
'''simple docstring'''







from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Union[List[PIL.Image.Image], np.ndarray]
  __UpperCAmelCase				:				Optional[List[bool]]


if is_transformers_available() and is_torch_available():
  from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def 					UpperCAmelCase_		(__a      :    List[Any] ):






   """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 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   for char in word:
      _a				:	Union[str, Any]			=   ord(__a )
      if not _is_chinese_char(__a ):
         return 0
   return 1





def 					UpperCAmelCase_		(__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   set()

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





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    set() ):






   """simple docstring"""

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

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





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    LTP ,	__a      :    BertTokenizer ):






   """simple docstring"""

   _a				:	int			=   []

   for i in range(0 ,	len(__a ) ,	1_0_0 ):
      _a				:	Union[str, Any]			=   ltp_tokenizer.seg(lines[i : i + 1_0_0] )[0]
      _a				:	Optional[Any]			=   [get_chinese_word(__a ) for r in res]
      ltp_res.extend(__a )
   assert len(__a ) == len(__a )

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

   _a				:	List[str]			=   []
   for input_ids, chinese_word in zip(__a ,	__a ):
      _a				:	int			=   []
      for id in input_ids:
         _a				:	Optional[int]			=   bert_tokenizer._convert_id_to_token(__a )
         input_tokens.append(__a )
      _a				:	List[str]			=   add_sub_symbol(__a ,	__a )
      _a				:	Tuple			=   []
      # 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				:	str			=   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 					UpperCAmelCase_		(__a      :    Optional[Any] ):






   """simple docstring"""

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

   _a				:	int			=   prepare_ref(__a ,	__a ,	__a )

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


if __name__ == "__main__":
  __lowerCAmelCase         =    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""")

  __lowerCAmelCase         =    parser.parse_args()
  main(args)



