"""
Display utilities for the CodeReview Leaderboard
"""

from typing import List, Dict, Any, Optional, Tuple
import json
from datetime import datetime, timezone
from src.envs import PROGRAMMING_LANGUAGES, COMMENT_LANGUAGES, TAXONOMY_CATEGORIES, QUALITY_METRICS
from src.display.formatting import format_table_cell, format_timestamp

def filter_leaderboard_data(
    data: List[Dict],
    programming_language: str = "All",
    comment_language: str = "All", 
    taxonomy_category: str = "All",
    sort_by: str = "llm_pass_1",
    sort_order: str = "desc"
) -> List[Dict]:
    """Filter and sort leaderboard data based on criteria"""
    
    if not data:
        return []
    
    # Apply filters
    filtered_data = data.copy()
    
    if programming_language != "All":
        filtered_data = [
            entry for entry in filtered_data
            if entry.get("programming_language", "").lower() == programming_language.lower()
        ]
    
    if comment_language != "All":
        filtered_data = [
            entry for entry in filtered_data
            if entry.get("comment_language", "").lower() == comment_language.lower()
        ]
    
    if taxonomy_category != "All":
        filtered_data = [
            entry for entry in filtered_data
            if entry.get("taxonomy_category", "").lower() == taxonomy_category.lower()
        ]
    
    # Sort data
    reverse = sort_order.lower() == "desc"
    
    try:
        if sort_by in ["bleu", "llm_pass_1", "llm_pass_5", "llm_pass_10"]:
            filtered_data.sort(key=lambda x: x.get(sort_by, 0), reverse=reverse)
        elif sort_by in QUALITY_METRICS:
            filtered_data.sort(key=lambda x: x.get("metrics", {}).get(sort_by, 0), reverse=reverse)
        else:
            filtered_data.sort(key=lambda x: str(x.get(sort_by, "")), reverse=reverse)
    except Exception as e:
        print(f"Error sorting data: {e}")
        # Default sort by pass@1
        filtered_data.sort(key=lambda x: x.get("llm_pass_1", 0), reverse=True)
    
    return filtered_data

def get_main_leaderboard_data(
    data: List[Dict],
    programming_language: str = "All",
    comment_language: str = "All",
    taxonomy_category: str = "All",
    sort_by: str = "llm_pass_1"
) -> List[List[str]]:
    """Get formatted main leaderboard table data"""
    
    filtered_data = filter_leaderboard_data(
        data, programming_language, comment_language, taxonomy_category, sort_by
    )
    
    table_rows = []
    for entry in filtered_data:
        row = [
            format_table_cell(entry.get("model_name", ""), "model"),
            format_table_cell(entry.get("programming_language", ""), "programming language"),
            format_table_cell(entry.get("comment_language", ""), "comment language"),
            format_table_cell(entry.get("taxonomy_category", ""), "taxonomy"),
            format_table_cell(entry.get("bleu", 0), "bleu"),
            format_table_cell(entry.get("llm_pass_1", 0), "pass@1"),
            format_table_cell(entry.get("llm_pass_5", 0), "pass@5"),
            format_table_cell(entry.get("llm_pass_10", 0), "pass@10"),
        ]
        table_rows.append(row)
    
    return table_rows

def get_quality_metrics_data(
    data: List[Dict],
    programming_language: str = "All",
    comment_language: str = "All",
    taxonomy_category: str = "All",
    sort_by: str = "llm_pass_1"
) -> List[List[str]]:
    """Get formatted quality metrics table data"""
    
    filtered_data = filter_leaderboard_data(
        data, programming_language, comment_language, taxonomy_category, sort_by
    )
    
    table_rows = []
    for entry in filtered_data:
        metrics = entry.get("metrics", {})
        row = [format_table_cell(entry.get("model_name", ""), "model")]
        
        for metric in QUALITY_METRICS:
            formatted_value = format_table_cell(metrics.get(metric, 0), metric.replace("_", " "))
            row.append(formatted_value)
        
        table_rows.append(row)
    
    return table_rows

def get_submission_history_data(
    data: List[Dict],
    programming_language: str = "All",
    comment_language: str = "All",
    taxonomy_category: str = "All",
    limit: int = 50
) -> List[List[str]]:
    """Get formatted submission history data"""
    
    filtered_data = filter_leaderboard_data(
        data, programming_language, comment_language, taxonomy_category, "submission_date", "desc"
    )
    
    # Limit results
    filtered_data = filtered_data[:limit]
    
    table_rows = []
    for entry in filtered_data:
        row = [
            format_table_cell(entry.get("model_name", ""), "model"),
            format_table_cell(entry.get("programming_language", ""), "programming language"),
            format_table_cell(entry.get("comment_language", ""), "comment language"),
            format_table_cell(entry.get("taxonomy_category", ""), "taxonomy"),
            format_table_cell(entry.get("llm_pass_1", 0), "pass@1"),
            format_timestamp(entry.get("submission_date", "")),
            entry.get("submission_ip", "").split(".")[0] + ".xxx.xxx.xxx" if entry.get("submission_ip") else "Unknown"
        ]
        table_rows.append(row)
    
    return table_rows

def get_statistics_summary(data: List[Dict]) -> Dict[str, Any]:
    """Get summary statistics for the leaderboard"""
    
    if not data:
        return {
            "total_models": 0,
            "total_submissions": 0,
            "avg_pass_1": 0,
            "best_model": "None",
            "languages_covered": 0,
            "categories_covered": 0
        }
    
    # Calculate statistics
    total_models = len(set(entry.get("model_name", "") for entry in data))
    total_submissions = len(data)
    
    pass_1_scores = [entry.get("llm_pass_1", 0) for entry in data if entry.get("llm_pass_1") is not None]
    avg_pass_1 = sum(pass_1_scores) / len(pass_1_scores) if pass_1_scores else 0
    
    best_entry = max(data, key=lambda x: x.get("llm_pass_1", 0)) if data else None
    best_model = best_entry.get("model_name", "None") if best_entry else "None"
    
    languages_covered = len(set(entry.get("programming_language", "") for entry in data if entry.get("programming_language")))
    categories_covered = len(set(entry.get("taxonomy_category", "") for entry in data if entry.get("taxonomy_category")))
    
    return {
        "total_models": total_models,
        "total_submissions": total_submissions,
        "avg_pass_1": avg_pass_1,
        "best_model": best_model,
        "languages_covered": languages_covered,
        "categories_covered": categories_covered
    }

def validate_submission_data(data: Dict[str, Any]) -> Tuple[bool, str]:
    """Validate submission data"""
    
    required_fields = ["model_name", "programming_language", "comment_language", "taxonomy_category"]
    
    # Check required fields
    for field in required_fields:
        if not data.get(field):
            return False, f"Missing required field: {field}"
    
    # Validate scores
    score_fields = ["bleu", "llm_pass_1", "llm_pass_5", "llm_pass_10"]
    for field in score_fields:
        value = data.get(field)
        if value is None:
            return False, f"Missing score: {field}"
        if not isinstance(value, (int, float)):
            return False, f"Invalid score format: {field}"
        if not 0 <= value <= 1:
            return False, f"Score out of range (0-1): {field}"
    
    # Validate metrics
    metrics = data.get("metrics", {})
    for metric in QUALITY_METRICS:
        value = metrics.get(metric)
        if value is None:
            return False, f"Missing metric: {metric}"
        if not isinstance(value, (int, float)):
            return False, f"Invalid metric format: {metric}"
        if not 0 <= value <= 10:
            return False, f"Metric out of range (0-10): {metric}"
    
    # Validate language and category choices
    if data.get("programming_language") not in PROGRAMMING_LANGUAGES:
        return False, "Invalid programming language"
    
    if data.get("comment_language") not in COMMENT_LANGUAGES:
        return False, "Invalid comment language"
    
    if data.get("taxonomy_category") not in TAXONOMY_CATEGORIES:
        return False, "Invalid taxonomy category"
    
    return True, "Valid submission"

def get_leaderboard_insights(data: List[Dict]) -> Dict[str, Any]:
    """Get insights and trends from leaderboard data"""
    
    if not data:
        return {}
    
    # Language performance analysis
    lang_performance = {}
    for lang in PROGRAMMING_LANGUAGES[1:]:  # Skip "All"
        lang_data = [entry for entry in data if entry.get("programming_language") == lang]
        if lang_data:
            avg_score = sum(entry.get("llm_pass_1", 0) for entry in lang_data) / len(lang_data)
            lang_performance[lang] = {
                "avg_score": avg_score,
                "model_count": len(lang_data),
                "best_model": max(lang_data, key=lambda x: x.get("llm_pass_1", 0)).get("model_name", "")
            }
    
    # Category performance analysis
    category_performance = {}
    for category in TAXONOMY_CATEGORIES[1:]:  # Skip "All"
        cat_data = [entry for entry in data if entry.get("taxonomy_category") == category]
        if cat_data:
            avg_score = sum(entry.get("llm_pass_1", 0) for entry in cat_data) / len(cat_data)
            category_performance[category] = {
                "avg_score": avg_score,
                "model_count": len(cat_data),
                "best_model": max(cat_data, key=lambda x: x.get("llm_pass_1", 0)).get("model_name", "")
            }
    
    return {
        "language_performance": lang_performance,
        "category_performance": category_performance,
        "top_performers": sorted(data, key=lambda x: x.get("llm_pass_1", 0), reverse=True)[:5]
    }

def export_leaderboard_data(data: List[Dict], format_type: str = "json") -> str:
    """Export leaderboard data in specified format"""
    
    if format_type.lower() == "json":
        return json.dumps(data, indent=2, ensure_ascii=False)
    elif format_type.lower() == "csv":
        # Simple CSV export
        if not data:
            return ""
        
        # Get headers
        headers = ["model_name", "programming_language", "comment_language", "taxonomy_category", 
                  "bleu", "llm_pass_1", "llm_pass_5", "llm_pass_10"]
        headers.extend(QUALITY_METRICS)
        
        lines = [",".join(headers)]
        
        for entry in data:
            row = []
            for header in headers:
                if header in QUALITY_METRICS:
                    value = entry.get("metrics", {}).get(header, "")
                else:
                    value = entry.get(header, "")
                row.append(str(value))
            lines.append(",".join(row))
        
        return "\n".join(lines)
    else:
        return "Unsupported format"