



'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_				:			Any        =						logging.get_logger(__name__)

snake_case_				:			Optional[Any]        =						{
    'google/mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'
}






class 							lowercase__			(							lowercase     ):
	lowercase__						       =    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	lowercase__						       =    """mobilebert"""





	def __init__(				self      :       Optional[int]				,lowerCamelCase__      :       str=30522				,lowerCamelCase__      :       Any=512				,lowerCamelCase__      :       List[Any]=24				,lowerCamelCase__      :       Dict=4				,lowerCamelCase__      :       str=512				,lowerCamelCase__      :       Union[str, Any]="relu"				,lowerCamelCase__      :       Dict=0.0				,lowerCamelCase__      :       str=0.1				,lowerCamelCase__      :       Dict=512				,lowerCamelCase__      :       Optional[Any]=2				,lowerCamelCase__      :       int=0.0_2				,lowerCamelCase__      :       str=1E-12				,lowerCamelCase__      :       int=0				,lowerCamelCase__      :       Any=128				,lowerCamelCase__      :       Dict=True				,lowerCamelCase__      :       Any=True				,lowerCamelCase__      :       Tuple=128				,lowerCamelCase__      :       Optional[Any]=False				,lowerCamelCase__      :       List[str]=True				,lowerCamelCase__      :       Any=4				,lowerCamelCase__      :       List[str]="no_norm"				,lowerCamelCase__      :       List[str]=True				,lowerCamelCase__      :       Optional[Any]=None				,**lowerCamelCase__      :       str				,):







							'''simple docstring'''
							super().__init__(pad_token_id=lowerCamelCase__				,**lowerCamelCase__				)

							_UpperCamelCase      :	Optional[int]				       =     vocab_size
							_UpperCamelCase      :	List[Any]				       =     hidden_size
							_UpperCamelCase      :	Optional[Any]				       =     num_hidden_layers
							_UpperCamelCase      :	List[Any]				       =     num_attention_heads
							_UpperCamelCase      :	List[str]				       =     hidden_act
							_UpperCamelCase      :	Optional[Any]				       =     intermediate_size
							_UpperCamelCase      :	str				       =     hidden_dropout_prob
							_UpperCamelCase      :	Dict				       =     attention_probs_dropout_prob
							_UpperCamelCase      :	List[str]				       =     max_position_embeddings
							_UpperCamelCase      :	List[str]				       =     type_vocab_size
							_UpperCamelCase      :	Dict				       =     initializer_range
							_UpperCamelCase      :	Union[str, Any]				       =     layer_norm_eps
							_UpperCamelCase      :	str				       =     embedding_size
							_UpperCamelCase      :	Union[str, Any]				       =     trigram_input
							_UpperCamelCase      :	List[str]				       =     use_bottleneck
							_UpperCamelCase      :	List[Any]				       =     intra_bottleneck_size
							_UpperCamelCase      :	List[str]				       =     use_bottleneck_attention
							_UpperCamelCase      :	List[Any]				       =     key_query_shared_bottleneck
							_UpperCamelCase      :	Dict				       =     num_feedforward_networks
							_UpperCamelCase      :	Optional[int]				       =     normalization_type
							_UpperCamelCase      :	Any				       =     classifier_activation

							if self.use_bottleneck:
													_UpperCamelCase      :	Optional[int]				       =     intra_bottleneck_size
							else:
													_UpperCamelCase      :	List[Any]				       =     hidden_size

							_UpperCamelCase      :	Dict				       =     classifier_dropout






class 							lowercase__			(							lowercase     ):





	@property
	def 			UpperCamelCase_						(				self      :       str				):







							'''simple docstring'''
							if self.task == "multiple-choice":
													_UpperCamelCase      :	List[str]				       =     {0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
													_UpperCamelCase      :	int				       =     {0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							        ('token_type_ids', dynamic_axis),
							    ]				)







'''simple docstring'''
import math






def A__					(  UpperCAmelCase_							):

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






def A__					(  UpperCAmelCase_ = 1_0_0_0						,						UpperCAmelCase_ = 1_0_0_0							):
						_UpperCamelCase      :	str				       =     [0, 0, 0]  # length, a, b
						for a in range((a_limit * -1) + 1						,						UpperCAmelCase_							):
												for b in range(2						,						UpperCAmelCase_							):
																		if is_prime(UpperCAmelCase_							):
																								_UpperCamelCase      :	List[str]				       =     0
																								_UpperCamelCase      :	Union[str, Any]				       =     0
																								while is_prime((n**2) + (a * n) + b							):
																														count += 1
																														n += 1
																								if count > longest[0]:
																														_UpperCamelCase      :	Any				       =     [count, a, b]
						_UpperCamelCase      :	Dict				       =     longest[1] * longest[2]
						return ans


if __name__ == "__main__":
					print(solution(1000, 1000))


