



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import VideoMAEImageProcessor



class       _lowercase				(  unittest.TestCase      ):





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						,			lowerCamelCase_=7						,			lowerCamelCase_=3						,			lowerCamelCase_=10						,			lowerCamelCase_=18						,			lowerCamelCase_=30						,			lowerCamelCase_=400						,			lowerCamelCase_=True						,			lowerCamelCase_=None						,			lowerCamelCase_=True						,			lowerCamelCase_=[0.5, 0.5, 0.5]						,			lowerCamelCase_=[0.5, 0.5, 0.5]						,			lowerCamelCase_=None						,			):
						"""simple docstring"""

						a   	=  size if size is not None else {"shortest_edge": 18}
						a   	=  crop_size if crop_size is not None else {"height": 18, "width": 18}

						a   	=  parent
						a   	=  batch_size
						a   	=  num_channels
						a   	=  num_frames
						a   	=  image_size
						a   	=  min_resolution
						a   	=  max_resolution
						a   	=  do_resize
						a   	=  size
						a   	=  do_normalize
						a   	=  image_mean
						a   	=  image_std
						a   	=  crop_size




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

						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "crop_size": self.crop_size,
						}




@require_torch
@require_vision
class       _lowercase				(  lowerCAmelCase, unittest.TestCase      ):





	"""simple docstring"""
	__A		      =				VideoMAEImageProcessor if is_vision_available() else None




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

						a   	=  VideoMAEImageProcessingTester(self						)




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

						return self.image_processor_tester.prepare_image_processor_dict()




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

						a   	=  self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(lowerCamelCase_						,			"image_mean"						)						)
						self.assertTrue(hasattr(lowerCamelCase_						,			"image_std"						)						)
						self.assertTrue(hasattr(lowerCamelCase_						,			"do_normalize"						)						)
						self.assertTrue(hasattr(lowerCamelCase_						,			"do_resize"						)						)
						self.assertTrue(hasattr(lowerCamelCase_						,			"do_center_crop"						)						)
						self.assertTrue(hasattr(lowerCamelCase_						,			"size"						)						)




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

						a   	=  self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size						,			{"shortest_edge": 18}						)
						self.assertEqual(image_processor.crop_size						,			{"height": 18, "width": 18}						)

						a   	=  self.image_processing_class.from_dict(self.image_processor_dict						,			size=42						,			crop_size=84						)
						self.assertEqual(image_processor.size						,			{"shortest_edge": 42}						)
						self.assertEqual(image_processor.crop_size						,			{"height": 84, "width": 84}						)




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

						pass




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

						a   	=  self.image_processing_class(**self.image_processor_dict						)
						# create random PIL videos
						a   	=  prepare_video_inputs(self.image_processor_tester						,			equal_resolution=lowerCamelCase_						)
						for video in video_inputs:
											self.assertIsInstance(lowerCamelCase_						,			lowerCamelCase_						)
											self.assertIsInstance(video[0]						,			Image.Image						)

						# Test not batched input
						a   	=  image_processing(video_inputs[0]						,			return_tensors="pt"						).pixel_values
						self.assertEqual(
						    encoded_videos.shape						,			(
						        1,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )						,			)

						# Test batched
						a   	=  image_processing(lowerCamelCase_						,			return_tensors="pt"						).pixel_values
						self.assertEqual(
						    encoded_videos.shape						,			(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )						,			)




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

						a   	=  self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						a   	=  prepare_video_inputs(self.image_processor_tester						,			equal_resolution=lowerCamelCase_						,			numpify=lowerCamelCase_						)
						for video in video_inputs:
											self.assertIsInstance(lowerCamelCase_						,			lowerCamelCase_						)
											self.assertIsInstance(video[0]						,			np.ndarray						)

						# Test not batched input
						a   	=  image_processing(video_inputs[0]						,			return_tensors="pt"						).pixel_values
						self.assertEqual(
						    encoded_videos.shape						,			(
						        1,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )						,			)

						# Test batched
						a   	=  image_processing(lowerCamelCase_						,			return_tensors="pt"						).pixel_values
						self.assertEqual(
						    encoded_videos.shape						,			(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )						,			)




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

						a   	=  self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						a   	=  prepare_video_inputs(self.image_processor_tester						,			equal_resolution=lowerCamelCase_						,			torchify=lowerCamelCase_						)
						for video in video_inputs:
											self.assertIsInstance(lowerCamelCase_						,			lowerCamelCase_						)
											self.assertIsInstance(video[0]						,			torch.Tensor						)

						# Test not batched input
						a   	=  image_processing(video_inputs[0]						,			return_tensors="pt"						).pixel_values
						self.assertEqual(
						    encoded_videos.shape						,			(
						        1,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )						,			)

						# Test batched
						a   	=  image_processing(lowerCamelCase_						,			return_tensors="pt"						).pixel_values
						self.assertEqual(
						    encoded_videos.shape						,			(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_frames,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )						,			)





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()
