





"""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"""



from decimal import Decimal, getcontext
from math import ceil, factorial







def 		__lowercase					(       snake_case_  :  int						)       ->str:



    '''simple docstring'''







    if not isinstance(snake_case_    ,snake_case_						):
        raise TypeError('''Undefined for non-integers'''						)
    elif precision < 1:
        raise ValueError('''Undefined for non-natural numbers'''						)

    __A   :						int									=					precision
    __A   :						Tuple									=					ceil(precision / 14						)
    __A   :						Dict									=					426880 * Decimal(10005						).sqrt()
    __A   :						Optional[Any]									=					1
    __A   :						int									=					13591409
    __A   :						Optional[int]									=					Decimal(snake_case_						)
    for k in range(1    ,snake_case_						):
        __A   :						int									=					factorial(6 * k						) // (factorial(3 * k						) * factorial(snake_case_						) ** 3)
        linear_term += 545140134
        exponential_term *= -262537412640768000
        partial_sum += Decimal(multinomial_term * linear_term						) / exponential_term
    return str(constant_term / partial_sum						)[:-1]


if __name__ == "__main__":
       a_			=      50
       print(f'''The first {n} digits of pi is: {pi(n)}''')




