
'''simple docstring'''







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 				lowerCAmelCase__     (						lowerCamelCase_  ):
					lowerCAmelCase_       =  ['''image_processor''', '''tokenizer''']
					lowerCAmelCase_       =  '''AutoImageProcessor'''
					lowerCAmelCase_       =  '''AutoTokenizer'''






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  Tuple									= 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       ,							)
																	lowercase_			:  str									= kwargs.pop('''feature_extractor'''	)

											lowercase_			:  Tuple									= 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	)
											lowercase_			:  Tuple									= self.image_processor
											lowercase_			:  Any									= False






					def __call__(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if self._in_target_context_manager:
																	return self.current_processor(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											lowercase_			:  List[str]									= kwargs.pop('''images'''       ,							__SCREAMING_SNAKE_CASE	)
											lowercase_			:  List[Any]									= kwargs.pop('''text'''       ,							__SCREAMING_SNAKE_CASE	)
											if len(__SCREAMING_SNAKE_CASE	) > 0:
																	lowercase_			:  Dict									= args[0]
																	lowercase_			:  Union[str, 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:
																	lowercase_			:  List[str]									= self.image_processor(__SCREAMING_SNAKE_CASE       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)
											if text is not None:
																	lowercase_			:  int									= self.tokenizer(__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if text is None:
																	return inputs
											elif images is None:
																	return encodings
											else:
																	lowercase_			:  List[Any]									= encodings['''input_ids''']
																	return inputs






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





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






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





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






					@contextmanager
					def 			_snake_case					(     self	):


											"""simple docstring"""





											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.'''	)
											lowercase_			:  Any									= True
											lowercase_			:  Optional[Any]									= self.tokenizer
											yield
											lowercase_			:  List[str]									= self.image_processor
											lowercase_			:  int									= False






					@property
					def 			_snake_case					(     self	):


											"""simple docstring"""





											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 			_snake_case					(     self	):


											"""simple docstring"""





											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'''







from itertools import combinations, product
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						if xa == ya == 0 or xa == ya == 0:
												return False
						lowercase_			:  Optional[Any]									= xa * xa + ya * ya
						lowercase_			:  List[str]									= xa * xa + ya * ya
						lowercase_			:  Any									= (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
						return (
						    a_square + b_square == c_square
						    or a_square + c_square == b_square
						    or b_square + c_square == a_square
						)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 50							):



						"""simple docstring"""





						return sum(
						    1
						    for pta, pta in combinations(product(range(limit + 1							)   ,   repeat=2							)   ,   2							)
						    if is_right(*__SCREAMING_SNAKE_CASE   ,   *__SCREAMING_SNAKE_CASE							)							)


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



