





"""simple docstring"""
lowerCAmelCase:	List[Any]							      =(1, 153, 370, 371, 1634, 24678051, 115132219018763992565095597973971522401)
lowerCAmelCase:	tuple							      =(-153, -1, 0, 1.2, 200, "A", [], {}, None)




def 			__snake_case						(      __A    )		->				bool:
   if not isinstance(__A ,__A    ) or n < 1:
      return False

   # Initialization of sum and number of digits.
   lowercase    :			Optional[Any]  =       0
   lowercase    :			Tuple  =       0
   lowercase    :			Any  =       n
   # Calculation of digits of the number
   while temp > 0:
      number_of_digits += 1
      temp //= 10
   # Dividing number into separate digits and find Armstrong number
   lowercase    :			List[Any]  =       n
   while temp > 0:
      lowercase    :			Union[str, Any]  =       temp % 10
      total += rem**number_of_digits
      temp //= 10
   return n == total




def 			__snake_case						(      __A    )		->				bool:
   if not isinstance(__A ,__A    ) or n < 1:
      return False

   # Init a "histogram" of the digits
   lowercase    :			Union[str, Any]  =       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
   lowercase    :			Union[str, Any]  =       0
   lowercase    :			int  =       0
   lowercase    :			List[Any]  =       n
   while temp > 0:
      lowercase  ,					lowercase    :			int  =       divmod(__A ,10    )
      digit_histogram[rem] += 1
      digit_total += 1

   for cnt, i in zip(__A ,range(len(__A    )    )    ):
      total += cnt * i**digit_total

   return n == total




def 			__snake_case						(      __A    )		->				bool:
   if not isinstance(__A ,__A    ) or n < 1:
      return False
   lowercase    :			Optional[int]  =       len(str(__A    )    )  # the power that all digits will be raised to
   # check if sum of each digit multiplied expo times is equal to number
   return n == sum(int(__A    ) ** expo for i in str(__A    )    )




def 			__snake_case						(      )		->				int:
   lowercase    :			List[Any]  =       int(input("""Enter an integer to see if it is an Armstrong number: """    ).strip()    )
   print(F'''{num} is {'' if armstrong_number(__A    ) else 'not '}an Armstrong number.'''    )
   print(F'''{num} is {'' if narcissistic_number(__A    ) else 'not '}an Armstrong number.'''    )
   print(F'''{num} is {'' if pluperfect_number(__A    ) else 'not '}an Armstrong number.'''    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()









"""simple docstring"""
from typing import TYPE_CHECKING

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


lowerCAmelCase:	Union[str, Any]							      ={
    "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:	Dict							      =["DebertaV2TokenizerFast"]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase:	Optional[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:	str							      =[
       "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:	Optional[Any]							      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


