



'''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 LevitImageProcessor






class 						a_		(     unittest.TestCase    ):


							def __init__(				self   :						int      ,						a_   :						Any      ,						a_   :						Tuple=7      ,						a_   :						Tuple=3      ,						a_   :						List[str]=1_8      ,						a_   :						Union[str, Any]=3_0      ,						a_   :						Any=4_0_0      ,						a_   :						Any=True      ,						a_   :						List[Any]=None      ,						a_   :						Union[str, Any]=True      ,						a_   :						Any=None      ,						a_   :						Tuple=True      ,						a_   :						Dict=[0.5, 0.5, 0.5]      ,						a_   :						int=[0.5, 0.5, 0.5]      ,						)			->					Optional[Any]:
										snake_case:    Optional[Any]      							=size if size is not None else {'shortest_edge': 1_8}
										snake_case:    Any      							=crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
										snake_case:    Dict      							=parent
										snake_case:    int      							=batch_size
										snake_case:    Optional[Any]      							=num_channels
										snake_case:    Optional[Any]      							=image_size
										snake_case:    List[Any]      							=min_resolution
										snake_case:    Union[str, Any]      							=max_resolution
										snake_case:    List[Any]      							=do_resize
										snake_case:    Tuple      							=size
										snake_case:    Union[str, Any]      							=do_center_crop
										snake_case:    Union[str, Any]      							=crop_size
										snake_case:    Tuple      							=do_normalize
										snake_case:    int      							=image_mean
										snake_case:    Tuple      							=image_std




							def    UpperCamelCase     (				self   :						Any			)			->					Tuple:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "do_center_crop": self.do_center_crop,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}






@require_torch
@require_vision
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Tuple        =				LevitImageProcessor if is_vision_available() else None


							def    UpperCamelCase     (				self   :						Any			)			->					List[Any]:
										snake_case:    List[str]      							=LevitImageProcessingTester(self			)


							@property
							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Optional[int]:
										return self.image_processor_tester.prepare_image_processor_dict()


							def    UpperCamelCase     (				self   :						List[str]			)			->					List[str]:
										snake_case:    List[str]      							=self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(a_      ,						'image_mean'			)			)
										self.assertTrue(hasattr(a_      ,						'image_std'			)			)
										self.assertTrue(hasattr(a_      ,						'do_normalize'			)			)
										self.assertTrue(hasattr(a_      ,						'do_resize'			)			)
										self.assertTrue(hasattr(a_      ,						'do_center_crop'			)			)
										self.assertTrue(hasattr(a_      ,						'size'			)			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					str:
										snake_case:    int      							=self.image_processing_class.from_dict(self.image_processor_dict			)
										self.assertEqual(image_processor.size      ,						{'shortest_edge': 1_8}			)
										self.assertEqual(image_processor.crop_size      ,						{'height': 1_8, 'width': 1_8}			)

										snake_case:    Union[str, Any]      							=self.image_processing_class.from_dict(self.image_processor_dict      ,						size=4_2      ,						crop_size=8_4			)
										self.assertEqual(image_processor.size      ,						{'shortest_edge': 4_2}			)
										self.assertEqual(image_processor.crop_size      ,						{'height': 8_4, 'width': 8_4}			)


							def    UpperCamelCase     (				self   :						int			)			->					str:
										pass


							def    UpperCamelCase     (				self   :						Tuple			)			->					Dict:
										# Initialize image_processing
										snake_case:    Any      							=self.image_processing_class(**self.image_processor_dict			)
										# create random PIL images
										snake_case:    Optional[Any]      							=prepare_image_inputs(self.image_processor_tester      ,						equal_resolution=a_			)
										for image in image_inputs:
													self.assertIsInstance(a_      ,						Image.Image			)

										# Test not batched input
										snake_case:    Optional[int]      							=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
										snake_case:    Any      							=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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )      ,						)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					List[str]:
										# Initialize image_processing
										snake_case:    List[str]      							=self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										snake_case:    str      							=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
										snake_case:    List[str]      							=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
										snake_case:    List[str]      							=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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )      ,						)




							def    UpperCamelCase     (				self   :						List[str]			)			->					Tuple:
										# Initialize image_processing
										snake_case:    Union[str, Any]      							=self.image_processing_class(**self.image_processor_dict			)
										# create random PyTorch tensors
										snake_case:    Optional[int]      							=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
										snake_case:    int      							=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
										snake_case:    Tuple      							=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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )      ,						)









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=0
			if start < end:
						snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=a[end]
						snake_case:    List[Any]      							=a[pivot]
						snake_case:    int      							=temp

						snake_case							,  snake_case:    Optional[int]      							=_in_place_partition(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							__UpperCAmelCase				,							p - 1					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							p + 1				,							__UpperCAmelCase					)
			return count







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=0
			snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=a[end]
			snake_case:    List[str]      							=a[pivot]
			snake_case:    Dict      							=temp
			snake_case:    Union[str, Any]      							=start - 1
			for index in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									snake_case:    Optional[Any]      							=new_pivot_index + 1
									snake_case:    Dict      							=a[new_pivot_index]
									snake_case:    Union[str, Any]      							=a[index]
									snake_case:    List[Any]      							=temp

			snake_case:    Union[str, Any]      							=a[new_pivot_index + 1]
			snake_case:    Tuple      							=a[end]
			snake_case:    Union[str, Any]      							=temp
			return new_pivot_index + 1, count


a							   =				TemporaryFile()
a							   =				100  # 1000 elements are to be sorted


a   ,      a							   =				0, 1  # mean and standard deviation
a							   =				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
a							   =				np.load(outfile)
a							   =				len(M) - 1
a							   =				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)







print(z)




