utils/masking_methods.py

import random
import torch
import logging
import string
from transformers import BertTokenizer, BertForMaskedLM
from nltk.corpus import stopwords
import nltk
from tqdm import tqdm

# Set logging to WARNING for a cleaner terminal.
logging.basicConfig(level=logging.WARNING, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

# Ensure stopwords are downloaded
try:
    nltk.data.find('corpora/stopwords')
except LookupError:
    nltk.download('stopwords')

def clean_word(word):
    """More robust cleaning for consistent matching"""
    # Remove possessive 's before other punctuation
    if word.lower().endswith("'s"):
        word = word[:-2]
    return word.lower().strip().translate(str.maketrans('', '', string.punctuation))

class MaskingProcessor:
    def __init__(self, tokenizer, model):
        self.tokenizer = tokenizer
        self.model = model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.stop_words = set(stopwords.words('english'))
        tqdm.write(f"[MaskingProcessor] Initialized on device: {self.device}")

    def is_stopword(self, word):
        """Check if a word is a stopword, handling punctuation and case"""
        return clean_word(word) in self.stop_words

    def verify_and_correct_ngram_positions(self, sentence, common_ngrams):
        """Verify ngram positions match actual words in sentence and correct if needed."""
        words = sentence.split()
        corrected_ngrams = {}
        
        for ngram, positions in common_ngrams.items():
            corrected_positions = []
            ngram_words = ngram.split()
            
            # Convert ngram words to clean format for matching
            clean_ngram_words = [clean_word(word) for word in ngram_words]
            
            # Scan the sentence to find actual occurrences of the ngram
            for i in range(len(words) - len(ngram_words) + 1):
                is_match = True
                for j, ngram_word in enumerate(clean_ngram_words):
                    if clean_word(words[i + j]) != ngram_word:
                        is_match = False
                        break
                
                if is_match:
                    # Found a matching position, add it
                    corrected_positions.append((i, i + len(ngram_words) - 1))
            
            if corrected_positions:
                corrected_ngrams[ngram] = corrected_positions
            else:
                # Log the issue and perform a more flexible search
                print(f"Warning: Could not find exact match for '{ngram}' in the sentence.")
                print(f"Attempting flexible matching...")
                
                # Try a more flexible approach by looking for individual words
                for i in range(len(words)):
                    if clean_word(words[i]) == clean_ngram_words[0]:
                        # We found the first word of the ngram
                        if len(ngram_words) == 1 or (
                            i + len(ngram_words) <= len(words) and 
                            all(clean_word(words[i+j]).startswith(clean_ngram_words[j]) for j in range(len(ngram_words)))
                        ):
                            corrected_positions.append((i, i + len(ngram_words) - 1))
                
                if corrected_positions:
                    print(f"Found flexible matches for '{ngram}': {corrected_positions}")
                    corrected_ngrams[ngram] = corrected_positions
                else:
                    # If still no match, keep original positions as fallback
                    print(f"No matches found for '{ngram}'. Keeping original positions.")
                    corrected_ngrams[ngram] = positions
        
        # Log changes
        if corrected_ngrams != common_ngrams:
            print(f"Original ngram positions: {common_ngrams}")
            print(f"Corrected ngram positions: {corrected_ngrams}")
        
        return corrected_ngrams
    def in_any_ngram(self, idx, ngram_positions):
            """Check if an original sentence index is part of any n-gram span"""
            return any(start <= idx <= end for start, end in ngram_positions)
    def create_fallback_mask(self, sentence, ngrams):
        """Create a fallback mask when normal strategies fail."""
        try:
            words = sentence.split()
            if not words:
                return None
            
            # Find any non-stopword that isn't in an ngram
            ngram_positions = []
            for positions in ngrams.values():
                for start, end in positions:
                    ngram_positions.append((start, end))
            ngram_positions.sort()
            
            # Find first eligible word
            for idx, word in enumerate(words):
                if not self.is_stopword(word) and not self.in_any_ngram(idx, ngram_positions):
                    masked_words = words.copy()
                    masked_words[idx] = self.tokenizer.mask_token
                    tqdm.write(f"[INFO] Fallback mask created at position {idx}: '{word}'")
                    return " ".join(masked_words), [idx]
            
            # If no eligible word found, just mask the first non-stop word
            for idx, word in enumerate(words):
                if not self.is_stopword(word):
                    masked_words = words.copy()
                    masked_words[idx] = self.tokenizer.mask_token
                    tqdm.write(f"[INFO] Last resort fallback mask created at position {idx}: '{word}'")
                    return " ".join(masked_words), [idx]
            
            # If still nothing, mask the first word
            if words:
                masked_words = words.copy()
                masked_words[0] = self.tokenizer.mask_token
                return " ".join(masked_words), [0]
                
            return None
        except Exception as e:
            tqdm.write(f"[ERROR] Error creating fallback mask: {e}")
            return None

    def mask_sentence_random(self, sentence, common_ngrams):
        """Mask random non-stopwords that are not part of common ngrams with controlled positioning."""
        common_ngrams = self.verify_and_correct_ngram_positions(sentence, common_ngrams)
        tqdm.write(f"[MaskingProcessor] Masking (random) sentence: {sentence}")
        original_words = sentence.split()

        # Handle punctuation
        has_punctuation = False
        punctuation = ''
        if original_words and original_words[-1][-1] in ['.', ',', '!', '?', ';', ':', '"', "'"]:
            has_punctuation = True
            punctuation = original_words[-1][-1]
            original_words[-1] = original_words[-1][:-1]
            if not original_words[-1]:  # If the word was just punctuation
                original_words.pop()

        # Get flattened ngram positions
        ngram_positions = []
        for positions in common_ngrams.values():
            for start, end in positions:
                ngram_positions.append((start, end))
        ngram_positions.sort()
        
        # Find all candidate indices (non-stopwords not in ngrams)
        candidate_indices = []
        for idx, word in enumerate(original_words):
            if not self.is_stopword(word) and not self.in_any_ngram(idx, ngram_positions):
                candidate_indices.append(idx)
        
        # Debug print candidate words
        print("Candidate words for masking:")
        for idx in candidate_indices:
            print(f"  Position {idx}: '{original_words[idx]}'")
        
        selected_indices = []
        if ngram_positions:
            # Before first ngram
            before_first = [idx for idx in candidate_indices if idx < ngram_positions[0][0]]
            if before_first:
                num_to_select = min(1, len(before_first))  # Select 1 word
                if num_to_select > 0:
                    selected = random.sample(before_first, num_to_select)
                    selected_indices.extend(selected)

            # Between ngrams
            for i in range(len(ngram_positions) - 1):
                between = [idx for idx in candidate_indices 
                        if ngram_positions[i][1] < idx < ngram_positions[i+1][0]]
                if between:
                    num_to_select = min(2, len(between))  # Select between 1-2 words
                    if num_to_select > 0:
                        selected = random.sample(between, num_to_select)
                        selected_indices.extend(selected)

            # After last ngram
            after_last = [idx for idx in candidate_indices if idx > ngram_positions[-1][1]]
            if after_last:
                num_to_select = min(1, len(after_last))  # Select 1 word
                if num_to_select > 0:
                    selected = random.sample(after_last, num_to_select)
                    selected_indices.extend(selected)
        else:
            # If no ngrams, pick up to 6 random candidates
            if candidate_indices:
                selected_indices = random.sample(candidate_indices, 
                                            min(6, len(candidate_indices)))
        
        masked_words = original_words.copy()
        for idx in selected_indices:
            masked_words[idx] = self.tokenizer.mask_token

        if has_punctuation:
            masked_words.append(punctuation)

        # Debug prints
        print("Original sentence:", sentence)
        print("Common ngrams:", common_ngrams)
        print("Common ngram positions:", ngram_positions)
        print("Candidate indices for masking:", candidate_indices)
        print("Selected for masking:", selected_indices)
        print("Masked sentence:", " ".join(masked_words))

        return " ".join(masked_words), selected_indices

    def mask_sentence_pseudorandom(self, sentence, common_ngrams):
        """Mask specific non-stopwords based on their position relative to ngrams."""
        common_ngrams = self.verify_and_correct_ngram_positions(sentence, common_ngrams)
        tqdm.write(f"[MaskingProcessor] Masking (pseudorandom) sentence: {sentence}")
        random.seed(3)  # Fixed seed for pseudorandom behavior
        original_words = sentence.split()

        # Handle punctuation
        has_punctuation = False
        punctuation = ''
        if original_words and original_words[-1][-1] in ['.', ',', '!', '?', ';', ':', '"', "'"]:
            has_punctuation = True
            punctuation = original_words[-1][-1]
            original_words[-1] = original_words[-1][:-1]
            if not original_words[-1]:  # If the word was just punctuation
                original_words.pop()

        # Get flattened ngram positions
        ngram_positions = []
        for positions in common_ngrams.values():
            for start, end in positions:
                ngram_positions.append((start, end))
        ngram_positions.sort()
        
        # Find all candidate indices (non-stopwords not in ngrams)
        candidate_indices = []
        for idx, word in enumerate(original_words):
            if not self.is_stopword(word) and not self.in_any_ngram(idx, ngram_positions):
                candidate_indices.append(idx)
        
        # Debug print candidate words
        print("Candidate words for masking:")
        for idx in candidate_indices:
            print(f"  Position {idx}: '{original_words[idx]}'")
        
        # PSEUDORANDOM SPECIFIC LOGIC: 
        selected_indices = []
        if ngram_positions:
            # Before first ngram
            before_first = [idx for idx in candidate_indices if idx < ngram_positions[0][0]]
            if before_first:
                num_to_select = min(1, len(before_first))  # Select 1 word
                if num_to_select > 0:
                    selected = random.sample(before_first, num_to_select)
                    selected_indices.extend(selected)

            # Between ngrams
            for i in range(len(ngram_positions) - 1):
                between = [idx for idx in candidate_indices 
                        if ngram_positions[i][1] < idx < ngram_positions[i+1][0]]
                if between:
                    num_to_select = min(2, len(between))  # Select between 1-2 words
                    if num_to_select > 0:
                        selected = random.sample(between, num_to_select)
                        selected_indices.extend(selected)

            # After last ngram
            after_last = [idx for idx in candidate_indices if idx > ngram_positions[-1][1]]
            if after_last:
                num_to_select = min(1, len(after_last))  # Select 1 word
                if num_to_select > 0:
                    selected = random.sample(after_last, num_to_select)
                    selected_indices.extend(selected)
        else:
            # If no ngrams, pick up to 6 random candidates
            if candidate_indices:
                selected_indices = random.sample(candidate_indices, 
                                            min(6, len(candidate_indices)))
        
        masked_words = original_words.copy()
        for idx in selected_indices:
            masked_words[idx] = self.tokenizer.mask_token

        if has_punctuation:
            masked_words.append(punctuation)

        # Debug prints
        print("Original sentence:", sentence)
        print("Common ngrams:", common_ngrams)
        print("Common ngram positions:", ngram_positions)
        print("Candidate indices for masking:", candidate_indices)
        print("Selected for masking:", selected_indices)
        print("Masked sentence:", " ".join(masked_words))

        return " ".join(masked_words), selected_indices

    def mask_sentence_entropy(self, sentence, common_ngrams):
        """Mask words with highest entropy that are not part of common ngrams."""
        common_ngrams = self.verify_and_correct_ngram_positions(sentence, common_ngrams)
        tqdm.write(f"[MaskingProcessor] Masking (entropy) sentence: {sentence}")
        original_words = sentence.split()

        # Handle punctuation
        has_punctuation = False
        punctuation = ''
        if original_words and original_words[-1][-1] in ['.', ',', '!', '?', ';', ':', '"', "'"]:
            has_punctuation = True
            punctuation = original_words[-1][-1]
            original_words[-1] = original_words[-1][:-1]
            if not original_words[-1]:  # If the word was just punctuation
                original_words.pop()

        # Get flattened ngram positions
        ngram_positions = []
        for positions in common_ngrams.values():
            for start, end in positions:
                ngram_positions.append((start, end))
        ngram_positions.sort()
        
        # Find all candidate indices (non-stopwords not in ngrams)
        candidate_indices = []
        for idx, word in enumerate(original_words):
            if not self.is_stopword(word) and not self.in_any_ngram(idx, ngram_positions):
                candidate_indices.append(idx)
        
        # Debug print candidate words
        print("Candidate words for masking:")
        for idx in candidate_indices:
            print(f"  Position {idx}: '{original_words[idx]}'")
        
        # ENTROPY SPECIFIC LOGIC:
        # Calculate entropy for each candidate word
        selected_indices = []
        if candidate_indices:
            # Organize candidates by position relative to ngrams
            if ngram_positions:
                # Group candidates by position
                before_first = []
                between_ngrams = {}
                after_last = []
                
                for idx in candidate_indices:
                    if idx < ngram_positions[0][0]:
                        before_first.append(idx)
                    elif idx > ngram_positions[-1][1]:
                        after_last.append(idx)
                    else:
                        # Find which ngram gap this belongs to
                        for i in range(len(ngram_positions) - 1):
                            if ngram_positions[i][1] < idx < ngram_positions[i+1][0]:
                                if i not in between_ngrams:
                                    between_ngrams[i] = []
                                between_ngrams[i].append(idx)
                
                # Before first ngram: select 1 highest entropy words
                if before_first:
                    entropies = [(idx, self.calculate_word_entropy(sentence, idx)) for idx in before_first]
                    entropies.sort(key=lambda x: x[1], reverse=True)  # Sort by entropy (highest first)
                    num_to_select = min(1, len(entropies))  # Select 1 word
                    selected_indices.extend([idx for idx, _ in entropies[:num_to_select]])
                    
                # For each gap between ngrams: select 1-2 highest entropy words
                for group, indices in between_ngrams.items():
                    if indices:
                        entropies = [(idx, self.calculate_word_entropy(sentence, idx)) for idx in indices]
                        entropies.sort(key=lambda x: x[1], reverse=True)  # Sort by entropy (highest first)
                        num_to_select = min(2, len(entropies))  # Select between 1-2 words
                        selected_indices.extend([idx for idx, _ in entropies[:num_to_select]])
                        
                # After last ngram: select 1 highest entropy words
                if after_last:
                    entropies = [(idx, self.calculate_word_entropy(sentence, idx)) for idx in after_last]
                    entropies.sort(key=lambda x: x[1], reverse=True)  # Sort by entropy (highest first)
                    num_to_select = min(1, len(entropies))  # Select 1 word
                    selected_indices.extend([idx for idx, _ in entropies[:num_to_select]])
            else:
                # If no ngrams, calculate entropy for all candidates
                entropies = [(idx, self.calculate_word_entropy(sentence, idx)) for idx in candidate_indices]
                # Sort by entropy (highest first)
                entropies.sort(key=lambda x: x[1], reverse=True)
                # Take top 6 or all if fewer
                selected_indices = [idx for idx, _ in entropies[:min(6, len(entropies))]]
        
        masked_words = original_words.copy()
        for idx in selected_indices:
            masked_words[idx] = self.tokenizer.mask_token

        if has_punctuation:
            masked_words.append(punctuation)

        # Debug prints
        print("Original sentence:", sentence)
        print("Common ngrams:", common_ngrams)
        print("Common ngram positions:", ngram_positions)
        print("Candidate indices for masking:", candidate_indices)
        print("Selected for masking:", selected_indices)
        print("Masked sentence:", " ".join(masked_words))

        return " ".join(masked_words), selected_indices

    def calculate_mask_logits(self, original_sentence, original_mask_indices):
        """Calculate logits for masked positions."""
        logger.info(f"Calculating mask logits for sentence: {original_sentence}")
        words = original_sentence.split()
        mask_logits = {}
        for idx in original_mask_indices:
            masked_words = words.copy()
            masked_words[idx] = self.tokenizer.mask_token
            masked_sentence = " ".join(masked_words)
            input_ids = self.tokenizer(masked_sentence, return_tensors="pt")["input_ids"].to(self.device)
            mask_token_index = torch.where(input_ids == self.tokenizer.mask_token_id)[1]
            with torch.no_grad():
                outputs = self.model(input_ids)
                logits = outputs.logits
            mask_logits_tensor = logits[0, mask_token_index, :]
            top_mask_logits, top_mask_indices = torch.topk(mask_logits_tensor, 100, dim=-1)
            top_tokens = []
            top_logits = []
            seen_words = set()
            for token_id, logit in zip(top_mask_indices[0], top_mask_logits[0]):
                token = self.tokenizer.convert_ids_to_tokens(token_id.item())
                if token.startswith('##'):
                    continue
                word = self.tokenizer.convert_tokens_to_string([token]).strip()
                if word and word not in seen_words:
                    seen_words.add(word)
                    top_tokens.append(word)
                    top_logits.append(logit.item())
                    if len(top_tokens) == 50:
                        break
            mask_logits[idx] = {
                "tokens": top_tokens,
                "logits": top_logits
            }
        logger.info("Completed calculating mask logits.")
        return mask_logits

    def calculate_word_entropy(self, sentence, word_position):
        """Calculate entropy for a word at a specific position."""
        logger.info(f"Calculating word entropy for position {word_position} in sentence: {sentence}")
        words = sentence.split()
        masked_words = words.copy()
        masked_words[word_position] = self.tokenizer.mask_token
        masked_sentence = " ".join(masked_words)
        input_ids = self.tokenizer(masked_sentence, return_tensors="pt")["input_ids"].to(self.device)
        mask_token_index = torch.where(input_ids == self.tokenizer.mask_token_id)[1]
        with torch.no_grad():
            outputs = self.model(input_ids)
            logits = outputs.logits
        probs = torch.nn.functional.softmax(logits[0, mask_token_index], dim=-1)
        entropy = -torch.sum(probs * torch.log(probs + 1e-9))
        logger.info(f"Computed entropy: {entropy.item()}")
        return entropy.item()

    def process_sentences(self, sentences_list, common_grams, method="random"):
        """Process multiple sentences with the specified masking method."""
        tqdm.write(f"[MaskingProcessor] Processing sentences using method: {method}")
        results = {}
        for sentence in tqdm(sentences_list, desc="Masking Sentences"):
            try:
                ngrams = common_grams.get(sentence, {})
                
                if method == "random":
                    masked_sentence, original_mask_indices = self.mask_sentence_random(sentence, ngrams)
                elif method == "pseudorandom":
                    masked_sentence, original_mask_indices = self.mask_sentence_pseudorandom(sentence, ngrams)
                else:  # entropy
                    masked_sentence, original_mask_indices = self.mask_sentence_entropy(sentence, ngrams)
                
                # Skip if no masks were applied
                if not original_mask_indices:
                    tqdm.write(f"[WARNING] No mask indices found for sentence with method {method}: {sentence[:50]}...")
                    # Create a fallback masked sentence with at least one mask
                    fallback_result = self.create_fallback_mask(sentence, ngrams)
                    if fallback_result:
                        masked_sentence, original_mask_indices = fallback_result
                        tqdm.write(f"[INFO] Created fallback mask for sentence")
                    else:
                        tqdm.write(f"[WARNING] Could not create fallback mask, skipping sentence")
                        continue
                
                logits = self.calculate_mask_logits(sentence, original_mask_indices)
                results[sentence] = {
                    "masked_sentence": masked_sentence,
                    "mask_logits": logits
                }
                logger.info(f"Processed sentence: {sentence}")
            except Exception as e:
                tqdm.write(f"[ERROR] Failed to process sentence with method {method}: {e}")
                tqdm.write(f"Sentence: {sentence[:100]}...")
                import traceback
                tqdm.write(traceback.format_exc())
        tqdm.write("[MaskingProcessor] Completed processing sentences.")
        return results

    @staticmethod
    def identify_common_ngrams(sentences, entities):
        """Enhanced to handle possessive forms better"""
        common_grams = {}
        
        # Pre-process entities to handle variations
        processed_entities = []
        for entity in entities:
            processed_entities.append(entity)
            # Add possessive form if not already there
            if not entity.endswith("'s") and not entity.endswith("s"):
                processed_entities.append(f"{entity}'s")
        
        for sentence in sentences:
            words = sentence.split()
            common_grams[sentence] = {}
            
            # Look for each entity in the sentence
            for entity in processed_entities:
                entity_words = entity.split()
                entity_len = len(entity_words)
                
                # Convert entity words for matching
                clean_entity_words = [clean_word(word) for word in entity_words]
                
                # Find all occurrences
                for i in range(len(words) - entity_len + 1):
                    is_match = True
                    for j, entity_word in enumerate(clean_entity_words):
                        if clean_word(words[i + j]) != entity_word:
                            is_match = False
                            break
                    
                    if is_match:
                        # Use canonical form from entity list for consistency
                        base_entity = entity
                        if entity.endswith("'s") and any(e == entity[:-2] for e in processed_entities):
                            base_entity = entity[:-2]
                            
                        if base_entity not in common_grams[sentence]:
                            common_grams[sentence][base_entity] = []
                        common_grams[sentence][base_entity].append((i, i + entity_len - 1))
        
        return common_grams
if __name__ == "__main__":
    #example test
    # test_sentence = "Kevin De Bruyne scored for Manchester City as they won the 2019-20 Premier League title."
    # entities to preserve
    # entities = ["Kevin De Bruyne", "Manchester City", "Premier League"]
    # Identify common n-grams
    common_grams = MaskingProcessor.identify_common_ngrams([test_sentence], entities)
    
    # Print detected n-grams
    print(f"Detected common n-grams: {common_grams[test_sentence]}")
    
    # Initialize the processor
    processor = MaskingProcessor(
        BertTokenizer.from_pretrained("bert-large-cased-whole-word-masking"), 
        BertForMaskedLM.from_pretrained("bert-large-cased-whole-word-masking")
    )
    
    # Test all three masking methods
    print("\nTesting Random Masking:")
    masked_random, indices_random = processor.mask_sentence_random(test_sentence, common_grams[test_sentence])
    
    print("\nTesting Pseudorandom Masking:")
    masked_pseudorandom, indices_pseudorandom = processor.mask_sentence_pseudorandom(test_sentence, common_grams[test_sentence])
    
    print("\nTesting Entropy Masking:")
    masked_entropy, indices_entropy = processor.mask_sentence_entropy(test_sentence, common_grams[test_sentence])
    
    # Print results
    print("\nResults:")
    print(f"Original: {test_sentence}")
    print(f"Random Masked: {masked_random}")
    print(f"Pseudorandom Masked: {masked_pseudorandom}")
    print(f"Entropy Masked: {masked_entropy}")