code: import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  UpperCAmelCase					=				logging.get_logger(__name__)
  
  UpperCAmelCase					=				{}
  
  
  
  
  class    __lowerCAmelCase  (       a_						):
  
  
  
   '''simple docstring'''
   _lowerCamelCase						:	Dict				    = 'fsmt_decoder'
  
   def __init__(			self       :		Optional[Any]       ,			lowerCamelCase       :		Optional[Any]=0       ,			lowerCamelCase       :		Any=0  ) ->							Union[str, Any]:
          super().__init__()
          _UpperCamelCase     :       Optional[int]  						=  vocab_size
          _UpperCamelCase     :       str  						=  bos_token_id
  
  
  
  
  
  
  
  class    __lowerCAmelCase  (       a_						):
  
  
  
   '''simple docstring'''
   _lowerCamelCase						:	Tuple				    = 'fsmt'
   _lowerCamelCase						:	List[str]				    = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
  
   def __init__(			self       :		List[str]       ,			lowerCamelCase       :		List[Any]=["en", "de"]       ,			lowerCamelCase       :		List[str]=4_2_0_2_4       ,			lowerCamelCase       :		List[str]=4_2_0_2_4       ,			lowerCamelCase       :		Tuple="relu"       ,			lowerCamelCase       :		List[Any]=1_0_2_4       ,			lowerCamelCase       :		Union[str, Any]=2_0_0       ,			lowerCamelCase       :		Tuple=1_0_2_4       ,			lowerCamelCase       :		List[str]=4_0_9_6       ,			lowerCamelCase       :		Union[str, Any]=1_2       ,			lowerCamelCase       :		Optional[Any]=1_6       ,			lowerCamelCase       :		Optional[int]=0.0       ,			lowerCamelCase       :		Any=4_0_9_6       ,			lowerCamelCase       :		List[str]=1_2       ,			lowerCamelCase       :		Union[str, Any]=1_6       ,			lowerCamelCase       :		Any=0.0       ,			lowerCamelCase       :		int=0.0       ,			lowerCamelCase       :		Optional[Any]=0.1       ,			lowerCamelCase       :		Tuple=0.0       ,			lowerCamelCase       :		Any=0.0_2       ,			lowerCamelCase       :		List[str]=2       ,			lowerCamelCase       :		Any=True       ,			lowerCamelCase       :		Dict=True       ,			lowerCamelCase       :		Tuple=False       ,			lowerCamelCase       :		int=5       ,			lowerCamelCase       :		Union[str, Any]=1.0       ,			lowerCamelCase       :		Optional[Any]=False       ,			lowerCamelCase       :		Tuple=True       ,			lowerCamelCase       :		Tuple=1       ,			lowerCamelCase       :		Dict=0       ,			lowerCamelCase       :		Optional[Any]=2       ,			lowerCamelCase       :		int=2       ,			**lowerCamelCase       :		Optional[Any]       ,			) ->							Tuple:
          _UpperCamelCase     :       Any  						=  langs
          _UpperCamelCase     :       Union[str, Any]  						=  src_vocab_size
          _UpperCamelCase     :       Optional[Any]  						=  tgt_vocab_size
          _UpperCamelCase     :       Optional[Any]  						=  d_model  # encoder_embed_dim and decoder_embed_dim
  
          _UpperCamelCase     :       Optional[int]  						=  encoder_ffn_dim
          _UpperCamelCase     :       Optional[int]  						=  encoder_layers
          _UpperCamelCase     :       Union[str, Any]  						=  encoder_attention_heads
          _UpperCamelCase     :       str  						=  encoder_layerdrop
          _UpperCamelCase     :       int  						=  decoder_layerdrop
          _UpperCamelCase     :       Optional[int]  						=  decoder_ffn_dim
          _UpperCamelCase     :       Optional[int]  						=  decoder_layers
          _UpperCamelCase     :       List[Any]  						=  decoder_attention_heads
          _UpperCamelCase     :       List[Any]  						=  max_position_embeddings
          _UpperCamelCase     :       Dict  						=  init_std  # Normal(0, this parameter)
          _UpperCamelCase     :       List[Any]  						=  activation_function
  
          _UpperCamelCase     :       Dict  						=  DecoderConfig(vocab_size=lowerCamelCase       ,			bos_token_id=lowerCamelCase  )
          if "decoder" in common_kwargs:
                 del common_kwargs["decoder"]
  
          _UpperCamelCase     :       Optional[int]  						=  scale_embedding  # scale factor will be sqrt(d_model) if True
  
          # 3 Types of Dropout
          _UpperCamelCase     :       Optional[Any]  						=  attention_dropout
          _UpperCamelCase     :       str  						=  activation_dropout
          _UpperCamelCase     :       Optional[Any]  						=  dropout
  
          _UpperCamelCase     :       int  						=  use_cache
          super().__init__(
              pad_token_id=lowerCamelCase       ,			bos_token_id=lowerCamelCase       ,			eos_token_id=lowerCamelCase       ,			decoder_start_token_id=lowerCamelCase       ,			is_encoder_decoder=lowerCamelCase       ,			tie_word_embeddings=lowerCamelCase       ,			forced_eos_token_id=lowerCamelCase       ,			max_length=lowerCamelCase       ,			num_beams=lowerCamelCase       ,			length_penalty=lowerCamelCase       ,			early_stopping=lowerCamelCase       ,			**lowerCamelCase       ,			)
  
   def 					__snake_case					(			self       :		int  ) ->							int:
          _UpperCamelCase     :       Optional[Any]  						=  copy.deepcopy(self.__dict__  )
          _UpperCamelCase     :       Optional[Any]  						=  self.decoder.to_dict()
          _UpperCamelCase     :       Any  						=  self.__class__.model_type
          return output
  
  
  
  
style_context: import math
  from collections.abc import Generator
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	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		,						lowercase_				):
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	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     :       Any  						=  int(math.sqrt(lowercase_				)				) + 1
                for j in range(2		,						lowercase_				):
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     lowercase_:	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     :       Tuple  						=  int(math.sqrt(lowercase_				)				) + 1
                for j in range(3		,						lowercase_		,						2				):
                       # As we removed the even numbers, we don't need them now
                       if (i % j) == 0:
                              break
                else:
                       yield i
  
  
  def SCREAMING_SNAKE_CASE				(     ):
         from timeit import timeit
  
         _UpperCamelCase     :       Optional[Any]  						=  'from __main__ import slow_primes, primes, fast_primes'
         print(timeit('slow_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
         print(timeit('primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
         print(timeit('fast_primes(1_000_000_000_000)'		,						setup=lowercase_		,						number=1000000				)				)
  
  
  if __name__ == "__main__":
   UpperCAmelCase					=				int(input('''Calculate primes up to:\n>> ''').strip())
   for ret in primes(number):
    print(ret)
   benchmark()
  
  
  
  