Language Model Strategy
=======================

The Language Model Strategy represents the most sophisticated normalization approach in SilverSpeak, utilizing pre-trained masked language models to predict contextually optimal character replacements. This strategy leverages the deep linguistic understanding encoded in transformer models to make highly informed decisions about homoglyph normalization based on semantic and syntactic context.

Overview
--------

This strategy addresses complex normalization scenarios where traditional rule-based or frequency-based approaches may fail. By using masked language models (MLMs) like BERT, RoBERTa, or similar architectures, the strategy can understand nuanced linguistic contexts and select replacements that preserve both meaning and grammatical correctness.

The approach is particularly powerful for handling:
- Mixed-script texts where context determines the appropriate script
- Ambiguous characters that could belong to multiple writing systems
- Domain-specific terminology requiring specialized understanding
- Complex linguistic constructions where simple character replacement would break meaning

**Key Innovations**:
- **Context-Aware Masking**: Selectively masks characters/words containing homoglyphs for targeted prediction
- **Confidence Scoring**: Uses model confidence to validate replacement quality
- **Batch Processing**: Efficiently processes multiple masked positions simultaneously
- **Multi-Level Analysis**: Supports both character-level and word-level normalization approaches

Implementation Details
-----------------------

The language model strategy implementation employs sophisticated techniques to maximize accuracy while maintaining efficiency:

1. **Model Initialization and Validation**:
   The strategy begins by loading and validating the language model for masked language modeling capability:

   .. code-block:: python

      from silverspeak.homoglyphs.normalization import apply_language_model_strategy
      from transformers import AutoTokenizer, AutoModelForMaskedLM
      import torch

      # Load model and tokenizer
      model_name = "bert-base-multilingual-cased"
      tokenizer = AutoTokenizer.from_pretrained(model_name)
      model = AutoModelForMaskedLM.from_pretrained(model_name)
      
      # Verify MLM capability
      if not hasattr(model, 'get_output_embeddings'):
          raise ValueError("Model does not support masked language modeling")

   The strategy automatically detects GPU availability and optimizes device placement for performance.

2. **Homoglyph Detection and Mapping**:
   The system creates bidirectional mappings to identify potential homoglyphs efficiently:

   .. code-block:: python

      # Create reverse mapping for efficient lookup
      from collections import defaultdict
      
      reverse_mapping = defaultdict(list)
      for orig_char, homoglyphs in mapping.items():
          for homoglyph in homoglyphs:
              reverse_mapping[homoglyph].append(orig_char)
      
      # Also add original characters to reverse mapping
      for orig_char in mapping.keys():
          if orig_char not in reverse_mapping:
              reverse_mapping[orig_char] = []

3. **Word-Level vs Character-Level Processing**:
   The strategy supports two processing modes for optimal accuracy:

   **Word-Level Processing** (Default - Higher Accuracy):

   .. code-block:: python

      def find_homoglyph_words(text_segment):
          """Identify words containing potential homoglyphs."""
          words = []
          word_pattern = r'\b\w+\b'
          
          for match in re.finditer(word_pattern, text_segment):
              word = match.group()
              contains_homoglyph = any(char in mapping or char in reverse_mapping 
                                     for char in word)
              if contains_homoglyph:
                  words.append((match.start(), match.end(), word))
          return words

   **Character-Level Processing** (Fallback):

   .. code-block:: python

      # Process individual character positions
      positions_to_mask = [
          (pos, char) for pos, char in enumerate(segment) 
          if char in mapping or char in reverse_mapping
      ]

4. **Advanced Masking and Prediction**:
   The strategy employs sophisticated masking techniques for optimal context preservation:

   .. code-block:: python

      # Create masked versions maintaining token alignment
      mask_token = tokenizer.mask_token
      mask_token_id = tokenizer.mask_token_id
      
      # For word-level: replace entire words with appropriate number of masks
      chars = list(normalized_segment)
      mask_length = end_pos - start_pos
      chars[start_pos:end_pos] = [mask_token] * mask_length
      masked_segment = "".join(chars)
      
      # Tokenize and prepare for model
      inputs = tokenizer(
          masked_segments,
          return_tensors="pt",
          padding=True,
          truncation=True,
          max_length=512
      )

5. **Confidence-Based Selection**:
   The strategy incorporates confidence thresholding to ensure quality replacements:

   .. code-block:: python

      # Extract predictions with confidence scores
      with torch.no_grad():
          outputs = model(**inputs)
          logits = outputs.logits
          
      # Apply softmax for probability distribution
      probs = torch.softmax(logits, dim=-1)
      top_values, top_indices = torch.topk(probs, k=10)
      
      # Filter by confidence threshold
      candidates = [
          (token, confidence) for token, confidence in zip(top_tokens, top_values)
          if confidence >= min_confidence
      ]

Advanced Usage Examples
-----------------------

**Basic Language Model Normalization**:

.. code-block:: python

   from silverspeak.homoglyphs.normalization import apply_language_model_strategy
   from transformers import AutoTokenizer, AutoModelForMaskedLM

   # Load model components
   model_name = "bert-base-multilingual-cased"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   model = AutoModelForMaskedLM.from_pretrained(model_name)
   
   text = "Тhе quісk brоwn fох јumps оvеr thе lаzу dоg."  # Mixed Cyrillic homoglyphs
   normalization_map = {
       "Т": ["T"], "е": ["e"], "і": ["i"], "с": ["c"], "k": ["k"],
       "о": ["o"], "w": ["w"], "n": ["n"], "х": ["x"], "ј": ["j"], 
       "m": ["m"], "р": ["p"], "s": ["s"], "v": ["v"], "r": ["r"],
       "h": ["h"], "l": ["l"], "а": ["a"], "z": ["z"], "у": ["y"], "g": ["g"]
   }

   normalized_text = apply_language_model_strategy(
       text=text,
       mapping=normalization_map,
       language_model=model,
       tokenizer=tokenizer
   )
   print(f"Original:   {text}")
   print(f"Normalized: {normalized_text}")

**Alternative Usage via normalize_text**:

.. code-block:: python

   from silverspeak.homoglyphs import normalize_text
   from silverspeak.homoglyphs.utils import NormalizationStrategies

   # Automatic model loading (recommended for simplicity)
   text = "Mathеmatical ехprеssion: ∫f(х)dx = ln|х| + C"
   normalized_text = normalize_text(
       text, 
       strategy=NormalizationStrategies.LANGUAGE_MODEL,
       model_name="distilbert-base-multilingual-cased",  # Faster alternative
       min_confidence=0.5,  # Adjust confidence threshold
       batch_size=4         # Process multiple masks per batch
   )
   print(normalized_text)

**Advanced Configuration Options**:

.. code-block:: python

   # Fine-tuned configuration for specific use cases
   normalized_text = apply_language_model_strategy(
       text=text,
       mapping=normalization_map,
       model_name="microsoft/mdeberta-v3-base",  # More advanced model
       batch_size=8,           # Larger batches for efficiency
       max_length=256,         # Shorter sequences for speed
       min_confidence=0.7,     # Higher confidence threshold
       word_level=True,        # Word-level processing (default)
       device="cuda"           # Force GPU usage
   )

**Domain-Specific Model Usage**:

.. code-block:: python

   # Using domain-specific models for better accuracy
   domain_models = {
       "scientific": "allenai/scibert_scivocab_uncased",
       "clinical": "emilyalsentzer/Bio_ClinicalBERT", 
       "legal": "nlpaueb/legal-bert-base-uncased",
       "financial": "ProsusAI/finbert"
   }
   
   scientific_text = "Thе protеin structurе shows ехcеllеnt stаbility."
   result = normalize_text(
       scientific_text,
       strategy=NormalizationStrategies.LANGUAGE_MODEL,
       model_name=domain_models["scientific"]
   )

**Multilingual Processing**:

.. code-block:: python

   # Optimized for multilingual content
   multilingual_text = "Hеllo, こんにちは, Hаllо, مرحبا"  # Mixed scripts
   result = apply_language_model_strategy(
       text=multilingual_text,
       mapping=normalization_map,
       model_name="bert-base-multilingual-cased",
       batch_size=2,
       min_confidence=0.6
   )

Performance Characteristics
---------------------------

**Computational Complexity**:
- **Time Complexity**: O(n × b × s) where n is text length, b is batch size, and s is sequence length
- **Space Complexity**: O(m + v) where m is model size and v is vocabulary size
- **GPU Memory**: 1-8GB depending on model size (BERT-base: ~1GB, Large models: 4-8GB)

**Processing Speed Benchmarks** (approximate, varies by hardware):

.. code-block:: python

   # Performance comparison across different models
   model_performance = {
       "distilbert-base-multilingual-cased": {
           "speed": "Fast (2-3x faster than BERT)",
           "accuracy": "Good (90-95% of BERT performance)",
           "memory": "~512MB GPU"
       },
       "bert-base-multilingual-cased": {
           "speed": "Medium (baseline)",
           "accuracy": "Very Good (reference standard)",
           "memory": "~1GB GPU"
       },
       "microsoft/mdeberta-v3-base": {
           "speed": "Slow (2-3x slower than BERT)",
           "accuracy": "Excellent (best performance)",
           "memory": "~2GB GPU"
       }
   }

**Optimization Strategies**:

.. code-block:: python

   # Performance optimization techniques
   optimized_config = {
       "batch_size": 8,        # Process multiple positions simultaneously
       "max_length": 256,      # Reduce sequence length for speed
       "device": "cuda",       # Use GPU acceleration
       "torch_dtype": torch.float16,  # Use half precision for memory efficiency
   }
   
   # Enable mixed precision for faster training
   with torch.cuda.amp.autocast():
       outputs = model(**inputs)

Security Considerations
-----------------------

**Model Security**:

.. code-block:: python

   def secure_model_loading(model_name, verify_ssl=True, trust_remote_code=False):
       """Securely load language models with safety checks."""
       try:
           # Verify model source and integrity
           if not model_name.startswith(("bert-", "distilbert-", "microsoft/")):
               raise ValueError(f"Untrusted model source: {model_name}")
           
           # Load with security constraints
           model = AutoModelForMaskedLM.from_pretrained(
               model_name,
               trust_remote_code=trust_remote_code,
               use_auth_token=False  # Avoid automatic token usage
           )
           return model
       except Exception as e:
           logger.error(f"Failed to securely load model: {e}")
           raise

**Resource Management**:

.. code-block:: python

   # Implement resource limits and monitoring
   def resource_aware_normalization(text, max_memory_gb=4, timeout_seconds=300):
       """Apply language model strategy with resource constraints."""
       import psutil
       import signal
       
       # Check available memory
       available_memory = psutil.virtual_memory().available / (1024**3)
       if available_memory < max_memory_gb:
           raise RuntimeError(f"Insufficient memory: {available_memory:.1f}GB < {max_memory_gb}GB")
       
       # Set timeout for processing
       def timeout_handler(signum, frame):
           raise TimeoutError("Processing exceeded time limit")
       
       signal.signal(signal.SIGALRM, timeout_handler)
       signal.alarm(timeout_seconds)
       
       try:
           result = apply_language_model_strategy(text, mapping)
           return result
       finally:
           signal.alarm(0)  # Disable timeout

**Privacy and Data Protection**:

.. code-block:: python

   # Implement data protection measures
   def privacy_aware_processing(text, mask_sensitive=True):
       """Process text while protecting sensitive information."""
       import re
       
       if mask_sensitive:
           # Mask potential PII before processing
           patterns = {
               'email': r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
               'phone': r'\b\d{3}-\d{3}-\d{4}\b',
               'ssn': r'\b\d{3}-\d{2}-\d{4}\b'
           }
           
           masked_text = text
           for pattern_name, pattern in patterns.items():
               masked_text = re.sub(pattern, f'[MASKED_{pattern_name.upper()}]', masked_text)
           
           return masked_text
       return text

Best Practices
--------------

**Model Selection Guidelines**:

.. code-block:: python

   # Choose models based on requirements
   def select_optimal_model(requirements):
       """Select the best model based on specific requirements."""
       
       if requirements.get("speed") == "critical":
           return "distilbert-base-multilingual-cased"
       elif requirements.get("accuracy") == "critical":
           return "microsoft/mdeberta-v3-base" 
       elif requirements.get("multilingual") == True:
           return "bert-base-multilingual-cased"
       elif requirements.get("domain") == "scientific":
           return "allenai/scibert_scivocab_uncased"
       else:
           return "bert-base-multilingual-cased"  # Default balanced choice

**Error Handling and Fallbacks**:

.. code-block:: python

   def robust_language_model_normalization(text, mapping, **kwargs):
       """Apply language model strategy with comprehensive error handling."""
       
       fallback_strategies = [
           ("language_model", apply_language_model_strategy),
           ("local_context", apply_local_context_strategy),
           ("dominant_script", apply_dominant_script_strategy)
       ]
       
       for strategy_name, strategy_func in fallback_strategies:
           try:
               if strategy_name == "language_model":
                   return strategy_func(text, mapping, **kwargs)
               else:
                   return strategy_func(text, mapping)
               
           except ImportError as e:
               logger.warning(f"{strategy_name} dependencies not available: {e}")
               continue
           except RuntimeError as e:
               logger.warning(f"{strategy_name} failed: {e}")
               continue
           except Exception as e:
               logger.error(f"Unexpected error in {strategy_name}: {e}")
               continue
       
       # Final fallback: return original text
       logger.error("All normalization strategies failed")
       return text

**Configuration Management**:

.. code-block:: python

   # Centralized configuration for different use cases
   LANGUAGE_MODEL_CONFIGS = {
       "production": {
           "model_name": "distilbert-base-multilingual-cased",
           "batch_size": 4,
           "min_confidence": 0.8,
           "max_length": 256,
           "device": "auto"
       },
       "research": {
           "model_name": "microsoft/mdeberta-v3-base",
           "batch_size": 1,
           "min_confidence": 0.5,
           "max_length": 512,
           "device": "cuda"
       },
       "development": {
           "model_name": "bert-base-multilingual-cased",
           "batch_size": 2,
           "min_confidence": 0.6,
           "max_length": 256,
           "device": "auto"
       }
   }

Limitations and Advanced Considerations
---------------------------------------

**Known Limitations**:
- **Computational Requirements**: Requires significant GPU memory and processing power
- **Model Bias**: May inherit biases from training data, affecting certain languages or domains
- **Context Window**: Limited by model's maximum sequence length (typically 512 tokens)
- **Tokenization Artifacts**: Subword tokenization can affect character-level predictions

**When to Use This Strategy**:
- ✅ Maximum accuracy is required regardless of computational cost
- ✅ Context-dependent normalization is critical
- ✅ GPU resources are available
- ✅ Processing time is not the primary constraint
- ✅ Complex, mixed-script texts need normalization

**When to Consider Alternatives**:
- ❌ Real-time processing is required
- ❌ Limited computational resources
- ❌ Simple, deterministic normalization is sufficient
- ❌ Privacy constraints prevent using pre-trained models

**Integration Patterns**:

.. code-block:: python

   # Hybrid approach combining multiple strategies
   def hybrid_normalization(text, mapping):
       """Combine language model with faster strategies for optimal results."""
       
       # First pass: fast screening with dominant script
       quick_result = apply_dominant_script_strategy(text, mapping)
       
       # Second pass: language model on remaining ambiguous cases
       if text != quick_result:
           # Only apply expensive strategy if changes were made
           final_result = apply_language_model_strategy(quick_result, mapping)
           return final_result
       
       return quick_result

**Evaluation and Quality Metrics**:

.. code-block:: python

   def evaluate_normalization_quality(original, normalized, ground_truth=None):
       """Evaluate the quality of language model normalization."""
       
       metrics = {
           "character_changes": sum(c1 != c2 for c1, c2 in zip(original, normalized)),
           "length_preservation": len(normalized) == len(original),
           "script_consistency": analyze_script_consistency(normalized)
       }
       
       if ground_truth:
           metrics["accuracy"] = calculate_accuracy(normalized, ground_truth)
           
       return metrics

The Language Model Strategy represents the pinnacle of homoglyph normalization accuracy, leveraging cutting-edge NLP technology to achieve context-aware, semantically coherent text normalization. While it demands significant computational resources, its ability to understand complex linguistic patterns makes it invaluable for applications where accuracy is paramount.