



import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      if (
          (cp >= 0x4E_00 and cp <= 0x9F_FF)
          or (cp >= 0x34_00 and cp <= 0x4D_BF)  #
          or (cp >= 0x2_00_00 and cp <= 0x2_A6_DF)  #
          or (cp >= 0x2_A7_00 and cp <= 0x2_B7_3F)  #
          or (cp >= 0x2_B7_40 and cp <= 0x2_B8_1F)  #
          or (cp >= 0x2_B8_20 and cp <= 0x2_CE_AF)  #
          or (cp >= 0xF9_00 and cp <= 0xFA_FF)
          or (cp >= 0x2_F8_00 and cp <= 0x2_FA_1F)  #
      ):  #
            return True

      return False

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      for char in word:
            lowerCamelCase								=		ord(UpperCAmelCase__ )
            if not _is_chinese_char(UpperCAmelCase__ ):
                  return 0
      return 1

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		set()

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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

      lowerCamelCase								=		bert_tokens
      lowerCamelCase			,							lowerCamelCase								=		0, len(UpperCAmelCase__ )
      while start < end:
            lowerCamelCase								=		True
            if is_chinese(bert_word[start] ):
                  lowerCamelCase								=		min(end - start   ,   UpperCAmelCase__ )
                  for i in range(UpperCAmelCase__   ,   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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		[]

      for i in range(0   ,   len(UpperCAmelCase__ )   ,   100 ):
            lowerCamelCase								=		ltp_tokenizer.seg(lines[i : i + 100] )[0]
            lowerCamelCase								=		[get_chinese_word(UpperCAmelCase__ ) for r in res]
            ltp_res.extend(UpperCAmelCase__ )
      assert len(UpperCAmelCase__ ) == len(UpperCAmelCase__ )

      lowerCamelCase								=		[]
      for i in range(0   ,   len(UpperCAmelCase__ )   ,   100 ):
            lowerCamelCase								=		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__ )

      lowerCamelCase								=		[]
      for input_ids, chinese_word in zip(UpperCAmelCase__   ,   UpperCAmelCase__ ):
            lowerCamelCase								=		[]
            for id in input_ids:
                  lowerCamelCase								=		bert_tokenizer._convert_id_to_token(UpperCAmelCase__ )
                  input_tokens.append(UpperCAmelCase__ )
            lowerCamelCase								=		add_sub_symbol(UpperCAmelCase__   ,   UpperCAmelCase__ )
            lowerCamelCase								=		[]
            # 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] == "##":
                        lowerCamelCase								=		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     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      with open(args.file_name   ,   "r"   ,   encoding="utf-8" ) as f:
            lowerCamelCase								=		f.readlines()
      lowerCamelCase								=		[line.strip() for line in data if len(UpperCAmelCase__ ) > 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(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

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


if __name__ == "__main__":
     a_						:     Tuple	     =		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_						:     Optional[Any]	     =		parser.parse_args()
     main(args)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_						:     List[str]	     =		{
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Any	     =		[
         'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
         'CLIPSegModel',
         'CLIPSegPreTrainedModel',
         'CLIPSegTextModel',
         'CLIPSegVisionModel',
         'CLIPSegForImageSegmentation',
     ]

if TYPE_CHECKING:
     from .configuration_clipseg import (
         CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPSegConfig,
         CLIPSegTextConfig,
         CLIPSegVisionConfig,
     )
     from .processing_clipseg import CLIPSegProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clipseg import (
              CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPSegForImageSegmentation,
              CLIPSegModel,
              CLIPSegPreTrainedModel,
              CLIPSegTextModel,
              CLIPSegVisionModel,
          )

else:
     import sys

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