





from ... import PretrainedConfig


lowercase_		:				Any				=						{
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}


class 			lowercase				(							UpperCAmelCase__     ):



						"""simple docstring"""

						_UpperCamelCase  :	str								=	NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
						_UpperCamelCase  :	Optional[Any]								=	'nezha'







						def __init__(    self			:		Dict ,				lowerCamelCase_			:		List[str]=2_11_28 ,				lowerCamelCase_			:		Any=7_68 ,				lowerCamelCase_			:		Optional[Any]=12 ,				lowerCamelCase_			:		List[str]=12 ,				lowerCamelCase_			:		Optional[Any]=30_72 ,				lowerCamelCase_			:		Optional[int]="gelu" ,				lowerCamelCase_			:		Any=0.1 ,				lowerCamelCase_			:		List[str]=0.1 ,				lowerCamelCase_			:		Tuple=5_12 ,				lowerCamelCase_			:		Any=64 ,				lowerCamelCase_			:		Optional[int]=2 ,				lowerCamelCase_			:		Union[str, Any]=0.02 ,				lowerCamelCase_			:		List[Any]=1e-12 ,				lowerCamelCase_			:		int=0.1 ,				lowerCamelCase_			:		int=0 ,				lowerCamelCase_			:		Any=2 ,				lowerCamelCase_			:		Optional[int]=3 ,				lowerCamelCase_			:		int=True ,				**lowerCamelCase_			:		Dict ,				):





										'''simple docstring'''







										super().__init__(pad_token_id=lowerCamelCase_ ,				bos_token_id=lowerCamelCase_ ,				eos_token_id=lowerCamelCase_ ,				**lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   vocab_size
										_snake_case :			Optional[Any]  						=   hidden_size
										_snake_case :			Tuple  						=   num_hidden_layers
										_snake_case :			Any  						=   num_attention_heads
										_snake_case :			Any  						=   hidden_act
										_snake_case :			Optional[int]  						=   intermediate_size
										_snake_case :			int  						=   hidden_dropout_prob
										_snake_case :			Any  						=   attention_probs_dropout_prob
										_snake_case :			int  						=   max_position_embeddings
										_snake_case :			Optional[int]  						=   max_relative_position
										_snake_case :			Union[str, Any]  						=   type_vocab_size
										_snake_case :			Optional[int]  						=   initializer_range
										_snake_case :			Any  						=   layer_norm_eps
										_snake_case :			List[str]  						=   classifier_dropout
										_snake_case :			str  						=   use_cache













import math

def       A__(		__lowerCAmelCase ):

				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(__lowerCAmelCase ) + 1 )	,			6 ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True

def       A__(		__lowerCAmelCase = 1_00_01 ):

				try:
								_snake_case :			int  						=   int(__lowerCAmelCase )
				except (TypeError, ValueError):
								raise TypeError('Parameter nth must be int or castable to int.' ) from None
				if nth <= 0:
								raise ValueError('Parameter nth must be greater than or equal to one.' )
				_snake_case :			list[int]  						=   []
				_snake_case :			List[Any]  						=   2
				while len(__lowerCAmelCase ) < nth:
								if is_prime(__lowerCAmelCase ):
												primes.append(__lowerCAmelCase )
												num += 1
								else:
												num += 1
				return primes[len(__lowerCAmelCase ) - 1]


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






