



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Tuple					):






 '''simple docstring'''

 if (
     (cp >= 0x4_e00 and cp <= 0x9_fff)
     or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
     or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
     or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
     or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
     or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
     or (cp >= 0xf_900 and cp <= 0xf_aff)
     or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
 ):  #
  return True

 return False


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str					):






 '''simple docstring'''

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[str]					):






 '''simple docstring'''

 a						=		set()

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :set()					):






 '''simple docstring'''

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

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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :LTP ,					UpperCAmelCase__ :BertTokenizer					):






 '''simple docstring'''

 a						=		[]

 for i in range(0 ,					len(UpperCAmelCase__					) ,					1_00					):
  a						=		ltp_tokenizer.pipeline(lines[i : i + 1_00] ,					tasks=["cws"]					).cws
  a						=		[get_chinese_word(UpperCAmelCase__					) for r in res]
  ltp_res.extend(UpperCAmelCase__					)
 assert len(UpperCAmelCase__					) == len(UpperCAmelCase__					)

 a						=		[]
 for i in range(0 ,					len(UpperCAmelCase__					) ,					1_00					):
  a						=		bert_tokenizer(lines[i : i + 1_00] ,					add_special_tokens=UpperCAmelCase__ ,					truncation=UpperCAmelCase__ ,					max_length=5_12					)
  bert_res.extend(res["input_ids"]					)
 assert len(UpperCAmelCase__					) == len(UpperCAmelCase__					)

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






 '''simple docstring'''

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

 a						=		prepare_ref(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

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


if __name__ == "__main__":
     A_		:     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''',
     )

     A_		:     List[str]        =     parser.parse_args()
     main(args)






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 		_lowercase		(       UpperCAmelCase__	):

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"							)
     a						=		input_file.read()
     a						=		regexp.search(__lowerCAmelCase							)

    return match

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str							)       ->				Dict:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"							,   re.DOTALL							)
     a						=		input_file.read()
     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
     a						=		regexp.finditer(__lowerCAmelCase							)

    a						=		[match for match in matches if match is not None and match.group(1							) is not None]
    return matches[0] if matches else None

   def 		A							(      self			:       List[str]							)       ->				List[Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_encoding_on_file_open(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""							)




   def 		A							(      self			:       Tuple							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_print_statements(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""							)

