# Copyright (c) 2026 SandAI. 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.

import torch

from .parallel_state import get_tp_rank, get_tp_world_size


def is_last_rank():
    return torch.distributed.get_rank() == (torch.distributed.get_world_size() - 1)


def is_last_tp_cp_rank():
    return get_tp_rank(with_context_parallel=True) == get_tp_world_size(with_context_parallel=True) - 1


def get_world_size():
    if torch.distributed.is_available() and torch.distributed.is_initialized():
        world_size = torch.distributed.get_world_size()
    else:
        world_size = 1
    return world_size


def get_device(local_rank=None):
    backend = torch.distributed.get_backend()
    if backend == "nccl":
        if local_rank is None:
            device = torch.device("cuda")
        else:
            device = torch.device(f"cuda:{local_rank}")
    elif backend == "gloo":
        device = torch.device("cpu")
    else:
        raise RuntimeError
    return device