# 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, Any

import paddle
from paddle.distributed.communication import stream
from paddle.distributed.communication.group import (
    _get_global_group,
    _warn_cur_rank_not_in_group,
)
from paddle.distributed.communication.serialization_utils import (
    convert_tensor_to_object,
)

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


def recv(
    tensor: Tensor,
    src: int = 0,
    group: Group | None = None,
    sync_op: bool = True,
) -> task:
    """
    Receive a tensor to the sender.

    Args:
        tensor (Tensor): The tensor to receive. Its data type
            should be float16, float32, float64, int32, int64, int8, uint8, bool or bfloat16.
        src (int): The source rank id.
        group (Group, optional): The group instance return by new_group or None for global default group. Default: None.
        sync_op (bool, optional): Whether this op is a sync op. The default value is True.

    Returns:
        Return a task object.

    Examples:
        .. code-block:: pycon

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

            >>> dist.init_parallel_env()
            >>> if dist.get_rank() == 0:
            ...     data = paddle.to_tensor([7, 8, 9])
            ...     dist.send(data, dst=1)
            >>> else:
            ...     data = paddle.to_tensor([1, 2, 3])
            ...     dist.recv(data, src=0)
            >>> print(data)
            >>> # [7, 8, 9] (2 GPUs)
    """
    return stream.recv(
        tensor, src=src, group=group, sync_op=sync_op, use_calc_stream=False
    )


def irecv(
    tensor: Tensor, src: int | None = None, group: Group | None = None
) -> task:
    """
    Receive a tensor to the sender.

    Args:
        tensor (Tensor): The Tensor to receive. Its data type
            should be float16, float32, float64, int32, int64, int8, uint8, bool or bfloat16.
        src (int): The source rank id.
        group (Group, optional): The group instance return by new_group or None for global default group. Default: None.

    Returns:
        Return a task object.

    Warning:
        This API only supports the dygraph mode.

    Examples:
        .. code-block:: pycon

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

            >>> dist.init_parallel_env()
            >>> if dist.get_rank() == 0:
            ...     data = paddle.to_tensor([7, 8, 9])
            ...     task = dist.isend(data, dst=1)
            >>> else:
            ...     data = paddle.to_tensor([1, 2, 3])
            ...     task = dist.irecv(data, src=0)
            >>> task.wait()  # type: ignore[union-attr]
            >>> print(data)
            >>> # [7, 8, 9] (2 GPUs)
    """
    return recv(tensor, src, group, sync_op=False)


def recv_object_list(
    object_list: list[Any],
    src: int | None = None,
    group: Group | None = None,
    src_in_group: int | None = None,
):
    """
    Receive a list of Python objects from the sender.

    Args:
        object_list (list): The list to store received objects. Must be pre-allocated with correct size.
        src (int, optional): The source rank id. Default: 0.
        group (Group, optional): The group instance return by new_group or None for global default group. Default: None.
        src_in_group (int, optional): The source rank within the group. Cannot be specified together with src. Default: None.

    Returns:
        This function does not return any value.

    Examples:
        .. code-block:: pycon

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

            >>> dist.init_parallel_env()
            >>> if dist.get_rank() == 0:
            ...     data = ["hello", {"key": 100}, [1, 2, 3]]
            ...     dist.send_object_list(data, dst=1)
            >>> else:
            ...     data = [None] * 3
            ...     dist.recv_object_list(data, src=0)
            >>> print(data)
            >>> # ["hello", {"key": 100}, [1, 2, 3]] (2 GPUs)
    """
    if object_list is None or len(object_list) == 0:
        raise ValueError("object_list cannot be None or empty")

    group = _get_global_group() if group is None else group
    if _warn_cur_rank_not_in_group(group):
        return

    if src_in_group is not None:
        if src is not None:
            raise ValueError(
                "Cannot specify both 'src' and 'src_in_group' arguments."
            )
        src = group.get_global_rank(src_in_group)
    else:
        src = 0 if src is None else src

    object_sizes_tensor = paddle.empty((len(object_list),), dtype='int64')
    recv(object_sizes_tensor, src=src, group=group)

    total_size = paddle.sum(object_sizes_tensor).item()
    object_tensor = paddle.empty((total_size,), dtype=paddle.uint8)
    recv(object_tensor, src=src, group=group)

    offset = 0
    for i, obj_size in enumerate(object_sizes_tensor):
        obj_size = obj_size.item()
        obj_view = object_tensor[offset : offset + obj_size]
        object_list[i] = convert_tensor_to_object(obj_view, obj_size)
        offset += obj_size