



"""simple docstring"""

from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class 						lowerCAmelCase__	:


	'''simple docstring'''







	def __init__(			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Dict						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    vocab
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    normalizers.BertNormalizer(
					    clean_text=a_    ,					handle_chinese_chars=a_    ,					strip_accents=a_    ,					lowercase=a_    ,					)
					try:
									import rjieba
					except ImportError:
									raise ImportError(
									    'You need to install rjieba to use RoFormerTokenizer. '
									    'See https://pypi.org/project/rjieba/ for installation.'						)
					SCREAMING_SNAKE_CASE	:       int 							=    rjieba





	def  _lowerCAmelCase    (			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					NormalizedString						)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    []

					# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
					for token, start, end in self.jieba.tokenize(str(a_						)    ,					hmm=a_						):
									if token in self.vocab:
													splits.append(normalized_string[start:end]						)
									else:
													SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.normalizers.normalize_str(a_						).split()
													for token in token_list:
																	if token:
																					SCREAMING_SNAKE_CASE	:       int 							=    start + len(a_						)
																					splits.append(normalized_string[start:end]						)
																					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    end

									 # this code test_alignement_methods can't pass but fast (300ms)
									 # for token in self.jieba.cut(str(normalized_string), False):
									 #     if token in self.vocab:
									 #         splits.append(NormalizedString(token))
									 #     else:
									 #         token_list = self.normalizers.normalize_str(token).split()
									 #         for token in token_list:
									 #             if token:
									 #                 splits.append(NormalizedString(token))

					return splits







	def  _lowerCAmelCase    (			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					PreTokenizedString						)	->  Optional[Any]:





					"""simple docstring"""


					pretok.split(self.jieba_split						)










"""simple docstring"""

import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


A_	:						Any				=			logging.get_logger(__name__)
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					*_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  None:





					"""simple docstring"""


					warnings.warn(
					    'The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
					    ' Please use ConvNextImageProcessor instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
					super().__init__(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





