





import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def     __A      (			a_						:Dict)		->		Union[str, Any]:
				# 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     __A      (			a_						:str)		->		Union[str, Any]:
				# word like '180' or '身高' or '神'
				for char in word:
								__a :						List[str]			       =  ord(SCREAMING_SNAKE_CASE_)
								if not _is_chinese_char(SCREAMING_SNAKE_CASE_):
												return 0
				return 1

def     __A      (			a_						:Any)		->		Dict:
				__a :						Union[str, Any]			       =  set()

				for token in tokens:
								__a :						Tuple			       =  len(SCREAMING_SNAKE_CASE_) > 1 and is_chinese(SCREAMING_SNAKE_CASE_)
								if chinese_word:
												word_set.add(SCREAMING_SNAKE_CASE_)
				__a :						Union[str, Any]			       =  list(SCREAMING_SNAKE_CASE_)
				return word_list

def     __A      (			a_						:Tuple   ,       a_						:List[Any])		->		int:
				if not chinese_word_set:
								return bert_tokens
				__a :						Dict			       =  max([len(SCREAMING_SNAKE_CASE_) for w in chinese_word_set])

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

def     __A      (			a_						:Optional[int]   ,       a_						:Optional[int]   ,       a_						:Union[str, Any])		->		Any:
				__a :						Tuple			       =  []

				for i in range(0   ,       len(SCREAMING_SNAKE_CASE_)   ,       1_00):
								__a :						Tuple			       =  ltp_tokenizer.seg(lines[i : i + 1_00])[0]
								__a :						List[Any]			       =  [get_chinese_word(SCREAMING_SNAKE_CASE_) for r in res]
								ltp_res.extend(SCREAMING_SNAKE_CASE_)
				assert len(SCREAMING_SNAKE_CASE_) == len(SCREAMING_SNAKE_CASE_)

				__a :						int			       =  []
				for i in range(0   ,       len(SCREAMING_SNAKE_CASE_)   ,       1_00):
								__a :						Tuple			       =  bert_tokenizer(lines[i : i + 1_00]   ,       add_special_tokens=SCREAMING_SNAKE_CASE_   ,       truncation=SCREAMING_SNAKE_CASE_   ,       max_length=5_12)
								bert_res.extend(res['''input_ids'''])
				assert len(SCREAMING_SNAKE_CASE_) == len(SCREAMING_SNAKE_CASE_)

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

				assert len(SCREAMING_SNAKE_CASE_) == len(SCREAMING_SNAKE_CASE_)

				return ref_ids

def     __A      (			a_						:List[str])		->		Optional[Any]:
				# 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 :						Any			       =  f.readlines()
				__a :						Optional[Any]			       =  [line.strip() for line in data if len(SCREAMING_SNAKE_CASE_) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
				__a :						Dict			       =  LTP(args.ltp)  # faster in GPU device
				__a :						Tuple			       =  BertTokenizer.from_pretrained(args.bert)

				__a :						Tuple			       =  prepare_ref(SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_)

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


if __name__ == "__main__":
		A 			=  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''')

		A 			=  parser.parse_args()
		main(args)






"""simple docstring"""






import copy
import os
from typing import Union

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


A 			=  logging.get_logger(__name__)

A 			=  {
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''git_vision_model'''







					def __init__(						self	,			_UpperCAmelCase=768	,			_UpperCAmelCase=3072	,			_UpperCAmelCase=12	,			_UpperCAmelCase=12	,			_UpperCAmelCase=3	,			_UpperCAmelCase=224	,			_UpperCAmelCase=16	,			_UpperCAmelCase="quick_gelu"	,			_UpperCAmelCase=1e-5	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.0_2	,			**_UpperCAmelCase	,			):
									super().__init__(**_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  hidden_size
									__a :						Optional[int]			       =  intermediate_size
									__a :						Tuple			       =  num_hidden_layers
									__a :						Tuple			       =  num_attention_heads
									__a :						Optional[int]			       =  num_channels
									__a :						Tuple			       =  patch_size
									__a :						Any			       =  image_size
									__a :						Optional[Any]			       =  initializer_range
									__a :						Optional[int]			       =  attention_dropout
									__a :						Any			       =  layer_norm_eps
									__a :						Dict			       =  hidden_act







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									cls._set_token_in_kwargs(_UpperCAmelCase     )

									__a       ,						__a :						Dict			       =  cls.get_config_dict(_UpperCAmelCase	,			**_UpperCAmelCase     )

									# get the vision config dict if we are loading from GITConfig
									if config_dict.get('''model_type'''     ) == "git":
													__a :						int			       =  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(_UpperCAmelCase	,			**_UpperCAmelCase     )






class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''git'''







					def __init__(						self	,			_UpperCAmelCase=None	,			_UpperCAmelCase=30522	,			_UpperCAmelCase=768	,			_UpperCAmelCase=6	,			_UpperCAmelCase=12	,			_UpperCAmelCase=3072	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=1024	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=1e-1_2	,			_UpperCAmelCase=0	,			_UpperCAmelCase="absolute"	,			_UpperCAmelCase=True	,			_UpperCAmelCase=False	,			_UpperCAmelCase=101	,			_UpperCAmelCase=102	,			_UpperCAmelCase=None	,			**_UpperCAmelCase	,			):
									super().__init__(bos_token_id=_UpperCAmelCase	,			eos_token_id=_UpperCAmelCase	,			pad_token_id=_UpperCAmelCase	,			**_UpperCAmelCase     )

									if vision_config is None:
													__a :						Union[str, Any]			       =  {}
													logger.info('''vision_config is None. initializing the GitVisionConfig with default values.'''     )

									__a :						Union[str, Any]			       =  GitVisionConfig(**_UpperCAmelCase     )
									__a :						List[str]			       =  vocab_size
									__a :						Any			       =  hidden_size
									__a :						Dict			       =  num_hidden_layers
									__a :						Tuple			       =  num_attention_heads
									__a :						List[str]			       =  hidden_act
									__a :						Any			       =  intermediate_size
									__a :						int			       =  hidden_dropout_prob
									__a :						Dict			       =  attention_probs_dropout_prob
									__a :						Tuple			       =  max_position_embeddings
									__a :						int			       =  initializer_range
									__a :						List[str]			       =  layer_norm_eps
									__a :						Tuple			       =  position_embedding_type
									__a :						Optional[int]			       =  use_cache
									__a :						str			       =  tie_word_embeddings
									__a :						Optional[Any]			       =  num_image_with_embedding

									__a :						str			       =  bos_token_id
									__a :						Dict			       =  eos_token_id







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  copy.deepcopy(self.__dict__     )
									__a :						int			       =  self.vision_config.to_dict()
									__a :						Tuple			       =  self.__class__.model_type
									return output