_from_dict_or_env(
            values,
            "openai_api_type",
            "OPENAI_API_TYPE",
            default="azure",
        )
        # 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:
                values["openai_api_base"] = (
                    values["openai_api_base"].rstrip("/") + "/openai"
                )
                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 `deployment_name` (or"
                    " alias `azure_deployment`) is specified then"
                    " `openai_api_base` (or alias `base_url`) should"
                    " not be. Instead use `deployment_name` (or alias"
                    " `azure_deployment`) and `azure_endpoint`."
                )
                values["deployment_name"] = None
        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"],
            "http_client": values["http_client"],
        }
        values["client"] = openai.AzureOpenAI(
            **client_params
        ).completions
        values["async_client"] = openai.AsyncAzureOpenAI(
            **client_params
        ).completions

        return values

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        return {
            **{"deployment_name": self.deployment_name},
            **super()._identifying_params,
        }

    @property
    def _invocation_params(self) -> dict[str, Any]:
        openai_params = {"model": self.deployment_name}
        return {**openai_params, **super()._invocation_params}

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

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