


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


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

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


SCREAMING_SNAKE_CASE__       			=				{
    """AutoTokenizer""": """PreTrainedTokenizerBase""",
    """AutoFeatureExtractor""": """FeatureExtractionMixin""",
    """AutoImageProcessor""": """ImageProcessingMixin""",
}







class     __lowerCamelCase     (  __a     ):


							"""simple docstring"""

							lowerCAmelCase__          =				["feature_extractor", "tokenizer"]
							# Names need to be attr_class for attr in attributes
							lowerCAmelCase__          =				None
							lowerCAmelCase__          =				None
							lowerCAmelCase__          =				None




							def __init__(  self						,       *UpperCAmelCase						,       **UpperCAmelCase  )     ->       Dict:






								'''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(lowerCAmelCase_						,       self.attributes  ):
									if attribute_name in kwargs:
										raise TypeError(F'Got multiple values for argument {attribute_name}.'  )
									else:
										lowercase_			 = arg

								if len(lowerCAmelCase_  ) != len(self.attributes  ):
									raise ValueError(
									    F'This processor requires {len(self.attributes  )} arguments: {", ".join(self.attributes  )}. Got '
									    F'{len(lowerCAmelCase_  )} 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():
									lowercase_			 = getattr(self						,       F'{attribute_name}_class'  )
									# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
									lowercase_			 = AUTO_TO_BASE_CLASS_MAPPING.get(lowerCAmelCase_						,       lowerCAmelCase_  )
									if isinstance(lowerCAmelCase_						,       lowerCAmelCase_  ):
										lowercase_			 = tuple(getattr(lowerCAmelCase_						,       lowerCAmelCase_  ) for n in class_name if n is not None  )
									else:
										lowercase_			 = getattr(lowerCAmelCase_						,       lowerCAmelCase_  )

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

									setattr(self						,       lowerCAmelCase_						,       lowerCAmelCase_  )




							def __repr__(  self  )     ->       Optional[Any]:






								'''simple docstring'''






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




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = False						,       **UpperCAmelCase  )     ->       Union[str, Any]:






								'''simple docstring'''






								os.makedirs(lowerCAmelCase_						,       exist_ok=lowerCAmelCase_  )

								if push_to_hub:
									lowercase_			 = kwargs.pop("commit_message"						,       lowerCAmelCase_  )
									lowercase_			 = kwargs.pop("repo_id"						,       save_directory.split(os.path.sep  )[-1]  )
									lowercase_			 = self._create_repo(lowerCAmelCase_						,       **lowerCAmelCase_  )
									lowercase_			 = self._get_files_timestamps(lowerCAmelCase_  )
								# 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:
									lowercase_			 = [getattr(self						,       lowerCAmelCase_  ) for attribute_name in self.attributes]
									lowercase_			 = [(a.init_kwargs if isinstance(lowerCAmelCase_						,       lowerCAmelCase_  ) else a) for a in attrs]
									custom_object_save(self						,       lowerCAmelCase_						,       config=lowerCAmelCase_  )

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

								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:
										lowercase_			 = getattr(self						,       lowerCAmelCase_  )
										if isinstance(lowerCAmelCase_						,       lowerCAmelCase_  ):
											del attribute.init_kwargs["auto_map"]

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




							@classmethod
							def 				A__      (  cls						,       UpperCAmelCase						,       UpperCAmelCase = None						,       UpperCAmelCase = False						,       UpperCAmelCase = False						,       UpperCAmelCase = None						,       UpperCAmelCase = "main"						,       **UpperCAmelCase						,       )     ->       Any:






								'''simple docstring'''






								lowercase_			 = cache_dir
								lowercase_			 = force_download
								lowercase_			 = local_files_only
								lowercase_			 = revision

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

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

								lowercase_			 = cls._get_arguments_from_pretrained(lowerCAmelCase_						,       **lowerCAmelCase_  )
								return cls(*lowerCAmelCase_  )




							@classmethod
							def 				A__      (  cls						,       UpperCAmelCase="AutoProcessor"  )     ->       int:






								'''simple docstring'''






								if not isinstance(lowerCAmelCase_						,       lowerCAmelCase_  ):
									lowercase_			 = auto_class.__name__

								import transformers.models.auto as auto_module

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

								lowercase_			 = auto_class




							@classmethod
							def 				A__      (  cls						,       UpperCAmelCase						,       **UpperCAmelCase  )     ->       Optional[int]:






								'''simple docstring'''






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

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




							@property
							def 				A__      (  self  )     ->       Any:






								'''simple docstring'''






								lowercase_			 = getattr(self						,       self.attributes[0]  )
								return getattr(lowerCAmelCase_						,       "model_input_names"						,       lowerCAmelCase_  )







SCREAMING_SNAKE_CASE__       			=				copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
							SCREAMING_SNAKE_CASE__       			=				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 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"pytorch_model.bin"							)							,						map_location="cpu"							)

     a_									=0  # Number of remaining (not pruned) params in the encoder
     a_									=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":
                    a_									=TopKBinarizer.apply(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    a_									=s * (r - l) + l
                    a_									=s_bar.clamp(min=0.0							,						max=1.0							)
                    a_									=(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(lowercase__							)							)
          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__":
  lowercase											=      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''',
  )

  lowercase											=      parser.parse_args()

  main(args)


