code: 
  
  
  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 TvltImageProcessor
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase=10				,UpperCAmelCase=10				,UpperCAmelCase=False				,UpperCAmelCase=False      )->		Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       []
  					for i in range(image_processor_tester.num_frames      ):
  										video.append(np.random.randint(255				,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
  										SCREAMING_SNAKE_CASE_	      =       [Image.fromarray(np.moveaxis(UpperCAmelCase				,0				,-1      )      ) for frame in video]
  
  					if torchify:
  										SCREAMING_SNAKE_CASE_	      =       [torch.from_numpy(UpperCAmelCase      ) for frame in video]
  
  					return video
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase=False				,UpperCAmelCase=False				,UpperCAmelCase=False      )->		Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
  					SCREAMING_SNAKE_CASE_	      =       []
  					for i in range(image_processor_tester.batch_size      ):
  										if equal_resolution:
  															SCREAMING_SNAKE_CASE_	      =       SCREAMING_SNAKE_CASE_	      =       image_processor_tester.max_resolution
  										else:
  															SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       np.random.choice(
  															    np.arange(image_processor_tester.min_resolution				,image_processor_tester.max_resolution      )				,2      )
  															SCREAMING_SNAKE_CASE_	      =       prepare_video(
  															    image_processor_tester=UpperCAmelCase				,width=UpperCAmelCase				,height=UpperCAmelCase				,numpify=UpperCAmelCase				,torchify=UpperCAmelCase				,)
  										video_inputs.append(UpperCAmelCase      )
  
  					return video_inputs
  
  class 						snake_case			(       unittest.TestCase	):
  			'''simple docstring'''
  
  			def __init__(	self     :     str							,							lowerCAmelCase_     :     Optional[Any]							,							lowerCAmelCase_     :     Dict=7							,							lowerCAmelCase_     :     Tuple=3							,							lowerCAmelCase_     :     Optional[Any]=4							,							lowerCAmelCase_     :     Tuple=18							,							lowerCAmelCase_     :     List[Any]=30							,							lowerCAmelCase_     :     Union[str, Any]=400							,							lowerCAmelCase_     :     Tuple=True							,							lowerCAmelCase_     :     str=None							,							lowerCAmelCase_     :     Union[str, Any]=True							,							lowerCAmelCase_     :     int=[0.5, 0.5, 0.5]							,							lowerCAmelCase_     :     Dict=[0.5, 0.5, 0.5]							,							lowerCAmelCase_     :     List[Any]=True							,							lowerCAmelCase_     :     Any=None							,							)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       size if size is not None else {'''shortest_edge''': 18}
  								SCREAMING_SNAKE_CASE_	      =       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
  
  								SCREAMING_SNAKE_CASE_	      =       parent
  								SCREAMING_SNAKE_CASE_	      =       batch_size
  								SCREAMING_SNAKE_CASE_	      =       num_channels
  								SCREAMING_SNAKE_CASE_	      =       num_frames
  								SCREAMING_SNAKE_CASE_	      =       image_size
  								SCREAMING_SNAKE_CASE_	      =       min_resolution
  								SCREAMING_SNAKE_CASE_	      =       max_resolution
  								SCREAMING_SNAKE_CASE_	      =       do_resize
  								SCREAMING_SNAKE_CASE_	      =       size
  								SCREAMING_SNAKE_CASE_	      =       do_normalize
  								SCREAMING_SNAKE_CASE_	      =       image_mean
  								SCREAMING_SNAKE_CASE_	      =       image_std
  								SCREAMING_SNAKE_CASE_	      =       do_center_crop
  								SCREAMING_SNAKE_CASE_	      =       crop_size
  
  
  
  
  			def _lowercase (	self     :     Any					)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return {
  								    "image_mean": self.image_mean,
  								    "image_std": self.image_std,
  								    "do_normalize": self.do_normalize,
  								    "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 						snake_case			(       lowerCAmelCase__	, unittest.TestCase	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       List[Any]       = TvltImageProcessor if is_vision_available() else None
  
  			def _lowercase (	self     :     List[Any]					)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       TvltImageProcessorTester(self					)
  
  			@property
  			def _lowercase (	self     :     Tuple					)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.image_processor_tester.prepare_image_processor_dict()
  
  			def _lowercase (	self     :     Optional[Any]					)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.image_processing_class(**self.image_processor_dict					)
  								self.assertTrue(hasattr(lowerCAmelCase_							,							'''image_mean'''					)					)
  								self.assertTrue(hasattr(lowerCAmelCase_							,							'''image_std'''					)					)
  								self.assertTrue(hasattr(lowerCAmelCase_							,							'''do_normalize'''					)					)
  								self.assertTrue(hasattr(lowerCAmelCase_							,							'''do_resize'''					)					)
  								self.assertTrue(hasattr(lowerCAmelCase_							,							'''do_center_crop'''					)					)
  								self.assertTrue(hasattr(lowerCAmelCase_							,							'''size'''					)					)
  
  			def _lowercase (	self     :     Dict					)					->					Optional[int]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.image_processing_class(**self.image_processor_dict					)
  								# create random PIL videos
  								SCREAMING_SNAKE_CASE_	      =       prepare_video_inputs(self.image_processor_tester							,							equal_resolution=lowerCAmelCase_					)
  								for video in video_inputs:
  													self.assertIsInstance(lowerCAmelCase_							,							lowerCAmelCase_					)
  													self.assertIsInstance(video[0]							,							Image.Image					)
  
  								# Test not batched input
  								SCREAMING_SNAKE_CASE_	      =       image_processor(video_inputs[0]							,							return_tensors='''pt'''					).pixel_values
  								self.assertEqual(
  								    encoded_videos.shape							,							(
  								        1,
  								        self.image_processor_tester.num_frames,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size['''height'''],
  								        self.image_processor_tester.crop_size['''width'''],
  								    )							,							)
  
  								# Test batched
  								SCREAMING_SNAKE_CASE_	      =       image_processor(lowerCAmelCase_							,							return_tensors='''pt'''					).pixel_values
  								self.assertEqual(
  								    encoded_videos.shape							,							(
  								        self.image_processor_tester.batch_size,
  								        self.image_processor_tester.num_frames,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size['''height'''],
  								        self.image_processor_tester.crop_size['''width'''],
  								    )							,							)
  
  			def _lowercase (	self     :     Tuple					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.image_processing_class(**self.image_processor_dict					)
  								# create random numpy tensors
  								SCREAMING_SNAKE_CASE_	      =       prepare_video_inputs(self.image_processor_tester							,							equal_resolution=lowerCAmelCase_							,							numpify=lowerCAmelCase_					)
  								for video in video_inputs:
  													self.assertIsInstance(lowerCAmelCase_							,							lowerCAmelCase_					)
  													self.assertIsInstance(video[0]							,							np.ndarray					)
  
  								# Test not batched input
  								SCREAMING_SNAKE_CASE_	      =       image_processor(video_inputs[0]							,							return_tensors='''pt'''					).pixel_values
  								self.assertEqual(
  								    encoded_videos.shape							,							(
  								        1,
  								        self.image_processor_tester.num_frames,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size['''height'''],
  								        self.image_processor_tester.crop_size['''width'''],
  								    )							,							)
  
  								# Test batched
  								SCREAMING_SNAKE_CASE_	      =       image_processor(lowerCAmelCase_							,							return_tensors='''pt'''					).pixel_values
  								self.assertEqual(
  								    encoded_videos.shape							,							(
  								        self.image_processor_tester.batch_size,
  								        self.image_processor_tester.num_frames,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size['''height'''],
  								        self.image_processor_tester.crop_size['''width'''],
  								    )							,							)
  
  
  
  
  			def _lowercase (	self     :     Dict					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.image_processing_class(**self.image_processor_dict					)
  								# create random PyTorch tensors
  								SCREAMING_SNAKE_CASE_	      =       prepare_video_inputs(self.image_processor_tester							,							equal_resolution=lowerCAmelCase_							,							torchify=lowerCAmelCase_					)
  								for video in video_inputs:
  													self.assertIsInstance(lowerCAmelCase_							,							lowerCAmelCase_					)
  													self.assertIsInstance(video[0]							,							torch.Tensor					)
  
  								# Test not batched input
  								SCREAMING_SNAKE_CASE_	      =       image_processor(video_inputs[0]							,							return_tensors='''pt'''					).pixel_values
  								self.assertEqual(
  								    encoded_videos.shape							,							(
  								        1,
  								        self.image_processor_tester.num_frames,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size['''height'''],
  								        self.image_processor_tester.crop_size['''width'''],
  								    )							,							)
  
  								# Test batched
  								SCREAMING_SNAKE_CASE_	      =       image_processor(lowerCAmelCase_							,							return_tensors='''pt'''					).pixel_values
  								self.assertEqual(
  								    encoded_videos.shape							,							(
  								        self.image_processor_tester.batch_size,
  								        self.image_processor_tester.num_frames,
  								        self.image_processor_tester.num_channels,
  								        self.image_processor_tester.crop_size['''height'''],
  								        self.image_processor_tester.crop_size['''width'''],
  								    )							,							)
  
  
  
style_context: 
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase      )->		int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					return (input_a, input_a).count(0      ) % 2
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     )->		None:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					assert xor_gate(0				,0      ) == 0
  					assert xor_gate(0				,1      ) == 1
  					assert xor_gate(1				,0      ) == 1
  					assert xor_gate(1				,1      ) == 0
  
  
  if __name__ == "__main__":
  		print(xor_gate(0, 0))
  		print(xor_gate(0, 1))
  
  
  