






"""simple docstring"""







import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

def _a			(		_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	args.pruning_method
      UpperCAmelCase          =	args.threshold

      UpperCAmelCase          =	args.model_name_or_path.rstrip("""/"""							)
      UpperCAmelCase          =	args.target_model_path

      print(F'''Load fine-pruned model from {model_name_or_path}'''							)
      UpperCAmelCase          =	torch.load(os.path.join(_snake_case				,	"""pytorch_model.bin"""							)							)
      UpperCAmelCase          =	{}

      for name, tensor in model.items():
            if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
                  UpperCAmelCase          =	tensor
                  print(F'''Copied layer {name}'''							)
            elif "classifier" in name or "qa_output" in name:
                  UpperCAmelCase          =	tensor
                  print(F'''Copied layer {name}'''							)
            elif "bias" in name:
                  UpperCAmelCase          =	tensor
                  print(F'''Copied layer {name}'''							)
            else:
                  if pruning_method == "magnitude":
                        UpperCAmelCase          =	MagnitudeBinarizer.apply(inputs=_snake_case				,	threshold=_snake_case							)
                        UpperCAmelCase          =	tensor * mask
                        print(F'''Pruned layer {name}'''							)
                  elif pruning_method == "topK":
                        if "mask_scores" in name:
                              continue
                        UpperCAmelCase          =	name[:-6]
                        UpperCAmelCase          =	model[F'''{prefix_}mask_scores''']
                        UpperCAmelCase          =	TopKBinarizer.apply(_snake_case				,	_snake_case							)
                        UpperCAmelCase          =	tensor * mask
                        print(F'''Pruned layer {name}'''							)
                  elif pruning_method == "sigmoied_threshold":
                        if "mask_scores" in name:
                              continue
                        UpperCAmelCase          =	name[:-6]
                        UpperCAmelCase          =	model[F'''{prefix_}mask_scores''']
                        UpperCAmelCase          =	ThresholdBinarizer.apply(_snake_case				,	_snake_case				,	_snake_case							)
                        UpperCAmelCase          =	tensor * mask
                        print(F'''Pruned layer {name}'''							)
                  elif pruning_method == "l0":
                        if "mask_scores" in name:
                              continue
                        UpperCAmelCase          =	name[:-6]
                        UpperCAmelCase          =	model[F'''{prefix_}mask_scores''']
                        UpperCAmelCase      , UpperCAmelCase          =	-0.1, 1.1
                        UpperCAmelCase          =	torch.sigmoid(_snake_case							)
                        UpperCAmelCase          =	s * (r - l) + l
                        UpperCAmelCase          =	s_bar.clamp(min=0.0				,	max=1.0							)
                        UpperCAmelCase          =	tensor * mask
                        print(F'''Pruned layer {name}'''							)
                  else:
                        raise ValueError("""Unknown pruning method"""							)

      if target_model_path is None:
            UpperCAmelCase          =	os.path.join(
                os.path.dirname(_snake_case							)				,	F'''bertarized_{os.path.basename(_snake_case							)}'''							)

      if not os.path.isdir(_snake_case							):
            shutil.copytree(_snake_case				,	_snake_case							)
            print(F'''\nCreated folder {target_model_path}'''							)

      torch.save(_snake_case				,	os.path.join(_snake_case				,	"""pytorch_model.bin"""							)							)
      print("""\nPruned model saved! See you later!"""							)


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

  parser.add_argument(
      """--pruning_method""",
      choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
      type=str,
      required=True,
      help=(
          """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
          """ sigmoied_threshold = Soft movement pruning)"""
      ),
  )
  parser.add_argument(
      """--threshold""",
      type=float,
      required=False,
      help=(
          """For `magnitude` and `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(
      """--model_name_or_path""",
      type=str,
      required=True,
      help="""Folder containing the model that was previously fine-pruned""",
  )
  parser.add_argument(
      """--target_model_path""",
      default=None,
      type=str,
      required=False,
      help="""Folder containing the model that was previously fine-pruned""",
  )

  _UpperCamelCase					      =  parser.parse_args()

  main(args)











"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  _UpperCamelCase					      =  None


_UpperCamelCase					      =  logging.get_logger(__name__)


_UpperCamelCase					      =  """▁"""

_UpperCamelCase					      =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase					      =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

_UpperCamelCase					      =  {
    """google/pegasus-xsum""": 512,
}







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      PegasusTokenizer
    SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']






    def __init__(			self     ,A=None     ,A=None     ,A="<pad>"     ,A="</s>"     ,A="<unk>"     ,A="<mask_2>"     ,A="<mask_1>"     ,A=None     ,A=103     ,**A     ,):
          UpperCAmelCase          =	offset

          if additional_special_tokens is not None:
                if not isinstance(A     ,A ):
                      raise TypeError(
                          F'''additional_special_tokens should be of type {type(A )}, but is'''
                          F''' {type(A )}''' )

                UpperCAmelCase          =	(
                    ([mask_token_sent] + additional_special_tokens)
                    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                    else additional_special_tokens
                )
                # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                additional_special_tokens_extended += [
                    F'''<unk_{i}>''' for i in range(len(A )     ,self.offset - 1 )
                ]

                if len(set(A ) ) != len(A ):
                      raise ValueError(
                          """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                          F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
                UpperCAmelCase          =	additional_special_tokens_extended
          else:
                UpperCAmelCase          =	[mask_token_sent] if mask_token_sent is not None else []
                additional_special_tokens += [F'''<unk_{i}>''' for i in range(2     ,self.offset )]

          super().__init__(
              A     ,tokenizer_file=A     ,pad_token=A     ,eos_token=A     ,unk_token=A     ,mask_token=A     ,mask_token_sent=A     ,offset=A     ,additional_special_tokens=A     ,**A     ,)
          UpperCAmelCase          =	vocab_file
          UpperCAmelCase          =	False if not self.vocab_file else True






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	set(self.all_special_ids )  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

          if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
                raise ValueError(
                    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
                    F''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )

          return [1 if x in all_special_ids else 0 for x in seq]






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = False ):
          if already_has_special_tokens:
                return self._special_token_mask(A )
          elif token_ids_a is None:
                return self._special_token_mask(A ) + [1]
          else:
                return self._special_token_mask(token_ids_a + token_ids_a ) + [1]






    def     _UpperCamelCase							(			self     ,A     ,A=None ):
          if token_ids_a is None:
                return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer.""" )

          if not os.path.isdir(A ):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                return
          UpperCAmelCase          =	os.path.join(
              A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
                copyfile(self.vocab_file     ,A )

          return (out_vocab_file,)



