





"""simple docstring"""




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


a		:					List[Any]	      =     logging.get_logger(__name__)

a		:					List[Any]	      =     {
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}


class 			__UpperCamelCase							(		a__						,			a__		):
							lowerCamelCase  :   List[Any]    ="""dinat"""

							lowerCamelCase  :   Optional[Any]    ={
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}






							def __init__(  self ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=3 ,   lowerCAmelCase__=64 ,   lowerCAmelCase__=[3, 4, 6, 5] ,   lowerCAmelCase__=[2, 4, 8, 16] ,   lowerCAmelCase__=7 ,   lowerCAmelCase__=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] ,   lowerCAmelCase__=3.0 ,   lowerCAmelCase__=True ,   lowerCAmelCase__=0.0 ,   lowerCAmelCase__=0.0 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__="gelu" ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=1E-5 ,   lowerCAmelCase__=0.0 ,   lowerCAmelCase__=None ,   lowerCAmelCase__=None ,   **lowerCAmelCase__ ,   )      ->					Optional[int]:
								super().__init__(**lowerCAmelCase__ )

								a :						Optional[Any]      =							patch_size
								a :						Any      =							num_channels
								a :						int      =							embed_dim
								a :						Dict      =							depths
								a :						int      =							len(lowerCAmelCase__ )
								a :						Optional[int]      =							num_heads
								a :						str      =							kernel_size
								a :						Dict      =							dilations
								a :						Union[str, Any]      =							mlp_ratio
								a :						str      =							qkv_bias
								a :						Union[str, Any]      =							hidden_dropout_prob
								a :						List[str]      =							attention_probs_dropout_prob
								a :						Optional[Any]      =							drop_path_rate
								a :						Dict      =							hidden_act
								a :						int      =							layer_norm_eps
								a :						int      =							initializer_range
								# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								a :						Tuple      =							int(embed_dim * 2 ** (len(lowerCAmelCase__ ) - 1) )
								a :						Union[str, Any]      =							layer_scale_init_value
								a :						List[Any]      =							["stem"] + [f"""stage{idx}""" for idx in range(1 ,   len(lowerCAmelCase__ ) + 1 )]
								a,			a :						Tuple      =							get_aligned_output_features_output_indices(
								    out_features=lowerCAmelCase__ ,   out_indices=lowerCAmelCase__ ,   stage_names=self.stage_names )









"""simple docstring"""




from numpy import exp, pi, sqrt






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple       ,			_lowercase		:       float = 0.0       ,			_lowercase		:       float = 1.0   )					->int:






	'''simple docstring'''






	return 1 / sqrt(2 * pi * sigma**2   ) * exp(-((x - mu) ** 2) / (2 * sigma**2)   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


