code: 
  
  
  
  
  """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"],
  					    )				, )
  
  
style_context: 
  
  
  
  
  """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))
  
  