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 TvltImageProcessor
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						List[Any]   ,     _snake_case   :						Optional[Any]=10   ,     _snake_case   :						int=10   ,     _snake_case   :						List[Any]=False   ,     _snake_case   :						int=False     )       ->	Optional[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	List[str]			     =  []
  	for i in range(image_processor_tester.num_frames     ):
  		video.append(np.random.randint(255   ,     size=(image_processor_tester.num_channels, width, height)   ,     dtype=np.uinta     )     )
  
  	if not numpify and not torchify:
  		# PIL expects the channel dimension as last dimension
  		__magic_name__	:	Optional[Any]			     =  [Image.fromarray(np.moveaxis(_snake_case   ,     0   ,     -1     )     ) for frame in video]
  
  	if torchify:
  		__magic_name__	:	Dict			     =  [torch.from_numpy(_snake_case     ) for frame in video]
  
  	return video
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int   ,     _snake_case   :						Tuple=False   ,     _snake_case   :						Optional[Any]=False   ,     _snake_case   :						Any=False     )       ->	Optional[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
  	__magic_name__	:	List[Any]			     =  []
  	for i in range(image_processor_tester.batch_size     ):
  		if equal_resolution:
  			__magic_name__	:	Tuple			     =  image_processor_tester.max_resolution
  		else:
  			__magic_name__     ,					__magic_name__	:	Tuple			     =  np.random.choice(
  			    np.arange(image_processor_tester.min_resolution   ,     image_processor_tester.max_resolution     )   ,     2     )
  			__magic_name__	:	Tuple			     =  prepare_video(
  			    image_processor_tester=_snake_case   ,     width=_snake_case   ,     height=_snake_case   ,     numpify=_snake_case   ,     torchify=_snake_case   ,     )
  		video_inputs.append(_snake_case     )
  
  	return video_inputs
  
  
  
  
  
  class    _snake_case      (  unittest.TestCase       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a=7		,     _a=3		,     _a=4		,     _a=18		,     _a=30		,     _a=400		,     _a=True		,     _a=None		,     _a=True		,     _a=[0.5, 0.5, 0.5]		,     _a=[0.5, 0.5, 0.5]		,     _a=True		,     _a=None		,     ):
  				__magic_name__	:	str			     =  size if size is not None else {"shortest_edge": 18}
  				__magic_name__	:	Tuple			     =  crop_size if crop_size is not None else {"height": 18, "width": 18}
  
  				__magic_name__	:	str			     =  parent
  				__magic_name__	:	Optional[Any]			     =  batch_size
  				__magic_name__	:	List[str]			     =  num_channels
  				__magic_name__	:	Any			     =  num_frames
  				__magic_name__	:	Tuple			     =  image_size
  				__magic_name__	:	List[Any]			     =  min_resolution
  				__magic_name__	:	List[Any]			     =  max_resolution
  				__magic_name__	:	List[str]			     =  do_resize
  				__magic_name__	:	Any			     =  size
  				__magic_name__	:	str			     =  do_normalize
  				__magic_name__	:	str			     =  image_mean
  				__magic_name__	:	Union[str, Any]			     =  image_std
  				__magic_name__	:	Any			     =  do_center_crop
  				__magic_name__	:	int			     =  crop_size
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return {
  				    "image_mean": self.image_mean,
  				    "image_std": self.image_std,
  				    "do_normalize": self.do_normalize,
  				    "do_resize": self.do_resize,
  				    "size": self.size,
  				    "do_center_crop": self.do_center_crop,
  				    "crop_size": self.crop_size,
  				}
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class    _snake_case      (  snake_case       ,		unittest.TestCase       ):
  			UpperCamelCase__					   =					TvltImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[int]			     =  TvltImageProcessorTester(self	)
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Dict			     =  self.image_processing_class(**self.image_processor_dict	)
  				self.assertTrue(hasattr(_a		,     "image_mean"	)	)
  				self.assertTrue(hasattr(_a		,     "image_std"	)	)
  				self.assertTrue(hasattr(_a		,     "do_normalize"	)	)
  				self.assertTrue(hasattr(_a		,     "do_resize"	)	)
  				self.assertTrue(hasattr(_a		,     "do_center_crop"	)	)
  				self.assertTrue(hasattr(_a		,     "size"	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				# Initialize image_processor
  				__magic_name__	:	int			     =  self.image_processing_class(**self.image_processor_dict	)
  				# create random PIL videos
  				__magic_name__	:	Optional[int]			     =  prepare_video_inputs(self.image_processor_tester		,     equal_resolution=_a	)
  				for video in video_inputs:
  					self.assertIsInstance(_a		,     _a	)
  					self.assertIsInstance(video[0]		,     Image.Image	)
  
  				# Test not batched input
  				__magic_name__	:	Any			     =  image_processor(video_inputs[0]		,     return_tensors="pt"	).pixel_values
  				self.assertEqual(
  				    encoded_videos.shape		,     (
  				        1,
  				        self.image_processor_tester.num_frames,
  				        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_processor(_a		,     return_tensors="pt"	).pixel_values
  				self.assertEqual(
  				    encoded_videos.shape		,     (
  				        self.image_processor_tester.batch_size,
  				        self.image_processor_tester.num_frames,
  				        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_processor
  				__magic_name__	:	int			     =  self.image_processing_class(**self.image_processor_dict	)
  				# create random numpy tensors
  				__magic_name__	:	Tuple			     =  prepare_video_inputs(self.image_processor_tester		,     equal_resolution=_a		,     numpify=_a	)
  				for video in video_inputs:
  					self.assertIsInstance(_a		,     _a	)
  					self.assertIsInstance(video[0]		,     np.ndarray	)
  
  				# Test not batched input
  				__magic_name__	:	Tuple			     =  image_processor(video_inputs[0]		,     return_tensors="pt"	).pixel_values
  				self.assertEqual(
  				    encoded_videos.shape		,     (
  				        1,
  				        self.image_processor_tester.num_frames,
  				        self.image_processor_tester.num_channels,
  				        self.image_processor_tester.crop_size["height"],
  				        self.image_processor_tester.crop_size["width"],
  				    )		,     )
  
  				# Test batched
  				__magic_name__	:	str			     =  image_processor(_a		,     return_tensors="pt"	).pixel_values
  				self.assertEqual(
  				    encoded_videos.shape		,     (
  				        self.image_processor_tester.batch_size,
  				        self.image_processor_tester.num_frames,
  				        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_processor
  				__magic_name__	:	Union[str, Any]			     =  self.image_processing_class(**self.image_processor_dict	)
  				# create random PyTorch tensors
  				__magic_name__	:	int			     =  prepare_video_inputs(self.image_processor_tester		,     equal_resolution=_a		,     torchify=_a	)
  				for video in video_inputs:
  					self.assertIsInstance(_a		,     _a	)
  					self.assertIsInstance(video[0]		,     torch.Tensor	)
  
  				# Test not batched input
  				__magic_name__	:	Any			     =  image_processor(video_inputs[0]		,     return_tensors="pt"	).pixel_values
  				self.assertEqual(
  				    encoded_videos.shape		,     (
  				        1,
  				        self.image_processor_tester.num_frames,
  				        self.image_processor_tester.num_channels,
  				        self.image_processor_tester.crop_size["height"],
  				        self.image_processor_tester.crop_size["width"],
  				    )		,     )
  
  				# Test batched
  				__magic_name__	:	Union[str, Any]			     =  image_processor(_a		,     return_tensors="pt"	).pixel_values
  				self.assertEqual(
  				    encoded_videos.shape		,     (
  				        self.image_processor_tester.batch_size,
  				        self.image_processor_tester.num_frames,
  				        self.image_processor_tester.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__)
  