"""simple docstring"""





from typing import TYPE_CHECKING

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


lowerCAmelCase_     =	{
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	['''ConvNextFeatureExtractor''']
			lowerCAmelCase_     =	['''ConvNextImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ConvNextForImageClassification''',
			    '''ConvNextModel''',
			    '''ConvNextPreTrainedModel''',
			    '''ConvNextBackbone''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''TFConvNextForImageClassification''',
			    '''TFConvNextModel''',
			    '''TFConvNextPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_convnext import ConvNextFeatureExtractor
						from .image_processing_convnext import ConvNextImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convnext import (
						    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvNextBackbone,
						    ConvNextForImageClassification,
						    ConvNextModel,
						    ConvNextPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
			import sys

			lowerCAmelCase_     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure)



"""simple docstring"""





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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    str:

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

							_SCREAMING_SNAKE_CASE		:						int			  =    precision
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ceil(precision / 14  )
							_SCREAMING_SNAKE_CASE		:						int			  =    426_880 * Decimal(10_005  ).sqrt()
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    1
							_SCREAMING_SNAKE_CASE		:						str			  =    13_591_409
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    Decimal(__SCREAMING_SNAKE_CASE  )
							for k in range(1     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    factorial(6 * k  ) // (factorial(3 * k  ) * factorial(__SCREAMING_SNAKE_CASE  ) ** 3)
														linear_term += 545_140_134
														exponential_term *= -262_537_412_640_768_000
														partial_sum += Decimal(multinomial_term * linear_term  ) / exponential_term
							return str(constant_term / partial_sum  )[:-1]


if __name__ == "__main__":
			lowerCAmelCase_     =	50
			print(F"The first {n} digits of pi is: {pi(n)}")


