


'''simple docstring'''
import math
from collections.abc import Generator

def 				lowerCamelCase_   (   __UpperCamelCase				:					int		)				->  Generator[int, None, None]:







     """simple docstring"""
     _A				=       (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 				lowerCamelCase_   (   __UpperCamelCase				:					int		)				->  Generator[int, None, None]:







     """simple docstring"""
     _A				=       (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)
          _A				=       int(math.sqrt(__UpperCamelCase		)		) + 1
          for j in range(2       ,						__UpperCamelCase		):
               if (i % j) == 0:
                    break
          else:
               yield i

def 				lowerCamelCase_   (   __UpperCamelCase				:					int		)				->  Generator[int, None, None]:







     """simple docstring"""
     _A				=       (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):
          _A				=       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 				lowerCamelCase_   (   )				->  Optional[Any]:







     """simple docstring"""
     from timeit import timeit

     _A				=       'from __main__ import slow_primes, primes, fast_primes'
     print(timeit('slow_primes(1_000_000_000_000)'       ,						setup=__UpperCamelCase       ,						number=1_0_0_0_0_0_0		)		)
     print(timeit('primes(1_000_000_000_000)'       ,						setup=__UpperCamelCase       ,						number=1_0_0_0_0_0_0		)		)
     print(timeit('fast_primes(1_000_000_000_000)'       ,						setup=__UpperCamelCase       ,						number=1_0_0_0_0_0_0		)		)


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









'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase  =				{
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase  =				[
        """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvNextV2ForImageClassification""",
        """ConvNextV2Model""",
        """ConvNextV2PreTrainedModel""",
        """ConvNextV2Backbone""",
    ]


if TYPE_CHECKING:
    from .configuration_convnextva import (
        CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ConvNextVaConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnextva import (
            CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextVaBackbone,
            ConvNextVaForImageClassification,
            ConvNextVaModel,
            ConvNextVaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase  =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)





