


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		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	):




		"""simple docstring"""






		for char in word:
				lowerCAmelCase__       :      Union[str, Any]   					= ord(_a	)
				if not _is_chinese_char(_a	):
						return 0
		return 1






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= set()

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






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






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

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






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Dict   					= []

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

		lowerCAmelCase__       :      int   					= []
		for i in range(0     ,       len(_a	)     ,       100	):
				lowerCAmelCase__       :      List[Any]   					= 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	)

		lowerCAmelCase__       :      Optional[Any]   					= []
		for input_ids, chinese_word in zip(_a     ,       _a	):
				lowerCAmelCase__       :      Union[str, Any]   					= []
				for id in input_ids:
						lowerCAmelCase__       :      Any   					= bert_tokenizer._convert_id_to_token(_a	)
						input_tokens.append(_a	)
				lowerCAmelCase__       :      Dict   					= add_sub_symbol(_a     ,       _a	)
				lowerCAmelCase__       :      Any   					= []
				# 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] == "##":
								lowerCAmelCase__       :      int   					= 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	):




		"""simple docstring"""






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

		lowerCAmelCase__       :      str   					= prepare_ref(_a     ,       _a     ,       _a	)

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


if __name__ == "__main__":
		lowerCamelCase						=		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''',
		)

		lowerCamelCase						=		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


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class  _a							(    _lowercase):
	_a  :  Dict				   =							'''convbert'''




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		str=3_0522		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=12		, _SCREAMING_SNAKE_CASE						:		Any=3072		, _SCREAMING_SNAKE_CASE						:		List[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=0.1		, _SCREAMING_SNAKE_CASE						:		int=512		, _SCREAMING_SNAKE_CASE						:		Any=2		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.02		, _SCREAMING_SNAKE_CASE						:		Optional[int]=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0		, _SCREAMING_SNAKE_CASE						:		Dict=2		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=768		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=2		, _SCREAMING_SNAKE_CASE						:		Tuple=9		, _SCREAMING_SNAKE_CASE						:		int=1		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	List[str]:
			super().__init__(
			    pad_token_id=_SCREAMING_SNAKE_CASE		, bos_token_id=_SCREAMING_SNAKE_CASE		, eos_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      List[Any]   					= hidden_size
			lowerCAmelCase__       :      Tuple   					= num_hidden_layers
			lowerCAmelCase__       :      Union[str, Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Tuple   					= hidden_act
			lowerCAmelCase__       :      Dict   					= hidden_dropout_prob
			lowerCAmelCase__       :      str   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      Tuple   					= type_vocab_size
			lowerCAmelCase__       :      Optional[Any]   					= initializer_range
			lowerCAmelCase__       :      List[Any]   					= layer_norm_eps
			lowerCAmelCase__       :      int   					= embedding_size
			lowerCAmelCase__       :      Union[str, Any]   					= head_ratio
			lowerCAmelCase__       :      Optional[int]   					= conv_kernel_size
			lowerCAmelCase__       :      List[str]   					= num_groups
			lowerCAmelCase__       :      Dict   					= classifier_dropout





class  _a							(    _lowercase):




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCAmelCase__       :      Optional[Any]   					= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					lowerCAmelCase__       :      List[str]   					= {0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			        ('''token_type_ids''', dynamic_axis),
			    ]		)




