


'''simple docstring'''



from doctest import testmod
from math import sqrt



def       UpperCAmelCase__ (     UpperCAmelCase_   :    int							)					->		list:
     __lowerCamelCase       :     list[int]        =		[]
     if num < 1:
          return facs
     facs.append(1							)
     if num == 1:
          return facs
     facs.append(UpperCAmelCase_							)
     for i in range(2				,   int(sqrt(UpperCAmelCase_							)							) + 1							):
          if num % i == 0:  # If i is a factor of num
               facs.append(UpperCAmelCase_							)
               __lowerCamelCase       :     Union[str, Any]        =		num // i  # num//i is the other factor of num
               if d != i:  # If d and i are distinct
                    facs.append(UpperCAmelCase_							)  # we have found another factor
     facs.sort()
     return facs


if __name__ == "__main__":
      testmod(name="""factors_of_a_number""", verbose=True)










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__		: str												=  {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: List[str]												=  [
          """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """HubertForCTC""",
          """HubertForSequenceClassification""",
          """HubertModel""",
          """HubertPreTrainedModel""",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: Optional[int]												=  [
          """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFHubertForCTC""",
          """TFHubertModel""",
          """TFHubertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_hubert import (
                HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                HubertForCTC,
                HubertForSequenceClassification,
                HubertModel,
                HubertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_hubert import (
                TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFHubertForCTC,
                TFHubertModel,
                TFHubertPreTrainedModel,
            )


else:
      import sys

      A__		: Optional[int]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






