#pragma once
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#include <torch/csrc/WindowsTorchApiMacro.h>
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#include <torch/data/samplers/base.h>
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#include <torch/types.h>
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#include <cstddef>
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#include <vector>
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namespace torch {
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namespace serialize {
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class OutputArchive;
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class InputArchive;
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} // namespace serialize
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} // namespace torch
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namespace torch {
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namespace data {
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namespace samplers {
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/// A `Sampler` that returns indices sequentially.
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class TORCH_API SequentialSampler : public Sampler<> {
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public:
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/// Creates a `SequentialSampler` that will return indices in the range
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/// `0...size - 1`.
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explicit SequentialSampler(size_t size);
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/// Resets the `SequentialSampler` to zero.
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void reset(optional<size_t> new_size = nullopt) override;
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/// Returns the next batch of indices.
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optional<std::vector<size_t>> next(size_t batch_size) override;
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/// Serializes the `SequentialSampler` to the `archive`.
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void save(serialize::OutputArchive& archive) const override;
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/// Deserializes the `SequentialSampler` from the `archive`.
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void load(serialize::InputArchive& archive) override;
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/// Returns the current index of the `SequentialSampler`.
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size_t index() const noexcept;
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private:
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size_t size_;
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size_t index_{0};
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};
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} // namespace samplers
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} // namespace data
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} // namespace torch
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