"""
Utility functions for Multi-Language TTS application
"""

import os
import tempfile
import logging
from typing import Optional, Tuple, List
import numpy as np
import torch
import librosa
from pathlib import Path

logger = logging.getLogger(__name__)

def get_device() -> str:
    """Get the best available device for inference"""
    if torch.cuda.is_available():
        return "cuda"
    elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
        return "mps"  # Apple Silicon
    else:
        return "cpu"

def validate_text(text: str, max_length: int = 1000) -> str:
    """Validate and clean input text"""
    if not text or not text.strip():
        raise ValueError("Text cannot be empty")
    
    text = text.strip()
    if len(text) > max_length:
        logger.warning(f"Text truncated from {len(text)} to {max_length} characters")
        text = text[:max_length]
    
    return text

def validate_audio_file(file_path: str) -> bool:
    """Validate audio file format and accessibility"""
    if not file_path or not os.path.exists(file_path):
        return False
    
    supported_formats = ['.wav', '.mp3', '.flac', '.ogg', '.m4a']
    file_ext = Path(file_path).suffix.lower()
    
    return file_ext in supported_formats

def create_temp_audio_file(suffix: str = ".wav") -> str:
    """Create a temporary audio file"""
    temp_file = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
    temp_file.close()
    return temp_file.name

def cleanup_temp_file(file_path: str) -> None:
    """Safely remove temporary file"""
    try:
        if file_path and os.path.exists(file_path):
            os.unlink(file_path)
    except Exception as e:
        logger.warning(f"Failed to cleanup temp file {file_path}: {e}")

def load_audio(file_path: str, target_sr: int = 22050) -> Tuple[np.ndarray, int]:
    """Load audio file with proper error handling"""
    try:
        audio, sr = librosa.load(file_path, sr=target_sr)
        return audio, sr
    except Exception as e:
        logger.error(f"Failed to load audio from {file_path}: {e}")
        raise ValueError(f"Could not load audio file: {e}")

def normalize_audio(audio: np.ndarray) -> np.ndarray:
    """Normalize audio to prevent clipping"""
    if len(audio) == 0:
        return audio
    
    # Normalize to [-1, 1] range
    max_val = np.max(np.abs(audio))
    if max_val > 0:
        audio = audio / max_val
    
    return audio

def get_supported_languages() -> List[str]:
    """Get list of supported languages"""
    from config import LANGUAGE_MODELS
    return list(LANGUAGE_MODELS.keys())

def format_duration(seconds: float) -> str:
    """Format duration in seconds to human readable format"""
    if seconds < 1:
        return f"{seconds*1000:.0f}ms"
    elif seconds < 60:
        return f"{seconds:.1f}s"
    else:
        minutes = int(seconds // 60)
        seconds = seconds % 60
        return f"{minutes}m {seconds:.1f}s"

def estimate_synthesis_time(text_length: int, language: str = "English") -> float:
    """Estimate synthesis time based on text length and language"""
    # Base time estimates (seconds per character)
    base_times = {
        "English": 0.05,
        "Korean": 0.08,
        "German": 0.06,
        "Spanish": 0.05
    }
    
    base_time = base_times.get(language, 0.06)
    return text_length * base_time + 2.0  # Add 2s overhead

def log_system_info():
    """Log system information for debugging"""
    logger.info(f"Device: {get_device()}")
    logger.info(f"PyTorch version: {torch.__version__}")
    logger.info(f"CUDA available: {torch.cuda.is_available()}")
    
    if torch.cuda.is_available():
        logger.info(f"CUDA device: {torch.cuda.get_device_name()}")
        logger.info(f"CUDA memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f}GB")

class AudioProcessor:
    """Audio processing utilities"""
    
    @staticmethod
    def resample_audio(audio: np.ndarray, orig_sr: int, target_sr: int) -> np.ndarray:
        """Resample audio to target sample rate"""
        if orig_sr == target_sr:
            return audio
        return librosa.resample(audio, orig_sr=orig_sr, target_sr=target_sr)
    
    @staticmethod
    def trim_silence(audio: np.ndarray, sr: int, threshold: float = 0.01) -> np.ndarray:
        """Trim silence from beginning and end of audio"""
        return librosa.effects.trim(audio, top_db=20)[0]
    
    @staticmethod
    def apply_fade(audio: np.ndarray, sr: int, fade_duration: float = 0.1) -> np.ndarray:
        """Apply fade in/out to audio"""
        fade_samples = int(fade_duration * sr)
        if len(audio) <= 2 * fade_samples:
            return audio
        
        # Fade in
        audio[:fade_samples] *= np.linspace(0, 1, fade_samples)
        # Fade out
        audio[-fade_samples:] *= np.linspace(1, 0, fade_samples)
        
        return audio