import torch.nn.functional as F
from torch.nn import Transformer
from torch.utils.data import Dataset, DataLoader


class ChainOfThoughtDataset(Dataset):
    """
    A dataset that represents a chain of thoughts, where each thought represents a step in the code generation process.
    """

    def __init__(self, thoughts):
        self.thoughts = thoughts

    def __len__(self):
        return len(self.thoughts)

    def __getitem__(self, idx):
        return self.thoughts[idx]


class ChainOfThoughtTransformer(Transformer):
    """
    A Transformer model that enhances the code generation process by maintaining a chain of thoughts.
    This Transformer model uses attention mechanisms to learn the dependencies between different thoughts in the chain.
    """

    def __init__(self, d_model, nhead, num_encoder_layers, num_decoder_layers):
        super().__init__(d_model, nhead, num_encoder_layers, num_decoder_layers)
        self.d_model = d_model

    def forward(self, src, tgt, src_mask=None, tgt_mask=None, memory_mask=None):
        memory = self.encoder(src, mask=src_mask)
        output = self.decoder(tgt, memory, tgt_mask=tgt_mask, memory_mask=memory_mask)
        return output


def train_transformer_chain_of_thought(thoughts, epochs):
    """
    Trains the ChainOfThoughtTransformer model using the given chain of thoughts.

    :param thoughts: A list of thoughts, where each thought represents a step in the code generation process.
    :param epochs: The number of training epochs.

    :return: Trained ChainOfThoughtTransformer model.
    """
    # Prepare the dataset and the data loader
    dataset = ChainOfThoughtDataset(thoughts)
    data_loader = DataLoader(dataset, batch_size=64)

    # Initialize the model
    model = ChainOfThoughtTransformer(d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6)

    # Define the loss function and the optimizer
    criterion = torch.nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

    # Train the model
    model.train()
    for epoch in range(epochs):
        for idx, data in enumerate(data_loader):
            optimizer.zero_grad()

            # Run the forward pass
            output = model(data)

            # Compute the loss
            loss = criterion(output, data)

            # Perform backpropagation and optimization
            loss.backward()
            optimizer.step()

        print(f"Epoch: {epoch + 1}, Loss: {loss.item()}")

    return model


def generate_code_with_chain_of_thought(model, thoughts):
    """
    Generates code using the model and given chain of thoughts.

    :param model: The learnt ChainOfThoughtTransformer model.
    :param thoughts: Initial chain of thoughts to continue generating from.

    :return: Generated code.
    """
    model.eval()

    thoughts_tensor = torch.tensor(thoughts).unsqueeze(0)  # Unsqueezing to add the batch dimension
    output = model(thoughts_tensor, thoughts_tensor)

    # Apply softmax to the output to get probabilities
    output_probs = F.softmax(output, dim=-1)

    # Sample from the output probabilities to get the next thought
    next_thought = torch.multinomial(output_probs[0, -1], 1)

    generated_thoughts = thoughts + next_thought.tolist()
    generated_code = ''.join(generated_thoughts)  # Join the thoughts into a complete code

    return generated_code
