import numpy as np
from transformer import SimpleTransformer, create_padding_mask, create_look_ahead_mask


def main():
    vocab_size = 1000
    d_model = 512
    num_heads = 8
    num_layers = 6
    d_ff = 2048
    max_seq_len = 100
    
    model = SimpleTransformer(vocab_size, d_model, num_heads, num_layers, d_ff, max_seq_len)
    
    batch_size = 2
    seq_len = 10
    x = np.random.randint(0, vocab_size, (batch_size, seq_len))
    
    print("=== Simple Transformer Example ===")
    print(f"Vocabulary size: {vocab_size}")
    print(f"Model dimension: {d_model}")
    print(f"Number of heads: {num_heads}")
    print(f"Number of layers: {num_layers}")
    print(f"Feed-forward dimension: {d_ff}")
    print(f"Max sequence length: {max_seq_len}")
    print()
    
    print(f"Input shape: {x.shape}")
    print(f"Input sample: {x[0]}")
    print()
    
    output = model.forward(x)
    
    print(f"Output shape: {output.shape}")
    print(f"Output sample (first 5 values): {output[0, 0, :5]}")
    print()
    
    total_params = model.count_parameters()
    print(f"Total parameters: {total_params:,}")
    print()
    
    print("=== Attention Mask Examples ===")
    padding_mask = create_padding_mask(x)
    print(f"Padding mask shape: {padding_mask.shape}")
    
    look_ahead_mask = create_look_ahead_mask(seq_len)
    print(f"Look-ahead mask shape: {look_ahead_mask.shape}")
    print(f"Look-ahead mask sample:\n{look_ahead_mask[:5, :5]}")


if __name__ == "__main__":
    main()