code: 
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
     import PIL
  
  
  lowerCAmelCase_   =   logging.get_logger(__name__)
  
  class 				A    (__UpperCAmelCase				):
        _SCREAMING_SNAKE_CASE					=							["pixel_values"]
  
  
  
        def __init__(					self     ,  lowercase_ = True     ,  lowercase_ = None     ,  lowercase_ = PIL.Image.NEAREST     ,  lowercase_ = False     ,  lowercase_ = None     ,  lowercase_ = 1 / 255     ,  lowercase_ = False     ,  lowercase_ = True     ,  lowercase_ = True     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = True     ,  **lowercase_     ,  )      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               super().__init__(**UpperCamelCase_					)
               _snake_case      :							int     				=						size if size is not None else {'''height''': 346, '''width''': 346}
               _snake_case      :							Dict     				=						get_size_dict(UpperCamelCase_					)
               _snake_case      :							List[Any]     				=						crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
               _snake_case      :							Dict     				=						get_size_dict(UpperCamelCase_     ,  param_name='''crop_size'''					)
  
               _snake_case      :							str     				=						do_resize
               _snake_case      :							Optional[Any]     				=						size
               _snake_case      :							Optional[int]     				=						resample
               _snake_case      :							str     				=						do_center_crop
               _snake_case      :							Optional[Any]     				=						crop_size
               _snake_case      :							str     				=						do_rescale
               _snake_case      :							str     				=						rescale_factor
               _snake_case      :							Dict     				=						rescale_offset
               _snake_case      :							List[str]     				=						do_normalize
               _snake_case      :							int     				=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
               _snake_case      :							Optional[int]     				=						image_std if image_std is not None else IMAGENET_STANDARD_STD
               _snake_case      :							List[str]     				=						include_top
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = PIL.Image.NEAREST     ,  lowercase_ = None     ,  **lowercase_     ,  )      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						get_size_dict(UpperCamelCase_					)
               if "height" not in size or "width" not in size:
                      raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''					)
               return resize(
                   UpperCamelCase_     ,  size=(size['''height'''], size['''width'''])     ,  resample=UpperCamelCase_     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = None     ,  **lowercase_     ,  )      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							int     				=						get_size_dict(UpperCamelCase_					)
               if "height" not in size or "width" not in size:
                      raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''					)
               return center_crop(UpperCamelCase_     ,  size=(size['''height'''], size['''width'''])     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = True     ,  lowercase_ = None     ,  **lowercase_     ,  )      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               if offset:
                      _snake_case      :							str     				=						(image - 127.5) * scale
                      if data_format is not None:
                             _snake_case      :							Any     				=						to_channel_dimension_format(UpperCamelCase_     ,  UpperCamelCase_					)
                      _snake_case      :							Dict     				=						rescaled_image.astype(np.floataa					)
               else:
                      _snake_case      :							Any     				=						rescale(UpperCamelCase_     ,  scale=UpperCamelCase_     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)
               return rescaled_image
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_ = None     ,  **lowercase_     ,  )      ->   str:
  
  
  
               '''simple docstring'''
  
               return normalize(UpperCamelCase_     ,  mean=UpperCamelCase_     ,  std=UpperCamelCase_     ,  data_format=UpperCamelCase_     ,  **UpperCamelCase_					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_=None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = None     ,  lowercase_ = ChannelDimension.FIRST     ,  **lowercase_     ,  )      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						do_resize if do_resize is not None else self.do_resize
               _snake_case      :							int     				=						resample if resample is not None else self.resample
               _snake_case      :							Any     				=						do_center_crop if do_center_crop is not None else self.do_center_crop
               _snake_case      :							List[str]     				=						do_rescale if do_rescale is not None else self.do_rescale
               _snake_case      :							Optional[Any]     				=						rescale_factor if rescale_factor is not None else self.rescale_factor
               _snake_case      :							List[str]     				=						rescale_offset if rescale_offset is not None else self.rescale_offset
               _snake_case      :							Optional[int]     				=						do_normalize if do_normalize is not None else self.do_normalize
               _snake_case      :							List[str]     				=						image_mean if image_mean is not None else self.image_mean
               _snake_case      :							int     				=						image_std if image_std is not None else self.image_std
               _snake_case      :							List[Any]     				=						include_top if include_top is not None else self.include_top
  
               _snake_case      :							Any     				=						size if size is not None else self.size
               _snake_case      :							List[Any]     				=						get_size_dict(UpperCamelCase_					)
               _snake_case      :							Optional[Any]     				=						crop_size if crop_size is not None else self.crop_size
               _snake_case      :							Optional[Any]     				=						get_size_dict(UpperCamelCase_     ,  param_name='''crop_size'''					)
  
               _snake_case      :							Any     				=						make_list_of_images(UpperCamelCase_					)
  
               if not valid_images(UpperCamelCase_					):
                      raise ValueError(
                          '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                          '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)
  
               if do_resize and size is None or resample is None:
                      raise ValueError('''Size and resample must be specified if do_resize is True.'''					)
  
               if do_center_crop and crop_size is None:
                      raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)
  
               if do_rescale and rescale_factor is None:
                      raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)
  
               if do_normalize and (image_mean is None or image_std is None):
                      raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)
  
               # All transformations expect numpy arrays.
               _snake_case      :							Optional[Any]     				=						[to_numpy_array(UpperCamelCase_					) for image in images]
  
               if do_resize:
                      _snake_case      :							Union[str, Any]     				=						[self.resize(image=UpperCamelCase_     ,  size=UpperCamelCase_     ,  resample=UpperCamelCase_					) for image in images]
  
               if do_center_crop:
                      _snake_case      :							Dict     				=						[self.center_crop(image=UpperCamelCase_     ,  size=UpperCamelCase_					) for image in images]
  
               if do_rescale:
                      _snake_case      :							Union[str, Any]     				=						[self.rescale(image=UpperCamelCase_     ,  scale=UpperCamelCase_     ,  offset=UpperCamelCase_					) for image in images]
  
               if do_normalize:
                      _snake_case      :							Dict     				=						[self.normalize(image=UpperCamelCase_     ,  mean=UpperCamelCase_     ,  std=UpperCamelCase_					) for image in images]
  
               if include_top:
                      _snake_case      :							int     				=						[self.normalize(image=UpperCamelCase_     ,  mean=[0, 0, 0]     ,  std=UpperCamelCase_					) for image in images]
  
               _snake_case      :							Dict     				=						[to_channel_dimension_format(UpperCamelCase_     ,  UpperCamelCase_					) for image in images]
  
               _snake_case      :							List[str]     				=						{'''pixel_values''': images}
               return BatchFeature(data=UpperCamelCase_     ,  tensor_type=UpperCamelCase_					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  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 				A    (unittest.TestCase				):
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_=7     ,  lowercase_=3     ,  lowercase_=18     ,  lowercase_=30     ,  lowercase_=400     ,  lowercase_=True     ,  lowercase_=None     ,  lowercase_=0.875     ,  lowercase_=True     ,  lowercase_=[0.5, 0.5, 0.5]     ,  lowercase_=[0.5, 0.5, 0.5]     ,  )      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						size if size is not None else {'''shortest_edge''': 20}
               _snake_case      :							List[Any]     				=						parent
               _snake_case      :							Optional[int]     				=						batch_size
               _snake_case      :							str     				=						num_channels
               _snake_case      :							Union[str, Any]     				=						image_size
               _snake_case      :							Optional[Any]     				=						min_resolution
               _snake_case      :							Union[str, Any]     				=						max_resolution
               _snake_case      :							Tuple     				=						do_resize
               _snake_case      :							List[Any]     				=						size
               _snake_case      :							Optional[int]     				=						crop_pct
               _snake_case      :							Any     				=						do_normalize
               _snake_case      :							Optional[int]     				=						image_mean
               _snake_case      :							Union[str, Any]     				=						image_std
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''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,
                   "crop_pct": self.crop_pct,
               }
  
  
  @require_torch
  @require_vision
  class 				A    (__UpperCAmelCase       ,unittest.TestCase				):
        _SCREAMING_SNAKE_CASE					=							ConvNextImageProcessor if is_vision_available() else None
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						ConvNextImageProcessingTester(self					)
  
  
  
        @property
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						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    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							int     				=						self.image_processing_class.from_dict(self.image_processor_dict					)
               self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 20}					)
  
               _snake_case      :							Tuple     				=						self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42					)
               self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 42}					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               pass
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
               # create random PIL images
               _snake_case      :							List[str]     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
               for image in image_inputs:
                      self.assertIsInstance(lowercase_     ,  Image.Image					)
  
               # Test not batched input
               _snake_case      :							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
               _snake_case      :							int     				=						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    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
               # create random numpy tensors
               _snake_case      :							Tuple     				=						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
               _snake_case      :							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
               _snake_case      :							Union[str, Any]     				=						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    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						self.image_processing_class(**self.image_processor_dict					)
               # create random PyTorch tensors
               _snake_case      :							Dict     				=						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
               _snake_case      :							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
               _snake_case      :							List[Any]     				=						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'''],
                   )     ,  )
  
  
  
  
  
  
  