



'''simple docstring'''





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 __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[int]          =       ['image_processor', 'tokenizer']
 __lowerCamelCase    :       List[str]          =       'Pix2StructImageProcessor'
 __lowerCamelCase    :       int          =       ('T5Tokenizer', 'T5TokenizerFast')



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


      """simple docstring"""



      _a      =  False
      super().__init__(A ,   A       )



 def __call__(self ,   A=None ,   A = None ,   A = True ,   A = False ,   A = None ,   A = None ,   A = 2_048 ,   A = 0 ,   A = None ,   A = None ,   A = False ,   A = False ,   A = False ,   A = False ,   A = False ,   A = True ,   A = None ,   **A ,   )    ->     BatchEncoding:


      """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 and not self.image_processor.is_vqa:
           _a      =  self.tokenizer
           _a      =  self.tokenizer(
               text=A ,   add_special_tokens=A ,   padding=A ,   truncation=A ,   max_length=A ,   stride=A ,   pad_to_multiple_of=A ,   return_attention_mask=A ,   return_overflowing_tokens=A ,   return_special_tokens_mask=A ,   return_offsets_mapping=A ,   return_token_type_ids=A ,   return_length=A ,   verbose=A ,   return_tensors=A ,   **A ,   )
           return text_encoding

      if not self.image_processor.is_vqa:
           # add pixel_values
           _a      =  self.image_processor(
               A ,   return_tensors=A ,   max_patches=A ,   **A       )
      else:
           # add pixel_values and bbox
           _a      =  self.image_processor(
               A ,   return_tensors=A ,   max_patches=A ,   header_text=A ,   **A       )

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

           if "attention_mask" in text_encoding:
                _a      =  text_encoding.pop('''attention_mask'''       )
           if "input_ids" in text_encoding:
                _a      =  text_encoding.pop('''input_ids'''       )
      else:
           _a      =  None

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

      return encoding_image_processor



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


      """simple docstring"""



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



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


      """simple docstring"""



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



 @property
 def 					a__  (self       )    ->     int:


      """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       )       )








'''simple docstring'''





def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  1
          for i in range(0  ,	len(__A)):
               total *= numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  0
          for i in range(0  ,	len(__A)):
               total += numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



