


"""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 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					['''image_processor''', '''tokenizer''']
   __UpperCAmelCase							       =					'''Pix2StructImageProcessor'''
   __UpperCAmelCase							       =					('''T5Tokenizer''', '''T5TokenizerFast''')





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      _lowerCamelCase	:					Union[str, Any]   =		False
      super().__init__(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def __call__(							self      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = False      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = 2048      ,   SCREAMING_SNAKE_CASE = 0      ,   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 = False      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = None      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  BatchEncoding:
      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:
         _lowerCamelCase	:					List[Any]   =		self.tokenizer
         _lowerCamelCase	:					Any   =		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_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_token_type_ids=SCREAMING_SNAKE_CASE      ,   return_length=SCREAMING_SNAKE_CASE      ,   verbose=SCREAMING_SNAKE_CASE      ,   return_tensors=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   )
         return text_encoding

      if not self.image_processor.is_vqa:
         # add pixel_values
         _lowerCamelCase	:					Any   =		self.image_processor(
             SCREAMING_SNAKE_CASE      ,   return_tensors=SCREAMING_SNAKE_CASE      ,   max_patches=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
      else:
         # add pixel_values and bbox
         _lowerCamelCase	:					Union[str, Any]   =		self.image_processor(
             SCREAMING_SNAKE_CASE      ,   return_tensors=SCREAMING_SNAKE_CASE      ,   max_patches=SCREAMING_SNAKE_CASE      ,   header_text=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

      if text is not None and not self.image_processor.is_vqa:
         _lowerCamelCase	:					Tuple   =		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_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_token_type_ids=SCREAMING_SNAKE_CASE      ,   return_length=SCREAMING_SNAKE_CASE      ,   verbose=SCREAMING_SNAKE_CASE      ,   return_tensors=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   )

         if "attention_mask" in text_encoding:
            _lowerCamelCase	:					int   =		text_encoding.pop("""attention_mask""")
         if "input_ids" in text_encoding:
            _lowerCamelCase	:					str   =		text_encoding.pop("""input_ids""")
      else:
         _lowerCamelCase	:					List[str]   =		None

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

      return encoding_image_processor





   def 		UpperCamelCase_  (							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  int:
      return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  int:
      return self.tokenizer.decode(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)





   @property
   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					Any   =		self.tokenizer.model_input_names
      _lowerCamelCase	:					int   =		self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	bool:







     """simple docstring"""
     if num < 0:
          return False

     _A				=							num
     _A				=							0
     while num > 0:
          _A				=							rev_num * 1_0 + (num % 1_0)
          num //= 1_0

     return num_copy == rev_num


if __name__ == "__main__":
    import doctest

    doctest.testmod()





