import copy
import json
import os
from typing import Dict, List, Optional, Union

from azure.identity import DefaultAzureCredential, get_bearer_token_provider
from openai import AzureOpenAI

from mem0.configs.llms.azure import AzureOpenAIConfig
from mem0.configs.llms.base import BaseLlmConfig
from mem0.llms.base import LLMBase
from mem0.memory.utils import extract_json

SCOPE = "https://cognitiveservices.azure.com/.default"


class AzureOpenAILLM(LLMBase):
    def __init__(self, config: Optional[Union[BaseLlmConfig, AzureOpenAIConfig, Dict]] = None):
        # Convert to AzureOpenAIConfig if needed
        if config is None:
            config = AzureOpenAIConfig()
        elif isinstance(config, dict):
            config = AzureOpenAIConfig(**config)
        elif isinstance(config, BaseLlmConfig) and not isinstance(config, AzureOpenAIConfig):
            # Convert BaseLlmConfig to AzureOpenAIConfig
            config = AzureOpenAIConfig(
                model=config.model,
                temperature=config.temperature,
                api_key=config.api_key,
                max_tokens=config.max_tokens,
                top_p=config.top_p,
                top_k=config.top_k,
                enable_vision=config.enable_vision,
                vision_details=config.vision_details,
                reasoning_effort=getattr(config, 'reasoning_effort', None),
                http_client_proxies=config.http_client_proxies,
                is_reasoning_model=getattr(config, 'is_reasoning_model', None),
            )

        super().__init__(config)

        # Model name should match the custom deployment name chosen for it.
        if not self.config.model:
            self.config.model = "gpt-5-mini"

        api_key = self.config.azure_kwargs.api_key or os.getenv("LLM_AZURE_OPENAI_API_KEY")
        azure_deployment = self.config.azure_kwargs.azure_deployment or os.getenv("LLM_AZURE_DEPLOYMENT")
        azure_endpoint = self.config.azure_kwargs.azure_endpoint or os.getenv("LLM_AZURE_ENDPOINT")
        api_version = self.config.azure_kwargs.api_version or os.getenv("LLM_AZURE_API_VERSION")
        default_headers = self.config.azure_kwargs.default_headers

        # If the API key is not provided or is a placeholder, use DefaultAzureCredential.
        if api_key is None or api_key == "" or api_key == "your-api-key":
            self.credential = DefaultAzureCredential()
            azure_ad_token_provider = get_bearer_token_provider(
                self.credential,
                SCOPE,
            )
            api_key = None
        else:
            azure_ad_token_provider = None

        self.client = AzureOpenAI(
            azure_deployment=azure_deployment,
            azure_endpoint=azure_endpoint,
            azure_ad_token_provider=azure_ad_token_provider,
            api_version=api_version,
            api_key=api_key,
            http_client=self.config.http_client,
            default_headers=default_headers,
        )

    @staticmethod
    def _rewrite_assistant_keyword(messages):
        """
        Return a copy of ``messages`` with the word "assistant" replaced by "ai"
        in the last message's textual content.

        Azure's content management policy can flag the literal word "assistant",
        which makes ``add`` fail (see issue #2636). The rewrite targets that
        trigger without mutating the caller's messages and without assuming the
        content is a string, so multimodal (list) content passes through untouched.
        """
        if not messages:
            return messages

        messages = copy.deepcopy(messages)
        last_content = messages[-1].get("content")
        if isinstance(last_content, str):
            messages[-1]["content"] = last_content.replace("assistant", "ai")
        return messages

    def _parse_response(self, response, tools):
        """
        Process the response based on whether tools are used or not.

        Args:
            response: The raw response from API.
            tools: The list of tools provided in the request.

        Returns:
            str or dict: The processed response.
        """
        if tools:
            processed_response = {
                "content": response.choices[0].message.content,
                "tool_calls": [],
            }

            if response.choices[0].message.tool_calls:
                for tool_call in response.choices[0].message.tool_calls:
                    processed_response["tool_calls"].append(
                        {
                            "name": tool_call.function.name,
                            "arguments": json.loads(extract_json(tool_call.function.arguments)),
                        }
                    )

            return processed_response
        else:
            return response.choices[0].message.content

    def generate_response(
        self,
        messages: List[Dict[str, str]],
        response_format=None,
        tools: Optional[List[Dict]] = None,
        tool_choice: str = "auto",
        **kwargs,
    ):
        """
        Generate a response based on the given messages using Azure OpenAI.

        Args:
            messages (list): List of message dicts containing 'role' and 'content'.
            response_format (str or object, optional): Format of the response. Defaults to "text".
            tools (list, optional): List of tools that the model can call. Defaults to None.
            tool_choice (str, optional): Tool choice method. Defaults to "auto".
            **kwargs: Additional Azure OpenAI-specific parameters.

        Returns:
            str: The generated response.
        """

        # Azure's "Indirect Attacks" content filter can flag the literal word
        # "assistant" in the prompt, so it is rewritten to "ai" before the request.
        # Work on a copy so the caller's messages are left untouched and string-only
        # content is handled without breaking multimodal (list) content.
        messages = self._rewrite_assistant_keyword(messages)

        params = self._get_supported_params(messages=messages, **kwargs)

        # Add model and messages
        params.update({
            "model": self.config.model,
            "messages": messages,
        })

        if response_format:
            params["response_format"] = response_format
        if tools:
            params["tools"] = tools
            params["tool_choice"] = tool_choice

        response = self.client.chat.completions.create(**params)
        return self._parse_response(response, tools)