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 CLIPImageProcessor
  
  
  class 		_SCREAMING_SNAKE_CASE    (	unittest.TestCase    ):
  
  
       def __init__(     self       ,				A_       ,				A_=7       ,				A_=3       ,				A_=18       ,				A_=30       ,				A_=4_00       ,				A_=True       ,				A_=None       ,				A_=True       ,				A_=None       ,				A_=True       ,				A_=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]       ,				A_=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]       ,				A_=True       ,				)     ->   Optional[Any]:
         _UpperCAmelCase		:       int       =    size if size is not None else {"""shortest_edge""": 20}
         _UpperCAmelCase		:       Dict       =    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
         _UpperCAmelCase		:       List[str]       =    parent
         _UpperCAmelCase		:       Optional[int]       =    batch_size
         _UpperCAmelCase		:       Tuple       =    num_channels
         _UpperCAmelCase		:       List[Any]       =    image_size
         _UpperCAmelCase		:       Union[str, Any]       =    min_resolution
         _UpperCAmelCase		:       Optional[Any]       =    max_resolution
         _UpperCAmelCase		:       List[Any]       =    do_resize
         _UpperCAmelCase		:       Any       =    size
         _UpperCAmelCase		:       Dict       =    do_center_crop
         _UpperCAmelCase		:       Any       =    crop_size
         _UpperCAmelCase		:       str       =    do_normalize
         _UpperCAmelCase		:       Any       =    image_mean
         _UpperCAmelCase		:       Optional[int]       =    image_std
         _UpperCAmelCase		:       Dict       =    do_convert_rgb
  
  
       def 					__snake_case(     self						)     ->   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 					__snake_case(     self       ,				A_=False       ,				A_=False       ,				A_=False						)     ->   List[Any]:
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
         if equal_resolution:
           _UpperCAmelCase		:       Optional[int]       =    []
           for i in range(self.batch_size						):
             image_inputs.append(
                 np.random.randint(
                     2_55       ,				size=(self.num_channels, self.max_resolution, self.max_resolution)       ,				dtype=np.uinta						)						)
         else:
           _UpperCAmelCase		:       Union[str, Any]       =    []
           for i in range(self.batch_size						):
             _UpperCAmelCase		:       str       =    np.random.choice(np.arange(self.min_resolution       ,				self.max_resolution						)       ,				2						)
             image_inputs.append(np.random.randint(2_55       ,				size=(self.num_channels, width, height)       ,				dtype=np.uinta						)						)
  
         if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           _UpperCAmelCase		:       int       =    [Image.fromarray(np.moveaxis(A_       ,				0       ,				-1						)						) for x in image_inputs]
  
         if torchify:
           _UpperCAmelCase		:       Union[str, Any]       =    [torch.from_numpy(A_						) for x in image_inputs]
  
         return image_inputs
  
  
  @require_torch
  @require_vision
  class 		_SCREAMING_SNAKE_CASE    (	A       ,  unittest.TestCase    ):
       __SCREAMING_SNAKE_CASE    				=						CLIPImageProcessor if is_vision_available() else None
  
  
       def 					__snake_case(     self						)     ->   Tuple:
         _UpperCAmelCase		:       str       =    CLIPImageProcessingTester(self						)
  
  
       @property
       def 					__snake_case(     self						)     ->   int:
         return self.image_processor_tester.prepare_image_processor_dict()
  
  
       def 					__snake_case(     self						)     ->   Dict:
         _UpperCAmelCase		:       List[str]       =    self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(A_       ,				"""do_resize"""						)						)
         self.assertTrue(hasattr(A_       ,				"""size"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_center_crop"""						)						)
         self.assertTrue(hasattr(A_       ,				"""center_crop"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_normalize"""						)						)
         self.assertTrue(hasattr(A_       ,				"""image_mean"""						)						)
         self.assertTrue(hasattr(A_       ,				"""image_std"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_convert_rgb"""						)						)
  
  
       def 					__snake_case(     self						)     ->   Union[str, Any]:
         _UpperCAmelCase		:       Tuple       =    self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 20}						)
         self.assertEqual(image_processor.crop_size       ,				{"""height""": 18, """width""": 18}						)
  
         _UpperCAmelCase		:       List[str]       =    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 					__snake_case(     self						)     ->   List[Any]:
         pass
  
  
       def 					__snake_case(     self						)     ->   int:
         # Initialize image_processing
         _UpperCAmelCase		:       str       =    self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         _UpperCAmelCase		:       List[str]       =    self.image_processor_tester.prepare_inputs(equal_resolution=A_						)
         for image in image_inputs:
           self.assertIsInstance(A_       ,				Image.Image						)
  
         # Test not batched input
         _UpperCAmelCase		:       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
         _UpperCAmelCase		:       Optional[int]       =    image_processing(A_       ,				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 					__snake_case(     self						)     ->   int:
         # Initialize image_processing
         _UpperCAmelCase		:       List[str]       =    self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         _UpperCAmelCase		:       Optional[int]       =    self.image_processor_tester.prepare_inputs(equal_resolution=A_       ,				numpify=A_						)
         for image in image_inputs:
           self.assertIsInstance(A_       ,				np.ndarray						)
  
         # Test not batched input
         _UpperCAmelCase		:       Optional[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
         _UpperCAmelCase		:       int       =    image_processing(A_       ,				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 					__snake_case(     self						)     ->   Any:
         # Initialize image_processing
         _UpperCAmelCase		:       Union[str, Any]       =    self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         _UpperCAmelCase		:       Optional[Any]       =    self.image_processor_tester.prepare_inputs(equal_resolution=A_       ,				torchify=A_						)
         for image in image_inputs:
           self.assertIsInstance(A_       ,				torch.Tensor						)
  
         # Test not batched input
         _UpperCAmelCase		:       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
         _UpperCAmelCase		:       Dict       =    image_processing(A_       ,				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 		_SCREAMING_SNAKE_CASE    (	A       ,  unittest.TestCase    ):
       __SCREAMING_SNAKE_CASE    				=						CLIPImageProcessor if is_vision_available() else None
  
  
       def 					__snake_case(     self						)     ->   List[Any]:
         _UpperCAmelCase		:       List[str]       =    CLIPImageProcessingTester(self       ,				num_channels=4						)
         _UpperCAmelCase		:       Optional[int]       =    3
  
  
       @property
       def 					__snake_case(     self						)     ->   Any:
         return self.image_processor_tester.prepare_image_processor_dict()
  
  
       def 					__snake_case(     self						)     ->   Union[str, Any]:
         _UpperCAmelCase		:       Any       =    self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(A_       ,				"""do_resize"""						)						)
         self.assertTrue(hasattr(A_       ,				"""size"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_center_crop"""						)						)
         self.assertTrue(hasattr(A_       ,				"""center_crop"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_normalize"""						)						)
         self.assertTrue(hasattr(A_       ,				"""image_mean"""						)						)
         self.assertTrue(hasattr(A_       ,				"""image_std"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_convert_rgb"""						)						)
  
  
       def 					__snake_case(     self						)     ->   Any:
         pass
  
  
  
  
       def 					__snake_case(     self						)     ->   Tuple:
         # Initialize image_processing
         _UpperCAmelCase		:       Union[str, Any]       =    self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         _UpperCAmelCase		:       str       =    self.image_processor_tester.prepare_inputs(equal_resolution=A_						)
         for image in image_inputs:
           self.assertIsInstance(A_       ,				Image.Image						)
  
         # Test not batched input
         _UpperCAmelCase		:       List[str]       =    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
         _UpperCAmelCase		:       Optional[int]       =    image_processing(A_       ,				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"""],
             )       ,				)
  
  
  
  
  
  
style_context: 
  
  
  from typing import Any
  
  
  class 		_SCREAMING_SNAKE_CASE    :
  
  
       def __init__(     self						):
         _UpperCAmelCase		:       Union[str, Any]       =    []
         _UpperCAmelCase		:       Dict       =    0
  
  
       def __str__(     self						):
         _UpperCAmelCase		:       Union[str, Any]       =    """<""" + str(self.stack						)[1:-1] + """>"""
         return printed
  
  
       def 					__snake_case(     self       ,				A_						):
         self.stack.append(A_						)
         _UpperCAmelCase		:       Optional[Any]       =    self.length + 1
  
  
       def 					__snake_case(     self						):
         self.rotate(1						)
         _UpperCAmelCase		:       List[Any]       =    self.stack[self.length - 1]
         _UpperCAmelCase		:       Optional[Any]       =    self.stack[:-1]
         self.rotate(self.length - 1						)
         _UpperCAmelCase		:       Union[str, Any]       =    self.length - 1
         return dequeued
  
  
       def 					__snake_case(     self       ,				A_						):
         for _ in range(A_						):
           _UpperCAmelCase		:       Dict       =    self.stack[0]
           _UpperCAmelCase		:       List[Any]       =    self.stack[1:]
           self.put(A_						)
           _UpperCAmelCase		:       Dict       =    self.length - 1
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Tuple       =    self.get()
         self.put(A_						)
         self.rotate(self.length - 1						)
         return front
  
  
       def 					__snake_case(     self						):
         return self.length
  
  
  
  
  
  