/* 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. */

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

// TODO(freeliuzc): Delete old infershape
// New infershape has already in unary.h and backward.h

namespace paddle {
namespace operators {

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

  void InferShape(framework::InferShapeContext *ctx) const override {
    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "CropTensor");
    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "CropTensor");
    auto x_dim = ctx->GetInputDim("X");
    auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
    auto offsets = ctx->Attrs().Get<std::vector<int>>("offsets");
    if (ctx->HasInputs("ShapeTensor")) {
      // top prority shape
      auto inputs_name = ctx->Inputs("ShapeTensor");
      PADDLE_ENFORCE_GT(
          inputs_name.size(),
          0,
          platform::errors::InvalidArgument(
              "The number of elements of the input 'ShapeTensor' for "
              "CropTensor must be greater than zero, "
              "but the value received is %d.",
              inputs_name.size()));
      auto out_dims = std::vector<int>(inputs_name.size(), -1);
      for (size_t i = 0; i < shape.size(); ++i) {
        if (shape[i] > 0) {
          out_dims[i] = static_cast<int64_t>(shape[i]);
        } else {
          if (shape[i] == -1 && offsets[i] != -1 && x_dim[i] != -1) {
            out_dims[i] = x_dim[i] - static_cast<int64_t>(offsets[i]);
          }
        }
      }
      ctx->SetOutputDim("Out", phi::make_ddim(out_dims));

      return;
    }

    if (ctx->HasInput("Shape")) {
      auto shape_dim = ctx->GetInputDim("Shape");
      PADDLE_ENFORCE_EQ(shape_dim.size(),
                        1,
                        platform::errors::InvalidArgument(
                            "The number of dimensions of the input "
                            "'Shape' for CropTensor must be 1, "
                            "but the value received is %d.",
                            shape_dim.size()));
      PADDLE_ENFORCE_EQ(shape_dim[0],
                        x_dim.size(),
                        platform::errors::InvalidArgument(
                            "The number of elements (%d) of the input 'Shape' "
                            "for CropTensor must be equal to the number of"
                            " dimensions (%d) of the input.",
                            shape_dim[0],
                            x_dim.size()));
      if (ctx->IsRuntime()) {
        // If true, set the shape of Output(Out) according to Input(Shape) in
        // CropKernel with ExecutionContext. Also check LoD in
        // CropKernel.
        ctx->ShareLoD("X", /*->*/ "Out");
      } else {
        auto out_dims = std::vector<int>(shape_dim[0], -1);
        ctx->SetOutputDim("Out", phi::make_ddim(out_dims));
      }
      return;
    }
    PADDLE_ENFORCE_EQ(
        int64_t(shape.size()),
        x_dim.size(),
        platform::errors::InvalidArgument(
            "The number of elements (%d) of attribute 'shape' for "
            "CropTensor must be equal to the number of "
            "dimensions (%d) of the input.",
            shape.size(),
            x_dim.size()));
    std::vector<int64_t> out_shape(shape.size(), -1);
    for (size_t i = 0; i < shape.size(); ++i) {
      if (shape[i] > 0) {
        out_shape[i] = static_cast<int64_t>(shape[i]);
      } else {
        if (shape[i] == -1 && offsets[i] != -1 && x_dim[i] != -1) {
          out_shape[i] = x_dim[i] - static_cast<int64_t>(offsets[i]);
        }
      }
    }
    ctx->SetOutputDim("Out", phi::make_ddim(out_shape));
  }

  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext &ctx) const override {
    return framework::OpKernelType(
        OperatorWithKernel::IndicateVarDataType(ctx, "X"),
        ctx.device_context());
  }

  framework::OpKernelType GetKernelTypeForVar(
      const std::string &var_name,
      const phi::DenseTensor &tensor,
      const framework::OpKernelType &expected_kernel_type) const override {
    if (var_name == "ShapeTensor" || var_name == "OffsetsTensor" ||
        var_name == "Shape" || var_name == "Offsets") {
      return expected_kernel_type;
    }

    return framework::OpKernelType(
        expected_kernel_type.data_type_, tensor.place(), tensor.layout());
  }
};

class CropTensorOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X",
             "The input of pad op. "
             "The input should be a k-D tensor(k > 0 and k < 7).");
    AddInput("Shape",
             "The input used to describe shape of output, which is a "
             "1-D vector whose size equals to the rank of input 'X'. The "
             "elements data type must be int. It has a higher priority than "
             "the shape attribute")
        .AsDispensable();
    AddInput("Offsets",
             "The input used to describe offsets in runtime, which is a "
             "1-D vector whose size equals to the rank of input 'X'. The "
             "elements data type must be int. It has a higher priority than "
             "the offsets attribute")
        .AsDispensable();
    AddInput("ShapeTensor",
             "(vector<Tensor<int32>>, optional). If provided, crop_tensor will "
             "use this. The shape of the tensor in vector MUST BE [1]. "
             "It has the highest priority compare with Input(Shape) and "
             "attr(shape).")
        .AsDuplicable()
        .AsDispensable();
    AddInput("OffsetsTensor",
             "(vector<Tensor<int32>>, optional). If provided, crop_tensor will "
             "use this. The shape of the tensor in vector MUST BE [1]. "
             "It has the highest priority compare with Input(Offsets) and "
             "attr(offsets).")
        .AsDuplicable()
        .AsDispensable();
    AddOutput("Out",
              "The output of crop_tensor op, "
              "which is of the same dimensions as X.");
    AddAttr<std::vector<int>>("offsets",
                              "A list<int> describing offsets to be cropped. "
                              "The size of offsets list should be the same as "
                              "the dimension size of input X.")
        .SetDefault(std::vector<int>());
    AddAttr<std::vector<int>>("shape",
                              "A list<int> describing the shape of output. "
                              "The size of shape list should be the same as "
                              "the dimension size of input X.")
        .SetDefault(std::vector<int>());
    AddComment(R"DOC(
CropTensor Operator.

Crop input into output, as specified by offsets and shape.

There are three ways to set the offsets:
1. Input 'OffsetsTensor: It is a tensor list. It should be set as a list that
                         contains tensor variable in python configure script.
                         This way is suitable for dynamic offsets.
2. Input 'Offsets': It is a variable and can be output of other operators.
                    This way is suitable for dynamic offsets.
3. Attribute 'offsets': It will be set in python configure script. This way
                        is suitable for fixed offsets.

You CANNOT use these three ways at the same time. An exception will be raised
if input 'OffsetsTensor' or 'Offset' is configured and meanwhile the attribute 'offsets' is
not empty.

There are three ways to set shape:
1. Input 'ShapeTensor': It is a tensor list. It should be set as a list that contains
                        tensor variable in python configure script. This way is suitable
                        for dynamic shape.
2. Input 'Shape': It is a Variable and can be output of other operators. This way is suitable
                  for dynamic shape.
2. Attribute 'shape': crop input X into the shape described by a list<int>. The size of shape
                      list should be the same as the dimension size of input X. This way is
                      suitable for fixed shape.

The input should be a k-D tensor(k > 0 and k < 7). As an example:

Case 1:
Given

    X = [[0, 1, 2, 0, 0]
         [0, 3, 4, 0, 0]
         [0, 0, 0, 0, 0]],

and

    offsets = [0, 1],

and

    shape = [2, 2],

we get:

    Out = [[1, 2],
           [3, 4]].


Case 2:
Given

    X = [[0, 1, 2, 5, 0]
         [0, 3, 4, 6, 0]
         [0, 0, 0, 0, 0]],

and offsets is a list that contains tensor variable,
in runtime offses_var' s value is 1.

    offsets = [0, offsets_var],

and shape is a list that contains tensor variable,
in runtime dim's value is 2.

    shape = [dim, 3]

we get:

    Out = [[1, 2, 5],
           [3, 4, 6]].
)DOC");
  }
};

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

  void InferShape(framework::InferShapeContext *ctx) const override {
    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "CropTensorGrad");
    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")),
                   "Input",
                   framework::GradVarName("Out"),
                   "CropTensorGrad");
    auto x_dims = ctx->GetInputDim("X");
    auto x_grad_name = framework::GradVarName("X");
    if (ctx->HasOutput(x_grad_name)) {
      ctx->SetOutputDim(x_grad_name, x_dims);
    }
  }

  framework::OpKernelType GetExpectedKernelType(
      const framework::ExecutionContext &ctx) const override {
    return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
                                       ctx, framework::GradVarName("Out")),
                                   ctx.device_context());
  }

  framework::OpKernelType GetKernelTypeForVar(
      const std::string &var_name,
      const phi::DenseTensor &tensor,
      const framework::OpKernelType &expected_kernel_type) const override {
    if (var_name == "ShapeTensor" || var_name == "OffsetsTensor" ||
        var_name == "Shape" || var_name == "Offsets") {
      return expected_kernel_type;
    }

    return framework::OpKernelType(
        expected_kernel_type.data_type_, tensor.place(), tensor.layout());
  }
};

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

 protected:
  void Apply(GradOpPtr<T> op) const override {
    op->SetType("crop_tensor_grad");
    op->SetInput("X", this->Input("X"));
    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
    if (this->HasInput("OffsetsTensor")) {
      op->SetInput("OffsetsTensor", this->Input("OffsetsTensor"));
    }
    if (this->HasInput("Offsets")) {
      op->SetInput("Offsets", this->Input("Offsets"));
    }
    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
    op->SetAttrMap(this->Attrs());
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;

REGISTER_OPERATOR(crop_tensor,
                  ops::CropTensorOp,
                  ops::CropTensorOpMaker,
                  ops::CropTensorGradOpMaker<paddle::framework::OpDesc>,
                  ops::CropTensorGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(crop_tensor_grad, ops::CropTensorOpGrad);
