






'''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_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ConvNextImageProcessor





class 		SCREAMING_SNAKE_CASE__   (     unittest.TestCase    ):



		def __init__(    self:    Union[str, Any]				,   a:    List[str]				,   a:    Dict=7				,   a:    Optional[Any]=3				,   a:    Optional[int]=18				,   a:    List[str]=30				,   a:    Any=4_00				,   a:    Optional[int]=True				,   a:    Optional[Any]=None				,   a:    Optional[int]=0.875				,   a:    Any=True				,   a:    Union[str, Any]=[0.5, 0.5, 0.5]				,   a:    Tuple=[0.5, 0.5, 0.5]				,   )  ->Any:






								'''simple docstring'''


								a_				     =   size if size is not None else {"shortest_edge": 20}
								a_				     =   parent
								a_				     =   batch_size
								a_				     =   num_channels
								a_				     =   image_size
								a_				     =   min_resolution
								a_				     =   max_resolution
								a_				     =   do_resize
								a_				     =   size
								a_				     =   crop_pct
								a_				     =   do_normalize
								a_				     =   image_mean
								a_				     =   image_std




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






								'''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_pct": self.crop_pct,
								}





@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__   (     lowercase_		,		unittest.TestCase    ):
		_UpperCAmelCase         =ConvNextImageProcessor if is_vision_available() else None



		def _lowerCAmelCase     (    self:    Optional[int])  ->List[str]:






								'''simple docstring'''


								a_				     =   ConvNextImageProcessingTester(self)



		@property
		def _lowerCAmelCase     (    self:    List[Any])  ->Tuple:






								'''simple docstring'''


								return self.image_processor_tester.prepare_image_processor_dict()



		def _lowerCAmelCase     (    self:    Tuple)  ->List[str]:






								'''simple docstring'''


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



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






								'''simple docstring'''


								a_				     =   self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size				,   {"shortest_edge": 20})

								a_				     =   self.image_processing_class.from_dict(self.image_processor_dict				,   size=42)
								self.assertEqual(image_processor.size				,   {"shortest_edge": 42})



		def _lowerCAmelCase     (    self:    Dict)  ->Optional[int]:






								'''simple docstring'''


								pass



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






								'''simple docstring'''


								a_				     =   self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								a_				     =   prepare_image_inputs(self.image_processor_tester				,   equal_resolution=a)
								for image in image_inputs:
														self.assertIsInstance(a				,   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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )				,   )

								# Test batched
								a_				     =   image_processing(a				,   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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )				,   )



		def _lowerCAmelCase     (    self:    Dict)  ->Any:






								'''simple docstring'''


								a_				     =   self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								a_				     =   prepare_image_inputs(self.image_processor_tester				,   equal_resolution=a				,   numpify=a)
								for image in image_inputs:
														self.assertIsInstance(a				,   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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )				,   )

								# Test batched
								a_				     =   image_processing(a				,   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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )				,   )




		def _lowerCAmelCase     (    self:    str)  ->Optional[int]:






								'''simple docstring'''


								a_				     =   self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								a_				     =   prepare_image_inputs(self.image_processor_tester				,   equal_resolution=a				,   torchify=a)
								for image in image_inputs:
														self.assertIsInstance(a				,   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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )				,   )

								# Test batched
								a_				     =   image_processing(a				,   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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )				,   )











'''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 		SCREAMING_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:    Union[str, Any])  ->str:






								'''simple docstring'''


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





def 			__UpperCAmelCase				(lowercase__				)		->				Node | None:

						'''simple docstring'''


						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 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				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
						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 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


						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 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				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]
						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 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__	,lowercase__				)		->				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
						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()



