


"""simple docstring"""







import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def 					lowercase      (    A_	)->				Dict:
  '''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 					lowercase      (    A_	)->				Optional[Any]:
  '''simple docstring'''







  for char in word:
    a						:		List[Any]											=      ord(A_	)
    if not _is_chinese_char(A_	):
      return 0
  return 1


def 					lowercase      (    A_	)->				Union[str, Any]:
  '''simple docstring'''







  a						:		List[str]											=      set()

  for token in tokens:
    a						:		Union[str, Any]											=      len(A_	) > 1 and is_chinese(A_	)
    if chinese_word:
      word_set.add(A_	)
  a						:		List[Any]											=      list(A_	)
  return word_list


def 					lowercase      (    A_  ,       A_	)->				Tuple:
  '''simple docstring'''







  if not chinese_word_set:
    return bert_tokens
  a						:		Any											=      max([len(A_	) for w in chinese_word_set]	)

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


def 					lowercase      (    A_  ,       A_  ,       A_	)->				str:
  '''simple docstring'''







  a						:		Any											=      []

  for i in range(0  ,       len(A_	)  ,       100	):
    a						:		List[Any]											=      ltp_tokenizer.seg(lines[i : i + 100]	)[0]
    a						:		Union[str, Any]											=      [get_chinese_word(A_	) for r in res]
    ltp_res.extend(A_	)
  assert len(A_	) == len(A_	)

  a						:		int											=      []
  for i in range(0  ,       len(A_	)  ,       100	):
    a						:		int											=      bert_tokenizer(lines[i : i + 100]  ,       add_special_tokens=A_  ,       truncation=A_  ,       max_length=512	)
    bert_res.extend(res["input_ids"]	)
  assert len(A_	) == len(A_	)

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

  assert len(A_	) == len(A_	)

  return ref_ids


def 					lowercase      (    A_	)->				str:
  '''simple docstring'''







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

  a						:		Optional[Any]											=      prepare_ref(A_  ,       A_  ,       A_	)

  with open(args.save_path  ,       "w"  ,       encoding="utf-8"	) as f:
    a						:		List[str]											=      [json.dumps(A_	) + "\n" for ref in ref_ids]
    f.writelines(A_	)


if __name__ == "__main__":
 __lowercase    =				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""")

 __lowercase    =				parser.parse_args()
 main(args)










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase    =				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """InstructBlipQFormerModel""",
     """InstructBlipPreTrainedModel""",
     """InstructBlipForConditionalGeneration""",
     """InstructBlipVisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_instructblip import (
     INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     InstructBlipConfig,
     InstructBlipQFormerConfig,
     InstructBlipVisionConfig,
 )
 from .processing_instructblip import InstructBlipProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_instructblip import (
      INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      InstructBlipForConditionalGeneration,
      InstructBlipPreTrainedModel,
      InstructBlipQFormerModel,
      InstructBlipVisionModel,
  )

else:
 import sys

 __lowercase    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






