"""
Local LLM Provider Implementations
===================================

This module provides client implementations for various local LLM deployment
frameworks. Currently, Ollama is fully implemented. Other providers have
stub implementations ready for development.

Supported Providers:
- Ollama (✓ Fully Implemented)
- LlamaCpp (Stub)
- vLLM (Stub)
- Text Generation WebUI/Oobabooga (Stub)
- LocalAI (Stub)
- LM Studio (Stub)

Contributing:
If you'd like to implement a provider, follow the pattern in LocalOllamaClient
and implement the BaseOllamaClient interface.
"""

from __future__ import annotations

import logging
from typing import Any, AsyncGenerator, Dict, Iterable, List, Optional, Union

from .ollama_framework import BaseOllamaClient, ChatMessage, ChatResponse

logger = logging.getLogger(__name__)


class LlamaCppClient(BaseOllamaClient):
    """
    Client for llama.cpp server deployments.

    llama.cpp provides a lightweight C++ implementation with OpenAI-compatible API.
    Server URL typically: http://localhost:8080

    Status: STUB - Not yet implemented

    To implement:
    - Connect to llama.cpp server's /completion and /chat/completions endpoints
    - Handle llama.cpp-specific parameters (n_predict, temperature, etc.)
    - Support embeddings via /embeddings endpoint

    References:
    - https://github.com/ggerganov/llama.cpp
    - Server docs: https://github.com/ggerganov/llama.cpp/tree/master/examples/server
    """

    def __init__(
        self,
        model_name: str,
        api_base: str = "http://localhost:8080",
        embed_model: Optional[str] = None,
    ) -> None:
        super().__init__(model_name, embed_model=embed_model)
        self.api_base = api_base.rstrip("/")
        raise NotImplementedError(
            "LlamaCppClient is not yet implemented. "
            "Contributions welcome! See providers.py for implementation guidelines."
        )

    async def generate_embedding(self, text: str) -> List[float]:
        """Generate embedding using llama.cpp /embeddings endpoint."""
        raise NotImplementedError("LlamaCppClient embedding not implemented")

    async def chat(
        self,
        messages: Iterable[Union[ChatMessage, Dict[str, Any]]],
        stream: bool = True,
        temperature: Optional[float] = None,
        top_p: Optional[float] = None,
        max_tokens: Optional[int] = None,
        **options: Any,
    ) -> AsyncGenerator[ChatResponse, None]:
        """Send chat request to llama.cpp /chat/completions endpoint."""
        raise NotImplementedError("LlamaCppClient chat not implemented")
        yield  # Make it a generator


class VLLMClient(BaseOllamaClient):
    """
    Client for vLLM (Very Large Language Model) deployments.

    vLLM is a high-throughput serving engine with PagedAttention.
    Provides OpenAI-compatible API endpoints.
    Server URL typically: http://localhost:8000

    Status: STUB - Not yet implemented

    To implement:
    - Connect to vLLM's OpenAI-compatible /v1/chat/completions endpoint
    - Support vLLM-specific features (continuous batching, PagedAttention)
    - Handle embeddings via /v1/embeddings

    References:
    - https://github.com/vllm-project/vllm
    - API docs: https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html
    """

    def __init__(
        self,
        model_name: str,
        api_base: str = "http://localhost:8000",
        embed_model: Optional[str] = None,
    ) -> None:
        super().__init__(model_name, embed_model=embed_model)
        self.api_base = api_base.rstrip("/")
        raise NotImplementedError(
            "VLLMClient is not yet implemented. "
            "Contributions welcome! See providers.py for implementation guidelines."
        )

    async def generate_embedding(self, text: str) -> List[float]:
        """Generate embedding using vLLM /v1/embeddings endpoint."""
        raise NotImplementedError("VLLMClient embedding not implemented")

    async def chat(
        self,
        messages: Iterable[Union[ChatMessage, Dict[str, Any]]],
        stream: bool = True,
        temperature: Optional[float] = None,
        top_p: Optional[float] = None,
        max_tokens: Optional[int] = None,
        **options: Any,
    ) -> AsyncGenerator[ChatResponse, None]:
        """Send chat request to vLLM /v1/chat/completions endpoint."""
        raise NotImplementedError("VLLMClient chat not implemented")
        yield


class TextGenWebUIClient(BaseOllamaClient):
    """
    Client for Text Generation WebUI (Oobabooga) deployments.

    Text Generation WebUI is a gradio interface with API extensions.
    Server URL typically: http://localhost:5000

    Status: STUB - Not yet implemented

    To implement:
    - Connect to Text Generation WebUI API endpoints
    - Support both streaming and non-streaming modes
    - Handle character/instruction templates

    References:
    - https://github.com/oobabooga/text-generation-webui
    - API docs: Extensions > API
    """

    def __init__(
        self,
        model_name: str,
        api_base: str = "http://localhost:5000",
        embed_model: Optional[str] = None,
    ) -> None:
        super().__init__(model_name, embed_model=embed_model)
        self.api_base = api_base.rstrip("/")
        raise NotImplementedError(
            "TextGenWebUIClient is not yet implemented. "
            "Contributions welcome! See providers.py for implementation guidelines."
        )

    async def generate_embedding(self, text: str) -> List[float]:
        """Generate embedding using Text Generation WebUI API."""
        raise NotImplementedError("TextGenWebUIClient embedding not implemented")

    async def chat(
        self,
        messages: Iterable[Union[ChatMessage, Dict[str, Any]]],
        stream: bool = True,
        temperature: Optional[float] = None,
        top_p: Optional[float] = None,
        max_tokens: Optional[int] = None,
        **options: Any,
    ) -> AsyncGenerator[ChatResponse, None]:
        """Send chat request to Text Generation WebUI API."""
        raise NotImplementedError("TextGenWebUIClient chat not implemented")
        yield


class LocalAIClient(BaseOllamaClient):
    """
    Client for LocalAI deployments.

    LocalAI is a drop-in OpenAI replacement with support for multiple backends.
    Server URL typically: http://localhost:8080

    Status: STUB - Not yet implemented

    To implement:
    - Connect to LocalAI's OpenAI-compatible endpoints
    - Support LocalAI-specific model loading
    - Handle embeddings and image generation

    References:
    - https://github.com/mudler/LocalAI
    - API docs: https://localai.io/api-endpoints/
    """

    def __init__(
        self,
        model_name: str,
        api_base: str = "http://localhost:8080",
        embed_model: Optional[str] = None,
    ) -> None:
        super().__init__(model_name, embed_model=embed_model)
        self.api_base = api_base.rstrip("/")
        raise NotImplementedError(
            "LocalAIClient is not yet implemented. "
            "Contributions welcome! See providers.py for implementation guidelines."
        )

    async def generate_embedding(self, text: str) -> List[float]:
        """Generate embedding using LocalAI /v1/embeddings endpoint."""
        raise NotImplementedError("LocalAIClient embedding not implemented")

    async def chat(
        self,
        messages: Iterable[Union[ChatMessage, Dict[str, Any]]],
        stream: bool = True,
        temperature: Optional[float] = None,
        top_p: Optional[float] = None,
        max_tokens: Optional[int] = None,
        **options: Any,
    ) -> AsyncGenerator[ChatResponse, None]:
        """Send chat request to LocalAI /v1/chat/completions endpoint."""
        raise NotImplementedError("LocalAIClient chat not implemented")
        yield


class LMStudioClient(BaseOllamaClient):
    """
    Client for LM Studio local server.

    LM Studio provides a user-friendly interface with OpenAI-compatible API.
    Server URL typically: http://localhost:1234

    Status: STUB - Not yet implemented

    To implement:
    - Connect to LM Studio's OpenAI-compatible /v1/chat/completions endpoint
    - Support LM Studio model management
    - Handle embeddings if available

    References:
    - https://lmstudio.ai/
    - API is OpenAI-compatible
    """

    def __init__(
        self,
        model_name: str,
        api_base: str = "http://localhost:1234",
        embed_model: Optional[str] = None,
    ) -> None:
        super().__init__(model_name, embed_model=embed_model)
        self.api_base = api_base.rstrip("/")
        raise NotImplementedError(
            "LMStudioClient is not yet implemented. "
            "Contributions welcome! See providers.py for implementation guidelines."
        )

    async def generate_embedding(self, text: str) -> List[float]:
        """Generate embedding using LM Studio API."""
        raise NotImplementedError("LMStudioClient embedding not implemented")

    async def chat(
        self,
        messages: Iterable[Union[ChatMessage, Dict[str, Any]]],
        stream: bool = True,
        temperature: Optional[float] = None,
        top_p: Optional[float] = None,
        max_tokens: Optional[int] = None,
        **options: Any,
    ) -> AsyncGenerator[ChatResponse, None]:
        """Send chat request to LM Studio /v1/chat/completions endpoint."""
        raise NotImplementedError("LMStudioClient chat not implemented")
        yield


# Export all provider clients
__all__ = [
    "LlamaCppClient",
    "VLLMClient",
    "TextGenWebUIClient",
    "LocalAIClient",
    "LMStudioClient",
]
