


def 	_lowerCAmelCase					( UpperCamelCase__:  int = 2_00    )			-> int:





	"""simple docstring"""







	A             =					[1, 2, 5, 10, 20, 50, 1_00, 2_00]
	A             =					[0] * (pence + 1)
	A             =					1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(UpperCamelCase__				,       pence + 1				,       1    ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
				assert solution(200) == 7_3682







import numpy as np

from transformers import Pipeline





def 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					np.max(UpperCamelCase__				,       axis=-1				,       keepdims=UpperCamelCase__    )
	A             =					np.exp(outputs - maxes    )
	return shifted_exp / shifted_exp.sum(axis=-1				,       keepdims=UpperCamelCase__    )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self					, **a__ )						->					Union[str, Any]:
					A             =					{}
					if "second_text" in kwargs:
						A             =					kwargs["""second_text"""]
					return preprocess_kwargs, {}, {}

				def   _UpperCAmelCase       (    self					, a__					, a__=None )						->					str:
					return self.tokenizer(a__					, text_pair=a__					, return_tensors=self.framework )

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					return self.model(**a__ )

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[int]:
					A             =					model_outputs.logits[0].numpy()
					A             =					softmax(a__ )

					A             =					np.argmax(a__ )
					A             =					self.model.config.idalabel[best_class]
					A             =					probabilities[best_class].item()
					A             =					logits.tolist()
					return {"label": label, "score": score, "logits": logits}



