# Copyright (c) 2022 Graphcore Ltd. All rights reserved.
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License
# The LICENSE referenced above is reproduced below:
# MIT License
#
#     Copyright (c) Microsoft Corporation.
#
#     Permission is hereby granted, free of charge, to any person obtaining a copy
#     of this software and associated documentation files (the "Software"), to deal
#     in the Software without restriction, including without limitation the rights
#     to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#     copies of the Software, and to permit persons to whom the Software is
#     furnished to do so, subject to the following conditions:
#
#     The above copyright notice and this permission notice shall be included in all
#     copies or substantial portions of the Software.
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#     THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#     IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#     FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#     AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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#     OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#     SOFTWARE
# Written by Ze Liu
# --------------------------------------------------------

import torch


class SubsetRandomSampler(torch.utils.data.Sampler):
    r"""Samples elements randomly from a given list of indices, without replacement.

    Arguments:
        indices (sequence): a sequence of indices
    """

    def __init__(self, indices):
        self.epoch = 0
        self.indices = indices

    def __iter__(self):
        return (self.indices[i] for i in torch.randperm(len(self.indices)))

    def __len__(self):
        return len(self.indices)

    def set_epoch(self, epoch):
        self.epoch = epoch