






"""simple docstring"""



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


A__	:    Optional[int]			 = logging.get_logger(__name__)

A__	:    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        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:Tuple             =			"dinat"

			_UpperCAmelCase		:int             =			{
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			}


			def __init__(				self					:  List[str]       ,	snake_case__					:  Any=4       ,	snake_case__					:  str=3       ,	snake_case__					:  int=64       ,	snake_case__					:  Union[str, Any]=[3, 4, 6, 5]       ,	snake_case__					:  Dict=[2, 4, 8, 16]       ,	snake_case__					:  Dict=7       ,	snake_case__					:  str=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]       ,	snake_case__					:  Optional[int]=3.0       ,	snake_case__					:  Any=True       ,	snake_case__					:  List[str]=0.0       ,	snake_case__					:  List[Any]=0.0       ,	snake_case__					:  List[str]=0.1       ,	snake_case__					:  Union[str, Any]="gelu"       ,	snake_case__					:  Tuple=0.02       ,	snake_case__					:  str=1E-5       ,	snake_case__					:  Optional[Any]=0.0       ,	snake_case__					:  int=None       ,	snake_case__					:  Tuple=None       ,	**snake_case__					:  str       ,	):
										super().__init__(**snake_case__ )

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







"""simple docstring"""



from __future__ import annotations

from collections import namedtuple

def 			_snake_case    (					lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float    ,						lowerCamelCase__     :   float  )    ->     tuple:
							lowerCamelCase_	:							Optional[Any]									=namedtuple("result"    ,						"name value"  )
							if (voltage, current, power).count(0  ) != 1:
														raise ValueError("Only one argument must be 0"  )
							elif power < 0:
														raise ValueError(
														    "Power cannot be negative in any electrical/electronics system"  )
							elif voltage == 0:
														return result("voltage"    ,						power / current  )
							elif current == 0:
														return result("current"    ,						power / voltage  )
							elif power == 0:
														return result("power"    ,						float(round(abs(voltage * current  )    ,						2  )  )  )
							else:
														raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



