





"""simple docstring"""





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


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__					=   {
    "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"
        ),
    },
}

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

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE					=      ['input_ids', 'attention_mask']
      _SCREAMING_SNAKE_CASE					=      RobertaTokenizer





      def __init__(       self      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase="replace"      ,						lowercase="<s>"      ,						lowercase="</s>"      ,						lowercase="</s>"      ,						lowercase="<s>"      ,						lowercase="<unk>"      ,						lowercase="<pad>"      ,						lowercase="<mask>"      ,						lowercase=False      ,						lowercase=True      ,						**lowercase      ,						)		->     Optional[int]:
            super().__init__(
                lowercase      ,						lowercase      ,						tokenizer_file=lowercase      ,						errors=lowercase      ,						bos_token=lowercase      ,						eos_token=lowercase      ,						sep_token=lowercase      ,						cls_token=lowercase      ,						unk_token=lowercase      ,						pad_token=lowercase      ,						mask_token=lowercase      ,						add_prefix_space=lowercase      ,						trim_offsets=lowercase      ,						**lowercase      ,						)

            lowerCAmelCase  						=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get("""add_prefix_space"""      ,						lowercase ) != add_prefix_space:
                  lowerCAmelCase  						=       getattr(lowercase      ,						pre_tok_state.pop("""type""" ) )
                  lowerCAmelCase  						=       add_prefix_space
                  lowerCAmelCase  						=       pre_tok_class(**lowercase )

            lowerCAmelCase  						=       add_prefix_space

            lowerCAmelCase  						=       """post_processor"""
            lowerCAmelCase  						=       getattr(self.backend_tokenizer      ,						lowercase      ,						lowercase )
            if tokenizer_component_instance:
                  lowerCAmelCase  						=       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:
                        lowerCAmelCase  						=       tuple(state["""sep"""] )
                  if "cls" in state:
                        lowerCAmelCase  						=       tuple(state["""cls"""] )

                  lowerCAmelCase  						=       False

                  if state.get("""add_prefix_space"""      ,						lowercase ) != add_prefix_space:
                        lowerCAmelCase  						=       add_prefix_space
                        lowerCAmelCase  						=       True

                  if state.get("""trim_offsets"""      ,						lowercase ) != trim_offsets:
                        lowerCAmelCase  						=       trim_offsets
                        lowerCAmelCase  						=       True

                  if changes_to_apply:
                        lowerCAmelCase  						=       getattr(lowercase      ,						state.pop("""type""" ) )
                        lowerCAmelCase  						=       component_class(**lowercase )
                        setattr(self.backend_tokenizer      ,						lowercase      ,						lowercase )





      @property
      def     _snake_case			(       self )		->     str:
            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     _snake_case			(       self      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       AddedToken(lowercase      ,						lstrip=lowercase      ,						rstrip=lowercase ) if isinstance(lowercase      ,						lowercase ) else value
            lowerCAmelCase  						=       value





      def     _snake_case			(       self      ,						*lowercase      ,						**lowercase )		->     BatchEncoding:
            lowerCAmelCase  						=       kwargs.get("""is_split_into_words"""      ,						lowercase )
            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(*lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						*lowercase      ,						**lowercase )		->     BatchEncoding:
            lowerCAmelCase  						=       kwargs.get("""is_split_into_words"""      ,						lowercase )

            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(*lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     Tuple[str]:
            lowerCAmelCase  						=       self._tokenizer.model.save(lowercase      ,						name=lowercase )
            return tuple(lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=None )		->     List[Any]:
            lowerCAmelCase  						=       [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     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     List[int]:
            lowerCAmelCase  						=       [self.sep_token_id]
            lowerCAmelCase  						=       [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)


