#!/usr/bin/env python3

import numpy as np
import sys
import os
import argparse
from pathlib import Path
from common import get_model_name_from_env_path  # type: ignore[import-not-found]

def calculate_nmse(reference, test):
    mse = np.mean((test - reference) ** 2)
    ref_var = np.var(reference)
    if ref_var == 0:
        nmse = float('inf') if mse > 0 else 0.0
        return mse, mse, ref_var

    nmse = mse / ref_var

    return nmse, mse, ref_var

def load_logits(file_path):
    if not os.path.exists(file_path):
        raise FileNotFoundError(f"File not found: {file_path}")

    if file_path.suffix == '.npy':
        return np.load(file_path)
    elif file_path.suffix == '.bin':
        return np.fromfile(file_path, dtype=np.float32)
    else:
        # Try to load as text file
        try:
            # If it has index format "0: value", extract just values
            data = []
            with open(file_path, 'r') as f:
                for line in f:
                    if ':' in line:
                        # Format: "index: value"
                        value = float(line.split(':')[1].strip())
                    else:
                        # Just the value
                        value = float(line.strip())
                    data.append(value)
            return np.array(data, dtype=np.float32)
        except:
            return np.loadtxt(file_path, dtype=np.float32)

def interpret_nmse(nmse):
    """Provide interpretation of NMSE value"""
    if nmse == 0:
        return "Perfect match", "🎉"
    elif nmse < 1e-6:
        return "Essentially identical", "✅"
    elif nmse < 1e-4:
        return "Excellent match", "✅"
    elif nmse < 1e-3:
        return "Very good match", "👍"
    elif nmse < 1e-2:
        return "Good match", "👍"
    elif nmse < 0.1:
        return "Acceptable match", "⚠️"
    elif nmse < 1.0:
        return "Poor match", "❌"
    else:
        return "Very poor match (worse than noise)", "❌"

def main():
    parser = argparse.ArgumentParser(description='Validate model logits')
    parser.add_argument('-m', '--model-path', required=True,  help='Path to the model directory')
    args = parser.parse_args()

    model_name = get_model_name_from_env_path('MODEL_PATH')
    data_dir = Path("data")

    pytorch_file = data_dir / f"pytorch-{model_name}.bin"

    llamacpp_model_name = get_model_name_from_env_path('CONVERTED_MODEL')
    llamacpp_file = data_dir / f"llamacpp-{llamacpp_model_name}.bin"

    print(f"Model name: {model_name}")
    print(f"PyTorch logits file: {pytorch_file}")
    print(f"llama.cpp logits file: {llamacpp_file}")

    reference_file = pytorch_file
    test_file = llamacpp_file

    print("📊 NMSE Check for Model Comparison")
    print("=" * 50)
    print(f"Reference (ground truth): {reference_file}")
    print(f"Test (to evaluate):       {test_file}")
    print()

    try:
        print("Loading reference logits...")
        reference = load_logits(reference_file)
        print(f"  Shape: {reference.shape}, Type: {reference.dtype}")

        print("Loading test logits...")
        test = load_logits(test_file)
        print(f"  Shape: {test.shape}, Type: {test.dtype}")

        # Check shapes match
        if reference.shape != test.shape:
            print(f"\n❌ Error: Shape mismatch!")
            print(f"  Reference: {reference.shape}")
            print(f"  Test: {test.shape}")
            sys.exit(1)

        print(f"\n✅ Shapes match: {reference.shape}")

        nmse, mse, ref_var = calculate_nmse(reference, test)

        # Additional metrics
        max_abs_error = np.max(np.abs(test - reference))
        mean_abs_error = np.mean(np.abs(test - reference))

        # Results
        print(f"\n📈 METRICS")
        print("=" * 30)
        print(f"MSE (Mean Squared Error):     {mse:.6e}")
        print(f"Reference Variance:           {ref_var:.6e}")
        print(f"NMSE:                         {nmse:.6e}")
        print(f"Max Absolute Error:           {max_abs_error:.6f}")
        print(f"Mean Absolute Error:          {mean_abs_error:.6f}")

        # NMSE in dB (common in signal processing)
        if nmse > 0:
            nmse_db = 10 * np.log10(nmse)
            print(f"NMSE (dB):                    {nmse_db:.2f} dB")

        # Interpretation
        interpretation, emoji = interpret_nmse(nmse)
        print(f"\n🎯 INTERPRETATION")
        print("=" * 30)
        print(f"{emoji} {interpretation}")

        # Detailed guidance
        print(f"\n📋 GUIDANCE")
        print("=" * 30)
        if nmse < 1e-3:
            print("✅ EXCELLENT: Your GGML conversion is working very well!")
            print("   The differences are negligible for practical use.")
        elif nmse < 1e-2:
            print("👍 GOOD: Your GGML conversion is working well.")
            print("   Small differences are likely due to precision/quantization.")
        elif nmse < 0.1:
            print("⚠️  ACCEPTABLE: Conversion is working but with some differences.")
            print("   Check if you're using quantization (Q4, Q8, etc.)")
            print("   Test generation quality to see if it's acceptable.")
        else:
            print("❌ PROBLEMATIC: Large differences detected.")
            print("   Check your conversion process for potential issues.")
            print("   Verify you're using the same model weights.")

        # NMSE benchmarks
        print(f"\n📚 NMSE BENCHMARKS")
        print("=" * 30)
        print("< 1e-6:  Essentially identical")
        print("< 1e-4:  Excellent (typical for good conversions)")
        print("< 1e-3:  Very good")
        print("< 1e-2:  Good (acceptable for most use cases)")
        print("< 0.1:   Acceptable (may need verification)")
        print("> 1.0:   Poor (worse than random)")

        # Exit code based on NMSE
        if nmse < 1e-2:
            print(f"\n✅ RESULT: PASS (NMSE = {nmse:.2e})")
            sys.exit(0)
        else:
            print(f"\n❌ RESULT: NEEDS REVIEW (NMSE = {nmse:.2e})")
            sys.exit(1)

    except Exception as e:
        print(f"❌ Error: {e}")
        sys.exit(1)

if __name__ == "__main__":
    main()