
'''simple docstring'''

import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer







def 	A  (   UpperCamelCase_       :  Optional[int]   )       ->							Any:







			'''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 	A  (   UpperCamelCase_       :  str   )       ->							Any:







			'''simple docstring'''



			for char in word:
						lowerCAmelCase__										=				ord(UpperCamelCase_   )
						if not _is_chinese_char(UpperCamelCase_   ):
									return 0
			return 1







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







			'''simple docstring'''



			lowerCAmelCase__										=				set()

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







def 	A  (   UpperCamelCase_       :  List[str]    ,						UpperCamelCase_       :  set()   )       ->							str:







			'''simple docstring'''



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

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







def 	A  (   UpperCamelCase_       :  List[str]    ,						UpperCamelCase_       :  LTP    ,						UpperCamelCase_       :  BertTokenizer   )       ->							str:







			'''simple docstring'''



			lowerCAmelCase__										=				[]

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

			lowerCAmelCase__										=				[]
			for i in range(0    ,						len(UpperCamelCase_   )    ,						1_00   ):
						lowerCAmelCase__										=				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_   )

			lowerCAmelCase__										=				[]
			for input_ids, chinese_word in zip(UpperCamelCase_    ,						UpperCamelCase_   ):
						lowerCAmelCase__										=				[]
						for id in input_ids:
									lowerCAmelCase__										=				bert_tokenizer._convert_id_to_token(UpperCamelCase_   )
									input_tokens.append(UpperCamelCase_   )
						lowerCAmelCase__										=				add_sub_symbol(UpperCamelCase_    ,						UpperCamelCase_   )
						lowerCAmelCase__										=				[]
						# 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] == "##":
												lowerCAmelCase__										=				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 	A  (   UpperCamelCase_       :  List[str]   )       ->							str:







			'''simple docstring'''



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

			lowerCAmelCase__										=				prepare_ref(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_   )

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


if __name__ == "__main__":
		UpperCAmelCase__ :			Any											=		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",
		)

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










SCREAMING_SNAKE_CASE__   			=   '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

SCREAMING_SNAKE_CASE__   			=   [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE__   			=   {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




