//   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/operators/one_hot_v2_op.h"
#include <string>
#include <vector>

namespace paddle {
namespace operators {

class OneHotV2Op : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;
  void InferShape(framework::InferShapeContext* ctx) const override {
    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "one_hot_v2");
    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "one_hot_v2");

    auto x_dims = ctx->GetInputDim("X");
    PADDLE_ENFORCE_GE(x_dims.size(), 1,
                      platform::errors::InvalidArgument(
                          "Rank of Input(X) should be at least 1."));

    int depth = ctx->Attrs().Get<int>("depth");
    if (ctx->HasInput("depth_tensor")) {
      depth = -1;
    }

    auto out_dims_vec = phi::vectorize(x_dims);
    out_dims_vec.push_back(depth);
    auto out_dims = phi::make_ddim(out_dims_vec);
    ctx->SetOutputDim("Out", out_dims);
    ctx->ShareLoD("X", /* --> */ "Out");
  }

 protected:
  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 Tensor& tensor,
      const framework::OpKernelType& expected_kernel_type) const override {
    if (var_name == "depth_tensor") {
      return expected_kernel_type;
    }
    return framework::OpKernelType(expected_kernel_type.data_type_,
                                   tensor.place(), tensor.layout());
  }
};

class OneHotV2OpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("X",
             "(LoDTensor, LoDTensor<int>) Input variable with rank at least 2. "
             "The last dimension of X should be 1. Each value of X is an index "
             "to indicate the position.");
    AddInput("depth_tensor", "(Tensor, Tensor<int>), Length of one-hot vector")
        .AsDispensable();
    AddOutput("Out",
              "(Tensor, Tensor<float>) Output tensor with same rank as X. "
              "The tensor consists of one-hot representations of values in X.");

    AddAttr<int>("depth",
                 "A positive integer to specify the length of one-hot vector.")
        .SetDefault(-1);
    AddAttr<int>("dtype",
                 "An integer to specify the data type of one-hot "
                 "vector. The default value is FP32.")
        .SetDefault(paddle::framework::proto::VarType::FP32);
    AddAttr<bool>("allow_out_of_range",
                  "If it is set true and the input data is out of range, "
                  "the output tensor will be filled zeros. The default value "
                  "is false.")
        .SetDefault(false);
    AddComment(R"DOC(
One Hot Operator. This operator creates the one-hot representations for input
index values. The following example will help to explain the function of this
operator:

X is a LoDTensor:
  X.lod = [[0, 1, 4]]
  X.shape = [4]
  X.data = [1, 1, 3, 0]

set depth = 4

Out is a LoDTensor:
  Out.lod = [[0, 1, 4]]
  Out.shape = [4, 4]
  Out.data = [[0., 1., 0., 0.],
              [0., 1., 0., 0.],
              [0., 0., 0., 1.],
              [1., 0., 0., 0.]]
)DOC");
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(
    one_hot_v2, ops::OneHotV2Op, ops::OneHotV2OpMaker,
    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OP_CPU_KERNEL(
    one_hot_v2, ops::OneHotV2Kernel<paddle::platform::CPUDeviceContext, int>,
    ops::OneHotV2Kernel<paddle::platform::CPUDeviceContext, int64_t>);