from dataclasses import dataclass
from typing import List, Union
from functools import reduce

from torch.distributed.remote_device import _remote_device

@dataclass
class ShardMetadata(object):
    """
    Represents a shard of the overall Tensor including its
    offsets, lengths and device placement.

    Args:
        shard_offsets(List[int]): Offsets in the original tensor indicating
            the start offsets for this shard. Should have the same rank as
            the original tensor.
        shard_sizes(List[int]): Integers indicating the size of each
            dimension for this shard. Should have the same rank as the
            original tensor.
        placement(:class:`torch.distributed._remote_device`):
            Specifies the placement of this shard.
    """

    __slots__ = ['shard_offsets', 'shard_sizes', 'placement']

    shard_offsets: List[int]
    shard_sizes: List[int]
    placement: Union[str, _remote_device]

    def __hash__(self):
        def _hash_reduce(a, b):
            return (a << 8) + hash(b)

        res = reduce(_hash_reduce, self.shard_offsets, 37)
        res = reduce(_hash_reduce, self.shard_sizes, res)
        res = _hash_reduce(res, self.placement)
        return res

    def __post_init__(self):
        if isinstance(self.placement, str):
            self.placement = _remote_device(self.placement)

        if len(self.shard_offsets) != len(self.shard_sizes):
            raise ValueError(
                f'shard_offsets and shard_sizes should have '
                f'the same number of elements, found {len(self.shard_offsets)} '
                f'and {self.shard_sizes} respectively')

        for i in range(len(self.shard_offsets)):
            if self.shard_offsets[i] < 0:
                raise ValueError('shard_offsets should be >=0')
            if self.shard_sizes[i] < 0:
                raise ValueError('shard_sizes should be >= 0')