tf_data_pipeline.py

import os
import gc
import numpy as np
import faiss
import tensorflow as tf
import h5py
import math
from tqdm import tqdm
import json
from pathlib import Path
from typing import Union, Optional, List, Tuple, Generator
from transformers import AutoTokenizer
from typing import List, Tuple, Generator
from transformers import AutoTokenizer
from gpu_monitor import GPUMemoryMonitor

from logger_config import config_logger
logger = config_logger(__name__)

class TFDataPipeline:
    def __init__(
        self,
        config,
        tokenizer,
        encoder,
        index_file_path: str,
        response_pool: List[str],
        max_length: int,
        query_embeddings_cache: dict,
        neg_samples: int = 3,
        index_type: str = 'IndexFlatIP',
        nlist: int = 100,
        max_retries: int = 3
    ):
        self.config = config
        self.tokenizer = tokenizer
        self.encoder = encoder
        self.index_file_path = index_file_path
        self.response_pool = response_pool
        self.max_length = max_length
        self.neg_samples = neg_samples
        self.query_embeddings_cache = query_embeddings_cache # In-memory cache for embeddings
        self.index_type = index_type
        self.nlist = nlist
        self.embedding_batch_size = 16 if len(response_pool) < 100 else 64
        self.search_batch_size = 16 if len(response_pool) < 100 else 64
        self.max_batch_size = 16 if len(response_pool) < 100 else 64
        self.memory_monitor = GPUMemoryMonitor()
        self.max_retries = max_retries

        if os.path.exists(index_file_path):
            logger.info(f"Loading existing FAISS index from {index_file_path}...")
            self.index = faiss.read_index(index_file_path)
            self.validate_faiss_index()
            logger.info("FAISS index loaded and validated successfully.")
        else:
            # Initialize FAISS index
            dimension = self.encoder.config.embedding_dim
            self.index = faiss.IndexFlatIP(dimension)
            logger.info(f"Initialized FAISS IndexFlatIP with dimension {dimension}.")
            
        if not self.index.is_trained:
            # Train the index if it's not trained. # TODO: Replace 'dimension' with embedding size
            dimension = self.query_embeddings_cache[next(iter(self.query_embeddings_cache))].shape[0]
            self.index.train(np.array(list(self.query_embeddings_cache.values())).astype(np.float32))
            self.index.add(np.array(list(self.query_embeddings_cache.values())).astype(np.float32))
        
    def save_embeddings_cache_hdf5(self, cache_file_path: str):
        """Save the embeddings cache to an HDF5 file."""
        with h5py.File(cache_file_path, 'w') as hf:
            for query, emb in self.query_embeddings_cache.items():
                hf.create_dataset(query, data=emb)
        logger.info(f"Embeddings cache saved to {cache_file_path}.")

    def load_embeddings_cache_hdf5(self, cache_file_path: str):
        """Load the embeddings cache from an HDF5 file."""
        with h5py.File(cache_file_path, 'r') as hf:
            for query in hf.keys():
                self.query_embeddings_cache[query] = hf[query][:]
        logger.info(f"Embeddings cache loaded from {cache_file_path}.")
        
    def save_faiss_index(self, index_file_path: str):
        faiss.write_index(self.index, index_file_path)
        logger.info(f"FAISS index saved to {index_file_path}")
    
    def load_faiss_index(self, index_file_path: str):
        """Load the FAISS index from the specified file path."""
        if os.path.exists(index_file_path):
            self.index = faiss.read_index(index_file_path)
            logger.info(f"FAISS index loaded from {index_file_path}.")
        else:
            logger.error(f"FAISS index file not found at {index_file_path}.")
            raise FileNotFoundError(f"FAISS index file not found at {index_file_path}.")
    
    def validate_faiss_index(self):
        """Validates that the FAISS index has the correct dimensionality."""
        expected_dim = self.encoder.config.embedding_dim
        if self.index.d != expected_dim:
            logger.error(f"FAISS index dimension {self.index.d} does not match encoder embedding dimension {expected_dim}.")
            raise ValueError("FAISS index dimensionality mismatch.")
        logger.info("FAISS index dimension validated successfully.")
    
    def save_tokenizer(self, tokenizer_dir: str):
        self.tokenizer.save_pretrained(tokenizer_dir)
        logger.info(f"Tokenizer saved to {tokenizer_dir}")
    
    def load_tokenizer(self, tokenizer_dir: str):
        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir)
        logger.info(f"Tokenizer loaded from {tokenizer_dir}")
    
    @staticmethod
    def load_json_training_data(data_path: Union[str, Path], debug_samples: Optional[int] = None) -> List[dict]:
        """
        Load training data from a JSON file.
        
        Args:
            data_path (Union[str, Path]): Path to the JSON file containing dialogues.
            debug_samples (Optional[int]): Number of samples to load for debugging.
        
        Returns:
            List[dict]: List of dialogue dictionaries.
        """
        logger.info(f"Loading training data from {data_path}...")
        data_path = Path(data_path)
        if not data_path.exists():
            logger.error(f"Data file {data_path} does not exist.")
            return []
        
        with open(data_path, 'r', encoding='utf-8') as f:
            dialogues = json.load(f)
        
        if debug_samples is not None:
            dialogues = dialogues[:debug_samples]
            logger.info(f"Debug mode: Limited to {debug_samples} dialogues")
        
        logger.info(f"Loaded {len(dialogues)} dialogues.")
        return dialogues
    
    def collect_responses(self, dialogues: List[dict]) -> List[str]:
        """Extract unique assistant responses from dialogues."""
        response_set = set()
        for dialogue in tqdm(dialogues, desc="Processing Dialogues", unit="dialogue"):
            turns = dialogue.get('turns', [])
            for turn in turns:
                speaker = turn.get('speaker')
                text = turn.get('text', '').strip()
                if speaker == 'assistant' and text:
                    # Ensure we don't exclude valid shorter responses
                    if len(text) <= self.max_length:
                        response_set.add(text)
        logger.info(f"Collected {len(response_set)} unique assistant responses from dialogues.")
        return list(response_set)
    
    def _extract_pairs_from_dialogue(self, dialogue: dict) -> List[Tuple[str, str]]:
        """Extract query-response pairs from a dialogue."""
        pairs = []
        turns = dialogue.get('turns', [])
        
        for i in range(len(turns) - 1):
            current_turn = turns[i]
            next_turn = turns[i+1]
            
            if (current_turn.get('speaker') == 'user' and
                next_turn.get('speaker') == 'assistant' and
                'text' in current_turn and 
                'text' in next_turn):
                
                query = current_turn['text'].strip()
                positive = next_turn['text'].strip()
                pairs.append((query, positive))
                
        return pairs
    
    def compute_and_index_response_embeddings(self):
        """
        Computes embeddings for the response pool and adds them to the FAISS index with progress bars.
        """
        logger.info("Computing embeddings for the response pool...")

        # Ensure all responses are strings
        if not all(isinstance(response, str) for response in self.response_pool):
            logger.error("All elements in response_pool must be strings.")
            raise ValueError("Invalid data type in response_pool.")

        # Tokenization
        logger.info("Tokenizing responses...")
        encoded_responses = self.tokenizer(
            self.response_pool,
            padding=True,
            truncation=True,
            max_length=self.max_length,
            return_tensors='tf'
        )
        response_ids = encoded_responses['input_ids']

        # Compute embeddings in batches with progress bar
        batch_size = getattr(self, 'embedding_batch_size', 64)  # Default to 64 if not set
        total_responses = len(response_ids)
        logger.info(f"Computing embeddings in batches of {batch_size}...")
        embeddings = []

        with tqdm(total=total_responses, desc="Computing Embeddings", unit="response") as pbar:
            for i in range(0, total_responses, batch_size):
                batch_ids = response_ids[i:i + batch_size]
                # Compute embeddings
                batch_embeddings = self.encoder(batch_ids, training=False).numpy()
                # Normalize embeddings for cosine similarity
                faiss.normalize_L2(batch_embeddings)
                embeddings.append(batch_embeddings)
                pbar.update(len(batch_ids))

        if embeddings:
            embeddings = np.vstack(embeddings).astype(np.float32)
            # Add embeddings to FAISS index with progress bar
            logger.info(f"Adding {len(embeddings)} response embeddings to FAISS index...")
            
            # Determine number of batches for indexing
            index_batch_size = getattr(self, 'index_batch_size', 1000)  # Adjust as needed
            total_embeddings = len(embeddings)
            num_index_batches = math.ceil(total_embeddings / index_batch_size)
            
            with tqdm(total=total_embeddings, desc="Indexing Embeddings", unit="embedding") as pbar_index:
                for i in range(0, total_embeddings, index_batch_size):
                    batch = embeddings[i:i + index_batch_size]
                    self.index.add(batch)
                    pbar_index.update(len(batch))
            
            logger.info("Response embeddings added to FAISS index.")
        else:
            logger.warning("No embeddings to add to FAISS index.")

        # **Sanity Check:** Verify the number of embeddings in FAISS index
        logger.info(f"Total embeddings in FAISS index after addition: {self.index.ntotal}")
    
    def _find_hard_negatives_batch(self, queries: List[str], positives: List[str]) -> List[List[str]]:
        """Find hard negatives for a batch of queries with error handling and retries."""
        retry_count = 0
        total_responses = len(self.response_pool)

        # Set k to be neg_samples + additional candidates to improve negative selection
        k = self.neg_samples + 0

        while retry_count < self.max_retries:
            try:
                # Compute embeddings in sub-batches to manage memory
                batch_size = 128  # Example sub-batch size; adjust as needed
                query_embeddings = []
                for i in range(0, len(queries), batch_size):
                    sub_queries = queries[i:i + batch_size]
                    sub_embeddings = np.vstack([
                        self.query_embeddings_cache[q] for q in sub_queries
                    ]).astype(np.float32)
                    faiss.normalize_L2(sub_embeddings)
                    query_embeddings.append(sub_embeddings)
                query_embeddings = np.vstack(query_embeddings)

                # Ensure contiguous memory layout
                query_embeddings = np.ascontiguousarray(query_embeddings)

                # Perform FAISS search on CPU
                distances, indices = self.index.search(query_embeddings, k)

                all_negatives = []
                for query_indices, query, positive in zip(indices, queries, positives):
                    negatives = []
                    positive_strip = positive.strip()
                    seen = {positive_strip}

                    for idx in query_indices:
                        if idx >= 0 and idx < total_responses:
                            candidate = self.response_pool[idx].strip()
                            if candidate and candidate not in seen:
                                seen.add(candidate)
                                negatives.append(candidate)
                                if len(negatives) >= self.neg_samples:
                                    break

                    # If not enough negatives are found, pad with a special token
                    while len(negatives) < self.neg_samples:
                        negatives.append("<EMPTY_NEGATIVE>")  # Use a special token

                    all_negatives.append(negatives)

                return all_negatives

            except KeyError as ke:
                retry_count += 1
                logger.warning(f"Hard negative search attempt {retry_count} failed due to missing embeddings: {ke}")
                if retry_count == self.max_retries:
                    logger.error("Max retries reached for hard negative search due to missing embeddings.")
                    return [["<EMPTY_NEGATIVE>"] * self.neg_samples for _ in queries]
                # Perform memory cleanup
                gc.collect()
                if tf.config.list_physical_devices('GPU'):
                    tf.keras.backend.clear_session()
            except Exception as e:
                retry_count += 1
                logger.warning(f"Hard negative search attempt {retry_count} failed: {e}")
                if retry_count == self.max_retries:
                    logger.error("Max retries reached for hard negative search.")
                    return [["<EMPTY_NEGATIVE>"] * self.neg_samples for _ in queries]
                # Perform memory cleanup
                gc.collect()
                if tf.config.list_physical_devices('GPU'):
                    tf.keras.backend.clear_session()

    def encode_query(self, query: str, context: Optional[List[Tuple[str, str]]] = None) -> np.ndarray:
        """
        Encode a query with optional conversation context into an embedding vector.

        Args:
            query (str): The user query.
            context (Optional[List[Tuple[str, str]]]): Optional conversation history as a list of (user, assistant) tuples.

        Returns:
            np.ndarray: The normalized embedding vector for the query.
        """
        # Prepare query with context
        if context:
            context_str = ' '.join([
                f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<USER>')]} {q} "
                f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<ASSISTANT>')]} {r}"
                for q, r in context[-self.config.max_context_turns:]
            ])
            query = f"{context_str} {self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<USER>')]}" \
                    f" {query}"
        else:
            query = f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<USER>')]} {query}"
        
        # Tokenize and encode
        encodings = self.tokenizer(
            [query],
            padding='max_length',
            truncation=True,
            max_length=self.max_length,
            return_tensors='np'  # Use NumPy arrays for compatibility with FAISS
        )
        input_ids = encodings['input_ids']
        
        # Verify token IDs
        max_id = np.max(input_ids)
        new_vocab_size = len(self.tokenizer)
        
        if max_id >= new_vocab_size:
            logger.error(f"Token ID {max_id} exceeds the vocabulary size {new_vocab_size}.")
            raise ValueError("Token ID exceeds vocabulary size.")
        
        # Get embeddings from the shared encoder
        embeddings = self.encoder(input_ids, training=False).numpy()
        
        # Normalize embeddings for cosine similarity
        faiss.normalize_L2(embeddings)
        
        return embeddings[0]  # Return as a 1D array

    def encode_responses(self, responses: List[str], context: Optional[List[Tuple[str, str]]] = None) -> np.ndarray:
        """
        Encode a list of responses into embedding vectors.

        Args:
            responses (List[str]): List of response texts.
            context (Optional[List[Tuple[str, str]]]): Optional conversation history as a list of (user, assistant) tuples.

        Returns:
            np.ndarray: Array of normalized embedding vectors.
        """
        # Prepare responses with context if provided
        if context:
            prepared_responses = []
            for response in responses:
                context_str = ' '.join([
                    f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<USER>')]} {q} "
                    f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<ASSISTANT>')]} {r}"
                    for q, r in context[-self.config.max_context_turns:]
                ])
                full_response = f"{context_str} {self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<ASSISTANT>')]} {response}"
                prepared_responses.append(full_response)
        else:
            prepared_responses = [
                f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<ASSISTANT>')]} {resp}"
                for resp in responses
            ]
        
        # Tokenize and encode
        encodings = self.tokenizer(
            prepared_responses,
            padding='max_length',
            truncation=True,
            max_length=self.max_length,
            return_tensors='np'  # Use NumPy arrays for compatibility with FAISS
        )
        input_ids = encodings['input_ids']
        
        # Verify token IDs
        max_id = np.max(input_ids)
        new_vocab_size = len(self.tokenizer)
        
        if max_id >= new_vocab_size:
            logger.error(f"Token ID {max_id} exceeds the vocabulary size {new_vocab_size}.")
            raise ValueError("Token ID exceeds vocabulary size.")
        
        # Get embeddings from the shared encoder
        embeddings = self.encoder(input_ids, training=False).numpy()
        
        # Normalize embeddings for cosine similarity
        faiss.normalize_L2(embeddings)
        
        return embeddings.astype('float32')
    
    def prepare_and_save_data(self, dialogues: List[dict], tf_record_path: str, batch_size: int = 32):
        """
        Processes dialogues in batches and saves to a TFRecord file using optimized batch tokenization and encoding.
        
        Args:
            dialogues (List[dict]): List of dialogue dictionaries.
            tf_record_path (str): Path to save the TFRecord file.
            batch_size (int): Number of dialogues to process per batch.
        """
        logger.info(f"Preparing and saving data to {tf_record_path}...")
        
        num_dialogues = len(dialogues)
        num_batches = math.ceil(num_dialogues / batch_size)
        
        with tf.io.TFRecordWriter(tf_record_path) as writer:
            # Initialize progress bar
            with tqdm(total=num_batches, desc="Preparing Data Batches", unit="batch") as pbar:
                for i in range(num_batches):
                    start_idx = i * batch_size
                    end_idx = min(start_idx + batch_size, num_dialogues)
                    batch_dialogues = dialogues[start_idx:end_idx]
                    
                    # Extract all query-positive pairs in the batch
                    queries = []
                    positives = []
                    for dialogue in batch_dialogues:
                        pairs = self._extract_pairs_from_dialogue(dialogue)
                        for query, positive in pairs:
                            if len(query) <= self.max_length and len(positive) <= self.max_length:
                                queries.append(query)
                                positives.append(positive)
                    
                    if not queries:
                        pbar.update(1)
                        continue  # Skip if no valid queries
                    
                    # Compute and cache query embeddings
                    try:
                        self._compute_embeddings(queries)
                    except Exception as e:
                        logger.error(f"Error computing embeddings: {e}")
                        pbar.update(1)
                        continue  # Skip to the next batch
                    
                    # Find hard negatives for the batch
                    try:
                        hard_negatives = self._find_hard_negatives_batch(queries, positives)
                    except Exception as e:
                        logger.error(f"Error finding hard negatives: {e}")
                        pbar.update(1)
                        continue  # Skip to the next batch
                    
                    # Tokenize and encode all queries, positives, and negatives in the batch
                    try:
                        encoded_queries = self.tokenizer.batch_encode_plus(
                            queries,
                            max_length=self.config.max_context_token_limit,
                            truncation=True,
                            padding='max_length',
                            return_tensors='tf'
                        )
                        encoded_positives = self.tokenizer.batch_encode_plus(
                            positives,
                            max_length=self.config.max_context_token_limit,
                            truncation=True,
                            padding='max_length',
                            return_tensors='tf'
                        )
                    except Exception as e:
                        logger.error(f"Error during tokenization: {e}")
                        pbar.update(1)
                        continue  # Skip to the next batch
                    
                    # Flatten hard_negatives while maintaining alignment
                    # Assuming hard_negatives is a list of lists, where each sublist corresponds to a query
                    try:
                        flattened_negatives = [neg for sublist in hard_negatives for neg in sublist]
                        encoded_negatives = self.tokenizer.batch_encode_plus(
                            flattened_negatives,
                            max_length=self.config.max_context_token_limit,
                            truncation=True,
                            padding='max_length',
                            return_tensors='tf'
                        )
                        
                        # Reshape encoded_negatives['input_ids'] to [num_queries, num_negatives, max_length]
                        num_negatives = self.config.neg_samples
                        reshaped_negatives = encoded_negatives['input_ids'].numpy().reshape(-1, num_negatives, self.config.max_context_token_limit)
                    except Exception as e:
                        logger.error(f"Error during negatives tokenization: {e}")
                        pbar.update(1)
                        continue  # Skip to the next batch
                    
                    # Serialize each example and write to TFRecord
                    for j in range(len(queries)):
                        try:
                            q_id = encoded_queries['input_ids'][j].numpy()
                            p_id = encoded_positives['input_ids'][j].numpy()
                            n_id = reshaped_negatives[j]
                            
                            feature = {
                                'query_ids': tf.train.Feature(int64_list=tf.train.Int64List(value=q_id)),
                                'positive_ids': tf.train.Feature(int64_list=tf.train.Int64List(value=p_id)),
                                'negative_ids': tf.train.Feature(int64_list=tf.train.Int64List(value=n_id.flatten())),
                            }
                            example = tf.train.Example(features=tf.train.Features(feature=feature))
                            writer.write(example.SerializeToString())
                        except Exception as e:
                            logger.error(f"Error serializing example {j} in batch {i}: {e}")
                            continue  # Skip to the next example
                    
                    # Update progress bar
                    pbar.update(1)
        
        logger.info(f"Data preparation complete. TFRecord saved.")
        
    def _compute_embeddings(self, queries: List[str]) -> None:
        new_queries = [q for q in queries if q not in self.query_embeddings_cache]
        if not new_queries:
            return  # All queries already cached

        # Compute embeddings for new queries
        new_embeddings = []
        for i in range(0, len(new_queries), self.embedding_batch_size):
            batch_queries = new_queries[i:i + self.embedding_batch_size]
            encoded = self.tokenizer(
                batch_queries,
                padding=True,
                truncation=True,
                max_length=self.max_length,
                return_tensors='tf'
            )
            batch_embeddings = self.encoder(encoded['input_ids'], training=False).numpy()
            faiss.normalize_L2(batch_embeddings)
            new_embeddings.extend(batch_embeddings)

        # Update the cache
        for query, emb in zip(new_queries, new_embeddings):
            self.query_embeddings_cache[query] = emb

    def data_generator(self, dialogues: List[dict]) -> Generator[Tuple[str, str, List[str]], None, None]:
        """
        Generates training examples: (query, positive, hard_negatives).
        Wrapped the outer loop with tqdm for progress tracking.
        """
        total_dialogues = len(dialogues)
        logger.debug(f"Total dialogues to process: {total_dialogues}")

        # Initialize tqdm progress bar
        with tqdm(total=total_dialogues, desc="Processing Dialogues", unit="dialogue") as pbar:
            for dialogue in dialogues:
                pairs = self._extract_pairs_from_dialogue(dialogue)
                for query, positive in pairs:
                    # Ensure embeddings are computed, find hard negatives, etc.
                    self._compute_embeddings([query])
                    hard_negatives = self._find_hard_negatives_batch([query], [positive])[0]
                    yield (query, positive, hard_negatives)
                pbar.update(1)

    def get_tf_dataset(self, dialogues: List[dict], batch_size: int) -> tf.data.Dataset:
        """
        Creates a tf.data.Dataset for streaming training that yields
        (input_ids_query, input_ids_positive, input_ids_negatives).
        """
        # 1) Start with a generator dataset
        dataset = tf.data.Dataset.from_generator(
            lambda: self.data_generator(dialogues),
            output_signature=(
                tf.TensorSpec(shape=(), dtype=tf.string),        # Query (single string)
                tf.TensorSpec(shape=(), dtype=tf.string),        # Positive (single string)
                tf.TensorSpec(shape=(self.neg_samples,), dtype=tf.string)    # Hard Negatives (list of strings)
            )
        )
        
        # 2) Batch the raw strings
        dataset = dataset.batch(batch_size, drop_remainder=True)

        # 3) Map them through a tokenize step using `tf.py_function`
        dataset = dataset.map(
            lambda q, p, n: self._tokenize_triple(q, p, n),
            num_parallel_calls=1 #tf.data.AUTOTUNE
        )

        dataset = dataset.prefetch(tf.data.AUTOTUNE)
        return dataset
    
    def _tokenize_triple(
        self, 
        q: tf.Tensor, 
        p: tf.Tensor, 
        n: tf.Tensor
    ) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor]:
        """
        Wraps a Python function via tf.py_function to convert tf.Tensors of strings 
        -> Python lists of strings -> HF tokenizer -> Tensors of IDs.
        
        q is shape [batch_size], p is shape [batch_size], 
        n is shape [batch_size, neg_samples] (i.e., each row is a list of negatives).
        """
        # Use tf.py_function with limited parallelism
        q_ids, p_ids, n_ids = tf.py_function(
            func=self._tokenize_triple_py,
            inp=[q, p, n, tf.constant(self.max_length), tf.constant(self.neg_samples)],
            Tout=[tf.int32, tf.int32, tf.int32]
        )

        # Manually set shape information
        q_ids.set_shape([None, self.max_length])                # [batch_size, max_length]
        p_ids.set_shape([None, self.max_length])                # [batch_size, max_length]
        n_ids.set_shape([None, self.neg_samples, self.max_length])  # [batch_size, neg_samples, max_length]

        return q_ids, p_ids, n_ids

    def _tokenize_triple_py(
        self, 
        q: tf.Tensor, 
        p: tf.Tensor, 
        n: tf.Tensor,
        max_len: tf.Tensor,
        neg_samples: tf.Tensor
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        """
        Python function that:
        - Decodes each tf.string Tensor to a Python list of strings
        - Calls the HF tokenizer
        - Reshapes negatives
        - Returns np.array of int32s for (q_ids, p_ids, n_ids).
        
        q: shape [batch_size], p: shape [batch_size]
        n: shape [batch_size, neg_samples]
        max_len: scalar int
        neg_samples: scalar int
        """
        max_len = int(max_len.numpy())       # Convert to Python int
        neg_samples = int(neg_samples.numpy())

        # 1) Convert Tensors -> Python lists of strings
        q_list = [q_i.decode("utf-8") for q_i in q.numpy()]  # shape [batch_size]
        p_list = [p_i.decode("utf-8") for p_i in p.numpy()]  # shape [batch_size]

        # shape [batch_size, neg_samples], decode each row
        n_list = []
        for row in n.numpy():
            # row is shape [neg_samples], each is a tf.string
            decoded = [neg.decode("utf-8") for neg in row]
            n_list.append(decoded)

        # 2) Tokenize queries & positives
        q_enc = self.tokenizer(
            q_list,
            padding="max_length",
            truncation=True,
            max_length=max_len,
            return_tensors="np"
        )
        p_enc = self.tokenizer(
            p_list,
            padding="max_length",
            truncation=True,
            max_length=max_len,
            return_tensors="np"
        )

        # 3) Tokenize negatives
        # Flatten [batch_size, neg_samples] -> single list
        flattened_negatives = [neg for row in n_list for neg in row]
        if len(flattened_negatives) == 0:
            # No negatives at all: return a zero array
            n_ids = np.zeros((len(q_list), neg_samples, max_len), dtype=np.int32)
        else:
            n_enc = self.tokenizer(
                flattened_negatives,
                padding="max_length",
                truncation=True,
                max_length=max_len,
                return_tensors="np"
            )
            # shape [batch_size * neg_samples, max_len]
            n_input_ids = n_enc["input_ids"]

            # We want to reshape to [batch_size, neg_samples, max_len]
            # Handle cases where there might be fewer negatives
            batch_size = len(q_list)
            n_ids_list = []
            for i in range(batch_size):
                start_idx = i * neg_samples
                end_idx = start_idx + neg_samples
                row_negs = n_input_ids[start_idx:end_idx]

                # If fewer negatives, pad with zeros
                if row_negs.shape[0] < neg_samples:
                    deficit = neg_samples - row_negs.shape[0]
                    pad_arr = np.zeros((deficit, max_len), dtype=np.int32)
                    row_negs = np.concatenate([row_negs, pad_arr], axis=0)

                n_ids_list.append(row_negs)

            # stack them -> shape [batch_size, neg_samples, max_len]
            n_ids = np.stack(n_ids_list, axis=0)

        # 4) Return as np.int32 arrays
        q_ids = q_enc["input_ids"].astype(np.int32)  # shape [batch_size, max_len]
        p_ids = p_enc["input_ids"].astype(np.int32)  # shape [batch_size, max_len]
        n_ids = n_ids.astype(np.int32)               # shape [batch_size, neg_samples, max_len]

        return q_ids, p_ids, n_ids