

from __future__ import annotations

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







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	str(_A   )
						SCREAMING_SNAKE_CASE_:			Any  		=	[n]
						for i in range(1    ,     len(_A   )   ):
												list_nums.append(int(str_num[i:]   )   )
												list_nums.append(int(str_num[:-i]   )   )
						return list_nums







def       A_				(   _UpperCAmelCase   ):







						if len(str(_A   )   ) > 3:
												if not is_prime(int(str(_A   )[-3:]   )   ) or not is_prime(int(str(_A   )[:3]   )   ):
																		return False
						return True







def       A_				(   _UpperCAmelCase = 11   ):
						SCREAMING_SNAKE_CASE_:			list[int]  		=	[]
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	13
						while len(_A   ) != count:
												if validate(_A   ):
																		SCREAMING_SNAKE_CASE_:			Any  		=	list_truncated_nums(_A   )
																		if all(is_prime(_A   ) for i in list_nums   ):
																								list_truncated_primes.append(_A   )
												num += 2
						return list_truncated_primes







def       A_				(   ):
						return sum(compute_truncated_primes(11   )   )


if __name__ == "__main__":
						print(f'''{sum(compute_truncated_primes(11)) = }''')









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase							:  Optional[int]            =						logging.get_logger(__name__)

lowerCAmelCase							:  int            =						{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					'''fnet'''



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[str]=3_2000       ,						lowerCAmelCase__  :					Union[str, Any]=768       ,						lowerCAmelCase__  :					Optional[Any]=12       ,						lowerCAmelCase__  :					Tuple=3072       ,						lowerCAmelCase__  :					Any="gelu_new"       ,						lowerCAmelCase__  :					Union[str, Any]=0.1       ,						lowerCAmelCase__  :					Optional[Any]=512       ,						lowerCAmelCase__  :					int=4       ,						lowerCAmelCase__  :					List[Any]=0.02       ,						lowerCAmelCase__  :					Union[str, Any]=1E-12       ,						lowerCAmelCase__  :					Optional[int]=False       ,						lowerCAmelCase__  :					Any=512       ,						lowerCAmelCase__  :					Optional[int]=3       ,						lowerCAmelCase__  :					int=1       ,						lowerCAmelCase__  :					List[Any]=2       ,						**lowerCAmelCase__  :					Optional[int]       ,						):
									super().__init__(pad_token_id=lowerCAmelCase__       ,						bos_token_id=lowerCAmelCase__       ,						eos_token_id=lowerCAmelCase__       ,						**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	vocab_size
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	max_position_embeddings
									SCREAMING_SNAKE_CASE_:			Tuple  		=	hidden_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	num_hidden_layers
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	intermediate_size
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	hidden_act
									SCREAMING_SNAKE_CASE_:			Any  		=	hidden_dropout_prob
									SCREAMING_SNAKE_CASE_:			int  		=	initializer_range
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	type_vocab_size
									SCREAMING_SNAKE_CASE_:			int  		=	layer_norm_eps
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	use_tpu_fourier_optimizations
									SCREAMING_SNAKE_CASE_:			int  		=	tpu_short_seq_length






