






"""simple docstring"""


from math import pi, sqrt




def       snake_case  (A_				:float				):







  '''simple docstring'''

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




def       snake_case  ():







  '''simple docstring'''

  assert gamma(0.5				) == sqrt(_lowerCAmelCase				)
  assert gamma(1				) == 1.0
  assert gamma(2				) == 1.0


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

   testmod()
   _UpperCamelCase    :   List[str]   			=						1.0
   while num:
      _UpperCamelCase    :   List[str]   			=						float(input('Gamma of: '))
      print(f'''gamma({num}) = {gamma(num)}''')
      print('\nEnter 0 to exit...')












"""simple docstring"""


import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


_UpperCamelCase    :   Dict   			=						logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					field(default=UpperCAmelCase       ,			metadata={'''help''': '''Whether to use SortishSampler or not.'''}	)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}	)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={
            '''help''': (
                '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                '''to the `max_length` value of the model configuration.'''
            )
        }       ,			)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={
            '''help''': (
                '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                '''to the `num_beams` value of the model configuration.'''
            )
        }       ,			)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={
            '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
        }       ,			)





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      List[Any]    =       super().to_dict()
      for k, v in d.items():
        if isinstance(A    ,		A      ):
          a					:      str    =       v.to_dict()
      return d




