


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_           =	{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_           =	["""DebertaV2TokenizerFast"""]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_           =	[
           """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFDebertaV2ForMaskedLM""",
           """TFDebertaV2ForQuestionAnswering""",
           """TFDebertaV2ForSequenceClassification""",
           """TFDebertaV2ForTokenClassification""",
           """TFDebertaV2Model""",
           """TFDebertaV2PreTrainedModel""",
       ]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_           =	[
           """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """DebertaV2ForMaskedLM""",
           """DebertaV2ForMultipleChoice""",
           """DebertaV2ForQuestionAnswering""",
           """DebertaV2ForSequenceClassification""",
           """DebertaV2ForTokenClassification""",
           """DebertaV2Model""",
           """DebertaV2PreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_deberta_va import (
           DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
           DebertaVaConfig,
           DebertaVaOnnxConfig,
       )
       from .tokenization_deberta_va import DebertaVaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta_va import (
                  TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaVaForMaskedLM,
                  TFDebertaVaForQuestionAnswering,
                  TFDebertaVaForSequenceClassification,
                  TFDebertaVaForTokenClassification,
                  TFDebertaVaModel,
                  TFDebertaVaPreTrainedModel,
              )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta_va import (
                  DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaVaForMaskedLM,
                  DebertaVaForMultipleChoice,
                  DebertaVaForQuestionAnswering,
                  DebertaVaForSequenceClassification,
                  DebertaVaForTokenClassification,
                  DebertaVaModel,
                  DebertaVaPreTrainedModel,
              )

else:
       import sys

       lowercase_           =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









"""simple docstring"""



from doctest import testmod
from math import sqrt


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      int							)->						list:






     '''simple docstring'''



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


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


