






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def      _snake_case     (__lowercase):
   # 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      _snake_case     (__lowercase):
   # word like '180' or '身高' or '神'
   for char in word:
      UpperCamelCase_						=  ord(lowerCamelCase__)
      if not _is_chinese_char(lowerCamelCase__):
         return 0
   return 1






def      _snake_case     (__lowercase):
   UpperCamelCase_						=  set()

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






def      _snake_case     (__lowercase     ,   __lowercase):
   if not chinese_word_set:
      return bert_tokens
   UpperCamelCase_						=  max([len(lowerCamelCase__) for w in chinese_word_set])

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






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  []

   for i in range(0     ,   len(lowerCamelCase__)     ,   100):
      UpperCamelCase_						=  ltp_tokenizer.pipeline(lines[i : i + 100]     ,   tasks=['cws']).cws
      UpperCamelCase_						=  [get_chinese_word(lowerCamelCase__) for r in res]
      ltp_res.extend(lowerCamelCase__)
   assert len(lowerCamelCase__) == len(lowerCamelCase__)

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

   UpperCamelCase_						=  []
   for input_ids, chinese_word in zip(lowerCamelCase__     ,   lowerCamelCase__):
      UpperCamelCase_						=  []
      for id in input_ids:
         UpperCamelCase_						=  bert_tokenizer._convert_id_to_token(lowerCamelCase__)
         input_tokens.append(lowerCamelCase__)
      UpperCamelCase_						=  add_sub_symbol(lowerCamelCase__     ,   lowerCamelCase__)
      UpperCamelCase_						=  []
      # 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] == "##":
            UpperCamelCase_						=  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      _snake_case     (__lowercase):
   # 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:
      UpperCamelCase_						=  f.readlines()
   UpperCamelCase_						=  [line.strip() for line in data if len(lowerCamelCase__) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   UpperCamelCase_						=  LTP(args.ltp)  # faster in GPU device
   UpperCamelCase_						=  BertTokenizer.from_pretrained(args.bert)

   UpperCamelCase_						=  prepare_ref(lowerCamelCase__     ,   lowerCamelCase__     ,   lowerCamelCase__)

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


if __name__ == "__main__":
 snake_case__   :						int													=   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""",
 )

 snake_case__   :						Union[str, Any]													=   parser.parse_args()
 main(args)









"""simple docstring"""


import requests
from bsa import BeautifulSoup


def    _lowerCamelCase   (     lowerCamelCase__	:							str = "https://www.worldometers.info/coronavirus"  ):
 lowercase__       :       Optional[Any]				=						BeautifulSoup(requests.get(lowerCamelCase__  ).text       ,		"""html.parser"""  )
 lowercase__       :       Union[str, Any]				=						soup.findAll("""h1"""  )
 lowercase__       :       List[str]				=						soup.findAll("""div"""       ,		{"""class""": """maincounter-number"""}  )
 keys += soup.findAll("""span"""       ,		{"""class""": """panel-title"""}  )
 values += soup.findAll("""div"""       ,		{"""class""": """number-table-main"""}  )
 return {key.text.strip(): value.text.strip() for key, value in zip(lowerCamelCase__       ,		lowerCamelCase__  )}


if __name__ == "__main__":
   print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
   for key, value in world_covidaa_stats().items():
      print(F"{key}\n{value}\n")