import torch
from torch import autograd







class __snake_case							(		autograd.Function						):




   @staticmethod
   def       __a		(       _lowercase     :							Union[str, Any]			,      _lowercase     :							torch.tensor			,      _lowercase     :							float			,      _lowercase     :							bool ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= inputs.numel()
     SCREAMING_SNAKE_CASE__     				= int(0.0_05 * nb_elems ) + 1
     if sigmoid:
       SCREAMING_SNAKE_CASE__     				= (torch.sigmoid(_lowercase ) > threshold).type(inputs.type() )
     else:
       SCREAMING_SNAKE_CASE__     				= (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
       SCREAMING_SNAKE_CASE__     				= inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1 ) ).values
       SCREAMING_SNAKE_CASE__     				= (inputs > k_threshold).type(inputs.type() )
     return mask




   @staticmethod
   def       __a		(       _lowercase     :							int			,      _lowercase     :							Union[str, Any] ):



     """simple docstring"""






     return gradOutput, None, None







class __snake_case							(		autograd.Function						):




   @staticmethod
   def       __a		(       _lowercase     :							Dict			,      _lowercase     :							torch.tensor			,      _lowercase     :							float ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= inputs.clone()
     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= inputs.flatten().sort(descending=_lowercase )
     SCREAMING_SNAKE_CASE__     				= int(threshold * inputs.numel() )

     # flat_out and mask access the same memory.
     SCREAMING_SNAKE_CASE__     				= mask.flatten()
     SCREAMING_SNAKE_CASE__     				= 0
     SCREAMING_SNAKE_CASE__     				= 1
     return mask




   @staticmethod
   def       __a		(       _lowercase     :							Optional[int]			,      _lowercase     :							List[Any] ):



     """simple docstring"""






     return gradOutput, None







class __snake_case							(		lowerCamelCase_						):




   @staticmethod
   def       __a		(       _lowercase     :							torch.tensor			,      _lowercase     :							float ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= inputs.clone()
     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= inputs.abs().flatten().sort(descending=_lowercase )
     SCREAMING_SNAKE_CASE__     				= int(threshold * inputs.numel() )

     # flat_out and mask access the same memory.
     SCREAMING_SNAKE_CASE__     				= mask.flatten()
     SCREAMING_SNAKE_CASE__     				= 0
     SCREAMING_SNAKE_CASE__     				= 1
     return mask

import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


__lowerCamelCase     :					Union[str, Any]				  =						logging.get_logger(__name__)







class __snake_case							(		lowerCamelCase_						):




   def __init__(       self     :							str			,      *_lowercase     :							str			,      **_lowercase     :							str ):



     """simple docstring"""






     warnings.warn(
         """The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use ViTImageProcessor instead."""			,      _lowercase			,      )
     super().__init__(*_lowercase			,      **_lowercase )
