
'''simple docstring'''




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


lowercase     :							Optional[Any]       						=     logging.get_logger(__name__)





class 					A							(				__snake_case       ,     __snake_case		):
     __magic_name__          =						'''maskformer-swin'''

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





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






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE      )

         A			:  Optional[int]   =		image_size
         A			:  List[str]   =		patch_size
         A			:  Tuple   =		num_channels
         A			:  Dict   =		embed_dim
         A			:  Union[str, Any]   =		depths
         A			:  int   =		len(SCREAMING_SNAKE_CASE      )
         A			:  Optional[Any]   =		num_heads
         A			:  Dict   =		window_size
         A			:  Dict   =		mlp_ratio
         A			:  str   =		qkv_bias
         A			:  Optional[Any]   =		hidden_dropout_prob
         A			:  Any   =		attention_probs_dropout_prob
         A			:  List[str]   =		drop_path_rate
         A			:  Dict   =		hidden_act
         A			:  Tuple   =		use_absolute_embeddings
         A			:  int   =		layer_norm_eps
         A			:  Optional[Any]   =		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
         A			:  List[Any]   =		int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE      ) - 1)      )
         A			:  Tuple   =		['''stem'''] + [F'stage{idx}' for idx in range(1		,     len(SCREAMING_SNAKE_CASE      ) + 1      )]
         A,    A			:  List[Any]   =		get_aligned_output_features_output_indices(
             out_features=SCREAMING_SNAKE_CASE		,     out_indices=SCREAMING_SNAKE_CASE		,     stage_names=self.stage_names      )






'''simple docstring'''




import math
from collections.abc import Callable







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  float   =		xa
    A			:  float   =		xa
    while True:
        if x_n == x_na or function(snake_case__		) == function(snake_case__		):
            raise ZeroDivisionError('''float division by zero, could not find root'''		)
        A			:  float   =		x_na - (
            function(snake_case__		) / ((function(snake_case__		) - function(snake_case__		)) / (x_na - x_n))
        )
        if abs(x_na - x_na		) < 10**-5:
            return x_na
        A			:  Any   =		x_na
        A			:  Any   =		x_na







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    return math.pow(snake_case__						,							3		) - (2 * x) - 5


if __name__ == "__main__":
      print(intersection(f, 3, 3.5))




