




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def 		a_							( __lowerCAmelCase				):
  # This defines a "chinese character" as anything in the CJK Unicode block:
  #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
  #
  # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
  # despite its name. The modern Korean Hangul alphabet is a different block,
  # as is Japanese Hiragana and Katakana. Those alphabets are used to write
  # space-separated words, so they are not treated specially and handled
  # like the all of the other languages.
  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 		a_							( __lowerCAmelCase				):
  # word like '180' or '身高' or '神'
  for char in word:
    lowerCAmelCase__										=      ord(__lowerCAmelCase				)
    if not _is_chinese_char(__lowerCAmelCase				):
      return 0
  return 1
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      set()

  for token in tokens:
    lowerCAmelCase__										=      len(__lowerCAmelCase				) > 1 and is_chinese(__lowerCAmelCase				)
    if chinese_word:
      word_set.add(__lowerCAmelCase				)
  lowerCAmelCase__										=      list(__lowerCAmelCase				)
  return word_list
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  if not chinese_word_set:
    return bert_tokens
  lowerCAmelCase__										=      max([len(__lowerCAmelCase				) for w in chinese_word_set]				)

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

  for i in range(0   , len(__lowerCAmelCase				)   , 1_00				):
    lowerCAmelCase__										=      ltp_tokenizer.pipeline(lines[i : i + 1_00]   , tasks=['''cws''']				).cws
    lowerCAmelCase__										=      [get_chinese_word(__lowerCAmelCase				) for r in res]
    ltp_res.extend(__lowerCAmelCase				)
  assert len(__lowerCAmelCase				) == len(__lowerCAmelCase				)

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

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

  assert len(__lowerCAmelCase				) == len(__lowerCAmelCase				)

  return ref_ids
def 		a_							( __lowerCAmelCase				):
  # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
  # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
  with open(args.file_name   , '''r'''   , encoding='''utf-8'''				) as f:
    lowerCAmelCase__										=      f.readlines()
  lowerCAmelCase__										=      [line.strip() for line in data if len(__lowerCAmelCase				) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  lowerCAmelCase__										=      LTP(args.ltp				)  # faster in GPU device
  lowerCAmelCase__										=      BertTokenizer.from_pretrained(args.bert				)

  lowerCAmelCase__										=      prepare_ref(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				)

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


if __name__ == "__main__":
      __magic_name__  :  Any					=       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""",
      )

      __magic_name__  :  Tuple					=       parser.parse_args()
      main(args)







from typing import TYPE_CHECKING

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


__magic_name__  :  Union[str, Any]					=       {
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  str					=       [
          """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """AltCLIPPreTrainedModel""",
          """AltCLIPModel""",
          """AltCLIPTextModel""",
          """AltCLIPVisionModel""",
      ]


if TYPE_CHECKING:
      from .configuration_altclip import (
          ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AltCLIPConfig,
          AltCLIPTextConfig,
          AltCLIPVisionConfig,
      )
      from .processing_altclip import AltCLIPProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_altclip import (
                ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                AltCLIPModel,
                AltCLIPPreTrainedModel,
                AltCLIPTextModel,
                AltCLIPVisionModel,
            )


else:
      import sys

      __magic_name__  :  str					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

