


import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     List[Any]:




						'''simple docstring'''







						if (
						    (cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F)
						    or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F)  #
						    or (cp >= 0X2_0_0_0_0 and cp <= 0X2_A_6_D_F)  #
						    or (cp >= 0X2_A_7_0_0 and cp <= 0X2_B_7_3_F)  #
						    or (cp >= 0X2_B_7_4_0 and cp <= 0X2_B_8_1_F)  #
						    or (cp >= 0X2_B_8_2_0 and cp <= 0X2_C_E_A_F)  #
						    or (cp >= 0XF_9_0_0 and cp <= 0XF_A_F_F)
						    or (cp >= 0X2_F_8_0_0 and cp <= 0X2_F_A_1_F)  #
						):  #
												return True

						return False




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Optional[int]:




						'''simple docstring'''







						for char in word:
												__UpperCAmelCase			:							Dict	    =	ord(lowercase_      )
												if not _is_chinese_char(lowercase_      ):
																		return 0
						return 1




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Union[str, Any]	    =	set()

						for token in tokens:
												__UpperCAmelCase			:							List[str]	    =	len(lowercase_      ) > 1 and is_chinese(lowercase_      )
												if chinese_word:
																		word_set.add(lowercase_      )
						__UpperCAmelCase			:							Any	    =	list(lowercase_      )
						return word_list




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     str:




						'''simple docstring'''







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

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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      )		->     Union[str, Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							List[Any]	    =	[]

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

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

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

						assert len(lowercase_      ) == len(lowercase_      )

						return ref_ids




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     int:




						'''simple docstring'''







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

						__UpperCAmelCase			:							Union[str, Any]	    =	prepare_ref(lowercase_      ,       lowercase_      ,       lowercase_      )

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


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


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						'''gpt_neox_japanese'''


	def __init__(    self				,     lowercase__=3_2_0_0_0				,     lowercase__=2_5_6_0				,     lowercase__=3_2				,     lowercase__=3_2				,     lowercase__=4				,     lowercase__="gelu"				,     lowercase__=1.0_0				,     lowercase__=1_0_0_0_0				,     lowercase__=2_0_4_8				,     lowercase__=0.0_2				,     lowercase__=1e-5				,     lowercase__=True				,     lowercase__=3_1_9_9_6				,     lowercase__=3_1_9_9_9				,     lowercase__=0.1				,     lowercase__=0.0				,     **lowercase__				,     ):
							super().__init__(bos_token_id=lowercase__				,     eos_token_id=lowercase__				,     **lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	vocab_size
							__UpperCAmelCase			:							Dict	    =	max_position_embeddings
							__UpperCAmelCase			:							int	    =	hidden_size
							__UpperCAmelCase			:							List[Any]	    =	num_hidden_layers
							__UpperCAmelCase			:							Union[str, Any]	    =	num_attention_heads
							__UpperCAmelCase			:							Optional[int]	    =	intermediate_multiple_size
							__UpperCAmelCase			:							Any	    =	hidden_act
							__UpperCAmelCase			:							str	    =	rotary_pct
							__UpperCAmelCase			:							Any	    =	rotary_emb_base
							__UpperCAmelCase			:							Dict	    =	initializer_range
							__UpperCAmelCase			:							Tuple	    =	layer_norm_eps
							__UpperCAmelCase			:							Optional[int]	    =	use_cache
							__UpperCAmelCase			:							str	    =	attention_dropout
							__UpperCAmelCase			:							Any	    =	hidden_dropout

