'''simple docstring'''

from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers





class 				lowercase    :


   def __init__( self					:						Optional[Any] ,		__lowerCAmelCase					:						Union[str, Any])   ->       None:
         lowercase_     			=   vocab
         lowercase_     			=   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.")
         lowercase_     			=   rjieba


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						NormalizedString)   ->       List[NormalizedString]:
         lowercase_     			=   []

         # 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:
                     lowercase_     			=   self.normalizers.normalize_str(__lowerCAmelCase).split()
                     for token in token_list:
                           if token:
                                 lowercase_     			=   start + len(__lowerCAmelCase)
                                 splits.append(normalized_string[start:end])
                                 lowercase_     			=   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 __UpperCAmelCase							( self					:						Tuple ,		__lowerCAmelCase					:						PreTokenizedString)   ->       int:
         pretok.split(self.jieba_split)







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_			:			Tuple    	=   {"configuration_vilt": ["VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig"]}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Optional[int]    	=   ["ViltFeatureExtractor"]
       lowerCAmelCase_			:			str    	=   ["ViltImageProcessor"]
       lowerCAmelCase_			:			Any    	=   ["ViltProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_			:			Any    	=   [
           "VILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ViltForImageAndTextRetrieval",
           "ViltForImagesAndTextClassification",
           "ViltForTokenClassification",
           "ViltForMaskedLM",
           "ViltForQuestionAnswering",
           "ViltLayer",
           "ViltModel",
           "ViltPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vilt import ViltFeatureExtractor
              from .image_processing_vilt import ViltImageProcessor
              from .processing_vilt import ViltProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vilt import (
                  VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViltForImageAndTextRetrieval,
                  ViltForImagesAndTextClassification,
                  ViltForMaskedLM,
                  ViltForQuestionAnswering,
                  ViltForTokenClassification,
                  ViltLayer,
                  ViltModel,
                  ViltPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_			:			str    	=   _LazyModule(__name__, globals()["__file__"], _import_structure)






