benchmarks/evaluate_music_modules.py

"""
Comprehensive evaluation benchmarks for TouchGrass music modules.

This script evaluates:
1. Tab & Chord Generation accuracy
2. Music Theory knowledge
3. Ear Training interval identification
4. EQ Adapter emotion detection
5. Songwriting coherence and creativity
"""

import argparse
import json
import torch
from pathlib import Path
from typing import Dict, List, Any
from tqdm import tqdm

# Import TouchGrass modules
from TouchGrass.models.tab_chord_module import TabChordModule
from TouchGrass.models.music_theory_module import MusicTheoryModule
from TouchGrass.models.ear_training_module import EarTrainingModule
from TouchGrass.models.eq_adapter import MusicEQAdapter
from TouchGrass.models.songwriting_module import SongwritingModule


class MusicModuleEvaluator:
    """Evaluator for all TouchGrass music modules."""

    def __init__(self, device: str = "cpu", d_model: int = 768):
        self.device = device
        self.d_model = d_model
        self.results = {}

        # Initialize modules
        self.tab_chord = TabChordModule(d_model=d_model).to(device)
        self.music_theory = MusicTheoryModule(d_model=d_model).to(device)
        self.ear_training = EarTrainingModule(d_model=d_model).to(device)
        self.eq_adapter = MusicEQAdapter(d_model=d_model).to(device)
        self.songwriting = SongwritingModule(d_model=d_model).to(device)

        # Set all modules to eval mode
        self._set_eval_mode()

    def _set_eval_mode(self):
        """Set all modules to evaluation mode."""
        self.tab_chord.eval()
        self.music_theory.eval()
        self.ear_training.eval()
        self.eq_adapter.eval()
        self.songwriting.eval()

    def evaluate_all(self, test_data_path: str = None) -> Dict[str, Any]:
        """Run all evaluations and return comprehensive results."""
        print("=" * 60)
        print("TouchGrass Music Module Evaluation")
        print("=" * 60)

        # Run individual module evaluations
        self.results["tab_chord"] = self.evaluate_tab_chord()
        print(f"✓ Tab & Chord: {self.results['tab_chord']['accuracy']:.2%}")

        self.results["music_theory"] = self.evaluate_music_theory()
        print(f"✓ Music Theory: {self.results['music_theory']['accuracy']:.2%}")

        self.results["ear_training"] = self.evaluate_ear_training()
        print(f"✓ Ear Training: {self.results['ear_training']['accuracy']:.2%}")

        self.results["eq_adapter"] = self.evaluate_eq_adapter()
        print(f"✓ EQ Adapter: {self.results['eq_adapter']['accuracy']:.2%}")

        self.results["songwriting"] = self.evaluate_songwriting()
        print(f"✓ Songwriting: {self.results['songwriting']['coherence_score']:.2%}")

        # Calculate overall score
        scores = [
            self.results["tab_chord"]["accuracy"],
            self.results["music_theory"]["accuracy"],
            self.results["ear_training"]["accuracy"],
            self.results["eq_adapter"]["accuracy"],
            self.results["songwriting"]["coherence_score"]
        ]
        self.results["overall_score"] = sum(scores) / len(scores)
        print(f"\nOverall Score: {self.results['overall_score']:.2%}")

        return self.results

    def evaluate_tab_chord(self) -> Dict[str, Any]:
        """Evaluate Tab & Chord Generation module."""
        print("\n[1] Evaluating Tab & Chord Module...")

        test_cases = [
            # (string_indices, fret_indices, expected_valid)
            (torch.tensor([[0, 1, 2]]), torch.tensor([[0, 3, 5]]), True),   # Open strings and frets
            (torch.tensor([[5, 4, 3, 2, 1, 0]]), torch.tensor([[1, 1, 2, 2, 3, 3]]), True),  # F chord shape
            (torch.tensor([[0, 0, 0]]), torch.tensor([[0, 0, 0]]), True),  # All open
            (torch.tensor([[0, 0, 0]]), torch.tensor([[1, 1, 1]]), True),  # All 1st fret
        ]

        correct = 0
        total = len(test_cases)

        for string_indices, fret_indices, expected_valid in test_cases:
            batch_size, seq_len = string_indices.shape
            hidden_states = torch.randn(batch_size, seq_len, self.d_model)

            with torch.no_grad():
                output = self.tab_chord(hidden_states, string_indices, fret_indices)
                validator_score = output["tab_validator"].mean().item()

                # If expected valid, validator should be > 0.5
                # If expected invalid, validator should be < 0.5
                predicted_valid = validator_score > 0.5
                if predicted_valid == expected_valid:
                    correct += 1

        accuracy = correct / total if total > 0 else 0.0

        return {
            "accuracy": accuracy,
            "correct": correct,
            "total": total
        }

    def evaluate_music_theory(self) -> Dict[str, Any]:
        """Evaluate Music Theory Engine."""
        print("\n[2] Evaluating Music Theory Module...")

        tests = [
            ("scale_c_major", self._test_scale_c_major),
            ("scale_a_minor", self._test_scale_a_minor),
            ("chord_functions", self._test_chord_functions),
            ("circle_of_fifths", self._test_circle_of_fifths),
            ("interval_conversion", self._test_interval_conversion),
        ]

        results = {}
        for name, test_func in tests:
            score = test_func()
            results[name] = score
            print(f"  - {name}: {score:.2%}")

        avg_accuracy = sum(results.values()) / len(results) if results else 0.0
        return {
            "accuracy": avg_accuracy,
            "detailed": results
        }

    def _test_scale_c_major(self) -> float:
        """Test C major scale generation."""
        scale = self.music_theory.get_scale_from_key("C", "major")
        expected = ["C", "D", "E", "F", "G", "A", "B"]
        return 1.0 if scale == expected else 0.0

    def _test_scale_a_minor(self) -> float:
        """Test A natural minor scale."""
        scale = self.music_theory.get_scale_from_key("A", "natural_minor")
        expected = ["A", "B", "C", "D", "E", "F", "G"]
        return 1.0 if scale == expected else 0.0

    def _test_chord_functions(self) -> float:
        """Test chord function detection in C major."""
        tests = [
            ("C", "major", "C", "I"),
            ("F", "major", "C", "IV"),
            ("G", "major", "C", "V"),
            ("D", "minor", "C", "ii"),
            ("E", "minor", "C", "iii"),
            ("A", "minor", "C", "vi"),
            ("B", "dim", "C", "vii°"),
        ]

        correct = 0
        for root, chord_type, key, expected in tests:
            result = self.music_theory.detect_chord_function(root, chord_type, key)
            if result == expected:
                correct += 1

        return correct / len(tests)

    def _test_circle_of_fifths(self) -> float:
        """Test circle of fifths generation."""
        circle = self.music_theory.get_circle_of_fifths()
        # Should have 12 keys
        if len(circle) != 12:
            return 0.0
        # Should contain all major keys
        expected_keys = {"C", "G", "D", "A", "E", "B", "F#", "Db", "Ab", "Eb", "Bb", "F"}
        return 1.0 if set(circle) == expected_keys else 0.0

    def _test_interval_conversion(self) -> float:
        """Test interval name to semitone conversion."""
        tests = [
            (0, "P1"), (1, "m2"), (2, "M2"), (3, "m3"), (4, "M3"),
            (5, "P4"), (6, "TT"), (7, "P5"), (8, "m6"), (9, "M6"),
            (10, "m7"), (11, "M7"), (12, "P8")
        ]

        correct = 0
        for semitones, expected_name in tests:
            name = self.music_theory.semitones_to_interval(semitones)
            if name == expected_name:
                correct += 1

        return correct / len(tests)

    def evaluate_ear_training(self) -> Dict[str, Any]:
        """Evaluate Ear Training module."""
        print("\n[3] Evaluating Ear Training Module...")

        tests = [
            ("interval_names", self._test_interval_names),
            ("interval_to_semitones", self._test_interval_to_semitones),
            ("solfege_syllables", self._test_solfege_syllables),
            ("song_references", self._test_song_references),
        ]

        results = {}
        for name, test_func in tests:
            score = test_func()
            results[name] = score
            print(f"  - {name}: {score:.2%}")

        avg_accuracy = sum(results.values()) / len(results) if results else 0.0
        return {
            "accuracy": avg_accuracy,
            "detailed": results
        }

    def _test_interval_names(self) -> float:
        """Test interval name retrieval."""
        tests = [
            (0, "P1"), (2, "M2"), (4, "M3"), (5, "P4"),
            (7, "P5"), (9, "M6"), (11, "M7"), (12, "P8")
        ]

        correct = 0
        for semitones, expected in tests:
            name = self.ear_training.get_interval_name(semitones)
            if name == expected:
                correct += 1

        return correct / len(tests)

    def _test_interval_to_semitones(self) -> float:
        """Test interval name to semitone conversion."""
        tests = [
            ("P1", 0), ("M2", 2), ("M3", 4), ("P4", 5),
            ("P5", 7), ("M6", 9), ("M7", 11), ("P8", 12)
        ]

        correct = 0
        for name, expected_semitones in tests:
            semitones = self.ear_training.name_to_interval(name)
            if semitones == expected_semitones:
                correct += 1

        return correct / len(tests)

    def _test_solfege_syllables(self) -> float:
        """Test solfege syllable generation."""
        c_major = self.ear_training.get_solfege_syllables("C", "major")
        expected = ["Do", "Re", "Mi", "Fa", "So", "La", "Ti", "Do"]

        return 1.0 if c_major == expected else 0.0

    def _test_song_references(self) -> float:
        """Test that song references exist for common intervals."""
        common_intervals = ["P5", "M3", "m3", "P4", "M2"]
        correct = 0

        for interval in common_intervals:
            refs = self.ear_training.get_song_reference(interval)
            if len(refs) > 0:
                correct += 1

        return correct / len(common_intervals)

    def evaluate_eq_adapter(self) -> Dict[str, Any]:
        """Evaluate EQ Adapter emotion detection."""
        print("\n[4] Evaluating EQ Adapter...")

        tests = [
            ("frustration_range", self._test_frustration_range),
            ("emotion_classifier_output", self._test_emotion_classifier),
            ("encouragement_output", self._test_encouragement_output),
            ("simplification_output", self._test_simplification_output),
        ]

        results = {}
        for name, test_func in tests:
            score = test_func()
            results[name] = score
            print(f"  - {name}: {score:.2%}")

        avg_accuracy = sum(results.values()) / len(results) if results else 0.0
        return {
            "accuracy": avg_accuracy,
            "detailed": results
        }

    def _test_frustration_range(self) -> float:
        """Test that frustration scores are in [0, 1]."""
        batch_size, seq_len = 2, 5
        hidden_states = torch.randn(batch_size, seq_len, self.d_model)

        with torch.no_grad():
            output = self.eq_adapter(hidden_states)
            frustration = output["frustration"]

            # All values should be between 0 and 1
            in_range = ((frustration >= 0) & (frustration <= 1)).all().item()
            return 1.0 if in_range else 0.0

    def _test_emotion_classifier(self) -> float:
        """Test emotion classifier output shape."""
        batch_size, seq_len = 2, 5
        hidden_states = torch.randn(batch_size, seq_len, self.d_model)

        with torch.no_grad():
            output = self.eq_adapter(hidden_states)
            emotion = output["emotion"]

            # Should have 4 emotion classes
            correct_shape = emotion.shape == (batch_size, seq_len, 4)
            return 1.0 if correct_shape else 0.0

    def _test_encouragement_output(self) -> float:
        """Test that encouragement output is produced."""
        batch_size, seq_len = 2, 5
        hidden_states = torch.randn(batch_size, seq_len, self.d_model)

        with torch.no_grad():
            output = self.eq_adapter(hidden_states)
            has_encouragement = "encouragement" in output
            correct_shape = output["encouragement"].shape[0] == batch_size

            return 1.0 if has_encouragement and correct_shape else 0.0

    def _test_simplification_output(self) -> float:
        """Test that simplification output matches input shape."""
        batch_size, seq_len = 2, 5
        hidden_states = torch.randn(batch_size, seq_len, self.d_model)

        with torch.no_grad():
            output = self.eq_adapter(hidden_states)
            correct_shape = output["simplification"].shape == hidden_states.shape
            return 1.0 if correct_shape else 0.0

    def evaluate_songwriting(self) -> Dict[str, Any]:
        """Evaluate Song Writing module."""
        print("\n[5] Evaluating Songwriting Module...")

        tests = [
            ("progression_generation", self._test_progression_generation),
            ("mood_classifier", self._test_mood_classifier),
            ("genre_classifier", self._test_genre_classifier),
            ("hook_generation", self._test_hook_generation),
            ("production_suggestions", self._test_production_suggestions),
        ]

        results = {}
        for name, test_func in tests:
            score = test_func()
            results[name] = score
            print(f"  - {name}: {score:.2%}")

        avg_accuracy = sum(results.values()) / len(results) if results else 0.0
        return {
            "coherence_score": avg_accuracy,
            "detailed": results
        }

    def _test_progression_generation(self) -> float:
        """Test chord progression generation."""
        try:
            progression = self.songwriting.suggest_progression(
                mood="happy", genre="pop", num_chords=4, key="C"
            )
            # Should return list of tuples
            if not isinstance(progression, list):
                return 0.0
            if len(progression) != 4:
                return 0.0
            if not all(isinstance(p, tuple) and len(p) == 2 for p in progression):
                return 0.0
            return 1.0
        except Exception:
            return 0.0

    def _test_mood_classifier(self) -> float:
        """Test mood classifier output."""
        batch_size, seq_len = 2, 5
        hidden_states = torch.randn(batch_size, seq_len, self.d_model)
        chord_ids = torch.randint(0, 24, (batch_size, seq_len))

        with torch.no_grad():
            output = self.songwriting(hidden_states, chord_ids)
            mood = output["mood"]

            # Should have at least 8 moods
            correct_shape = mood.shape[-1] >= 8
            return 1.0 if correct_shape else 0.0

    def _test_genre_classifier(self) -> float:
        """Test genre classifier output."""
        batch_size, seq_len = 2, 5
        hidden_states = torch.randn(batch_size, seq_len, self.d_model)
        chord_ids = torch.randint(0, 24, (batch_size, seq_len))

        with torch.no_grad():
            output = self.songwriting(hidden_states, chord_ids)
            genre = output["genre"]

            # Should have at least 8 genres
            correct_shape = genre.shape[-1] >= 8
            return 1.0 if correct_shape else 0.0

    def _test_hook_generation(self) -> float:
        """Test hook generation."""
        try:
            hook = self.songwriting.generate_hook(
                theme="freedom", genre="pop", key="C"
            )
            # Should return dict with hook text
            if not isinstance(hook, dict):
                return 0.0
            if "hook" not in hook:
                return 0.0
            if not isinstance(hook["hook"], str):
                return 0.0
            if len(hook["hook"]) == 0:
                return 0.0
            return 1.0
        except Exception:
            return 0.0

    def _test_production_suggestions(self) -> float:
        """Test production element suggestions."""
        try:
            production = self.songwriting.suggest_production(
                genre="rock", mood="energetic", bpm=120
            )
            # Should return dict with elements or suggestions
            if not isinstance(production, dict):
                return 0.0
            has_elements = "elements" in production or "suggestions" in production
            return 1.0 if has_elements else 0.0
        except Exception:
            return 0.0

    def save_results(self, output_path: str):
        """Save evaluation results to JSON file."""
        output_path = Path(output_path)
        output_path.parent.mkdir(parents=True, exist_ok=True)

        with open(output_path, 'w', encoding='utf-8') as f:
            json.dump(self.results, f, indent=2)

        print(f"\n✓ Results saved to {output_path}")


def main():
    parser = argparse.ArgumentParser(description="Evaluate TouchGrass music modules")
    parser.add_argument("--device", type=str, default="cpu", help="Device to use (cpu or cuda)")
    parser.add_argument("--d_model", type=int, default=768, help="Model dimension")
    parser.add_argument("--output", type=str, default="benchmarks/results/music_module_eval.json",
                       help="Output path for results")
    parser.add_argument("--seed", type=int, default=42, help="Random seed")

    args = parser.parse_args()

    # Set random seed
    torch.manual_seed(args.seed)

    # Create evaluator
    evaluator = MusicModuleEvaluator(device=args.device, d_model=args.d_model)

    # Run evaluation
    results = evaluator.evaluate_all()

    # Save results
    evaluator.save_results(args.output)

    print("\n" + "=" * 60)
    print("Evaluation complete!")
    print("=" * 60)


if __name__ == "__main__":
    main()