#   Copyright (c) 2021 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.

from __future__ import annotations

from typing import TYPE_CHECKING, Literal

import numpy as np

from paddle import _C_ops
from paddle.base.data_feeder import (
    check_dtype,
    check_type,
    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
from paddle.geometric.message_passing.utils import (
    convert_out_size_to_list,
    get_out_size_tensor_inputs,
)
from paddle.utils import deprecated

if TYPE_CHECKING:
    from paddle import Tensor


@deprecated(
    since="2.4.0",
    update_to="paddle.geometric.send_u_recv",
    level=1,
    reason="graph_send_recv in paddle.incubate will be removed in future",
)
def graph_send_recv(
    x: Tensor,
    src_index: Tensor,
    dst_index: Tensor,
    pool_type: Literal["sum", "mean", "max", "min"] = "sum",
    out_size: int | Tensor | None = None,
    name: str | None = None,
) -> Tensor:
    """

    Graph Learning Send_Recv combine operator.

    This operator is mainly used in Graph Learning domain, and the main purpose is to reduce intermediate memory
    consumption in the process of message passing. Take `x` as the input tensor, we first use `src_index`
    to gather the corresponding data, and then use `dst_index` to update the corresponding position of output tensor
    in different pooling types, like sum, mean, max, or min. Besides, we can set `out_size` to get necessary output shape.

    .. code-block:: text

           Given:

           X = [[0, 2, 3],
                [1, 4, 5],
                [2, 6, 7]]

           src_index = [0, 1, 2, 0]

           dst_index = [1, 2, 1, 0]

           pool_type = "sum"

           out_size = None

           Then:

           Out = [[0, 2, 3],
                  [2, 8, 10],
                  [1, 4, 5]]

    Args:
        x (Tensor): The input tensor, and the available data type is float32, float64, int32, int64.
        src_index (Tensor): An 1-D tensor, and the available data type is int32, int64.
        dst_index (Tensor): An 1-D tensor, and should have the same shape as `src_index`.
                            The available data type is int32, int64.
        pool_type (str): The pooling types of graph_send_recv, including `sum`, `mean`, `max`, `min`.
                         Default value is `sum`.
        out_size (int|Tensor|None): We can set `out_size` to get necessary output shape. If not set or
                                    out_size is smaller or equal to 0, then this input will not be used.
                                    Otherwise, `out_size` should be equal with or larger than
                                    max(dst_index) + 1.
        name (str, optional): Name for the operation (optional, default is None).
                              For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        out (Tensor): The output tensor, should have the same shape and same dtype as input tensor `x`.
                      If `out_size` is set correctly, then it should have the same shape as `x` except
                      the 0th dimension.

    Examples:

        .. code-block:: pycon

            >>> import paddle

            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
            >>> indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
            >>> src_index = indexes[:, 0]
            >>> dst_index = indexes[:, 1]
            >>> out = paddle.incubate.graph_send_recv(x, src_index, dst_index, pool_type="sum")  # type: ignore[operator]
            >>> print(out)
            Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
            [[0. , 2. , 3. ],
             [2. , 8. , 10.],
             [1. , 4. , 5. ]])

            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
            >>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
            >>> src_index = indexes[:, 0]
            >>> dst_index = indexes[:, 1]
            >>> out_size = paddle.max(dst_index) + 1
            >>> out = paddle.incubate.graph_send_recv(x, src_index, dst_index, pool_type="sum", out_size=out_size)  # type: ignore[operator]
            >>> print(out)
            Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
            [[0. , 2. , 3. ],
             [2. , 8. , 10.]])

            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
            >>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
            >>> src_index = indexes[:, 0]
            >>> dst_index = indexes[:, 1]
            >>> out = paddle.incubate.graph_send_recv(x, src_index, dst_index, pool_type="sum")  # type: ignore[operator]
            >>> print(out)
            Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
            [[0. , 2. , 3. ],
             [2. , 8. , 10.],
             [0. , 0. , 0. ]])
    """

    if pool_type not in ["sum", "mean", "max", "min"]:
        raise ValueError(
            f"pool_type should be `sum`, `mean`, `max` or `min`, but received {pool_type}"
        )

    # TODO(daisiming): Should we add judgement for out_size: max(dst_index) + 1.
    if in_dynamic_or_pir_mode():
        out_size = convert_out_size_to_list(out_size, 'graph_send_recv')
        return _C_ops.send_u_recv(
            x, src_index, dst_index, pool_type.upper(), out_size
        )
    else:
        check_variable_and_dtype(
            x, "X", ("float32", "float64", "int32", "int64"), "graph_send_recv"
        )
        check_variable_and_dtype(
            src_index, "Src_index", ("int32", "int64"), "graph_send_recv"
        )
        check_variable_and_dtype(
            dst_index, "Dst_index", ("int32", "int64"), "graph_send_recv"
        )
        if out_size:
            check_type(
                out_size,
                'out_size',
                (int, np.int32, np.int64, Variable),
                'graph_send_recv',
            )
        if isinstance(out_size, Variable):
            check_dtype(
                out_size.dtype,
                'out_size',
                ['int32', 'int64'],
                'graph_send_recv',
            )

        helper = LayerHelper("graph_send_recv", **locals())
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
        dst_count = helper.create_variable_for_type_inference(
            dtype="int32", stop_gradient=True
        )

        inputs = {"X": x, "Src_index": src_index, "Dst_index": dst_index}
        attrs = {"reduce_op": pool_type.upper()}
        get_out_size_tensor_inputs(
            inputs=inputs,
            attrs=attrs,
            out_size=out_size,
            op_type='graph_send_recv',
        )

        helper.append_op(
            type="graph_send_recv",
            inputs=inputs,
            outputs={"Out": out, "Dst_count": dst_count},
            attrs=attrs,
        )
    return out