



'''simple docstring'''





from typing import List, Optional, Union

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






class __A			(  __lowerCamelCase						):



 '''simple docstring'''







 __lowerCamelCase    :       List[Any]          =       ['image_processor', 'tokenizer']
 __lowerCamelCase    :       int          =       'BlipImageProcessor'
 __lowerCamelCase    :       Tuple          =       ('BertTokenizer', 'BertTokenizerFast')



 def __init__(self ,   A ,   A       )    ->     int:


      """simple docstring"""



      _a      =  False
      super().__init__(UpperCamelCase_ ,   UpperCamelCase_       )
      _a      =  self.image_processor



 def __call__(self ,   A = None ,   A = None ,   A = True ,   A = False ,   A = None ,   A = None ,   A = 0 ,   A = None ,   A = None ,   A = False ,   A = False ,   A = False ,   A = False ,   A = False ,   A = True ,   A = None ,   **A ,   )    ->     str:


      """simple docstring"""



      if images is None and text is None:
           raise ValueError('''You have to specify either images or text.'''       )

      # Get only text
      if images is None:
           _a      =  self.tokenizer
           _a      =  self.tokenizer(
               text=UpperCamelCase_ ,   add_special_tokens=UpperCamelCase_ ,   padding=UpperCamelCase_ ,   truncation=UpperCamelCase_ ,   max_length=UpperCamelCase_ ,   stride=UpperCamelCase_ ,   pad_to_multiple_of=UpperCamelCase_ ,   return_attention_mask=UpperCamelCase_ ,   return_overflowing_tokens=UpperCamelCase_ ,   return_special_tokens_mask=UpperCamelCase_ ,   return_offsets_mapping=UpperCamelCase_ ,   return_token_type_ids=UpperCamelCase_ ,   return_length=UpperCamelCase_ ,   verbose=UpperCamelCase_ ,   return_tensors=UpperCamelCase_ ,   **UpperCamelCase_ ,   )
           return text_encoding

      # add pixel_values
      _a      =  self.image_processor(UpperCamelCase_ ,   return_tensors=UpperCamelCase_       )

      if text is not None:
           _a      =  self.tokenizer(
               text=UpperCamelCase_ ,   add_special_tokens=UpperCamelCase_ ,   padding=UpperCamelCase_ ,   truncation=UpperCamelCase_ ,   max_length=UpperCamelCase_ ,   stride=UpperCamelCase_ ,   pad_to_multiple_of=UpperCamelCase_ ,   return_attention_mask=UpperCamelCase_ ,   return_overflowing_tokens=UpperCamelCase_ ,   return_special_tokens_mask=UpperCamelCase_ ,   return_offsets_mapping=UpperCamelCase_ ,   return_token_type_ids=UpperCamelCase_ ,   return_length=UpperCamelCase_ ,   verbose=UpperCamelCase_ ,   return_tensors=UpperCamelCase_ ,   **UpperCamelCase_ ,   )
      else:
           _a      =  None

      if text_encoding is not None:
           encoding_image_processor.update(UpperCamelCase_       )

      return encoding_image_processor



 def 					a__  (self ,   *A ,   **A       )    ->     Tuple:


      """simple docstring"""



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



 def 					a__  (self ,   *A ,   **A       )    ->     Optional[Any]:


      """simple docstring"""



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



 @property
 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  self.tokenizer.model_input_names
      _a      =  self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )









def 					lowerCamelCase__       (						A__       :					list	):





       '''simple docstring'''



       __lowerCamelCase   =						len(A__	)
       for _ in range(A__	):
              for i in range(_ % 2  ,			arr_size - 1  ,			2	):
                     if arr[i + 1] < arr[i]:
                            __lowerCamelCase,   __lowerCamelCase   =						arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
       UpperCAmelCase_									=	list(range(10, 0, -1))
       print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")

