


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def 						lowerCamelCase(   a__):
				# 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 						lowerCamelCase(   a__):
				# word like '180' or '身高' or '神'
				for char in word:
								_SCREAMING_SNAKE_CASE			  =ord(a__)
								if not _is_chinese_char(a__):
												return 0
				return 1







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =set()

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







def 						lowerCamelCase(   a__	,a__):
				if not chinese_word_set:
								return bert_tokens
				_SCREAMING_SNAKE_CASE			  =max([len(a__) for w in chinese_word_set])

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







def 						lowerCamelCase(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =[]

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

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

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

				assert len(a__) == len(a__)

				return ref_ids







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

				_SCREAMING_SNAKE_CASE			  =prepare_ref(a__	,a__	,a__)

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


if __name__ == "__main__":
							snake_case_						:			Tuple													=							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''')

							snake_case_						:			List[Any]													=							parser.parse_args()
							main(args)



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_						:			str													=							logging.get_logger(__name__)

snake_case_						:			str													=							{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"data2vec-text"




						def __init__(       self					:      Optional[int]				,    _a					:      Union[str, Any]=3_0522				,    _a					:      Any=768				,    _a					:      List[Any]=12				,    _a					:      Any=12				,    _a					:      List[str]=3072				,    _a					:      Optional[Any]="gelu"				,    _a					:      List[str]=0.1				,    _a					:      Optional[Any]=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Any=2				,    _a					:      Optional[Any]=0.02				,    _a					:      Dict=1E-12				,    _a					:      int=1				,    _a					:      Any=0				,    _a					:      List[Any]=2				,    _a					:      Dict="absolute"				,    _a					:      Optional[Any]=True				,    _a					:      Dict=None				,    **_a					:      Optional[Any]				,    )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    bos_token_id=_a				,    eos_token_id=_a				,    **_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =use_cache
										_SCREAMING_SNAKE_CASE			  =classifier_dropout

class 						A__							(		UpperCamelCase__  ):




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.task == "multiple-choice":
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
														_SCREAMING_SNAKE_CASE			  ={0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]  )