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

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


_lowerCamelCase        =logging.get_logger(__name__)

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

_lowerCamelCase        ={
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}





class 							A__		(     __SCREAMING_SNAKE_CASE):
      def __init__(				self   ,  __magic_name__=None   ,  __magic_name__=None   ,  __magic_name__=False   ,  __magic_name__=True   ,  __magic_name__=False   ,  __magic_name__="<s>"   ,  __magic_name__="</s>"   ,  __magic_name__="<unk>"   ,  __magic_name__="<sep>"   ,  __magic_name__="<pad>"   ,  __magic_name__="<cls>"   ,  __magic_name__="<mask>"   ,  __magic_name__=["<eop>", "<eod>"]   ,  **__magic_name__   ,  ):
       lowerCamelCase		:     List[str]          =				AddedToken(__magic_name__   ,  lstrip=__magic_name__   ,  rstrip=__magic_name__				) if isinstance(__magic_name__   ,  __magic_name__				) else mask_token

       super().__init__(
           vocab_file=__magic_name__   ,  tokenizer_file=__magic_name__   ,  do_lower_case=__magic_name__   ,  remove_space=__magic_name__   ,  keep_accents=__magic_name__   ,  bos_token=__magic_name__   ,  eos_token=__magic_name__   ,  unk_token=__magic_name__   ,  sep_token=__magic_name__   ,  pad_token=__magic_name__   ,  cls_token=__magic_name__   ,  mask_token=__magic_name__   ,  additional_special_tokens=__magic_name__   ,  **__magic_name__   ,  )

       lowerCamelCase		:     Optional[Any]          =				3
       lowerCamelCase		:     str          =				do_lower_case
       lowerCamelCase		:     List[Any]          =				remove_space
       lowerCamelCase		:     str          =				keep_accents
       lowerCamelCase		:     Tuple          =				vocab_file
       lowerCamelCase		:     Union[str, Any]          =				False if not self.vocab_file else True

       try:
        import jieba
       except ModuleNotFoundError as error:
        raise error.__class__(
            """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
            """See https://pypi.org/project/jieba/ for installation."""				)
       lowerCamelCase		:     Optional[Any]          =				jieba
       lowerCamelCase		:     str          =				str.maketrans(""" \n"""   ,  """\u2582\u2583"""				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = None				):
       lowerCamelCase		:     Any          =				[self.sep_token_id]
       lowerCamelCase		:     List[Any]          =				[self.cls_token_id]
       if token_ids_a is None:
        return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = None				):
       lowerCamelCase		:     Any          =				[self.sep_token_id]
       lowerCamelCase		:     Optional[Any]          =				[2]

       if token_ids_a is None:
        return len(token_ids_a + sep				) * [0] + cls_segment_id
       return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = 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(__magic_name__				):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
        return
       lowerCamelCase		:     int          =				os.path.join(
           __magic_name__   ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

       return (out_vocab_file,)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  *__magic_name__   ,  **__magic_name__				):
       lowerCamelCase		:     Union[str, Any]          =				[
           """ """.join([x.translate(self.translator				) for x in self.jieba.cut(__magic_name__   ,  cut_all=__magic_name__				)]				)
           for text in batch_text_or_text_pairs
       ]
       return super()._batch_encode_plus(__magic_name__   ,  *__magic_name__   ,  **__magic_name__				)






      def 						UpperCamelCase__							(				self   ,  *__magic_name__   ,  **__magic_name__				):
       lowerCamelCase		:     str          =				super()._decode(*__magic_name__   ,  **__magic_name__				)
       lowerCamelCase		:     Optional[int]          =				text.replace(""" """   ,  """"""				).replace("""\u2582"""   ,  """ """				).replace("""\u2583"""   ,  """\n"""				)
       return text






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     Dict          =				args.serialization_dir
 lowerCamelCase		:     List[Any]          =				args.pruning_method
 lowerCamelCase		:     List[Any]          =				args.threshold

 lowerCamelCase		:     str          =				torch.load(os.path.join(lowerCamelCase,     """pytorch_model.bin"""							),     map_location="""cpu"""							)

 lowerCamelCase		:     Optional[int]          =				0  # Number of remaining (not pruned) params in the encoder
 lowerCamelCase		:     Dict          =				0  # Number of params in the encoder

 print("""name""".ljust(60,     """ """							),     """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":
    lowerCamelCase		:     List[str]          =				TopKBinarizer.apply(lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "sigmoied_threshold":
    lowerCamelCase		:     Optional[int]          =				ThresholdBinarizer.apply(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "l0":
    lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				-0.1, 1.1
    lowerCamelCase		:     Any          =				torch.sigmoid(lowerCamelCase							)
    lowerCamelCase		:     str          =				s * (r - l) + l
    lowerCamelCase		:     str          =				s_bar.clamp(min=0.0,     max=1.0							)
    lowerCamelCase		:     Union[str, Any]          =				(mask > 0.0).sum().item()
   else:
    raise ValueError("""Unknown pruning method"""							)
   remaining_count += mask_ones
   print(name.ljust(60,     """ """							),     str(round(100 * mask_ones / param.numel(),     3							)							).ljust(20,     """ """							),     str(lowerCamelCase							)							)
  else:
   encoder_count += param.numel()
   if "bias" in name or "LayerNorm" in name:
    remaining_count += param.numel()

 print(""""""							)
 print("""Remaining Weights (global) %: """,     100 * remaining_count / encoder_count							)


if __name__ == "__main__":
      _lowerCamelCase        =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""",
      )

      _lowerCamelCase        =parser.parse_args()

      main(args)




