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 MobileNetVaImageProcessor
  
  
  
  
  
  class 					snake_case__  ( unittest.TestCase       ):
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_=7				,    UpperCamelCase_=3				,    UpperCamelCase_=18				,    UpperCamelCase_=30				,    UpperCamelCase_=400				,    UpperCamelCase_=True				,    UpperCamelCase_=None				,    UpperCamelCase_=True				,    UpperCamelCase_=None				,    )		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[str]			=	size if size is not None else {"""shortest_edge""": 20}
  								a_       :   List[Any]			=	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
  								a_       :   List[Any]			=	parent
  								a_       :   Union[str, Any]			=	batch_size
  								a_       :   Optional[int]			=	num_channels
  								a_       :   List[str]			=	image_size
  								a_       :   Any			=	min_resolution
  								a_       :   Optional[Any]			=	max_resolution
  								a_       :   str			=	do_resize
  								a_       :   Union[str, Any]			=	size
  								a_       :   List[str]			=	do_center_crop
  								a_       :   Optional[Any]			=	crop_size
  
  
  
  
  
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return {
  								    "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__  ( __A      ,    unittest.TestCase       ):
  	UpperCAmelCase   :							Any         = MobileNetVaImageProcessor if is_vision_available() else None
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	MobileNetVaImageProcessingTester(self							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  	def     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   int			=	self.image_processing_class(**self.image_processor_dict							)
  								self.assertTrue(hasattr(UpperCamelCase_				,    """do_resize"""							)							)
  								self.assertTrue(hasattr(UpperCamelCase_				,    """size"""							)							)
  								self.assertTrue(hasattr(UpperCamelCase_				,    """do_center_crop"""							)							)
  								self.assertTrue(hasattr(UpperCamelCase_				,    """center_crop"""							)							)
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	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}							)
  
  								a_       :   Optional[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     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								pass
  
  
  
  
  
  	def     A				(  self							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   int			=	self.image_processing_class(**self.image_processor_dict							)
  								# create random PIL images
  								a_       :   List[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_       :   int			=	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
  								a_       :   int			=	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.crop_size["""height"""],
  								        self.image_processor_tester.crop_size["""width"""],
  								    )				,    )
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[int]			=	self.image_processing_class(**self.image_processor_dict							)
  								# create random numpy tensors
  								a_       :   Dict			=	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_       :   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.crop_size["""height"""],
  								        self.image_processor_tester.crop_size["""width"""],
  								    )				,    )
  
  								# Test batched
  								a_       :   Union[str, 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.crop_size["""height"""],
  								        self.image_processor_tester.crop_size["""width"""],
  								    )				,    )
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	self.image_processing_class(**self.image_processor_dict							)
  								# create random PyTorch tensors
  								a_       :   List[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_       :   Optional[int]			=	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
  								a_       :   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.crop_size["""height"""],
  								        self.image_processor_tester.crop_size["""width"""],
  								    )				,    )
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Tuple			=	sorted(SCREAMING_SNAKE_CASE_      )
  							a_       :   Tuple			=	len(SCREAMING_SNAKE_CASE_      )
  							a_       :   Tuple			=	length >> 1
  							return (
  							    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
  							    if length % 2 == 0
  							    else sorted_list[mid_index]
  							)
  
  
  
  
  
  def 						_lowerCamelCase		(							):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							import doctest
  
  							doctest.testmod()
  
  
  if __name__ == "__main__":
  	main()
  
  
  