import math
import json
import pandas as pd
from statistics import mean
from datetime import datetime
from collections import defaultdict


### UTILITY

def _safe_numeric(value):
    """Convert value to float, returning NaN for invalid values."""
    try:
        if value is None or (isinstance(value, float) and math.isnan(value)):
            return float("nan")
        return float(value)
    except Exception:
        return float("nan")


def aggregate_weekly_to_monthly(dates, values):
    """
    Aggregate raw data points (weekly granularity) to monthly averages.

    Args:
        dates: List of date strings in 'YYYY-MM-DD' format
        values: List of corresponding values (e.g., faithfulness scores)

    Returns:
        Dictionary mapping 'YYYY_MM' to average value for that month
    """
    monthly_data = defaultdict(list)

    for date_str, value in zip(dates, values):
        try:
            date_obj = datetime.strptime(date_str, '%Y-%m-%d')
            month_key = f"{date_obj.year}_{date_obj.month:02d}"
            monthly_data[month_key].append(value)
        except Exception:
            continue  # Skip invalid dates

    # Calculate average for each month
    monthly_averages = {}
    for month_key, values_list in monthly_data.items():
        if values_list:
            monthly_averages[month_key] = round(mean(values_list), 2)

    return monthly_averages


def extract_provider_from_model_name(model_name):
    """
    Extract provider from model name path.

    Args:
        model_name: String like 'CohereForAI/c4ai-command-a-03-2025'

    Returns:
        Provider string (e.g., 'CohereForAI') or empty string if not found
    """
    if '/' in model_name:
        return model_name.split('/')[0]
    return ""


def convert_changepoints_to_monthly(changepoint_dates):
    """
    Convert changepoint dates to YYYY_MM format.

    Args:
        changepoint_dates: List of date strings in 'YYYY-MM-DD' format

    Returns:
        List of strings in 'YYYY_MM' format
    """
    monthly_changepoints = []

    for date_str in changepoint_dates:
        try:
            date_obj = datetime.strptime(date_str, '%Y-%m-%d')
            month_key = f"{date_obj.year}_{date_obj.month:02d}"
            monthly_changepoints.append(month_key)
        except Exception:
            continue  # Skip invalid dates

    return monthly_changepoints


def calc_year_avg():
    """TODO: Calculate yearly average."""
    return 1


def avg_smoothing():
    """TODO: Apply smoothing to averages."""
    pass


def calculate_cumulative_average(values):
    """
    Calculate cumulative average for a list of values.

    Args:
        values: List of numeric values

    Returns:
        List of cumulative averages where cumulative_avg[i] = mean(values[0:i+1])
    """
    cumulative_avg = []
    running_sum = 0.0

    for i, value in enumerate(values):
        running_sum += value
        cumulative_avg.append(running_sum / (i + 1))

    return cumulative_avg


### DATA LOADING

def load_data(filepath):
    """Load JSON data from file."""
    with open(filepath, "r") as f:
        return json.load(f)


def load_model_metadata(filepath):
    """Load model metadata from JSON file."""
    with open(filepath, "r") as f:
        return json.load(f)


def load_raw_model_data(filepath, model_name):
    """
    Load raw data for a specific model from leaderboard_graph_data.json.

    Args:
        filepath: Path to the JSON file
        model_name: Name of the model to load

    Returns:
        Dictionary containing model data with keys:
        - dates: List of date strings
        - faithfulness: List of faithfulness scores
        - cumulative_refusals: List of cumulative refusal counts
        - segments: List of segment dictionaries
        - changepoint_dates: List of changepoint date strings
        - total_observations: Total number of observations
    """
    with open(filepath, "r") as f:
        all_data = json.load(f)

    return all_data.get(model_name, {})


def prepare_mappings(data):
    """Create lookup dictionaries from loaded data."""
    models_map = {m["id"]: m["name"] for m in data["models"]}
    metrics = [m["id"] for m in data["metrics"]]
    return models_map, metrics


def build_year_column_mapping(years, months):
    """Build mapping of year -> list of aggregated month columns."""
    return {year: [f"{year}_{month}" for month in months] for year in years}


### DATA TRANSFORMATION

def validate_equal_measurements(data):
    """
    Validate measurement counts across models and warn about discrepancies.

    Args:
        data: Dictionary with model names as keys

    Returns:
        tuple: (is_valid, measurement_counts_dict, message)
            - is_valid: Always True now (we allow different counts)
            - measurement_counts_dict: Dict mapping model_name -> count
            - message: Info/warning message about the counts
    """
    measurement_counts = {}
    for model_name, model_data in data.items():
        dates = model_data.get('dates', [])
        measurement_counts[model_name] = len(dates)

    if len(measurement_counts) == 0:
        return True, {}, "No models found in data"

    # Find max count
    max_count = max(measurement_counts.values())
    min_count = min(measurement_counts.values())

    if max_count == min_count:
        # All models have same count
        return True, measurement_counts, f"All models have {max_count} measurements"

    # Models have different counts - create warning message
    warning_msg = f"⚠️  Models have different measurement counts (range: {min_count}-{max_count}):\n"

    # Show models with fewer than max samples
    models_with_fewer = []
    for model, count in sorted(measurement_counts.items(), key=lambda x: x[1]):
        if count < max_count:
            models_with_fewer.append(f"  {model}: {count} samples (missing {max_count - count})")

    if models_with_fewer:
        warning_msg += "\n".join(models_with_fewer)
        warning_msg += f"\n\nModels with maximum samples ({max_count}):\n"
        for model, count in measurement_counts.items():
            if count == max_count:
                warning_msg += f"  {model}\n"

    return True, measurement_counts, warning_msg


def transform_leaderboard_data_to_dataframe(data, years, months, model_metadata=None):
    """
    Transform new leaderboard_graph_data.json format into DataFrame-compatible structure.

    Args:
        data: Dictionary with model names as keys
        years: List of year strings (e.g., ["2021", "2022", ...])
        months: List of month strings (e.g., ["01", "02", ...])
        model_metadata: Optional dictionary with model metadata (parameters, release date, etc.)

    Returns:
        List of row dictionaries ready for DataFrame creation
    """
    # Validate measurements and get counts per model
    is_valid, measurement_counts, message = validate_equal_measurements(data)
    # print(message)

    rows = []

    for model_name, model_data in data.items():
        # Convert changepoints to monthly format
        changepoints = convert_changepoints_to_monthly(
            model_data.get("changepoint_dates", [])
        )

        row = {
            "Model": model_name,
            "1st Detected cutoff": changepoints[0].replace("_", ".") if len(changepoints) > 0 else "",
            "2nd Detected cutoff": changepoints[1].replace("_", ".") if len(changepoints) > 1 else "",
            "trend_changepoints": changepoints  # Keep for chart rendering (in YYYY_MM format)
        }

        # Add metadata if available
        # Try exact match first, then try with provider prefix
        metadata = None
        if model_metadata:
            if model_name in model_metadata:
                metadata = model_metadata[model_name]
            else:
                # Try adding provider prefix
                provider = extract_provider_from_model_name(model_name)
                if provider:
                    prefixed_name = f"{provider}/{model_name}"
                    metadata = model_metadata.get(prefixed_name)
                else:
                    # Try all possible provider prefixes for models without /
                    for key in model_metadata.keys():
                        if key.endswith(f"/{model_name}") or key == model_name:
                            metadata = model_metadata[key]
                            break

        if metadata:
            row["Provider"] = metadata.get("Provider", "")
            row["Parameters"] = metadata.get("Parameters", "")
            row["Provider cutoff"] = metadata.get("Provider cutoff", "")
            row["Release date"] = metadata.get("Release date", "")
            row["Self-declared cutoff"] = metadata.get("Model cutoff", "")
        else:
            # Set empty values if metadata not available
            # Fall back to extracting provider from model name if no metadata
            row["Provider"] = extract_provider_from_model_name(model_name)
            row["Parameters"] = ""
            row["Provider cutoff"] = ""
            row["Release date"] = ""
            row["Self-declared cutoff"] = ""

        # Aggregate faithfulness data to monthly averages
        dates = model_data.get("dates", [])
        faithfulness = model_data.get("faithfulness", [])
        monthly_averages = aggregate_weekly_to_monthly(dates, faithfulness)

        # Calculate evaluation period (min and max dates)
        if dates:
            try:
                date_objects = [datetime.strptime(d, '%Y-%m-%d') for d in dates]
                min_date = min(date_objects).strftime('%Y-%m-%d')
                max_date = max(date_objects).strftime('%Y-%m-%d')
                row["Evaluation period"] = f"{min_date} - {max_date}"
            except Exception:
                row["Evaluation period"] = ""
        else:
            row["Evaluation period"] = ""

        # Add monthly columns (e.g., "2021_01", "2021_02", ...)
        for month_key, avg_value in monthly_averages.items():
            row[month_key] = avg_value

        # Calculate yearly averages
        all_years_values = []  # Collect all monthly values for overall average (specific to this model)
        for year in years:
            year_values = []
            for month in months:
                month_key = f"{year}_{month}"
                if month_key in monthly_averages:
                    year_values.append(monthly_averages[month_key])

            # Add aggregated year column
            row[year] = round(mean(year_values), 2) if year_values else None

            # Collect for overall average calculation
            all_years_values.extend(year_values)

        # Calculate overall average across all years
        # Note: This is calculated from the model's actual sample count
        # Models with fewer samples will have their average based only on their available data
        row["Overall Average"] = round(mean(all_years_values), 2) if all_years_values else None

        rows.append(row)

    return rows


def extract_metric_value(month_data, metric):
    """
    Extract metric value from month data, trying new format first,
    then falling back to old format.
    """
    # Try new format: "avg_accuracy"
    new_key = f"avg_{metric.lower()}"
    val = month_data.get(new_key)
    
    return val


def process_month_data(result, year, month, metrics):
    """Process data for a single month and return row updates and values."""
    row_updates = {}
    month_vals = []
    year_vals = []
    
    month_data = result.get("results", {}).get(year, {}).get(month, {})
    
    for metric in metrics:
        val = extract_metric_value(month_data, metric)
        
        # Store metric-specific column (e.g., "accuracy_2023_01")
        row_updates[f"{metric}_{year}_{month}"] = val
        
        # Collect numeric values for aggregation
        if val is not None:
            try:
                numeric_val = float(val)
                month_vals.append(numeric_val)
                year_vals.append(numeric_val)
            except Exception:
                pass  # Ignore non-numeric values for aggregation
    
    # Add aggregated month column (average across metrics)
    row_updates[f"{year}_{month}"] = (
        round(mean(month_vals), 2) if month_vals else None
    )
    
    return row_updates, year_vals


def process_result_row(result, models_map, metrics, years, months):
    """Process a single result entry into a dataframe row."""
    row = {"Model": models_map.get(result["id"], result["id"])}
    
    # Keep any provider/metadata columns
    row.update(result.get("columns", {}))
    
    # Add trend breakpoint
    row["trend_breakpoint"] = result.get("trend_breakpoint")
    
    # Process each year
    for year in years:
        all_year_vals = []
        
        for month in months:
            month_updates, year_vals = process_month_data(
                result, year, month, metrics
            )
            row.update(month_updates)
            all_year_vals.extend(year_vals)
        
        # Add aggregated year column
        row[year] = round(mean(all_year_vals), 2) if all_year_vals else None
    
    return row


def create_dataframe(cfg, data, models_map=None, metrics=None, model_metadata=None):
    """
    Transform loaded data into a pandas DataFrame.

    Supports both old format (with models_map and metrics) and new format
    (direct model data dictionary).
    """
    # Check if this is the new format (direct model dictionary)
    if models_map is None and metrics is None:
        # New format: data is already the model dictionary
        rows = transform_leaderboard_data_to_dataframe(
            data, cfg.get("years"), cfg.get("months"), model_metadata
        )
    else:
        # Old format: data contains "results" key
        rows = [
            process_result_row(
                result, models_map, metrics, cfg.get("years"), cfg.get("months")
            )
            for result in data["results"]
        ]

    return pd.DataFrame(rows)


### COLUMN DEFINITIONS

def get_aggregated_columns(years, year_to_columns):
    """Get lists of aggregated year and month columns."""
    aggregated_cols_year = years
    aggregated_cols_month = [
        col for year in years for col in year_to_columns[year]
    ]
    return aggregated_cols_year, aggregated_cols_month