






"""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__)

lowercase    			=       {
    '''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 						A_							(      snake_case__					,							snake_case__  ):
				UpperCAmelCase__        =				'''dinat'''

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







				def __init__(							self							:						List[Any]						,    __lowerCamelCase							:						str=4						,    __lowerCamelCase							:						Dict=3						,    __lowerCamelCase							:						Any=6_4						,    __lowerCamelCase							:						Optional[int]=[3, 4, 6, 5]						,    __lowerCamelCase							:						int=[2, 4, 8, 1_6]						,    __lowerCamelCase							:						List[Any]=7						,    __lowerCamelCase							:						str=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]						,    __lowerCamelCase							:						Dict=3.0						,    __lowerCamelCase							:						Tuple=True						,    __lowerCamelCase							:						Tuple=0.0						,    __lowerCamelCase							:						int=0.0						,    __lowerCamelCase							:						Optional[Any]=0.1						,    __lowerCamelCase							:						Tuple="gelu"						,    __lowerCamelCase							:						Any=0.02						,    __lowerCamelCase							:						str=1e-5						,    __lowerCamelCase							:						Tuple=0.0						,    __lowerCamelCase							:						Optional[int]=None						,    __lowerCamelCase							:						List[str]=None						,    **__lowerCamelCase							:						List[str]						,    )				->					List[str]:
										super().__init__(**_A	)

										__magic_name__            =     patch_size
										__magic_name__            =     num_channels
										__magic_name__            =     embed_dim
										__magic_name__            =     depths
										__magic_name__            =     len(_A	)
										__magic_name__            =     num_heads
										__magic_name__            =     kernel_size
										__magic_name__            =     dilations
										__magic_name__            =     mlp_ratio
										__magic_name__            =     qkv_bias
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     drop_path_rate
										__magic_name__            =     hidden_act
										__magic_name__            =     layer_norm_eps
										__magic_name__            =     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
										__magic_name__            =     int(embed_dim * 2 ** (len(_A	) - 1)	)
										__magic_name__            =     layer_scale_init_value
										__magic_name__            =     ['stem'] + [f'''stage{idx}''' for idx in range(1						,    len(_A	) + 1	)]
										__magic_name__            =     get_aligned_output_features_output_indices(
										    out_features=_A						,    out_indices=_A						,    stage_names=self.stage_names	)











"""simple docstring"""

from __future__ import annotations

from random import choice






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return choice(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     random_pivot(__lowerCamelCase   )

						# partition based on pivot
						# linear time
						__magic_name__            =     [e for e in lst if e < pivot]
						__magic_name__            =     [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(__lowerCamelCase   ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(__lowerCamelCase   ) < k - 1:
												return kth_number(__lowerCamelCase ,						k - len(__lowerCamelCase   ) - 1   )
						# pivot is in elements smaller than k
						else:
												return kth_number(__lowerCamelCase ,						__lowerCamelCase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



