


'''simple docstring'''







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 				__lowerCamelCase				( a_ ):
    """simple docstring"""
    a									=					["image_processor", "tokenizer"]
    a									=					"AutoImageProcessor"
    a									=					"AutoTokenizer"


    def __init__(						self    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=None   ,							SCREAMING_SNAKE_CASE    :							str=None   ,							**SCREAMING_SNAKE_CASE    :							List[str]):
           _A  :			str											=      None
           if "feature_extractor" in kwargs:
                  warnings.warn(
                      'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                      ' instead.'   ,							SCREAMING_SNAKE_CASE   ,							)
                  _A  :			Any											=      kwargs.pop('feature_extractor')

           _A  :			Optional[int]											=      image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                  raise ValueError('You need to specify an `image_processor`.')
           if tokenizer is None:
                  raise ValueError('You need to specify a `tokenizer`.')

           super().__init__(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
           _A  :			Optional[Any]											=      self.image_processor
           _A  :			int											=      False


    def __call__(						self    :							Any   ,							*SCREAMING_SNAKE_CASE    :							int   ,							**SCREAMING_SNAKE_CASE    :							Optional[Any]):
           # For backward compatibility
           if self._in_target_context_manager:
                  return self.current_processor(*SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)

           _A  :			Optional[int]											=      kwargs.pop('images'   ,							SCREAMING_SNAKE_CASE)
           _A  :			Tuple											=      kwargs.pop('text'   ,							SCREAMING_SNAKE_CASE)
           if len(SCREAMING_SNAKE_CASE) > 0:
                  _A  :			Any											=      args[0]
                  _A  :			Any											=      args[1:]

           if images is None and text is None:
                  raise ValueError('You need to specify either an `images` or `text` input to process.')

           if images is not None:
                  _A  :			List[str]											=      self.image_processor(SCREAMING_SNAKE_CASE   ,							*SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)
           if text is not None:
                  _A  :			str											=      self.tokenizer(SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)

           if text is None:
                  return inputs
           elif images is None:
                  return encodings
           else:
                  _A  :			Union[str, Any]											=      encodings['input_ids']
                  return inputs


    def      A					(						self    :							Tuple   ,							*SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							**SCREAMING_SNAKE_CASE    :							Optional[int]):
           return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Optional[int]   ,							*SCREAMING_SNAKE_CASE    :							Tuple   ,							**SCREAMING_SNAKE_CASE    :							Optional[int]):
           return self.tokenizer.decode(*SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE)


    @contextmanager
    def      A					(						self    :							Union[str, Any]):
           warnings.warn(
               '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
               'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
               'your images inputs, or in a separate call.')
           _A  :			int											=      True
           _A  :			str											=      self.tokenizer
           yield
           _A  :			Optional[int]											=      self.image_processor
           _A  :			List[str]											=      False


    @property
    def      A					(						self    :							Dict):
           warnings.warn(
               '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,							SCREAMING_SNAKE_CASE   ,							)
           return self.image_processor_class


    @property
    def      A					(						self    :							List[str]):
           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,							SCREAMING_SNAKE_CASE   ,							)
           return self.image_processor










'''simple docstring'''







import itertools






def    lowerCAmelCase__ (       lowerCamelCase  :    int = 1000000     ):
       _A  :			str											=      [1]

       for i in itertools.count(len(lowerCamelCase     )     ):
              _A  :			Optional[Any]											=      0
              for j in itertools.count(1     ):
                     _A  :			int											=      -1 if j % 2 == 0 else +1
                     _A  :			Optional[Any]											=      (j * j * 3 - j) // 2
                     if index > i:
                            break
                     item += partitions[i - index] * sign
                     item %= number
                     index += j
                     if index > i:
                            break
                     item += partitions[i - index] * sign
                     item %= number

              if item == 0:
                     return i
              partitions.append(lowerCamelCase     )

       return 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")






