
'''simple docstring'''







from string import ascii_uppercase

_lowercase			:  Tuple   							=	{str(ord(c) - 5_5): c for c in ascii_uppercase}
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			Any							):



						"""simple docstring"""





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

						return new_value[::-1]


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							for base in range(2, 3_7):
														for num in range(1_0_0_0):
																					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'''







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


_lowercase			:  Tuple   							=	logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__					)
class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					field(default=lowerCamelCase_   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}					)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						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.'''
					        )
					    }   ,						)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						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.'''
					        )
					    }   ,						)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={
					        '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
					    }   ,						)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	super().to_dict()
											for k, v in d.items():
																	if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
																							lowercase_     :					str													=	v.to_dict()
											return d



