utils/watermark.py

"""
This file contains the code to watermark given sentences using PECCAVI
"""
import os
import sys
import time
import random
import torch
from utils.paraphraser import Paraphraser
from utils.entailment import EntailmentAnalyzer
from utils.sampling import SamplingProcessor
# from tokenizer import tokenize_sentence, tokenize_sentences
from utils.non_melting_point import NgramProcessor
from utils.masking_methods import MaskingProcessor
from tqdm import tqdm  # add this import at the top if not already present

sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from renderers.highlighter import highlight_common_words,reparaphrased_sentences_html
from renderers.tree import generate_subplot1, generate_subplot2
from renderers.plot_3d import gen_three_D_plot
# from metrics.detectability import SentenceDetectabilityCalculator
# from metrics.distortion import SentenceDistortionCalculator
# from metrics.euclidean_distance import SentenceEuclideanDistanceCalculator
from transformers import pipeline, AutoTokenizer, AutoModelForMaskedLM
from transformers import BertTokenizer, BertForMaskedLM
from pathlib import Path


from utils.config import load_config
import logging

project_root = Path(__file__).parent.parent
config_path = project_root / "utils" / "config.yaml"

# Update logging configuration to reduce clutter
logging.basicConfig(level=logging.WARNING, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

class Watermarker:
    def __init__(self, config):
        self.config = config
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        tqdm.write(f"[Watermarker] Initializing on device: {self.device}")
        self.user_prompt = None
        self.paraphrased_sentences = None
        self.analyzed_paraphrased_sentences = None
        self.selected_sentences = None
        self.discarded_sentences = None
        self.common_grams = None
        # self.subsequences = None
        self.common_grams_position = None
        self.masked_sentences = None
        self.masked_words = None
        self.masked_logits = None
        self.sampled_sentences = None
        self.reparaphrased_sentences = None
        self.distortion_list = None
        self.detectability_list = None
        self.euclidean_dist_list = None

        self.masking_strategies = ['random', 'pseudorandom','entropy']
        self.sampling_strategies = ['inverse_transform', 'exponential_minimum', 'temperature', 'greedy']
        self.masking_results = dict()
        self.sampling_results = dict()

        # Move the model to GPU if available.
        self.tokenizer = BertTokenizer.from_pretrained("bert-large-cased-whole-word-masking")
        self.model = BertForMaskedLM.from_pretrained("bert-large-cased-whole-word-masking").to(self.device)
        
        self.paraphraser = Paraphraser(self.config['Paraphrase'])
        self.entailment_analyzer = EntailmentAnalyzer(self.config['Entailment'])
        self.ngram_processor = NgramProcessor()
        self.masker = MaskingProcessor(self.tokenizer, self.model)
        self.sampler = SamplingProcessor(self.tokenizer)

        # self.detectability_calculator = SentenceDetectabilityCalculator(self.config['Metrics'])
        # self.distortion_calculator = SentenceDistortionCalculator(self.config['Metrics'])
        # self.euclidean_distance_calculator = SentenceEuclideanDistanceCalculator(self.config['Metrics'])


    def Paraphrase(self, prompt:str, threshold:int=0.7):
        """
        This function paraphrases the given prompt using PECCAVI
        Args:
            prompt: str: The prompt to be paraphrased
            threshold: int: The threshold for the similarity score
        Returns:
            str: The paraphrased sentence
        """
        start_time = time.time()
        self.user_prompt = prompt
        self.paraphrased_sentences = self.paraphraser.paraphrase(self.user_prompt)
        if self.paraphrased_sentences is None:
            print("Error in generating paraphrases", "Error: Could not complete step")
            return None
        
        self.analyzed_paraphrased_sentences, self.selected_sentences, self.discarded_sentences = self.entailment_analyzer.analyze_entailment(self.user_prompt, self.paraphrased_sentences, threshold)

        self.selected_sentences_list = [key for key in self.selected_sentences.keys()]
        self.discarded_sentences_list = [key for key in self.discarded_sentences.keys()]
        self.full_list = self.selected_sentences_list.copy()
        self.full_list.extend(self.discarded_sentences_list)
        self.full_list.append(self.user_prompt) 


        # self.user_prompt_tokenized = tokenize_sentence(self.user_prompt)
        # self.selected_sentences_tokenized = tokenize_sentences(self.selected_sentences)
        # self.discarded_sentences_tokenized = tokenize_sentences(self.discarded_sentences)

        # all_tokenized_sentences = []
        # all_tokenized_sentences.append(self.user_prompt_tokenized)
        # all_tokenized_sentences.extend(self.selected_sentences_tokenized)
        # all_tokenized_sentences.extend(self.discarded_sentences_tokenized)

        self.common_grams = self.ngram_processor.find_filtered_ngrams(self.full_list)
        print(f"Common grams: {self.common_grams}")

        if self.user_prompt in self.full_list:
            self.full_list.remove(self.user_prompt)

        # highlighted_user_prompt = highlight_common_words(self.common_grams, [self.user_prompt], "Highlighted LCS in the User Prompt")
        # highlighted_accepted_sentences = highlight_common_words(self.common_grams, self.selected_sentences, "Highlighted LCS in the Accepted Sentences")
        # highlighted_discarded_sentences = highlight_common_words(self.common_grams, self.discarded_sentences, "Highlighted LCS in the Discarded Sentences")

        execution_time = time.time() - start_time
        time_info = f"Step 1 completed in {execution_time:.2f} seconds"

        # return [
        #     highlighted_user_prompt, 
        #     highlighted_accepted_sentences, 
        #     highlighted_discarded_sentences,
        #     time_info
        # ]
    
    def Masking(self) : 
        """
        For each masking strategy in self.masking_strategies, mask the sentences in self.selected_sentences_list
        Return structure:
                    {
                        "<masking_strategy1>": 
                        {
                            "Original sentence 1": 
                            {
                                "masked_sentence": "The sentence with appropriate [MASK] tokens",
                                "mask_logits": 
                                {
                                    3: 
                                    {              # Example: mask index 3
                                        "tokens": ["word1", "word2", ...],  # Top predicted tokens
                                        "logits": [score1, score2, ...]       # Corresponding predicted scores
                                    },
                                    7: 
                                    {
                                        "tokens": ["wordA", "wordB", ...],
                                        "logits": [scoreA, scoreB, ...]
                                    },
                                    # ... possibly additional mask positions
                                }
                            },
                            "Original sentence 2": 
                            {
                                "masked_sentence": "Another masked sentence",
                                "mask_logits": { ... }
                            },
                            # ... more sentences processed for this strategy
                        },
                        "<masking_strategy2>": 
                        {
                            # Similar structure for each original sentence processed with masking_strategy2
                        },
                        # ... additional masking strategies if defined in self.masking_strategies
                    }
        """
        tqdm.write("[Watermarker] Starting Masking process.")
        for strategy in self.masking_strategies:
            tqdm.write(f"[Watermarker] Processing masking strategy: {strategy}")
            results = self.masker.process_sentences(self.full_list, self.common_grams, strategy)
            self.masking_results[strategy] = results
        tqdm.write("[Watermarker] Masking process completed.")
        return self.masking_results

    
    def Sampling(self) : 
        """
        For each masking strategy in self.masking_results, sample a sentence from the 
        masked sentences using the given sampling strategy.
        Return structure:
                            {
                    "inverse_transform (SAMPLING STRATEGY)": 
                    {
                        "random (MASKING STRATEGY)": 
                        {
                            "Original sentence 1": 
                            {
                                "masked_sentence": "Masked version of sentence 1",
                                "sampled_sentence": "Sampled version of sentence 1"
                            },
                            "Original sentence 2": 
                            {
                                "masked_sentence": "Masked version of sentence 2",
                                "sampled_sentence": "Sampled version of sentence 2"
                            },
                            # ... additional original sentences
                        },
                        "pseudorandom": 
                        {
                            # Similar structure for each original sentence
                        },
                        "entropy": 
                        {
                            # Similar structure for each original sentence
                        },
                    },
                    "exponential_minimum": 
                    {
                        # Similar nested dictionaries for each masking strategy and original sentence
                    },
                    "greedy": 
                    {
                        # Similar nested dictionaries for each masking strategy and original sentence
                    }
                }
        """
        tqdm.write("[Watermarker] Starting Sampling process.")
        for strategy in self.sampling_strategies:
            tqdm.write(f"[Watermarker] Processing sampling strategy: {strategy}")
            self.sampling_results[strategy] = {}
            for mask_strategy in self.masking_strategies:
                results = self.sampler.process_masked_sentences(
                    self.masking_results[mask_strategy], 
                    sampling_technique=strategy, 
                    temperature=1.0
                )
                self.sampling_results[strategy][mask_strategy] = results
        tqdm.write("[Watermarker] Sampling process completed.")
        return self.sampling_results

    def re_paraphrasing(self):
        tqdm.write("[Watermarker] Starting re-paraphrasing process.")
        self.reparaphrasing_results = {}
        for sampling_strategy, mask_dict in tqdm(self.sampling_results.items(), desc="Sampling Strategies", leave=True):
            self.reparaphrasing_results[sampling_strategy] = {}
            for mask_strategy, sentences_data in tqdm(mask_dict.items(), desc="Masking Strategies", leave=False):
                self.reparaphrasing_results[sampling_strategy][mask_strategy] = {}
                for original_sentence, result in tqdm(sentences_data.items(), desc="Sentences", leave=False):
                    sampled_sentence = result.get("sampled_sentence", None)
                    if sampled_sentence:
                        new_paraphrases = self.paraphraser.paraphrase(sampled_sentence,
                                                                      num_return_sequences=10,
                                                                      num_beams=10)
                    else:
                        new_paraphrases = []
                    self.reparaphrasing_results[sampling_strategy][mask_strategy][original_sentence] = {
                        "masking_strategy": mask_strategy,
                        "sampling_strategy": sampling_strategy,
                        "sampled_sentence": sampled_sentence,
                        "re_paraphrased_sentences": new_paraphrases
                    }
        tqdm.write("[Watermarker] Re-paraphrasing process completed.")
        return self.reparaphrasing_results
    
    def calculate_distortion(self):
        return None

if __name__ == "__main__":
    # config_path = '/home/jigyasu/PECCAVI-Text/utils/config.yaml'
    config = load_config(config_path)['PECCAVI_TEXT']
    watermarker = Watermarker(config)
    
    logger.info("Starting main Watermarker process.")
    print("==> Paraphrasing:")
    watermarker.Paraphrase("The quick brown fox jumps over small cat the lazy dog everyday again and again.")
    logger.info("Paraphrasing completed.")
    
    # Prepare a list to accumulate result strings
    results_str = []
    results_str.append("========== WATERMARKING RESULTS ==========\n\n")
    
    # --- Step 2: Common N-grams ---
    results_str.append("==> Common N-grams:\n")
    if watermarker.common_grams:
        for ngram, positions in watermarker.common_grams.items():
            results_str.append(f"  {ngram}: {positions}\n")
    else:
        results_str.append("  No common n-grams found.\n")
    
    # --- Step 3: Selected Sentences ---
    results_str.append("\n==> Selected Sentences:\n")
    if watermarker.selected_sentences:
        for sentence in watermarker.selected_sentences:
            results_str.append(f"  {sentence}\n")
    else:
        results_str.append("  No selected sentences available.\n")
    
    # --- Step 4: Masking Results (without logits) ---
    results_str.append("\n==> Masking Results:\n")
    masking_results = watermarker.Masking()
    for masking_strategy, results_dict in masking_results.items():
        results_str.append(f"\n-- Masking Strategy: {masking_strategy} --\n")
        for original_sentence, data in results_dict.items():
            masked_sentence = data.get("masked_sentence", "")
            results_str.append("Original:\n")
            results_str.append(f"  {original_sentence}\n")
            results_str.append("Masked:\n")
            results_str.append(f"  {masked_sentence}\n")
            results_str.append("-----\n")
    
    # --- Step 5: Sampling Results ---
    results_str.append("\n==> Sampling Results:\n")
    sampling_results = watermarker.Sampling()
    for sampling_strategy, mask_strategy_dict in sampling_results.items():
        results_str.append(f"\n-- Sampling Strategy: {sampling_strategy} --\n")
        for mask_strategy, sentences in mask_strategy_dict.items():
            results_str.append(f"\n  Masking Strategy: {mask_strategy}\n")
            for original_sentence, res in sentences.items():
                masked_sentence = res.get("masked_sentence", "")
                sampled_sentence = res.get("sampled_sentence", "")
                results_str.append("    Original:\n")
                results_str.append(f"      {original_sentence}\n")
                results_str.append("    Masked:\n")
                results_str.append(f"      {masked_sentence}\n")
                results_str.append("    Sampled:\n")
                results_str.append(f"      {sampled_sentence}\n")
                results_str.append("    -----\n")
    
    # --- Step 6: Re-paraphrasing Results ---
    results_str.append("\n==> Re-paraphrasing Results:\n")
    reparaphrasing_results = watermarker.re_paraphrasing()
    for sampling_strategy, mask_dict in reparaphrasing_results.items():
        results_str.append(f"\n-- Sampling Strategy: {sampling_strategy} --\n")
        for mask_strategy, orig_sentence_dict in mask_dict.items():
            results_str.append(f"\n  Masking Strategy: {mask_strategy}\n")
            for original_sentence, data in orig_sentence_dict.items():
                sampled_sentence = data.get("sampled_sentence", "")
                re_paraphrases = data.get("re_paraphrased_sentences", [])
                results_str.append("    Original:\n")
                results_str.append(f"      {original_sentence}\n")
                results_str.append("    Sampled:\n")
                results_str.append(f"      {sampled_sentence}\n")
                results_str.append("    Re-paraphrased (first 3 examples):\n")
                # Display only the first 3 re-paraphrases for brevity
                for idx, rp in enumerate(re_paraphrases[:3]):
                    results_str.append(f"      {idx+1}. {rp}\n")
                results_str.append("    -----\n")
    
    # Write all results to the output file
    output_file = "watermarking_results.txt"
    with open(output_file, "w", encoding="utf-8") as f:
        f.writelines(results_str)
    
    logger.info("Writing results to output file.")
    print("\nResults have been written to", output_file)