/* 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 <memory>
#include <string>
#include <unordered_map>

#include "paddle/fluid/framework/op_registry.h"

namespace paddle {
namespace operators {

using Tensor = phi::DenseTensor;
using LoDTensor = phi::DenseTensor;
using DataLayout = framework::DataLayout;

class InstanceNormOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext &ctx) const override;
};

class InstanceNormGradOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext &ctx) const override;
};

class InstanceNormDoubleGradOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

 protected:
  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext &ctx) const override;
};

class InstanceNormOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override;
};

template <typename T>
class InstanceNormGradMaker : public framework::SingleGradOpMaker<T> {
 public:
  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  void Apply(GradOpPtr<T> op) const override {
    op->SetType("instance_norm_grad");
    op->SetInput("X", this->Input("X"));
    op->SetInput(framework::GradVarName("Y"), this->OutputGrad("Y"));

    op->SetInput("Scale", this->Input("Scale"));
    op->SetInput("SavedMean", this->Output("SavedMean"));
    op->SetInput("SavedVariance", this->Output("SavedVariance"));

    op->SetAttrMap(this->Attrs());
    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
    op->SetOutput(framework::GradVarName("Scale"), this->InputGrad("Scale"));
    op->SetOutput(framework::GradVarName("Bias"), this->InputGrad("Bias"));
  }
};

template <typename T>
class InstanceNormDoubleGradMaker : public framework::SingleGradOpMaker<T> {
 public:
  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;

 protected:
  void Apply(GradOpPtr<T> op) const override {
    op->SetType("instance_norm_grad_grad");
    op->SetInput("X", this->Input("X"));
    op->SetInput("Scale", this->Input("Scale"));
    op->SetInput("SavedMean", this->Input("SavedMean"));
    op->SetInput("SavedVariance", this->Input("SavedVariance"));
    op->SetInput("DDX", this->OutputGrad(framework::GradVarName("X")));
    op->SetInput("DDScale", this->OutputGrad(framework::GradVarName("Scale")));
    op->SetInput("DDBias", this->OutputGrad(framework::GradVarName("Bias")));
    op->SetInput("DY", this->Input(framework::GradVarName("Y")));

    op->SetAttrMap(this->Attrs());
    op->SetOutput("DX", this->InputGrad("X"));
    op->SetOutput("DScale", this->InputGrad("Scale"));
    op->SetOutput("DDY", this->InputGrad(framework::GradVarName("Y")));
  }
};

class InstanceNormOpInferVarType
    : public framework::PassInDtypeAndVarTypeToOutput {
 protected:
  std::unordered_map<std::string, std::string> &GetInputOutputWithSameType()
      const override {
    static std::unordered_map<std::string, std::string> m{{"X", "Y"}};
    return m;
  }
};

}  // namespace operators
}  // namespace paddle
