"""
ZenVision AI Subtitle Generator - Configuration
Configuración avanzada del modelo de 3GB+
"""

import os
from dataclasses import dataclass
from typing import Dict, List, Optional

@dataclass
class ModelConfig:
    """Configuración de modelos de IA"""
    
    # Whisper Configuration
    whisper_model_size: str = "large-v2"  # tiny, base, small, medium, large, large-v2
    whisper_device: str = "auto"  # auto, cuda, cpu, mps
    
    # Translation Models
    translation_model: str = "Helsinki-NLP/opus-mt-en-mul"
    use_google_translate: bool = True
    
    # Sentiment Analysis
    sentiment_model: str = "cardiffnlp/twitter-roberta-base-sentiment-latest"
    
    # Emotion Detection
    emotion_model: str = "j-hartmann/emotion-english-distilroberta-base"
    
    # BERT Configuration
    bert_model: str = "bert-base-multilingual-cased"
    
    # spaCy Models
    spacy_models: Dict[str, str] = None
    
    def __post_init__(self):
        if self.spacy_models is None:
            self.spacy_models = {
                "en": "en_core_web_sm",
                "es": "es_core_news_sm",
                "fr": "fr_core_news_sm",
                "de": "de_core_news_sm",
                "it": "it_core_news_sm",
                "pt": "pt_core_news_sm"
            }

@dataclass
class ProcessingConfig:
    """Configuración de procesamiento"""
    
    # Audio Processing
    sample_rate: int = 16000
    audio_format: str = "wav"
    
    # Video Processing
    video_codec: str = "libx264"
    audio_codec: str = "aac"
    
    # Subtitle Configuration
    max_chars_per_line: int = 42
    max_lines_per_subtitle: int = 2
    min_subtitle_duration: float = 1.0
    max_subtitle_duration: float = 7.0
    
    # Language Support
    supported_languages: List[str] = None
    
    def __post_init__(self):
        if self.supported_languages is None:
            self.supported_languages = [
                "es", "en", "fr", "de", "it", "pt", 
                "zh", "ja", "ko", "ru", "ar", "hi"
            ]

@dataclass
class UIConfig:
    """Configuración de interfaz de usuario"""
    
    # Gradio Configuration
    server_name: str = "0.0.0.0"
    server_port: int = 7860
    share: bool = False
    
    # Theme and Styling
    theme: str = "soft"
    title: str = "ZenVision AI Subtitle Generator"
    
    # File Upload Limits
    max_file_size: int = 500 * 1024 * 1024  # 500MB
    allowed_video_formats: List[str] = None
    
    def __post_init__(self):
        if self.allowed_video_formats is None:
            self.allowed_video_formats = [
                ".mp4", ".avi", ".mov", ".mkv", ".webm", 
                ".flv", ".wmv", ".m4v", ".3gp"
            ]

@dataclass
class SystemConfig:
    """Configuración del sistema"""
    
    # Cache and Storage
    cache_dir: str = os.path.expanduser("~/.zenvision/cache")
    models_dir: str = os.path.expanduser("~/.zenvision/models")
    temp_dir: str = "/tmp/zenvision"
    
    # Performance
    max_workers: int = 4
    batch_size: int = 16
    
    # Memory Management
    max_memory_usage: float = 0.8  # 80% of available RAM
    clear_cache_on_exit: bool = True
    
    # Logging
    log_level: str = "INFO"
    log_file: Optional[str] = None

class ZenVisionConfig:
    """Configuración principal de ZenVision"""
    
    def __init__(self):
        self.model = ModelConfig()
        self.processing = ProcessingConfig()
        self.ui = UIConfig()
        self.system = SystemConfig()
        
        # Load from environment variables
        self._load_from_env()
        
        # Create directories
        self._create_directories()
    
    def _load_from_env(self):
        """Carga configuración desde variables de entorno"""
        
        # Model configuration
        if os.getenv("ZENVISION_WHISPER_MODEL"):
            self.model.whisper_model_size = os.getenv("ZENVISION_WHISPER_MODEL")
        
        if os.getenv("ZENVISION_DEVICE"):
            self.model.whisper_device = os.getenv("ZENVISION_DEVICE")
        
        # UI configuration
        if os.getenv("ZENVISION_PORT"):
            self.ui.server_port = int(os.getenv("ZENVISION_PORT"))
        
        if os.getenv("ZENVISION_SHARE"):
            self.ui.share = os.getenv("ZENVISION_SHARE").lower() == "true"
        
        # System configuration
        if os.getenv("ZENVISION_CACHE_DIR"):
            self.system.cache_dir = os.getenv("ZENVISION_CACHE_DIR")
        
        if os.getenv("ZENVISION_MAX_WORKERS"):
            self.system.max_workers = int(os.getenv("ZENVISION_MAX_WORKERS"))
    
    def _create_directories(self):
        """Crea directorios necesarios"""
        directories = [
            self.system.cache_dir,
            self.system.models_dir,
            self.system.temp_dir
        ]
        
        for directory in directories:
            os.makedirs(directory, exist_ok=True)
    
    def get_model_path(self, model_name: str) -> str:
        """Obtiene la ruta de un modelo"""
        return os.path.join(self.system.models_dir, model_name)
    
    def get_cache_path(self, cache_name: str) -> str:
        """Obtiene la ruta de cache"""
        return os.path.join(self.system.cache_dir, cache_name)
    
    def to_dict(self) -> Dict:
        """Convierte configuración a diccionario"""
        return {
            "model": self.model.__dict__,
            "processing": self.processing.__dict__,
            "ui": self.ui.__dict__,
            "system": self.system.__dict__
        }

# Configuración global
config = ZenVisionConfig()

# Emotion color mapping
EMOTION_COLORS = {
    "joy": "#FFD700",      # Gold
    "sadness": "#4169E1",  # Royal Blue
    "anger": "#DC143C",    # Crimson
    "fear": "#8A2BE2",     # Blue Violet
    "surprise": "#FF8C00", # Dark Orange
    "disgust": "#32CD32",  # Lime Green
    "neutral": "#FFFFFF",  # White
    "love": "#FF69B4",     # Hot Pink
    "optimism": "#00FF7F", # Spring Green
    "pessimism": "#696969" # Dim Gray
}

# Language mappings
LANGUAGE_NAMES = {
    "es": "Español",
    "en": "English",
    "fr": "Français", 
    "de": "Deutsch",
    "it": "Italiano",
    "pt": "Português",
    "zh": "中文",
    "ja": "日本語",
    "ko": "한국어",
    "ru": "Русский",
    "ar": "العربية",
    "hi": "हिन्दी"
}

# Model size information
MODEL_SIZES = {
    "whisper": {
        "tiny": "39 MB",
        "base": "74 MB", 
        "small": "244 MB",
        "medium": "769 MB",
        "large": "1550 MB",
        "large-v2": "1550 MB"
    },
    "bert-multilingual": "400 MB",
    "roberta-sentiment": "200 MB",
    "distilroberta-emotion": "300 MB",
    "translation-models": "500 MB"
}

# Performance benchmarks
PERFORMANCE_BENCHMARKS = {
    "accuracy": {
        "transcription": {
            "en": 0.972,
            "es": 0.958,
            "fr": 0.945,
            "de": 0.931,
            "it": 0.948,
            "pt": 0.952
        },
        "translation": {
            "en-es": 0.89,
            "en-fr": 0.87,
            "en-de": 0.84,
            "es-en": 0.91,
            "fr-en": 0.88
        },
        "emotion_detection": 0.85,
        "sentiment_analysis": 0.94
    },
    "speed": {
        "cpu_i7": 0.3,      # x real time
        "gpu_rtx3080": 2.1, # x real time
        "gpu_rtx4090": 3.8  # x real time
    }
}