





from typing import Dict

from .base import GenericTensor, Pipeline


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			def        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   Any=None							,     __lowerCAmelCase       :   Any=None							,     __lowerCAmelCase       :   str=None							,     **__lowerCAmelCase       :   Optional[Any]       )     ->     List[str]:







					"""simple docstring"""






					if tokenize_kwargs is None:
							A__  				=       {}

					if truncation is not None:
							if "truncation" in tokenize_kwargs:
									raise ValueError(
									    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""       )
							A__  				=       truncation

					A__  				=       tokenize_kwargs

					A__  				=       {}
					if return_tensors is not None:
							A__  				=       return_tensors

					return preprocess_params, {}, postprocess_params






			def        a_	(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   Union[str, Any]							,     **__lowerCAmelCase       :   List[str]       )     ->     Dict[str, GenericTensor]:







					"""simple docstring"""






					A__  				=       self.framework
					A__  				=       self.tokenizer(__lowerCAmelCase							,     return_tensors=__lowerCAmelCase							,     **__lowerCAmelCase       )
					return model_inputs






			def        a_	(      self       :   str							,     __lowerCAmelCase       :   Any       )     ->     Union[str, Any]:







					"""simple docstring"""






					A__  				=       self.model(**__lowerCAmelCase       )
					return model_outputs






			def        a_	(      self       :   str							,     __lowerCAmelCase       :   Union[str, Any]							,     __lowerCAmelCase       :   List[str]=False       )     ->     Dict:







					"""simple docstring"""






					if return_tensors:
							return model_outputs[0]
					if self.framework == "pt":
							return model_outputs[0].tolist()
					elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()







			def __call__(      self       :   str							,     *__lowerCAmelCase       :   Union[str, Any]							,     **__lowerCAmelCase       :   List[str]       )     ->     Dict:







					"""simple docstring"""






					return super().__call__(*__lowerCAmelCase							,     **__lowerCAmelCase       )










import math







def        __lowerCamelCase (							__a       :int   )		->    bool:






		"""simple docstring"""

		A__  				=       math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

		return exponent == int(__a   )







def        __lowerCamelCase (							__a       :float = 1 / 1_2_3_4_5   )		->    int:






		"""simple docstring"""

		A__  				=       0
		A__  				=       0

		A__  				=       3
		while True:
				A__  				=       (integer**2 - 1) / 4
				# if candidate is an integer, then there is a partition for k
				if partition_candidate == int(__a   ):
						A__  				=       int(__a   )
						total_partitions += 1
						if check_partition_perfect(__a   ):
								perfect_partitions += 1
				if perfect_partitions > 0:
						if perfect_partitions / total_partitions < max_proportion:
								return int(__a   )
				integer += 1


if __name__ == "__main__":
					print(F'''{solution() = }''')



