


import math




def    snake_case_     (  snake_case     )  ->				str:
       lowercase__:   str  			=   0
       lowercase__:   str  			=   0
       while num > 0:
              lowercase__:   Optional[Any]  			=   num % 8
              lowercase__:   Tuple  			=   octal + (remainder * math.floor(math.pow(10			,							snake_case     )     ))
              counter += 1
              lowercase__:   List[str]  			=   math.floor(num / 8     )  # basically /= 8 without remainder if any
              # This formatting removes trailing '.0' from `octal`.
       return f'0o{int(snake_case     )}'




def    snake_case_     (  )  ->				None:
       print('\n2 in octal is:'     )
       print(decimal_to_octal(2     )     )  # = 2
       print('\n8 in octal is:'     )
       print(decimal_to_octal(8     )     )  # = 10
       print('\n65 in octal is:'     )
       print(decimal_to_octal(65     )     )  # = 101
       print('\n216 in octal is:'     )
       print(decimal_to_octal(2_16     )     )  # = 330
       print('\n512 in octal is:'     )
       print(decimal_to_octal(5_12     )     )  # = 1000
       print('\n'     )


if __name__ == "__main__":
 main()






from typing import TYPE_CHECKING

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


__lowerCAmelCase		       =							{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CanineForMultipleChoice''',
     '''CanineForQuestionAnswering''',
     '''CanineForSequenceClassification''',
     '''CanineForTokenClassification''',
     '''CanineLayer''',
     '''CanineModel''',
     '''CaninePreTrainedModel''',
     '''load_tf_weights_in_canine''',
 ]


if TYPE_CHECKING:
 from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
 from .tokenization_canine import CanineTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_canine import (
      CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
      CanineForMultipleChoice,
      CanineForQuestionAnswering,
      CanineForSequenceClassification,
      CanineForTokenClassification,
      CanineLayer,
      CanineModel,
      CaninePreTrainedModel,
      load_tf_weights_in_canine,
  )


else:
 import sys

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


