



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


A__  :  int								=					logging.get_logger(__name__)

A__  :  int								=					{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}



class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):
							_a			      = '''bit'''
							_a			      = ['''preactivation''', '''bottleneck''']
							_a			      = ['''SAME''', '''VALID''']




							def __init__(       self     :       Any				,				A_     :       Dict=3				,				A_     :       Optional[Any]=6_4				,				A_     :       Any=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]				,				A_     :       Optional[Any]=[3, 4, 6, 3]				,				A_     :       Tuple="preactivation"				,				A_     :       Tuple="relu"				,				A_     :       List[str]=None				,				A_     :       Dict=3_2				,				A_     :       Union[str, Any]=0.0				,				A_     :       Union[str, Any]=False				,				A_     :       Any=3_2				,				A_     :       List[str]=1				,				A_     :       Any=None				,				A_     :       Tuple=None				,				**A_     :       Tuple				,				):
									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:
													lowerCAmelCase_			:     str									= global_padding.upper()
											else:
													raise ValueError(F"""Padding strategy {global_padding} not supported""")
									lowerCAmelCase_			:     str									= num_channels
									lowerCAmelCase_			:     int									= embedding_size
									lowerCAmelCase_			:     Optional[Any]									= hidden_sizes
									lowerCAmelCase_			:     Any									= depths
									lowerCAmelCase_			:     Optional[int]									= layer_type
									lowerCAmelCase_			:     List[str]									= hidden_act
									lowerCAmelCase_			:     Dict									= global_padding
									lowerCAmelCase_			:     int									= num_groups
									lowerCAmelCase_			:     Tuple									= drop_path_rate
									lowerCAmelCase_			:     Dict									= embedding_dynamic_padding
									lowerCAmelCase_			:     Optional[int]									= output_stride
									lowerCAmelCase_			:     Tuple									= width_factor

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






"""simple docstring"""
import math







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->bool:

  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(_SCREAMING_SNAKE_CASE   ) + 1   )		,     6   ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True







def 						__a   (	_SCREAMING_SNAKE_CASE = 0.1   )	->int:
  a__:     str										=	3
  a__:     Optional[Any]										=	3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1		,     (j + 2) * (j + 2)		,     j + 1   ):
      primes += is_prime(_SCREAMING_SNAKE_CASE   )
    j += 2
  return j


if __name__ == "__main__":
 import doctest

 doctest.testmod()


