



"""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 MobileNetVaImageProcessor







class    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__=7   ,		lowercase__=3   ,		lowercase__=18   ,		lowercase__=30   ,		lowercase__=4_00   ,		lowercase__=True   ,		lowercase__=None   ,		lowercase__=True   ,		lowercase__=None   ,		):
											snake_case_					:		Dict 					=					size if size is not None else {"""shortest_edge""": 20}
											snake_case_					:		List[str] 					=					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											snake_case_					:		str 					=					parent
											snake_case_					:		Optional[Any] 					=					batch_size
											snake_case_					:		Union[str, Any] 					=					num_channels
											snake_case_					:		Optional[int] 					=					image_size
											snake_case_					:		Dict 					=					min_resolution
											snake_case_					:		Union[str, Any] 					=					max_resolution
											snake_case_					:		Any 					=					do_resize
											snake_case_					:		Dict 					=					size
											snake_case_					:		Union[str, Any] 					=					do_center_crop
											snake_case_					:		Dict 					=					crop_size







						def 						__UpperCamelCase							(self       ):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}







@require_torch
@require_vision
class    __lowercase     (					_UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		MobileNetVaImageProcessor if is_vision_available() else None







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					MobileNetVaImageProcessingTester(self       )







						@property
						def 						__UpperCamelCase							(self       ):
											return self.image_processor_tester.prepare_image_processor_dict()







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[str] 					=					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__   ,		"""crop_size"""       )       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Tuple 					=					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}       )

											snake_case_					:		Union[str, Any] 					=					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 						__UpperCamelCase							(self       ):
											pass







						def 						__UpperCamelCase							(self       ):
											# Initialize image_processing
											snake_case_					:		Tuple 					=					self.image_processing_class(**self.image_processor_dict       )
											# create random PIL images
											snake_case_					:		Optional[int] 					=					prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=lowercase__       )
											for image in image_inputs:
																self.assertIsInstance(lowercase__   ,		Image.Image       )

											# Test not batched input
											snake_case_					:		Any 					=					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_					:		int 					=					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 						__UpperCamelCase							(self       ):
											# Initialize image_processing
											snake_case_					:		Optional[int] 					=					self.image_processing_class(**self.image_processor_dict       )
											# create random numpy tensors
											snake_case_					:		Tuple 					=					prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=lowercase__   ,		numpify=lowercase__       )
											for image in image_inputs:
																self.assertIsInstance(lowercase__   ,		np.ndarray       )

											# Test not batched input
											snake_case_					:		Dict 					=					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[Any] 					=					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 						__UpperCamelCase							(self       ):
											# Initialize image_processing
											snake_case_					:		Tuple 					=					self.image_processing_class(**self.image_processor_dict       )
											# create random PyTorch tensors
											snake_case_					:		int 					=					prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=lowercase__   ,		torchify=lowercase__       )
											for image in image_inputs:
																self.assertIsInstance(lowercase__   ,		torch.Tensor       )

											# Test not batched input
											snake_case_					:		Tuple 					=					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_					:		int 					=					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"""],
											    )   ,		)









"""simple docstring"""

from random import randint
from tempfile import TemporaryFile

import numpy as np







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					0
					if start < end:
										snake_case_					:		Union[str, Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					a[end]
										snake_case_					:		Dict 					=					a[pivot]
										snake_case_					:		Any 					=					temp

										snake_case_					,			snake_case_					:		Dict 					=					_in_place_partition(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    p - 1			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    p + 1				,    SCREAMING_SNAKE_CASE__			)
					return count







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





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

					snake_case_					:		Union[str, Any] 					=					a[new_pivot_index + 1]
					snake_case_					:		Union[str, Any] 					=					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)




