





"""simple docstring"""





import torch
from torch import autograd





class 			snake_case		(   autograd.Function   ):

      @staticmethod
      def 						SCREAMING_SNAKE_CASE_			(			_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :torch.tensor						,							_lowerCamelCase   :float						,							_lowerCamelCase   :bool						):
       __SCREAMING_SNAKE_CASE :			int												=       inputs.numel()
       __SCREAMING_SNAKE_CASE :			str												=       int(0.0_0_5 * nb_elems						) + 1
       if sigmoid:
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       (torch.sigmoid(_lowerCamelCase						) > threshold).type(inputs.type()						)
       else:
        __SCREAMING_SNAKE_CASE :			int												=       (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 :			List[Any]												=       inputs.flatten().kthvalue(max(nb_elems - nb_min						,							1						)						).values
        __SCREAMING_SNAKE_CASE :			int												=       (inputs > k_threshold).type(inputs.type()						)
       return mask



      @staticmethod
      def 						SCREAMING_SNAKE_CASE_			(			_lowerCamelCase   :List[str]						,							_lowerCamelCase   :List[str]						):
       return gradOutput, None, None







class 			snake_case		(   autograd.Function   ):

      @staticmethod
      def 						SCREAMING_SNAKE_CASE_			(			_lowerCamelCase   :Tuple						,							_lowerCamelCase   :torch.tensor						,							_lowerCamelCase   :float						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       inputs.clone()
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       inputs.flatten().sort(descending=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       int(threshold * inputs.numel()						)

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



      @staticmethod
      def 						SCREAMING_SNAKE_CASE_			(			_lowerCamelCase   :Dict						,							_lowerCamelCase   :Optional[int]						):
       return gradOutput, None







class 			snake_case		(   __UpperCAmelCase   ):

      @staticmethod
      def 						SCREAMING_SNAKE_CASE_			(			_lowerCamelCase   :torch.tensor						,							_lowerCamelCase   :float						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       inputs.clone()
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       inputs.abs().flatten().sort(descending=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			int												=       int(threshold * inputs.numel()						)

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








"""simple docstring"""





from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore





class 			snake_case		(   __UpperCAmelCase   ):
      lowerCamelCase__						= c_float

      @property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       return sizeof(self.value						)







def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							int ,      lowercase_   :							int = 0		):

 '''simple docstring'''






 if dimension <= 0:
  __SCREAMING_SNAKE_CASE :			str												=       fixed_dimension

 dimension -= num_token_to_add
 return dimension




def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							ParameterFormat		):

 '''simple docstring'''






 return num_parameters * dtype.size






def 		lowerCAmelCase_	(  lowercase_   :							str		):

 '''simple docstring'''






 from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

 try:
  return AutoProcessor.from_pretrained(lowercase_		)
 except (ValueError, OSError, KeyError):
  __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       None, None
  try:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       AutoTokenizer.from_pretrained(lowercase_		)
  except (OSError, KeyError):
   pass
  try:
   __SCREAMING_SNAKE_CASE :			Optional[Any]												=       AutoFeatureExtractor.from_pretrained(lowercase_		)
  except (OSError, KeyError):
   pass

  if tokenizer is not None and feature_extractor is not None:
   raise ValueError(
       F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''		)
  elif tokenizer is None and feature_extractor is None:
   return None
  elif tokenizer is not None:
   return tokenizer
  else:
   return feature_extractor

