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    _snake_case      (  unittest.TestCase       ):
  
  
  
  
  
  
  
  						def __init__(       self		,     _a		,     _a=7		,     _a=3		,     _a=18		,     _a=30		,     _a=400		,     _a=True		,     _a=None		,     _a=True		,     _a=None		,     _a=True		,     _a=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]		,     _a=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]		,     _a=True		,     ):
  							__magic_name__	:	str			     =  size if size is not None else {"shortest_edge": 20}
  							__magic_name__	:	int			     =  crop_size if crop_size is not None else {"height": 18, "width": 18}
  							__magic_name__	:	int			     =  parent
  							__magic_name__	:	List[Any]			     =  batch_size
  							__magic_name__	:	Optional[int]			     =  num_channels
  							__magic_name__	:	Any			     =  image_size
  							__magic_name__	:	Dict			     =  min_resolution
  							__magic_name__	:	Tuple			     =  max_resolution
  							__magic_name__	:	int			     =  do_resize
  							__magic_name__	:	Optional[Any]			     =  size
  							__magic_name__	:	int			     =  do_center_crop
  							__magic_name__	:	Union[str, Any]			     =  crop_size
  							__magic_name__	:	str			     =  do_normalize
  							__magic_name__	:	Tuple			     =  image_mean
  							__magic_name__	:	List[str]			     =  image_std
  							__magic_name__	:	Any			     =  do_convert_rgb
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							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		,     _a=False		,     _a=False		,     _a=False	):
  							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
  							if equal_resolution:
  								__magic_name__	:	Dict			     =  []
  								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:
  								__magic_name__	:	int			     =  []
  								for i in range(self.batch_size	):
  									__magic_name__	:	Tuple			     =  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
  								__magic_name__	:	Union[str, Any]			     =  [Image.fromarray(np.moveaxis(_a		,     0		,     -1	)	) for x in image_inputs]
  
  							if torchify:
  								__magic_name__	:	str			     =  [torch.from_numpy(_a	) for x in image_inputs]
  
  							return image_inputs
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class    _snake_case      (  snake_case       ,		unittest.TestCase       ):
  						UpperCamelCase__					   =					CLIPImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							__magic_name__	:	Dict			     =  CLIPImageProcessingTester(self	)
  
  
  
  
  
  
  
  						@property
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							__magic_name__	:	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     SCREAMING_SNAKE_CASE					(       self	):
  							__magic_name__	:	Union[str, Any]			     =  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}	)
  
  							__magic_name__	:	List[Any]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
  							pass
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							# Initialize image_processing
  							__magic_name__	:	List[str]			     =  self.image_processing_class(**self.image_processor_dict	)
  							# create random PIL images
  							__magic_name__	:	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
  							__magic_name__	:	Tuple			     =  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
  							__magic_name__	:	List[Any]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
  							# Initialize image_processing
  							__magic_name__	:	Optional[int]			     =  self.image_processing_class(**self.image_processor_dict	)
  							# create random numpy tensors
  							__magic_name__	:	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
  							__magic_name__	:	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
  							__magic_name__	:	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     SCREAMING_SNAKE_CASE					(       self	):
  							# Initialize image_processing
  							__magic_name__	:	str			     =  self.image_processing_class(**self.image_processor_dict	)
  							# create random PyTorch tensors
  							__magic_name__	:	str			     =  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
  							__magic_name__	:	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.crop_size["height"],
  							        self.image_processor_tester.crop_size["width"],
  							    )		,     )
  
  							# Test batched
  							__magic_name__	:	Tuple			     =  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    _snake_case      (  snake_case       ,		unittest.TestCase       ):
  						UpperCamelCase__					   =					CLIPImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							__magic_name__	:	List[str]			     =  CLIPImageProcessingTester(self		,     num_channels=4	)
  							__magic_name__	:	Any			     =  3
  
  
  
  
  
  
  
  						@property
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							__magic_name__	:	int			     =  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     SCREAMING_SNAKE_CASE					(       self	):
  							pass
  
  
  
  
  
  
  
  						def     SCREAMING_SNAKE_CASE					(       self	):
  							# Initialize image_processing
  							__magic_name__	:	List[Any]			     =  self.image_processing_class(**self.image_processor_dict	)
  							# create random PIL images
  							__magic_name__	:	Optional[int]			     =  self.image_processor_tester.prepare_inputs(equal_resolution=_a	)
  							for image in image_inputs:
  								self.assertIsInstance(_a		,     Image.Image	)
  
  							# Test not batched input
  							__magic_name__	:	Optional[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
  							__magic_name__	:	List[Any]			     =  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 TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  snake_case				:	Optional[int]							   =						{
      "configuration_mobilevitv2": [
          "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "MobileViTV2Config",
          "MobileViTV2OnnxConfig",
      ],
  }
  
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	Optional[int]							   =						[
  				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "MobileViTV2ForImageClassification",
  				    "MobileViTV2ForSemanticSegmentation",
  				    "MobileViTV2Model",
  				    "MobileViTV2PreTrainedModel",
  				]
  
  if TYPE_CHECKING:
  				from .configuration_mobilevitva import (
  				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  				    MobileViTVaConfig,
  				    MobileViTVaOnnxConfig,
  				)
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_mobilevitva import (
  								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    MobileViTVaForImageClassification,
  								    MobileViTVaForSemanticSegmentation,
  								    MobileViTVaModel,
  								    MobileViTVaPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  