
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def  __lowerCamelCase					( _lowercase			)       ->      Tuple:
     # 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 >= 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  __lowerCamelCase					( _lowercase			)       ->      Dict:
     # word like '180' or '身高' or '神'
     for char in word:
          UpperCamelCase						    =					ord(_lowercase			)
          if not _is_chinese_char(_lowercase			):
               return 0
     return 1

def  __lowerCamelCase					( _lowercase			)       ->      Optional[Any]:
     UpperCamelCase						    =					set()

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

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      int:
     if not chinese_word_set:
          return bert_tokens
     UpperCamelCase						    =					max([len(_lowercase			) for w in chinese_word_set]			)

     UpperCamelCase						    =					bert_tokens
     UpperCamelCase    ,		UpperCamelCase						    =					0, len(_lowercase			)
     while start < end:
          UpperCamelCase						    =					True
          if is_chinese(bert_word[start]			):
               UpperCamelCase						    =					min(end - start						,     _lowercase			)
               for i in range(_lowercase						,     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  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase			)       ->      List[str]:
     UpperCamelCase						    =					[]

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

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

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

     assert len(_lowercase			) == len(_lowercase			)

     return ref_ids

def  __lowerCamelCase					( _lowercase			)       ->      Dict:
     # 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(_lowercase			) > 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(_lowercase						,     _lowercase						,     _lowercase			)

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


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




from decimal import Decimal, getcontext
from math import ceil, factorial

def  __lowerCamelCase					( _lowercase			)       ->      str:

     if not isinstance(_lowercase						,     _lowercase			):
          raise TypeError('Undefined for non-integers'			)
     elif precision < 1:
          raise ValueError('Undefined for non-natural numbers'			)

     UpperCamelCase						    =					precision
     UpperCamelCase						    =					ceil(precision / 14			)
     UpperCamelCase						    =					426880 * Decimal(10005			).sqrt()
     UpperCamelCase						    =					1
     UpperCamelCase						    =					13591409
     UpperCamelCase						    =					Decimal(_lowercase			)
     for k in range(1						,     _lowercase			):
          UpperCamelCase						    =					factorial(6 * k			) // (factorial(3 * k			) * factorial(_lowercase			) ** 3)
          linear_term += 545140134
          exponential_term *= -262537412640768000
          partial_sum += Decimal(multinomial_term * linear_term			) / exponential_term
     return str(constant_term / partial_sum			)[:-1]


if __name__ == "__main__":
 _snake_case      = 50
 print(F"The first {n} digits of pi is: {pi(n)}")


