
import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def 							a		(				snake_case__:   Tuple   ):


				'''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 >= 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		(				snake_case__:   str   ):


				'''simple docstring'''
				# word like '180' or '身高' or '神'
				for char in word:
								lowercase_	    =   ord(snake_case__   )
								if not _is_chinese_char(snake_case__   ):
												return 0
				return 1




def 							a		(				snake_case__:   List[str]   ):


				'''simple docstring'''
				lowercase_	    =   set()

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




def 							a		(				snake_case__:   List[str]		,  snake_case__:   set()   ):


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

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




def 							a		(				snake_case__:   List[str]		,  snake_case__:   LTP		,  snake_case__:   BertTokenizer   ):


				'''simple docstring'''
				lowercase_	    =   []

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

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

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

				assert len(snake_case__   ) == len(snake_case__   )

				return ref_ids




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

				lowercase_	    =   prepare_ref(snake_case__		,  snake_case__		,  snake_case__   )

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE:     Any									= {
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     List[Any]									= [
						    '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''PegasusXForConditionalGeneration''',
						    '''PegasusXModel''',
						    '''PegasusXPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_pegasus_x import (
												    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PegasusXForConditionalGeneration,
												    PegasusXModel,
												    PegasusXPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     List[Any]									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)