import torch


def ensure_divisibility(numerator, denominator):
    """Ensure that numerator is divisible by the denominator."""
    assert numerator % denominator == 0, "{} is not divisible by {}".format(numerator, denominator)


def divide(numerator, denominator):
    """Ensure that numerator is divisible by the denominator and return
    the division value."""
    ensure_divisibility(numerator, denominator)
    return numerator // denominator


def split_tensor_along_last_dim(tensor, num_partitions, contiguous_split_chunks=False):
    """Split a tensor along its last dimension.
    Arguments:
        tensor: input tensor.
        num_partitions: number of partitions to split the tensor
        contiguous_split_chunks: If True, make each chunk contiguous
                                 in memory.
    """
    # Get the size and dimension.
    last_dim = tensor.dim() - 1
    last_dim_size = divide(tensor.size()[last_dim], num_partitions)
    # Split.
    tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
    # Note: torch.split does not create contiguous tensors by default.
    if contiguous_split_chunks:
        return tuple(chunk.contiguous() for chunk in tensor_list)

    return tensor_list


class VocabUtility:
    """Split the vocabulary into `world_size` chunks amd return the
    first and last index of the vocabulary belonging to the `rank`
    partition: Note that indices in [fist, last)"""

    @staticmethod
    def vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank, world_size):
        index_f = rank * per_partition_vocab_size
        index_l = index_f + per_partition_vocab_size
        return index_f, index_l

    @staticmethod
    def vocab_range_from_global_vocab_size(global_vocab_size, rank, world_size):
        per_partition_vocab_size = divide(global_vocab_size, world_size)
        return VocabUtility.vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank, world_size)
