



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE_  (   UpperCamelCase_    ,			UpperCamelCase_   ):
						__magic_name__:     Tuple	 =    """bit"""
						__magic_name__:     Dict	 =    ["""preactivation""", """bottleneck"""]
						__magic_name__:     Dict	 =    ["""SAME""", """VALID"""]






						def __init__( self : List[Any]						,				_A : int=3						,				_A : Union[str, Any]=64						,				_A : Any=[256, 512, 1024, 2048]						,				_A : Union[str, Any]=[3, 4, 6, 3]						,				_A : Union[str, Any]="preactivation"						,				_A : str="relu"						,				_A : Any=None						,				_A : Union[str, Any]=32						,				_A : Optional[Any]=0.0						,				_A : Any=False						,				_A : Any=32						,				_A : Any=1						,				_A : Any=None						,				_A : Tuple=None						,				**_A : Optional[Any]						,				)							->      str:







							"""simple docstring"""


							super().__init__(**_a		)
							if layer_type not in self.layer_types:
								raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types		)}"""		)
							if global_padding is not None:
								if global_padding.upper() in self.supported_padding:
									snake_case_						:     Any     =		global_padding.upper()
								else:
									raise ValueError(F"""Padding strategy {global_padding} not supported"""		)
							snake_case_						:     Tuple     =		num_channels
							snake_case_						:     str     =		embedding_size
							snake_case_						:     str     =		hidden_sizes
							snake_case_						:     Tuple     =		depths
							snake_case_						:     List[str]     =		layer_type
							snake_case_						:     Union[str, Any]     =		hidden_act
							snake_case_						:     Tuple     =		global_padding
							snake_case_						:     int     =		num_groups
							snake_case_						:     Any     =		drop_path_rate
							snake_case_						:     Dict     =		embedding_dynamic_padding
							snake_case_						:     List[str]     =		output_stride
							snake_case_						:     Dict     =		width_factor

							snake_case_						:     str     =		["""stem"""] + [F"""stage{idx}""" for idx in range(1						,				len(_a		) + 1		)]
							snake_case_						:     str     =		get_aligned_output_features_output_indices(
							    out_features=_a						,				out_indices=_a						,				stage_names=self.stage_names		)








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


def 							SCREAMING_SNAKE_CASE__  (      __a				):

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

	snake_case_						:     Dict     =		precision
	snake_case_						:     str     =		ceil(precision / 14				)
	snake_case_						:     str     =		42_68_80 * Decimal(1_00_05				).sqrt()
	snake_case_						:     Tuple     =		1
	snake_case_						:     int     =		13_59_14_09
	snake_case_						:     Tuple     =		Decimal(__a				)
	for k in range(1				,	__a				):
		snake_case_						:     List[Any]     =		factorial(6 * k				) // (factorial(3 * k				) * factorial(__a				) ** 3)
		linear_term += 5_45_14_01_34
		exponential_term *= -26_25_37_41_26_40_76_80_00
		partial_sum += Decimal(multinomial_term * linear_term				) / exponential_term
	return str(constant_term / partial_sum				)[:-1]


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

