



"""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       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     ["""image_processor""", """tokenizer"""]
      _SCREAMING_SNAKE_CASE  				=     """Pix2StructImageProcessor"""
      _SCREAMING_SNAKE_CASE  				=     ("""T5Tokenizer""", """T5TokenizerFast""")



      def __init__(      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        lowerCAmelCase_   :				List[Any]								= False
        super().__init__(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )



      def __call__(      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Any=None    ,			SCREAMING_SNAKE_CASE_				:      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      Union[bool, str, PaddingStrategy] = False    ,			SCREAMING_SNAKE_CASE_				:      Union[bool, str, TruncationStrategy] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = 2_0_4_8    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[bool] = None    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[str, TensorType]] = None    ,			**SCREAMING_SNAKE_CASE_				:      List[str]    ,			):
        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_   :				Dict								= 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_   :				int								= 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_   :				Dict								= 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_   :				List[Any]								= text_encoding.pop('attention_mask'       )
          if "input_ids" in text_encoding:
            lowerCAmelCase_   :				Optional[int]								= text_encoding.pop('input_ids'       )
        else:
          lowerCAmelCase_   :				List[Any]								= None

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

        return encoding_image_processor



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			*SCREAMING_SNAKE_CASE_				:      List[Any]    ,			**SCREAMING_SNAKE_CASE_				:      str       ):
        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			*SCREAMING_SNAKE_CASE_				:      Dict    ,			**SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )





      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				Any								= self.tokenizer.model_input_names
        lowerCAmelCase_   :				Dict								= self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )










"""simple docstring"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				int = 100						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= (n * (n + 1) // 2) ** 2
  lowerCAmelCase_   :				Optional[int]								= n * (n + 1) * (2 * n + 1) // 6
  return sum_cubes - sum_squares


if __name__ == "__main__":
     print(f'{solution() = }')





