__all__ = [

    "is_hccl_available", "reinit_process_group", "reduce_scatter_tensor_uneven", "all_gather_into_tensor_uneven"

]



import torch_npu

from torch_npu.utils._error_code import ErrCode, dist_error





def is_available():

    """

    Returns ``True`` if the distributed package is available. Otherwise,

    ``torch.distributed`` does not expose any other APIs. Currently,

    ``torch.distributed`` is available on Linux, MacOS and Windows. Set

    ``USE_DISTRIBUTED=1`` to enable it when building PyTorch from source.

    Currently, the default value is ``USE_DISTRIBUTED=1`` for Linux and Windows,

    ``USE_DISTRIBUTED=0`` for MacOS.

    """

    return hasattr(torch_npu._C, "_c10d_npu_init")





if is_available() and not torch_npu._C._c10d_npu_init():

    raise RuntimeError("Failed to initialize torch_npu.distributed" + dist_error(ErrCode.INTERNAL))





from torch_npu._C._distributed_c10d import (

    ParallelStore,

    _verify_params_across_processes,

    _is_support_hccl_comm_name,

)





from torch_npu.distributed import rendezvous, tensor

from .distributed_c10d import is_hccl_available, reinit_process_group, _reduce_scatter_tensor_uneven as reduce_scatter_tensor_uneven, _all_gather_into_tensor_uneven as all_gather_into_tensor_uneven



rendezvous._rendezvous_init()