/* 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/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/norm_utils.h"

namespace paddle {
namespace operators {

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

template <typename T>
using EigenArrayMap =
    Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenArrayMap =
    Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
    Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;

class InstanceNormOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;
  void InferShape(framework::InferShapeContext *ctx) const override;

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

class InstanceNormGradOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;
  void InferShape(framework::InferShapeContext *ctx) const override;

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

class InstanceNormDoubleGradOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;
  void InferShape(framework::InferShapeContext *ctx) const override;

 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:
  std::unique_ptr<T> Apply() const override {
    auto *op = new T();
    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("Bias", this->Input("Bias"));
    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"));

    return std::unique_ptr<T>(op);
  }
};

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

 protected:
  std::unique_ptr<T> Apply() const override {
    auto *op = new T();
    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")));
    return std::unique_ptr<T>(op);
  }
};

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

template <typename DeviceContext, typename T>
class InstanceNormKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override;
};

template <typename DeviceContext, typename T>
class InstanceNormGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override;
};

template <typename DeviceContext, typename T>
class InstanceNormDoubleGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &ctx) const override;
};

}  // namespace operators
}  // namespace paddle