


'''simple docstring'''





from __future__ import annotations

import math






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5,int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ),6 ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True


UpperCAmelCase__  			=				[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if not isinstance(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE ):
						raise ValueError('n must be an integer' )
			if n <= 0:
						raise ValueError('n must be >= 0' )

			__A=	[]
			for num in range(len(_SCREAMING_SNAKE_CASE ) ):
						__A=	0
						while 2 * i * i <= odd_composites[num]:
									__A=	odd_composites[num] - 2 * i * i
									if is_prime(_SCREAMING_SNAKE_CASE ):
												break
									i += 1
						else:
									list_nums.append(odd_composites[num] )
									if len(_SCREAMING_SNAKE_CASE ) == n:
												return list_nums

			return []






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			return compute_nums(1 )[0]


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







'''simple docstring'''





import numpy as np

from transformers import Pipeline






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any ):






			"""simple docstring"""




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






class 						a__					(					a_   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    **lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						__A=	{}
						if "second_text" in kwargs:
									__A=	kwargs['second_text']
						return preprocess_kwargs, {}, {}
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]=None	)      ->		int:
						return self.tokenizer(lowerCAmelCase_			,    text_pair=lowerCAmelCase_			,    return_tensors=self.framework	)
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Tuple:
						return self.model(**lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict	)      ->		Optional[int]:
						__A=	model_outputs.logits[0].numpy()
						__A=	softmax(lowerCAmelCase_	)

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



