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#
# Licensed under the Apache License, Version 2.0 (the "License");
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# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.framework import _non_static_mode
from paddle.fluid.data_feeder import check_variable_and_dtype
from paddle import _legacy_C_ops


def graph_khop_sampler(row,
                       colptr,
                       input_nodes,
                       sample_sizes,
                       sorted_eids=None,
                       return_eids=False,
                       name=None):
    """
    Graph Khop Sampler API.

    This API is mainly used in Graph Learning domain, and the main purpose is to
    provide high performance graph khop sampling method with subgraph reindex step.
    For example, we get the CSC(Compressed Sparse Column) format of the input graph
    edges as `row` and `colptr`, so as to covert graph data into a suitable format
    for sampling. And the `input_nodes` means the nodes we need to sample neighbors,
    and `sample_sizes` means the number of neighbors and number of layers we want
    to sample.

    Args:
        row (Tensor): One of the components of the CSC format of the input graph, and
                      the shape should be [num_edges, 1] or [num_edges]. The available
                      data type is int32, int64.
        colptr (Tensor): One of the components of the CSC format of the input graph,
                         and the shape should be [num_nodes + 1, 1] or [num_nodes].
                         The data type should be the same with `row`.
        input_nodes (Tensor): The input nodes we need to sample neighbors for, and the
                              data type should be the same with `row`.
        sample_sizes (list|tuple): The number of neighbors and number of layers we want
                                   to sample. The data type should be int, and the shape
                                   should only have one dimension.
        sorted_eids (Tensor): The sorted edge ids, should not be None when `return_eids`
                              is True. The shape should be [num_edges, 1], and the data
                              type should be the same with `row`.
        return_eids (bool): Whether to return the id of the sample edges. Default is False.
        name (str, optional): Name for the operation (optional, default is None).
                              For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        edge_src (Tensor): The src index of the output edges, also means the first column of
                           the edges. The shape is [num_sample_edges, 1] currently.
        edge_dst (Tensor): The dst index of the output edges, also means the second column
                           of the edges. The shape is [num_sample_edges, 1] currently.
        sample_index (Tensor): The original id of the input nodes and sampled neighbor nodes.
        reindex_nodes (Tensor): The reindex id of the input nodes.
        edge_eids (Tensor): Return the id of the sample edges if `return_eids` is True.

    Examples:

        .. code-block:: python

        import paddle

        row = [3, 7, 0, 9, 1, 4, 2, 9, 3, 9, 1, 9, 7]
        colptr = [0, 2, 4, 5, 6, 7, 9, 11, 11, 13, 13]
        nodes = [0, 8, 1, 2]
        sample_sizes = [2, 2]
        row = paddle.to_tensor(row, dtype="int64")
        colptr = paddle.to_tensor(colptr, dtype="int64")
        nodes = paddle.to_tensor(nodes, dtype="int64")

        edge_src, edge_dst, sample_index, reindex_nodes = \
            paddle.incubate.graph_khop_sampler(row, colptr, nodes, sample_sizes, False)

    """

    if _non_static_mode():
        if return_eids:
            if sorted_eids is None:
                raise ValueError("`sorted_eid` should not be None "
                                 "if return_eids is True.")
            edge_src, edge_dst, sample_index, reindex_nodes, edge_eids = \
                _legacy_C_ops.graph_khop_sampler(row, sorted_eids,
                                              colptr, input_nodes,
                                              "sample_sizes", sample_sizes,
                                              "return_eids", True)
            return edge_src, edge_dst, sample_index, reindex_nodes, edge_eids
        else:
            edge_src, edge_dst, sample_index, reindex_nodes, _ = \
                _legacy_C_ops.graph_khop_sampler(row, None,
                                              colptr, input_nodes,
                                              "sample_sizes", sample_sizes,
                                              "return_eids", False)
            return edge_src, edge_dst, sample_index, reindex_nodes

    check_variable_and_dtype(row, "Row", ("int32", "int64"),
                             "graph_khop_sampler")

    if return_eids:
        if sorted_eids is None:
            raise ValueError("`sorted_eid` should not be None "
                             "if return_eids is True.")
        check_variable_and_dtype(sorted_eids, "Eids", ("int32", "int64"),
                                 "graph_khop_sampler")

    check_variable_and_dtype(colptr, "Col_Ptr", ("int32", "int64"),
                             "graph_khop_sampler")
    check_variable_and_dtype(input_nodes, "X", ("int32", "int64"),
                             "graph_khop_sampler")

    helper = LayerHelper("graph_khop_sampler", **locals())
    edge_src = helper.create_variable_for_type_inference(dtype=row.dtype)
    edge_dst = helper.create_variable_for_type_inference(dtype=row.dtype)
    sample_index = helper.create_variable_for_type_inference(dtype=row.dtype)
    reindex_nodes = helper.create_variable_for_type_inference(dtype=row.dtype)
    edge_eids = helper.create_variable_for_type_inference(dtype=row.dtype)
    helper.append_op(type="graph_khop_sampler",
                     inputs={
                         "Row": row,
                         "Eids": sorted_eids,
                         "Col_Ptr": colptr,
                         "X": input_nodes
                     },
                     outputs={
                         "Out_Src": edge_src,
                         "Out_Dst": edge_dst,
                         "Sample_Index": sample_index,
                         "Reindex_X": reindex_nodes,
                         "Out_Eids": edge_eids
                     },
                     attrs={
                         "sample_sizes": sample_sizes,
                         "return_eids": return_eids
                     })
    if return_eids:
        return edge_src, edge_dst, sample_index, reindex_nodes, edge_eids
    else:
        return edge_src, edge_dst, sample_index, reindex_nodes
