
'''simple docstring'''







from __future__ import annotations






class 					lowerCAmelCase__  :






					def __init__(  self							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	str						=			value
											lowercase_      :	Node | None						=			None
											lowercase_      :	Node | None						=			None







class 					lowerCAmelCase__  :
					lowerCAmelCase_	  =	42






					def __init__(  self						):


											"""simple docstring"""





											lowercase_      :	Dict						=			0






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											if node is None:
																	return

											if path_sum == self.target:
																	self.paths += 1

											if node.left:
																	self.depth_first_search(node.left							,				path_sum + node.left.value						)
											if node.right:
																	self.depth_first_search(node.right							,				path_sum + node.right.value						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE = None						):


											"""simple docstring"""





											if node is None:
																	return 0
											if target is not None:
																	lowercase_      :	List[str]						=			target

											self.depth_first_search(__SCREAMING_SNAKE_CASE							,				node.value						)
											self.path_sum(node.left						)
											self.path_sum(node.right						)

											return self.paths


if __name__ == "__main__":
							import doctest

							doctest.testmod()





'''simple docstring'''







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






class 					lowerCAmelCase__  (    lowerCamelCase_	):
					lowerCAmelCase_	  =	['''image_processor''', '''tokenizer''']
					lowerCAmelCase_	  =	'''EfficientNetImageProcessor'''
					lowerCAmelCase_	  =	('''BertTokenizer''', '''BertTokenizerFast''')






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											super().__init__(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					def __call__(  self							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE="max_length"							,				__SCREAMING_SNAKE_CASE=64							,				__SCREAMING_SNAKE_CASE=None							,				**__SCREAMING_SNAKE_CASE						):


											"""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:
																	lowercase_      :	str						=			self.tokenizer(
																	    __SCREAMING_SNAKE_CASE							,				padding=__SCREAMING_SNAKE_CASE							,				max_length=__SCREAMING_SNAKE_CASE							,				return_tensors=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)

											if images is not None:
																	lowercase_      :	int						=			self.image_processor(__SCREAMING_SNAKE_CASE							,				return_tensors=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)

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






					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						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Dict						=			self.tokenizer.model_input_names
											lowercase_      :	Optional[int]						=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)



