// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#pragma once

#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/tensor_array.h"
#include "paddle/phi/infermeta/unary.h"

namespace phi {

template <typename T, typename Context>
void SliceKernel(const Context& ctx,
                 const DenseTensor& input,
                 const std::vector<int64_t>& axes,
                 const IntArray& starts,
                 const IntArray& ends,
                 const std::vector<int64_t>& infer_flags,
                 const std::vector<int64_t>& decrease_axis,
                 DenseTensor* out);

template <typename T, typename Context>
void SliceArrayKernel(const Context& dev_ctx,
                      const TensorArray& input,
                      const IntArray& starts,
                      const IntArray& ends,
                      TensorArray* out);

template <typename T, typename Context>
void SliceArrayDenseKernel(const Context& dev_ctx,
                           const TensorArray& input,
                           const IntArray& starts,
                           DenseTensor* out);

template <typename Context>
void SliceStridedKernel(const Context& ctx,
                        const DenseTensor& input,
                        const std::vector<int64_t>& axes,
                        const IntArray& starts,
                        const IntArray& ends,
                        const std::vector<int64_t>& infer_flags,
                        const std::vector<int64_t>& decrease_axis,
                        DenseTensor* out);

template <typename T, typename Context>
DenseTensor Slice(const Context& ctx,
                  const DenseTensor& input,
                  const std::vector<int64_t>& axes,
                  const IntArray& starts,
                  const IntArray& ends) {
  DenseTensor dense_out;
  MetaTensor meta_out(&dense_out);
  std::vector<int64_t> infer_flags = {1};
  std::vector<int64_t> decrease_axis = {};
  SliceRawInferMeta(
      input, axes, starts, ends, infer_flags, decrease_axis, &meta_out);
  SliceKernel<T, Context>(
      ctx, input, axes, starts, ends, infer_flags, decrease_axis, &dense_out);
  return dense_out;
}

}  // namespace phi
