/**
 * Copyright 2019-2020 Huawei Technologies Co., Ltd
 *
 * 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.
 */

/*!
 * \file ragged_conversion_ops.h
 * \brief
 */
#ifndef OPS_BUILT_IN_OP_PROTO_INC_RAGGED_CONVERSION_OPS_H_
#define OPS_BUILT_IN_OP_PROTO_INC_RAGGED_CONVERSION_OPS_H_
#include "graph/operator_reg.h"

namespace ge {

/**
*@brief Converts a RaggedTensor into a SparseTensor with the same values . \n

*@par Inputs:
*Two inputs, including:
*@li rt_nested_splits: A list of at least 1 Tensor objects with the same type
in: int32, int64. The row_splits for the RaggedTensor. It's a dynamic input.
*@li rt_dense_values: A Tensor. The flat_values for the RaggedTensor
Must be one of the following types: bool, int8, int16, uint16, int32,
int64, double, float, float16 . \n

*@par Attributes:
*@li RAGGED_RANK: the dynamic of input rt_nested_splits with type int.
*@li Tsplits: A required attribute, the type is int64 . \n

*@par Outputs:
*@li sparse_indices: A Tensor of type int64.
*@li sparse_values: A Tensor. Has the same type as rt_dense_values.
*@li sparse_dense_shape: A Tensor of type int64 . \n

*@par Third-party framework compatibility
* Compatible with TensorFlow operator RaggedTensorToSparse.
*/
REG_OP(RaggedTensorToSparse)
    .DYNAMIC_INPUT(rt_nested_splits, TensorType({DT_INT32, DT_INT64}))
    .INPUT(rt_dense_values, TensorType({DT_BOOL, DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(sparse_indices, TensorType({DT_INT64}))
    .OUTPUT(sparse_values, TensorType({DT_BOOL, DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
    .OUTPUT(sparse_dense_shape, TensorType({DT_INT64}))
    .ATTR(RAGGED_RANK, Int, 1)
    .ATTR(Tsplits, Type, DT_INT64)
    .OP_END_FACTORY_REG(RaggedTensorToSparse)

/**
*@brief Create a dense tensor from a ragged tensor, possibly altering its shape . \n

*@par Inputs:
*Six inputs, including:
*@li shape:A `Tensor`. Must be one of the following types: `int64`, `int32`.
*@li values:A 1D tensor representing the values of the ragged tensor.
*@li default_value:A `Tensor`. Must have the same type as `values`.
*@li row_partition_tensors:A list of at least 1 `Tensor` objects with the same
type in: `int64`, `int32` . It's a dynamic input.\n

*@par Attributes:
*@li num_row_partition_tensors:Numbers of row partition tensors.
*@li row_partition_types: A list of `strings`.
The types of the row partition tensors. At present, these can be:
* "ROW_SPLITS": the row_splits tensor from the ragged tensor.
* "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor.
* "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then it
is preceeded by "FIRST_DIM_SIZE" . \n

*@par Outputs:
*@li result: A `Tensor`. Has the same type as `values`.
*/
REG_OP(RaggedTensorToTensor)
    .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
    .INPUT(values, TensorType({DT_BOOL, DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
                          DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
    .INPUT(default_value, TensorType({DT_BOOL, DT_INT8, DT_UINT8, DT_INT16,
              DT_UINT16, DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
    .DYNAMIC_INPUT(row_partition_tensors, TensorType({DT_INT32, DT_INT64}))
    .OUTPUT(result, TensorType({DT_BOOL, DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
                          DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
    .REQUIRED_ATTR(num_row_partition_tensors, Int)
    .REQUIRED_ATTR(row_partition_types, ListString)
    .OP_END_FACTORY_REG(RaggedTensorToTensor)


} // namespace ge
#endif  // OPS_BUILT_IN_OP_PROTO_INC_RAGGED_CONVERSION_OPS_H_