#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# This file is a part of the vllm-ascend project.
#
# 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 typing import List, Optional, Union

import torch
import vllm
from torch.distributed import Backend
from vllm.distributed.parallel_state import (GroupCoordinator,
                                             _get_unique_name, _register_group)

from vllm_npu.distributed.communicator import NPUCommunicator
from vllm_npu.utils import create_hccl_pg_options


class GroupCoordinatorPatch(GroupCoordinator):

    def __init__(
        self,
        group_ranks: list[list[int]],
        local_rank: int,
        torch_distributed_backend: Union[str, Backend],
        use_device_communicator: bool,  # whether to use device communicator
        use_message_queue_broadcaster: bool = False,
        group_name: Optional[str] = None,
    ):
        group_name = group_name or "anonymous"
        self.unique_name = _get_unique_name(group_name)
        _register_group(self)

        self.rank = torch.distributed.get_rank()
        self.local_rank = local_rank

        self_device_group = None
        self_cpu_group = None
        hccl_pg_options = create_hccl_pg_options(group_name)

        for ranks in group_ranks:
            device_group = torch.distributed.new_group(
                ranks,
                backend=torch_distributed_backend,
                pg_options=hccl_pg_options)

            # a group with `gloo` backend, to allow direct coordination between
            # processes through the CPU.
            cpu_group = torch.distributed.new_group(ranks, backend="gloo")
            if self.rank in ranks:
                self.ranks = ranks
                self.world_size = len(ranks)
                self.rank_in_group = ranks.index(self.rank)
                self_device_group = device_group
                self_cpu_group = cpu_group

        assert self_cpu_group is not None
        assert self_device_group is not None

        self.cpu_group = self_cpu_group
        self.device_group = self_device_group
        self.device = torch.npu.current_device()

        self.use_device_communicator = use_device_communicator
        self.device_communicator = None
        if use_device_communicator and self.world_size > 1:
            self.device_communicator = NPUCommunicator(
                cpu_group=self.cpu_group,
                device=self.device,
                device_group=self.device_group,
                unique_name=self.unique_name,
            )

        from vllm.distributed.device_communicators.shm_broadcast import \
            MessageQueue
        self.mq_broadcaster: Optional[MessageQueue] = None
        if use_message_queue_broadcaster and self.world_size > 1:
            self.mq_broadcaster = MessageQueue.create_from_process_group(
                self.cpu_group, 1 << 22, 6)

        self.use_custom_op_call = False
        self.use_cpu_custom_send_recv = False

    def all_to_all(self,
                   input_: torch.Tensor,
                   scatter_dim: int = 0,
                   gather_dim: int = -1,
                   scatter_sizes: Optional[List[int]] = None,
                   gather_sizes: Optional[List[int]] = None) -> torch.Tensor:
        if self.world_size == 1:
            return input_
        assert -input_.dim() <= scatter_dim < input_.dim(), (
            f"Invalid scatter dim ({scatter_dim}) for input tensor with shape {input_.size()}"
        )
        assert -input_.dim() <= gather_dim < input_.dim(), (
            f"Invalid gather dim ({gather_dim}) for input tensor with shape {input_.size()}"
        )
        assert self.device_communicator is not None, "device_communicator should be initialized when world_size > 1"
        return self.device_communicator.all_to_all(input_, scatter_dim,
                                                   gather_dim, scatter_sizes,
                                                   gather_sizes)


vllm.distributed.parallel_state.GroupCoordinator = GroupCoordinatorPatch