
'''simple docstring'''
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      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def __init__(	self							:       Dict   ,     UpperCamelCase__							:       str   ,     UpperCamelCase__							:       Optional[Any]=7   ,     UpperCamelCase__							:       Union[str, Any]=3   ,     UpperCamelCase__							:       str=10   ,     UpperCamelCase__							:       List[Any]=18   ,     UpperCamelCase__							:       Optional[Any]=30   ,     UpperCamelCase__							:       Optional[Any]=400   ,     UpperCamelCase__							:       List[Any]=True   ,     UpperCamelCase__							:       Union[str, Any]=None   ,     UpperCamelCase__							:       int=True   ,     UpperCamelCase__							:       int=[0.5, 0.5, 0.5]   ,     UpperCamelCase__							:       Optional[Any]=[0.5, 0.5, 0.5]   ,     UpperCamelCase__							:       List[Any]=None   ,     )		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    Dict			       =		size if size is not None else {'''shortest_edge''': 18}
				snake_case   :    Union[str, Any]			       =		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

				snake_case   :    Optional[int]			       =		parent
				snake_case   :    Any			       =		batch_size
				snake_case   :    Union[str, Any]			       =		num_channels
				snake_case   :    Optional[int]			       =		num_frames
				snake_case   :    List[str]			       =		image_size
				snake_case   :    int			       =		min_resolution
				snake_case   :    List[Any]			       =		max_resolution
				snake_case   :    Dict			       =		do_resize
				snake_case   :    str			       =		size
				snake_case   :    List[Any]			       =		do_normalize
				snake_case   :    Optional[Any]			       =		image_mean
				snake_case   :    List[Any]			       =		image_std
				snake_case   :    List[Any]			       =		crop_size



		def 	lowerCAmelCase							(	self							:       Union[str, Any]	)		->       Optional[Any]:






				"""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      snake_case__    (	__SCREAMING_SNAKE_CASE  , unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		VideoMAEImageProcessor if is_vision_available() else None



		def 	lowerCAmelCase							(	self							:       Union[str, Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    str			       =		VideoMAEImageProcessingTester(self	)



		@property
		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    int			       =		self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_mean'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_std'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_normalize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_resize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_center_crop'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''size'''	)	)



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Optional[Any]:






				"""simple docstring"""







				snake_case   :    Any			       =		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}	)

				snake_case   :    Optional[int]			       =		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 	lowerCAmelCase							(	self							:       Tuple	)		->       Optional[Any]:






				"""simple docstring"""







				pass



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PIL videos
				snake_case   :    int			       =		prepare_video_inputs(self.image_processor_tester   ,     equal_resolution=UpperCamelCase__	)
				for video in video_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     UpperCamelCase__	)
						self.assertIsInstance(video[0]   ,     Image.Image	)

				# Test not batched input
				snake_case   :    Union[str, Any]			       =		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
				snake_case   :    Optional[int]			       =		image_processing(UpperCamelCase__   ,     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 	lowerCAmelCase							(	self							:       List[Any]	)		->       Union[str, Any]:






				"""simple docstring"""







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

				# Test not batched input
				snake_case   :    Optional[int]			       =		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
				snake_case   :    int			       =		image_processing(UpperCamelCase__   ,     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 	lowerCAmelCase							(	self							:       Any	)		->       List[Any]:






				"""simple docstring"""







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

				# Test not batched input
				snake_case   :    Union[str, Any]			       =		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
				snake_case   :    Dict			       =		image_processing(UpperCamelCase__   ,     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'''],
				    )   ,     )






"""simple docstring"""


from __future__ import annotations

a_											=			[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class snake_case					:







		def __init__(    self					:				Optional[int]			,      a__					:				int						)				->  None:





						'''simple docstring'''







						_A         =	-1
						_A         =	-1
						_A         =	[-1] * length
						_A         =	None
						_A         =	None







		def __repr__(    self					:				Any						)				->  str:





						'''simple docstring'''







						return F"""Node(min_value={self.minn} max_value={self.maxx})"""






def    a__       ( __lowercase      )		->		Node | None:
				_A         =	Node(len(__lowercase      )      )
				_A     ,						_A         =	min(__lowercase      ), max(__lowercase      )
				# Leaf node case where the node contains only one unique value
				if root.minn == root.maxx:
								return root
				_A         =	(root.minn + root.maxx) // 2

				_A         =	[]
				_A         =	[]

				for index, num in enumerate(__lowercase      ):
								if num <= pivot:
												left_arr.append(__lowercase      )
								else:
												right_arr.append(__lowercase      )
								_A         =	len(__lowercase      )
				_A         =	build_tree(__lowercase      )
				_A         =	build_tree(__lowercase      )
				return root






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if index < 0 or node is None:
								return 0
				# Leaf node cases
				if node.minn == node.maxx:
								return index + 1 if node.minn == num else 0
				_A         =	(node.minn + node.maxx) // 2
				if num <= pivot:
								# go the left subtree and map index to the left subtree
								return rank_till_index(node.left   ,	__lowercase   ,	node.map_left[index] - 1      )
				else:
								# go to the right subtree and map index to the right subtree
								return rank_till_index(node.right   ,	__lowercase   ,	index - node.map_left[index]      )






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if start > end:
								return 0
				_A         =	rank_till_index(__lowercase   ,	__lowercase   ,	__lowercase      )
				_A         =	rank_till_index(__lowercase   ,	__lowercase   ,	start - 1      )
				return rank_till_end - rank_before_start






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if index > (end - start) or start > end or node is None:
								return -1
				# Leaf node case
				if node.minn == node.maxx:
								return node.minn
				# Number of elements in the left subtree in interval [start, end]
				_A         =	node.map_left[end] - (
				    node.map_left[start - 1] if start else 0
				)
				if num_elements_in_left_tree > index:
								return quantile(
								    node.left   ,	__lowercase   ,	(node.map_left[start - 1] if start else 0)   ,	node.map_left[end] - 1   ,	)
				else:
								return quantile(
								    node.right   ,	index - num_elements_in_left_tree   ,	start - (node.map_left[start - 1] if start else 0)   ,	end - node.map_left[end]   ,	)






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if (
				    start > end
				    or node is None
				    or start_num > end_num
				    or node.minn > end_num
				    or node.maxx < start_num
				):
								return 0
				if start_num <= node.minn and node.maxx <= end_num:
								return end - start + 1
				_A         =	range_counting(
				    node.left   ,	(node.map_left[start - 1] if start else 0)   ,	node.map_left[end] - 1   ,	__lowercase   ,	__lowercase   ,	)
				_A         =	range_counting(
				    node.right   ,	start - (node.map_left[start - 1] if start else 0)   ,	end - node.map_left[end]   ,	__lowercase   ,	__lowercase   ,	)
				return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()