
"""simple docstring"""

from string import ascii_uppercase

SCREAMING_SNAKE_CASE_            =    {str(ord(c) - 55): c for c in ascii_uppercase}




def      lowercase__    (       lowerCAmelCase					:       int			,					lowerCAmelCase					:       int			)			->   str:






    """simple docstring"""




    if isinstance(lowerCAmelCase			,					lowerCAmelCase			):
        raise TypeError('int() can\'t convert non-string with explicit base'			)
    if num < 0:
        raise ValueError('parameter must be positive int'			)
    if isinstance(lowerCAmelCase			,					lowerCAmelCase			):
        raise TypeError('\'str\' object cannot be interpreted as an integer'			)
    if isinstance(lowerCAmelCase			,					lowerCAmelCase			):
        raise TypeError('\'float\' object cannot be interpreted as an integer'			)
    if base in (0, 1):
        raise ValueError('base must be >= 2'			)
    if base > 36:
        raise ValueError('base must be <= 36'			)
    UpperCAmelCase          =       ''
    UpperCAmelCase          =       0
    UpperCAmelCase          =       0
    while div != 1:
        UpperCAmelCase      ,				UpperCAmelCase          =       divmod(lowerCAmelCase			,					lowerCAmelCase			)
        if base >= 11 and 9 < mod < 36:
            UpperCAmelCase          =       ALPHABET_VALUES[str(lowerCAmelCase			)]
        else:
            UpperCAmelCase          =       str(lowerCAmelCase			)
        new_value += actual_value
        UpperCAmelCase          =       num // base
        UpperCAmelCase          =       div
        if div == 0:
            return str(new_value[::-1]			)
        elif div == 1:
            new_value += str(lowerCAmelCase			)
            return str(new_value[::-1]			)

    return new_value[::-1]


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   for base in range(2, 37):
      for num in range(1000):
         assert int(decimal_to_any(num, base), base) == num, (
             num,
             base,
             decimal_to_any(num, base),
             int(decimal_to_any(num, base), base),
         )





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE_            =    {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''BeitFeatureExtractor''']
   SCREAMING_SNAKE_CASE_            =    ['''BeitImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BeitForImageClassification''',
       '''BeitForMaskedImageModeling''',
       '''BeitForSemanticSegmentation''',
       '''BeitModel''',
       '''BeitPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''FlaxBeitForImageClassification''',
       '''FlaxBeitForMaskedImageModeling''',
       '''FlaxBeitModel''',
       '''FlaxBeitPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_beit import BeitFeatureExtractor
      from .image_processing_beit import BeitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_beit import (
          BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
          BeitPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_beit import (
          FlaxBeitForImageClassification,
          FlaxBeitForMaskedImageModeling,
          FlaxBeitModel,
          FlaxBeitPreTrainedModel,
      )


else:
   import sys

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



