
'''simple docstring'''



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


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: Optional[Any]										=  '''maskformer-swin'''

						_lowercase						: Union[str, Any]										=  {
						    '''num_attention_heads''': '''num_heads''',
						    '''num_hidden_layers''': '''num_layers''',
						}




						def __init__(   self       ,  _lowercase=224       ,  _lowercase=4       ,  _lowercase=3       ,  _lowercase=96       ,  _lowercase=[2, 2, 6, 2]       ,  _lowercase=[3, 6, 12, 24]       ,  _lowercase=7       ,  _lowercase=4.0       ,  _lowercase=True       ,  _lowercase=0.0       ,  _lowercase=0.0       ,  _lowercase=0.1       ,  _lowercase="gelu"       ,  _lowercase=False       ,  _lowercase=0.02       ,  _lowercase=1e-5       ,  _lowercase=None       ,  _lowercase=None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(**_lowercase )

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


'''simple docstring'''



def A ():
						for n in range(1					,      1000000						):
												yield n * (n + 1) // 2


def A (__lowerCamelCase :List[Any]						):
						_lowerCAmelCase    		=					1
						_lowerCAmelCase    		=					2
						while i * i <= n:
												_lowerCAmelCase    		=					0
												while n % i == 0:
																		n //= i
																		multiplicity += 1
												divisors_count *= multiplicity + 1
												i += 1
						if n > 1:
												divisors_count *= 2
						return divisors_count


def A ():
						return next(i for i in triangle_number_generator() if count_divisors(__lowerCamelCase						) > 500						)


if __name__ == "__main__":
						print(solution())
