



'''simple docstring'''





lowercase_			=  (1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
lowercase_			=  (-153, -1, 0, 1.2, 200, "A", [], {}, None)




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,     __A) or n < 1:
          return False

     # Initialization of sum and number of digits.
     _a     =  0
     _a     =  0
     _a     =  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
     _a     =  n
     while temp > 0:
          _a     =  temp % 10
          total += rem**number_of_digits
          temp //= 10
     return n == total




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,     __A) or n < 1:
          return False

     # Init a "histogram" of the digits
     _a     =  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
     _a     =  0
     _a     =  0
     _a     =  n
     while temp > 0:
          _a  ,   _a     =  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 lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,     __A) or n < 1:
          return False
     _a     =  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 lowerCAmelCase  ():



     """simple docstring"""



     _a     =  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_torch_available,
    is_vision_available,
)


lowercase_			=  {
    "configuration_segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig", "SegformerOnnxConfig"]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  ["SegformerFeatureExtractor"]
  lowercase_			=  ["SegformerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SegformerDecodeHead",
      "SegformerForImageClassification",
      "SegformerForSemanticSegmentation",
      "SegformerLayer",
      "SegformerModel",
      "SegformerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_			=  [
      "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFSegformerDecodeHead",
      "TFSegformerForImageClassification",
      "TFSegformerForSemanticSegmentation",
      "TFSegformerModel",
      "TFSegformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_segformer import SegformerFeatureExtractor
    from .image_processing_segformer import SegformerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_segformer import (
        SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SegformerDecodeHead,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerLayer,
        SegformerModel,
        SegformerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_segformer import (
        TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSegformerDecodeHead,
        TFSegformerForImageClassification,
        TFSegformerForSemanticSegmentation,
        TFSegformerModel,
        TFSegformerPreTrainedModel,
    )

else:
  import sys

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



