






"""simple docstring"""







import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


lowerCAmelCase_		:			Optional[int]				   =					logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
lowerCAmelCase_		:			str				   =					direct_transformers_import(Path(__file__).parent)


lowerCAmelCase_		:			Dict				   =					{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}





class      UpperCamelCase_       (				a_     ):
  _A				:    int =		['feature_extractor', 'tokenizer']
  # Names need to be attr_class for attr in attributes
  _A				:    Any =		None
  _A				:    str =		None
  _A				:    Tuple =		None



  def __init__(					self       ,						*snake_case__       ,						**snake_case__ )	->	Optional[Any]:



         """simple docstring"""





         for key in kwargs:
                if key not in self.attributes:
                       raise TypeError(f'''Unexpected keyword argument {key}.''' )
         for arg, attribute_name in zip(snake_case__       ,						self.attributes ):
                if attribute_name in kwargs:
                       raise TypeError(f'''Got multiple values for argument {attribute_name}.''' )
                else:
                       UpperCAmelCase					=	arg

         if len(snake_case__ ) != len(self.attributes ):
                raise ValueError(
                    f'''This processor requires {len(self.attributes )} arguments: {', '.join(self.attributes )}. Got '''
                    f'''{len(snake_case__ )} arguments instead.''' )

         # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
         for attribute_name, arg in kwargs.items():
                UpperCAmelCase					=	getattr(self       ,						f'''{attribute_name}_class''' )
                # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
                UpperCAmelCase					=	AUTO_TO_BASE_CLASS_MAPPING.get(snake_case__       ,						snake_case__ )
                if isinstance(snake_case__       ,						snake_case__ ):
                       UpperCAmelCase					=	tuple(getattr(snake_case__       ,						snake_case__ ) for n in class_name if n is not None )
                else:
                       UpperCAmelCase					=	getattr(snake_case__       ,						snake_case__ )

                if not isinstance(snake_case__       ,						snake_case__ ):
                       raise ValueError(
                           f'''Received a {type(snake_case__ ).__name__} for argument {attribute_name}, but a {class_name} was expected.''' )

                setattr(self       ,						snake_case__       ,						snake_case__ )



  def __repr__(					self )	->	int:



         """simple docstring"""





         UpperCAmelCase					=	[f'''- {name}: {repr(getattr(self       ,						snake_case__ ) )}''' for name in self.attributes]
         UpperCAmelCase					=	"""\n""".join(snake_case__ )
         return f'''{self.__class__.__name__}:\n{attributes_repr}'''



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = False       ,						**snake_case__ )	->	List[str]:



         """simple docstring"""





         os.makedirs(snake_case__       ,						exist_ok=snake_case__ )

         if push_to_hub:
                UpperCAmelCase					=	kwargs.pop("""commit_message"""       ,						snake_case__ )
                UpperCAmelCase					=	kwargs.pop("""repo_id"""       ,						save_directory.split(os.path.sep )[-1] )
                UpperCAmelCase					=	self._create_repo(snake_case__       ,						**snake_case__ )
                UpperCAmelCase					=	self._get_files_timestamps(snake_case__ )
         # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
         # loaded from the Hub.
         if self._auto_class is not None:
                UpperCAmelCase					=	[getattr(self       ,						snake_case__ ) for attribute_name in self.attributes]
                UpperCAmelCase					=	[(a.init_kwargs if isinstance(snake_case__       ,						snake_case__ ) else a) for a in attrs]
                custom_object_save(self       ,						snake_case__       ,						config=snake_case__ )

         for attribute_name in self.attributes:
                UpperCAmelCase					=	getattr(self       ,						snake_case__ )
                # Include the processor class in the attribute config so this processor can then be reloaded with the
                # `AutoProcessor` API.
                if hasattr(snake_case__       ,						"""_set_processor_class""" ):
                       attribute._set_processor_class(self.__class__.__name__ )
                attribute.save_pretrained(snake_case__ )

         if self._auto_class is not None:
                # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
                for attribute_name in self.attributes:
                       UpperCAmelCase					=	getattr(self       ,						snake_case__ )
                       if isinstance(snake_case__       ,						snake_case__ ):
                              del attribute.init_kwargs["auto_map"]

         if push_to_hub:
                self._upload_modified_files(
                    snake_case__       ,						snake_case__       ,						snake_case__       ,						commit_message=snake_case__       ,						token=kwargs.get("""use_auth_token""" )       ,						)



  @classmethod
  def 						UpperCamelCase_ (					cls       ,						snake_case__       ,						snake_case__ = None       ,						snake_case__ = False       ,						snake_case__ = False       ,						snake_case__ = None       ,						snake_case__ = "main"       ,						**snake_case__       ,						)	->	Tuple:



         """simple docstring"""





         UpperCAmelCase					=	cache_dir
         UpperCAmelCase					=	force_download
         UpperCAmelCase					=	local_files_only
         UpperCAmelCase					=	revision

         UpperCAmelCase					=	kwargs.pop("""use_auth_token"""       ,						snake_case__ )
         if use_auth_token is not None:
                warnings.warn(
                    """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."""       ,						snake_case__ )
                if token is not None:
                       raise ValueError(
                           """`token` and `use_auth_token` are both specified. Please set only the argument `token`.""" )
                UpperCAmelCase					=	use_auth_token

         if token is not None:
                # change to `token` in a follow-up PR
                UpperCAmelCase					=	token

         UpperCAmelCase					=	cls._get_arguments_from_pretrained(snake_case__       ,						**snake_case__ )
         return cls(*snake_case__ )



  @classmethod
  def 						UpperCamelCase_ (					cls       ,						snake_case__="AutoProcessor" )	->	List[str]:



         """simple docstring"""





         if not isinstance(snake_case__       ,						snake_case__ ):
                UpperCAmelCase					=	auto_class.__name__

         import transformers.models.auto as auto_module

         if not hasattr(snake_case__       ,						snake_case__ ):
                raise ValueError(f'''{auto_class} is not a valid auto class.''' )

         UpperCAmelCase					=	auto_class



  @classmethod
  def 						UpperCamelCase_ (					cls       ,						snake_case__       ,						**snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	[]
         for attribute_name in cls.attributes:
                UpperCAmelCase					=	getattr(cls       ,						f'''{attribute_name}_class''' )
                if isinstance(snake_case__       ,						snake_case__ ):
                       UpperCAmelCase					=	tuple(getattr(snake_case__       ,						snake_case__ ) if n is not None else None for n in class_name )
                       UpperCAmelCase					=	kwargs.get("""use_fast"""       ,						snake_case__ )
                       if use_fast and classes[1] is not None:
                              UpperCAmelCase					=	classes[1]
                       else:
                              UpperCAmelCase					=	classes[0]
                else:
                       UpperCAmelCase					=	getattr(snake_case__       ,						snake_case__ )

                args.append(attribute_class.from_pretrained(snake_case__       ,						**snake_case__ ) )
         return args



  @property
  def 						UpperCamelCase_ (					self )	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	getattr(self       ,						self.attributes[0] )
         return getattr(snake_case__       ,						"""model_input_names"""       ,						snake_case__ )





lowerCAmelCase_		:			Optional[Any]				   =					copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
 lowerCAmelCase_		:			Tuple				   =					ProcessorMixin.push_to_hub.__doc__.format(
     object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
 )



'''simple docstring'''






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Optional[int]    )							->   str:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Any     =       args.serialization_dir
      SCREAMING_SNAKE_CASE_      :			List[str]     =       args.pruning_method
      SCREAMING_SNAKE_CASE_      :			str     =       args.threshold

      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       torch.load(os.path.join(SCREAMING_SNAKE_CASE_  ,  "pytorch_model.bin"    )  ,  map_location="cpu"    )

      SCREAMING_SNAKE_CASE_      :			List[Any]     =       0  # Number of remaining (not pruned) params in the encoder
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       0  # Number of params in the encoder

      print("name".ljust(6_0  ,  " "    )  ,  "Remaining Weights %"  ,  "Remaining Weight"    )
      for name, param in st.items():
            if "encoder" not in name:
                  continue

            if "mask_scores" in name:
                  if pruning_method == "topK":
                        SCREAMING_SNAKE_CASE_      :			int     =       TopKBinarizer.apply(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ).sum().item()
                  elif pruning_method == "sigmoied_threshold":
                        SCREAMING_SNAKE_CASE_      :			Tuple     =       ThresholdBinarizer.apply(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ).sum().item()
                  elif pruning_method == "l0":
                        SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Dict     =       -0.1, 1.1
                        SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       torch.sigmoid(SCREAMING_SNAKE_CASE_    )
                        SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       s * (r - l) + l
                        SCREAMING_SNAKE_CASE_      :			str     =       s_bar.clamp(min=0.0  ,  max=1.0    )
                        SCREAMING_SNAKE_CASE_      :			Dict     =       (mask > 0.0).sum().item()
                  else:
                        raise ValueError("Unknown pruning method"    )
                  remaining_count += mask_ones
                  print(name.ljust(6_0  ,  " "    )  ,  str(round(1_0_0 * mask_ones / param.numel()  ,  3    )    ).ljust(2_0  ,  " "    )  ,  str(SCREAMING_SNAKE_CASE_    )    )
            else:
                  encoder_count += param.numel()
                  if "bias" in name or "LayerNorm" in name:
                        remaining_count += param.numel()

      print(""    )
      print("Remaining Weights (global) %: "  ,  1_0_0 * remaining_count / encoder_count    )


if __name__ == "__main__":
      snake_case_       =       argparse.ArgumentParser()

      parser.add_argument(
          '--pruning_method',
          choices=['l0', 'topK', 'sigmoied_threshold'],
          type=str,
          required=True,
          help=(
              'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
              ' pruning)'
          ),
      )
      parser.add_argument(
          '--threshold',
          type=float,
          required=False,
          help=(
              'For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
              'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
              'Not needed for `l0`'
          ),
      )
      parser.add_argument(
          '--serialization_dir',
          type=str,
          required=True,
          help='Folder containing the model that was previously fine-pruned',
      )

      snake_case_       =       parser.parse_args()

      main(args)

