






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer
def      a_ (      _A  )			->			List[str]:







						"""simple docstring"""
						# 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 >= 0X20000 and cp <= 0X2A6DF)  #
						    or (cp >= 0X2A700 and cp <= 0X2B73F)  #
						    or (cp >= 0X2B740 and cp <= 0X2B81F)  #
						    or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
						    or (cp >= 0XF900 and cp <= 0XFAFF)
						    or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
						):  #
												return True

						return False
def      a_ (      _A  )			->			List[Any]:







						"""simple docstring"""
						# word like '180' or '身高' or '神'
						for char in word:
												snake_case__									=     ord(_SCREAMING_SNAKE_CASE  )
												if not _is_chinese_char(_SCREAMING_SNAKE_CASE  ):
																		return 0
						return 1
def      a_ (      _A  )			->			Any:







						"""simple docstring"""
						snake_case__									=     set()

						for token in tokens:
												snake_case__									=     len(_SCREAMING_SNAKE_CASE  ) > 1 and is_chinese(_SCREAMING_SNAKE_CASE  )
												if chinese_word:
																		word_set.add(_SCREAMING_SNAKE_CASE  )
						snake_case__									=     list(_SCREAMING_SNAKE_CASE  )
						return word_list
def      a_ (      _A      ,   _A  )			->			Optional[Any]:







						"""simple docstring"""
						if not chinese_word_set:
												return bert_tokens
						snake_case__									=     max([len(_SCREAMING_SNAKE_CASE  ) for w in chinese_word_set]  )

						snake_case__									=     bert_tokens
						snake_case__							,						snake_case__									=     0, len(_SCREAMING_SNAKE_CASE  )
						while start < end:
												snake_case__									=     True
												if is_chinese(bert_word[start]  ):
																		snake_case__									=     min(end - start      ,   _SCREAMING_SNAKE_CASE  )
																		for i in range(_SCREAMING_SNAKE_CASE      ,   1      ,   -1  ):
																								snake_case__									=     ''.join(bert_word[start : start + i]  )
																								if whole_word in chinese_word_set:
																														for j in range(start + 1      ,   start + i  ):
																																				snake_case__									=     '##' + bert_word[j]
																														snake_case__									=     start + i
																														snake_case__									=     False
																														break
												if single_word:
																		start += 1
						return bert_word
def      a_ (      _A      ,   _A      ,   _A  )			->			Optional[Any]:







						"""simple docstring"""
						snake_case__									=     []

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

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

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

						assert len(_SCREAMING_SNAKE_CASE  ) == len(_SCREAMING_SNAKE_CASE  )

						return ref_ids
def      a_ (      _A  )			->			int:







						"""simple docstring"""
						# 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:
												snake_case__									=     f.readlines()
						snake_case__									=     [line.strip() for line in data if len(_SCREAMING_SNAKE_CASE  ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						snake_case__									=     LTP(args.ltp  )  # faster in GPU device
						snake_case__									=     BertTokenizer.from_pretrained(args.bert  )

						snake_case__									=     prepare_ref(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE  )

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


if __name__ == "__main__":
							__UpperCamelCase					:		Dict	  =						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""")

							__UpperCamelCase					:		Optional[int]	  =						parser.parse_args()
							main(args)












import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__UpperCamelCase					:		Optional[Any]	  =						logging.getLogger()


@unittest.skip("Temporarily disable the doc tests."	)
@require_torch
@require_tf
@slow
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      Path       ,	UpperCamelCase:      Union[str, None] = None       ,	UpperCamelCase:      Union[List[str], None] = None       ,	UpperCamelCase:      Union[str, List[str], None] = None       ,	UpperCamelCase:      bool = True       ,	)		->		int:
											snake_case__									=     [file for file in os.listdir(UpperCamelCase						) if os.path.isfile(os.path.join(UpperCamelCase       ,	UpperCamelCase						)						)]

											if identifier is not None:
															snake_case__									=     [file for file in files if identifier in file]

											if n_identifier is not None:
															if isinstance(UpperCamelCase       ,	UpperCamelCase						):
																			for n_ in n_identifier:
																							snake_case__									=     [file for file in files if n_ not in file]
															else:
																			snake_case__									=     [file for file in files if n_identifier not in file]

											snake_case__									=     ignore_files or []
											ignore_files.append('__init__.py'						)
											snake_case__									=     [file for file in files if file not in ignore_files]

											for file in files:
															# Open all files
															print('Testing'       ,	UpperCamelCase						)

															if only_modules:
																			snake_case__									=     file.split('.'						)[0]
																			try:
																							snake_case__									=     getattr(UpperCamelCase       ,	UpperCamelCase						)
																							snake_case__									=     doctest.DocTestSuite(UpperCamelCase						)
																							snake_case__									=     unittest.TextTestRunner().run(UpperCamelCase						)
																							self.assertIs(len(result.failures						)       ,	0						)
																			except AttributeError:
																							logger.info(F'''{module_identifier} is not a module.'''						)
															else:
																			snake_case__									=     doctest.testfile(str('..' / directory / file						)       ,	optionflags=doctest.ELLIPSIS						)
																			self.assertIs(result.failed       ,	0						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'modeling'
											snake_case__									=     [
											    'modeling_ctrl.py',
											    'modeling_tf_ctrl.py',
											]
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase       ,	ignore_files=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		str:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'tokenization'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		int:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     'configuration'
											self.analyze_directory(UpperCamelCase       ,	identifier=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											snake_case__									=     Path('src/transformers'						)
											snake_case__									=     ['configuration', 'modeling', 'tokenization']
											self.analyze_directory(UpperCamelCase       ,	n_identifier=UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     Path('docs/source'						)
											snake_case__									=     ['favicon.ico']
											self.analyze_directory(UpperCamelCase       ,	ignore_files=UpperCamelCase       ,	only_modules=UpperCamelCase						)




