
import warnings

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






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	['image_processor', 'tokenizer']
							_snake_case  		=	'AutoImageProcessor'
							_snake_case  		=	'AutoTokenizer'



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





													'''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.'''  ,						SCREAMING_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 have to specify an image_processor.'''      )
													if tokenizer is None:
																			raise ValueError('''You have to specify a tokenizer.'''      )

													super().__init__(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      self.image_processor



							def __call__(	self  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None  ,						**SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''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(SCREAMING_SNAKE_CASE_  ,						return_tensors=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )

													if images is not None:
																			__UpperCamelCase  	=      self.image_processor(SCREAMING_SNAKE_CASE_  ,						return_tensors=SCREAMING_SNAKE_CASE_  ,						**SCREAMING_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(**SCREAMING_SNAKE_CASE_      )  ,						tensor_type=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> Optional[Any]:





													'''simple docstring'''






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



							def      A__		(	self  ,						*SCREAMING_SNAKE_CASE_  ,						**SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






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



							@property
							def      A__		(	self      )-> List[str]:





													'''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      A__		(	self      )-> Optional[int]:





													'''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      A__		(	self      )-> List[str]:





													'''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 __future__ import annotations



class 		lowerCamelCase__   :




					'''simple docstring'''



					def __init__(	self					:						str  ,	UpperCamelCase_					:						int       )	->							None:



						'''simple docstring'''





						_lowercase       :							Any  	=		value
						_lowercase       :							Node | None  	=		None
						_lowercase       :							Node | None  	=		None






class 		lowerCamelCase__   :




					'''simple docstring'''



					A_         =    42




					def __init__(	self					:						List[Any]       )	->							None:



						'''simple docstring'''





						_lowercase       :							Optional[int]  	=		0




					def     __UpperCAmelCase  (	self					:						str  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int       )	->							None:



						'''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     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Node | None  ,	UpperCamelCase_					:						int | None = None       )	->							int:



						'''simple docstring'''





						if node is None:
							return 0
						if target is not None:
							_lowercase       :							Union[str, Any]  	=		target

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

						return self.paths


if __name__ == "__main__":
		import doctest

		doctest.testmod()
