




"""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 BlipImageProcessor
class 			lowerCamelCase__    (  unittest.TestCase			):


			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=7				, SCREAMING_SNAKE_CASE=3				, SCREAMING_SNAKE_CASE=18				, SCREAMING_SNAKE_CASE=30				, SCREAMING_SNAKE_CASE=400				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				, SCREAMING_SNAKE_CASE=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				, SCREAMING_SNAKE_CASE=True				, ):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				size if size is not None else {"height": 20, "width": 20}
					snake_case		:						Tuple								=				parent
					snake_case		:						List[Any]								=				batch_size
					snake_case		:						Tuple								=				num_channels
					snake_case		:						Union[str, Any]								=				image_size
					snake_case		:						Tuple								=				min_resolution
					snake_case		:						str								=				max_resolution
					snake_case		:						Any								=				do_resize
					snake_case		:						Optional[Any]								=				size
					snake_case		:						int								=				do_normalize
					snake_case		:						str								=				image_mean
					snake_case		:						Optional[Any]								=				image_std
					snake_case		:						int								=				do_pad
					snake_case		:						int								=				do_convert_rgb


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_convert_rgb": self.do_convert_rgb,
					    "do_pad": self.do_pad,
					}



			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=False							):



					"""simple docstring"""







					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					if equal_resolution:
							snake_case		:						Dict								=				[]
							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:
							snake_case		:						Optional[Any]								=				[]
							for i in range(self.batch_size							):
									snake_case			,				snake_case		:						Any								=				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
							snake_case		:						Optional[int]								=				[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE				, 0				, -1							)							) for x in image_inputs]

					if torchify:
							snake_case		:						Optional[Any]								=				[torch.from_numpy(SCREAMING_SNAKE_CASE							) for x in image_inputs]

					return image_inputs







@require_torch
@require_vision
class 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
			a__ :      Dict								=     BlipImageProcessor if is_vision_available() else None


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						str								=				BlipImageProcessingTester(self							)


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return self.image_processor_tester.prepare_image_processor_dict()


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				self.image_processing_class(**self.image_processor_dict							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_resize"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "size"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_normalize"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_mean"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_std"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_convert_rgb"							)							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					pass


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Any								=				self.image_processing_class(**self.image_processor_dict							)
					# create random PIL images
					snake_case		:						List[str]								=				self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE				, Image.Image							)

					# Test not batched input
					snake_case		:						Union[str, Any]								=				image_processor(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
					snake_case		:						Dict								=				image_processor(SCREAMING_SNAKE_CASE				, 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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Union[str, Any]								=				self.image_processing_class(**self.image_processor_dict							)
					# create random numpy tensors
					snake_case		:						Dict								=				self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE				, numpify=SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE				, np.ndarray							)

					# Test not batched input
					snake_case		:						Any								=				image_processor(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
					snake_case		:						List[str]								=				image_processor(SCREAMING_SNAKE_CASE				, 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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						int								=				self.image_processing_class(**self.image_processor_dict							)
					# create random PyTorch tensors
					snake_case		:						Union[str, Any]								=				self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE				, torchify=SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE				, torch.Tensor							)

					# Test not batched input
					snake_case		:						str								=				image_processor(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
					snake_case		:						List[Any]								=				image_processor(SCREAMING_SNAKE_CASE				, 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"],
					    )				, )







@require_torch
@require_vision
class 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
			a__ :      Union[str, Any]								=     BlipImageProcessor if is_vision_available() else None


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				BlipImageProcessingTester(self				, num_channels=4							)
					snake_case		:						Optional[int]								=				3


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return self.image_processor_tester.prepare_image_processor_dict()


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Tuple								=				self.image_processing_class(**self.image_processor_dict							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_resize"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "size"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_normalize"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_mean"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "image_std"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE				, "do_convert_rgb"							)							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					pass



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						List[Any]								=				self.image_processing_class(**self.image_processor_dict							)
					# create random PIL images
					snake_case		:						List[str]								=				self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE				, Image.Image							)

					# Test not batched input
					snake_case		:						Any								=				image_processor(image_inputs[0]				, return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_images.shape				, (
					        1,
					        self.expected_encoded_image_num_channels,
					        self.image_processor_tester.size["height"],
					        self.image_processor_tester.size["width"],
					    )				, )

					# Test batched
					snake_case		:						List[str]								=				image_processor(SCREAMING_SNAKE_CASE				, 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.size["height"],
					        self.image_processor_tester.size["width"],
					    )				, )







"""simple docstring"""

import random



def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):

		def is_sorted(lowercase__  :     int   ):
				for i in range(len(lowercase__   ) - 1   ):
						if collection[i] > collection[i + 1]:
								return False
				return True

		while not is_sorted(lowercase__   ):
				random.shuffle(lowercase__   )
		return collection


if __name__ == "__main__":
							__A 		=			input("Enter numbers separated by a comma:\n").strip()
							__A 		=			[int(item) for item in user_input.split(",")]
							print(bogo_sort(unsorted))

