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# There is a minimum version of pytorch required for shard tensor.
# 2.6.0+ works
# 2.5.X and lower does not work

import torch

from physicsnemo.utils.version_check import check_module_requirements

from .autograd import all_gather_v, gather_v, indexed_all_to_all_v, scatter_v
from .config import ProcessGroupConfig, ProcessGroupNode

# Load and register custom ops:
from .manager import (
    DistributedManager,
    PhysicsNeMoUndefinedGroupError,
    PhysicsNeMoUninitializedDistributedManagerWarning,
)
from .utils import (
    mark_module_as_shared,
    reduce_loss,
    unmark_module_as_shared,
)

try:
    check_module_requirements("physicsnemo.distributed.shard_tensor")

    # In minumum versions are met, we can import the shard tensor and spec.

    from ._shard_tensor_spec import ShardTensorSpec
    from .shard_tensor import ShardTensor, scatter_tensor

    def register_custom_ops():
        # These imports will register the custom ops with the ShardTensor class.
        # It's done here to avoid an import cycle.
        from .custom_ops import (
            mean_wrapper,
            sum_wrapper,
            unbind_rules,
        )
        from .shard_utils import register_shard_wrappers

        register_shard_wrappers()

    # Protect the automatic imports by checking cuda is available.
    if torch.cuda.is_available():
        register_custom_ops()

except ImportError:
    pass