code: 
  
  
  import json
  import os
  import tempfile
  
  from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  
  if is_torch_available():
  						import numpy as np
  						import torch
  
  if is_vision_available():
  						from PIL import Image
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_=False,	snake_case_=False,	snake_case_=False	)			->							int:
  				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
  				A__     :							str   				=[]
  				for i in range(image_processor_tester.batch_size	):
  								if equal_resolution:
  												A__     :							Dict   				=image_processor_tester.max_resolution
  								else:
  												# To avoid getting image width/height 0
  												A__     :							Any   				=image_processor_tester.min_resolution
  												if getattr(snake_case_,	'''size_divisor''',	snake_case_	):
  																# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
  																A__     :							Tuple   				=max(image_processor_tester.size_divisor,	snake_case_	)
  												A__				,  A__     :							Dict   				=np.random.choice(np.arange(snake_case_,	image_processor_tester.max_resolution	),	2	)
  								image_inputs.append(
  								    np.random.randint(2_5_5,	size=(image_processor_tester.num_channels, width, height),	dtype=np.uinta	)	)
  
  				if not numpify and not torchify:
  								# PIL expects the channel dimension as last dimension
  								A__     :							Optional[Any]   				=[Image.fromarray(np.moveaxis(snake_case_,	0,	-1	)	) for image in image_inputs]
  
  				if torchify:
  								A__     :							Dict   				=[torch.from_numpy(snake_case_	) for image in image_inputs]
  
  				return image_inputs
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_=1_0,	snake_case_=1_0,	snake_case_=False,	snake_case_=False	)			->							int:
  				A__     :							Optional[int]   				=[]
  				for i in range(image_processor_tester.num_frames	):
  								video.append(np.random.randint(2_5_5,	size=(image_processor_tester.num_channels, width, height),	dtype=np.uinta	)	)
  
  				if not numpify and not torchify:
  								# PIL expects the channel dimension as last dimension
  								A__     :							List[str]   				=[Image.fromarray(np.moveaxis(snake_case_,	0,	-1	)	) for frame in video]
  
  				if torchify:
  								A__     :							int   				=[torch.from_numpy(snake_case_	) for frame in video]
  
  				return video
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_=False,	snake_case_=False,	snake_case_=False	)			->							Optional[Any]:
  				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
  				A__     :							Optional[int]   				=[]
  				for i in range(image_processor_tester.batch_size	):
  								if equal_resolution:
  												A__     :							Union[str, Any]   				=image_processor_tester.max_resolution
  								else:
  												A__				,  A__     :							Union[str, Any]   				=np.random.choice(
  												    np.arange(image_processor_tester.min_resolution,	image_processor_tester.max_resolution	),	2	)
  												A__     :							str   				=prepare_video(
  												    image_processor_tester=snake_case_,	width=snake_case_,	height=snake_case_,	numpify=snake_case_,	torchify=snake_case_,	)
  								video_inputs.append(snake_case_	)
  
  				return video_inputs
  
  
  
  
  class 			a      :
  		__lowercase													=		None
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							str   				=self.image_processing_class(**self.image_processor_dict							)
  						A__     :							str   				=json.loads(image_processor.to_json_string()							)
  						for key, value in self.image_processor_dict.items():
  										self.assertEqual(obj[key]	,     __SCREAMING_SNAKE_CASE							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				=self.image_processing_class(**self.image_processor_dict							)
  
  						with tempfile.TemporaryDirectory() as tmpdirname:
  										A__     :							Dict   				=os.path.join(__SCREAMING_SNAKE_CASE	,     '''image_processor.json'''							)
  										image_processor_first.to_json_file(__SCREAMING_SNAKE_CASE							)
  										A__     :							Any   				=self.image_processing_class.from_json_file(__SCREAMING_SNAKE_CASE							)
  
  						self.assertEqual(image_processor_second.to_dict()	,     image_processor_first.to_dict()							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							int   				=self.image_processing_class(**self.image_processor_dict							)
  
  						with tempfile.TemporaryDirectory() as tmpdirname:
  										A__     :							Any   				=image_processor_first.save_pretrained(__SCREAMING_SNAKE_CASE							)[0]
  										check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE							)
  										A__     :							Optional[int]   				=self.image_processing_class.from_pretrained(__SCREAMING_SNAKE_CASE							)
  
  						self.assertEqual(image_processor_second.to_dict()	,     image_processor_first.to_dict()							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[str]:
  						'''simple docstring'''
  
  
  						A__     :							Any   				=self.image_processing_class()
  						self.assertIsNotNone(__SCREAMING_SNAKE_CASE							)
  
  
  
  
  
  
  		@require_torch
  		@require_vision
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						if self.test_cast_dtype is not None:
  										# Initialize image_processor
  										A__     :							Optional[int]   				=self.image_processing_class(**self.image_processor_dict							)
  
  										# create random PyTorch tensors
  										A__     :							Optional[Any]   				=prepare_image_inputs(self.image_processor_tester	,     equal_resolution=__SCREAMING_SNAKE_CASE	,     torchify=__SCREAMING_SNAKE_CASE							)
  
  										A__     :							Union[str, Any]   				=image_processor(__SCREAMING_SNAKE_CASE	,     return_tensors='''pt'''							)
  										# for layoutLM compatiblity
  										self.assertEqual(encoding.pixel_values.device	,     torch.device('''cpu'''							)							)
  										self.assertEqual(encoding.pixel_values.dtype	,     torch.floataa							)
  
  										A__     :							Dict   				=image_processor(__SCREAMING_SNAKE_CASE	,     return_tensors='''pt'''							).to(torch.floataa							)
  										self.assertEqual(encoding.pixel_values.device	,     torch.device('''cpu'''							)							)
  										self.assertEqual(encoding.pixel_values.dtype	,     torch.floataa							)
  
  										A__     :							int   				=image_processor(__SCREAMING_SNAKE_CASE	,     return_tensors='''pt'''							).to('''cpu'''	,     torch.bfloataa							)
  										self.assertEqual(encoding.pixel_values.device	,     torch.device('''cpu'''							)							)
  										self.assertEqual(encoding.pixel_values.dtype	,     torch.bfloataa							)
  
  										with self.assertRaises(__SCREAMING_SNAKE_CASE							):
  														A__     :							Optional[Any]   				=image_processor(__SCREAMING_SNAKE_CASE	,     return_tensors='''pt'''							).to(torch.bfloataa	,     '''cpu'''							)
  
  										# Try with text + image feature
  										A__     :							Union[str, Any]   				=image_processor(__SCREAMING_SNAKE_CASE	,     return_tensors='''pt'''							)
  										encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]							)}							)
  										A__     :							int   				=encoding.to(torch.floataa							)
  
  										self.assertEqual(encoding.pixel_values.device	,     torch.device('''cpu'''							)							)
  										self.assertEqual(encoding.pixel_values.dtype	,     torch.floataa							)
  										self.assertEqual(encoding.input_ids.dtype	,     torch.long							)
  
  
  
  
style_context: 
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Dict:
  				A__     :							Dict   				=len(snake_case_	)
  				for i in range(length - 1	):
  								A__     :							List[Any]   				=False
  								for j in range(length - 1 - i	):
  												if collection[j] > collection[j + 1]:
  																A__     :							Dict   				=True
  																A__				,  A__     :							Union[str, Any]   				=collection[j + 1], collection[j]
  								if not swapped:
  												break  # Stop iteration if the collection is sorted.
  				return collection
  
  
  if __name__ == "__main__":
  						import doctest
  						import time
  
  						doctest.testmod()
  
  						__lowerCamelCase       :       Union[str, Any]				   =			input("Enter numbers separated by a comma:").strip()
  						__lowerCamelCase       :       Optional[int]				   =			[int(item) for item in user_input.split(",")]
  						__lowerCamelCase       :       Optional[int]				   =			time.process_time()
  						print(*bubble_sort(unsorted), sep=",")
  						print(F"Processing time: {(time.process_time() - start)%1E9 + 7}")
  
  
  
  