import torch

def get_causal_attention_mask(embedding_dimension: int) -> torch.Tensor:
    return torch.triu(torch.ones(embedding_dimension, embedding_dimension, dtype=torch.bool), diagonal=1)

def get_causal_attention_mask_batched(embedding_dimension: int, batch_size: int ) -> torch.Tensor:
    base_mask = get_causal_attention_mask(embedding_dimension)
    return base_mask.unsqueeze(0).expand(batch_size, -1, -1)  # add another dimension at the beginning, big as batch_size   
    # the result is that z,x,y where x,y are repeated along z