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      ):




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase=7       ,			__UpperCamelCase=3       ,			__UpperCamelCase=10       ,			__UpperCamelCase=18       ,			__UpperCamelCase=30       ,			__UpperCamelCase=400       ,			__UpperCamelCase=True       ,			__UpperCamelCase=None       ,			__UpperCamelCase=True       ,			__UpperCamelCase=[0.5, 0.5, 0.5]       ,			__UpperCamelCase=[0.5, 0.5, 0.5]       ,			__UpperCamelCase=None       ,			)	->    List[str]:



									'''simple docstring'''







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

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




					def      __a     (  self						)	->    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    (     _lowerCamelCase       ,unittest.TestCase      ):
					__lowerCamelCase			   =  VideoMAEImageProcessor if is_vision_available() else None




					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= VideoMAEImageProcessingTester(self						)




					@property
					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									return self.image_processor_tester.prepare_image_processor_dict()




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									snake_case__   :      List[str]   				= 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      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Optional[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__   :      str   				= 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      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									pass




					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= 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__   :      List[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'],
									    )       ,			)




					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      int   				= self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									snake_case__   :      Tuple   				= 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__   :      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'],
									    )       ,			)




					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= 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__   :      List[str]   				= 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__   :      List[str]   				= 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'],
									    )       ,			)



import math





class 					__snake_case    :




					def __init__(  self       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= len(__UpperCamelCase						)
									snake_case__   :      Dict   				= [0] * (
									    4 * self.N
									)  # approximate the overall size of segment tree with array N
									self.build(1       ,			0       ,			self.N - 1						)




					def      __a     (  self       ,			__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									return idx * 2




					def      __a     (  self       ,			__UpperCamelCase						)	->    Optional[int]:



									'''simple docstring'''







									return idx * 2 + 1




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    int:  # noqa: E741



									'''simple docstring'''







									if l == r:
													snake_case__   :      Dict   				= A[l]
									else:
													snake_case__   :      Optional[Any]   				= (l + r) // 2
													self.build(self.left(__UpperCamelCase						)       ,			__UpperCamelCase       ,			__UpperCamelCase						)
													self.build(self.right(__UpperCamelCase						)       ,			mid + 1       ,			__UpperCamelCase						)
													snake_case__   :      int   				= max(self.st[self.left(__UpperCamelCase						)]       ,			self.st[self.right(__UpperCamelCase						)]						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    int:



									'''simple docstring'''







									return self.update_recursive(1       ,			0       ,			self.N - 1       ,			a - 1       ,			b - 1       ,			__UpperCamelCase						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Tuple:  # noqa: E741



									'''simple docstring'''







									if r < a or l > b:
													return True
									if l == r:
													snake_case__   :      List[str]   				= val
													return True
									snake_case__   :      str   				= (l + r) // 2
									self.update_recursive(self.left(__UpperCamelCase						)       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									self.update_recursive(self.right(__UpperCamelCase						)       ,			mid + 1       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									snake_case__   :      Tuple   				= max(self.st[self.left(__UpperCamelCase						)]       ,			self.st[self.right(__UpperCamelCase						)]						)
									return True




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    List[str]:



									'''simple docstring'''







									return self.query_recursive(1       ,			0       ,			self.N - 1       ,			a - 1       ,			b - 1						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Optional[int]:  # noqa: E741



									'''simple docstring'''







									if r < a or l > b:
													return -math.inf
									if l >= a and r <= b:
													return self.st[idx]
									snake_case__   :      str   				= (l + r) // 2
									snake_case__   :      Optional[Any]   				= self.query_recursive(self.left(__UpperCamelCase						)       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									snake_case__   :      List[str]   				= self.query_recursive(self.right(__UpperCamelCase						)       ,			mid + 1       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									return max(__UpperCamelCase       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Tuple   				= []
									for i in range(1       ,			N + 1						):
													show_list += [self.query(__UpperCamelCase       ,			__UpperCamelCase						)]
									print(__UpperCamelCase						)


if __name__ == "__main__":
				lowerCAmelCase__	:  Optional[int]						=  [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
				lowerCAmelCase__	:  Any						=  15
				lowerCAmelCase__	:  List[Any]						=  SegmentTree(A)
				print(segt.query(4, 6))
				print(segt.query(7, 11))
				print(segt.query(7, 12))
				segt.update(1, 3, 1_11)
				print(segt.query(1, 15))
				segt.update(7, 8, 2_35)
				segt.show_data()


