


"""simple docstring"""






import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class      lowerCamelCase__   (     __A				):







					"""simple docstring"""


					__a	       =							["""image_processor""", """tokenizer"""]
					__a	       =							"""FlavaImageProcessor"""
					__a	       =							("""BertTokenizer""", """BertTokenizerFast""")






					def __init__(       self    : List[Any] ,       UpperCamelCase    : Optional[int]=None ,       UpperCamelCase    : Optional[int]=None ,       **UpperCamelCase    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						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.""" ,       lowerCAmelCase_ ,       )
																			__UpperCAmelCase   :						Any      							=					kwargs.pop("""feature_extractor"""  )

												__UpperCAmelCase   :						str      							=					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__(lowerCAmelCase_ ,       lowerCAmelCase_  )
												__UpperCAmelCase   :						List[str]      							=					self.image_processor






					def __call__(       self    : Dict ,       UpperCamelCase    : Optional[ImageInput] = None ,       UpperCamelCase    : Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None ,       UpperCamelCase    : bool = True ,       UpperCamelCase    : Union[bool, str, PaddingStrategy] = False ,       UpperCamelCase    : Union[bool, str, TruncationStrategy] = False ,       UpperCamelCase    : Optional[int] = None ,       UpperCamelCase    : int = 0 ,       UpperCamelCase    : Optional[int] = None ,       UpperCamelCase    : Optional[bool] = None ,       UpperCamelCase    : Optional[bool] = None ,       UpperCamelCase    : Optional[bool] = None ,       UpperCamelCase    : Optional[bool] = None ,       UpperCamelCase    : bool = False ,       UpperCamelCase    : bool = False ,       UpperCamelCase    : bool = False ,       UpperCamelCase    : bool = False ,       UpperCamelCase    : bool = True ,       UpperCamelCase    : Optional[Union[str, TensorType]] = None ,       **UpperCamelCase    : List[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   :						Dict      							=					self.tokenizer(
																			    text=lowerCAmelCase_ ,       add_special_tokens=lowerCAmelCase_ ,       padding=lowerCAmelCase_ ,       truncation=lowerCAmelCase_ ,       max_length=lowerCAmelCase_ ,       stride=lowerCAmelCase_ ,       pad_to_multiple_of=lowerCAmelCase_ ,       return_token_type_ids=lowerCAmelCase_ ,       return_attention_mask=lowerCAmelCase_ ,       return_overflowing_tokens=lowerCAmelCase_ ,       return_special_tokens_mask=lowerCAmelCase_ ,       return_offsets_mapping=lowerCAmelCase_ ,       return_length=lowerCAmelCase_ ,       verbose=lowerCAmelCase_ ,       return_tensors=lowerCAmelCase_ ,       **lowerCAmelCase_ ,       )
												if images is not None:
																			__UpperCAmelCase   :						Dict      							=					self.image_processor(
																			    lowerCAmelCase_ ,       return_image_mask=lowerCAmelCase_ ,       return_codebook_pixels=lowerCAmelCase_ ,       return_tensors=lowerCAmelCase_ ,       **lowerCAmelCase_ ,       )

												if text is not None and images is not None:
																			encoding.update(lowerCAmelCase_  )
																			return encoding
												elif text is not None:
																			return encoding
												else:
																			return BatchEncoding(data=dict(**lowerCAmelCase_  ) ,       tensor_type=lowerCAmelCase_  )






					def lowerCamelCase__							(       self    : str ,       *UpperCamelCase    : Dict ,       **UpperCamelCase    : Any  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : str ,       *UpperCamelCase    : Union[str, Any] ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






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






					@property
					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					self.tokenizer.model_input_names
												__UpperCAmelCase   :						List[str]      							=					self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






					@property
					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






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






					@property
					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												warnings.warn(
												    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,       lowerCAmelCase_ ,       )
												return self.image_processor








"""simple docstring"""
from __future__ import annotations







class 			UpperCamelCase_       :





	def __init__(      self					: Any	,	lowerCAmelCase_					: int						)						->			None:
				UpperCAmelCase_     : Any			     =					data
				UpperCAmelCase_     : Node | None			     =					None
				UpperCAmelCase_     : Node | None			     =					None





def 				snake_case      (   A__						):  # In Order traversal of the tree
			if tree:
						display(tree.left						)
						print(tree.data						)
						display(tree.right						)

def 				snake_case      (   A__						):
			return 1 + max(depth_of_tree(tree.left						)	,depth_of_tree(tree.right						)						) if tree else 0

def 				snake_case      (   A__						):
			if not tree:
						return True
			if tree.left and tree.right:
						return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
			else:
						return not tree.left and not tree.right

def 				snake_case      (   ):  # Main function for testing.
			UpperCAmelCase_     : List[str]			     =					Node(1						)
			UpperCAmelCase_     : Any			     =					Node(2						)
			UpperCAmelCase_     : Optional[Any]			     =					Node(3						)
			UpperCAmelCase_     : Union[str, Any]			     =					Node(4						)
			UpperCAmelCase_     : int			     =					Node(5						)
			UpperCAmelCase_     : Optional[int]			     =					Node(6						)
			UpperCAmelCase_     : Any			     =					Node(7						)
			UpperCAmelCase_     : List[str]			     =					Node(8						)
			UpperCAmelCase_     : List[Any]			     =					Node(9						)

			print(is_full_binary_tree(A__						)						)
			print(depth_of_tree(A__						)						)
			print("Tree is: "						)
			display(A__						)


if __name__ == "__main__":
			main()



