




"""simple docstring"""

import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()






def 						_snake_case					( _snake_case							: int    ,  _snake_case							: Union[str, Any]    ,  _snake_case							: Tuple							)   -> Union[str, Any]:






      '''simple docstring'''


      _A        =       TaConfig.from_json_file(_snake_case							)
      print(F'''Building PyTorch model from configuration: {config}'''							)
      _A        =       TaForConditionalGeneration(_snake_case							)

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(_snake_case    ,  _snake_case    ,  _snake_case							)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
      model.save_pretrained(_snake_case							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
       )
       parser.add_argument(
           '''--config_file''',
           default=None,
           type=str,
           required=True,
           help=(
               '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
           ),
       )
       parser.add_argument(
           '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       a         =      parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







"""simple docstring"""

import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer






def 						_snake_case					( _snake_case							: Dict							)   -> Any:






      '''simple docstring'''


      if (
          (cp >= 0X4e00 and cp <= 0X9fff)
          or (cp >= 0X3400 and cp <= 0X4dbf)  #
          or (cp >= 0X2_0000 and cp <= 0X2_a6df)  #
          or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
          or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
          or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
          or (cp >= 0Xf900 and cp <= 0Xfaff)
          or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
      ):  #
            return True

      return False






def 						_snake_case					( _snake_case							: str							)   -> Tuple:






      '''simple docstring'''


      for char in word:
            _A        =       ord(_snake_case							)
            if not _is_chinese_char(_snake_case							):
                  return 0
      return 1






def 						_snake_case					( _snake_case							: List[str]							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       set()

      for token in tokens:
            _A        =       len(_snake_case							) > 1 and is_chinese(_snake_case							)
            if chinese_word:
                  word_set.add(_snake_case							)
      _A        =       list(_snake_case							)
      return word_list






def 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: set()							)   -> Optional[Any]:






      '''simple docstring'''


      if not chinese_word_set:
            return bert_tokens
      _A        =       max([len(_snake_case							) for w in chinese_word_set]							)

      _A        =       bert_tokens
      _A       ,   _A        =       0, len(_snake_case							)
      while start < end:
            _A        =       True
            if is_chinese(bert_word[start]							):
                  _A        =       min(end - start    ,  _snake_case							)
                  for i in range(_snake_case    ,  1    ,  -1							):
                        _A        =       ''.join(bert_word[start : start + i]							)
                        if whole_word in chinese_word_set:
                              for j in range(start + 1    ,  start + i							):
                                    _A        =       '##' + bert_word[j]
                              _A        =       start + i
                              _A        =       False
                              break
            if single_word:
                  start += 1
      return bert_word






def 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: LTP    ,  _snake_case							: BertTokenizer							)   -> str:






      '''simple docstring'''


      _A        =       []

      for i in range(0    ,  len(_snake_case							)    ,  1_00							):
            _A        =       ltp_tokenizer.seg(lines[i : i + 1_00]							)[0]
            _A        =       [get_chinese_word(_snake_case							) for r in res]
            ltp_res.extend(_snake_case							)
      assert len(_snake_case							) == len(_snake_case							)

      _A        =       []
      for i in range(0    ,  len(_snake_case							)    ,  1_00							):
            _A        =       bert_tokenizer(lines[i : i + 1_00]    ,  add_special_tokens=_snake_case    ,  truncation=_snake_case    ,  max_length=5_12							)
            bert_res.extend(res['input_ids']							)
      assert len(_snake_case							) == len(_snake_case							)

      _A        =       []
      for input_ids, chinese_word in zip(_snake_case    ,  _snake_case							):
            _A        =       []
            for id in input_ids:
                  _A        =       bert_tokenizer._convert_id_to_token(_snake_case							)
                  input_tokens.append(_snake_case							)
            _A        =       add_sub_symbol(_snake_case    ,  _snake_case							)
            _A        =       []
            # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
            for i, token in enumerate(_snake_case							):
                  if token[:2] == "##":
                        _A        =       token[2:]
                        # save chinese tokens' pos
                        if len(_snake_case							) == 1 and _is_chinese_char(ord(_snake_case							)							):
                              ref_id.append(_snake_case							)
            ref_ids.append(_snake_case							)

      assert len(_snake_case							) == len(_snake_case							)

      return ref_ids






def 						_snake_case					( _snake_case							: List[str]							)   -> Dict:






      '''simple docstring'''


      with open(args.file_name    ,  'r'    ,  encoding='utf-8'							) as f:
            _A        =       f.readlines()
      _A        =       [line.strip() for line in data if len(_snake_case							) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      _A        =       LTP(args.ltp							)  # faster in GPU device
      _A        =       BertTokenizer.from_pretrained(args.bert							)

      _A        =       prepare_ref(_snake_case    ,  _snake_case    ,  _snake_case							)

      with open(args.save_path    ,  'w'    ,  encoding='utf-8'							) as f:
            _A        =       [json.dumps(_snake_case							) + '\n' for ref in ref_ids]
            f.writelines(_snake_case							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser(description='''prepare_chinese_ref''')
       parser.add_argument(
           '''--file_name''',
           type=str,
           default='''./resources/chinese-demo.txt''',
           help='''file need process, same as training data in lm''',
       )
       parser.add_argument(
           '''--ltp''', type=str, default='''./resources/ltp''', help='''resources for LTP tokenizer, usually a path'''
       )
       parser.add_argument('''--bert''', type=str, default='''./resources/robert''', help='''resources for Bert tokenizer''')
       parser.add_argument('''--save_path''', type=str, default='''./resources/ref.txt''', help='''path to save res''')

       a         =      parser.parse_args()
       main(args)

