"""
Music Theory Engine for TouchGrass.
Understands music theory relationships, scales, chords, progressions.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, List, Dict, Tuple


class MusicTheoryModule(nn.Module):
    """
    Understands music theory relationships.
    
    Knows:
    - Circle of fifths and key relationships
    - Scale degrees and chord functions (I, ii, iii, IV, V, vi, vii°)
    - All modes: Ionian, Dorian, Phrygian, Lydian, Mixolydian, Aeolian, Locrian
    - Interval relationships (major/minor/perfect/augmented/diminished)
    - Chord tensions and extensions (7ths, 9ths, 11ths, 13ths)
    - Common progressions (I-IV-V, ii-V-I, I-V-vi-IV, 12-bar blues, etc)
    - Voice leading principles
    - Modulation techniques
    """

    # Chromatic notes (C-based)
    CHROMATIC_NOTES = ["C", "C#", "D", "Db", "E", "Eb", "F", "F#", "G", "Gb", "A", "Ab", "B", "Bb"]
    # Actually 12 notes, but listing enharmonics for flexibility

    # Scale degrees in major (Ionian)
    SCALE_DEGREES = ["I", "ii", "iii", "IV", "V", "vi", "vii°"]

    # Common chord types
    CHORD_TYPES = [
        "major", "minor", "diminished", "augmented",
        "major7", "minor7", "dominant7", "half-dim7", "dim7",
        "major9", "minor9", "dominant9",
        "sus2", "sus4", "add9", "6", "maj6",
    ]

    # Modes
    MODES = [
        "ionian", "dorian", "phrygian", "lydian",
        "mixolydian", "aeolian", "locrian"
    ]

    # Common progressions (by scale degrees)
    COMMON_PROGRESSIONS = {
        "I-IV-V-I": "Classical cadential",
        "ii-V-I": "Jazz turnaround",
        "I-V-vi-IV": "Pop progression (4-chord)",
        "vi-IV-I-V": "Pop variant",
        "I-vi-ii-V": "Circle progression",
        "I-vi-IV-V": "50s progression",
        "IV-V-I": "Plagal cadence",
        "V-I": "Authentic cadence",
        "12-bar blues": "Blues",
        "i-iv-v": "Minor blues",
    }

    def __init__(self, d_model: int):
        """
        Initialize MusicTheoryModule.

        Args:
            d_model: Hidden dimension from base model
        """
        super().__init__()
        self.d_model = d_model

        # Embeddings
        # 12 chromatic notes × 4 octave context = 48 total pitch classes
        self.note_embed = nn.Embedding(48, 128)  # 12 notes × 4 octaves
        self.chord_type_embed = nn.Embedding(15, 128)
        self.mode_embed = nn.Embedding(7, 128)
        self.key_embed = nn.Embedding(24, 128)  # 12 major + 12 minor keys

        # Theory relationship head
        self.relationship_proj = nn.Linear(d_model, d_model)

        # Chord function classifier (tonic, subdominant, dominant)
        self.chord_function_head = nn.Linear(d_model, 3)

        # Scale degree predictor
        self.scale_degree_head = nn.Linear(d_model, 7)

        # Interval classifier (unison through 13th)
        self.interval_head = nn.Linear(d_model, 14)

        # Progression predictor (next chord in progression)
        self.progression_head = nn.Linear(d_model, 7)

        # Key detection head
        self.key_detection_head = nn.Linear(d_model, 24)

        # Mode classifier
        self.mode_classifier = nn.Linear(d_model, 7)

    def forward(
        self,
        hidden_states: torch.Tensor,
        query: Optional[str] = None,
    ) -> Dict[str, torch.Tensor]:
        """
        Forward pass through MusicTheoryModule.

        Args:
            hidden_states: Base model hidden states [batch, seq_len, d_model]
            query: Optional text query about music theory

        Returns:
            Dictionary with theory-related predictions
        """
        batch_size, seq_len, _ = hidden_states.shape

        # Pool hidden states
        pooled = hidden_states.mean(dim=1)  # [batch, d_model]

        # Predict chord function
        chord_function_logits = self.chord_function_head(pooled)  # [batch, 3]

        # Predict scale degree
        scale_degree_logits = self.scale_degree_head(pooled)  # [batch, 7]

        # Predict interval
        interval_logits = self.interval_head(pooled)  # [batch, 14]

        # Predict next chord in progression
        progression_logits = self.progression_head(pooled)  # [batch, 7]

        # Detect key
        key_logits = self.key_detection_head(pooled)  # [batch, 24]

        # Classify mode
        mode_logits = self.mode_classifier(pooled)  # [batch, 7]

        outputs = {
            "chord_function_logits": chord_function_logits,
            "scale_degree_logits": scale_degree_logits,
            "interval_logits": interval_logits,
            "progression_logits": progression_logits,
            "key_logits": key_logits,
            "mode_logits": mode_logits,
        }

        return outputs

    def get_chord_function(self, scale_degree: str) -> str:
        """
        Get chord function (tonic, subdominant, dominant).

        Args:
            scale_degree: Roman numeral (I, ii, V, etc)

        Returns:
            Chord function string
        """
        tonic = ["I", "vi"]
        subdominant = ["ii", "IV", "vi"]
        dominant = ["V", "vii°", "iii"]

        if scale_degree in tonic:
            return "tonic"
        elif scale_degree in subdominant:
            return "subdominant"
        elif scale_degree in dominant:
            return "dominant"
        else:
            return "unknown"

    def get_scale_from_key(self, key: str, mode: str = "ionian") -> List[str]:
        """
        Generate scale notes from key and mode.

        Args:
            key: Root note (C, D, E, etc)
            mode: Mode name (ionian, dorian, etc)

        Returns:
            List of notes in the scale
        """
        # Define intervals for each mode (semitones from root)
        mode_intervals = {
            "ionian": [0, 2, 4, 5, 7, 9, 11],
            "dorian": [0, 2, 3, 5, 7, 9, 10],
            "phrygian": [0, 1, 3, 5, 7, 8, 10],
            "lydian": [0, 2, 4, 6, 7, 9, 11],
            "mixolydian": [0, 2, 4, 5, 7, 9, 10],
            "aeolian": [0, 2, 3, 5, 7, 8, 10],
            "locrian": [0, 1, 3, 5, 6, 8, 10],
        }

        # Note to semitone mapping (C=0)
        note_to_semitone = {
            "C": 0, "C#": 1, "Db": 1, "D": 2, "D#": 3, "Eb": 3,
            "E": 4, "F": 5, "F#": 6, "Gb": 6, "G": 7, "G#": 8,
            "Ab": 8, "A": 9, "A#": 10, "Bb": 10, "B": 11,
        }

        if mode not in mode_intervals:
            raise ValueError(f"Unknown mode: {mode}")

        root_semitone = note_to_semitone.get(key)
        if root_semitone is None:
            raise ValueError(f"Unknown key: {key}")

        # Build scale
        intervals = mode_intervals[mode]
        scale = []
        for interval in intervals:
            semitone = (root_semitone + interval) % 12
            # Find note name
            note_name = self._semitone_to_note(semitone)
            scale.append(note_name)

        return scale

    def _semitone_to_note(self, semitone: int) -> str:
        """Convert semitone number to note name."""
        semitone_to_note = {
            0: "C", 1: "C#", 2: "D", 3: "Eb", 4: "E", 5: "F",
            6: "F#", 7: "G", 8: "Ab", 9: "A", 10: "Bb", 11: "B",
        }
        return semitone_to_note[semitone]

    def get_progression_chords(
        self,
        progression_name: str,
        key: str = "C",
    ) -> List[Tuple[str, str]]:
        """
        Get chord progression as list of (degree, chord).

        Args:
            progression_name: Name of progression (e.g., "I-IV-V-I")
            key: Root key

        Returns:
            List of (scale_degree, chord) tuples
        """
        if progression_name not in self.COMMON_PROGRESSIONS:
            raise ValueError(f"Unknown progression: {progression_name}")

        # Parse progression degrees
        degrees = progression_name.split("-")

        # Get scale for key
        scale = self.get_scale_from_key(key, mode="ionian")

        chords = []
        for degree in degrees:
            # Convert Roman numeral to scale index
            roman_map = {"I": 0, "ii": 1, "iii": 2, "IV": 3, "V": 4, "vi": 5, "vii°": 6}
            idx = roman_map.get(degree)
            if idx is None:
                continue

            root_note = scale[idx]
            # Determine chord quality based on degree
            if degree in ["ii", "iii", "vi"]:
                quality = "minor"
            elif degree == "vii°":
                quality = "diminished"
            else:
                quality = "major"

            chord = f"{root_note} {quality}"
            chords.append((degree, chord))

        return chords

    def suggest_progression(
        self,
        mood: str = "happy",
        genre: str = "pop",
        num_chords: int = 4,
    ) -> List[str]:
        """
        Suggest chord progression based on mood and genre.

        Args:
            mood: Emotional mood (happy, sad, tense, etc)
            genre: Music genre
            num_chords: Number of chords in progression

        Returns:
            List of chord names
        """
        # Simple rule-based suggestions
        if mood == "happy" and genre == "pop":
            if num_chords == 4:
                return ["I", "V", "vi", "IV"]
            elif num_chords == 3:
                return ["I", "IV", "V"]
        elif mood == "sad" or mood == "melancholy":
            return ["vi", "IV", "I", "V"]
        elif mood == "tense" or mood == "dramatic":
            return ["i", "iv", "V", "i"]  # Minor with dominant
        elif mood == "jazzy":
            return ["ii", "V", "I", "vi"]
        else:
            return ["I", "IV", "V", "I"]  # Default

        return ["I", "IV", "V", "I"]

    def validate_progression(
        self,
        progression: List[str],
        key: str = "C",
    ) -> Tuple[bool, List[str]]:
        """
        Validate chord progression for theoretical correctness.

        Args:
            progression: List of Roman numerals or chord names
            key: Key center

        Returns:
            (is_valid, issues)
        """
        issues = []

        # Check if all chords belong to the key
        scale = self.get_scale_from_key(key, mode="ionian")
        scale_notes = [note.rstrip("b#") for note in scale]  # Simplified

        for chord in progression:
            # Extract root note from chord name
            if " " in chord:
                root = chord.split(" ")[0]
                if root.rstrip("b#") not in scale_notes:
                    issues.append(f"Chord {chord} has root {root} not in key {key}")

        return len(issues) == 0, issues


def test_music_theory_module():
    """Test the MusicTheoryModule."""
    import torch

    # Create module
    module = MusicTheoryModule(d_model=4096)

    # Test input
    batch_size = 2
    seq_len = 10
    d_model = 4096
    hidden_states = torch.randn(batch_size, seq_len, d_model)

    # Forward pass
    outputs = module.forward(hidden_states)

    print("Music Theory Module outputs:")
    for key, value in outputs.items():
        print(f"  {key}: {value.shape}")

    # Test scale generation
    print("\nScale from C ionian:")
    scale = module.get_scale_from_key("C", "ionian")
    print(f"  {scale}")

    print("\nScale from A dorian:")
    scale = module.get_scale_from_key("A", "dorian")
    print(f"  {scale}")

    # Test progression
    print("\nProgression I-V-vi-IV in C:")
    chords = module.get_progression_chords("I-V-vi-IV", "C")
    for degree, chord in chords:
        print(f"  {degree}: {chord}")

    # Test suggestion
    print("\nSuggested progression (happy, pop, 4 chords):")
    prog = module.suggest_progression(mood="happy", genre="pop", num_chords=4)
    print(f"  {prog}")

    # Test validation
    print("\nValidate progression [I, IV, V, I] in C:")
    valid, issues = module.validate_progression(["I", "IV", "V", "I"], "C")
    print(f"  Valid: {valid}")
    if issues:
        print(f"  Issues: {issues}")

    print("\nMusic Theory Module test complete!")


if __name__ == "__main__":
    test_music_theory_module()