import logging
from typing import Any, Optional, Type

# Import discovery functions (will be created soon)
from .providers import _discovery
# Import type hints for request/response objects (will be created soon)
from .types.audio import SpeechCreateRequest # Example for TTS

logger = logging.getLogger(__name__)

class Speech:
    """Handles Text-to-Speech related API calls."""
    def __init__(self, client: 'A4F'):
        self._client = client

    def create(self, *, model: str, input: str, voice: str, **kwargs: Any) -> bytes:
        """
        Generates audio from the input text. Mimics OpenAI's audio.speech.create.

        Args:
            model: The model to use (e.g., "tts-1"). This might influence provider selection.
            input: The text to synthesize.
            voice: The voice to use (e.g., "alloy").
            **kwargs: Additional provider-specific arguments.

        Returns:
            The generated audio content as bytes.

        Raises:
            NotImplementedError: If no provider supports the 'tts' capability.
            Exception: If the selected provider's engine fails.
        """
        capability = "tts"
        logger.debug(f"Attempting to find provider for capability: {capability}")

        # 1. Find a provider for the capability
        # TODO: Enhance provider selection based on model or other criteria if needed.
        provider_name = _discovery.get_provider_for_capability(capability)
        if not provider_name:
            logger.error(f"No provider found supporting capability: {capability}")
            raise NotImplementedError(f"No configured provider supports the '{capability}' capability.")

        logger.info(f"Selected provider '{provider_name}' for capability '{capability}'")

        # 2. Get the engine function from the selected provider
        engine_func = _discovery.get_engine(provider_name, capability)
        if not engine_func:
            # This should ideally not happen if discovery worked correctly
            logger.error(f"Provider '{provider_name}' found but engine function for '{capability}' is missing.")
            raise RuntimeError(f"Internal error: Engine function missing for {provider_name}.{capability}")

        # 3. Prepare the request object (using Pydantic model recommended)
        # We assume the engine function expects an object matching the type hint
        # For simplicity here, we pass required args directly, but using the Pydantic
        # model is better for validation and structure.
        # request_obj = SpeechCreateRequest(model=model, input=input, voice=voice, **kwargs)

        # 4. Call the provider's engine function
        logger.debug(f"Calling engine function for {provider_name}.{capability}")
        try:
            # Pass arguments consistent with OpenAI's structure.
            # The engine function is responsible for translating these.
            # Using a structured request object is preferred:
            # return engine_func(request=request_obj)

            # Simplified call for now, assuming engine accepts kwargs or specific args:
            # Note: This assumes the engine function signature matches these named args.
            # A better approach is to pass a single request object.
            request_data = SpeechCreateRequest(model=model, input=input, voice=voice, **kwargs)
            return engine_func(request=request_data)

        except Exception as e:
            logger.exception(f"Error executing engine function for {provider_name}.{capability}: {e}")
            # Re-raise the exception for the caller to handle
            raise e

class Audio:
    """Groups audio-related capabilities."""
    def __init__(self, client: 'A4F'):
        self.speech = Speech(client)
        # Add other audio capabilities here (e.g., transcriptions)
        # self.transcriptions = Transcriptions(client)

# --- Placeholder for other capabilities ---
# class ChatCompletions: ...
# class Chat: def __init__(self, client): self.completions = ChatCompletions(client)
# class Images: ...

class A4F:
    """
    Main client class for interacting with various AI providers through a unified,
    OpenAI-compatible interface.
    """
    def __init__(
        self,
        # Potential future args: api_key, base_url (if needed for a default provider)
        # provider_config: Optional[Dict[str, Any]] = None # For passing config down
    ):
        """
        Initializes the A4F client.

        Currently, initialization is simple. It might be extended later to handle
        authentication or specific provider configurations.
        """
        # Ensure providers are discovered when the client is instantiated
        # (can be moved to package __init__ if preferred)
        _discovery.find_providers()
        logger.info(f"A4F Client Initialized. Discovered providers: {list(_discovery.PROVIDER_CAPABILITIES.keys())}")

        # Instantiate capability groups
        self.audio = Audio(self)
        # self.chat = Chat(self)
        # self.images = Images(self)
        # ... add other top-level capabilities like 'models', 'files' etc.

    # Potentially add methods for listing models, etc., if needed
    # def list_models(self): ...
