"""3-stage LLM Council orchestration."""

import time
from typing import List, Dict, Any, Tuple, Optional
from .llm_client import query_models_parallel, query_model
from .config import COUNCIL_MODELS, CHAIRMAN_MODEL, CHAIRMAN_TIMEOUT_SECONDS, TITLE_GENERATION_TIMEOUT_SECONDS


def _format_docs_context(docs_text: Optional[str]) -> str:
    if not docs_text or not docs_text.strip():
        return ""
    return (
        "\n\nREFERENCE DOCUMENTS (user-provided markdown):\n"
        "Use these as additional context if relevant. Quote sparingly and cite sections when helpful.\n"
        f"{docs_text.strip()}\n"
    )


async def stage1_collect_responses(user_query: str, docs_text: Optional[str] = None) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
    """
    Stage 1: Collect individual responses from all council models.

    Args:
        user_query: The user's question

    Returns:
        Tuple of (results list, metadata dict with timing info)
    """
    start_time = time.time()
    prompt = f"{user_query}{_format_docs_context(docs_text)}"
    messages = [{"role": "user", "content": prompt}]

    # Query all models in parallel
    responses = await query_models_parallel(COUNCIL_MODELS, messages)
    duration = time.time() - start_time

    # Format results
    stage1_results = []
    successful_models = []
    failed_models = []
    for model, response in responses.items():
        if response is not None:  # Only include successful responses
            stage1_results.append({
                "model": model,
                "response": response.get('content', '')
            })
            successful_models.append(model)
        else:
            failed_models.append(model)

    metadata = {
        "duration_seconds": round(duration, 2),
        "successful_models": successful_models,
        "failed_models": failed_models,
        "total_models": len(COUNCIL_MODELS)
    }

    return stage1_results, metadata


async def stage1_collect_responses_streaming(
    user_query: str, 
    docs_text: Optional[str] = None,
    on_response = None
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
    """
    Stage 1: Collect individual responses from all council models, streaming as they complete.

    Args:
        user_query: The user's question
        docs_text: Optional document context
        on_response: Optional callback(model, response_dict) called as each response completes

    Returns:
        Tuple of (results list, metadata dict with timing info)
    """
    import asyncio
    from .llm_client import query_model, LLM_TIMEOUT_SECONDS, MAX_TOKENS
    
    start_time = time.time()
    prompt = f"{user_query}{_format_docs_context(docs_text)}"
    messages = [{"role": "user", "content": prompt}]

    # Query all models in parallel, but yield results as they complete
    async def query_and_notify(model: str):
        response = await query_model(model, messages, timeout=LLM_TIMEOUT_SECONDS, max_tokens_override=MAX_TOKENS)
        if on_response and response is not None:
            result = {"model": model, "response": response.get('content', '')}
            await on_response(model, result)
        return model, response

    tasks = [query_and_notify(model) for model in COUNCIL_MODELS]
    responses_dict = {}
    
    # Use as_completed to process results as they finish
    for coro in asyncio.as_completed(tasks):
        model, response = await coro
        responses_dict[model] = response

    duration = time.time() - start_time

    # Format results
    stage1_results = []
    successful_models = []
    failed_models = []
    for model, response in responses_dict.items():
        if response is not None:  # Only include successful responses
            stage1_results.append({
                "model": model,
                "response": response.get('content', '')
            })
            successful_models.append(model)
        else:
            failed_models.append(model)

    metadata = {
        "duration_seconds": round(duration, 2),
        "successful_models": successful_models,
        "failed_models": failed_models,
        "total_models": len(COUNCIL_MODELS)
    }

    return stage1_results, metadata


async def stage2_collect_rankings(
    user_query: str,
    stage1_results: List[Dict[str, Any]],
    docs_text: Optional[str] = None,
) -> Tuple[List[Dict[str, Any]], Dict[str, str], Dict[str, Any]]:
    """
    Stage 2: Each model ranks the anonymized responses.

    Args:
        user_query: The original user query
        stage1_results: Results from Stage 1

    Returns:
        Tuple of (rankings list, label_to_model mapping, metadata dict with timing)
    """
    start_time = time.time()
    # Handle empty stage1_results
    if not stage1_results:
        return [], {}, {"duration_seconds": 0.0, "successful_models": [], "failed_models": [], "total_models": len(COUNCIL_MODELS)}
    
    # Create anonymized labels for responses (Response A, Response B, etc.)
    labels = [chr(65 + i) for i in range(len(stage1_results))]  # A, B, C, ...

    # Create mapping from label to model name
    label_to_model = {
        f"Response {label}": result['model']
        for label, result in zip(labels, stage1_results)
    }

    # Build the ranking prompt
    responses_text = "\n\n".join([
        f"Response {label}:\n{result['response']}"
        for label, result in zip(labels, stage1_results)
    ])

    ranking_prompt = f"""You are evaluating different responses to the following question:

Question: {user_query}
{_format_docs_context(docs_text)}

Here are the responses from different models (anonymized):

{responses_text}

Your task:
1. First, evaluate each response individually. For each response, explain what it does well and what it does poorly.
2. Then, at the very end of your response, provide a final ranking.

IMPORTANT: Your final ranking MUST be formatted EXACTLY as follows:
- Start with the line "FINAL RANKING:" (all caps, with colon)
- Then list the responses from best to worst as a numbered list
- Each line should be: number, period, space, then ONLY the response label (e.g., "1. Response A")
- Do not add any other text or explanations in the ranking section

Example of the correct format for your ENTIRE response:

Response A provides good detail on X but misses Y...
Response B is accurate but lacks depth on Z...
Response C offers the most comprehensive answer...

FINAL RANKING:
1. Response C
2. Response A
3. Response B

Now provide your evaluation and ranking:"""

    messages = [{"role": "user", "content": ranking_prompt}]

    # Get rankings from all council models in parallel
    responses = await query_models_parallel(COUNCIL_MODELS, messages)
    duration = time.time() - start_time

    # Format results
    stage2_results = []
    successful_models = []
    failed_models = []
    for model, response in responses.items():
        if response is not None:
            full_text = response.get('content', '')
            parsed = parse_ranking_from_text(full_text)
            stage2_results.append({
                "model": model,
                "ranking": full_text,
                "parsed_ranking": parsed
            })
            successful_models.append(model)
        else:
            failed_models.append(model)

    metadata = {
        "duration_seconds": round(duration, 2),
        "successful_models": successful_models,
        "failed_models": failed_models,
        "total_models": len(COUNCIL_MODELS)
    }

    return stage2_results, label_to_model, metadata


async def stage3_synthesize_final(
    user_query: str,
    stage1_results: List[Dict[str, Any]],
    stage2_results: List[Dict[str, Any]],
    docs_text: Optional[str] = None,
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
    """
    Stage 3: Chairman synthesizes final response.

    Args:
        user_query: The original user query
        stage1_results: Individual model responses from Stage 1
        stage2_results: Rankings from Stage 2

    Returns:
        Tuple of (result dict with 'model' and 'response' keys, metadata dict with timing)
    """
    start_time = time.time()
    # Handle empty inputs
    if not stage1_results:
        duration = time.time() - start_time
        return {
            "model": CHAIRMAN_MODEL,
            "response": "Error: Cannot synthesize final answer - no responses from Stage 1."
        }, {
            "duration_seconds": round(duration, 2),
            "model": CHAIRMAN_MODEL,
            "success": False
        }
    
    # Build comprehensive context for chairman
    # Truncate very long responses to avoid exceeding token/context limits
    # More aggressive truncation to keep total prompt under ~2000 tokens (~8000 chars)
    MAX_RESPONSE_LENGTH = 2000  # Characters per response
    MAX_RANKING_LENGTH = 1000    # Characters per ranking
    MAX_DOCS_LENGTH = 2000      # Max characters for docs context
    MAX_TOTAL_PROMPT_LENGTH = 8000  # Max total prompt length (safety limit)
    
    def truncate_text(text: str, max_length: int) -> str:
        """Truncate text to max_length, adding ellipsis if truncated."""
        if len(text) <= max_length:
            return text
        return text[:max_length-3] + "..."
    
    # Truncate docs_text if provided
    truncated_docs_text = docs_text
    if docs_text and len(docs_text) > MAX_DOCS_LENGTH:
        truncated_docs_text = truncate_text(docs_text, MAX_DOCS_LENGTH)
    
    stage1_text = "\n\n".join([
        f"Model: {result['model']}\nResponse: {truncate_text(result['response'], MAX_RESPONSE_LENGTH)}"
        for result in stage1_results
    ])

    stage2_text = "\n\n".join([
        f"Model: {result['model']}\nRanking: {truncate_text(result['ranking'], MAX_RANKING_LENGTH)}"
        for result in stage2_results
    ]) if stage2_results else "No rankings available from Stage 2."

    chairman_prompt = f"""You are the Chairman of an LLM Council. Multiple AI models have provided responses to a user's question, and then ranked each other's responses.

Original Question: {user_query}
{_format_docs_context(truncated_docs_text)}

STAGE 1 - Individual Responses:
{stage1_text}

STAGE 2 - Peer Rankings:
{stage2_text}

Your task as Chairman is to synthesize all of this information into a single, comprehensive, accurate answer to the user's original question. Consider:
- The individual responses and their insights
- The peer rankings and what they reveal about response quality
- Any patterns of agreement or disagreement

Provide a clear, well-reasoned final answer that represents the council's collective wisdom:"""

    # Apply final safety truncation if prompt is still too long
    if len(chairman_prompt) > MAX_TOTAL_PROMPT_LENGTH:
        # Truncate the prompt itself if it exceeds the limit
        chairman_prompt = chairman_prompt[:MAX_TOTAL_PROMPT_LENGTH - 100] + "\n\n[Content truncated due to length limits...]\n\nProvide a clear, well-reasoned final answer:"

    messages = [{"role": "user", "content": chairman_prompt}]

    # Query the chairman model
    # Note: For very long prompts, we might need to truncate or summarize
    # For now, we'll try with the full prompt and handle errors gracefully
    # Use the default max_tokens (2048) to stay within credit limits
    # If you have more credits, you can increase MAX_TOKENS in config.py
    response = await query_model(CHAIRMAN_MODEL, messages, timeout=CHAIRMAN_TIMEOUT_SECONDS)
    duration = time.time() - start_time

    if response is None:
        # Try to get more specific error info - check if prompt might be too long
        prompt_length = len(chairman_prompt)
        estimated_tokens = prompt_length // 4  # Rough estimate: ~4 chars per token
        
        error_msg = (
            "Error: Unable to generate final synthesis.\n\n"
            "The chairman model failed to respond. Possible causes:\n"
            "- Model '{}' not available on the server\n"
            "- Server not running or unreachable\n"
            "- Network/API errors\n"
            "- Prompt too long (estimated ~{} tokens)\n"
            "- Server timeout or overloaded\n\n"
            "Check the backend terminal logs for the exact error message."
        ).format(CHAIRMAN_MODEL, estimated_tokens)
        return {
            "model": CHAIRMAN_MODEL,
            "response": error_msg
        }, {
            "duration_seconds": round(duration, 2),
            "model": CHAIRMAN_MODEL,
            "success": False
        }

    return {
        "model": CHAIRMAN_MODEL,
        "response": response.get('content', '')
    }, {
        "duration_seconds": round(duration, 2),
        "model": CHAIRMAN_MODEL,
        "success": True
    }


def parse_ranking_from_text(ranking_text: str) -> List[str]:
    """
    Parse the FINAL RANKING section from the model's response.

    Args:
        ranking_text: The full text response from the model

    Returns:
        List of response labels in ranked order
    """
    import re

    # Look for "FINAL RANKING:" section
    if "FINAL RANKING:" in ranking_text:
        # Extract everything after "FINAL RANKING:"
        parts = ranking_text.split("FINAL RANKING:")
        if len(parts) >= 2:
            ranking_section = parts[1]
            # Try to extract numbered list format (e.g., "1. Response A")
            # This pattern looks for: number, period, optional space, "Response X"
            numbered_matches = re.findall(r'\d+\.\s*Response [A-Z]', ranking_section)
            if numbered_matches:
                # Extract just the "Response X" part
                return [re.search(r'Response [A-Z]', m).group() for m in numbered_matches]

            # Fallback: Extract all "Response X" patterns in order
            matches = re.findall(r'Response [A-Z]', ranking_section)
            return matches

    # Fallback: try to find any "Response X" patterns in order
    matches = re.findall(r'Response [A-Z]', ranking_text)
    return matches


def calculate_aggregate_rankings(
    stage2_results: List[Dict[str, Any]],
    label_to_model: Dict[str, str]
) -> List[Dict[str, Any]]:
    """
    Calculate aggregate rankings across all models.

    Args:
        stage2_results: Rankings from each model
        label_to_model: Mapping from anonymous labels to model names

    Returns:
        List of dicts with model name and average rank, sorted best to worst
    """
    from collections import defaultdict

    # Track positions for each model
    model_positions = defaultdict(list)

    for ranking in stage2_results:
        ranking_text = ranking['ranking']

        # Parse the ranking from the structured format
        parsed_ranking = parse_ranking_from_text(ranking_text)

        for position, label in enumerate(parsed_ranking, start=1):
            if label in label_to_model:
                model_name = label_to_model[label]
                model_positions[model_name].append(position)

    # Calculate average position for each model
    aggregate = []
    for model, positions in model_positions.items():
        if positions:
            avg_rank = sum(positions) / len(positions)
            aggregate.append({
                "model": model,
                "average_rank": round(avg_rank, 2),
                "rankings_count": len(positions)
            })

    # Sort by average rank (lower is better)
    aggregate.sort(key=lambda x: x['average_rank'])

    return aggregate


async def generate_conversation_title(user_query: str) -> str:
    """
    Generate a short title for a conversation based on the first user message.

    Args:
        user_query: The first user message

    Returns:
        A short title (3-5 words)
    """
    title_prompt = f"""Generate a very short title (3-5 words maximum) that summarizes the following question.
The title should be concise and descriptive. Do not use quotes or punctuation in the title.

Question: {user_query}

Title:"""

    messages = [{"role": "user", "content": title_prompt}]

    # Use chairman model for title generation
    # Use configurable timeout (may need longer for local models which load on first request)
    response = await query_model(CHAIRMAN_MODEL, messages, timeout=TITLE_GENERATION_TIMEOUT_SECONDS, max_tokens_override=50)

    if response is None:
        # Fallback to a generic title
        return "New Conversation"

    title = response.get('content', 'New Conversation').strip()

    # Clean up the title - remove quotes, limit length
    title = title.strip('"\'')

    # Truncate if too long
    if len(title) > 50:
        title = title[:47] + "..."

    return title


async def run_full_council(user_query: str, docs_text: Optional[str] = None) -> Tuple[List, List, Dict, Dict]:
    """
    Run the complete 3-stage council process.

    Args:
        user_query: The user's question

    Returns:
        Tuple of (stage1_results, stage2_results, stage3_result, metadata)
    """
    total_start_time = time.time()
    
    # Stage 1: Collect individual responses
    stage1_results, stage1_metadata = await stage1_collect_responses(user_query, docs_text=docs_text)

    # If no models responded successfully, return error with helpful message
    if not stage1_results:
        error_msg = (
            "All models failed to respond. This could be due to:\n"
            "- Server not running or unreachable\n"
            "- Model names not available on the server\n"
            "- Network/API errors\n"
            "- Server timeout or overloaded\n"
            "- Invalid OPENAI_COMPAT_BASE_URL configuration\n\n"
            "Check the backend logs for detailed error messages."
        )
        total_duration = time.time() - total_start_time
        return [], [], {
            "model": "error",
            "response": error_msg
        }, {
            "label_to_model": {},
            "aggregate_rankings": {},
            "stage1_metadata": stage1_metadata,
            "stage2_metadata": {},
            "stage3_metadata": {},
            "total_duration_seconds": round(total_duration, 2)
        }

    # Stage 2: Collect rankings
    stage2_results, label_to_model, stage2_metadata = await stage2_collect_rankings(user_query, stage1_results, docs_text=docs_text)

    # Calculate aggregate rankings
    aggregate_rankings = calculate_aggregate_rankings(stage2_results, label_to_model)

    # Stage 3: Synthesize final answer
    stage3_result, stage3_metadata = await stage3_synthesize_final(
        user_query,
        stage1_results,
        stage2_results,
        docs_text=docs_text,
    )

    total_duration = time.time() - total_start_time

    # Prepare metadata
    metadata = {
        "label_to_model": label_to_model,
        "aggregate_rankings": aggregate_rankings,
        "stage1_metadata": stage1_metadata,
        "stage2_metadata": stage2_metadata,
        "stage3_metadata": stage3_metadata,
        "total_duration_seconds": round(total_duration, 2)
    }

    return stage1_results, stage2_results, stage3_result, metadata