





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_   			=      {
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_   			=      ["""MobileNetV2FeatureExtractor"""]
       a_   			=      ["""MobileNetV2ImageProcessor"""]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_   			=      [
           """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MobileNetV2ForImageClassification""",
           """MobileNetV2ForSemanticSegmentation""",
           """MobileNetV2Model""",
           """MobileNetV2PreTrainedModel""",
           """load_tf_weights_in_mobilenet_v2""",
       ]


if TYPE_CHECKING:
       from .configuration_mobilenet_va import (
           MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
           MobileNetVaConfig,
           MobileNetVaOnnxConfig,
       )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
              from .image_processing_mobilenet_va import MobileNetVaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilenet_va import (
                  MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileNetVaForImageClassification,
                  MobileNetVaForSemanticSegmentation,
                  MobileNetVaModel,
                  MobileNetVaPreTrainedModel,
                  load_tf_weights_in_mobilenet_va,
              )


else:
       import sys

       a_   			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











"""simple docstring"""



import math
from collections.abc import Iterator
from itertools import takewhile







def 		__lowercase					(       snake_case_  :  int						)       ->bool:



    '''simple docstring'''







    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5    ,int(math.sqrt(snake_case_						) + 1						)    ,6						):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True







def 		__lowercase					(       )       ->Iterator[int]:



    '''simple docstring'''







    __A   :						int									=					2
    while True:
        if is_prime(snake_case_						):
            yield num
        num += 1







def 		__lowercase					(       snake_case_  :  int = 2000000						)       ->int:



    '''simple docstring'''







    return sum(takewhile(lambda snake_case_						: x < n    ,prime_generator()						)						)


if __name__ == "__main__":
       print(f'''{solution() = }''')




