



'''simple docstring'''
import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing



class _A     (			UpperCAmelCase__			):





		def __init__(   self       ,				__UpperCAmelCase = "▁"       ,				__UpperCAmelCase = True       ,				__UpperCAmelCase = "<unk>"       ,				__UpperCAmelCase = "</s>"       ,				__UpperCAmelCase = "<pad>"       ,				)				->   Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					int   =   {
					    """pad""": {"""id""": 0, """token""": pad_token},
					    """eos""": {"""id""": 1, """token""": eos_token},
					    """unk""": {"""id""": 2, """token""": unk_token},
					}

					__UpperCAmelCase      :					Optional[Any]   =   [None] * len(self.special_tokens )
					for token_dict in self.special_tokens.values():
								__UpperCAmelCase      :					Optional[Any]   =   token_dict["""token"""]

					__UpperCAmelCase      :					List[str]   =   Tokenizer(Unigram() )

					__UpperCAmelCase      :					Optional[int]   =   normalizers.Sequence(
					    [
					        normalizers.Nmt(),
					        normalizers.NFKC(),
					        normalizers.Replace(Regex(""" {2,}""" )       ,				""" """ ),
					        normalizers.Lowercase(),
					    ] )
					__UpperCAmelCase      :					List[Any]   =   pre_tokenizers.Sequence(
					    [
					        pre_tokenizers.Metaspace(replacement=UpperCAmelCase__       ,				add_prefix_space=UpperCAmelCase__ ),
					        pre_tokenizers.Digits(individual_digits=UpperCAmelCase__ ),
					        pre_tokenizers.Punctuation(),
					    ] )
					__UpperCAmelCase      :					int   =   decoders.Metaspace(replacement=UpperCAmelCase__       ,				add_prefix_space=UpperCAmelCase__ )

					__UpperCAmelCase      :					Optional[Any]   =   TemplateProcessing(
					    single=f'$A {self.special_tokens["eos"]["token"]}'       ,				special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])]       ,				)

					__UpperCAmelCase      :					int   =   {
					    """model""": """SentencePieceUnigram""",
					    """replacement""": replacement,
					    """add_prefix_space""": add_prefix_space,
					}

					super().__init__(UpperCAmelCase__       ,				UpperCAmelCase__ )





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase = 8_000       ,				__UpperCAmelCase = True       ,				)				->   Tuple:

					'''simple docstring'''
					__UpperCAmelCase      :					Dict   =   trainers.UnigramTrainer(
					    vocab_size=UpperCAmelCase__       ,				special_tokens=self.special_tokens_list       ,				show_progress=UpperCAmelCase__       ,				)

					if isinstance(UpperCAmelCase__       ,				UpperCAmelCase__ ):
								__UpperCAmelCase      :					int   =   [files]
					self._tokenizer.train(UpperCAmelCase__       ,				trainer=UpperCAmelCase__ )

					self.add_unk_id()





		def 							__A				(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase = 8_000       ,				__UpperCAmelCase = True       ,				)				->   List[Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					Union[str, Any]   =   trainers.UnigramTrainer(
					    vocab_size=UpperCAmelCase__       ,				special_tokens=self.special_tokens_list       ,				show_progress=UpperCAmelCase__       ,				)

					self._tokenizer.train_from_iterator(UpperCAmelCase__       ,				trainer=UpperCAmelCase__ )

					self.add_unk_id()





		def 							__A				(   self )				->   str:

					'''simple docstring'''
					__UpperCAmelCase      :					Union[str, Any]   =   json.loads(self._tokenizer.to_str() )

					__UpperCAmelCase      :					Optional[Any]   =   self.special_tokens["""unk"""]["""id"""]

					__UpperCAmelCase      :					str   =   Tokenizer.from_str(json.dumps(UpperCAmelCase__ ) )










import argparse
import os
import shutil

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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Dict:



       """simple docstring"""

       A__       =    args.pruning_method
       A__       =    args.threshold

       A__       =    args.model_name_or_path.rstrip('''/'''						)
       A__       =    args.target_model_path

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

       for name, tensor in model.items():
              if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              elif "classifier" in name or "qa_output" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              elif "bias" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              else:
                     if pruning_method == "magnitude":
                            A__       =    MagnitudeBinarizer.apply(inputs=lowercase_ ,				threshold=lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "topK":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            A__       =    TopKBinarizer.apply(lowercase_ ,				lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "sigmoied_threshold":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            A__       =    ThresholdBinarizer.apply(lowercase_ ,				lowercase_ ,				lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "l0":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            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__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     else:
                            raise ValueError('''Unknown pruning method'''						)

       if target_model_path is None:
              A__       =    os.path.join(
                  os.path.dirname(lowercase_						) ,				f"""bertarized_{os.path.basename(lowercase_						)}"""						)

       if not os.path.isdir(lowercase_						):
              shutil.copytree(lowercase_ ,				lowercase_						)
              print(f"""\nCreated folder {target_model_path}"""						)

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


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   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""",
  )

  _lowerCamelCase	:		int							       =   parser.parse_args()

  main(args)


