




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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	List[Any]	     =      ["""image_processor""", """tokenizer"""]
    lowerCamelCase_      :	Dict	     =      """BlipImageProcessor"""
    lowerCamelCase_      :	Any	     =      """AutoTokenizer"""







    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      Dict			 =					False
           super().__init__(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      List[Any]			 =					self.image_processor







    def __call__(    self     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = True     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = 0     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = None     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = False     ,  UpperCamelCase__ = True     ,  UpperCamelCase__ = None     ,  **UpperCamelCase__     ,  )     ->      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:
                  lowerCamelCase   :      int			 =					self.tokenizer
                  lowerCamelCase   :      str			 =					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
           lowerCamelCase   :      Dict			 =					self.image_processor(UpperCamelCase__     ,  return_tensors=UpperCamelCase__     )

           if text is not None:
                  lowerCamelCase   :      Union[str, Any]			 =					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:
                  lowerCamelCase   :      List[Any]			 =					None

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

           return encoding_image_processor







    def     _lowercase     (    self     ,  *UpperCamelCase__     ,  **UpperCamelCase__     )     ->      Optional[Any]:
           return self.tokenizer.batch_decode(*UpperCamelCase__     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  *UpperCamelCase__     ,  **UpperCamelCase__     )     ->      Union[str, Any]:
           return self.tokenizer.decode(*UpperCamelCase__     ,  **UpperCamelCase__     )







    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      str			 =					self.tokenizer.model_input_names
           lowerCamelCase   :      Optional[int]			 =					self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )













def lowerCamelCase__    (							)      ->				int:
   return [
       a * b * (1000 - a - b)
       for a in range(1       ,      999       )
       for b in range(snake_case_       ,      999       )
       if (a * a + b * b == (1000 - a - b) ** 2)
   ][0]


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

