#!/usr/bin/env python3
"""
Temporal Dynamics Score Calculator for Audio Evaluation

Calculates temporal dynamics similarity scores between ground truth and output audio files
based on normalized temporal differences of magnitude spectrograms.

The metric:
1. Converts audio to magnitude spectrogram M(f, t) via STFT
2. Takes temporal differences ΔM(f, t) = M(f, t+1) - M(f, t)
3. L2-normalizes each frequency bin's time-difference vector
4. Computes cosine similarity between normalized pred and target
5. Returns cosine similarity (1 = perfect alignment, -1 = opposite)

Usage:
    python calculate_temporal_dynamics.py [--input-dir INPUT_DIR] [--n-fft N_FFT] [--hop-length HOP_LENGTH]
"""

import os
import sys
import argparse
import logging
from pathlib import Path
from typing import List, Tuple, Dict, Optional
import warnings

import torch
import torchaudio
import numpy as np
import pandas as pd
from tqdm import tqdm
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

# Setup logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# Suppress warnings
warnings.filterwarnings('ignore')


class AudioProcessor:
    """Handles audio loading and preprocessing for temporal dynamics calculation."""
    
    def __init__(self, target_sample_rate: int = 16000, n_fft: int = 1024, 
                 hop_length: int = 512, eps: float = 1e-8):
        """
        Initialize audio processor.
        
        Args:
            target_sample_rate: Sample rate to resample audio to
            n_fft: FFT window size for STFT
            hop_length: Hop length for STFT
            eps: Epsilon for numerical stability in normalization
        """
        self.target_sample_rate = target_sample_rate
        self.n_fft = n_fft
        self.hop_length = hop_length
        self.eps = eps
        self.resampler = None
        self._last_sr = None
    
    def load_audio(self, audio_path: str) -> torch.Tensor:
        """
        Load audio file and resample to target sample rate.
        
        Args:
            audio_path: Path to the audio file
            
        Returns:
            Audio tensor at target sample rate, shape (channels, time)
        """
        try:
            # Load audio
            waveform, sample_rate = torchaudio.load(audio_path)
            
            # Resample if necessary
            if sample_rate != self.target_sample_rate:
                if self.resampler is None or self._last_sr != sample_rate:
                    self.resampler = torchaudio.transforms.Resample(
                        orig_freq=sample_rate,
                        new_freq=self.target_sample_rate
                    )
                    self._last_sr = sample_rate
                waveform = self.resampler(waveform)
            
            return waveform
            
        except Exception as e:
            logger.error(f"Error loading audio {audio_path}: {e}")
            raise
    
    def compute_magnitude_spectrogram(self, waveform: torch.Tensor) -> torch.Tensor:
        """
        Compute magnitude spectrogram using STFT.
        
        Args:
            waveform: Audio tensor, shape (channels, time)
            
        Returns:
            Magnitude spectrogram, shape (channels, freq_bins, time_frames)
        """
        # Convert to mono by averaging channels if stereo
        if waveform.shape[0] > 1:
            waveform = torch.mean(waveform, dim=0, keepdim=True)
        
        # Compute STFT
        stft = torch.stft(
            waveform.squeeze(0),
            n_fft=self.n_fft,
            hop_length=self.hop_length,
            window=torch.hann_window(self.n_fft),
            return_complex=True
        )
        
        # Get magnitude
        magnitude = torch.abs(stft)
        
        return magnitude  # Shape: (freq_bins, time_frames)
    
    def compute_temporal_differences(self, magnitude: torch.Tensor) -> torch.Tensor:
        """
        Compute frame-to-frame temporal differences.
        
        Args:
            magnitude: Magnitude spectrogram, shape (freq_bins, time_frames)
            
        Returns:
            Temporal differences ΔM(f, t) = M(f, t+1) - M(f, t), shape (freq_bins, time_frames-1)
        """
        # Compute differences: M(f, t+1) - M(f, t)
        delta_m = magnitude[:, 1:] - magnitude[:, :-1]
        
        return delta_m
    
    def normalize_frequency_bins(self, delta_m: torch.Tensor) -> torch.Tensor:
        """
        L2-normalize each frequency bin's temporal difference vector.
        
        Args:
            delta_m: Temporal differences, shape (freq_bins, time_frames)
            
        Returns:
            Normalized temporal differences, shape (freq_bins, time_frames)
        """
        # Compute L2 norm for each frequency bin (across time)
        # Shape: (freq_bins,)
        norms = torch.sqrt(torch.sum(delta_m ** 2, dim=1, keepdim=True))
        
        # Clamp norms with epsilon to avoid division by zero
        norms = torch.clamp(norms, min=self.eps)
        
        # Normalize
        delta_m_normalized = delta_m / norms
        
        return delta_m_normalized
    
    def compute_temporal_dynamics_score(self, pred_path: str, target_path: str) -> float:
        """
        Compute temporal dynamics similarity score between prediction and target.
        
        Args:
            pred_path: Path to predicted audio file
            target_path: Path to target (ground truth) audio file
            
        Returns:
            Cosine similarity score (1 = perfect alignment, -1 = opposite)
        """
        # Load audio
        pred_waveform = self.load_audio(pred_path)
        target_waveform = self.load_audio(target_path)
        
        # Compute magnitude spectrograms
        pred_mag = self.compute_magnitude_spectrogram(pred_waveform)
        target_mag = self.compute_magnitude_spectrogram(target_waveform)
        
        # Handle different lengths by truncating to minimum length
        min_time = min(pred_mag.shape[1], target_mag.shape[1])
        pred_mag = pred_mag[:, :min_time]
        target_mag = target_mag[:, :min_time]
        
        # Compute temporal differences
        pred_delta = self.compute_temporal_differences(pred_mag)
        target_delta = self.compute_temporal_differences(target_mag)
        
        # Normalize frequency bins
        pred_delta_norm = self.normalize_frequency_bins(pred_delta)
        target_delta_norm = self.normalize_frequency_bins(target_delta)
        
        # Flatten to vectors
        pred_vector = pred_delta_norm.flatten()
        target_vector = target_delta_norm.flatten()
        
        # Compute cosine similarity
        cosine_sim = torch.nn.functional.cosine_similarity(
            pred_vector.unsqueeze(0),
            target_vector.unsqueeze(0),
            dim=1
        ).item()
        
        return cosine_sim


class FilePairMatcher:
    """Matches ground truth and output audio files."""
    
    @staticmethod
    def find_audio_pairs(directory: Path) -> List[Tuple[str, str]]:
        """
        Find matching gt_*.wav and output_*.wav pairs in a directory.
        Pairs all gt files with all output files in the same directory.
        
        Args:
            directory: Directory to search for audio pairs
            
        Returns:
            List of tuples (gt_file, output_file)
        """
        pairs = []
        
        # Get all gt and output files
        gt_files = sorted(directory.glob("gt_*.wav"))
        output_files = sorted(directory.glob("output_*.wav"))
        
        if not gt_files or not output_files:
            return pairs
        
        # Pair each output file with each gt file
        # In practice, there's typically one gt file per directory
        for output_file in output_files:
            for gt_file in gt_files:
                pairs.append((str(gt_file), str(output_file)))
        
        return pairs


def calculate_temporal_dynamics_scores(
    input_dir: str,
    sample_rate: int = 16000,
    n_fft: int = 1024,
    hop_length: int = 512,
    eps: float = 1e-8
) -> pd.DataFrame:
    """
    Calculate temporal dynamics scores for all audio pairs in subdirectories.
    
    Args:
        input_dir: Path to eval_outputs directory
        sample_rate: Target sample rate for audio processing
        n_fft: FFT window size for STFT
        hop_length: Hop length for STFT
        eps: Epsilon for numerical stability
        
    Returns:
        DataFrame with columns: subdirectory, gt_file, output_file, temporal_dynamics_score
    """
    input_path = Path(input_dir)
    if not input_path.exists():
        raise ValueError(f"Input directory does not exist: {input_dir}")
    
    # Initialize processor
    audio_processor = AudioProcessor(
        target_sample_rate=sample_rate,
        n_fft=n_fft,
        hop_length=hop_length,
        eps=eps
    )
    file_matcher = FilePairMatcher()
    
    # Collect all subdirectories
    subdirs = sorted([d for d in input_path.iterdir() if d.is_dir()])
    logger.info(f"Found {len(subdirs)} subdirectories to process")
    logger.info(f"STFT parameters: n_fft={n_fft}, hop_length={hop_length}, sample_rate={sample_rate}")
    
    # Results storage
    results = []
    
    # Process each subdirectory
    for subdir in tqdm(subdirs, desc="Processing directories"):
        subdir_name = subdir.name
        
        # Find audio pairs
        pairs = file_matcher.find_audio_pairs(subdir)
        
        if not pairs:
            logger.warning(f"No audio pairs found in {subdir_name}")
            continue
        
        # Process each pair
        for gt_file, output_file in pairs:
            try:
                # Calculate temporal dynamics score
                td_score = audio_processor.compute_temporal_dynamics_score(
                    pred_path=output_file,
                    target_path=gt_file
                )
                
                # Store results
                result = {
                    'subdirectory': subdir_name,
                    'gt_file': Path(gt_file).name,
                    'output_file': Path(output_file).name,
                    'temporal_dynamics_score': td_score
                }
                
                results.append(result)
                
            except Exception as e:
                logger.error(
                    f"Error processing pair in {subdir_name}: "
                    f"{Path(gt_file).name} vs {Path(output_file).name}: {e}"
                )
                continue
    
    # Create DataFrame
    df = pd.DataFrame(results)
    logger.info(f"Processed {len(results)} audio pairs successfully")
    
    return df


def generate_visualization_plot(
    df: pd.DataFrame,
    input_dir: str,
    audio_processor: AudioProcessor,
    output_path: str
) -> None:
    """
    Generate a visualization plot showing 5 examples (one from each score range).
    
    Args:
        df: DataFrame with temporal dynamics scores
        input_dir: Path to eval_outputs directory
        audio_processor: AudioProcessor instance for computing spectrograms
        output_path: Path to save the plot
    """
    # Define score ranges
    score_ranges = [
        (0.0, 0.2, "[0.0-0.2)"),
        (0.2, 0.4, "[0.2-0.4)"),
        (0.4, 0.6, "[0.4-0.6)"),
        (0.6, 0.8, "[0.6-0.8)"),
        (0.8, 1.0, "[0.8-1.0]")
    ]
    
    # Select one example from each range
    examples = []
    for min_score, max_score, label in score_ranges:
        if max_score == 1.0:
            mask = (df['temporal_dynamics_score'] >= min_score) & (df['temporal_dynamics_score'] <= max_score)
        else:
            mask = (df['temporal_dynamics_score'] >= min_score) & (df['temporal_dynamics_score'] < max_score)
        
        candidates = df[mask]
        if len(candidates) > 0:
            # Pick the example closest to the middle of the range
            target_score = (min_score + max_score) / 2
            idx = (candidates['temporal_dynamics_score'] - target_score).abs().idxmin()
            example = candidates.loc[idx]
            examples.append((example, label))
        else:
            logger.warning(f"No examples found in range {label}")
    
    if not examples:
        logger.warning("No examples found for visualization")
        return
    
    # Create figure with subplots
    n_examples = len(examples)
    fig = plt.figure(figsize=(20, 4 * n_examples))
    gs = gridspec.GridSpec(n_examples, 4, figure=fig, hspace=0.4, wspace=0.3)
    
    input_path = Path(input_dir)
    
    for idx, (example, range_label) in enumerate(examples):
        score = example['temporal_dynamics_score']
        subdir = example['subdirectory']
        gt_file = example['gt_file']
        output_file = example['output_file']
        
        # Construct full paths
        gt_path = str(input_path / subdir / gt_file)
        output_path_full = str(input_path / subdir / output_file)
        
        try:
            # Load audio
            gt_waveform = audio_processor.load_audio(gt_path)
            output_waveform = audio_processor.load_audio(output_path_full)
            
            # Compute magnitude spectrograms
            gt_mag = audio_processor.compute_magnitude_spectrogram(gt_waveform)
            output_mag = audio_processor.compute_magnitude_spectrogram(output_waveform)
            
            # Handle different lengths
            min_time = min(gt_mag.shape[1], output_mag.shape[1])
            gt_mag = gt_mag[:, :min_time]
            output_mag = output_mag[:, :min_time]
            
            # Compute temporal differences
            gt_delta = audio_processor.compute_temporal_differences(gt_mag)
            output_delta = audio_processor.compute_temporal_differences(output_mag)
            
            # Normalize frequency bins
            gt_delta_norm = audio_processor.normalize_frequency_bins(gt_delta)
            output_delta_norm = audio_processor.normalize_frequency_bins(output_delta)
            
            # Convert to numpy for plotting
            gt_mag_np = gt_mag.numpy()
            output_mag_np = output_mag.numpy()
            gt_delta_norm_np = gt_delta_norm.numpy()
            output_delta_norm_np = output_delta_norm.numpy()
            
            # Plot GT magnitude spectrogram
            ax1 = fig.add_subplot(gs[idx, 0])
            im1 = ax1.imshow(
                20 * np.log10(gt_mag_np + 1e-8),
                aspect='auto',
                origin='lower',
                cmap='viridis',
                interpolation='nearest'
            )
            ax1.set_title(f'GT Magnitude Spectrogram\n{subdir}', fontsize=10)
            ax1.set_ylabel('Frequency Bin')
            ax1.set_xlabel('Time Frame')
            plt.colorbar(im1, ax=ax1, label='dB')
            
            # Plot Output magnitude spectrogram
            ax2 = fig.add_subplot(gs[idx, 1])
            im2 = ax2.imshow(
                20 * np.log10(output_mag_np + 1e-8),
                aspect='auto',
                origin='lower',
                cmap='viridis',
                interpolation='nearest'
            )
            ax2.set_title(f'Output Magnitude Spectrogram\n{output_file}', fontsize=10)
            ax2.set_ylabel('Frequency Bin')
            ax2.set_xlabel('Time Frame')
            plt.colorbar(im2, ax=ax2, label='dB')
            
            # Plot GT normalized temporal differences
            ax3 = fig.add_subplot(gs[idx, 2])
            vmax = max(np.abs(gt_delta_norm_np).max(), 0.1)
            im3 = ax3.imshow(
                gt_delta_norm_np,
                aspect='auto',
                origin='lower',
                cmap='RdBu_r',
                vmin=-vmax,
                vmax=vmax,
                interpolation='nearest'
            )
            ax3.set_title('GT Normalized Temporal Δ', fontsize=10)
            ax3.set_ylabel('Frequency Bin')
            ax3.set_xlabel('Time Frame')
            plt.colorbar(im3, ax=ax3, label='Normalized Δ')
            
            # Plot Output normalized temporal differences
            ax4 = fig.add_subplot(gs[idx, 3])
            vmax = max(np.abs(output_delta_norm_np).max(), 0.1)
            im4 = ax4.imshow(
                output_delta_norm_np,
                aspect='auto',
                origin='lower',
                cmap='RdBu_r',
                vmin=-vmax,
                vmax=vmax,
                interpolation='nearest'
            )
            ax4.set_title(f'Output Normalized Temporal Δ\nScore: {score:.4f} {range_label}', fontsize=10)
            ax4.set_ylabel('Frequency Bin')
            ax4.set_xlabel('Time Frame')
            plt.colorbar(im4, ax=ax4, label='Normalized Δ')
            
        except Exception as e:
            logger.error(f"Error processing example {subdir}/{output_file}: {e}")
            continue
    
    plt.suptitle('Temporal Dynamics Metric Visualization\n(Red=energy increase, Blue=energy decrease)', 
                 fontsize=14, fontweight='bold', y=0.995)
    
    # Save plot
    plt.savefig(output_path, dpi=150, bbox_inches='tight')
    plt.close()
    logger.info(f"Saved visualization plot to: {output_path}")


def generate_summary_statistics(df: pd.DataFrame) -> str:
    """
    Generate summary statistics from temporal dynamics scores.
    
    Args:
        df: DataFrame with temporal dynamics scores
        
    Returns:
        Formatted summary statistics string
    """
    summary = []
    summary.append("=" * 70)
    summary.append("TEMPORAL DYNAMICS SCORE SUMMARY STATISTICS")
    summary.append("=" * 70)
    summary.append("")
    summary.append(f"Total Comparisons: {len(df)}")
    summary.append("")
    
    if 'temporal_dynamics_score' in df.columns:
        scores = df['temporal_dynamics_score'].values
        summary.append("─" * 70)
        summary.append("Temporal Dynamics Score (1 = perfect, -1 = opposite):")
        summary.append("─" * 70)
        summary.append(f"  Mean:     {np.mean(scores):.6f}")
        summary.append(f"  Median:   {np.median(scores):.6f}")
        summary.append(f"  Std Dev:  {np.std(scores):.6f}")
        summary.append(f"  Min:      {np.min(scores):.6f}")
        summary.append(f"  Max:      {np.max(scores):.6f}")
        summary.append("")
        summary.append("  Quartiles:")
        summary.append(f"    Q1 (25%): {np.percentile(scores, 25):.6f}")
        summary.append(f"    Q2 (50%): {np.percentile(scores, 50):.6f}")
        summary.append(f"    Q3 (75%): {np.percentile(scores, 75):.6f}")
        summary.append("")
        summary.append("  Distribution:")
        summary.append(f"    [-1.0-0.0):  {np.sum((scores >= -1.0) & (scores < 0.0))} samples")
        summary.append(f"    [0.0-0.2):   {np.sum((scores >= 0.0) & (scores < 0.2))} samples")
        summary.append(f"    [0.2-0.4):   {np.sum((scores >= 0.2) & (scores < 0.4))} samples")
        summary.append(f"    [0.4-0.6):   {np.sum((scores >= 0.4) & (scores < 0.6))} samples")
        summary.append(f"    [0.6-0.8):   {np.sum((scores >= 0.6) & (scores < 0.8))} samples")
        summary.append(f"    [0.8-1.0]:   {np.sum((scores >= 0.8) & (scores <= 1.0))} samples")
        summary.append("")
    
    summary.append("=" * 70)
    
    return "\n".join(summary)


def main():
    """Main function to run temporal dynamics score calculation."""
    parser = argparse.ArgumentParser(
        description="Calculate temporal dynamics scores for audio evaluation"
    )
    parser.add_argument(
        '--input-dir',
        type=str,
        default='/home/karan/sda_link/GitHub/EMMA2/EMMA2_text_conditioning_contextual/eval_outputs',
        help='Input directory containing subdirectories with audio files'
    )
    parser.add_argument(
        '--sample-rate',
        type=int,
        default=16000,
        help='Target sample rate for audio processing (default: 16000)'
    )
    parser.add_argument(
        '--n-fft',
        type=int,
        default=1024,
        help='FFT window size for STFT (default: 1024)'
    )
    parser.add_argument(
        '--hop-length',
        type=int,
        default=256,
        help='Hop length for STFT (default: 512)'
    )
    parser.add_argument(
        '--eps',
        type=float,
        default=1e-8,
        help='Epsilon for numerical stability in normalization (default: 1e-8)'
    )
    
    args = parser.parse_args()
    
    logger.info("Starting temporal dynamics score calculation...")
    logger.info(f"Input directory: {args.input_dir}")
    logger.info(f"STFT parameters: n_fft={args.n_fft}, hop_length={args.hop_length}")
    logger.info(f"Sample rate: {args.sample_rate} Hz")
    logger.info(f"Epsilon: {args.eps}")
    
    # Create audio processor for later visualization use
    audio_processor = AudioProcessor(
        target_sample_rate=args.sample_rate,
        n_fft=args.n_fft,
        hop_length=args.hop_length,
        eps=args.eps
    )
    
    # Calculate temporal dynamics scores
    df = calculate_temporal_dynamics_scores(
        args.input_dir,
        sample_rate=args.sample_rate,
        n_fft=args.n_fft,
        hop_length=args.hop_length,
        eps=args.eps
    )
    
    if df.empty:
        logger.error("No results generated. Please check your input directory.")
        return
    
    # Save CSV
    csv_path = Path(args.input_dir) / "temporal_dynamics_scores.csv"
    df.to_csv(csv_path, index=False)
    logger.info(f"Saved CSV results to: {csv_path}")
    
    # Generate and save summary
    summary = generate_summary_statistics(df)
    summary_path = Path(args.input_dir) / "temporal_dynamics_summary.txt"
    with open(summary_path, 'w') as f:
        f.write(summary)
    logger.info(f"Saved summary statistics to: {summary_path}")
    
    # Print summary to console
    print("\n" + summary)
    
    # Generate visualization plot
    logger.info("Generating visualization plot...")
    plot_path = Path(args.input_dir) / "temporal_dynamics_visualization.png"
    generate_visualization_plot(df, args.input_dir, audio_processor, str(plot_path))
    
    logger.info("Temporal dynamics score calculation completed successfully!")


if __name__ == "__main__":
    main()