// Copyright (c) 2019 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 <string>
#include <vector>
#include "paddle/fluid/lite/core/compatible_tensor.h"
#include "paddle/fluid/lite/core/kernel.h"
#include "paddle/fluid/lite/core/op_lite.h"
#include "paddle/fluid/lite/core/scope.h"
#include "paddle/fluid/lite/operators/op_params.h"
#include "paddle/fluid/lite/utils/all.h"

namespace paddle {
namespace lite {
namespace operators {

inline int ConvOutputSize(int input_size, int filter_size, int dilation,
                          int padding, int stride) {
  const int dkernel = dilation * (filter_size - 1) + 1;
  int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
  CHECK_OR_FALSE(output_size > 0);

  return output_size;
}

inline bool IsExpand(const std::vector<int64_t>& filter_dim,
                     const std::vector<int>& strides,
                     const std::vector<int>& paddings,
                     const std::vector<int>& dilations) {
  bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
  for (size_t j = 0; j < strides.size(); ++j) {
    filter_1 = filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
    strides_1 = strides_1 && (strides[j] == 1);
    padding_0 = padding_0 && (paddings[j] == 0);
    dilation_1 = dilation_1 && (dilations[j] == 1);
  }
  return !(filter_1 && strides_1 && padding_0 && dilation_1);
}

class ConvOpLite : public OpLite {
 public:
  ConvOpLite() {}

  explicit ConvOpLite(const std::string& type) : OpLite(type) {}

  bool CheckShape() const override;

  bool InferShape() const override;

  void AttachKernel(KernelBase* kernel) override { kernel->SetParam(param_); }
  // TODO(Superjomn) replace framework::OpDesc with a lite one.
  bool AttachImpl(const cpp::OpDesc& op_desc, lite::Scope* scope) override {
    auto X = op_desc.Input("Input").front();
    auto Filter = op_desc.Input("Filter").front();
    auto Out = op_desc.Output("Output").front();

    param_.x = scope->FindVar(X)->GetMutable<lite::Tensor>();
    param_.filter = scope->FindVar(Filter)->GetMutable<lite::Tensor>();
    param_.output = scope->FindVar(Out)->GetMutable<lite::Tensor>();

    std::vector<std::string> input_arg_names = op_desc.InputArgumentNames();
    if (std::find(input_arg_names.begin(), input_arg_names.end(), "Bias") !=
        input_arg_names.end()) {
      auto bias_arguments = op_desc.Input("Bias");
      if (bias_arguments.size() != 0) {
        auto bias_var = scope->FindVar(bias_arguments.front());
        if (bias_var != nullptr) {
          param_.bias = &bias_var->Get<lite::Tensor>();
        }
      }
    }
    if (std::find(input_arg_names.begin(), input_arg_names.end(),
                  "ResidualData") != input_arg_names.end()) {
      auto res_argument = op_desc.Input("ResidualData");
      if (res_argument.size() != 0) {
        auto residual_data_var = scope->FindVar(res_argument.front());
        if (residual_data_var != nullptr) {
          param_.residualData = &residual_data_var->Get<lite::Tensor>();
        }
      }
    }

    param_.strides = op_desc.GetAttr<std::vector<int>>("strides");
    param_.paddings = op_desc.GetAttr<std::vector<int>>("paddings");
    param_.groups = op_desc.GetAttr<int>("groups");
    param_.dilations = op_desc.GetAttr<std::vector<int>>("dilations");

    return true;
  }

  std::string DebugString() const override { return "conv2d"; }

 private:
  mutable ConvParam param_;
};

}  // namespace operators
}  // namespace lite
}  // namespace paddle