# Copyright (c) 2022 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

from paddle import framework
from paddle.base import data_feeder
from paddle.distributed.communication.group import (
    _get_global_group,
    _get_or_throw_group_rank,
    _warn_cur_rank_not_in_group,
)

if TYPE_CHECKING:
    from paddle import Tensor
    from paddle.base.core import task
    from paddle.distributed.communication.group import Group


def _recv_in_dygraph(
    tensor, src_rank_in_group, group, sync_op, use_calc_stream
):
    if use_calc_stream:
        return group.process_group.recv_on_calc_stream(
            tensor, src_rank_in_group
        )

    task = group.process_group.recv(tensor, src_rank_in_group, sync_op)
    if sync_op:
        task.wait()

    return task


def _recv_in_static_mode(
    tensor, src_rank_in_group, group, sync_op, use_calc_stream
):
    op_type = 'recv_v2'
    data_feeder.check_variable_and_dtype(
        tensor,
        'tensor',
        ['float16', 'float32', 'float64', 'int32', 'int64', 'uint16'],
        'recv',
    )
    ring_id = 0 if group is None else group.id
    helper = framework.LayerHelper(op_type, **locals())
    helper.append_op(
        type=op_type,
        outputs={'Out': [tensor]},
        attrs={
            'ring_id': ring_id,
            'peer': src_rank_in_group,
            'out_shape': tensor.shape,
            'dtype': tensor.dtype,
            'use_calc_stream': sync_op,
        },
    )


def recv(
    tensor: Tensor,
    src: int = 0,
    group: Group | None = None,
    sync_op: bool = True,
    use_calc_stream: bool = False,
) -> task | None:
    """

    Receive a tensor from the source device.

    Args:
        tensor (Tensor): The tensor to receive. Support float16, float32, float64, int32, int64, int8, uint8 or bool as its data type.
        src (int, optional): Rank of the source device. If none is given, use `0` as default.
        group (Group|None, optional): Communicate in which group. If none is given, use the global group as default.
        sync_op (bool, optional): Indicate whether the communication is sync or not. If none is given, use true as default.
        use_calc_stream (bool, optional): Indicate whether the communication is done on calculation stream. If none is given, use false as default. This
            option is designed for high performance demand, be careful to turn it on except you are clearly know its meaning.

    Returns:
        Return a task object.

    Examples:
        .. code-block:: python

            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
            >>> import paddle
            >>> import paddle.distributed as dist

            >>> dist.init_parallel_env()
            >>> local_rank = dist.get_rank()
            >>> if local_rank == 0:
            ...     data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
            ...     task = dist.stream.send(data, dst=1, sync_op=False)
            >>> else:
            ...     data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
            ...     task = dist.stream.recv(data, src=0, sync_op=False)
            >>> task.wait()  # type: ignore[union-attr]
            >>> out = data.numpy()
            >>> print(out)
            >>> # [[4, 5, 6], [4, 5, 6]] (2 GPUs)
    """
    if _warn_cur_rank_not_in_group(group):
        return

    if not sync_op and use_calc_stream:
        raise RuntimeError(
            "use_calc_stream can only be True in sync op behavior."
        )

    if framework.in_dynamic_mode():
        group = _get_global_group() if group is None else group
        src_rank_in_group = _get_or_throw_group_rank(src, group)

        return _recv_in_dygraph(
            tensor, src_rank_in_group, group, sync_op, use_calc_stream
        )
    else:
        assert group is None, (
            "Group can not be used in static graph mode for now."
        )
        return _recv_in_static_mode(
            tensor, src, group, sync_op, use_calc_stream
        )