
"""simple docstring"""




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


_A      :	List[Any]       		=	logging.get_logger(__name__)

_A      :	Optional[int]       		=	{
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}

class 		a__						(				a_						):
   __lowerCAmelCase						=    """graphormer"""
   __lowerCAmelCase						=    ["""past_key_values"""]
   def __init__(      self     ,   _a = 1     ,   _a = 512 * 9     ,   _a = 512 * 3     ,   _a = 512     ,   _a = 512     ,   _a = 512     ,   _a = 128     ,   _a = 5     ,   _a = 1_024     ,   _a = "multi_hop"     ,   _a = 512     ,   _a = False     ,   _a = 12     ,   _a = 768     ,   _a = 768     ,   _a = 32     ,   _a = 0.1     ,   _a = 0.1     ,   _a = 0.0     ,   _a = False     ,   _a = False     ,   _a = False     ,   _a = "gelu"     ,   _a = None     ,   _a = False     ,   _a = 0     ,   _a = False     ,   _a = 0.0     ,   _a = 8     ,   _a = None     ,   _a = None     ,   _a = True     ,   _a = True     ,   _a=0     ,   _a=1     ,   _a=2     ,   **_a     ,   ):
          lowercase   :    Any			=							num_classes
          lowercase   :    Optional[Any]			=							num_atoms
          lowercase   :    Optional[Any]			=							num_in_degree
          lowercase   :    Any			=							num_out_degree
          lowercase   :    Optional[int]			=							num_edges
          lowercase   :    Any			=							num_spatial
          lowercase   :    Any			=							num_edge_dis
          lowercase   :    str			=							edge_type
          lowercase   :    Union[str, Any]			=							multi_hop_max_dist
          lowercase   :    List[str]			=							spatial_pos_max
          lowercase   :    Dict			=							max_nodes
          lowercase   :    str			=							num_hidden_layers
          lowercase   :    Union[str, Any]			=							embedding_dim
          lowercase   :    Optional[int]			=							embedding_dim
          lowercase   :    int			=							ffn_embedding_dim
          lowercase   :    List[Any]			=							num_attention_heads
          lowercase   :    Optional[int]			=							dropout
          lowercase   :    Dict			=							attention_dropout
          lowercase   :    int			=							layerdrop
          lowercase   :    int			=							encoder_normalize_before
          lowercase   :    List[str]			=							pre_layernorm
          lowercase   :    Union[str, Any]			=							apply_graphormer_init
          lowercase   :    Tuple			=							activation_fn
          lowercase   :    Tuple			=							embed_scale
          lowercase   :    List[Any]			=							freeze_embeddings
          lowercase   :    List[Any]			=							num_trans_layers_to_freeze
          lowercase   :    str			=							share_input_output_embed
          lowercase   :    List[str]			=							traceable
          lowercase   :    str			=							q_noise
          lowercase   :    List[str]			=							qn_block_size

          # These parameters are here for future extensions
          # atm, the model only supports self attention
          lowercase   :    Optional[int]			=							kdim
          lowercase   :    str			=							vdim
          lowercase   :    List[Any]			=							self_attention
          lowercase   :    Any			=							bias

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




"""simple docstring"""




import math
from collections.abc import Generator


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    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					,			__snake_case  ):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    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)
              lowercase   :    Dict			=							int(math.sqrt(__snake_case  )  ) + 1
              for j in range(2					,			__snake_case  ):
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				__snake_case    :	int  )			->    Generator[int, None, None]:
       lowercase   :    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):
              lowercase   :    int			=							int(math.sqrt(__snake_case  )  ) + 1
              for j in range(3					,			__snake_case					,			2  ):
                     # As we removed the even numbers, we don't need them now
                     if (i % j) == 0:
                            break
              else:
                     yield i


def   __magic_name__     (				)			->    Tuple:
       from timeit import timeit

       lowercase   :    List[str]			=							"from __main__ import slow_primes, primes, fast_primes"
       print(timeit("slow_primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )
       print(timeit("primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )
       print(timeit("fast_primes(1_000_000_000_000)"					,			setup=__snake_case					,			number=100_0000  )  )


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


