Translation Systems

class NeuralTranslationSystem:
    """
    Production-scale neural machine translation system
    Based on Google Translate architecture with transformer models
    """
    
    def __init__(self, config):
        self.config = config
        self.language_detector = self.initialize_language_detector()
        self.translation_models = self.load_translation_models()
        self.quality_estimator = self.setup_quality_estimation()
        self.post_processor = self.setup_post_processing()
        self.cache_manager = self.setup_translation_cache()
        
    def initialize_language_detector(self):
        """Initialize language detection system"""
        class LanguageDetector:
            def __init__(self):
                self.character_based_detector = self.load_char_detector()
                self.neural_detector = self.load_neural_detector()
                self.confidence_threshold = 0.9
                
            def detect_language(self, text):
                """Detect source language with confidence scoring"""
                # Character-based detection (fast)
                char_prediction = self.character_based_detector.predict(text)
                
                # Neural detection (accurate)
                neural_prediction = self.neural_detector.predict(text)
                
                # Ensemble prediction
                final_prediction = self.ensemble_predictions(
                    char_prediction, neural_prediction
                )
                
                return {
                    'language': final_prediction['language'],
                    'confidence': final_prediction['confidence'],
                    'alternatives': final_prediction['alternatives'][:3]
                }
            
            def load_char_detector(self):
                """Character n-gram based language detection"""
                class CharDetector:
                    def __init__(self):
                        self.char_profiles = self.load_language_profiles()
                        
                    def predict(self, text):
                        # Extract character n-grams
                        trigrams = self.extract_trigrams(text)
                        
                        # Calculate similarity to each language profile
                        scores = {}
                        for lang, profile in self.char_profiles.items():
                            score = self.calculate_similarity(trigrams, profile)
                            scores[lang] = score
                        
                        # Sort by similarity
                        sorted_langs = sorted(scores.items(), key=lambda x: x[1], reverse=True)
                        
                        return {
                            'language': sorted_langs[0][0],
                            'confidence': sorted_langs[0][1],
                            'scores': dict(sorted_langs[:5])
                        }
                    
                    def extract_trigrams(self, text):
                        """Extract character trigrams from text"""
                        text = text.lower().replace(' ', '_')
                        trigrams = {}
                        
                        for i in range(len(text) - 2):
                            trigram = text[i:i+3]
                            trigrams[trigram] = trigrams.get(trigram, 0) + 1
                        
                        # Normalize frequencies
                        total = sum(trigrams.values())
                        return {k: v/total for k, v in trigrams.items()}
                
                return CharDetector()
            
            def load_neural_detector(self):
                """Neural language detection model"""
                # Transformer-based language identification
                from transformers import pipeline
                
                return pipeline(
                    "text-classification",
                    model="facebook/fasttext-language-identification"
                )
        
        return LanguageDetector()
    
    def load_translation_models(self):
        """Load optimized translation models"""
        models = {}
        
        # Multilingual models for popular language pairs
        models['multilingual'] = self.load_multilingual_model()
        
        # Specialized high-quality models for major pairs
        major_pairs = [
            ('en', 'es'), ('en', 'fr'), ('en', 'de'), ('en', 'zh'),
            ('en', 'ja'), ('en', 'ko'), ('en', 'ar'), ('en', 'hi')
        ]
        
        for src, tgt in major_pairs:
            model_key = f"{src}-{tgt}"
            models[model_key] = self.load_specialized_model(src, tgt)
            
            # Bidirectional support
            reverse_key = f"{tgt}-{src}"
            models[reverse_key] = self.load_specialized_model(tgt, src)
        
        return models
    
    def load_multilingual_model(self):
        """Load multilingual transformer model"""
        class MultilingualTransformer:
            def __init__(self):
                from transformers import MarianMTModel, MarianTokenizer
                
                # Load multilingual model
                self.model_name = "facebook/m2m100_418M"
                self.tokenizer = MarianTokenizer.from_pretrained(self.model_name)
                self.model = MarianMTModel.from_pretrained(self.model_name)
                
                # Language codes mapping
                self.lang_codes = self.load_language_codes()
                
            def translate(self, text, source_lang, target_lang):
                """Translate text between any supported language pair"""
                # Set source and target language tokens
                src_lang_token = self.lang_codes.get(source_lang)
                tgt_lang_token = self.lang_codes.get(target_lang)
                
                if not src_lang_token or not tgt_lang_token:
                    raise ValueError(f"Unsupported language pair: {source_lang}-{target_lang}")
                
                # Prepare input with language tokens
                input_text = f"{src_lang_token} {text}"
                
                # Tokenize
                inputs = self.tokenizer(
                    input_text,
                    return_tensors="pt",
                    padding=True,
                    truncation=True,
                    max_length=512
                )
                
                # Generate translation
                with torch.no_grad():
                    outputs = self.model.generate(
                        **inputs,
                        forced_bos_token_id=self.tokenizer.lang_code_to_id[tgt_lang_token],
                        max_length=512,
                        num_beams=4,
                        early_stopping=True,
                        temperature=0.7
                    )
                
                # Decode translation
                translation = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
                
                return {
                    'translation': translation,
                    'model_type': 'multilingual',
                    'confidence': self.estimate_confidence(inputs, outputs)
                }
        
        return MultilingualTransformer()
    
    def load_specialized_model(self, source_lang, target_lang):
        """Load specialized high-quality model for language pair"""
        class SpecializedTransformer:
            def __init__(self, src_lang, tgt_lang):
                self.src_lang = src_lang
                self.tgt_lang = tgt_lang
                
                # Load specialized model (e.g., opus-mt models)
                model_name = f"Helsinki-NLP/opus-mt-{src_lang}-{tgt_lang}"
                
                from transformers import MarianMTModel, MarianTokenizer
                self.tokenizer = MarianTokenizer.from_pretrained(model_name)
                self.model = MarianMTModel.from_pretrained(model_name)
                
                # Domain adaptation layers
                self.domain_adapters = self.load_domain_adapters()
                
            def translate(self, text, domain='general'):
                """High-quality translation with domain adaptation"""
                # Preprocess text
                preprocessed = self.preprocess_text(text, domain)
                
                # Tokenize
                inputs = self.tokenizer(
                    preprocessed,
                    return_tensors="pt",
                    padding=True,
                    truncation=True,
                    max_length=512
                )
                
                # Apply domain adapter if available
                if domain in self.domain_adapters:
                    inputs = self.domain_adapters[domain].adapt_input(inputs)
                
                # Generate translation with optimized parameters
                with torch.no_grad():
                    outputs = self.model.generate(
                        **inputs,
                        max_length=512,
                        num_beams=5,  # Higher beam search for quality
                        early_stopping=True,
                        length_penalty=1.0,
                        repetition_penalty=1.1,
                        temperature=0.6
                    )
                
                # Decode and post-process
                translation = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
                translation = self.postprocess_translation(translation, domain)
                
                return {
                    'translation': translation,
                    'model_type': 'specialized',
                    'language_pair': f"{self.src_lang}-{self.tgt_lang}",
                    'domain': domain,
                    'confidence': self.calculate_confidence_score(inputs, outputs)
                }
        
        return SpecializedTransformer(source_lang, target_lang)
    
    async def translate_text(self, text, source_lang=None, target_lang='en', domain='general'):
        """Main translation pipeline"""
        # Step 1: Language detection if not provided
        if source_lang is None:
            detection = self.language_detector.detect_language(text)
            source_lang = detection['language']
            
            if detection['confidence'] < 0.8:
                return {
                    'error': 'Language detection confidence too low',
                    'detected_language': source_lang,
                    'confidence': detection['confidence']
                }
        
        # Step 2: Check cache
        cache_key = self.generate_cache_key(text, source_lang, target_lang)
        cached_result = self.cache_manager.get(cache_key)
        if cached_result:
            return cached_result
        
        # Step 3: Select best model
        model = self.select_best_model(source_lang, target_lang)
        
        # Step 4: Translate
        translation_result = model.translate(text, source_lang, target_lang)
        
        # Step 5: Quality estimation
        quality_score = self.quality_estimator.estimate_quality(
            text, translation_result['translation'], source_lang, target_lang
        )
        
        # Step 6: Post-processing
        final_translation = self.post_processor.process(
            translation_result['translation'], source_lang, target_lang
        )
        
        # Step 7: Prepare response
        result = {
            'source_text': text,
            'translated_text': final_translation,
            'source_language': source_lang,
            'target_language': target_lang,
            'confidence': translation_result['confidence'],
            'quality_score': quality_score,
            'model_used': translation_result['model_type'],
            'processing_time_ms': 0  # Will be set by wrapper
        }
        
        # Cache result
        self.cache_manager.set(cache_key, result, ttl=3600)
        
        return result
    
    def select_best_model(self, source_lang, target_lang):
        """Select optimal model for language pair"""
        # Check for specialized model first
        specialized_key = f"{source_lang}-{target_lang}"
        if specialized_key in self.translation_models:
            return self.translation_models[specialized_key]
        
        # Fall back to multilingual model
        return self.translation_models['multilingual']