




'''simple docstring'''
from __future__ import annotations

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



					'''simple docstring'''





					def __init__(  self    :	Any			,					_lowercase    :	int)						->       None:
								A_       		=   -1
								A_       		=   -1
								A_       		=   [-1] * length
								A_       		=   None
								A_       		=   None



					def __repr__(  self    :	Any)						->       str:
								return F'Node(min_value={self.minn} max_value={self.maxx})'





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		Node | None:
			A_       		=   Node(len(SCREAMING_SNAKE_CASE_    )    )
			A_		,   A_       		=   min(SCREAMING_SNAKE_CASE_    ), max(SCREAMING_SNAKE_CASE_    )
			# 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(SCREAMING_SNAKE_CASE_    ):
						if num <= pivot:
									left_arr.append(SCREAMING_SNAKE_CASE_    )
						else:
									right_arr.append(SCREAMING_SNAKE_CASE_    )
						A_       		=   len(SCREAMING_SNAKE_CASE_    )
			A_       		=   build_tree(SCREAMING_SNAKE_CASE_    )
			A_       		=   build_tree(SCREAMING_SNAKE_CASE_    )
			return root





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		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					,SCREAMING_SNAKE_CASE_					,node.map_left[index] - 1    )
			else:
						# go to the right subtree and map index to the right subtree
						return rank_till_index(node.right					,SCREAMING_SNAKE_CASE_					,index - node.map_left[index]    )





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		int:
			if start > end:
						return 0
			A_       		=   rank_till_index(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )
			A_       		=   rank_till_index(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,start - 1    )
			return rank_till_end - rank_before_start





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		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					,SCREAMING_SNAKE_CASE_					,(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  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		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					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,)
			A_       		=   range_counting(
			    node.right					,start - (node.map_left[start - 1] if start else 0)					,end - node.map_left[end]					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,)
			return left + right


if __name__ == "__main__":
							import doctest

							doctest.testmod()










'''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


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import CLIPImageProcessor
class      __UpperCAmelCase							(    unittest.TestCase							):



					'''simple docstring'''





					def __init__(  self    :	Dict			,					_lowercase    :	List[Any]			,					_lowercase    :	List[Any]=7			,					_lowercase    :	Dict=3			,					_lowercase    :	List[Any]=18			,					_lowercase    :	List[str]=30			,					_lowercase    :	List[str]=400			,					_lowercase    :	Any=True			,					_lowercase    :	Any=None			,					_lowercase    :	Any=True			,					_lowercase    :	Dict=None			,					_lowercase    :	Optional[int]=True			,					_lowercase    :	int=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,					_lowercase    :	Any=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,					_lowercase    :	Optional[Any]=True			,					)						->       List[str]:
								A_       		=   size if size is not None else {'shortest_edge': 20}
								A_       		=   crop_size if crop_size is not None else {'height': 18, 'width': 18}
								A_       		=   parent
								A_       		=   batch_size
								A_       		=   num_channels
								A_       		=   image_size
								A_       		=   min_resolution
								A_       		=   max_resolution
								A_       		=   do_resize
								A_       		=   size
								A_       		=   do_center_crop
								A_       		=   crop_size
								A_       		=   do_normalize
								A_       		=   image_mean
								A_       		=   image_std
								A_       		=   do_convert_rgb



					def 						__snake_case	(  self    :	Optional[Any])						->       List[Any]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								}



					def 						__snake_case	(  self    :	Dict			,					_lowercase    :	Tuple=False			,					_lowercase    :	Tuple=False			,					_lowercase    :	Union[str, Any]=False)						->       List[Any]:
								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
											A_       		=   []
											for i in range(self.batch_size):
														image_inputs.append(
														    np.random.randint(
														        255			,					size=(self.num_channels, self.max_resolution, self.max_resolution)			,					dtype=np.uinta))
								else:
											A_       		=   []
											for i in range(self.batch_size):
														A_		,   A_       		=   np.random.choice(np.arange(self.min_resolution			,					self.max_resolution)			,					2)
														image_inputs.append(np.random.randint(255			,					size=(self.num_channels, width, height)			,					dtype=np.uinta))

								if not numpify and not torchify:
											# PIL expects the channel dimension as last dimension
											A_       		=   [Image.fromarray(np.moveaxis(_lowercase			,					0			,					-1)) for x in image_inputs]

								if torchify:
											A_       		=   [torch.from_numpy(_lowercase) for x in image_inputs]

								return image_inputs





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



					'''simple docstring'''





					_UpperCamelCase  	=		CLIPImageProcessor if is_vision_available() else None



					def 						__snake_case	(  self    :	Optional[Any])						->       List[str]:
								A_       		=   CLIPImageProcessingTester(self)



					@property
					def 						__snake_case	(  self    :	Dict)						->       Optional[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 						__snake_case	(  self    :	Optional[Any])						->       str:
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase			,					'do_resize'))
								self.assertTrue(hasattr(_lowercase			,					'size'))
								self.assertTrue(hasattr(_lowercase			,					'do_center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'do_normalize'))
								self.assertTrue(hasattr(_lowercase			,					'image_mean'))
								self.assertTrue(hasattr(_lowercase			,					'image_std'))
								self.assertTrue(hasattr(_lowercase			,					'do_convert_rgb'))



					def 						__snake_case	(  self    :	Union[str, Any])						->       Optional[int]:
								A_       		=   self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size			,					{'shortest_edge': 20})
								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 						__snake_case	(  self    :	str)						->       List[str]:
								pass



					def 						__snake_case	(  self    :	List[str])						->       Dict:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					Image.Image)

								# Test not batched input
								A_       		=   image_processing(image_inputs[0]			,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape			,					(
								        1,
								        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(_lowercase			,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape			,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)



					def 						__snake_case	(  self    :	Union[str, Any])						->       Dict:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase			,					numpify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					np.ndarray)

								# Test not batched input
								A_       		=   image_processing(image_inputs[0]			,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape			,					(
								        1,
								        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(_lowercase			,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape			,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)



					def 						__snake_case	(  self    :	Optional[Any])						->       Tuple:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase			,					torchify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					torch.Tensor)

								# Test not batched input
								A_       		=   image_processing(image_inputs[0]			,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape			,					(
								        1,
								        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(_lowercase			,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape			,					(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)





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



					'''simple docstring'''





					_UpperCamelCase  	=		CLIPImageProcessor if is_vision_available() else None



					def 						__snake_case	(  self    :	int)						->       List[Any]:
								A_       		=   CLIPImageProcessingTester(self			,					num_channels=4)
								A_       		=   3



					@property
					def 						__snake_case	(  self    :	str)						->       Tuple:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 						__snake_case	(  self    :	Any)						->       Dict:
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase			,					'do_resize'))
								self.assertTrue(hasattr(_lowercase			,					'size'))
								self.assertTrue(hasattr(_lowercase			,					'do_center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'center_crop'))
								self.assertTrue(hasattr(_lowercase			,					'do_normalize'))
								self.assertTrue(hasattr(_lowercase			,					'image_mean'))
								self.assertTrue(hasattr(_lowercase			,					'image_std'))
								self.assertTrue(hasattr(_lowercase			,					'do_convert_rgb'))



					def 						__snake_case	(  self    :	Any)						->       Optional[int]:
								pass



					def 						__snake_case	(  self    :	str)						->       List[Any]:
								# Initialize image_processing
								A_       		=   self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								A_       		=   self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase			,					Image.Image)

								# Test not batched input
								A_       		=   image_processing(image_inputs[0]			,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape			,					(
								        1,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)

								# Test batched
								A_       		=   image_processing(_lowercase			,					return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape			,					(
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,					)




