chatbot_model.py

import os
from transformers import TFAutoModel, AutoTokenizer
import tensorflow as tf
from typing import List, Tuple, Dict, Optional, Union, Any
import math
from dataclasses import dataclass
import json
from pathlib import Path
import datetime       
import faiss
import gc
from tf_data_pipeline import TFDataPipeline
from response_quality_checker import ResponseQualityChecker
from cross_encoder_reranker import CrossEncoderReranker
from conversation_summarizer import DeviceAwareModel, Summarizer
from gpu_monitor import GPUMemoryMonitor
import absl.logging
from logger_config import config_logger
from tqdm.auto import tqdm

absl.logging.set_verbosity(absl.logging.WARNING)
logger = config_logger(__name__)

@dataclass
class ChatbotConfig:
    """Configuration for the RetrievalChatbot."""
    max_context_token_limit: int = 512
    embedding_dim: int = 768
    encoder_units: int = 256
    num_attention_heads: int = 8
    dropout_rate: float = 0.2
    l2_reg_weight: float = 0.001
    learning_rate: float = 0.001
    min_text_length: int = 3
    max_context_turns: int = 5
    warmup_steps: int = 200
    pretrained_model: str = 'distilbert-base-uncased'
    dtype: str = 'float32'
    freeze_embeddings: bool = False
    embedding_batch_size: int = 64
    search_batch_size: int = 64
    max_batch_size: int = 64
    neg_samples: int = 3
    max_retries: int = 3

    def to_dict(self) -> Dict:
        """Convert config to dictionary."""
        return {k: (str(v) if isinstance(v, Path) else v) 
                for k, v in self.__dict__.items()}

    @classmethod
    def from_dict(cls, config_dict: Dict) -> 'ChatbotConfig':
        """Create config from dictionary."""
        return cls(**{k: v for k, v in config_dict.items() 
                     if k in cls.__dataclass_fields__})

class EncoderModel(tf.keras.Model):
    """Dual encoder model with pretrained embeddings."""
    def __init__(
        self,
        config: ChatbotConfig,
        name: str = "encoder",
        **kwargs
    ):
        super().__init__(name=name, **kwargs)
        self.config = config

        # Load pretrained model and freeze layers based on config
        self.pretrained = TFAutoModel.from_pretrained(config.pretrained_model)
        self._freeze_layers()
        
        # Add Pooling layer (Global Average Pooling), Projection layer, Dropout, and Normalization
        self.pooler = tf.keras.layers.GlobalAveragePooling1D()
        self.projection = tf.keras.layers.Dense(
            config.embedding_dim,
            activation='tanh',
            name="projection"
        )
        self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
        self.normalize = tf.keras.layers.Lambda(
            lambda x: tf.nn.l2_normalize(x, axis=1),
            name="l2_normalize"
        )

    def _freeze_layers(self):
        """Freeze layers of the pretrained model based on configuration."""
        if self.config.freeze_embeddings:
            self.pretrained.trainable = False
            logger.info("All pretrained layers frozen.")
        else:
            # Freeze only the first 'n' transformer layers
            for i, layer in enumerate(self.pretrained.layers):
                if isinstance(layer, tf.keras.layers.Layer):
                    if hasattr(layer, 'trainable'):
                        # Freeze the first transformer block
                        if i < 1:
                            layer.trainable = False
                            logger.info(f"Layer {i} frozen.")
                        else:
                            layer.trainable = True
                            
    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
        """Forward pass."""
        # Get pretrained embeddings
        pretrained_outputs = self.pretrained(inputs, training=training)
        x = pretrained_outputs.last_hidden_state # Shape: [batch_size, seq_len, embedding_dim]

        # Apply pooling, projection, dropout, and normalization
        x = self.pooler(x)                      # Shape: [batch_size, 768]
        x = self.projection(x)                  # Shape: [batch_size, 768]
        x = self.dropout(x, training=training)
        x = self.normalize(x)                   # Shape: [batch_size, 768]

        return x

    def get_config(self) -> dict:
        """Return the config of the model."""
        config = super().get_config()
        config.update({
            "config": self.config.to_dict(),
            "name": self.name
        })
        return config
        
class RetrievalChatbot(DeviceAwareModel):
    """Retrieval-based chatbot using pretrained embeddings and FAISS for similarity search."""
    def __init__(
        self,
        config: ChatbotConfig,
        device: str = None,
        strategy=None,
        reranker: Optional[CrossEncoderReranker] = None,
        summarizer: Optional[Summarizer] = None,
        mode: str = 'training' 
    ):
        super().__init__()
        self.config = config
        self.strategy = strategy
        self.device = device or self._setup_default_device()
        self.mode = mode.lower()
        
        # Initialize reranker, summarizer, tokenizer, encoder, and memory monitor
        self.reranker = reranker or self._initialize_reranker()
        self.tokenizer = self._initialize_tokenizer()
        self.encoder = self._initialize_encoder()
        self.summarizer = summarizer or self._initialize_summarizer()
        self.memory_monitor = GPUMemoryMonitor()
        
        # Initialize data pipeline
        logger.info("Initializing TFDataPipeline.")
        self.data_pipeline = TFDataPipeline(
            config=self.config,
            tokenizer=self.tokenizer,
            encoder=self.encoder,
            index_file_path='path/to/index',  # Update as needed # TODO: Update this path
            response_pool=[],
            max_length=self.config.max_context_token_limit,
            query_embeddings_cache={},
            neg_samples=self.config.neg_samples,
            index_type='IndexFlatIP',
            nlist=100, # Not used with IndexFlatIP
            max_retries=self.config.max_retries
        )
        
        # Collect unique responses from dialogues
        if self.mode == 'inference':
            logger.info("Mode set to 'inference'. Loading FAISS index and response pool.")
            self._load_faiss_index_and_responses()
        elif self.mode != 'training':
            logger.error(f"Unsupported mode in RetrievalChatbot init: {self.mode}")
            raise ValueError(f"Unsupported mode in RetrievalChatbot init: {self.mode}")
            
        # Initialize training history
        self.history = {
            "train_loss": [],
            "val_loss": [],
            "train_metrics": {},
            "val_metrics": {}
        }

    
    def _setup_default_device(self) -> str:
        """Set up default device if none is provided."""
        if tf.config.list_physical_devices('GPU'):
            return 'GPU'
        else:
            return 'CPU'

    def _initialize_reranker(self) -> CrossEncoderReranker:
        """Initialize the CrossEncoderReranker."""
        logger.info("Initializing default CrossEncoderReranker...")
        return CrossEncoderReranker(model_name="cross-encoder/ms-marco-MiniLM-L-12-v2")

    def _initialize_summarizer(self) -> Summarizer:
        """Initialize the Summarizer."""
        return Summarizer(
            tokenizer=self.tokenizer,
            model_name="t5-small",
            max_summary_length=self.config.max_context_token_limit // 4,
            device=self.device,
            max_summary_rounds=2
        )

    def _initialize_tokenizer(self) -> AutoTokenizer:
        """Initialize the tokenizer and add special tokens."""
        logger.info("Initializing tokenizer and adding special tokens...")
        tokenizer = AutoTokenizer.from_pretrained(self.config.pretrained_model)
        special_tokens = {
            "user": "<USER>",
            "assistant": "<ASSISTANT>",
            "context": "<CONTEXT>",
            "sep": "<SEP>"
        }
        tokenizer.add_special_tokens(
            {'additional_special_tokens': list(special_tokens.values())}
        )
        return tokenizer
    
    def _initialize_encoder(self) -> EncoderModel:
        """Initialize the EncoderModel and resize token embeddings."""
        logger.info("Initializing encoder model...")
        encoder = EncoderModel(
            self.config,
            name="shared_encoder",
        )
        
        new_vocab_size = len(self.tokenizer)
        encoder.pretrained.resize_token_embeddings(new_vocab_size)
        logger.info(f"Token embeddings resized to: {new_vocab_size}")
        return encoder
    
    def _load_faiss_index_and_responses(self) -> None:
        """Load FAISS index and response pool for inference."""
        try:
            logger.info(f"Loading FAISS index from {self.data_pipeline.index_file_path}...")
            self.data_pipeline.load_faiss_index(self.data_pipeline.index_file_path)
            logger.info("FAISS index loaded successfully.")
            
            # Load response pool associated with the FAISS index
            response_pool_path = self.data_pipeline.index_file_path.replace('.index', '_responses.json')
            if os.path.exists(response_pool_path):
                with open(response_pool_path, 'r', encoding='utf-8') as f:
                    self.data_pipeline.response_pool = json.load(f)
                logger.info(f"Loaded {len(self.data_pipeline.response_pool)} responses from {response_pool_path}.")
            else:
                logger.error(f"Response pool file not found at {response_pool_path}.")
                raise FileNotFoundError(f"Response pool file not found at {response_pool_path}.")
                
            # Validate FAISS index and response pool
            self.data_pipeline.validate_faiss_index()
            logger.info("FAISS index and response pool validated successfully.")
            
        except Exception as e:
            logger.error(f"Failed to load FAISS index and response pool: {e}")
            raise
    
    @classmethod
    def load_model(cls, load_dir: Union[str, Path], mode: str = 'training') -> 'RetrievalChatbot':
        """
        Load saved models and configuration.
        
        Args:
            load_dir (Union[str, Path]): Directory containing saved model files
            mode (str): Either 'training' or 'inference'. In inference mode, 
                    also loads FAISS index and response pool.
        """
        load_dir = Path(load_dir)
        
        # Load config
        with open(load_dir / "config.json", "r") as f:
            config = ChatbotConfig.from_dict(json.load(f))
        
        # Initialize chatbot with appropriate mode
        chatbot = cls(config, mode=mode)
        
        # Load models
        chatbot.encoder.pretrained = TFAutoModel.from_pretrained(
            load_dir / "shared_encoder",
            config=config
        )
        
        # Load tokenizer
        chatbot.tokenizer = AutoTokenizer.from_pretrained(load_dir / "tokenizer")
        logger.info(f"Models and tokenizer loaded from {load_dir}")
        
        # If in inference mode, load additional components
        if mode == 'inference':
            cls._prepare_model_for_inference(chatbot, load_dir)
        
        return chatbot
    
    @classmethod
    def _prepare_model_for_inference(cls, chatbot: 'RetrievalChatbot', load_dir: Path) -> None:
        """Internal method to load inference components."""
        try:
            # Load FAISS index
            faiss_path = load_dir / 'faiss_index.bin'
            if faiss_path.exists():
                chatbot.index = faiss.read_index(str(faiss_path))
                logger.info("FAISS index loaded successfully")
            else:
                raise FileNotFoundError(f"FAISS index not found at {faiss_path}")
            
            # Load response pool
            response_pool_path = load_dir / 'response_pool.json'
            if response_pool_path.exists():
                with open(response_pool_path, 'r') as f:
                    chatbot.response_pool = json.load(f)
                logger.info(f"Loaded {len(chatbot.response_pool)} responses")
            else:
                raise FileNotFoundError(f"Response pool not found at {response_pool_path}")
            
            # Verify dimensions match
            if chatbot.index.d != chatbot.config.embedding_dim:
                raise ValueError(
                    f"FAISS index dimension {chatbot.index.d} doesn't match "
                    f"model dimension {chatbot.config.embedding_dim}"
                )
                
        except Exception as e:
            logger.error(f"Error loading inference components: {e}")
            raise
    
    def save_models(self, save_dir: Union[str, Path]):
        """Save models and configuration."""
        save_dir = Path(save_dir)
        save_dir.mkdir(parents=True, exist_ok=True)
        
        # Save config
        with open(save_dir / "config.json", "w") as f:
            json.dump(self.config.to_dict(), f, indent=2)
        
        # Save models
        self.encoder.pretrained.save_pretrained(save_dir / "shared_encoder")
        
        # Save tokenizer
        self.tokenizer.save_pretrained(save_dir / "tokenizer")
        
        logger.info(f"Models and tokenizer saved to {save_dir}.")

    def retrieve_responses_cross_encoder(
        self,
        query: str,
        top_k: int,
        reranker: Optional[CrossEncoderReranker] = None,
        summarizer: Optional[Summarizer] = None,
        summarize_threshold: int = 512  # Summarize over 512 tokens
    ) -> List[Tuple[str, float]]:
        """
        Retrieve top-k from FAISS, then re-rank them with a cross-encoder.
        Optionally summarize the user query if it's too long.
        """
        if reranker is None:
            reranker = self.reranker
        if summarizer is None:
            summarizer = self.summarizer
            
        # Optional summarization
        if summarizer and len(query.split()) > summarize_threshold:
            logger.info(f"Query is long. Summarizing before cross-encoder. Original length: {len(query.split())}")
            query = summarizer.summarize_text(query)
            logger.info(f"Summarized query: {query}")

        # 2) Dense retrieval
        dense_topk = self.retrieve_responses_faiss(query, top_k=top_k)  # [(resp, dense_score), ...]
        
        if not dense_topk:
            return []

        # 3) Cross-encoder rerank
        candidate_texts = [pair[0] for pair in dense_topk]
        cross_scores = reranker.rerank(query, candidate_texts, max_length=256)

        # Combine
        combined = [(text, score) for (text, _), score in zip(dense_topk, cross_scores)]
        # Sort descending by cross-encoder score
        combined.sort(key=lambda x: x[1], reverse=True)

        return combined
    # def retrieve_responses_cross_encoder(
    #     self,
    #     query: str,
    #     top_k: int,
    #     reranker: Optional[CrossEncoderReranker] = None,
    #     summarizer: Optional[Summarizer] = None,
    #     summarize_threshold: int = 512  # Summarize over 512 tokens
    # ) -> List[Tuple[str, float]]:
    #     """
    #     Retrieve top-k from FAISS, then re-rank them with a cross-encoder.
    #     Optionally summarize the user query if it's too long.
    #     """
    #     if reranker is None:
    #         reranker = self.reranker
    #     if summarizer is None:
    #         summarizer = self.summarizer
            
    #     # Optional summarization
    #     if summarizer and len(query.split()) > summarize_threshold:
    #         logger.info(f"Query is long. Summarizing before cross-encoder. Original length: {len(query.split())}")
    #         query = summarizer.summarize_text(query)
    #         logger.info(f"Summarized query: {query}")

    #     # 2) Dense retrieval
    #     dense_topk = self.retrieve_responses_faiss(query, top_k=top_k)  # [(resp, dense_score), ...]

    #     if not dense_topk:
    #         return []

    #     # 3) Cross-encoder rerank
    #     candidate_texts = [pair[0] for pair in dense_topk]
    #     cross_scores = reranker.rerank(query, candidate_texts, max_length=256)

    #     # Combine
    #     combined = [(text, score) for (text, _), score in zip(dense_topk, cross_scores)]
    #     # Sort descending by cross-encoder score
    #     combined.sort(key=lambda x: x[1], reverse=True)

    #     return combined
    
    def retrieve_responses_faiss(self, query: str, top_k: int = 5) -> List[Tuple[str, float]]:
        """Retrieve top-k responses using FAISS."""
        if not hasattr(self.data_pipeline, 'index') or self.data_pipeline.index is None:
            logger.warning("FAISS index not initialized. Cannot retrieve responses.")
            return []
        
        # Encode the query using TFDataPipeline's method
        q_emb = self.data_pipeline.encode_query(query)  # Ensure encode_query is within TFDataPipeline
        q_emb_np = q_emb.numpy().astype('float32')  # Ensure type match
        
        # Normalize the query embedding for cosine similarity
        faiss.normalize_L2(q_emb_np)
        
        # Search the FAISS index
        distances, indices = self.data_pipeline.index.search(q_emb_np, top_k)
        
        # Map indices to responses and distances to similarities
        top_responses = []
        for i, idx in enumerate(indices[0]):
            if idx < len(self.data_pipeline.response_pool):
                top_responses.append((self.data_pipeline.response_pool[idx], float(distances[0][i])))
            else:
                logger.warning(f"FAISS returned invalid index {idx}. Skipping.")
        
        return top_responses
    # def retrieve_responses_faiss(self, query: str, top_k: int = 5) -> List[Tuple[str, float]]:
    #     """Retrieve top-k responses using FAISS."""
    #     if not hasattr(self, 'index') or self.index is None:
    #         logger.warning("FAISS index not initialized. Cannot retrieve responses.")
    #         return []
        
    #     # Encode the query
    #     q_emb = self.encode_query(query)  # Shape: [1, embedding_dim]
    #     q_emb_np = q_emb.numpy().astype('float32')  # Ensure type match
        
    #     # Normalize the query embedding for cosine similarity
    #     faiss.normalize_L2(q_emb_np)
        
    #     # Search the FAISS index
    #     distances, indices = self.index.search(q_emb_np, top_k)
        
    #     # Map indices to responses and distances to similarities
    #     top_responses = []
    #     for i, idx in enumerate(indices[0]):
    #         if idx < len(self.response_pool):
    #             top_responses.append((self.response_pool[idx], float(distances[0][i])))
    #         else:
    #             logger.warning(f"FAISS returned invalid index {idx}. Skipping.")
        
    #     return top_responses
    
    def chat(
        self,
        query: str,
        conversation_history: Optional[List[Tuple[str, str]]] = None,
        quality_checker: Optional['ResponseQualityChecker'] = None,
        top_k: int = 5,
    ) -> Tuple[str, List[Tuple[str, float]], Dict[str, Any]]:
        """
        Example chat method that always uses cross-encoder re-ranking 
        if self.reranker is available.
        """
        @self.run_on_device
        def get_response(self_arg, query_arg):
            # 1) Build conversation context string
            conversation_str = self_arg._build_conversation_context(query_arg, conversation_history)
            
            # 2) Retrieve + cross-encoder re-rank
            results = self_arg.retrieve_responses_cross_encoder(
                query=conversation_str,
                top_k=top_k,
                reranker=self_arg.reranker,
                summarizer=self_arg.summarizer,
                summarize_threshold=512
            )

            # 3) Handle empty or confidence
            if not results:
                return (
                    "I'm sorry, but I couldn't find a relevant response.",
                    [],
                    {}
                )

            if quality_checker:
                metrics = quality_checker.check_response_quality(query_arg, results)
                if not metrics.get('is_confident', False):
                    return (
                        "I need more information to provide a good answer. Could you please clarify?",
                        results,
                        metrics
                    )
                return results[0][0], results, metrics
            
            return results[0][0], results, {}
        
        return get_response(self, query)
    # def chat(
    #     self,
    #     query: str,
    #     conversation_history: Optional[List[Tuple[str, str]]] = None,
    #     quality_checker: Optional['ResponseQualityChecker'] = None,
    #     top_k: int = 5,
    # ) -> Tuple[str, List[Tuple[str, float]], Dict[str, Any]]:
    #     """
    #     Example chat method that always uses cross-encoder re-ranking 
    #     if self.reranker is available.
    #     """
    #     @self.run_on_device
    #     def get_response(self_arg, query_arg):  # Add parameters that match decorator's expectations
    #         # 1) Build conversation context string
    #         conversation_str = self_arg._build_conversation_context(query_arg, conversation_history)
            
    #         # 2) Retrieve + cross-encoder re-rank
    #         results = self_arg.retrieve_responses_cross_encoder(
    #             query=conversation_str,
    #             top_k=top_k,
    #             reranker=self_arg.reranker,
    #             summarizer=self_arg.summarizer,
    #             summarize_threshold=512
    #         )

    #         # 3) Handle empty or confidence
    #         if not results:
    #             return (
    #                 "I'm sorry, but I couldn't find a relevant response.",
    #                 [],
    #                 {}
    #             )

    #         if quality_checker:
    #             metrics = quality_checker.check_response_quality(query_arg, results)
    #             if not metrics.get('is_confident', False):
    #                 return (
    #                     "I need more information to provide a good answer. Could you please clarify?",
    #                     results,
    #                     metrics
    #                 )
    #             return results[0][0], results, metrics
            
    #         return results[0][0], results, {}
        
    #     return get_response(self, query)

    def _build_conversation_context(
        self, 
        query: str, 
        conversation_history: Optional[List[Tuple[str, str]]]
    ) -> str:
        """Build conversation context with better memory management."""
        if not conversation_history:
            return f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<USER>')]} {query}"
            
        conversation_parts = []
        for user_txt, assistant_txt in conversation_history:
            conversation_parts.extend([
                f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<USER>')]} {user_txt}",
                f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<ASSISTANT>')]} {assistant_txt}"
            ])
            
        conversation_parts.append(f"{self.tokenizer.additional_special_tokens[self.tokenizer.additional_special_tokens.index('<USER>')]} {query}")
        return "\n".join(conversation_parts)
    # def _build_conversation_context(
    #     self, 
    #     query: str, 
    #     conversation_history: Optional[List[Tuple[str, str]]]
    # ) -> str:
    #     """Build conversation context with better memory management."""
    #     if not conversation_history:
    #         return f"{self.special_tokens['user']} {query}"
            
    #     conversation_parts = []
    #     for user_txt, assistant_txt in conversation_history:
    #         conversation_parts.extend([
    #             f"{self.special_tokens['user']} {user_txt}",
    #             f"{self.special_tokens['assistant']} {assistant_txt}"
    #         ])
            
    #     conversation_parts.append(f"{self.special_tokens['user']} {query}")
    #     return "\n".join(conversation_parts)
    
    def train_model(
        self,
        tfrecord_file_path: str,
        epochs: int = 20,
        batch_size: int = 16,
        validation_split: float = 0.2,
        checkpoint_dir: str = "checkpoints/",
        use_lr_schedule: bool = True,
        peak_lr: float = 1e-5,
        warmup_steps_ratio: float = 0.1,
        early_stopping_patience: int = 3,
        min_delta: float = 1e-4,
        test_mode: bool = False,
        initial_epoch: int = 0
    ) -> None:
        """Training using a pre-prepared TFRecord dataset."""
        logger.info("Starting training with pre-prepared TFRecord dataset...")
        
        def parse_tfrecord_fn(example_proto, max_length, neg_samples):
            """
            Parses a single TFRecord example.

            Args:
                example_proto: A serialized TFRecord example.
                max_length: The maximum sequence length for tokenization.
                neg_samples: The number of hard negatives per query.

            Returns:
                A tuple of (query_ids, positive_ids, negative_ids).
            """
            feature_description = {
                'query_ids': tf.io.FixedLenFeature([max_length], tf.int64),
                'positive_ids': tf.io.FixedLenFeature([max_length], tf.int64),
                'negative_ids': tf.io.FixedLenFeature([neg_samples * max_length], tf.int64),
            }
            parsed_features = tf.io.parse_single_example(example_proto, feature_description)

            query_ids = tf.cast(parsed_features['query_ids'], tf.int32)
            positive_ids = tf.cast(parsed_features['positive_ids'], tf.int32)
            negative_ids = tf.cast(parsed_features['negative_ids'], tf.int32)
            negative_ids = tf.reshape(negative_ids, [neg_samples, max_length])

            return query_ids, positive_ids, negative_ids        
        
        # Calculate total steps by counting the number of records in the TFRecord
        raw_dataset = tf.data.TFRecordDataset(tfrecord_file_path)
        total_pairs = sum(1 for _ in raw_dataset)
        logger.info(f"Total pairs in TFRecord: {total_pairs}")

        train_size = int(total_pairs * (1 - validation_split))
        val_size = total_pairs - train_size
        steps_per_epoch = math.ceil(train_size / batch_size)
        val_steps = math.ceil(val_size / batch_size)
        total_steps = steps_per_epoch * epochs
        buffer_size = total_pairs // 10 # 10% of the dataset

        logger.info(f"Training pairs: {train_size}")
        logger.info(f"Validation pairs: {val_size}")
        logger.info(f"Steps per epoch: {steps_per_epoch}")
        logger.info(f"Validation steps: {val_steps}")
        logger.info(f"Total steps: {total_steps}")

        # Set up optimizer with learning rate schedule
        if use_lr_schedule:
            warmup_steps = int(total_steps * warmup_steps_ratio)
            lr_schedule = self._get_lr_schedule(
                total_steps=total_steps,
                peak_lr=peak_lr,
                warmup_steps=warmup_steps
            )
            self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)
            logger.info("Using custom learning rate schedule.")
        else:
            self.optimizer = tf.keras.optimizers.Adam(learning_rate=peak_lr)
            logger.info("Using fixed learning rate.")

        # Initialize checkpoint manager
        checkpoint = tf.train.Checkpoint(
            epoch=tf.Variable(0),
            optimizer=self.optimizer, 
            model=self.encoder,
            variables=self.encoder.variables
        )
        manager = tf.train.CheckpointManager(checkpoint, checkpoint_dir, max_to_keep=3, checkpoint_name='ckpt')

        # Restore from checkpoint if available
        latest_checkpoint = manager.latest_checkpoint
        if latest_checkpoint:
            history_path = Path(checkpoint_dir) / 'training_history.json'
            if history_path.exists():
                try:
                    with open(history_path, 'r') as f:
                        self.history = json.load(f)
                    logger.info(f"Loaded previous training history from {history_path}")
                except Exception as e:
                    logger.warning(f"Could not load history, starting fresh: {e}")
                    self.history = {'train_loss': [], 'val_loss': [], 'learning_rate': []}
            else:
                self.history = {'train_loss': [], 'val_loss': [], 'learning_rate': []}
            
            status = checkpoint.restore(latest_checkpoint)
            status.expect_partial() 
            
            logger.info(f"Restored from checkpoint: {latest_checkpoint}")
            # Get the checkpoint number to validate initial_epoch
            ckpt_number = int(latest_checkpoint.split('ckpt-')[-1])
            if initial_epoch == 0:
                initial_epoch = ckpt_number
            logger.info(f"Resuming from epoch {initial_epoch}")
        else:
            logger.info("Starting training from scratch")
            initial_epoch = 0
        
        # Setup TensorBoard
        log_dir = Path(checkpoint_dir) / "tensorboard_logs"
        log_dir.mkdir(parents=True, exist_ok=True)
        current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
        train_log_dir = str(log_dir / f"train_{current_time}")
        val_log_dir = str(log_dir / f"val_{current_time}")
        train_summary_writer = tf.summary.create_file_writer(train_log_dir)
        val_summary_writer = tf.summary.create_file_writer(val_log_dir)
        logger.info(f"TensorBoard logs will be saved in {log_dir}")

        # Define the parsing function with the appropriate max_length and neg_samples
        parse_fn = lambda x: parse_tfrecord_fn(x, self.config.max_context_token_limit, self.config.neg_samples)

        # Create the full dataset
        dataset = tf.data.TFRecordDataset(tfrecord_file_path)
        
        # Test mode for debugging
        if test_mode:
            subset_size = 200
            dataset = dataset.take(subset_size)
            logger.info(f"TEST MODE: Using only {subset_size} examples")
            # Recalculate sizes
            total_pairs = subset_size
            train_size = int(total_pairs * (1 - validation_split))
            val_size = total_pairs - train_size
            steps_per_epoch = math.ceil(train_size / batch_size)
            val_steps = math.ceil(val_size / batch_size)
            total_steps = steps_per_epoch * epochs
            buffer_size = total_pairs // 10 # 10% of the dataset
            epochs = min(epochs, 5)  # Limit epochs in test mode
            early_stopping_patience = 2
            logger.info(f"New training pairs: {train_size}")
            logger.info(f"New validation pairs: {val_size}")
        
        dataset = dataset.map(parse_fn, num_parallel_calls=tf.data.AUTOTUNE)
        
        # Split into training and validation sets
        train_dataset = dataset.take(train_size)
        val_dataset = dataset.skip(train_size).take(val_size)

        # Shuffle the training data
        train_dataset = train_dataset.shuffle(buffer_size=buffer_size)

        # Batch both datasets
        train_dataset = train_dataset.batch(batch_size, drop_remainder=True)
        train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)

        val_dataset = val_dataset.batch(batch_size, drop_remainder=True)
        val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)
        val_dataset = val_dataset.cache()

        # Training loop
        best_val_loss = float("inf")
        epochs_no_improve = 0

        for epoch in range(initial_epoch + 1, epochs + 1):
            # --- Training Phase ---
            epoch_loss_avg = tf.keras.metrics.Mean()
            batches_processed = 0

            try:
                train_pbar = tqdm(total=steps_per_epoch, desc=f"Training Epoch {epoch}", unit="batch")
                is_tqdm_train = True
            except ImportError:
                train_pbar = None
                is_tqdm_train = False
                logger.info("Training progress bar disabled")

            for q_batch, p_batch, n_batch in train_dataset:
                loss, grad_norm, post_clip_norm = self.train_step(q_batch, p_batch, n_batch)

                # Check for gradient issues
                grad_norm_value = float(grad_norm.numpy())
                post_clip_value = float(post_clip_norm.numpy())
                if grad_norm_value < 1e-7:
                    logger.warning(f"Potential vanishing gradient detected: norm = {grad_norm_value:.2e}")
                elif grad_norm_value > 100:
                    logger.warning(f"Potential exploding gradient detected: norm = {grad_norm_value:.2e}")
                
                if grad_norm_value != post_clip_value:
                    logger.info(f"Gradient clipped: {grad_norm_value:.2e} -> {post_clip_value:.2e}")
                
                epoch_loss_avg(loss)
                batches_processed += 1

                # Log to TensorBoard
                with train_summary_writer.as_default():
                    step = (epoch - 1) * steps_per_epoch + batches_processed
                    tf.summary.scalar("loss", loss, step=step)
                    tf.summary.scalar("gradient_norm_pre_clip", grad_norm, step=step)
                    tf.summary.scalar("gradient_norm_post_clip", post_clip_norm, step=step)

                # Update progress bar
                if use_lr_schedule:
                    current_lr = float(lr_schedule(self.optimizer.iterations))
                else:
                    current_lr = float(self.optimizer.learning_rate.numpy())

                if is_tqdm_train:
                    train_pbar.update(1)
                    train_pbar.set_postfix({
                        "loss": f"{loss.numpy():.4f}",
                        "pre_clip": f"{grad_norm_value:.2e}",
                        "post_clip": f"{post_clip_value:.2e}",
                        "lr": f"{current_lr:.2e}",
                        "batches": f"{batches_processed}/{steps_per_epoch}"
                    })

                # Memory cleanup
                gc.collect()

                if batches_processed >= steps_per_epoch:
                    break

            if is_tqdm_train and train_pbar:
                train_pbar.close()

            # --- Validation Phase ---
            val_loss_avg = tf.keras.metrics.Mean()
            val_batches_processed = 0

            try:
                val_pbar = tqdm(total=val_steps, desc="Validation", unit="batch")
                is_tqdm_val = True
            except ImportError:
                val_pbar = None
                is_tqdm_val = False
                logger.info("Validation progress bar disabled")

            for q_batch, p_batch, n_batch in val_dataset:
                val_loss = self.validation_step(q_batch, p_batch, n_batch)
                val_loss_avg(val_loss)
                val_batches_processed += 1

                if is_tqdm_val:
                    val_pbar.update(1)
                    val_pbar.set_postfix({
                        "val_loss": f"{val_loss.numpy():.4f}",
                        "batches": f"{val_batches_processed}/{val_steps}"
                    })

                # Memory cleanup
                gc.collect()

                if val_batches_processed >= val_steps:
                    break

            if is_tqdm_val and val_pbar:
                val_pbar.close()

            # End of epoch: compute final epoch stats, log, and save checkpoint
            train_loss = epoch_loss_avg.result().numpy()
            val_loss = val_loss_avg.result().numpy()
            logger.info(f"Epoch {epoch} Complete: Train Loss={train_loss:.4f}, Val Loss={val_loss:.4f}")

            # Log epoch metrics
            with train_summary_writer.as_default():
                tf.summary.scalar("epoch_loss", train_loss, step=epoch)
            with val_summary_writer.as_default():
                tf.summary.scalar("val_loss", val_loss, step=epoch)

            # Save checkpoint
            manager.save()
            
            # Save model after each epoch for testing/inference
            model_save_path = Path(checkpoint_dir) / f"model_epoch_{epoch}"
            self.save_models(model_save_path)
            logger.info(f"Saved model for epoch {epoch} at {model_save_path}")

            # Store metrics in history
            self.history['train_loss'].append(train_loss)
            self.history['val_loss'].append(val_loss)

            if use_lr_schedule:
                current_lr = float(lr_schedule(self.optimizer.iterations))
            else:
                current_lr = float(self.optimizer.learning_rate.numpy())

            # Log learning rate
            self.history.setdefault('learning_rate', []).append(current_lr)

            # Save history to file
            with open(history_path, 'w') as f:
                json.dump(self.history, f)
            logger.info(f"Saved training history to {history_path}")
            
            # Early stopping logic
            if val_loss < best_val_loss - min_delta:
                best_val_loss = val_loss
                epochs_no_improve = 0
                logger.info(f"Validation loss improved to {val_loss:.4f}. Reset patience.")
            else:
                epochs_no_improve += 1
                logger.info(f"No improvement this epoch. Patience: {epochs_no_improve}/{early_stopping_patience}")
                if epochs_no_improve >= early_stopping_patience:
                    logger.info("Early stopping triggered.")
                    break

        logger.info("Training completed!")
        
    @tf.function
    def train_step(
        self, 
        q_batch: tf.Tensor, 
        p_batch: tf.Tensor, 
        n_batch: tf.Tensor
    ) -> tf.Tensor:
        """
        Single training step using queries, positives, and hard negatives.
        """
        with tf.GradientTape() as tape:
            # Encode queries
            q_enc = self.encoder(q_batch, training=True)  # [batch_size, embed_dim]

            # Encode positives
            p_enc = self.encoder(p_batch, training=True)  # [batch_size, embed_dim]

            # Encode negatives
            # n_batch: [batch_size, neg_samples, max_length]
            shape = tf.shape(n_batch)
            bs = shape[0]
            neg_samples = shape[1]

            # Flatten negatives to feed them in one pass:
            # => [batch_size * neg_samples, max_length]
            n_batch_flat = tf.reshape(n_batch, [bs * neg_samples, shape[2]])
            n_enc_flat = self.encoder(n_batch_flat, training=True)  # [bs*neg_samples, embed_dim]

            # Reshape back => [batch_size, neg_samples, embed_dim]
            n_enc = tf.reshape(n_enc_flat, [bs, neg_samples, -1])

            # Combine the positive embedding and negative embeddings along dim=1
            # => shape [batch_size, 1 + neg_samples, embed_dim]
            # The first column is the positive; subsequent columns are negatives
            combined_p_n = tf.concat(
                [tf.expand_dims(p_enc, axis=1), n_enc], 
                axis=1
            )  # [bs, (1+neg_samples), embed_dim]

            # Now compute scores: dot product of q_enc with each column in combined_p_n
            # We'll use `tf.einsum` to handle the batch dimension properly
            # dot_products => shape [batch_size, (1+neg_samples)]
            dot_products = tf.einsum('bd,bkd->bk', q_enc, combined_p_n)

            # The label for each row is 0 (the first column is the correct/positive)
            labels = tf.zeros([bs], dtype=tf.int32)

            # Cross-entropy over the [batch_size, 1+neg_samples] scores
            loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
                labels=labels,
                logits=dot_products
            )
            loss = tf.reduce_mean(loss)

        # Calculate gradients
        gradients = tape.gradient(loss, self.encoder.trainable_variables)
        gradients_norm = tf.linalg.global_norm(gradients)
        
        # Clip gradients if norm exceeds threshold
        max_grad_norm = 1.0
        gradients, _ = tf.clip_by_global_norm(gradients, max_grad_norm, gradients_norm)
        post_clip_norm = tf.linalg.global_norm(gradients)
        
        # Apply gradients
        self.optimizer.apply_gradients(zip(gradients, self.encoder.trainable_variables))
        
        return loss, gradients_norm, post_clip_norm

    @tf.function
    def validation_step(
        self, 
        q_batch: tf.Tensor, 
        p_batch: tf.Tensor, 
        n_batch: tf.Tensor
    ) -> tf.Tensor:
        """
        Single validation step using queries, positives, and hard negatives.
        """
        q_enc = self.encoder(q_batch, training=False)
        p_enc = self.encoder(p_batch, training=False)

        shape = tf.shape(n_batch)
        bs = shape[0]
        neg_samples = shape[1]

        n_batch_flat = tf.reshape(n_batch, [bs * neg_samples, shape[2]])
        n_enc_flat = self.encoder(n_batch_flat, training=False)
        n_enc = tf.reshape(n_enc_flat, [bs, neg_samples, -1])

        combined_p_n = tf.concat(
            [tf.expand_dims(p_enc, axis=1), n_enc],
            axis=1
        )

        dot_products = tf.einsum('bd,bkd->bk', q_enc, combined_p_n)
        labels = tf.zeros([bs], dtype=tf.int32)

        loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
            labels=labels,
            logits=dot_products
        )
        loss = tf.reduce_mean(loss)

        return loss
    
    def _get_lr_schedule(
        self,
        total_steps: int,
        peak_lr: float,
        warmup_steps: int
    ) -> tf.keras.optimizers.schedules.LearningRateSchedule:
        """Create a custom learning rate schedule with warmup and cosine decay."""
        class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
            def __init__(
                self,
                total_steps: int,
                peak_lr: float,
                warmup_steps: int
            ):
                super().__init__()
                self.total_steps = tf.cast(total_steps, tf.float32)
                self.peak_lr = tf.cast(peak_lr, tf.float32)
                
                # Adjust warmup_steps to not exceed half of total_steps
                adjusted_warmup_steps = min(warmup_steps, max(1, total_steps // 10))
                self.warmup_steps = tf.cast(adjusted_warmup_steps, tf.float32)
                
                # Calculate and store constants
                self.initial_lr = self.peak_lr * 0.1  # Start at 10% of peak
                self.min_lr = self.peak_lr * 0.01     # Minimum 1% of peak
                
                logger.info(f"Learning rate schedule initialized:")
                logger.info(f"  Initial LR: {float(self.initial_lr):.6f}")
                logger.info(f"  Peak LR: {float(self.peak_lr):.6f}")
                logger.info(f"  Min LR: {float(self.min_lr):.6f}")
                logger.info(f"  Warmup steps: {int(self.warmup_steps)}")
                logger.info(f"  Total steps: {int(self.total_steps)}")
            
            def __call__(self, step):
                step = tf.cast(step, tf.float32)
                
                # Warmup phase
                warmup_factor = tf.minimum(1.0, step / self.warmup_steps)
                warmup_lr = self.initial_lr + (self.peak_lr - self.initial_lr) * warmup_factor
                
                # Decay phase
                decay_steps = tf.maximum(1.0, self.total_steps - self.warmup_steps)
                decay_factor = (step - self.warmup_steps) / decay_steps
                decay_factor = tf.minimum(tf.maximum(0.0, decay_factor), 1.0)  # Clip to [0,1]
                
                cosine_decay = 0.5 * (1.0 + tf.cos(tf.constant(math.pi) * decay_factor))
                decay_lr = self.min_lr + (self.peak_lr - self.min_lr) * cosine_decay
                
                # Choose between warmup and decay
                final_lr = tf.where(step < self.warmup_steps, warmup_lr, decay_lr)
                
                # Ensure learning rate is valid
                final_lr = tf.maximum(self.min_lr, final_lr)
                final_lr = tf.where(tf.math.is_finite(final_lr), final_lr, self.min_lr)
                
                return final_lr
            
            def get_config(self):
                return {
                    "total_steps": self.total_steps,
                    "peak_lr": self.peak_lr,
                    "warmup_steps": self.warmup_steps,
                }
        
        return CustomSchedule(total_steps, peak_lr, warmup_steps)