

'''simple docstring'''

import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def 						__magic_name__       (							__UpperCAmelCase      )						->					Optional[int]:

   '''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 						__magic_name__       (							__UpperCAmelCase      )						->					Union[str, Any]:

   '''simple docstring'''







   for char in word:
      __SCREAMING_SNAKE_CASE      							=       ord(__snake_case      )
      if not _is_chinese_char(__snake_case      ):
         return 0
   return 1

def 						__magic_name__       (							__UpperCAmelCase      )						->					Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       set()

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

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







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

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

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []

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

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

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

   assert len(__snake_case      ) == len(__snake_case      )

   return ref_ids

def 						__magic_name__       (							__UpperCAmelCase      )						->					List[str]:

   '''simple docstring'''







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

   __SCREAMING_SNAKE_CASE      							=       prepare_ref(__snake_case							,	__snake_case							,	__snake_case      )

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


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












'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .multicontrolnet import MultiControlNetModel
    from .pipeline_controlnet import StableDiffusionControlNetPipeline
    from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
    from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
    from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline





