





"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  ['''image_processor''', '''tokenizer''']
       SCREAMING_SNAKE_CASE_       :				List[Any]     						=  '''ViltImageProcessor'''
       SCREAMING_SNAKE_CASE_       :				List[str]     						=  ('''BertTokenizer''', '''BertTokenizerFast''')




       def __init__(						self		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                       ''' instead.'''		,SCREAMING_SNAKE_CASE__		,)
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  kwargs.pop('''feature_extractor'''							)

             __SCREAMING_SNAKE_CASE       :Dict		    =  image_processor if image_processor is not None else feature_extractor
             if image_processor is None:
                   raise ValueError('''You need to specify an `image_processor`.'''							)
             if tokenizer is None:
                   raise ValueError('''You need to specify a `tokenizer`.'''							)

             super().__init__(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.image_processor




       def __call__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = True		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)      ->				BatchEncoding:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.tokenizer(
                 text=SCREAMING_SNAKE_CASE__		,add_special_tokens=SCREAMING_SNAKE_CASE__		,padding=SCREAMING_SNAKE_CASE__		,truncation=SCREAMING_SNAKE_CASE__		,max_length=SCREAMING_SNAKE_CASE__		,stride=SCREAMING_SNAKE_CASE__		,pad_to_multiple_of=SCREAMING_SNAKE_CASE__		,return_token_type_ids=SCREAMING_SNAKE_CASE__		,return_attention_mask=SCREAMING_SNAKE_CASE__		,return_overflowing_tokens=SCREAMING_SNAKE_CASE__		,return_special_tokens_mask=SCREAMING_SNAKE_CASE__		,return_offsets_mapping=SCREAMING_SNAKE_CASE__		,return_length=SCREAMING_SNAKE_CASE__		,verbose=SCREAMING_SNAKE_CASE__		,return_tensors=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)
             # add pixel_values + pixel_mask
             __SCREAMING_SNAKE_CASE       :Dict		    =  self.image_processor(SCREAMING_SNAKE_CASE__		,return_tensors=SCREAMING_SNAKE_CASE__							)
             encoding.update(SCREAMING_SNAKE_CASE__							)

             return encoding




       def    _UpperCamelCase					(						self		,*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)




       @property
       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.tokenizer.model_input_names
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




       @property
       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,SCREAMING_SNAKE_CASE__		,)
             return self.image_processor_class




       @property
       def    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		,SCREAMING_SNAKE_CASE__		,)
             return self.image_processor






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		str			,       a_					:		str							)      ->				str:
      __SCREAMING_SNAKE_CASE       :int		    =  len(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  len(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  (
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      __SCREAMING_SNAKE_CASE       :list		    =  []
      for char_count in range(a_							):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count]							)
            if char_count < second_str_length:
                  output_list.append(second_str[char_count]							)
      return "".join(a_							)


if __name__ == "__main__":
  print(alternative_string_arrange("AB", "XYZ"), end=" ")