



"""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 ChineseCLIPImageProcessor






class 			__snake_case			(      unittest.TestCase):




  def __init__(					self	:		Any		,   __lowerCAmelCase	:		Union[str, Any]		,   __lowerCAmelCase	:		Optional[Any]=7		,   __lowerCAmelCase	:		List[str]=3		,   __lowerCAmelCase	:		List[Any]=1_8		,   __lowerCAmelCase	:		Union[str, Any]=3_0		,   __lowerCAmelCase	:		Dict=4_0_0		,   __lowerCAmelCase	:		Optional[int]=True		,   __lowerCAmelCase	:		Optional[int]=None		,   __lowerCAmelCase	:		Tuple=True		,   __lowerCAmelCase	:		int=None		,   __lowerCAmelCase	:		Union[str, Any]=True		,   __lowerCAmelCase	:		Optional[Any]=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]		,   __lowerCAmelCase	:		Any=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]		,   __lowerCAmelCase	:		List[Any]=True		,   ):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
         _lowerCamelCase		:							Optional[Any]            =					crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
         _lowerCamelCase		:							Dict            =					parent
         _lowerCamelCase		:							str            =					batch_size
         _lowerCamelCase		:							Union[str, Any]            =					num_channels
         _lowerCamelCase		:							Dict            =					image_size
         _lowerCamelCase		:							Optional[int]            =					min_resolution
         _lowerCamelCase		:							Tuple            =					max_resolution
         _lowerCamelCase		:							Dict            =					do_resize
         _lowerCamelCase		:							Any            =					size
         _lowerCamelCase		:							Tuple            =					do_center_crop
         _lowerCamelCase		:							List[str]            =					crop_size
         _lowerCamelCase		:							Optional[int]            =					do_normalize
         _lowerCamelCase		:							Dict            =					image_mean
         _lowerCamelCase		:							Optional[Any]            =					image_std
         _lowerCamelCase		:							Optional[int]            =					do_convert_rgb




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		List[str]=False		,   __lowerCAmelCase	:		int=False		,   __lowerCAmelCase	:		Dict=False						):






         """simple docstring"""






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

         if equal_resolution:
                _lowerCamelCase		:							List[str]            =					[]
                for i in range(self.batch_size						):
                       image_inputs.append(
                           np.random.randint(
                               2_5_5		,   size=(self.num_channels, self.max_resolution, self.max_resolution)		,   dtype=np.uinta						)						)
         else:
                _lowerCamelCase		:							Union[str, Any]            =					[]
                for i in range(self.batch_size						):
                       _lowerCamelCase     ,       _lowerCamelCase		:							int            =					np.random.choice(np.arange(self.min_resolution		,   self.max_resolution						)		,   2						)
                       image_inputs.append(np.random.randint(2_5_5		,   size=(self.num_channels, width, height)		,   dtype=np.uinta						)						)

         if not numpify and not torchify:
                # PIL expects the channel dimension as last dimension
                _lowerCamelCase		:							int            =					[Image.fromarray(np.moveaxis(_UpperCamelCase		,   0		,   -1						)						) for x in image_inputs]

         if torchify:
                _lowerCamelCase		:							List[Any]            =					[torch.from_numpy(_UpperCamelCase						) for x in image_inputs]

         return image_inputs






@require_torch
@require_vision
class 			__snake_case			(      __a    ,	unittest.TestCase):
  snake_case__							:  List[str]         =		ChineseCLIPImageProcessor if is_vision_available() else None




  def 		SCREAMING_SNAKE_CASE							(					self	:		str						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					ChineseCLIPImageProcessingTester(self		,   do_center_crop=_UpperCamelCase						)




  @property
  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






         return self.image_processor_tester.prepare_image_processor_dict()




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''do_resize'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''size'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''do_center_crop'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''center_crop'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''do_normalize'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''image_mean'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''image_std'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''do_convert_rgb'''						)						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size		,   {'''height''': 2_2_4, '''width''': 2_2_4}						)
         self.assertEqual(image_processor.crop_size		,   {'''height''': 1_8, '''width''': 1_8}						)

         _lowerCamelCase		:							List[Any]            =					self.image_processing_class.from_dict(self.image_processor_dict		,   size=4_2		,   crop_size=8_4						)
         self.assertEqual(image_processor.size		,   {'''shortest_edge''': 4_2}						)
         self.assertEqual(image_processor.crop_size		,   {'''height''': 8_4, '''width''': 8_4}						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Any						):






         """simple docstring"""






         pass




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]						):






         """simple docstring"""






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

         # Test not batched input
         _lowerCamelCase		:							int            =					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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )		,   )

         # Test batched
         _lowerCamelCase		:							Optional[int]            =					image_processing(_UpperCamelCase		,   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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )		,   )




  def 		SCREAMING_SNAKE_CASE							(					self	:		Dict						):






         """simple docstring"""






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

         # Test not batched input
         _lowerCamelCase		:							Optional[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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )		,   )

         # Test batched
         _lowerCamelCase		:							Optional[int]            =					image_processing(_UpperCamelCase		,   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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )		,   )




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase		:							int            =					self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         _lowerCamelCase		:							Tuple            =					self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCamelCase		,   torchify=_UpperCamelCase						)
         for image in image_inputs:
                self.assertIsInstance(_UpperCamelCase		,   torch.Tensor						)

         # Test not batched input
         _lowerCamelCase		:							Optional[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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )		,   )

         # Test batched
         _lowerCamelCase		:							Any            =					image_processing(_UpperCamelCase		,   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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )		,   )






@require_torch
@require_vision
class 			__snake_case			(      __a    ,	unittest.TestCase):
  snake_case__							:  List[str]         =		ChineseCLIPImageProcessor if is_vision_available() else None




  def 		SCREAMING_SNAKE_CASE							(					self	:		str						):






         """simple docstring"""






         _lowerCamelCase		:							Dict            =					ChineseCLIPImageProcessingTester(self		,   num_channels=4		,   do_center_crop=_UpperCamelCase						)
         _lowerCamelCase		:							Optional[int]            =					3




  @property
  def 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """simple docstring"""






         return self.image_processor_tester.prepare_image_processor_dict()




  def 		SCREAMING_SNAKE_CASE							(					self	:		str						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''do_resize'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''size'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''do_center_crop'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''center_crop'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''do_normalize'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''image_mean'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''image_std'''						)						)
         self.assertTrue(hasattr(_UpperCamelCase		,   '''do_convert_rgb'''						)						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """simple docstring"""






         pass




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






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

         # Test not batched input
         _lowerCamelCase		:							List[Any]            =					image_processing(image_inputs[0]		,   return_tensors='''pt'''						).pixel_values
         self.assertEqual(
             encoded_images.shape		,   (
                 1,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )		,   )

         # Test batched
         _lowerCamelCase		:							List[Any]            =					image_processing(_UpperCamelCase		,   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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )		,   )




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 ChineseCLIPImageProcessor





class       _lowerCAmelCase   (      unittest.TestCase			):




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=7							,							_UpperCamelCase=3							,							_UpperCamelCase=18							,							_UpperCamelCase=30							,							_UpperCamelCase=400							,							_UpperCamelCase=True							,							_UpperCamelCase=None							,							_UpperCamelCase=True							,							_UpperCamelCase=None							,							_UpperCamelCase=True							,							_UpperCamelCase=[0.48145466, 0.4578275, 0.40821073]							,							_UpperCamelCase=[0.26862954, 0.26130258, 0.27577711]							,							_UpperCamelCase=True							,							)							-> Dict:
							lowerCAmelCase_         =      size if size is not None else {"height": 224, "width": 224}
							lowerCAmelCase_         =      crop_size if crop_size is not None else {"height": 18, "width": 18}
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      num_channels
							lowerCAmelCase_         =      image_size
							lowerCAmelCase_         =      min_resolution
							lowerCAmelCase_         =      max_resolution
							lowerCAmelCase_         =      do_resize
							lowerCAmelCase_         =      size
							lowerCAmelCase_         =      do_center_crop
							lowerCAmelCase_         =      crop_size
							lowerCAmelCase_         =      do_normalize
							lowerCAmelCase_         =      image_mean
							lowerCAmelCase_         =      image_std
							lowerCAmelCase_         =      do_convert_rgb




				def 	__a		(   self	)							-> List[Any]:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_convert_rgb": self.do_convert_rgb,
							}




				def 	__a		(   self							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							_UpperCamelCase=False	)							-> Dict:
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
										lowerCAmelCase_         =      []
										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:
										lowerCAmelCase_         =      []
										for i in range(self.batch_size	):
													lowerCAmelCase_      ,    lowerCAmelCase_         =      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
										lowerCAmelCase_         =      [Image.fromarray(np.moveaxis(_UpperCamelCase							,							0							,							-1	)	) for x in image_inputs]

							if torchify:
										lowerCAmelCase_         =      [torch.from_numpy(_UpperCamelCase	) for x in image_inputs]

							return image_inputs





@require_torch
@require_vision
class       _lowerCAmelCase   (      __a   , unittest.TestCase			):
				_lowercase     						=ChineseCLIPImageProcessor if is_vision_available() else None




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      ChineseCLIPImageProcessingTester(self							,							do_center_crop=_UpperCamelCase	)




				@property
				def 	__a		(   self	)							-> Tuple:
							return self.image_processor_tester.prepare_image_processor_dict()




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_resize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"size"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_normalize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_mean"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_std"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_convert_rgb"	)	)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.image_processing_class.from_dict(self.image_processor_dict	)
							self.assertEqual(image_processor.size							,							{"height": 224, "width": 224}	)
							self.assertEqual(image_processor.crop_size							,							{"height": 18, "width": 18}	)

							lowerCAmelCase_         =      self.image_processing_class.from_dict(self.image_processor_dict							,							size=42							,							crop_size=84	)
							self.assertEqual(image_processor.size							,							{"shortest_edge": 42}	)
							self.assertEqual(image_processor.crop_size							,							{"height": 84, "width": 84}	)




				def 	__a		(   self	)							-> str:
							pass




				def 	__a		(   self	)							-> Any:
							# Initialize image_processing
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							lowerCAmelCase_         =      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCamelCase	)
							for image in image_inputs:
										self.assertIsInstance(_UpperCamelCase							,							Image.Image	)

							# Test not batched input
							lowerCAmelCase_         =      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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)

							# Test batched
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)




				def 	__a		(   self	)							-> str:
							# Initialize image_processing
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							lowerCAmelCase_         =      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCamelCase							,							numpify=_UpperCamelCase	)
							for image in image_inputs:
										self.assertIsInstance(_UpperCamelCase							,							np.ndarray	)

							# Test not batched input
							lowerCAmelCase_         =      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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)

							# Test batched
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)




				def 	__a		(   self	)							-> Union[str, Any]:
							# Initialize image_processing
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							lowerCAmelCase_         =      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCamelCase							,							torchify=_UpperCamelCase	)
							for image in image_inputs:
										self.assertIsInstance(_UpperCamelCase							,							torch.Tensor	)

							# Test not batched input
							lowerCAmelCase_         =      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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)

							# Test batched
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)





@require_torch
@require_vision
class       _lowerCAmelCase   (      __a   , unittest.TestCase			):
				_lowercase     						=ChineseCLIPImageProcessor if is_vision_available() else None




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      ChineseCLIPImageProcessingTester(self							,							num_channels=4							,							do_center_crop=_UpperCamelCase	)
							lowerCAmelCase_         =      3




				@property
				def 	__a		(   self	)							-> List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_resize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"size"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_normalize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_mean"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_std"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_convert_rgb"	)	)




				def 	__a		(   self	)							-> int:
							pass




				def 	__a		(   self	)							-> str:
							# Initialize image_processing
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							lowerCAmelCase_         =      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCamelCase	)
							for image in image_inputs:
										self.assertIsInstance(_UpperCamelCase							,							Image.Image	)

							# Test not batched input
							lowerCAmelCase_         =      image_processing(image_inputs[0]							,							return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,							(
							        1,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)

							# Test batched
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)



