/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

   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. */

#include "paddle/operators/reshape_op.h"

namespace paddle {
namespace operators {

class ReshapeOp : public framework::OperatorWithKernel {
 public:
  ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
            const framework::VariableNameMap &outputs,
            const framework::AttributeMap &attrs)
      : OperatorWithKernel(type, inputs, outputs, attrs) {}

 protected:
  void InferShape(const framework::InferShapeContext &ctx) const override {
    // input check
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
                            "Input(X) of ReshapeOp should not be null.");
    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
                            "Output(Out) of ReshapeOp should not be null.");

    auto shape = ctx.Attr<std::vector<int>>("shape");
    PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
    for (auto dim : shape) {
      PADDLE_ENFORCE(dim > 0, "Each dimension of shape must be positive.");
    }
    // capacity check
    int64_t capacity =
        std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
    auto *in = ctx.Input<framework::Tensor>("X");
    PADDLE_ENFORCE_EQ(capacity, in->numel(),
                      "The size of Input(X) mismatches with Attr(shape).");
    // resize output
    std::vector<int64_t> shape_int64(shape.size(), 0);
    std::transform(shape.begin(), shape.end(), shape_int64.begin(),
                   [](int a) { return static_cast<int64_t>(a); });
    auto out_dims = framework::make_ddim(shape_int64);
    ctx.Output<framework::Tensor>("Out")->Resize(out_dims);
    if (shape[0] == in->dims()[0]) {
      // Only pass LoD when the first dimension is equal between
      // output and input.
      ctx.ShareLoD("X", /*->*/ "Out");
    }
  }
};

class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  ReshapeOpMaker(framework::OpProto *proto,
                 framework::OpAttrChecker *op_checker)
      : OpProtoAndCheckerMaker(proto, op_checker) {
    AddInput("X", "The input tensor of reshape operator.");
    AddOutput("Out", "The output tensor of reshape operator.");
    AddAttr<std::vector<int>>("shape", "Target shape of reshape operator.");
    AddComment(R"DOC(Reshape operator

Reshape Input(X) into the shape specified by Attr(shape).

An example:
Given a 2-D tensor X with 2 rows and 2 columns

    [[1, 2], [3, 4]]

with target shape = [1, 4], the reshape operator will transform 
the tensor X into a 1-D tensor:

    [1, 2, 3, 4]

)DOC");
  }
};

class ReshapeGradOp : public framework::OperatorWithKernel {
 public:
  ReshapeGradOp(const std::string &type,
                const framework::VariableNameMap &inputs,
                const framework::VariableNameMap &outputs,
                const framework::AttributeMap &attrs)
      : OperatorWithKernel(type, inputs, outputs, attrs) {}

 protected:
  void InferShape(const framework::InferShapeContext &ctx) const override {
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null.");
    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
                            "Input(Out@GRAD) shouldn't be null.");
    auto dims = ctx.Input<framework::Tensor>("X")->dims();
    auto *d_in = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
    d_in->Resize(dims);
  }
};

}  // namespace operators
}  // namespace paddle
namespace ops = paddle::operators;

REGISTER_OP(reshape, ops::ReshapeOp, ops::ReshapeOpMaker, reshape_grad,
            ops::ReshapeGradOp);
REGISTER_OP_CPU_KERNEL(reshape,
                       ops::ReshapeKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
    reshape_grad, ops::ReshapeGradKernel<paddle::platform::CPUPlace, float>);