






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_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase	,   UpperCAmelCase=7	,   UpperCAmelCase=3	,   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]	,   ):
							"""simple docstring"""







							_UpperCAmelCase     =      size if size is not None else {'height': 18, 'width': 18}
							_UpperCAmelCase     =      parent
							_UpperCAmelCase     =      batch_size
							_UpperCAmelCase     =      num_channels
							_UpperCAmelCase     =      image_size
							_UpperCAmelCase     =      min_resolution
							_UpperCAmelCase     =      max_resolution
							_UpperCAmelCase     =      do_resize
							_UpperCAmelCase     =      size
							_UpperCAmelCase     =      do_normalize
							_UpperCAmelCase     =      image_mean
							_UpperCAmelCase     =      image_std

	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,
							}





@require_torch
@require_vision
class __lowerCamelCase			(				snake_case__					,	unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=				ViTImageProcessor if is_vision_available() else None

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







							_UpperCAmelCase     =      ViTImageProcessingTester(self   )

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







							return self.image_processor_tester.prepare_image_processor_dict()

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







							_UpperCAmelCase     =      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	,   'size'   )   )

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







							_UpperCAmelCase     =      self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size	,   {'height': 18, 'width': 18}   )

							_UpperCAmelCase     =      self.image_processing_class.from_dict(self.image_processor_dict	,   size=42   )
							self.assertEqual(image_processor.size	,   {'height': 42, 'width': 42}   )

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







							pass

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







							_UpperCAmelCase     =      self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							_UpperCAmelCase     =      prepare_image_inputs(self.image_processor_tester	,   equal_resolution=UpperCAmelCase   )
							for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase	,   Image.Image   )

							# Test not batched input
							_UpperCAmelCase     =      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.size['height'],
							        self.image_processor_tester.size['width'],
							    )	,   )

							# Test batched
							_UpperCAmelCase     =      image_processing(UpperCAmelCase	,   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.size['height'],
							        self.image_processor_tester.size['width'],
							    )	,   )

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







							_UpperCAmelCase     =      self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							_UpperCAmelCase     =      prepare_image_inputs(self.image_processor_tester	,   equal_resolution=UpperCAmelCase	,   numpify=UpperCAmelCase   )
							for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase	,   np.ndarray   )

							# Test not batched input
							_UpperCAmelCase     =      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.size['height'],
							        self.image_processor_tester.size['width'],
							    )	,   )

							# Test batched
							_UpperCAmelCase     =      image_processing(UpperCAmelCase	,   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.size['height'],
							        self.image_processor_tester.size['width'],
							    )	,   )

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







							_UpperCAmelCase     =      self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							_UpperCAmelCase     =      prepare_image_inputs(self.image_processor_tester	,   equal_resolution=UpperCAmelCase	,   torchify=UpperCAmelCase   )
							for image in image_inputs:
													self.assertIsInstance(UpperCAmelCase	,   torch.Tensor   )

							# Test not batched input
							_UpperCAmelCase     =      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.size['height'],
							        self.image_processor_tester.size['width'],
							    )	,   )

							# Test batched
							_UpperCAmelCase     =      image_processing(UpperCAmelCase	,   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.size['height'],
							        self.image_processor_tester.size['width'],
							    )	,   )








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 __lowerCamelCase			:





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase   ):
							"""simple docstring"""







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

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







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



def __A   (				__lowerCAmelCase     )->  Node | None:





						"""simple docstring"""



						_UpperCAmelCase     =      Node(len(__lowerCAmelCase     )     )
						_UpperCAmelCase ,			_UpperCAmelCase     =      min(__lowerCAmelCase     ), max(__lowerCAmelCase     )
						# Leaf node case where the node contains only one unique value
						if root.minn == root.maxx:
												return root
						_UpperCAmelCase     =      (root.minn + root.maxx) // 2

						_UpperCAmelCase     =      []
						_UpperCAmelCase     =      []

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


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						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
						_UpperCAmelCase     =      (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			,		__lowerCAmelCase			,		node.map_left[index] - 1     )
						else:
												# go to the right subtree and map index to the right subtree
												return rank_till_index(node.right			,		__lowerCAmelCase			,		index - node.map_left[index]     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if start > end:
												return 0
						_UpperCAmelCase     =      rank_till_index(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						_UpperCAmelCase     =      rank_till_index(__lowerCAmelCase			,		__lowerCAmelCase			,		start - 1     )
						return rank_till_end - rank_before_start


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						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]
						_UpperCAmelCase     =      node.map_left[end] - (
						    node.map_left[start - 1] if start else 0
						)
						if num_elements_in_left_tree > index:
												return quantile(
												    node.left			,		__lowerCAmelCase			,		(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   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						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
						_UpperCAmelCase     =      range_counting(
						    node.left			,		(node.map_left[start - 1] if start else 0)			,		node.map_left[end] - 1			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)
						_UpperCAmelCase     =      range_counting(
						    node.right			,		start - (node.map_left[start - 1] if start else 0)			,		end - node.map_left[end]			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)
						return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()
