"""
Plot the threshold curve: average number of tokens above threshold vs. threshold.
"""

import pickle
import numpy as np
import matplotlib.pyplot as plt
import argparse


def main():
    parser = argparse.ArgumentParser(
        description="Plot threshold curve from analysis results"
    )
    parser.add_argument(
        "--input",
        type=str,
        default="threshold_analysis.pkl",
        help="Path to threshold analysis pickle file"
    )
    parser.add_argument(
        "--output",
        type=str,
        default="threshold_curve.png",
        help="Path to save plot"
    )

    args = parser.parse_args()

    # Load data
    print(f"Loading {args.input}...")
    with open(args.input, "rb") as f:
        stats = pickle.load(f)

    # Extract data
    avg_count_above = stats["avg_count_above"]  # [max_tokens, n_thresholds]
    thresholds = stats["thresholds"]  # [n_thresholds]

    print(f"Data shape: {avg_count_above.shape}")
    print(f"Token positions: {stats['max_tokens']}")
    print(f"Thresholds: {stats['n_thresholds']}")
    print(f"Prompts: {stats['n_prompts']}")
    print(f"Vocab size: {stats['vocab_size']}")

    # Average across all token positions
    avg_across_positions = np.nanmean(avg_count_above, axis=0)  # [n_thresholds]

    # Convert to "below threshold" (vocab_size - count_above)
    vocab_size = stats['vocab_size']
    avg_count_below = vocab_size - avg_across_positions  # [n_thresholds]

    print(f"\nAveraged curve shape: {avg_count_below.shape}")
    print(f"Sample values:")
    print(f"  threshold=0: {avg_count_below[0]:.1f} tokens below")
    print(f"  threshold=50: {avg_count_below[len(thresholds)//2]:.1f} tokens below")
    print(f"  threshold=100: {avg_count_below[-1]:.1f} tokens below")

    # Create plot
    plt.figure(figsize=(10, 6))
    plt.semilogy(thresholds, avg_count_below, linewidth=2)
    plt.xlabel("Threshold", fontsize=12)
    plt.ylabel("Average # of tokens below threshold (log scale)", fontsize=12)
    plt.title(f"Gumbel Score Threshold Curve\n({stats['n_prompts']} prompts, vocab size={stats['vocab_size']})", fontsize=14)
    plt.grid(True, alpha=0.3, which='both')
    plt.tight_layout()

    # Save
    plt.savefig(args.output, dpi=150, bbox_inches='tight')
    print(f"\nSaved plot to {args.output}")

    # Also show some statistics
    print(f"\nStatistics:")
    print(f"  Max count below: {avg_count_below.max():.1f}")
    print(f"  Min count below: {avg_count_below.min():.1f}")


if __name__ == "__main__":
    main()