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#
# Licensed under the Apache License, Version 2.0 (the "License");
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#
#     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,
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import paddle
from paddle import _C_ops
from paddle.base.data_feeder import check_variable_and_dtype
from paddle.base.framework import Variable
from paddle.base.layer_helper import LayerHelper
from paddle.framework import in_dynamic_or_pir_mode

__all__ = []


def reindex_graph(
    x, neighbors, count, value_buffer=None, index_buffer=None, name=None
):
    """

    Reindex Graph API.

    This API is mainly used in Graph Learning domain, which should be used
    in conjunction with `paddle.geometric.sample_neighbors` API. And the main purpose
    is to reindex the ids information of the input nodes, and return the
    corresponding graph edges after reindex.

    Take input nodes x = [0, 1, 2] as an example. If we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2],
    then we know that the neighbors of 0 is [8, 9], the neighbors of 1 is [0, 4, 7], and the neighbors of 2 is [6, 7].
    Then after graph_reindex, we will have 3 different outputs: reindex_src: [3, 4, 0, 5, 6, 7, 6], reindex_dst: [0, 0, 1, 1, 1, 2, 2]
    and out_nodes: [0, 1, 2, 8, 9, 4, 7, 6]. We can see that the numbers in `reindex_src` and `reindex_dst` is the corresponding index
    of nodes in `out_nodes`.

    Note:
        The number in x should be unique, otherwise it would cause potential errors. We will reindex all the nodes from 0.

    Args:
        x (Tensor): The input nodes which we sample neighbors for. The available
                    data type is int32, int64.
        neighbors (Tensor): The neighbors of the input nodes `x`. The data type
                            should be the same with `x`.
        count (Tensor): The neighbor count of the input nodes `x`. And the
                        data type should be int32.
        value_buffer (Tensor, optional): Value buffer for hashtable. The data type should be int32,
                                    and should be filled with -1. Only useful for gpu version. Default is None.
        index_buffer (Tensor, optional): Index buffer for hashtable. The data type should be int32,
                                    and should be filled with -1. Only useful for gpu version.
                                    `value_buffer` and `index_buffer` should be both not None
                                    if you want to speed up by using hashtable buffer. Default is None.
        name (str, optional): Name for the operation (optional, default is None).
                              For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        - reindex_src (Tensor), the source node index of graph edges after reindex.

        - reindex_dst (Tensor), the destination node index of graph edges after reindex.

        - out_nodes (Tensor), the index of unique input nodes and neighbors before reindex, where we put the input nodes `x` in the front, and put neighbor nodes in the back.

    Examples:
        .. code-block:: python

            >>> import paddle

            >>> x = [0, 1, 2]
            >>> neighbors = [8, 9, 0, 4, 7, 6, 7]
            >>> count = [2, 3, 2]
            >>> x = paddle.to_tensor(x, dtype="int64")
            >>> neighbors = paddle.to_tensor(neighbors, dtype="int64")
            >>> count = paddle.to_tensor(count, dtype="int32")
            >>> reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_graph(x, neighbors, count)
            >>> print(reindex_src.numpy())
            [3 4 0 5 6 7 6]
            >>> print(reindex_dst.numpy())
            [0 0 1 1 1 2 2]
            >>> print(out_nodes.numpy())
            [0 1 2 8 9 4 7 6]

    """
    use_buffer_hashtable = (
        True if value_buffer is not None and index_buffer is not None else False
    )

    if in_dynamic_or_pir_mode():
        reindex_src, reindex_dst, out_nodes = _C_ops.reindex_graph(
            x,
            neighbors,
            count,
            value_buffer,
            index_buffer,
        )
        return reindex_src, reindex_dst, out_nodes

    check_variable_and_dtype(x, "X", ("int32", "int64"), "graph_reindex")
    check_variable_and_dtype(
        neighbors, "Neighbors", ("int32", "int64"), "graph_reindex"
    )
    check_variable_and_dtype(count, "Count", ("int32"), "graph_reindex")

    if use_buffer_hashtable:
        check_variable_and_dtype(
            value_buffer, "HashTable_Value", ("int32"), "graph_reindex"
        )
        check_variable_and_dtype(
            index_buffer, "HashTable_Index", ("int32"), "graph_reindex"
        )

    helper = LayerHelper("reindex_graph", **locals())
    reindex_src = helper.create_variable_for_type_inference(dtype=x.dtype)
    reindex_dst = helper.create_variable_for_type_inference(dtype=x.dtype)
    out_nodes = helper.create_variable_for_type_inference(dtype=x.dtype)
    helper.append_op(
        type="graph_reindex",
        inputs={
            "X": x,
            "Neighbors": neighbors,
            "Count": count,
            "HashTable_Value": value_buffer if use_buffer_hashtable else None,
            "HashTable_Index": index_buffer if use_buffer_hashtable else None,
        },
        outputs={
            "Reindex_Src": reindex_src,
            "Reindex_Dst": reindex_dst,
            "Out_Nodes": out_nodes,
        },
    )
    return reindex_src, reindex_dst, out_nodes


def reindex_heter_graph(
    x, neighbors, count, value_buffer=None, index_buffer=None, name=None
):
    """

    Reindex HeterGraph API.

    This API is mainly used in Graph Learning domain, which should be used
    in conjunction with `paddle.geometric.sample_neighbors` API. And the main purpose
    is to reindex the ids information of the input nodes, and return the
    corresponding graph edges after reindex.

    Take input nodes x = [0, 1, 2] as an example. For graph A, suppose we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2],
    then we know that the neighbors of 0 is [8, 9], the neighbors of 1 is [0, 4, 7], and the neighbors of 2 is [6, 7]. For graph B,
    suppose we have neighbors = [0, 2, 3, 5, 1], and count = [1, 3, 1], then we know that the neighbors of 0 is [0], the neighbors of 1 is [2, 3, 5],
    and the neighbors of 3 is [1]. We will get following outputs: reindex_src: [3, 4, 0, 5, 6, 7, 6, 0, 2, 8, 9, 1], reindex_dst: [0, 0, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2]
    and out_nodes: [0, 1, 2, 8, 9, 4, 7, 6, 3, 5].

    Note:
        The number in x should be unique, otherwise it would cause potential errors. We support multi-edge-types neighbors reindexing in reindex_heter_graph api. We will reindex all the nodes from 0.

    Args:
        x (Tensor): The input nodes which we sample neighbors for. The available
                    data type is int32, int64.
        neighbors (list|tuple): The neighbors of the input nodes `x` from different graphs.
                                The data type should be the same with `x`.
        count (list|tuple): The neighbor counts of the input nodes `x` from different graphs.
                            And the data type should be int32.
        value_buffer (Tensor, optional): Value buffer for hashtable. The data type should be int32,
                                    and should be filled with -1. Only useful for gpu version. Default is None.
        index_buffer (Tensor, optional): Index buffer for hashtable. The data type should be int32,
                                    and should be filled with -1. Only useful for gpu version.
                                    `value_buffer` and `index_buffer` should be both not None
                                    if you want to speed up by using hashtable buffer. Default is None.
        name (str, optional): Name for the operation (optional, default is None).
                              For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        - reindex_src (Tensor), the source node index of graph edges after reindex.

        - reindex_dst (Tensor), the destination node index of graph edges after reindex.

        - out_nodes (Tensor), the index of unique input nodes and neighbors before reindex,
                              where we put the input nodes `x` in the front, and put neighbor
                              nodes in the back.

    Examples:
        .. code-block:: python

            >>> import paddle

            >>> x = [0, 1, 2]
            >>> neighbors_a = [8, 9, 0, 4, 7, 6, 7]
            >>> count_a = [2, 3, 2]
            >>> x = paddle.to_tensor(x, dtype="int64")
            >>> neighbors_a = paddle.to_tensor(neighbors_a, dtype="int64")
            >>> count_a = paddle.to_tensor(count_a, dtype="int32")
            >>> neighbors_b = [0, 2, 3, 5, 1]
            >>> count_b = [1, 3, 1]
            >>> neighbors_b = paddle.to_tensor(neighbors_b, dtype="int64")
            >>> count_b = paddle.to_tensor(count_b, dtype="int32")
            >>> neighbors = [neighbors_a, neighbors_b]
            >>> count = [count_a, count_b]
            >>> reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_heter_graph(x, neighbors, count)
            >>> print(reindex_src.numpy())
            [3 4 0 5 6 7 6 0 2 8 9 1]
            >>> print(reindex_dst.numpy())
            [0 0 1 1 1 2 2 0 1 1 1 2]
            >>> print(out_nodes.numpy())
            [0 1 2 8 9 4 7 6 3 5]

    """
    use_buffer_hashtable = (
        True if value_buffer is not None and index_buffer is not None else False
    )

    if in_dynamic_or_pir_mode():
        neighbors = paddle.concat(neighbors, axis=0)
        count = paddle.concat(count, axis=0)
        reindex_src, reindex_dst, out_nodes = _C_ops.reindex_graph(
            x,
            neighbors,
            count,
            value_buffer,
            index_buffer,
        )
        return reindex_src, reindex_dst, out_nodes

    if isinstance(neighbors, Variable):
        neighbors = [neighbors]
    if isinstance(count, Variable):
        count = [count]

    neighbors = paddle.concat(neighbors, axis=0)
    count = paddle.concat(count, axis=0)

    check_variable_and_dtype(x, "X", ("int32", "int64"), "heter_graph_reindex")
    check_variable_and_dtype(
        neighbors, "Neighbors", ("int32", "int64"), "graph_reindex"
    )
    check_variable_and_dtype(count, "Count", ("int32"), "graph_reindex")

    if use_buffer_hashtable:
        check_variable_and_dtype(
            value_buffer, "HashTable_Value", ("int32"), "graph_reindex"
        )
        check_variable_and_dtype(
            index_buffer, "HashTable_Index", ("int32"), "graph_reindex"
        )

    helper = LayerHelper("reindex_heter_graph", **locals())
    reindex_src = helper.create_variable_for_type_inference(dtype=x.dtype)
    reindex_dst = helper.create_variable_for_type_inference(dtype=x.dtype)
    out_nodes = helper.create_variable_for_type_inference(dtype=x.dtype)
    neighbors = paddle.concat(neighbors, axis=0)
    count = paddle.concat(count, axis=0)
    helper.append_op(
        type="graph_reindex",
        inputs={
            "X": x,
            "Neighbors": neighbors,
            "Count": count,
            "HashTable_Value": value_buffer if use_buffer_hashtable else None,
            "HashTable_Index": index_buffer if use_buffer_hashtable else None,
        },
        outputs={
            "Reindex_Src": reindex_src,
            "Reindex_Dst": reindex_dst,
            "Out_Nodes": out_nodes,
        },
    )
    return reindex_src, reindex_dst, out_nodes
