"""
AI-powered audio analysis and processing features.

This module provides capabilities for audio feature extraction, classification,
and enhancement using deep learning models.
"""

import torch
import torchaudio
import numpy as np
from typing import List, Dict, Tuple, Optional, Union
import logging
from dataclasses import dataclass
from pathlib import Path
import librosa

logger = logging.getLogger(__name__)

@dataclass
class AudioFeatures:
    """Extracted audio features."""
    mfcc: np.ndarray  # Mel-frequency cepstral coefficients
    spectral_centroid: np.ndarray
    spectral_rolloff: np.ndarray
    spectral_bandwidth: np.ndarray
    zero_crossing_rate: np.ndarray
    rms_energy: np.ndarray
    tempo: float
    beats: np.ndarray  # Beat timestamps

class AudioClassifier:
    """Audio classification using deep learning."""

    def __init__(self, model_name: str = "yamnet", device: str = "auto"):
        """
        Initialize audio classifier.

        Args:
            model_name: Name of classification model
            device: Device to run inference on ("cpu", "cuda", or "auto")
        """
        self.model_name = model_name

        # Determine device
        if device == "auto":
            self.device = "cuda" if torch.cuda.is_available() else "cpu"
        else:
            self.device = device

        # Load model
        try:
            if model_name == "yamnet":
                self.model = torch.hub.load("harritaylor/yamnet", "yamnet")
                self.model.to(self.device)
            else:
                raise ValueError(f"Unsupported model: {model_name}")
        except Exception as e:
            logger.error(f"Failed to load model {model_name}: {e}")
            raise

    def classify(self, audio: np.ndarray, sample_rate: int) -> List[Dict[str, float]]:
        """
        Classify audio segments.

        Args:
            audio: Audio samples (mono)
            sample_rate: Sample rate in Hz

        Returns:
            List of classifications with confidence scores
        """
        # Ensure audio is mono and float32
        if len(audio.shape) > 1:
            audio = np.mean(audio, axis=1)
        audio = audio.astype(np.float32)

        # Convert to tensor
        audio_tensor = torch.from_numpy(audio).to(self.device)

        # Run inference
        scores, embeddings, mel_spec = self.model(audio_tensor)

        # Get top predictions
        top_scores, top_indices = torch.topk(scores, k=5)

        # Convert to list of dictionaries
        results = []
        for score, idx in zip(top_scores, top_indices):
            results.append({
                "label": self.model.class_names[idx],
                "confidence": float(score)
            })

        return results

class AudioEnhancer:
    """AI-powered audio enhancement."""

    def __init__(self, model_name: str = "denoiser", device: str = "auto"):
        """
        Initialize audio enhancer.

        Args:
            model_name: Name of enhancement model
            device: Device to run inference on ("cpu", "cuda", or "auto")
        """
        self.model_name = model_name

        # Determine device
        if device == "auto":
            self.device = "cuda" if torch.cuda.is_available() else "cpu"
        else:
            self.device = device

        # Load model
        try:
            if model_name == "denoiser":
                self.model = torch.hub.load("facebookresearch/denoiser", "dns48")
                self.model.to(self.device)
            else:
                raise ValueError(f"Unsupported model: {model_name}")
        except Exception as e:
            logger.error(f"Failed to load model {model_name}: {e}")
            raise

    def enhance(self, audio: np.ndarray, sample_rate: int) -> np.ndarray:
        """
        Enhance audio quality.

        Args:
            audio: Audio samples
            sample_rate: Sample rate in Hz

        Returns:
            Enhanced audio samples
        """
        # Convert to tensor
        audio_tensor = torch.from_numpy(audio).to(self.device)

        # Add batch dimension if needed
        if audio_tensor.dim() == 1:
            audio_tensor = audio_tensor.unsqueeze(0)

        # Run enhancement
        with torch.no_grad():
            enhanced = self.model(audio_tensor)

        # Convert back to numpy
        return enhanced.cpu().numpy().squeeze()

class AudioFeatureExtractor:
    """Extract audio features for analysis."""

    def __init__(self, sample_rate: int = 22050, hop_length: int = 512):
        """
        Initialize feature extractor.

        Args:
            sample_rate: Target sample rate in Hz
            hop_length: Number of samples between frames
        """
        self.sample_rate = sample_rate
        self.hop_length = hop_length

    def extract_features(self, audio: np.ndarray) -> AudioFeatures:
        """
        Extract audio features.

        Args:
            audio: Audio samples

        Returns:
            AudioFeatures object
        """
        # Ensure audio is mono
        if len(audio.shape) > 1:
            audio = np.mean(audio, axis=1)

        # Extract features
        mfcc = librosa.feature.mfcc(y=audio, sr=self.sample_rate,
                                  hop_length=self.hop_length)

        spectral_centroid = librosa.feature.spectral_centroid(
            y=audio, sr=self.sample_rate, hop_length=self.hop_length)[0]

        spectral_rolloff = librosa.feature.spectral_rolloff(
            y=audio, sr=self.sample_rate, hop_length=self.hop_length)[0]

        spectral_bandwidth = librosa.feature.spectral_bandwidth(
            y=audio, sr=self.sample_rate, hop_length=self.hop_length)[0]

        zero_crossing_rate = librosa.feature.zero_crossing_rate(
            audio, hop_length=self.hop_length)[0]

        rms_energy = librosa.feature.rms(y=audio, hop_length=self.hop_length)[0]

        # Get tempo and beat frames
        tempo, beats = librosa.beat.beat_track(y=audio, sr=self.sample_rate)

        # Convert beat frames to timestamps
        beat_times = librosa.frames_to_time(beats, sr=self.sample_rate)

        return AudioFeatures(
            mfcc=mfcc,
            spectral_centroid=spectral_centroid,
            spectral_rolloff=spectral_rolloff,
            spectral_bandwidth=spectral_bandwidth,
            zero_crossing_rate=zero_crossing_rate,
            rms_energy=rms_energy,
            tempo=tempo,
            beats=beat_times
        )

class SpeechRecognizer:
    """Speech recognition using deep learning."""

    def __init__(self, model_name: str = "wav2vec2", device: str = "auto"):
        """
        Initialize speech recognizer.

        Args:
            model_name: Name of recognition model
            device: Device to run inference on ("cpu", "cuda", or "auto")
        """
        self.model_name = model_name

        # Determine device
        if device == "auto":
            self.device = "cuda" if torch.cuda.is_available() else "cpu"
        else:
            self.device = device

        # Load model
        try:
            if model_name == "wav2vec2":
                self.model = torch.hub.load("pytorch/fairseq", "wav2vec2_large_960h")
                self.model.to(self.device)
            else:
                raise ValueError(f"Unsupported model: {model_name}")
        except Exception as e:
            logger.error(f"Failed to load model {model_name}: {e}")
            raise

    def transcribe(self, audio: np.ndarray, sample_rate: int) -> str:
        """
        Transcribe speech to text.

        Args:
            audio: Audio samples
            sample_rate: Sample rate in Hz

        Returns:
            Transcribed text
        """
        # Ensure audio is mono and float32
        if len(audio.shape) > 1:
            audio = np.mean(audio, axis=1)
        audio = audio.astype(np.float32)

        # Convert to tensor
        audio_tensor = torch.from_numpy(audio).to(self.device)

        # Run inference
        with torch.no_grad():
            emissions = self.model(audio_tensor)
            emissions = torch.log_softmax(emissions, dim=-1)

        # Decode to text
        tokens = emissions.argmax(dim=-1)
        text = self.model.decode(tokens)

        return text

class MusicTranscriber:
    """Music transcription using deep learning."""

    def __init__(self, model_name: str = "omnizart", device: str = "auto"):
        """
        Initialize music transcriber.

        Args:
            model_name: Name of transcription model
            device: Device to run inference on ("cpu", "cuda", or "auto")
        """
        self.model_name = model_name

        # Determine device
        if device == "auto":
            self.device = "cuda" if torch.cuda.is_available() else "cpu"
        else:
            self.device = device

        # Load model
        try:
            if model_name == "omnizart":
                from omnizart.music import app
                self.model = app.MusicTranscription()
            else:
                raise ValueError(f"Unsupported model: {model_name}")
        except Exception as e:
            logger.error(f"Failed to load model {model_name}: {e}")
            raise

    def transcribe(self, audio: np.ndarray, sample_rate: int) -> Dict[str, np.ndarray]:
        """
        Transcribe music to MIDI-like representation.

        Args:
            audio: Audio samples
            sample_rate: Sample rate in Hz

        Returns:
            Dictionary with note events (onset, offset, pitch, velocity)
        """
        # Ensure audio is mono
        if len(audio.shape) > 1:
            audio = np.mean(audio, axis=1)

        # Run transcription
        transcription = self.model.transcribe(audio, sample_rate)

        return {
            "onset": transcription["onset"],
            "offset": transcription["offset"],
            "pitch": transcription["pitch"],
            "velocity": transcription["velocity"]
        }
