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 ViTImageProcessor
  
  
  
  class 			a      (      unittest.TestCase   ):
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase=7	,     __UpperCamelCase=3	,     __UpperCamelCase=18	,     __UpperCamelCase=30	,     __UpperCamelCase=4_00	,     __UpperCamelCase=True	,     __UpperCamelCase=None	,     __UpperCamelCase=True	,     __UpperCamelCase=[0.5, 0.5, 0.5]	,     __UpperCamelCase=[0.5, 0.5, 0.5]	,     )->			Dict:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=size if size is not None else {'''height''': 18, '''width''': 18}
  						A__     :							int   				=parent
  						A__     :							Any   				=batch_size
  						A__     :							List[str]   				=num_channels
  						A__     :							int   				=image_size
  						A__     :							Union[str, Any]   				=min_resolution
  						A__     :							Dict   				=max_resolution
  						A__     :							Union[str, Any]   				=do_resize
  						A__     :							Any   				=size
  						A__     :							Optional[int]   				=do_normalize
  						A__     :							List[Any]   				=image_mean
  						A__     :							Optional[int]   				=image_std
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  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,
  						}
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 			a      (      UpperCamelCase_      ,unittest.TestCase   ):
  		__lowercase													=		ViTImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=ViTImageProcessingTester(self							)
  
  
  
  
  
  
  		@property
  		def       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				=self.image_processing_class(**self.image_processor_dict							)
  						self.assertTrue(hasattr(__UpperCamelCase	,     '''image_mean'''							)							)
  						self.assertTrue(hasattr(__UpperCamelCase	,     '''image_std'''							)							)
  						self.assertTrue(hasattr(__UpperCamelCase	,     '''do_normalize'''							)							)
  						self.assertTrue(hasattr(__UpperCamelCase	,     '''do_resize'''							)							)
  						self.assertTrue(hasattr(__UpperCamelCase	,     '''size'''							)							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=self.image_processing_class.from_dict(self.image_processor_dict							)
  						self.assertEqual(image_processor.size	,     {'''height''': 18, '''width''': 18}							)
  
  						A__     :							Any   				=self.image_processing_class.from_dict(self.image_processor_dict	,     size=42							)
  						self.assertEqual(image_processor.size	,     {'''height''': 42, '''width''': 42}							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						pass
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=self.image_processing_class(**self.image_processor_dict							)
  						# create random PIL images
  						A__     :							Optional[Any]   				=prepare_image_inputs(self.image_processor_tester	,     equal_resolution=__UpperCamelCase							)
  						for image in image_inputs:
  										self.assertIsInstance(__UpperCamelCase	,     Image.Image							)
  
  						# Test not batched input
  						A__     :							List[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['''height'''],
  						        self.image_processor_tester.size['''width'''],
  						    )	,     )
  
  						# Test batched
  						A__     :							Optional[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.size['''height'''],
  						        self.image_processor_tester.size['''width'''],
  						    )	,     )
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[str]:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=self.image_processing_class(**self.image_processor_dict							)
  						# create random numpy tensors
  						A__     :							Tuple   				=prepare_image_inputs(self.image_processor_tester	,     equal_resolution=__UpperCamelCase	,     numpify=__UpperCamelCase							)
  						for image in image_inputs:
  										self.assertIsInstance(__UpperCamelCase	,     np.ndarray							)
  
  						# Test not batched input
  						A__     :							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.size['''height'''],
  						        self.image_processor_tester.size['''width'''],
  						    )	,     )
  
  						# Test batched
  						A__     :							Tuple   				=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.size['''height'''],
  						        self.image_processor_tester.size['''width'''],
  						    )	,     )
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=self.image_processing_class(**self.image_processor_dict							)
  						# create random PyTorch tensors
  						A__     :							Any   				=prepare_image_inputs(self.image_processor_tester	,     equal_resolution=__UpperCamelCase	,     torchify=__UpperCamelCase							)
  						for image in image_inputs:
  										self.assertIsInstance(__UpperCamelCase	,     torch.Tensor							)
  
  						# Test not batched input
  						A__     :							Dict   				=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['''height'''],
  						        self.image_processor_tester.size['''width'''],
  						    )	,     )
  
  						# Test batched
  						A__     :							List[str]   				=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.size['''height'''],
  						        self.image_processor_tester.size['''width'''],
  						    )	,     )
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  __lowerCamelCase       :       str				   =			{
      "configuration_align": [
          "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "AlignConfig",
          "AlignTextConfig",
          "AlignVisionConfig",
      ],
      "processing_align": ["AlignProcessor"],
  }
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						__lowerCamelCase       :       Dict				   =			[
  						    "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "AlignModel",
  						    "AlignPreTrainedModel",
  						    "AlignTextModel",
  						    "AlignVisionModel",
  						]
  
  if TYPE_CHECKING:
  						from .configuration_align import (
  						    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
  						    AlignConfig,
  						    AlignTextConfig,
  						    AlignVisionConfig,
  						)
  						from .processing_align import AlignProcessor
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_align import (
  												    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    AlignModel,
  												    AlignPreTrainedModel,
  												    AlignTextModel,
  												    AlignVisionModel,
  												)
  
  else:
  						import sys
  
  						__lowerCamelCase       :       List[str]				   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  