
"""simple docstring"""




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  if (
      (cp >= 0x4e00 and cp <= 0x9fff)
      or (cp >= 0x3400 and cp <= 0x4dbf)  #
      or (cp >= 0x20000 and cp <= 0x2a6df)  #
      or (cp >= 0x2a700 and cp <= 0x2b73f)  #
      or (cp >= 0x2b740 and cp <= 0x2b81f)  #
      or (cp >= 0x2b820 and cp <= 0x2ceaf)  #
      or (cp >= 0xf900 and cp <= 0xfaff)
      or (cp >= 0x2f800 and cp <= 0x2fa1f)  #
  ):  #
    return True

  return False




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  for char in word:
    _lowercase:      List[str]   			=  ord(_UpperCamelCase    )
    if not _is_chinese_char(_UpperCamelCase    ):
      return 0
  return 1




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[int]   			=  set()

  for token in tokens:
    _lowercase:      Any   			=  len(_UpperCamelCase    ) > 1 and is_chinese(_UpperCamelCase    )
    if chinese_word:
      word_set.add(_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  list(_UpperCamelCase    )
  return word_list




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if not chinese_word_set:
    return bert_tokens
  _lowercase:      Union[str, Any]   			=  max([len(_UpperCamelCase    ) for w in chinese_word_set]    )

  _lowercase:      Dict   			=  bert_tokens
  _lowercase			,					_lowercase:      Optional[int]   			=  0, len(_UpperCamelCase    )
  while start < end:
    _lowercase:      List[Any]   			=  True
    if is_chinese(bert_word[start]    ):
      _lowercase:      Dict   			=  min(end - start			,					_UpperCamelCase    )
      for i in range(_UpperCamelCase			,					1			,					-1    ):
        _lowercase:      List[str]   			=  ''''''.join(bert_word[start : start + i]    )
        if whole_word in chinese_word_set:
          for j in range(start + 1			,					start + i    ):
            _lowercase:      Union[str, Any]   			=  '''##''' + bert_word[j]
          _lowercase:      str   			=  start + i
          _lowercase:      str   			=  False
          break
    if single_word:
      start += 1
  return bert_word




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      str   			=  []

  for i in range(0			,					len(_UpperCamelCase    )			,					100    ):
    _lowercase:      List[Any]   			=  ltp_tokenizer.pipeline(lines[i : i + 100]			,					tasks=['''cws''']    ).cws
    _lowercase:      Tuple   			=  [get_chinese_word(_UpperCamelCase    ) for r in res]
    ltp_res.extend(_UpperCamelCase    )
  assert len(_UpperCamelCase    ) == len(_UpperCamelCase    )

  _lowercase:      Any   			=  []
  for i in range(0			,					len(_UpperCamelCase    )			,					100    ):
    _lowercase:      Optional[Any]   			=  bert_tokenizer(lines[i : i + 100]			,					add_special_tokens=_UpperCamelCase			,					truncation=_UpperCamelCase			,					max_length=512    )
    bert_res.extend(res['''input_ids''']    )
  assert len(_UpperCamelCase    ) == len(_UpperCamelCase    )

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

  assert len(_UpperCamelCase    ) == len(_UpperCamelCase    )

  return ref_ids




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



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

  _lowercase:      Any   			=  prepare_ref(_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )

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


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

  A__ :       Optional[Any]				= parser.parse_args()
  main(args)


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BioGptForCausalLM',
      'BioGptForTokenClassification',
      'BioGptForSequenceClassification',
      'BioGptModel',
      'BioGptPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  A__ :       int				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
