
from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_      :					Tuple				      =     logging.get_logger(__name__)

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





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				Optional[int]      =							'''regnet'''
							lowerCamelCase__:				List[Any]      =							['''x''', '''y''']



							def __init__(     self	,				__snake_case=3	,				__snake_case=3_2	,				__snake_case=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]	,				__snake_case=[2, 6, 1_2, 2]	,				__snake_case=6_4	,				__snake_case="y"	,				__snake_case="relu"	,				**__snake_case	,				)						->     Dict:






								"""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							)}'							)
								UpperCAmelCase:					Optional[int] 						=     num_channels
								UpperCAmelCase:					Optional[Any] 						=     embedding_size
								UpperCAmelCase:					int 						=     hidden_sizes
								UpperCAmelCase:					Tuple 						=     depths
								UpperCAmelCase:					Dict 						=     groups_width
								UpperCAmelCase:					Tuple 						=     layer_type
								UpperCAmelCase:					Tuple 						=     hidden_act
								# always downsample in the first stage
								UpperCAmelCase:					Any 						=     True


import torch
from torch import autograd





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     inputs.numel()
								UpperCAmelCase:					Tuple 						=     int(0.0_05 * nb_elems							) + 1
								if sigmoid:
									UpperCAmelCase:					int 						=     (torch.sigmoid(__snake_case							) > threshold).type(inputs.type()							)
								else:
									UpperCAmelCase:					List[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
									UpperCAmelCase:					List[Any] 						=     inputs.flatten().kthvalue(max(nb_elems - nb_min	,				1							)							).values
									UpperCAmelCase:					Optional[int] 						=     (inputs > k_threshold).type(inputs.type()							)
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return gradOutput, None, None





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     inputs.flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     int(threshold * inputs.numel()							)

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




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return gradOutput, None





class 		__lowerCamelCase     (   lowercase		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     inputs.abs().flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					str 						=     int(threshold * inputs.numel()							)

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