import asyncio
import logging
import os
from typing import Any, List, Optional

import anthropic
from anthropic import AsyncAnthropic

"""
Anthropic Provider Module for LLM2SLM

This module provides integration with Anthropic's Claude API for large language model operations,
such as text generation, within the LLM2SLM conversion pipeline. It is designed for
production deployment with robust error handling, logging, and security practices.

Key Features:
- Asynchronous API calls for non-blocking operations.
- Secure API key management via environment variables.
- Comprehensive logging for monitoring and debugging.
- Type hints and docstrings for maintainability.
- Error handling for common API issues (e.g., rate limits, authentication).

Dependencies:
- anthropic: Official Anthropic Python client.

Usage:
    provider = AnthropicProvider()
    response = await provider.generate(prompt="Hello, world!", model="claude-3-opus-20240229")
"""


# Configure logging
logger = logging.getLogger(__name__)


class AnthropicProvider:
    """
    Provider class for interacting with Anthropic's Claude API.

    This class handles authentication, request sending, and response parsing for Claude models.
    It uses asynchronous methods to support high-throughput operations in the LLM2SLM pipeline.

    Attributes:
        api_key (str): The Anthropic API key loaded from environment variables.
        client (AsyncAnthropic): The asynchronous Anthropic client instance.
    """

    def __init__(self, api_key: Optional[str] = None) -> None:
        """
        Initialize the Anthropic provider.

        Args:
            api_key (Optional[str]): The Anthropic API key. If not provided, it will be loaded
                from the ANTHROPIC_API_KEY environment variable.

        Raises:
            ValueError: If the API key is not provided or invalid.
        """
        self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY")
        if not self.api_key:
            raise ValueError(
                "Anthropic API key must be provided via parameter or ANTHROPIC_API_KEY environment "
                "variable."
            )

        self.client = AsyncAnthropic(api_key=self.api_key)
        logger.info("Anthropic provider initialized successfully.")

    async def generate(
        self, prompt: str, model: str = "claude-3-haiku-20240307", **kwargs: Any
    ) -> str:
        """
        Generate text using the specified Anthropic Claude model.

        Args:
            prompt (str): The input prompt for text generation.
            model (str): The Anthropic model to use (e.g., "claude-3-opus-20240229").
            **kwargs: Additional parameters to pass to the Anthropic API.

        Returns:
            str: The generated text response.

        Raises:
            anthropic.APIError: For API-related errors.
            Exception: For unexpected errors.
        """
        try:
            logger.info(
                f"Sending request to Anthropic model '{model}' with prompt: {prompt[:50]}..."
            )

            # Set default parameters
            max_tokens = kwargs.get("max_tokens", 1024)
            temperature = kwargs.get("temperature", 0.7)

            response = await self.client.messages.create(
                model=model,
                max_tokens=max_tokens,
                temperature=temperature,
                messages=[{"role": "user", "content": prompt}],
                **{k: v for k, v in kwargs.items() if k not in ["max_tokens", "temperature"]},
            )

            content = response.content[0].text if response.content else ""
            generated_text = str(content).strip() if content else ""  # type: ignore[no-any-return]
            logger.info(f"Successfully generated text: {generated_text[:50]}...")
            return generated_text

        except anthropic.RateLimitError as e:
            logger.error(f"Rate limit exceeded: {e}")
            await asyncio.sleep(1)  # Simple backoff; consider exponential backoff in production
            raise
        except anthropic.AuthenticationError as e:
            logger.error(f"Authentication failed: {e}")
            raise
        except Exception as e:
            logger.error(f"Unexpected error during text generation: {e}")
            raise

    async def embed(
        self, text: str, model: str = "claude-3-haiku-20240307", **kwargs: Any
    ) -> List[float]:
        """
        Generate embeddings for the given text.

        Note: Anthropic's Claude models are primarily text generation models and do not provide
        dedicated embedding endpoints. This method returns a stub implementation.

        Args:
            text (str): The input text to embed.
            model (str): The model to use (ignored for Anthropic).
            **kwargs: Additional parameters (ignored).

        Returns:
            List[float]: A placeholder embedding vector (all zeros).
        """
        logger.warning(
            "Anthropic Claude models do not provide embedding functionality. Returning stub."
        )
        # Return a fixed-size zero vector as a placeholder
        return [0.0] * 1536  # Common embedding dimension

    async def generate_response(self, prompt: str, **kwargs: Any) -> str:
        """
        Generate a response using the Anthropic provider (alias for generate).

        Args:
            prompt (str): The input prompt.
            **kwargs: Additional parameters.

        Returns:
            str: The generated response.
        """
        return await self.generate(prompt, **kwargs)

    async def convert_to_slm(self, model_id: str, **kwargs: Any) -> dict[str, Any]:
        """
        Convert an LLM to an SLM using the Anthropic provider.

        Args:
            model_id (str): Identifier of the model to convert.
            **kwargs: Additional conversion parameters.

        Returns:
            dict[str, Any]: Metadata about the conversion process.
        """
        logger.info(f"Starting SLM conversion for model: {model_id}")

        # This is a placeholder implementation
        # In a real implementation, this would involve model distillation,
        # quantization, or other compression techniques using Claude

        return {
            "converted": True,
            "model_id": model_id,
            "provider": "anthropic",
            "compression_ratio": kwargs.get("compression_ratio", 0.5),
            "method": "distillation",
        }

    async def close(self) -> None:
        """
        Close the Anthropic client session.

        This method should be called to properly clean up resources when the provider is no
        longer needed.
        """
        await self.client.close()
        logger.info("Anthropic provider session closed.")
