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  :   str      ,      lowerCAmelCase  :   int      ,      lowerCAmelCase  :   Dict=7      ,      lowerCAmelCase  :   Dict=3      ,      lowerCAmelCase  :   int=18      ,      lowerCAmelCase  :   Any=30      ,      lowerCAmelCase  :   str=400      ,      lowerCAmelCase  :   Tuple=True      ,      lowerCAmelCase  :   List[Any]=None      ,      lowerCAmelCase  :   Optional[Any]=0.875      ,      lowerCAmelCase  :   Tuple=True      ,      lowerCAmelCase  :   Tuple=[0.5, 0.5, 0.5]      ,      lowerCAmelCase  :   List[Any]=[0.5, 0.5, 0.5]      ,      )	-> List[str]:
        a__     		=     size if size is not None else {'''shortest_edge''': 20}
        a__     		=     parent
        a__     		=     batch_size
        a__     		=     num_channels
        a__     		=     image_size
        a__     		=     min_resolution
        a__     		=     max_resolution
        a__     		=     do_resize
        a__     		=     size
        a__     		=     crop_pct
        a__     		=     do_normalize
        a__     		=     image_mean
        a__     		=     image_std
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> str:
        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_				(							__UpperCAmelCase     ,     unittest.TestCase    ):
      UpperCAmelCase_			:       Tuple						 =							ConvNextImageProcessor if is_vision_available() else None
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> Dict:
        a__     		=     ConvNextImageProcessingTester(self   )
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   int   )	-> Dict:
        return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> str:
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(lowercase_      ,      "do_resize"   )   )
        self.assertTrue(hasattr(lowercase_      ,      "size"   )   )
        self.assertTrue(hasattr(lowercase_      ,      "crop_pct"   )   )
        self.assertTrue(hasattr(lowercase_      ,      "do_normalize"   )   )
        self.assertTrue(hasattr(lowercase_      ,      "image_mean"   )   )
        self.assertTrue(hasattr(lowercase_      ,      "image_std"   )   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> List[Any]:
        a__     		=     self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size      ,      {"shortest_edge": 20}   )
  
        a__     		=     self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42   )
        self.assertEqual(image_processor.size      ,      {"shortest_edge": 42}   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   str   )	-> List[Any]:
        pass
  
  
  
  
      def 						__lowerCamelCase (					self  :   str   )	-> int:
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        a__     		=     prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase_   )
        for image in image_inputs:
          self.assertIsInstance(lowercase_      ,      Image.Image   )
  
        # Test not batched input
        a__     		=     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
        a__     		=     image_processing(lowercase_      ,      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 						__lowerCamelCase (					self  :   List[str]   )	-> Dict:
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        a__     		=     prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase_      ,      numpify=lowercase_   )
        for image in image_inputs:
          self.assertIsInstance(lowercase_      ,      np.ndarray   )
  
        # Test not batched input
        a__     		=     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
        a__     		=     image_processing(lowercase_      ,      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 						__lowerCamelCase (					self  :   Dict   )	-> Union[str, Any]:
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        a__     		=     prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase_      ,      torchify=lowercase_   )
        for image in image_inputs:
          self.assertIsInstance(lowercase_      ,      torch.Tensor   )
  
        # Test not batched input
        a__     		=     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
        a__     		=     image_processing(lowercase_      ,      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: _SCREAMING_SNAKE_CASE   						=    [0.485, 0.456, 0.406]
  _SCREAMING_SNAKE_CASE   						=    [0.229, 0.224, 0.225]
  _SCREAMING_SNAKE_CASE   						=    [0.5, 0.5, 0.5]
  _SCREAMING_SNAKE_CASE   						=    [0.5, 0.5, 0.5]
  _SCREAMING_SNAKE_CASE   						=    [0.48145466, 0.4578275, 0.40821073]
  _SCREAMING_SNAKE_CASE   						=    [0.26862954, 0.26130258, 0.27577711]
  
  