





from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers




class 	_UpperCAmelCase						:




					def __init__(    self     ,   __lowerCAmelCase							)    ->	Optional[Any]:
							UpperCamelCase__								=						vocab
							UpperCamelCase__								=						normalizers.BertNormalizer(
							    clean_text=__lowerCamelCase     ,   handle_chinese_chars=__lowerCamelCase     ,   strip_accents=__lowerCamelCase     ,   lowercase=__lowerCamelCase     ,   )
							try:
									import rjieba
							except ImportError:
									raise ImportError(
									    """You need to install rjieba to use RoFormerTokenizer. """
									    """See https://pypi.org/project/rjieba/ for installation."""							)
							UpperCamelCase__								=						rjieba




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	Tuple:
							UpperCamelCase__								=						[]

							# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
							for token, start, end in self.jieba.tokenize(str(__lowerCamelCase							)     ,   hmm=__lowerCamelCase							):
									if token in self.vocab:
											splits.append(normalized_string[start:end]							)
									else:
											UpperCamelCase__								=						self.normalizers.normalize_str(__lowerCamelCase							).split()
											for token in token_list:
													if token:
															UpperCamelCase__								=						start + len(__lowerCamelCase							)
															splits.append(normalized_string[start:end]							)
															UpperCamelCase__								=						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 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	List[Any]:
							pretok.split(self.jieba_split							)






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      : Dict       ={'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase      : List[str]       =[
           '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''SEWDForCTC''',
           '''SEWDForSequenceClassification''',
           '''SEWDModel''',
           '''SEWDPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_sew_d import (
                  SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SEWDForCTC,
                  SEWDForSequenceClassification,
                  SEWDModel,
                  SEWDPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase      : str       =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



