



"""simple docstring"""







import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  set()

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





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

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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  []

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

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

					a__          =  []
					for input_ids, chinese_word in zip(lowerCamelCase_ ,     lowerCamelCase_):
										a__          =  []
										for id in input_ids:
															a__          =  bert_tokenizer._convert_id_to_token(lowerCamelCase_)
															input_tokens.append(lowerCamelCase_)
										a__          =  add_sub_symbol(lowerCamelCase_ ,     lowerCamelCase_)
										a__          =  []
										# 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] == "##":
																				a__          =  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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					# 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:
										a__          =  f.readlines()
					a__          =  [line.strip() for line in data if len(lowerCamelCase_) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
					a__          =  LTP(args.ltp)  # faster in GPU device
					a__          =  BertTokenizer.from_pretrained(args.bert)

					a__          =  prepare_ref(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)

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


if __name__ == "__main__":
	__a       :	Union[str, 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',
	)

	__a       :	List[str]             = parser.parse_args()
	main(args)










"""simple docstring"""







import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
	pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a       :	List[Any]             = logging.get_logger(__name__)

__a       :	Tuple             = {
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align_text_model'




							def __init__(   self:     Tuple				,		__A:     Optional[int]=30522				,		__A:     Tuple=768				,		__A:     Any=12				,		__A:     Any=12				,		__A:     Dict=3072				,		__A:     Tuple="gelu"				,		__A:     Union[str, Any]=0.1				,		__A:     List[str]=0.1				,		__A:     Optional[int]=512				,		__A:     Tuple=2				,		__A:     str=0.0_2				,		__A:     int=1e-12				,		__A:     Optional[int]=0				,		__A:     Optional[int]="absolute"				,		__A:     List[Any]=True				,		**__A:     Tuple				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  hidden_act
												a__          =  intermediate_size
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  initializer_range
												a__          =  layer_norm_eps
												a__          =  position_embedding_type
												a__          =  use_cache
												a__          =  pad_token_id





							@classmethod
							def  lowercase		(   cls:     Any				,		__A:     Union[str, os.PathLike]				,		**__A:     Tuple    ):

												'''simple docstring'''

												cls._set_token_in_kwargs(__A    )

												a__       ,a__          =  cls.get_config_dict(__A				,		**__A    )

												# get the text config dict if we are loading from AlignConfig
												if config_dict.get('''model_type'''    ) == "align":
																	a__          =  config_dict['''text_config''']

												if "model_type" in config_dict and hasattr(cls				,		'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

												return cls.from_dict(__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align_vision_model'




							def __init__(   self:     Dict				,		__A:     int = 3				,		__A:     int = 600				,		__A:     float = 2.0				,		__A:     float = 3.1				,		__A:     int = 8				,		__A:     List[int] = [3, 3, 5, 3, 5, 5, 3]				,		__A:     List[int] = [32, 16, 24, 40, 80, 112, 192]				,		__A:     List[int] = [16, 24, 40, 80, 112, 192, 320]				,		__A:     List[int] = []				,		__A:     List[int] = [1, 2, 2, 2, 1, 2, 1]				,		__A:     List[int] = [1, 2, 2, 3, 3, 4, 1]				,		__A:     List[int] = [1, 6, 6, 6, 6, 6, 6]				,		__A:     float = 0.2_5				,		__A:     str = "swish"				,		__A:     int = 2560				,		__A:     str = "mean"				,		__A:     float = 0.0_2				,		__A:     float = 0.0_0_1				,		__A:     float = 0.9_9				,		__A:     float = 0.2				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  num_channels
												a__          =  image_size
												a__          =  width_coefficient
												a__          =  depth_coefficient
												a__          =  depth_divisor
												a__          =  kernel_sizes
												a__          =  in_channels
												a__          =  out_channels
												a__          =  depthwise_padding
												a__          =  strides
												a__          =  num_block_repeats
												a__          =  expand_ratios
												a__          =  squeeze_expansion_ratio
												a__          =  hidden_act
												a__          =  hidden_dim
												a__          =  pooling_type
												a__          =  initializer_range
												a__          =  batch_norm_eps
												a__          =  batch_norm_momentum
												a__          =  drop_connect_rate
												a__          =  sum(__A    ) * 4





							@classmethod
							def  lowercase		(   cls:     Dict				,		__A:     Union[str, os.PathLike]				,		**__A:     List[Any]    ):

												'''simple docstring'''

												cls._set_token_in_kwargs(__A    )

												a__       ,a__          =  cls.get_config_dict(__A				,		**__A    )

												# get the vision config dict if we are loading from AlignConfig
												if config_dict.get('''model_type'''    ) == "align":
																	a__          =  config_dict['''vision_config''']

												if "model_type" in config_dict and hasattr(cls				,		'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

												return cls.from_dict(__A				,		**__A    )







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align'
							_SCREAMING_SNAKE_CASE    =True




							def __init__(   self:     Optional[int]				,		__A:     Optional[int]=None				,		__A:     Dict=None				,		__A:     List[str]=640				,		__A:     Optional[int]=1.0				,		__A:     str=0.0_2				,		**__A:     List[str]				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												if text_config is None:
																	a__          =  {}
																	logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''    )

												if vision_config is None:
																	a__          =  {}
																	logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''    )

												a__          =  AlignTextConfig(**__A    )
												a__          =  AlignVisionConfig(**__A    )

												a__          =  projection_dim
												a__          =  temperature_init_value
												a__          =  initializer_range




							@classmethod
							def  lowercase		(   cls:     Dict				,		__A:     AlignTextConfig				,		__A:     AlignVisionConfig				,		**__A:     str    ):

												'''simple docstring'''

												return cls(text_config=text_config.to_dict()				,		vision_config=vision_config.to_dict()				,		**__A    )





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  copy.deepcopy(self.__dict__    )
												a__          =  self.text_config.to_dict()
												a__          =  self.vision_config.to_dict()
												a__          =  self.__class__.model_type
												return output


