
"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


_A      :	Tuple       		=	logging.get_logger(__name__)

_A      :	Optional[int]       		=	{
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}

class 		a__						(				a_						):
			__lowerCAmelCase						=    """electra"""
			def __init__(      self     ,   _a=30_522     ,   _a=128     ,   _a=256     ,   _a=12     ,   _a=4     ,   _a=1_024     ,   _a="gelu"     ,   _a=0.1     ,   _a=0.1     ,   _a=512     ,   _a=2     ,   _a=0.0_2     ,   _a=1E-12     ,   _a="first"     ,   _a=True     ,   _a="gelu"     ,   _a=0.1     ,   _a=0     ,   _a="absolute"     ,   _a=True     ,   _a=None     ,   **_a     ,   ):
										super().__init__(pad_token_id=_a     ,   **_a    )

										lowercase   :    str			=							vocab_size
										lowercase   :    Union[str, Any]			=							embedding_size
										lowercase   :    Optional[Any]			=							hidden_size
										lowercase   :    Any			=							num_hidden_layers
										lowercase   :    Any			=							num_attention_heads
										lowercase   :    List[Any]			=							intermediate_size
										lowercase   :    Tuple			=							hidden_act
										lowercase   :    Union[str, Any]			=							hidden_dropout_prob
										lowercase   :    Optional[Any]			=							attention_probs_dropout_prob
										lowercase   :    List[Any]			=							max_position_embeddings
										lowercase   :    Tuple			=							type_vocab_size
										lowercase   :    Dict			=							initializer_range
										lowercase   :    Optional[int]			=							layer_norm_eps

										lowercase   :    Any			=							summary_type
										lowercase   :    Any			=							summary_use_proj
										lowercase   :    Tuple			=							summary_activation
										lowercase   :    List[str]			=							summary_last_dropout
										lowercase   :    Tuple			=							position_embedding_type
										lowercase   :    List[str]			=							use_cache
										lowercase   :    Union[str, Any]			=							classifier_dropout


class 		a__						(				a_						):
			@property
			def       __magic_name__      (      self    ):
										if self.task == "multiple-choice":
																	lowercase   :    Optional[int]			=							{0: "batch", 1: "choice", 2: "sequence"}
										else:
																	lowercase   :    Dict			=							{0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										        ("token_type_ids", dynamic_axis),
										    ]    )




"""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()


