



import warnings

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



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				["image_processor", "tokenizer"]
	__A		      =				"VideoMAEImageProcessor"
	__A		      =				("CLIPTokenizer", "CLIPTokenizerFast")




	def __init__(self						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			**lowerCamelCase_						):
						"""simple docstring"""

						a   	=  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_						,			)
											a   	=  kwargs.pop("feature_extractor"						)

						a   	=  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_						)
						a   	=  self.image_processor




	def __call__(self						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			**lowerCamelCase_						):
						"""simple docstring"""

						if text is None and videos is None:
											raise ValueError("You have to specify either text or videos. Both cannot be none."						)

						if text is not None:
											a   	=  self.tokenizer(lowerCamelCase_						,			return_tensors=lowerCamelCase_						,			**lowerCamelCase_						)

						if videos is not None:
											a   	=  self.image_processor(lowerCamelCase_						,			return_tensors=lowerCamelCase_						,			**lowerCamelCase_						)

						if text is not None and videos is not None:
											a   	=  image_features.pixel_values
											return encoding
						elif text is not None:
											return encoding
						else:
											return BatchEncoding(data=dict(**lowerCamelCase_						)						,			tensor_type=lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			*lowerCamelCase_						,			**lowerCamelCase_						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						,			*lowerCamelCase_						,			**lowerCamelCase_						):
						"""simple docstring"""

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




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return ["input_ids", "attention_mask", "position_ids", "pixel_values"]




	@property
	def 	UpperCamelCase_			(self						):
						"""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 	UpperCamelCase_			(self						):
						"""simple docstring"""

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





from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass



@dataclass
class       _lowercase				:





	"""simple docstring"""
	__A		      =				42
	__A		      =				None
	__A		      =				None






def        a( )						->			TreeNode:
					"""simple docstring"""





					return TreeNode(3					,	TreeNode(9			)					,	TreeNode(20					,	TreeNode(15			)					,	TreeNode(7			)			)			)






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def depth_first_search(
					    A			:       TreeNode | None					,	A			:       int					,	A			:       list[int]			) -> None:
										if not root:
															return

										if depth == len(A			):
															right_view.append(root.val			)

										depth_first_search(root.right					,	depth + 1					,	A			)
										depth_first_search(root.left					,	depth + 1					,	A			)

					a   	=  []
					if not root:
										return right_view

					depth_first_search(A					,	0					,	A			)
					return right_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def depth_first_search(
					    A			:       TreeNode | None					,	A			:       int					,	A			:       list[int]			) -> None:
										if not root:
															return

										if depth == len(A			):
															left_view.append(root.val			)

										depth_first_search(root.left					,	depth + 1					,	A			)
										depth_first_search(root.right					,	depth + 1					,	A			)

					a   	=  []
					if not root:
										return left_view

					depth_first_search(A					,	0					,	A			)
					return left_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					def breadth_first_search(A			:       TreeNode					,	A			:       list[int]			) -> None:
										a   	=  [(root, 0)]
										a   	=  defaultdict(A			)

										while queue:
															a   	=  queue.pop(0			)
															a	,							a   	=  first

															lookup[hd].append(node.val			)

															if node.left:
																				queue.append((node.left, hd - 1)			)
															if node.right:
																				queue.append((node.right, hd + 1)			)

										for pair in sorted(lookup.items()					,	key=lambda A			: each[0]			):
															top_view.append(pair[1][0]			)

					a   	=  []
					if not root:
										return top_view

					breadth_first_search(A					,	A			)
					return top_view






def        a( A			:       TreeNode			)						->			list[int]:
					"""simple docstring"""





					from collections import defaultdict

					def breadth_first_search(A			:       TreeNode					,	A			:       list[int]			) -> None:
										a   	=  [(root, 0)]
										a   	=  defaultdict(A			)

										while queue:
															a   	=  queue.pop(0			)
															a	,							a   	=  first
															lookup[hd].append(node.val			)

															if node.left:
																				queue.append((node.left, hd - 1)			)
															if node.right:
																				queue.append((node.right, hd + 1)			)

										for pair in sorted(lookup.items()					,	key=lambda A			: each[0]			):
															bottom_view.append(pair[1][-1]			)

					a   	=  []
					if not root:
										return bottom_view

					breadth_first_search(A					,	A			)
					return bottom_view


if __name__ == "__main__":
				import doctest

				doctest.testmod()
