
'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						=      {
    "flaubert/flaubert_small_cased": "https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json",
    "flaubert/flaubert_base_uncased": "https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json",
    "flaubert/flaubert_base_cased": "https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json",
    "flaubert/flaubert_large_cased": "https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json",
}




class     __snake_case			(    _a	):
		a_          =					"flaubert"
		a_          =					{
		    "hidden_size": "emb_dim",
		    "num_attention_heads": "n_heads",
		    "num_hidden_layers": "n_layers",
		    "n_words": "vocab_size",  # For backward compatibility
		}




		def __init__(  self					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:Dict=False			,    _SCREAMING_SNAKE_CASE					:Dict=0.0			,    _SCREAMING_SNAKE_CASE					:List[str]=3_0_1_4_5			,    _SCREAMING_SNAKE_CASE					:Optional[Any]=2_0_4_8			,    _SCREAMING_SNAKE_CASE					:Optional[Any]=1_2			,    _SCREAMING_SNAKE_CASE					:List[Any]=1_6			,    _SCREAMING_SNAKE_CASE					:Optional[int]=0.1			,    _SCREAMING_SNAKE_CASE					:Optional[int]=0.1			,    _SCREAMING_SNAKE_CASE					:Any=True			,    _SCREAMING_SNAKE_CASE					:Optional[int]=False			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=False			,    _SCREAMING_SNAKE_CASE					:List[Any]=False			,    _SCREAMING_SNAKE_CASE					:int=1			,    _SCREAMING_SNAKE_CASE					:Tuple=True			,    _SCREAMING_SNAKE_CASE					:Optional[int]=5_1_2			,    _SCREAMING_SNAKE_CASE					:str=2_0_4_8**-0.5			,    _SCREAMING_SNAKE_CASE					:Dict=1E-12			,    _SCREAMING_SNAKE_CASE					:List[str]=0.02			,    _SCREAMING_SNAKE_CASE					:List[Any]=0			,    _SCREAMING_SNAKE_CASE					:int=1			,    _SCREAMING_SNAKE_CASE					:Any=2			,    _SCREAMING_SNAKE_CASE					:Optional[int]=3			,    _SCREAMING_SNAKE_CASE					:List[Any]=5			,    _SCREAMING_SNAKE_CASE					:int=True			,    _SCREAMING_SNAKE_CASE					:Optional[int]="first"			,    _SCREAMING_SNAKE_CASE					:List[Any]=True			,    _SCREAMING_SNAKE_CASE					:List[str]=None			,    _SCREAMING_SNAKE_CASE					:Optional[Any]=True			,    _SCREAMING_SNAKE_CASE					:Dict=0.1			,    _SCREAMING_SNAKE_CASE					:Tuple=5			,    _SCREAMING_SNAKE_CASE					:List[Any]=5			,    _SCREAMING_SNAKE_CASE					:List[str]=0			,    _SCREAMING_SNAKE_CASE					:int=0			,    _SCREAMING_SNAKE_CASE					:Optional[int]=2			,    _SCREAMING_SNAKE_CASE					:List[str]=0			,    **_SCREAMING_SNAKE_CASE					:str			,    )					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     pre_norm
								UpperCamelCase_     :	str					     =     layerdrop
								UpperCamelCase_     :	Optional[Any]					     =     vocab_size
								UpperCamelCase_     :	Optional[int]					     =     emb_dim
								UpperCamelCase_     :	Optional[int]					     =     n_layers
								UpperCamelCase_     :	str					     =     n_heads
								UpperCamelCase_     :	Any					     =     dropout
								UpperCamelCase_     :	Dict					     =     attention_dropout
								UpperCamelCase_     :	Tuple					     =     gelu_activation
								UpperCamelCase_     :	Any					     =     sinusoidal_embeddings
								UpperCamelCase_     :	Tuple					     =     causal
								UpperCamelCase_     :	List[Any]					     =     asm
								UpperCamelCase_     :	Dict					     =     n_langs
								UpperCamelCase_     :	Any					     =     use_lang_emb
								UpperCamelCase_     :	Dict					     =     layer_norm_eps
								UpperCamelCase_     :	Any					     =     bos_index
								UpperCamelCase_     :	Dict					     =     eos_index
								UpperCamelCase_     :	str					     =     pad_index
								UpperCamelCase_     :	Union[str, Any]					     =     unk_index
								UpperCamelCase_     :	Dict					     =     mask_index
								UpperCamelCase_     :	str					     =     is_encoder
								UpperCamelCase_     :	Any					     =     max_position_embeddings
								UpperCamelCase_     :	Any					     =     embed_init_std
								UpperCamelCase_     :	Tuple					     =     init_std
								UpperCamelCase_     :	Optional[int]					     =     summary_type
								UpperCamelCase_     :	List[str]					     =     summary_use_proj
								UpperCamelCase_     :	Any					     =     summary_activation
								UpperCamelCase_     :	Optional[Any]					     =     summary_proj_to_labels
								UpperCamelCase_     :	Union[str, Any]					     =     summary_first_dropout
								UpperCamelCase_     :	Tuple					     =     start_n_top
								UpperCamelCase_     :	Tuple					     =     end_n_top
								UpperCamelCase_     :	Dict					     =     mask_token_id
								UpperCamelCase_     :	Optional[Any]					     =     lang_id

								if "n_words" in kwargs:
														UpperCamelCase_     :	Tuple					     =     kwargs["""n_words"""]

								super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE			,    bos_token_id=_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)







class     __snake_case			(    _a	):




		@property
		def 	_snake_case	(  self					:Union[str, Any])					->			Mapping[str, Mapping[int, str]]:






								'''simple docstring'''






								if self.task == "multiple-choice":
														UpperCamelCase_     :	List[Any]					     =     {0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
														UpperCamelCase_     :	Optional[Any]					     =     {0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								    ])






'''simple docstring'''


import math
from collections.abc import Generator
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Generator					     =     (i for i in range(1							,				(max_n + 1)	))
						for i in (n for n in numbers if n > 1):
												for j in range(2							,				UpperCAmelCase__	):
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Generator					     =     (i for i in range(1							,				(max_n + 1)	))
						for i in (n for n in numbers if n > 1):
												# only need to check for factors up to sqrt(i)
												UpperCamelCase_     :	List[Any]					     =     int(math.sqrt(UpperCAmelCase__	)	) + 1
												for j in range(2							,				UpperCAmelCase__	):
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Generator					     =     (i for i in range(1							,				(max_n + 1)							,				2	))
						# It's useless to test even numbers as they will not be prime
						if max_n > 2:
												yield 2  # Because 2 will not be tested, it's necessary to yield it now
						for i in (n for n in numbers if n > 1):
												UpperCamelCase_     :	Optional[int]					     =     int(math.sqrt(UpperCAmelCase__	)	) + 1
												for j in range(3							,				UpperCAmelCase__							,				2	):
																		# As we removed the even numbers, we don't need them now
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       ):
						from timeit import timeit

						UpperCamelCase_     :	int					     =     """from __main__ import slow_primes, primes, fast_primes"""
						print(timeit("""slow_primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)
						print(timeit("""primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)
						print(timeit("""fast_primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      int(input("Calculate primes up to:\n>> ").strip())
	for ret in primes(number):
		print(ret)
	benchmark()




