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, prepare_image_inputs
  
  
  if is_torch_available():
  		import torch
  
  if is_vision_available():
  		from PIL import Image
  
  		from transformers import ConvNextImageProcessor
  
  
  
  
  
  
  class 	UpperCamelCase_     (     unittest.TestCase       ):
  
  
  
  					def __init__(		self    :    int					,			lowerCamelCase    :    List[Any]					,			lowerCamelCase    :    int=7					,			lowerCamelCase    :    Dict=3					,			lowerCamelCase    :    List[str]=18					,			lowerCamelCase    :    List[Any]=30					,			lowerCamelCase    :    Any=4_00					,			lowerCamelCase    :    List[Any]=True					,			lowerCamelCase    :    Dict=None					,			lowerCamelCase    :    str=0.875					,			lowerCamelCase    :    int=True					,			lowerCamelCase    :    str=[0.5, 0.5, 0.5]					,			lowerCamelCase    :    Tuple=[0.5, 0.5, 0.5]					,			):
  							lowerCamelCase_  :    Tuple   	=			size if size is not None else {'shortest_edge': 20}
  							lowerCamelCase_  :    Tuple   	=			parent
  							lowerCamelCase_  :    Optional[int]   	=			batch_size
  							lowerCamelCase_  :    Dict   	=			num_channels
  							lowerCamelCase_  :    Union[str, Any]   	=			image_size
  							lowerCamelCase_  :    str   	=			min_resolution
  							lowerCamelCase_  :    int   	=			max_resolution
  							lowerCamelCase_  :    List[Any]   	=			do_resize
  							lowerCamelCase_  :    Tuple   	=			size
  							lowerCamelCase_  :    str   	=			crop_pct
  							lowerCamelCase_  :    List[str]   	=			do_normalize
  							lowerCamelCase_  :    List[str]   	=			image_mean
  							lowerCamelCase_  :    Any   	=			image_std
  
  
  
  
  
  					def      __a    (		self    :    int	):
  							return {
  							    "image_mean": self.image_mean,
  							    "image_std": self.image_std,
  							    "do_normalize": self.do_normalize,
  							    "do_resize": self.do_resize,
  							    "size": self.size,
  							    "crop_pct": self.crop_pct,
  							}
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 	UpperCamelCase_     (     snake_case__     ,    unittest.TestCase       ):
  					_a			:	Any							=				ConvNextImageProcessor if is_vision_available() else None
  
  
  
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    Union[str, Any]   	=			ConvNextImageProcessingTester(self	)
  
  
  
  					@property
  					def      __a    (		self    :    Union[str, Any]	):
  							return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    Optional[int]   	=			self.image_processing_class(**self.image_processor_dict	)
  							self.assertTrue(hasattr(lowerCamelCase					,			'do_resize'	)	)
  							self.assertTrue(hasattr(lowerCamelCase					,			'size'	)	)
  							self.assertTrue(hasattr(lowerCamelCase					,			'crop_pct'	)	)
  							self.assertTrue(hasattr(lowerCamelCase					,			'do_normalize'	)	)
  							self.assertTrue(hasattr(lowerCamelCase					,			'image_mean'	)	)
  							self.assertTrue(hasattr(lowerCamelCase					,			'image_std'	)	)
  
  
  
  					def      __a    (		self    :    Union[str, Any]	):
  							lowerCamelCase_  :    List[Any]   	=			self.image_processing_class.from_dict(self.image_processor_dict	)
  							self.assertEqual(image_processor.size					,			{'shortest_edge': 20}	)
  
  							lowerCamelCase_  :    List[str]   	=			self.image_processing_class.from_dict(self.image_processor_dict					,			size=42	)
  							self.assertEqual(image_processor.size					,			{'shortest_edge': 42}	)
  
  
  
  					def      __a    (		self    :    Tuple	):
  							pass
  
  
  
  					def      __a    (		self    :    Optional[Any]	):
  							# Initialize image_processing
  							lowerCamelCase_  :    Tuple   	=			self.image_processing_class(**self.image_processor_dict	)
  							# create random PIL images
  							lowerCamelCase_  :    List[Any]   	=			prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase	)
  							for image in image_inputs:
  									self.assertIsInstance(lowerCamelCase					,			Image.Image	)
  
  							# Test not batched input
  							lowerCamelCase_  :    List[str]   	=			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.size['shortest_edge'],
  							        self.image_processor_tester.size['shortest_edge'],
  							    )					,			)
  
  							# Test batched
  							lowerCamelCase_  :    Optional[Any]   	=			image_processing(lowerCamelCase					,			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['shortest_edge'],
  							        self.image_processor_tester.size['shortest_edge'],
  							    )					,			)
  
  
  
  					def      __a    (		self    :    Any	):
  							# Initialize image_processing
  							lowerCamelCase_  :    Optional[int]   	=			self.image_processing_class(**self.image_processor_dict	)
  							# create random numpy tensors
  							lowerCamelCase_  :    str   	=			prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase					,			numpify=lowerCamelCase	)
  							for image in image_inputs:
  									self.assertIsInstance(lowerCamelCase					,			np.ndarray	)
  
  							# Test not batched input
  							lowerCamelCase_  :    Union[str, 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.size['shortest_edge'],
  							        self.image_processor_tester.size['shortest_edge'],
  							    )					,			)
  
  							# Test batched
  							lowerCamelCase_  :    Tuple   	=			image_processing(lowerCamelCase					,			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['shortest_edge'],
  							        self.image_processor_tester.size['shortest_edge'],
  							    )					,			)
  
  
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							# Initialize image_processing
  							lowerCamelCase_  :    int   	=			self.image_processing_class(**self.image_processor_dict	)
  							# create random PyTorch tensors
  							lowerCamelCase_  :    Optional[int]   	=			prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase					,			torchify=lowerCamelCase	)
  							for image in image_inputs:
  									self.assertIsInstance(lowerCamelCase					,			torch.Tensor	)
  
  							# Test not batched input
  							lowerCamelCase_  :    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.size['shortest_edge'],
  							        self.image_processor_tester.size['shortest_edge'],
  							    )					,			)
  
  							# Test batched
  							lowerCamelCase_  :    Any   	=			image_processing(lowerCamelCase					,			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['shortest_edge'],
  							        self.image_processor_tester.size['shortest_edge'],
  							    )					,			)
  
style_context: 
  
  
  
  
  
  import os
  import unittest
  
  import pytest
  from parameterized import parameterized
  
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  
  
  # skipping in unittest tests
  
  _lowercase		:     Optional[Any] 					=[(1,)]
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    Union[str, Any]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
  		if run_slow:
  				assert True
  		else:
  				assert False, "should have been skipped"
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    List[str]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
  		if run_slow and torch_device == "cuda":
  				assert True
  		else:
  				assert False, "should have been skipped"
  
  
  
  
  
  
  @require_torch
  class 	UpperCamelCase_     (     unittest.TestCase       ):
  
  
  
  					@slow
  					@require_torch_gpu
  					def      __a    (		self    :    Tuple	):
  							check_slow_torch_cuda()
  
  
  
  					@require_torch_gpu
  					@slow
  					def      __a    (		self    :    Optional[int]	):
  							check_slow_torch_cuda()
  
  
  
  
  
  					@parameterized.expand(lowerCamelCase	)
  					@slow
  					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    Optional[Any]=None	):
  							check_slow()
  
  
  @slow
  @require_torch_gpu
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		check_slow_torch_cuda()
  
  
  @require_torch_gpu
  @slow
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		check_slow_torch_cuda()
  
  
  @slow
  @pytest.mark.parametrize('param'       ,[1]	)
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		check_slow()
  
  
  
  
  
  @pytest.mark.parametrize('param'       ,[1]	)
  @slow
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		check_slow()
  