"""Factory for creating embedders.

Provides a unified interface for instantiating different embedder backends.
"""

from __future__ import annotations

from typing import Any, Dict, List, Optional

from .base import BaseEmbedder


def get_embedder(
    backend: str = "fastembed",
    profile: str = "code",
    model: str = "default",
    use_gpu: bool = True,
    endpoints: Optional[List[Dict[str, Any]]] = None,
    strategy: str = "latency_aware",
    cooldown: float = 60.0,
    **kwargs: Any,
) -> BaseEmbedder:
    """Factory function to create embedder based on backend.

    Args:
        backend: Embedder backend to use. Options:
            - "fastembed": Use fastembed (ONNX-based) embedder (default)
            - "litellm": Use ccw-litellm embedder
        profile: Model profile for fastembed backend ("fast", "code", "multilingual", "balanced")
                Used only when backend="fastembed". Default: "code"
        model: Model identifier for litellm backend.
              Used only when backend="litellm". Default: "default"
        use_gpu: Whether to use GPU acceleration when available (default: True).
                Used only when backend="fastembed".
        endpoints: Optional list of endpoint configurations for multi-endpoint load balancing.
                  Each endpoint is a dict with keys: model, api_key, api_base, weight.
                  Used only when backend="litellm" and multiple endpoints provided.
        strategy: Selection strategy for multi-endpoint mode:
                 "round_robin", "latency_aware", "weighted_random".
                 Default: "latency_aware"
        cooldown: Default cooldown seconds for rate-limited endpoints (default: 60.0)
        **kwargs: Additional backend-specific arguments

    Returns:
        BaseEmbedder: Configured embedder instance

    Raises:
        ValueError: If backend is not recognized
        ImportError: If required backend dependencies are not installed

    Examples:
        Create fastembed embedder with code profile:
            >>> embedder = get_embedder(backend="fastembed", profile="code")

        Create fastembed embedder with fast profile and CPU only:
            >>> embedder = get_embedder(backend="fastembed", profile="fast", use_gpu=False)

        Create litellm embedder:
            >>> embedder = get_embedder(backend="litellm", model="text-embedding-3-small")

        Create rotational embedder with multiple endpoints:
            >>> endpoints = [
            ...     {"model": "openai/text-embedding-3-small", "api_key": "sk-..."},
            ...     {"model": "azure/my-embedding", "api_base": "https://...", "api_key": "..."},
            ... ]
            >>> embedder = get_embedder(backend="litellm", endpoints=endpoints)
    """
    if backend == "fastembed":
        from .embedder import Embedder
        return Embedder(profile=profile, use_gpu=use_gpu, **kwargs)
    elif backend == "litellm":
        # Check if multi-endpoint mode is requested
        if endpoints and len(endpoints) > 1:
            from .rotational_embedder import create_rotational_embedder
            return create_rotational_embedder(
                endpoints_config=endpoints,
                strategy=strategy,
                default_cooldown=cooldown,
            )
        elif endpoints and len(endpoints) == 1:
            # Single endpoint in list - use it directly
            ep = endpoints[0]
            ep_kwargs = {**kwargs}
            if "api_key" in ep:
                ep_kwargs["api_key"] = ep["api_key"]
            if "api_base" in ep:
                ep_kwargs["api_base"] = ep["api_base"]
            from .litellm_embedder import LiteLLMEmbedderWrapper
            return LiteLLMEmbedderWrapper(model=ep["model"], **ep_kwargs)
        else:
            # No endpoints list - use model parameter
            from .litellm_embedder import LiteLLMEmbedderWrapper
            return LiteLLMEmbedderWrapper(model=model, **kwargs)
    else:
        raise ValueError(
            f"Unknown backend: {backend}. "
            f"Supported backends: 'fastembed', 'litellm'"
        )