






'''simple docstring'''
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 	_a(					UpperCamelCase__			:  Optional[int]							):




      '''simple docstring'''






      if (
          (cp >= 0X4_E00 and cp <= 0X9_FFF)
          or (cp >= 0X3_400 and cp <= 0X4_DBF)  #
          or (cp >= 0X20_000 and cp <= 0X2A_6DF)  #
          or (cp >= 0X2A_700 and cp <= 0X2B_73F)  #
          or (cp >= 0X2B_740 and cp <= 0X2B_81F)  #
          or (cp >= 0X2B_820 and cp <= 0X2C_EAF)  #
          or (cp >= 0XF_900 and cp <= 0XF_AFF)
          or (cp >= 0X2F_800 and cp <= 0X2F_A1F)  #
      ):  #
            return True

      return False





def 	_a(					UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      for char in word:
            SCREAMING_SNAKE_CASE__       :			Dict													=ord(UpperCamelCase__							)
            if not _is_chinese_char(UpperCamelCase__							):
                  return 0
      return 1





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			str													=set()

      for token in tokens:
            SCREAMING_SNAKE_CASE__       :			List[str]													=len(UpperCamelCase__							) > 1 and is_chinese(UpperCamelCase__							)
            if chinese_word:
                  word_set.add(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			int													=list(UpperCamelCase__							)
      return word_list





def 	_a(					UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Optional[Any]							):




      '''simple docstring'''






      if not chinese_word_set:
            return bert_tokens
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=max([len(UpperCamelCase__							) for w in chinese_word_set]							)

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





def 	_a(					UpperCamelCase__			:  List[str],		UpperCamelCase__			:  Any,		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=[]

      for i in range(0,		len(UpperCamelCase__							),		1_0_0							):
            SCREAMING_SNAKE_CASE__       :			Tuple													=ltp_tokenizer.pipeline(lines[i : i + 1_0_0],		tasks=['''cws''']							).cws
            SCREAMING_SNAKE_CASE__       :			Tuple													=[get_chinese_word(UpperCamelCase__							) for r in res]
            ltp_res.extend(UpperCamelCase__							)
      assert len(UpperCamelCase__							) == len(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			List[str]													=[]
      for i in range(0,		len(UpperCamelCase__							),		1_0_0							):
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=bert_tokenizer(lines[i : i + 1_0_0],		add_special_tokens=UpperCamelCase__,		truncation=UpperCamelCase__,		max_length=5_1_2							)
            bert_res.extend(res['''input_ids''']							)
      assert len(UpperCamelCase__							) == len(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Optional[int]													=[]
      for input_ids, chinese_word in zip(UpperCamelCase__,		UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__       :			List[Any]													=[]
            for id in input_ids:
                  SCREAMING_SNAKE_CASE__       :			str													=bert_tokenizer._convert_id_to_token(UpperCamelCase__							)
                  input_tokens.append(UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__       :			Dict													=add_sub_symbol(UpperCamelCase__,		UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__       :			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] == "##":
                        SCREAMING_SNAKE_CASE__       :			str													=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 	_a(					UpperCamelCase__			:  Dict							):




      '''simple docstring'''






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

      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=prepare_ref(UpperCamelCase__,		UpperCamelCase__,		UpperCamelCase__							)

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


if __name__ == "__main__":
  a_					=	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_					=	parser.parse_args()
  main(args)







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_					=	{
    '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:
  a_					=	[
      '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

  a_					=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)