






"""simple docstring"""



from typing import TYPE_CHECKING

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


_lowerCAmelCase  :  Dict   =			{
    """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:
 _lowerCAmelCase  :  Tuple   =			["""DebertaV2TokenizerFast"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  List[Any]   =			[
     """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:
 _lowerCAmelCase  :  Any   =			[
     """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

 _lowerCAmelCase  :  Any   =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""



def   SCREAMING_SNAKE_CASE__					(     snake_case					:   int							)->		list:






     '''simple docstring'''



     UpperCAmelCase__	:						List[str]       =       [True for i in range(n + 1							)]
     UpperCAmelCase__	:						List[str]       =       [i - 1 for i in range(n + 1							)]
     UpperCAmelCase__	:						int       =       []
     for i in range(2 ,				n + 1							):
          if is_prime[i]:
               primes.append(snake_case							)
          for j in range(0 ,				len(snake_case							)							):
               if i * primes[j] >= n:
                    break
               UpperCAmelCase__	:						Optional[Any]       =       False

               if i % primes[j] == 0:
                    UpperCAmelCase__	:						str       =       totients[i] * primes[j]
                    break

               UpperCAmelCase__	:						Union[str, Any]       =       totients[i] * (primes[j] - 1)

     return totients


if __name__ == "__main__":
 import doctest

 doctest.testmod()


