Analyze the code, understand its purpose, make conclusions, find bugs, fix bugs, revise code, hunt for bugs again, fix them, return fixed code (This is an order): import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
import matplotlib.pyplot as plt
import math

# Parameters
SEQUENCE_LENGTH = 40
BATCH_SIZE = 128  # Ensure consistency across code
VOCAB_SIZE = 128  # Number of ASCII characters in the vocabulary
HIDDEN_SIZE = 256
NUM_HEADS = 8  # Number of attention heads for the Transformer
NUM_ENCODER_LAYERS = 2  # Number of encoder layers in the Transformer
NUM_DECODER_LAYERS = 2  # Number of decoder layers in the Transformer
DROPOUT = 0.1  # Dropout probability for regularization

# Replace ‘your_file.txt’ with the actual file path.
file_path = "C:/Users/Dell-PC/Desktop/The-Physics-Dataset.txt"

# Read the file and filter out blank lines
with open(file_path, "r", encoding="utf-8") as file:
    lines = file.readlines()
    text = "".join(line for line in lines if not line.isspace())

# Convert text to ASCII values
ascii_characters = [ord(c) for c in text]

# Prepare datasets
inputs = []
targets = []
for i in range(0, len(ascii_characters) - SEQUENCE_LENGTH):
    input_seq = ascii_characters[i:i+SEQUENCE_LENGTH]
    target = ascii_characters[i+1:i+SEQUENCE_LENGTH+1]
    inputs.append(torch.tensor(input_seq, dtype=torch.long))
    targets.append(torch.tensor(target, dtype=torch.long))

input_tensor = torch.stack(inputs)
target_tensor = torch.stack(targets)

print("Input tensor shape:", input_tensor.shape)
print("Target tensor shape:", target_tensor.shape)

# Load data into DataLoader for batch processing
dataset = TensorDataset(input_tensor, target_tensor)
data_loader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)

# Define the Transformer model
class Transformer(nn.Module):
    def __init__(self, vocab_size, hidden_size, num_heads, num_encoder_layers, num_decoder_layers, dropout):
        super(Transformer, self).__init__()
        self.embedding = nn.Embedding(vocab_size, hidden_size)
        self.pos_encoder = nn.positional_encoding(max_len=SEQUENCE_LENGTH, d_model=hidden_size)
        self.encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model=hidden_size, nhead=num_heads, dim_feedforward=4 * hidden_size, dropout=dropout), num_layers=num_encoder_layers)
        self.decoder = nn.TransformerDecoder(nn.TransformerDecoderLayer(d_model=hidden_size, nhead=num_heads, dim_feedforward=4 * hidden_size, dropout=dropout), num_layers=num_decoder_layers)
        self.fc = nn.Linear(hidden_size, vocab_size)

    def forward(self, src, tgt, src_mask, tgt_mask):
        src = self.embedding(src) * math.sqrt(self.embedding.d_model)
        src = self.pos_encoder(src)
        tgt = self.embedding(tgt) * math.sqrt(self.embedding.d_model)
        tgt = self.pos_encoder(tgt)
        memory = self.encoder(src, src_mask)
        output = self.decoder(tgt, memory, src_mask, tgt_mask)
        output = self.fc(output)
        return output

# Instantiate the model
model = Transformer(VOCAB_SIZE, HIDDEN_SIZE, NUM_HEADS, NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, DROPOUT)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())

# Training loop with visualization
for epoch in range(20):
    model.train()
    losses = []

    for batch, (src, tgt) in enumerate(data_loader):
        src_mask = model.generate_square_subsequent_mask(src.size(0))
        tgt_mask = model.generate_square_subsequent_mask(tgt.size(0))
        tgt_input = tgt[:, :-1]  # Use only decoder input (without last character)
        targets = tgt[:, 1:]  # Target is next character

        optimizer.zero_grad()
        output = model(src, tgt_input, src_mask, tgt_mask)
        loss = loss_fn(output.view(-1, VOCAB_SIZE), targets.view(-1))
        loss.backward()
        optimizer.step()

        losses.append(loss.item())

        # Plot the training loss after every epoch
        plt.clf()
        plt.plot(losses, label="Training Loss")
        plt.xlabel("Batch")
        plt.ylabel("Loss")
        plt.title(f"Training Loss at Epoch {epoch}")
        plt.legend()
        plt.grid(True)
        plt.pause(0.001)

    # Show the plot outside the loop to ensure it doesn't close automatically
    plt.show(block=True)

# Helper function for creating attention masks
def generate_square_subsequent_mask(size):
    mask = torch.zeros((size, size))
    for i in range(size):
        mask[i, i:] = 1
    return mask

# Interactive prediction function with temperature control
def predict_interactive(model, start_char='P', temperature=1.0):
    model.eval()
    hidden = model.init_hidden(1)
    input_char = start_char

    while True:
        print(f"\nCurrent text: {input_char}", end="")
        new_char, hidden = predict(model, input_char, hidden, temperature)
        input_char += new_char

        # Interactive prompt for user input
        user_input = input("\nNext character (or 'q' to quit): ")
        if user_input.lower() == 'q':
            break

# Generate text demonstration
predict_interactive(model)