


'''simple docstring'''

import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


a_  :				Dict						= logging.get_logger(__name__)

a_  :				int						= {"""vocab_file""": """spiece.model"""}

a_  :				Optional[int]						= {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=False			,  UpperCamelCase=True			,  UpperCamelCase=False			,  UpperCamelCase="<s>"			,  UpperCamelCase="</s>"			,  UpperCamelCase="<unk>"			,  UpperCamelCase="<sep>"			,  UpperCamelCase="<pad>"			,  UpperCamelCase="<cls>"			,  UpperCamelCase="<mask>"			,  UpperCamelCase=["<eop>", "<eod>"]			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  AddedToken(__A			,  lstrip=__A			,  rstrip=__A     ) if isinstance(__A			,  __A     ) else mask_token

   lowerCamelCase_      =  {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       do_lower_case=__A			,  remove_space=__A			,  keep_accents=__A			,  bos_token=__A			,  eos_token=__A			,  unk_token=__A			,  sep_token=__A			,  pad_token=__A			,  cls_token=__A			,  mask_token=__A			,  additional_special_tokens=__A			,  sp_model_kwargs=self.sp_model_kwargs			,  **__A			,  )

   lowerCamelCase_      =  3

   lowerCamelCase_      =  do_lower_case
   lowerCamelCase_      =  remove_space
   lowerCamelCase_      =  keep_accents
   lowerCamelCase_      =  vocab_file

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(__A     )

   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_      =  jieba
   lowerCamelCase_      =  str.maketrans(" \n"			,  "\u2582\u2583"     )






  @property
  # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
  def   snake_case		(    self     ):







   """simple docstring"""
   return len(self.sp_model     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  {self.convert_ids_to_tokens(__A     ): i for i in range(self.vocab_size     )}
   vocab.update(self.added_tokens_encoder     )
   return vocab






  def __getstate__(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.__dict__.copy()
   lowerCamelCase_      =  None
   return state






  def __setstate__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  d

   # for backward compatibility
   if not hasattr(self			,  "sp_model_kwargs"     ):
    lowerCamelCase_      =  {}

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(self.vocab_file     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""





   if self.remove_space:
    lowerCamelCase_      =  " ".join(inputs.strip().split()     )
   else:
    lowerCamelCase_      =  inputs
   lowerCamelCase_      =  outputs.replace("``"			,  "\""     ).replace("''"			,  "\""     )

   if not self.keep_accents:
    lowerCamelCase_      =  unicodedata.normalize("NFKD"			,  __A     )
    lowerCamelCase_      =  "".join([c for c in outputs if not unicodedata.combining(__A     )]     )
   if self.do_lower_case:
    lowerCamelCase_      =  outputs.lower()

   return outputs






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.preprocess_text(__A     )
   lowerCamelCase_      =  self.sp_model.encode(__A			,  out_type=__A     )
   lowerCamelCase_      =  []
   for piece in pieces:
    if len(__A     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
     lowerCamelCase_      =  self.sp_model.EncodeAsPieces(piece[:-1].replace(__A			,  ""     )     )
     if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
      if len(cur_pieces[0]     ) == 1:
       lowerCamelCase_      =  cur_pieces[1:]
      else:
       lowerCamelCase_      =  cur_pieces[0][1:]
     cur_pieces.append(piece[-1]     )
     new_pieces.extend(__A     )
    else:
     new_pieces.append(__A     )

   return new_pieces






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.PieceToId(__A     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.IdToPiece(__A     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  "".join(__A     ).replace(__A			,  " "     ).strip()
   return out_string






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = False     ):







   """simple docstring"""





   if already_has_special_tokens:
    return super().get_special_tokens_mask(
        token_ids_a=__A			,  token_ids_a=__A			,  already_has_special_tokens=__A     )

   if token_ids_a is not None:
    return ([0] * len(__A     )) + [1] + ([0] * len(__A     )) + [1, 1]
   return ([0] * len(__A     )) + [1, 1]






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""





   if not os.path.isdir(__A     ):
    logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
    return
   lowerCamelCase_      =  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     ) and os.path.isfile(self.vocab_file     ):
    copyfile(self.vocab_file			,  __A     )
   elif not os.path.isfile(self.vocab_file     ):
    with open(__A			,  "wb"     ) as fi:
     lowerCamelCase_      =  self.sp_model.serialized_model_proto()
     fi.write(__A     )

   return (out_vocab_file,)







  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  super()._decode(*__A			,  **__A     )
   lowerCamelCase_      =  text.replace(" "			,  ""     ).replace("\u2582"			,  " "     ).replace("\u2583"			,  "\n"     )
   return text







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if "model" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("model."      ,						""	)
  if "norm1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm1"      ,						"attention.output.LayerNorm"	)
  if "norm2" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm2"      ,						"output.LayerNorm"	)
  if "norm" in orig_key:
    lowerCamelCase :					Optional[Any]    =				orig_key.replace("norm"      ,						"LayerNorm"	)
  if "transformer" in orig_key:
    lowerCamelCase :					int    =				orig_key.split("."	)[0].split("_"	)[-1]
    lowerCamelCase :					Dict    =				orig_key.replace(f"""transformer_{layer_num}"""      ,						f"""encoder.layer.{layer_num}"""	)
  if "mha.attn" in orig_key:
    lowerCamelCase :					List[str]    =				orig_key.replace("mha.attn"      ,						"attention.self"	)
  if "mha" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mha"      ,						"attention"	)
  if "W_q" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("W_q"      ,						"self.query"	)
  if "W_k" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("W_k"      ,						"self.key"	)
  if "W_v" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("W_v"      ,						"self.value"	)
  if "ff1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("ff1"      ,						"intermediate.dense"	)
  if "ff2" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff2"      ,						"output.dense"	)
  if "ff" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff"      ,						"output.dense"	)
  if "mlm_class" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("mlm.mlm_class"      ,						"cls.predictions.decoder"	)
  if "mlm" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mlm"      ,						"cls.predictions.transform"	)
  if "cls" not in orig_key:
    lowerCamelCase :					int    =				"yoso." + orig_key

  return orig_key






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    lowerCamelCase :					List[str]    =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowerCamelCase :					Dict    =				val

  lowerCamelCase :					Dict    =				orig_state_dict["cls.predictions.decoder.bias"]
  lowerCamelCase :					Dict    =				torch.arange(SCREAMING_SNAKE_CASE_	).expand((1, -1)	) + 2

  return orig_state_dict






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				torch.load(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)["model_state_dict"]
  lowerCamelCase :					List[str]    =				YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				YosoForMaskedLM(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					List[Any]    =				convert_checkpoint_helper(config.max_position_embeddings      ,						SCREAMING_SNAKE_CASE_	)

  print(model.load_state_dict(SCREAMING_SNAKE_CASE_	)	)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE_	)

  print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The json file for YOSO model config.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   _snake_case             =     parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



