


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[Any]    ):
					# 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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str    ):
					# word like '180' or '身高' or '神'
					for char in word:
										__UpperCamelCase   							=ord(SCREAMING_SNAKE_CASE__    )
										if not _is_chinese_char(SCREAMING_SNAKE_CASE__    ):
															return 0
					return 1

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]    ):
					__UpperCamelCase   							=set()

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : set()    ):
					if not chinese_word_set:
										return bert_tokens
					__UpperCamelCase   							=max([len(SCREAMING_SNAKE_CASE__    ) for w in chinese_word_set]    )

					__UpperCamelCase   							=bert_tokens
					__UpperCamelCase							,				__UpperCamelCase   							=0, len(SCREAMING_SNAKE_CASE__    )
					while start < end:
										__UpperCamelCase   							=True
										if is_chinese(bert_word[start]    ):
															__UpperCamelCase   							=min(end - start      ,					SCREAMING_SNAKE_CASE__    )
															for i in range(SCREAMING_SNAKE_CASE__      ,					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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : List[str]      ,					SCREAMING_SNAKE_CASE__      : LTP      ,					SCREAMING_SNAKE_CASE__      : BertTokenizer    ):
					__UpperCamelCase   							=[]

					for i in range(0      ,					len(SCREAMING_SNAKE_CASE__    )      ,					1_00    ):
										__UpperCamelCase   							=ltp_tokenizer.pipeline(lines[i : i + 1_00]      ,					tasks=['cws']    ).cws
										__UpperCamelCase   							=[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__    )

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

					__UpperCamelCase   							=[]
					for input_ids, chinese_word in zip(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    ):
										__UpperCamelCase   							=[]
										for id in input_ids:
															__UpperCamelCase   							=bert_tokenizer._convert_id_to_token(SCREAMING_SNAKE_CASE__    )
															input_tokens.append(SCREAMING_SNAKE_CASE__    )
										__UpperCamelCase   							=add_sub_symbol(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
										__UpperCamelCase   							=[]
										# 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] == "##":
																				__UpperCamelCase   							=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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple    ):
					# 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(SCREAMING_SNAKE_CASE__    ) > 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(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

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


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









import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

_A			=						'python tqdm regex requests packaging filelock numpy tokenizers'.split()
if sys.version_info < (3, 7):
   pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
   pkgs_to_check_at_runtime.append('importlib_metadata')

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(f"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[int]=None    ):
     require_version(deps[pkg]      ,					SCREAMING_SNAKE_CASE__    )





