



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__lowerCAmelCase    (			A						):
			UpperCamelCase   					=     ['''image_processor''', '''tokenizer''']
			UpperCamelCase   					=     '''ChineseCLIPImageProcessor'''
			UpperCamelCase   					=     ('''BertTokenizer''', '''BertTokenizerFast''')



			def __init__(						self   :  Union[str, Any] ,		A   :  Union[str, Any]=None ,		A   :  List[Any]=None ,		**A   :  Tuple)   ->    Tuple:



						"""simple docstring"""






						_UpperCAmelCase		       =   None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
									    ' instead.' ,		_snake_case ,		)
									_UpperCAmelCase		       =   kwargs.pop('feature_extractor')

						_UpperCAmelCase		       =   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__(_snake_case ,		_snake_case)
						_UpperCAmelCase		       =   self.image_processor



			def __call__(						self   :  int ,		A   :  List[Any]=None ,		A   :  List[Any]=None ,		A   :  Optional[Any]=None ,		**A   :  Tuple)   ->    Optional[Any]:



						"""simple docstring"""






						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:
									_UpperCAmelCase		       =   self.tokenizer(_snake_case ,		return_tensors=_snake_case ,		**_snake_case)

						if images is not None:
									_UpperCAmelCase		       =   self.image_processor(_snake_case ,		return_tensors=_snake_case ,		**_snake_case)

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



			def     _lowerCamelCase       (						self   :  Tuple ,		*A   :  List[str] ,		**A   :  List[str])   ->    str:



						"""simple docstring"""






						return self.tokenizer.batch_decode(*_snake_case ,		**_snake_case)



			def     _lowerCamelCase       (						self   :  Union[str, Any] ,		*A   :  Optional[Any] ,		**A   :  Optional[int])   ->    Dict:



						"""simple docstring"""






						return self.tokenizer.decode(*_snake_case ,		**_snake_case)



			@property
			def     _lowerCamelCase       (						self   :  List[Any])   ->    Any:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.tokenizer.model_input_names
						_UpperCAmelCase		       =   self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



			@property
			def     _lowerCamelCase       (						self   :  str)   ->    Optional[Any]:



						"""simple docstring"""






						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,		_snake_case ,		)
						return self.image_processor_class


from __future__ import annotations

import typing
from collections import Counter


def     __lowerCAmelCase			(			a__  )       ->  typing.Counter[int]:
				__a										=    Counter()
				for base in range(1						,    max_perimeter + 1  ):
								for perpendicular in range(a__						,    max_perimeter + 1  ):
												__a										=    (base * base + perpendicular * perpendicular) ** 0.5
												if hypotenuse == int(a__  ):
																__a										=    int(base + perpendicular + hypotenuse  )
																if perimeter > max_perimeter:
																				continue
																triplets[perimeter] += 1
				return triplets


def     __lowerCAmelCase			(			a__ = 1000  )       ->  int:
				__a										=    pythagorean_triple(a__  )
				return triplets.most_common(1  )[0][0]


if __name__ == "__main__":
					print(F"Perimeter {solution()} has maximum solutions")