




'''simple docstring'''


def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							if not isinstance(UpperCamelCase			,						UpperCamelCase				):
														raise ValueError('''multiplicative_persistence() only accepts integral values'''				)
							if num < 0:
														raise ValueError('''multiplicative_persistence() does not accept negative values'''				)

							_a     					=    0
							_a     					=    str(UpperCamelCase				)

							while len(UpperCamelCase				) != 1:
														_a     					=    [int(UpperCamelCase				) for i in num_string]

														_a     					=    1
														for i in range(0			,						len(UpperCamelCase				)				):
																					total *= numbers[i]

														_a     					=    str(UpperCamelCase				)

														steps += 1
							return steps
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							if not isinstance(UpperCamelCase			,						UpperCamelCase				):
														raise ValueError('''additive_persistence() only accepts integral values'''				)
							if num < 0:
														raise ValueError('''additive_persistence() does not accept negative values'''				)

							_a     					=    0
							_a     					=    str(UpperCamelCase				)

							while len(UpperCamelCase				) != 1:
														_a     					=    [int(UpperCamelCase				) for i in num_string]

														_a     					=    0
														for i in range(0			,						len(UpperCamelCase				)				):
																					total += numbers[i]

														_a     					=    str(UpperCamelCase				)

														steps += 1
							return steps


if __name__ == "__main__":
				import doctest

				doctest.testmod()







'''simple docstring'''


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


_snake_case				:				Dict       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}

class       A   (		_a					,_a   ):
							lowercase_          =    'nat'

							lowercase_          =    {
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  str=4							,      lowerCAmelCase_	:  Any=3							,      lowerCAmelCase_	:  List[Any]=64							,      lowerCAmelCase_	:  Dict=[3, 4, 6, 5]							,      lowerCAmelCase_	:  Dict=[2, 4, 8, 16]							,      lowerCAmelCase_	:  str=7							,      lowerCAmelCase_	:  Dict=3.0							,      lowerCAmelCase_	:  int=True							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=0.0							,      lowerCAmelCase_	:  List[str]=0.1							,      lowerCAmelCase_	:  int="gelu"							,      lowerCAmelCase_	:  List[str]=0.0_2							,      lowerCAmelCase_	:  str=1e-5							,      lowerCAmelCase_	:  Tuple=0.0							,      lowerCAmelCase_	:  str=None							,      lowerCAmelCase_	:  int=None							,      **lowerCAmelCase_	:  List[Any]							,      )      ->							Any:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_ )

														_a     					=    patch_size
														_a     					=    num_channels
														_a     					=    embed_dim
														_a     					=    depths
														_a     					=    len(lowerCAmelCase_ )
														_a     					=    num_heads
														_a     					=    kernel_size
														_a     					=    mlp_ratio
														_a     					=    qkv_bias
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    drop_path_rate
														_a     					=    hidden_act
														_a     					=    layer_norm_eps
														_a     					=    initializer_range
														# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_a     					=    int(embed_dim * 2 ** (len(lowerCAmelCase_ ) - 1) )
														_a     					=    layer_scale_init_value
														_a     					=    ['''stem'''] + [F'stage{idx}' for idx in range(1							,      len(lowerCAmelCase_ ) + 1 )]
														_a					,							_a     					=    get_aligned_output_features_output_indices(
														    out_features=lowerCAmelCase_							,      out_indices=lowerCAmelCase_							,      stage_names=self.stage_names )

