




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


_lowerCamelCase	:		Optional[Any]							       =   logging.get_logger(__name__)

_lowerCamelCase	:		Optional[int]							       =   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase	:		List[Any]							       =   {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

_lowerCamelCase	:		Tuple							       =   {
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      VOCAB_FILES_NAMES
    UpperCAmelCase__							  =      PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =      ['''input_ids''', '''attention_mask''']
    UpperCAmelCase__							  =      RobertaTokenizer


    def __init__(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Tuple=None					, UpperCAmelCase__      :	Optional[int]=None					, UpperCAmelCase__      :	Tuple=None					, UpperCAmelCase__      :	Optional[int]="replace"					, UpperCAmelCase__      :	Any="<s>"					, UpperCAmelCase__      :	Optional[Any]="</s>"					, UpperCAmelCase__      :	List[str]="</s>"					, UpperCAmelCase__      :	Optional[int]="<s>"					, UpperCAmelCase__      :	Dict="<unk>"					, UpperCAmelCase__      :	List[str]="<pad>"					, UpperCAmelCase__      :	Dict="<mask>"					, UpperCAmelCase__      :	Tuple=False					, UpperCAmelCase__      :	Optional[int]=True					, **UpperCAmelCase__      :	Tuple					, )						->int:





           '''simple docstring'''

           super().__init__(
               UpperCAmelCase__					, UpperCAmelCase__					, tokenizer_file=UpperCAmelCase__					, errors=UpperCAmelCase__					, bos_token=UpperCAmelCase__					, eos_token=UpperCAmelCase__					, sep_token=UpperCAmelCase__					, cls_token=UpperCAmelCase__					, unk_token=UpperCAmelCase__					, pad_token=UpperCAmelCase__					, mask_token=UpperCAmelCase__					, add_prefix_space=UpperCAmelCase__					, trim_offsets=UpperCAmelCase__					, **UpperCAmelCase__					, )

           A__       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
           if pre_tok_state.get('''add_prefix_space'''					, UpperCAmelCase__) != add_prefix_space:
                  A__       =    getattr(UpperCAmelCase__					, pre_tok_state.pop('''type'''))
                  A__       =    add_prefix_space
                  A__       =    pre_tok_class(**UpperCAmelCase__)

           A__       =    add_prefix_space

           A__       =    '''post_processor'''
           A__       =    getattr(self.backend_tokenizer					, UpperCAmelCase__					, UpperCAmelCase__)
           if tokenizer_component_instance:
                  A__       =    json.loads(tokenizer_component_instance.__getstate__())

                  # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                  if "sep" in state:
                         A__       =    tuple(state['''sep'''])
                  if "cls" in state:
                         A__       =    tuple(state['''cls'''])

                  A__       =    False

                  if state.get('''add_prefix_space'''					, UpperCAmelCase__) != add_prefix_space:
                         A__       =    add_prefix_space
                         A__       =    True

                  if state.get('''trim_offsets'''					, UpperCAmelCase__) != trim_offsets:
                         A__       =    trim_offsets
                         A__       =    True

                  if changes_to_apply:
                         A__       =    getattr(UpperCAmelCase__					, state.pop('''type'''))
                         A__       =    component_class(**UpperCAmelCase__)
                         setattr(self.backend_tokenizer					, UpperCAmelCase__					, UpperCAmelCase__)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->str:





           '''simple docstring'''

           if self._mask_token is None:
                  if self.verbose:
                         logger.error('''Using mask_token, but it is not set yet.''')
                  return None
           return str(self._mask_token)


    @mask_token.setter
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple					, UpperCAmelCase__      :	List[str])						->Optional[Any]:





           '''simple docstring'''

           A__       =    AddedToken(UpperCAmelCase__					, lstrip=UpperCAmelCase__					, rstrip=UpperCAmelCase__) if isinstance(UpperCAmelCase__					, UpperCAmelCase__) else value
           A__       =    value


    def        SCREAMING_SNAKE_CASE							(			self      :	Any					, *UpperCAmelCase__      :	List[Any]					, **UpperCAmelCase__      :	Tuple)						->BatchEncoding:





           '''simple docstring'''

           A__       =    kwargs.get('''is_split_into_words'''					, UpperCAmelCase__)
           assert self.add_prefix_space or not is_split_into_words, (
               f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int]					, *UpperCAmelCase__      :	Any					, **UpperCAmelCase__      :	str)						->BatchEncoding:





           '''simple docstring'''

           A__       =    kwargs.get('''is_split_into_words'''					, UpperCAmelCase__)

           assert self.add_prefix_space or not is_split_into_words, (
               f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*UpperCAmelCase__					, **UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

           A__       =    self._tokenizer.model.save(UpperCAmelCase__					, name=UpperCAmelCase__)
           return tuple(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	int					, UpperCAmelCase__      :	Union[str, Any]=None)						->List[str]:





           '''simple docstring'''

           A__       =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                  return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, UpperCAmelCase__      :	List[int]					, UpperCAmelCase__      :	Optional[List[int]] = None)						->List[int]:





           '''simple docstring'''

           A__       =    [self.sep_token_id]
           A__       =    [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]








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)


