import torch
from torch.distributed._shard.metadata import ShardMetadata
from typing import Sequence

DEPRECATE_MSG = "Please use DTensor instead and we are deprecating ShardedTensor."

def narrow_tensor_by_index(tensor: torch.Tensor, offsets: Sequence[int], sizes: Sequence[int]) -> torch.Tensor:
    """
    Narrow the tensor according to ``offsets`` and ``sizes``.
    """
    narrowed_tensor = tensor
    for idx, (offset, size) in enumerate(zip(offsets, sizes)):
        if size < tensor.size(idx):
            # Reshape to get shard for this rank and we don't want autograd
            # recording here for the narrow op and 'local_shard' should be a
            # leaf variable in the autograd graph.
            narrowed_tensor = narrowed_tensor.narrow(
                idx,
                offset,
                size
            )
    return narrowed_tensor

def narrow_tensor(tensor: torch.Tensor, metadata: ShardMetadata) -> torch.Tensor:
    """
    Narrow the tensor according to the metadata
    """
    return narrow_tensor_by_index(tensor, metadata.shard_offsets, metadata.shard_sizes)