



import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def 			__a	(       lowerCAmelCase_  :   str							) ->					str:






						'''simple docstring'''



						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	(       lowerCAmelCase_  :   Optional[int]							) ->					Union[str, Any]:






						'''simple docstring'''



						for char in word:
												UpperCAmelCase_=  ord(lowerCamelCase_							)
												if not _is_chinese_char(lowerCamelCase_							):
																		return 0
						return 1



def 			__a	(       lowerCAmelCase_  :   List[Any]							) ->					Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=  set()

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



def 			__a	(       lowerCAmelCase_  :   List[Any]     ,lowerCAmelCase_  :   Union[str, Any]							) ->					Union[str, Any]:






						'''simple docstring'''



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

						UpperCAmelCase_=  bert_tokens
						UpperCAmelCase_,  UpperCAmelCase_=  0, len(lowerCamelCase_							)
						while start < end:
												UpperCAmelCase_=  True
												if is_chinese(bert_word[start]							):
																		UpperCAmelCase_=  min(end - start     ,lowerCamelCase_							)
																		for i in range(lowerCamelCase_     ,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 			__a	(       lowerCAmelCase_  :   Tuple     ,lowerCAmelCase_  :   Optional[Any]     ,lowerCAmelCase_  :   int							) ->					str:






						'''simple docstring'''



						UpperCAmelCase_=  []

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

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

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

						assert len(lowerCamelCase_							) == len(lowerCamelCase_							)

						return ref_ids



def 			__a	(       lowerCAmelCase_  :   Any							) ->					Tuple:






						'''simple docstring'''



						with open(args.file_name     ,"""r"""     ,encoding="""utf-8"""							) as f:
												UpperCAmelCase_=  f.readlines()
						UpperCAmelCase_=  [line.strip() for line in data if len(lowerCamelCase_							) > 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(lowerCamelCase_     ,lowerCamelCase_     ,lowerCamelCase_							)

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


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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_pegasus import PegasusTokenizer
else:
							A__      :     Optional[Any]       =					None


A__      :     str       =					logging.get_logger(__name__)


A__      :     Optional[int]       =					'▁'

A__      :     List[str]       =					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A__      :     Dict       =					{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

A__      :     int       =					{
    'google/pegasus-xsum': 5_12,
}
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			VOCAB_FILES_NAMES
				lowercase__											=			PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=			PegasusTokenizer
				lowercase__											=			["""input_ids""", """attention_mask"""]




				def __init__(	self		:       Dict,    lowerCamelCase		:       str=None,    lowerCamelCase		:       List[str]=None,    lowerCamelCase		:       int="<pad>",    lowerCamelCase		:       str="</s>",    lowerCamelCase		:       Optional[Any]="<unk>",    lowerCamelCase		:       Any="<mask_2>",    lowerCamelCase		:       Optional[Any]="<mask_1>",    lowerCamelCase		:       int=None,    lowerCamelCase		:       Dict=103,    **lowerCamelCase		:       List[Any],    ):

								'''simple docstring'''
								lowercase__  					=     offset

								if additional_special_tokens is not None:
												if not isinstance(lowerCamelCase,    lowerCamelCase    ):
																raise TypeError(
																    F"""additional_special_tokens should be of type {type(lowerCamelCase    )}, but is"""
																    F""" {type(lowerCamelCase    )}"""    )

												lowercase__  					=     (
												    ([mask_token_sent] + additional_special_tokens)
												    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
												    else additional_special_tokens
												)
												# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
												additional_special_tokens_extended += [
												    F"""<unk_{i}>""" for i in range(len(lowerCamelCase    ),    self.offset - 1    )
												]

												if len(set(lowerCamelCase    )    ) != len(lowerCamelCase    ):
																raise ValueError(
																    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
																    F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""    )
												lowercase__  					=     additional_special_tokens_extended
								else:
												lowercase__  					=     [mask_token_sent] if mask_token_sent is not None else []
												additional_special_tokens += [F"""<unk_{i}>""" for i in range(2,    self.offset    )]

								super().__init__(
								    lowerCamelCase,    tokenizer_file=lowerCamelCase,    pad_token=lowerCamelCase,    eos_token=lowerCamelCase,    unk_token=lowerCamelCase,    mask_token=lowerCamelCase,    mask_token_sent=lowerCamelCase,    offset=lowerCamelCase,    additional_special_tokens=lowerCamelCase,    **lowerCamelCase,    )
								lowercase__  					=     vocab_file
								lowercase__  					=     False if not self.vocab_file else True




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       int    ):

								'''simple docstring'''
								lowercase__  					=     set(self.all_special_ids    )  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

								if all_special_ids != set(range(len(self.additional_special_tokens    ) + 3    )    ):
												raise ValueError(
												    '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
												    F""" {len(self.additional_special_tokens    )} additional_special_tokens, but got {all_special_ids}"""    )

								return [1 if x in all_special_ids else 0 for x in seq]




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       List,    lowerCamelCase		:       Optional[List] = None,    lowerCamelCase		:       bool = False    ):

								'''simple docstring'''
								if already_has_special_tokens:
												return self._special_token_mask(lowerCamelCase    )
								elif token_ids_a is None:
												return self._special_token_mask(lowerCamelCase    ) + [1]
								else:
												return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       Optional[int],    lowerCamelCase		:       Optional[Any]=None    ):

								'''simple docstring'''
								if token_ids_a is None:
												return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       str,    lowerCamelCase		:       Optional[str] = None    ):

								'''simple docstring'''
								if not self.can_save_slow_tokenizer:
												raise ValueError(
												    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
												    '''tokenizer.'''    )

								if not os.path.isdir(lowerCamelCase    ):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
												return
								lowercase__  					=     os.path.join(
								    lowerCamelCase,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

								if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowerCamelCase    ):
												copyfile(self.vocab_file,    lowerCamelCase    )

								return (out_vocab_file,)




