
"""simple docstring"""
import torch
from torch import autograd




class    _snake_case  (						autograd.Function       ):






       @staticmethod
       def 							__lowercase  (					snake_case__						:			Dict						, snake_case__						:			torch.tensor						, snake_case__						:			float						, snake_case__						:			bool    ):






            """simple docstring"""



            __a:						Any		    =					inputs.numel()
            __a:						Optional[int]		    =					int(0.005 * nb_elems    ) + 1
            if sigmoid:
                 __a:						int		    =					(torch.sigmoid(UpperCamelCase__    ) > threshold).type(inputs.type()    )
            else:
                 __a:						Any		    =					(inputs > threshold).type(inputs.type()    )
            if mask.sum() < nb_min:
                 # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
                 __a:						Dict		    =					inputs.flatten().kthvalue(max(nb_elems - nb_min						, 1    )    ).values
                 __a:						Union[str, Any]		    =					(inputs > k_threshold).type(inputs.type()    )
            return mask






       @staticmethod
       def 							__lowercase  (					snake_case__						:			Union[str, Any]						, snake_case__						:			Tuple    ):






            """simple docstring"""



            return gradOutput, None, None






class    _snake_case  (						autograd.Function       ):






       @staticmethod
       def 							__lowercase  (					snake_case__						:			Any						, snake_case__						:			torch.tensor						, snake_case__						:			float    ):






            """simple docstring"""



            __a:						Dict		    =					inputs.clone()
            __a    ,				__a:						Any		    =					inputs.flatten().sort(descending=UpperCamelCase__    )
            __a:						Optional[Any]		    =					int(threshold * inputs.numel()    )

            # flat_out and mask access the same memory.
            __a:						int		    =					mask.flatten()
            __a:						Any		    =					0
            __a:						int		    =					1
            return mask






       @staticmethod
       def 							__lowercase  (					snake_case__						:			List[Any]						, snake_case__						:			List[Any]    ):






            """simple docstring"""



            return gradOutput, None






class    _snake_case  (						_SCREAMING_SNAKE_CASE       ):






       @staticmethod
       def 							__lowercase  (					snake_case__						:			torch.tensor						, snake_case__						:			float    ):






            """simple docstring"""



            __a:						Optional[int]		    =					inputs.clone()
            __a    ,				__a:						Optional[int]		    =					inputs.abs().flatten().sort(descending=UpperCamelCase__    )
            __a:						Tuple		    =					int(threshold * inputs.numel()    )

            # flat_out and mask access the same memory.
            __a:						List[str]		    =					mask.flatten()
            __a:						Optional[Any]		    =					0
            __a:						Optional[int]		    =					1
            return mask


"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase		:						int =					logging.get_logger(__name__)

lowercase		:						Any =					{
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''regnet'''
							SCREAMING_SNAKE_CASE		     =						['''x''', '''y''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			Any=3						, snake_case__						:			str=3_2						, snake_case__						:			Tuple=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]						, snake_case__						:			Union[str, Any]=[2, 6, 1_2, 2]						, snake_case__						:			str=6_4						, snake_case__						:			Optional[int]="y"						, snake_case__						:			Tuple="relu"						, **snake_case__						:			str						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												if layer_type not in self.layer_types:
																	raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types    )}"""    )
												__a:						Any		    =					num_channels
												__a:						List[Any]		    =					embedding_size
												__a:						Optional[int]		    =					hidden_sizes
												__a:						Optional[int]		    =					depths
												__a:						Optional[Any]		    =					groups_width
												__a:						Tuple		    =					layer_type
												__a:						str		    =					hidden_act
												# always downsample in the first stage
												__a:						List[str]		    =					True
