


"""simple docstring"""


from math import sqrt


def _lowerCamelCase	(		_UpperCamelCase = 100_0000      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			42

			while num_cuboids <= limit:
						max_cuboid_size += 1
						for sum_shortest_sides in range(2    , 2 * max_cuboid_size + 1      ):
									if sqrt(sum_shortest_sides**2 + max_cuboid_size**2      ).is_integer():
												num_cuboids += (
												    min(_UpperCamelCase    , sum_shortest_sides // 2      )
												    - max(1    , sum_shortest_sides - max_cuboid_size      )
												    + 1
												)

			return max_cuboid_size


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








"""simple docstring"""


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase						:    Optional[Any]				      ="""CLIPImageProcessor"""
							__UpperCAmelCase						:    Union[str, Any]				      =("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")

							def __init__(     self    ,   __a=None    ,   __a=None    ,   **__a	):
										__lowerCAmelCase				    =			None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
													    " instead."    ,   __a    ,   )
													__lowerCAmelCase				    =			kwargs.pop("feature_extractor"	)

										__lowerCAmelCase				    =			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__(__a    ,   __a	)

							def __call__(     self    ,   __a=None    ,   __a=None    ,   __a=None    ,   **__a	):

										if text is None and images is None:
													raise ValueError("You have to specify either text or images. Both cannot be none."	)

										if text is not None:
													__lowerCAmelCase				    =			self.tokenizer(__a    ,   return_tensors=__a    ,   **__a	)

										if images is not None:
													__lowerCAmelCase				    =			self.image_processor(__a    ,   return_tensors=__a    ,   **__a	)

										if text is not None and images is not None:
													__lowerCAmelCase				    =			image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										else:
													return BatchEncoding(data=dict(**__a	)    ,   tensor_type=__a	)

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.batch_decode(*__a    ,   **__a	)

							def 			snake_case				(     self    ,   *__a    ,   **__a	):
										return self.tokenizer.decode(*__a    ,   **__a	)



							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer.model_input_names
										__lowerCAmelCase				    =			self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)




