
"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A      :	Optional[Any]       		=	{"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :	Tuple       		=	[
			    """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """GPTJForCausalLM""",
			    """GPTJForQuestionAnswering""",
			    """GPTJForSequenceClassification""",
			    """GPTJModel""",
			    """GPTJPreTrainedModel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :	Optional[int]       		=	[
			    """TFGPTJForCausalLM""",
			    """TFGPTJForQuestionAnswering""",
			    """TFGPTJForSequenceClassification""",
			    """TFGPTJModel""",
			    """TFGPTJPreTrainedModel""",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :	List[Any]       		=	[
			    """FlaxGPTJForCausalLM""",
			    """FlaxGPTJModel""",
			    """FlaxGPTJPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gptj import (
						    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTJForCausalLM,
						    GPTJForQuestionAnswering,
						    GPTJForSequenceClassification,
						    GPTJModel,
						    GPTJPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_gptj import (
						    TFGPTJForCausalLM,
						    TFGPTJForQuestionAnswering,
						    TFGPTJForSequenceClassification,
						    TFGPTJModel,
						    TFGPTJPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
			import sys

			_A      :	Union[str, Any]       		=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




"""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()


