#!/usr/bin/env python3
"""
Utility functions for Translation AI Agent
"""

import os
import time
import tempfile
import logging
import hashlib
from typing import Optional, Tuple, List, Dict, Any
import numpy as np
import librosa
import soundfile as sf
from pathlib import Path

logger = logging.getLogger(__name__)

class AudioProcessor:
    """Audio processing utilities"""
    
    @staticmethod
    def load_audio(file_path: str, target_sr: int = 16000) -> Tuple[np.ndarray, int]:
        """Load and resample audio file"""
        try:
            audio, sr = librosa.load(file_path, sr=target_sr, mono=True)
            return audio, sr
        except Exception as e:
            logger.error(f"Error loading audio: {e}")
            raise
    
    @staticmethod
    def save_audio(audio: np.ndarray, file_path: str, sample_rate: int = 16000):
        """Save audio array to file"""
        try:
            sf.write(file_path, audio, sample_rate)
        except Exception as e:
            logger.error(f"Error saving audio: {e}")
            raise
    
    @staticmethod
    def get_audio_duration(file_path: str) -> float:
        """Get duration of audio file in seconds"""
        try:
            audio, sr = librosa.load(file_path, sr=None)
            return len(audio) / sr
        except Exception as e:
            logger.error(f"Error getting audio duration: {e}")
            return 0.0
    
    @staticmethod
    def validate_audio_file(file_path: str, max_duration: int = 300) -> bool:
        """Validate audio file format and duration"""
        if not os.path.exists(file_path):
            return False
        
        try:
            duration = AudioProcessor.get_audio_duration(file_path)
            return 0 < duration <= max_duration
        except:
            return False
    
    @staticmethod
    def normalize_audio(audio: np.ndarray) -> np.ndarray:
        """Normalize audio to [-1, 1] range"""
        if audio.max() > 1.0 or audio.min() < -1.0:
            audio = audio / np.max(np.abs(audio))
        return audio
    
    @staticmethod
    def add_silence(audio: np.ndarray, duration: float, sample_rate: int) -> np.ndarray:
        """Add silence to beginning and end of audio"""
        silence_samples = int(duration * sample_rate)
        silence = np.zeros(silence_samples)
        return np.concatenate([silence, audio, silence])

class LanguageDetector:
    """Language detection utilities"""
    
    def __init__(self, keywords_dict: Dict[str, List[str]]):
        self.keywords = keywords_dict
    
    def detect(self, text: str, threshold: int = 2) -> str:
        """Detect language from text using keyword matching"""
        text_lower = text.lower().split()
        scores = {}
        
        for lang, keywords in self.keywords.items():
            score = sum(1 for word in keywords if word in text_lower)
            scores[lang] = score
        
        # Get language with highest score
        if scores:
            detected_lang, score = max(scores.items(), key=lambda x: x[1])
            if score >= threshold:
                return detected_lang
        
        return 'en'  # Default to English
    
    def get_confidence(self, text: str, detected_lang: str) -> float:
        """Get confidence score for detected language"""
        text_lower = text.lower().split()
        keywords = self.keywords.get(detected_lang, [])
        
        if not keywords or not text_lower:
            return 0.0
        
        matches = sum(1 for word in keywords if word in text_lower)
        return min(matches / len(keywords), 1.0)

class FileManager:
    """File management utilities"""
    
    @staticmethod
    def create_temp_file(suffix: str = '.wav', prefix: str = 'temp_') -> str:
        """Create temporary file and return path"""
        temp_file = tempfile.NamedTemporaryFile(
            suffix=suffix, 
            prefix=prefix, 
            delete=False
        )
        temp_file.close()
        return temp_file.name
    
    @staticmethod
    def cleanup_temp_files(file_paths: List[str]):
        """Remove temporary files"""
        for file_path in file_paths:
            try:
                if os.path.exists(file_path):
                    os.remove(file_path)
            except Exception as e:
                logger.warning(f"Could not remove temp file {file_path}: {e}")
    
    @staticmethod
    def ensure_directory(directory: str):
        """Ensure directory exists, create if not"""
        Path(directory).mkdir(parents=True, exist_ok=True)
    
    @staticmethod
    def get_file_hash(file_path: str) -> str:
        """Get SHA256 hash of file"""
        try:
            with open(file_path, 'rb') as f:
                return hashlib.sha256(f.read()).hexdigest()
        except Exception as e:
            logger.error(f"Error computing file hash: {e}")
            return ""

class ModelManager:
    """Model loading and management utilities"""
    
    @staticmethod
    def check_cuda_availability() -> bool:
        """Check if CUDA is available"""
        try:
            import torch
            return torch.cuda.is_available()
        except ImportError:
            return False
    
    @staticmethod
    def get_device_info() -> Dict[str, Any]:
        """Get device information"""
        info = {"has_cuda": False, "device_count": 0, "device_names": []}
        
        try:
            import torch
            if torch.cuda.is_available():
                info["has_cuda"] = True
                info["device_count"] = torch.cuda.device_count()
                info["device_names"] = [
                    torch.cuda.get_device_name(i) 
                    for i in range(torch.cuda.device_count())
                ]
        except ImportError:
            pass
        
        return info
    
    @staticmethod
    def estimate_model_memory(model_name: str) -> int:
        """Estimate memory requirements for model in MB"""
        # Rough estimates based on common model sizes
        memory_estimates = {
            "whisper-tiny": 128,
            "whisper-base": 256,
            "whisper-small": 512,
            "whisper-medium": 1024,
            "nllb-200-distilled-600M": 1200,
            "nllb-200-1.3B": 2600,
            "speecht5": 800
        }
        
        for key, memory in memory_estimates.items():
            if key in model_name.lower():
                return memory
        
        return 1000  # Default estimate

class CacheManager:
    """Caching utilities"""
    
    def __init__(self, cache_dir: str, max_size: int = 1000, ttl: int = 3600):
        self.cache_dir = Path(cache_dir)
        self.max_size = max_size
        self.ttl = ttl  # Time to live in seconds
        self.cache_info = {}
        self.ensure_cache_dir()
    
    def ensure_cache_dir(self):
        """Ensure cache directory exists"""
        self.cache_dir.mkdir(parents=True, exist_ok=True)
    
    def get_cache_key(self, data: str) -> str:
        """Generate cache key from data"""
        return hashlib.md5(data.encode()).hexdigest()
    
    def is_cached(self, key: str) -> bool:
        """Check if key is in cache and not expired"""
        cache_file = self.cache_dir / f"{key}.cache"
        if not cache_file.exists():
            return False
        
        # Check TTL
        if key in self.cache_info:
            cache_time = self.cache_info[key]
            if time.time() - cache_time > self.ttl:
                self.remove_from_cache(key)
                return False
        
        return True
    
    def get_from_cache(self, key: str) -> Optional[Any]:
        """Get item from cache"""
        if not self.is_cached(key):
            return None
        
        try:
            cache_file = self.cache_dir / f"{key}.cache"
            with open(cache_file, 'r', encoding='utf-8') as f:
                return f.read()
        except Exception as e:
            logger.error(f"Error reading from cache: {e}")
            return None
    
    def add_to_cache(self, key: str, data: str):
        """Add item to cache"""
        try:
            cache_file = self.cache_dir / f"{key}.cache"
            with open(cache_file, 'w', encoding='utf-8') as f:
                f.write(data)
            
            self.cache_info[key] = time.time()
            self.cleanup_old_cache()
        except Exception as e:
            logger.error(f"Error writing to cache: {e}")
    
    def remove_from_cache(self, key: str):
        """Remove item from cache"""
        try:
            cache_file = self.cache_dir / f"{key}.cache"
            if cache_file.exists():
                cache_file.unlink()
            
            if key in self.cache_info:
                del self.cache_info[key]
        except Exception as e:
            logger.error(f"Error removing from cache: {e}")
    
    def cleanup_old_cache(self):
        """Remove old cache entries if over max size"""
        if len(self.cache_info) <= self.max_size:
            return
        
        # Sort by timestamp and remove oldest
        sorted_items = sorted(self.cache_info.items(), key=lambda x: x[1])
        items_to_remove = len(sorted_items) - self.max_size
        
        for key, _ in sorted_items[:items_to_remove]:
            self.remove_from_cache(key)

class MetricsTracker:
    """Track performance metrics"""
    
    def __init__(self):
        self.metrics = {
            "translations": 0,
            "speech_recognitions": 0,
            "text_to_speech": 0,
            "total_processing_time": 0,
            "average_processing_time": 0,
            "errors": 0
        }
        self.start_time = time.time()
    
    def record_translation(self, processing_time: float):
        """Record a translation event"""
        self.metrics["translations"] += 1
        self._update_timing(processing_time)
    
    def record_speech_recognition(self, processing_time: float):
        """Record a speech recognition event"""
        self.metrics["speech_recognitions"] += 1
        self._update_timing(processing_time)
    
    def record_tts(self, processing_time: float):
        """Record a text-to-speech event"""
        self.metrics["text_to_speech"] += 1
        self._update_timing(processing_time)
    
    def record_error(self):
        """Record an error event"""
        self.metrics["errors"] += 1
    
    def _update_timing(self, processing_time: float):
        """Update timing metrics"""
        self.metrics["total_processing_time"] += processing_time
        total_operations = (
            self.metrics["translations"] + 
            self.metrics["speech_recognitions"] + 
            self.metrics["text_to_speech"]
        )
        if total_operations > 0:
            self.metrics["average_processing_time"] = (
                self.metrics["total_processing_time"] / total_operations
            )
    
    def get_stats(self) -> Dict[str, Any]:
        """Get current statistics"""
        uptime = time.time() - self.start_time
        return {
            **self.metrics,
            "uptime_seconds": uptime,
            "operations_per_minute": (
                (self.metrics["translations"] + 
                 self.metrics["speech_recognitions"] + 
                 self.metrics["text_to_speech"]) / (uptime / 60)
                if uptime > 0 else 0
            )
        }

# Utility functions
def format_duration(seconds: float) -> str:
    """Format duration in human-readable format"""
    if seconds < 60:
        return f"{seconds:.1f}s"
    elif seconds < 3600:
        minutes = int(seconds // 60)
        secs = int(seconds % 60)
        return f"{minutes}m {secs}s"
    else:
        hours = int(seconds // 3600)
        minutes = int((seconds % 3600) // 60)
        return f"{hours}h {minutes}m"

def validate_language_code(code: str, supported_languages: Dict[str, str]) -> bool:
    """Validate language code"""
    return code in supported_languages

def extract_language_code(display_string: str) -> str:
    """Extract language code from display string like 'en - English'"""
    return display_string.split(' - ')[0] if ' - ' in display_string else display_string

def create_progress_callback(progress_bar=None):
    """Create progress callback for long-running operations"""
    def callback(current: int, total: int):
        if progress_bar:
            progress_bar.progress(current / total)
    return callback