"""Chat completions endpoint router."""

import json
import uuid
from typing import Union
from fastapi import APIRouter, HTTPException, Request
from fastapi.responses import StreamingResponse
from app.models.openai_models import (
    ChatCompletionRequest,
    ChatCompletionResponse,
    ErrorResponse,
    ErrorDetail,
)
from app.services.watsonx_service import watsonx_service
from app.utils.transformers import (
    transform_messages_to_watsonx,
    transform_tools_to_watsonx,
    transform_watsonx_to_openai_chat,
    transform_watsonx_to_openai_chat_chunk,
    format_sse_event,
)
from app.config import settings
import logging

logger = logging.getLogger(__name__)

router = APIRouter()


def normalize_messages_for_vllm(messages: list) -> list:
    """Normalize OpenAI message format for vLLM compatibility.
    
    vLLM is stricter than OpenAI API and requires:
    1. Message content as string (not array of content parts)
    2. Role must be system/user/assistant/function/tool (not "developer")
    
    Args:
        messages: List of message dicts
        
    Returns:
        Normalized list of messages
    """
    normalized = []
    
    for msg in messages:
        normalized_msg = msg.copy()
        
        # Normalize "developer" role to "system"
        if normalized_msg.get("role") == "developer":
            normalized_msg["role"] = "system"
            logger.debug("Normalized 'developer' role to 'system'")
        
        # Normalize array content to string for text-only messages
        content = normalized_msg.get("content")
        if isinstance(content, list):
            # Check if all parts are text-only
            if all(isinstance(p, dict) and p.get("type") == "text" for p in content):
                # Flatten to concatenated string
                normalized_msg["content"] = "\n".join(p.get("text", "") for p in content)
                logger.debug(f"Normalized array content to string: {len(content)} parts")
            else:
                # Has image_url or other non-text types - keep as is
                # vLLM may reject this, but we preserve the original format
                logger.warning("Message contains non-text content parts, keeping array format")
        
        normalized.append(normalized_msg)
    
    return normalized


@router.post(
    "/v1/chat/completions",
    response_model=Union[ChatCompletionResponse, ErrorResponse],
    responses={
        200: {"model": ChatCompletionResponse},
        400: {"model": ErrorResponse},
        401: {"model": ErrorResponse},
        500: {"model": ErrorResponse},
    },
)
async def create_chat_completion(
    request: ChatCompletionRequest,
    http_request: Request,
):
    """Create a chat completion using OpenAI-compatible API.
    
    This endpoint accepts OpenAI-formatted requests and translates them
    to watsonx.ai API calls.
    """
    try:
        # Map model name if needed
        watsonx_model = settings.map_model(request.model)
        logger.info(f"Chat completion request: {request.model} -> {watsonx_model}")
        
        # Normalize messages for vLLM compatibility (handles array content and developer role)
        normalized_messages = normalize_messages_for_vllm([msg.model_dump() for msg in request.messages])
        
        # Transform normalized messages to watsonx format
        # Convert back to ChatMessage objects for the transformer
        from app.models.openai_models import ChatMessage
        normalized_chat_messages = [ChatMessage(**msg) for msg in normalized_messages]
        watsonx_messages = transform_messages_to_watsonx(normalized_chat_messages)
        
        # Transform tools if present
        watsonx_tools = transform_tools_to_watsonx(request.tools)
        
        # Handle streaming
        if request.stream:
            return StreamingResponse(
                stream_chat_completion(
                    watsonx_model,
                    watsonx_messages,
                    request,
                    watsonx_tools,
                ),
                media_type="text/event-stream",
            )
        
        # Non-streaming response
        watsonx_response = await watsonx_service.chat_completion(
            model_id=watsonx_model,
            messages=watsonx_messages,
            temperature=request.temperature or 1.0,
            max_tokens=request.max_tokens,
            top_p=request.top_p or 1.0,
            stop=request.stop if isinstance(request.stop, list) else [request.stop] if request.stop else None,
            tools=watsonx_tools,
        )
        
        # Transform response
        openai_response = transform_watsonx_to_openai_chat(
            watsonx_response,
            request.model,
        )
        
        return openai_response
        
    except Exception as e:
        logger.error(f"Error in chat completion: {str(e)}", exc_info=True)
        raise HTTPException(
            status_code=500,
            detail={
                "error": {
                    "message": str(e),
                    "type": "internal_error",
                    "code": "internal_error",
                }
            },
        )


async def stream_chat_completion(
    watsonx_model: str,
    watsonx_messages: list,
    request: ChatCompletionRequest,
    watsonx_tools: list = None,
):
    """Stream chat completion responses.
    
    Args:
        watsonx_model: The watsonx model ID
        watsonx_messages: Transformed messages
        request: Original OpenAI request
        watsonx_tools: Transformed tools
        
    Yields:
        Server-Sent Events with chat completion chunks
    """
    request_id = f"chatcmpl-{uuid.uuid4().hex[:24]}"
    
    try:
        async for chunk in watsonx_service.chat_completion_stream(
            model_id=watsonx_model,
            messages=watsonx_messages,
            temperature=request.temperature or 1.0,
            max_tokens=request.max_tokens,
            top_p=request.top_p or 1.0,
            stop=request.stop if isinstance(request.stop, list) else [request.stop] if request.stop else None,
            tools=watsonx_tools,
        ):
            # Transform chunk to OpenAI format
            openai_chunk = transform_watsonx_to_openai_chat_chunk(
                chunk,
                request.model,
                request_id,
            )
            
            # Send as SSE
            yield format_sse_event(openai_chunk.model_dump_json())
        
        # Send [DONE] message
        yield format_sse_event("[DONE]")
        
    except Exception as e:
        logger.error(f"Error in streaming chat completion: {str(e)}", exc_info=True)
        error_response = ErrorResponse(
            error=ErrorDetail(
                message=str(e),
                type="internal_error",
                code="stream_error",
            )
        )
        yield format_sse_event(error_response.model_dump_json())