code: import torch
  from torch import autograd
  
  
  class _lowercase					(       autograd.Function						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							@staticmethod
  							def     _UpperCAmelCase       (    UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	inputs.numel()
  														_lowercase				=	int(0.005 * nb_elems  ) + 1
  														if sigmoid:
  																					_lowercase				=	(torch.sigmoid(SCREAMING_SNAKE_CASE_  ) > threshold).type(inputs.type()  )
  														else:
  																					_lowercase				=	(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
  																					_lowercase				=	inputs.flatten().kthvalue(max(nb_elems - nb_min     ,   1  )  ).values
  																					_lowercase				=	(inputs > k_threshold).type(inputs.type()  )
  														return mask
  
  
  
  							@staticmethod
  							def     _UpperCAmelCase       (    UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return gradOutput, None, None
  
  
  
  
  
  
  class _lowercase					(       autograd.Function						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							@staticmethod
  							def     _UpperCAmelCase       (    UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	inputs.clone()
  														_lowercase				,       _lowercase				=	inputs.flatten().sort(descending=SCREAMING_SNAKE_CASE_  )
  														_lowercase				=	int(threshold * inputs.numel()  )
  
  														# flat_out and mask access the same memory.
  														_lowercase				=	mask.flatten()
  														_lowercase				=	0
  														_lowercase				=	1
  														return mask
  
  
  
  							@staticmethod
  							def     _UpperCAmelCase       (    UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return gradOutput, None
  
  
  
  
  
  
  class _lowercase					(       lowerCamelCase__						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							@staticmethod
  							def     _UpperCAmelCase       (    UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	inputs.clone()
  														_lowercase				,       _lowercase				=	inputs.abs().flatten().sort(descending=SCREAMING_SNAKE_CASE_  )
  														_lowercase				=	int(threshold * inputs.numel()  )
  
  														# flat_out and mask access the same memory.
  														_lowercase				=	mask.flatten()
  														_lowercase				=	0
  														_lowercase				=	1
  														return mask
  
  
  
style_context: import warnings
  
  from ...utils import logging
  from .image_processing_mobilenet_va import MobileNetVaImageProcessor
  
  
  A_:      Tuple        =			logging.get_logger(__name__)
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   *UpperCAmelCase     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														warnings.warn(
  														    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
  														    """ Please use MobileNetV2ImageProcessor instead."""     ,   UpperCAmelCase     ,   )
  														super().__init__(*UpperCAmelCase     ,   **UpperCAmelCase  )
  
  
  