






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def 					_lowerCamelCase(			lowerCAmelCase__				:		str      ):





				'''simple docstring'''







				if (
				    (cp >= 0X4e_00 and cp <= 0X9f_ff)
				    or (cp >= 0X34_00 and cp <= 0X4d_bf)  #
				    or (cp >= 0X2_00_00 and cp <= 0X2_a6_df)  #
				    or (cp >= 0X2_a7_00 and cp <= 0X2_b7_3f)  #
				    or (cp >= 0X2_b7_40 and cp <= 0X2_b8_1f)  #
				    or (cp >= 0X2_b8_20 and cp <= 0X2_ce_af)  #
				    or (cp >= 0Xf9_00 and cp <= 0Xfa_ff)
				    or (cp >= 0X2_f8_00 and cp <= 0X2_fa_1f)  #
				):  #
								return True

				return False



def 					_lowerCamelCase(			lowerCAmelCase__				:		str      ):





				'''simple docstring'''







				for char in word:
								SCREAMING_SNAKE_CASE_   :	Tuple													=   ord(lowerCAmelCase__      )
								if not _is_chinese_char(lowerCAmelCase__      ):
												return 0
				return 1



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	int													=   set()

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



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		set()      ):





				'''simple docstring'''







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

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



def 					_lowerCamelCase(			lowerCAmelCase__				:		List[str]    ,    lowerCAmelCase__				:		LTP    ,    lowerCAmelCase__				:		BertTokenizer      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   []

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

				SCREAMING_SNAKE_CASE_   :	Any													=   []
				for i in range(0    ,    len(lowerCAmelCase__      )    ,    100      ):
								SCREAMING_SNAKE_CASE_   :	List[str]													=   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__      )

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





				'''simple docstring'''







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

				SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   prepare_ref(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

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


if __name__ == "__main__":
	A        =			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        =			parser.parse_args()
	main(args)







from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
				from .features import FeatureType


@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				List[str]
		UpperCAmelCase__      :				Optional[str]				    =						None
		# Automatically constructed
		UpperCAmelCase__      :				ClassVar[str]				    =						"dict"
		UpperCAmelCase__      :				ClassVar[Any]				    =						None
		UpperCAmelCase__      :				str				    =						field(default="""Translation""" ,	init=__A ,	repr=__A						)


		def __call__(						self       ):
						return pa.struct({lang: pa.string() for lang in sorted(self.languages       )}       )






		def   __snake_case			(						self       ):
						from .features import Value

						return {k: Value('string'       ) for k in sorted(self.languages       )}






@dataclass
class 							__a							:





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[List]				    =						None
		UpperCAmelCase__      :				Optional[int]				    =						None
		UpperCAmelCase__      :				Optional[str]				    =						None
		# Automatically constructed
		UpperCAmelCase__      :				ClassVar[str]				    =						"dict"
		UpperCAmelCase__      :				ClassVar[Any]				    =						None
		UpperCAmelCase__      :				str				    =						field(default="""TranslationVariableLanguages""" ,	init=__A ,	repr=__A						)


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   sorted(set(self.languages       )       ) if self.languages else None
						SCREAMING_SNAKE_CASE_   :	Dict													=   len(self.languages       ) if self.languages else None


		def __call__(						self       ):
						return pa.struct({'language': pa.list_(pa.string()       ), 'translation': pa.list_(pa.string()       )}       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   set(self.languages       )
						if self.languages and set(UpperCamelCase__       ) - lang_set:
										raise ValueError(
										    F'''Some languages in example ({', '.join(sorted(set(UpperCamelCase__       ) - lang_set       )       )}) are not in valid set ({', '.join(UpperCamelCase__       )}).'''       )

						# Convert dictionary into tuples, splitting out cases where there are
						# multiple translations for a single language.
						SCREAMING_SNAKE_CASE_   :	List[str]													=   []
						for lang, text in translation_dict.items():
										if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ):
														translation_tuples.append((lang, text)       )
										else:
														translation_tuples.extend([(lang, el) for el in text]       )

        # Ensure translations are in ascending order by language code.
						SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_   :	Dict													=   zip(*sorted(UpperCamelCase__       )       )

						return {"language": languages, "translation": translations}






		def   __snake_case			(						self       ):
						from .features import Sequence, Value

						return {
						    "language": Sequence(Value('string'       )       ),
						    "translation": Sequence(Value('string'       )       ),
						}