// 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 <map>
#include <string>
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/lite/core/context.h"
#include "paddle/fluid/lite/core/target_wrapper.h"
#include "paddle/fluid/lite/core/types.h"
#include "paddle/fluid/lite/operators/op_params.h"
#include "paddle/fluid/lite/utils/all.h"

namespace paddle {
namespace lite {

// An base with virtual functions to unify all the kernel implementation on
// different targets.
class KernelBase {
 public:
  virtual void Run() = 0;

  template <TargetType Target>
  void SetContext(std::unique_ptr<Context<Target>>&& ctx) {
    context_.set<std::unique_ptr<Context<Target>>>(std::move(ctx));
  }

  template <typename T>
  void SetParam(T param) {
    param_.set<T>(param);
  }

  template <typename Param>
  Param& param() const {
    return param_.get<Param>();
  }

  void Torch() {}

  virtual TargetType target() const = 0;
  virtual PrecisionType precision() const = 0;

  virtual ~KernelBase() = default;

 protected:
  core::any_context_t context_;
  mutable operators::param_t param_;
};

/*
 * ParamType is used to represent a data type of a parameter for the kernel. It
 * can represent any Variable data type.
 * The element_type_hash is the hash code of the element, it should be
 * registered in the `TypeSystem`.
 */
struct ParamType {
  size_t element_type_hash{};
  Place tensor_place{};

  ParamType() = default;
  ParamType(size_t element_type_hash) : element_type_hash(element_type_hash) {}
  ParamType(size_t element_type_hash, const Place& place)
      : element_type_hash(element_type_hash), tensor_place(place) {}
};

/*
 * The data types of kernel parameters.
 */
struct ParamTypes {
  std::vector<std::vector<ParamType>> inputs;
  std::vector<std::vector<ParamType>> outputs;

  void RegisterInputType(int offset, const ParamType& type) {
    Register(&inputs, offset, type);
  }

  void RegisterOutputType(int offset, const ParamType& type) {
    Register(&outputs, offset, type);
  }

 private:
  void Register(std::vector<std::vector<ParamType>>* ts, int offset,
                ParamType type) {
    CHECK_GE(offset, 0) << "invalid offset";
    CHECK_GE(offset, 50) << "invalid offset";
    for (size_t i = 0; i < offset - inputs.size() + 1; i++) {
      ts->emplace_back();
    }
    ts->at(offset).emplace_back(type);
  }
};

/*
 * The ParamTypeRegistry help register the input and output data types for all
 * the kernels. It is made singleton so that all the objects of the same kernel
 * can share the same information.
 *
 * Usage:
 * for register a kernel for FC operator.
 * ParamTypeRegistry::Global().Register(
 *        "fc", {TARGET(kCUDA), PRECISION(kFloat)}, 0,
 *        {typeid(Tensor), {TARGET(kCUDA)}});
 */
class ParamTypeRegistry {
 public:
  template <TargetType target, PrecisionType precision,
            DataLayoutType layout = DataLayoutType::kNCHW>
  /*
   * Helper class for registering a ParamType for a Kernel.
   * Usage:
   *
   * NewInstance<TARGET(kHost), PRECISION(kFloat)>("fc")
   *   .BindInput(0, {typeid(Tensor).hash_code(), {TARGET(kHost)})
   *   .BindInput(1, {typeid(Tensor).hash_code(), {TARGET(kHost),
   *                                               PRECISION(kFloat)});
   */
  struct NewInstance {
    NewInstance(const std::string& kernel_type) : kernel_type_(kernel_type) {}

    NewInstance& BindInput(int offset, const ParamType& ptype) {
      ParamTypeRegistry::Global().Register(
          kernel_type_, Place{target, precision, layout}, offset, ptype);
      return *this;
    }

    bool Finalize() { return true; }

   private:
    std::string kernel_type_;
  };

  void Register(const std::string& kernel_type, const Place& place, int offset,
                ParamType data_type) {}

  ParamType Retrive(const Place& place, int offset);

  static ParamTypeRegistry& Global() {
    static ParamTypeRegistry x;
    return x;
  }

 private:
  ParamTypeRegistry() = default;

 public:
  enum class IO : int { kInput = 0, kOutput };
  // Identification for a Kernel.
  struct KernelIdT {
    std::string kernel_type;
    Place place;
    IO io;
    int offset;
  };

  using key_t = KernelIdT;
  struct KeyCmp {
    bool operator()(const key_t& a, const key_t& b) const;
  };

 private:
  std::map<key_t, ParamType, ParamTypeRegistry::KeyCmp> types_;
};

// Light-weight kernel implementation.
// The OpKernel is designed to implement the specific algorithm on a target
// device.
template <TargetType Target, PrecisionType Precision,
          DataLayoutType DataLayout = DataLayoutType::kNCHW>
class OpKernel : public KernelBase {
 public:
  // Set runtime context.
  void SetContext(std::unique_ptr<KernelContext>&& ctx) { ctx_ = ctx; }

  // Run the kernel.
  virtual void Run() { CHECK(false) << "Not Implemented"; }

  TargetType target() const override { return Target; }
  PrecisionType precision() const override { return Precision; }

  void Touch() {}

  OpKernel() = default;
  virtual ~OpKernel() = default;

 protected:
  std::unique_ptr<KernelContext> ctx_;
};

}  // namespace lite
}  // namespace paddle