does the learning rate adjust if the loss doesn't drop after 2 epochs ? import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, Dataset
from collections import Counter
import json
from tqdm import tqdm
import math
import torch
import torch.optim.lr_scheduler as lr_scheduler



# Check if CUDA is available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def positional_encoding(seq_len, d_model, device):
    pos = torch.arange(seq_len, dtype=torch.float, device=device).unsqueeze(1)
    div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)).to(device)
    pe = torch.zeros(seq_len, d_model, device=device)
    pe[:, 0::2] = torch.sin(pos * div_term)
    pe[:, 1::2] = torch.cos(pos * div_term)
    return pe.unsqueeze(0)

# Expert Transformer Model
class TransformerExpert(nn.Module):
    def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers=1):
        super(TransformerExpert, self).__init__()
        self.d_model = d_model
        self.input_fc = nn.Linear(input_size, d_model)
        encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dim_feedforward=dim_feedforward, batch_first=True)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_encoder_layers)
        self.output_fc = nn.Linear(d_model, output_size)

    def forward(self, x):
        x = self.input_fc(x) + positional_encoding(x.size(1), self.d_model, x.device)
        transformer_output = self.transformer_encoder(x)
        output = self.output_fc(transformer_output)  # Apply output_fc to each time step in the sequence
        return output

# Gating Network
class GatingNetwork(nn.Module):
    def __init__(self, input_feature_dim, num_experts, hidden_dims=None, dropout_rate=0.1):
        super(GatingNetwork, self).__init__()
        layers = []
        last_dim = input_feature_dim
        
        # If hidden layers are specified, create them
        if hidden_dims is not None:
            for hidden_dim in hidden_dims:
                layers.append(nn.Linear(last_dim, hidden_dim))
                layers.append(nn.ReLU())  # You could make this a hyperparameter as well
                if dropout_rate > 0.0:
                    layers.append(nn.Dropout(dropout_rate))
                last_dim = hidden_dim
        
        # Final layer projecting to the number of experts
        layers.append(nn.Linear(last_dim, num_experts))
        self.fc_layers = nn.Sequential(*layers)
        self.softmax = nn.Softmax(dim=1)
    
    def forward(self, x):
        # Assuming x is of shape [batch_size, seq_len, d_model], aggregate across the sequence length
        x = x.mean(dim=1)  # Aggregate feature per instance
        x = self.fc_layers(x)  # Pass through gating network layers
        return self.softmax(x)
    
# Define hyperparameters specific to the transformer
d_model = 64 #128
nhead = 2 #8
dim_feedforward = 256 #256
num_encoder_layers = 6 #2
num_experts = 1 #2

model_name = "Alpha_Talk-V04-X"

# Mixture of Experts Model
class MixtureOfTransformerExperts(nn.Module):
    def __init__(self, input_size, d_model, output_size, nhead, dim_feedforward, num_experts, num_encoder_layers=1):
        super(MixtureOfTransformerExperts, self).__init__()
        self.num_experts = num_experts
        self.output_size = output_size  # Store output_size as an instance variable
        self.experts = nn.ModuleList([TransformerExpert(input_size, d_model, output_size, nhead, dim_feedforward, num_encoder_layers) for _ in range(num_experts)])
        self.gating_network = GatingNetwork(d_model, num_experts)

    def forward(self, x):
        gating_scores = self.gating_network(x)  # [batch_size, num_experts]

        expert_outputs = [expert(x) for expert in self.experts]  # List of [batch_size, seq_len, output_size]
        stacked_expert_outputs = torch.stack(expert_outputs)  # Shape: [num_experts, batch_size, seq_len, output_size]

        # Expand gating scores
        expanded_gating_scores = gating_scores.unsqueeze(2).unsqueeze(3)  # Shape: [batch_size, num_experts, 1, 1]
        expanded_gating_scores = expanded_gating_scores.expand(-1, -1, x.size(1), self.output_size)
        expanded_gating_scores = expanded_gating_scores.transpose(0, 1)  # Shape: [num_experts, batch_size, seq_len, output_size]

        # Now the shape of expanded_gating_scores matches stacked_expert_outputs, and broadcasting will work
        mixed_output = torch.sum(stacked_expert_outputs * expanded_gating_scores, dim=0)  # Sum weighted expert outputs for each time step
        return mixed_output

class QAJsonlDataset(Dataset):
    def __init__(self, path, seq_len):
        self.seq_len = seq_len
        self.pairs = self.load_data(path)
        # Flatten the pairs completely before passing them to build_vocab
        self.vocab, self.idx2token = self.build_vocab([word for pair in self.pairs for sublist in pair for word in sublist])
        self.tokenized_pairs = [(self.tokenize(q), self.tokenize(a)) for q, a in self.pairs]

    def load_data(self, path):
        pairs = []
        with open(path, "r", encoding="utf-8") as f:
            for line in f:
                data = json.loads(line.strip())
                question, answer = data.get("user", ""), data.get("content", "")
                pairs.append((question.split(), answer.split()))
        return pairs

    def tokenize(self, words):
    # Tokenize a sentence and pad if necessary
    # Add <eos> token at the end if there’s room
        tokens = [self.vocab.get(w, self.vocab["<unk>"]) for w in words]
        if len(tokens) < self.seq_len:
            tokens.append(self.vocab["<eos>"])  # Add <eos> token
            tokens.extend([self.vocab["<pad>"]] * (self.seq_len - len(tokens)))  # Pad the rest
        else:
            tokens = tokens[:self.seq_len - 1] + [self.vocab["<eos>"]]
        return tokens

    def build_vocab(self, words):
    # Start with special tokens with fixed indices
        vocab = {"<unk>": 0, "<pad>": 1, "<eos>": 2}
        start_index = len(vocab)

    # Use Counter to count word frequencies in the corpus
        counts = Counter(words)
    
    # Create the vocab dictionary with all words, starting indices after the special tokens
        for word, _ in counts.most_common():
            if word not in vocab:  # Skip special tokens
                vocab[word] = len(vocab)

    # Create the reverse mapping from indices to words
        idx2token = {idx: token for token, idx in vocab.items()}
        return vocab, idx2token
        
    def __len__(self):
        return len(self.tokenized_pairs)

    def __getitem__(self, idx):
        tokenized_question, tokenized_answer = self.tokenized_pairs[idx]
        return torch.tensor(tokenized_question, dtype=torch.long), torch.tensor(tokenized_answer, dtype=torch.long)

class MoETransformerModel(nn.Module):
    def __init__(self, vocab_size, d_model, moe):
        super(MoETransformerModel, self).__init__()
        self.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=d_model)
        self.moe = moe
        self.dropout = nn.Dropout(p=0.125)  # Dropout added for regularization

    def forward(self, x):
        embedded = self.dropout(self.embedding(x))
        return self.moe(embedded)  # Remove positional encoding addition here, as it’s already added in TransformerExpert

def collate_fn(batch):
    questions, answers = zip(*batch)
    questions = pad_sequence(questions, batch_first=True, padding_value=0)
    answers = pad_sequence(answers, batch_first=True, padding_value=0)
    return questions, answers

# Set the path to your jsonl file and define sequence length
path_to_text = 'Real_talk.jsonl'  # replace with the path to your jsonl file
seq_len = 32  # sequence length

# Create a dataset and data loader
dataset = QAJsonlDataset(path_to_text, seq_len)

# Save vocabulary to a text file
vocab_file = f"{model_name}_vocab.txt"
with open(vocab_file, "w", encoding="utf-8") as f:
    for token, id in dataset.vocab.items():
        f.write(f"{token}\t{id}\n")

# Model configuration parameters to be saved
model_config = {
    "d_model": d_model,
    "nhead": nhead,
    "dim_feedforward": dim_feedforward,
    "num_encoder_layers": num_encoder_layers,
    "num_experts": num_experts,
    "sequence-length":seq_len
}

# Save configuration to a JSON file
config_file = f"{model_name}_config.json"
with open(config_file, "w", encoding="utf-8") as f:
    json.dump(model_config, f, indent=4)

data_loader = DataLoader(dataset, batch_size=32, shuffle=True, collate_fn=collate_fn, pin_memory=True)
    
# Training loop - added gradient clipping to avoid exploding gradients
def train_model(model, criterion, optimizer, scheduler, num_epochs, data_loader, val_data_loader=None):
  """
  Trains the MoE Transformer model with learning rate scheduler and validation loss calculation.

  Args:
      model: The MoE Transformer model to train.
      criterion: The loss function (e.g., CrossEntropyLoss).
      optimizer: The optimizer (e.g., AdamW).
      scheduler: The learning rate scheduler (e.g., ReduceLROnPlateau).
      num_epochs: The number of epochs to train for.
      data_loader: The data loader for training data.
      val_data_loader (optional): The data loader for validation data (if desired).

  Returns:
      None
  """

  model.train()
  for epoch in range(num_epochs):
    total_loss = 0
    learning_rate = optimizer.param_groups[0]['lr']  # Access current learning rate
    progress_bar = tqdm(enumerate(data_loader), total=len(data_loader), desc=f"Epoch {epoch+1} - LR: {learning_rate:.6f}", leave=False)
    for i, (inputs, targets) in progress_bar:
      inputs, targets = inputs.to(device), targets.to(device)
      optimizer.zero_grad()
      predictions = model(inputs)
      predictions = predictions.view(-1, predictions.size(-1))
      targets = targets.view(-1)  # Flatten the targets to [batch_size * seq_len]
      loss = criterion(predictions, targets)
      loss.backward()
      torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)  # Gradient clipping
      optimizer.step()
      total_loss += loss.item()
      progress_bar.set_postfix({"Loss": loss.item()})

    # Validation (if validation data loader provided)
    if val_data_loader is not None:
      # Set model to evaluation mode
      model.eval()
      with torch.no_grad():
        running_val_loss = 0.0
        for batch_index, (val_inputs, val_targets) in enumerate(val_data_loader):
          val_inputs, val_targets = val_inputs.to(device), val_targets.to(device)
          # Forward pass
          val_outputs = model(val_inputs)
          # Calculate validation loss
          val_loss = criterion(val_outputs, val_targets)
          running_val_loss += val_loss.item() * val_inputs.size(0)  # Accumulate loss for the epoch

      epoch_val_loss = running_val_loss / len(val_data_loader.dataset)

      # Pass validation loss to scheduler
      scheduler.step(metrics={'val_loss': epoch_val_loss})

      # Set model back to training mode
      model.train()

    average_loss = total_loss / len(data_loader.dataset)
    print(f"Epoch {epoch+1}, Average Loss: {average_loss}")
    if val_data_loader is not None:
      print(f"Epoch {epoch+1}, Validation Loss: {epoch_val_loss}")

    # Update progress bar description to include current learning rate
    progress_bar.set_description(f"Epoch {epoch+1} - LR: {learning_rate:.6f}")

def generate_text(model, dataset, seed_text, num_generate, temperature=1.0):
    model.eval()  # Put the model in evaluation mode
    
    # List to store the generated tokens
    generated_tokens = []

    # Initial sequence (prefix) to start the generation process
    input_sequence = [dataset.vocab.get(word, dataset.vocab["<pad>"]) for word in seed_text.split()]  # Convert to token IDs
    current_sequence = torch.tensor(input_sequence, dtype=torch.long).unsqueeze(0)
    current_sequence = current_sequence.to(device)

    # Generate num_generate tokens
    for _ in range(num_generate):
        # Forward pass through the model
        with torch.no_grad():
            output = model(current_sequence)
        
        # Get probabilities, apply temperature scaling, and sample from the distribution
        probabilities = F.softmax(output[:, -1, :] / temperature, dim=-1).detach()
        next_token_idx = torch.multinomial(probabilities, 1).item()

        # Append token to the current sequence and to the generated tokens
        generated_tokens.append(next_token_idx)
        current_sequence = torch.cat((current_sequence, torch.tensor([[next_token_idx]])), 1).to(device)
    
    # Convert tokens to words
    generated_text = " ".join([dataset.idx2token.get(token, "<unk>") for token in generated_tokens])  # Use .get() to provide a default value for missing keys
    return generated_text

# Function to count the number of tokens in the dataset
def count_tokens_in_dataset(dataset):
    return sum([len(pair[0]) + len(pair[1]) for pair in dataset.pairs])

num_tokens = count_tokens_in_dataset(dataset)
print(f"Total number of tokens in the dataset: {num_tokens}")

vocab_size = len(dataset.vocab)  # Assume dataset.vocab is defined in the QAJsonlDataset class

# Instantiate resulting MoE transformer model and move it to device
moe = MixtureOfTransformerExperts(
    input_size=d_model,
    d_model=d_model,
    output_size=vocab_size,
    nhead=nhead,
    dim_feedforward=dim_feedforward,
    num_experts=num_experts,
    num_encoder_layers=num_encoder_layers
).to(device)

# Instantiate the MoE transformer model and move it to device
moe_transformer_model = MoETransformerModel(vocab_size, d_model, moe).to(device)

def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

# Example usage with your model:
total_params = count_parameters(moe_transformer_model)
print(f"Total trainable parameters: {total_params}")

# Training parameters
num_epochs = 4000
learning_rate = 0.0001
threshold_loss = 0.002  # Adjust as needed

# Define Loss Function and Optimizer for MoE model - using Label Smoothing for better generalization
criterion = nn.CrossEntropyLoss(label_smoothing=0.0)
optimizer = torch.optim.AdamW(moe_transformer_model.parameters(), lr=learning_rate, weight_decay=0.01)  # Using AdamW with weight decay

# Create the learning rate scheduler
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=2, threshold=threshold_loss, threshold_mode='rel', verbose=True)

# Train the model with the text data
train_model(moe_transformer_model, criterion, optimizer, scheduler, num_epochs, data_loader)

# Start a loop for the interactive chat-like text generation
while True:
    try:
        # Get user input
        seed_text = input("Enter seed text (type 'quit' to stop): ")
        
        # Check if user wants to quit the interaction
        if seed_text.lower() == "quit":
            print("Exiting text generation chat.")
            break
        
        # User input is not empty and not “quit”, generate text
        if seed_text.strip():
            num_generate = 64  # Number of words to generate
            temperature = 1.0  # Sampling temperature, higher will increase diversity
            
            # Use the trained model to generate text
            generated_text = generate_text(moe_transformer_model, dataset, seed_text, num_generate, temperature)
            print("Generated Text:", generated_text)
        else:
            print("Seed text cannot be empty.")
    except KeyboardInterrupt:
        # Handle KeyboardInterrupt (Ctrl+C) to gracefully exit
        print("\nExiting text generation chat.")
        break

torch.save(moe_transformer_model.state_dict(), f"{model_name}.pth")