





from math import pi, sqrt




def  __UpperCAmelCase(   lowercase_   ):
       if num <= 0:
              raise ValueError('''math domain error'''   )
       if num > 1_7_1.5:
              raise OverflowError('''math range error'''   )
       elif num - int(lowercase_   ) not in (0, 0.5):
              raise NotImplementedError('''num must be an integer or a half-integer'''   )
       elif num == 0.5:
              return sqrt(lowercase_   )
       else:
              return 1.0 if num == 1 else (num - 1) * gamma(num - 1   )




def  __UpperCAmelCase(   ):
       assert gamma(0.5   ) == sqrt(lowercase_   )
       assert gamma(1   ) == 1.0
       assert gamma(2   ) == 1.0


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       _lowerCamelCase      							=   1.0
       while num:
              _lowerCamelCase      							=   float(input('Gamma of: '))
              print(F'''gamma({num}) = {gamma(num)}''')
              print('\nEnter 0 to exit...')








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase      							=   {
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase      							=   [
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

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

