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        __lowerCAmelCase (					unittest.TestCase):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=7	,   lowerCAmelCase__=3	,   lowerCAmelCase__=1_8	,   lowerCAmelCase__=3_0	,   lowerCAmelCase__=4_0_0	,   lowerCAmelCase__=True	,   lowerCAmelCase__=None	,   lowerCAmelCase__=True	,   lowerCAmelCase__=None	,   )     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[int]            =size if size is not None else {'''shortest_edge''': 2_0}
          a__			:      Optional[Any]            =crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
          a__			:      Union[str, Any]            =parent
          a__			:      int            =batch_size
          a__			:      List[Any]            =num_channels
          a__			:      List[Any]            =image_size
          a__			:      str            =min_resolution
          a__			:      Union[str, Any]            =max_resolution
          a__			:      int            =do_resize
          a__			:      Optional[Any]            =size
          a__			:      Tuple            =do_center_crop
          a__			:      int            =crop_size
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[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        __lowerCAmelCase (					__UpperCAmelCase		,       unittest.TestCase):
        _lowercase       :      int        =    MobileNetVaImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      str            =MobileNetVaImageProcessingTester(self							)
  
  
  
  
  
  
  
        @property
        def      _lowercase						(					self							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[Any]            =self.image_processing_class(**self.image_processor_dict							)
          self.assertTrue(hasattr(lowercase_	,   "do_resize"							)							)
          self.assertTrue(hasattr(lowercase_	,   "size"							)							)
          self.assertTrue(hasattr(lowercase_	,   "do_center_crop"							)							)
          self.assertTrue(hasattr(lowercase_	,   "center_crop"							)							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Tuple:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[int]            =self.image_processing_class.from_dict(self.image_processor_dict							)
          self.assertEqual(image_processor.size	,   {"shortest_edge": 2_0}							)
          self.assertEqual(image_processor.crop_size	,   {"height": 1_8, "width": 1_8}							)
  
          a__			:      int            =self.image_processing_class.from_dict(self.image_processor_dict	,   size=4_2	,   crop_size=8_4							)
          self.assertEqual(image_processor.size	,   {"shortest_edge": 4_2}							)
          self.assertEqual(image_processor.crop_size	,   {"height": 8_4, "width": 8_4}							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          pass
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =self.image_processing_class(**self.image_processor_dict							)
          # create random PIL images
          a__			:      Dict            =prepare_image_inputs(self.image_processor_tester	,   equal_resolution=lowercase_							)
          for image in image_inputs:
            self.assertIsInstance(lowercase_	,   Image.Image							)
  
          # 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__			:      Tuple            =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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,   )
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Optional[Any]            =self.image_processing_class(**self.image_processor_dict							)
          # create random numpy tensors
          a__			:      List[Any]            =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
          a__			:      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
          a__			:      Tuple            =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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,   )
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =self.image_processing_class(**self.image_processor_dict							)
          # create random PyTorch tensors
          a__			:      Optional[Any]            =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
          a__			:      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
          a__			:      Optional[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.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )	,   )
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_detr import DetrImageProcessor
  
  
  UpperCAmelCase			:			List[Any]	  = logging.get_logger(__name__)
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
  
  
  
  
  
  
  
        def __init__(					self	,   *lowerCAmelCase__	,   **lowerCAmelCase__							)     ->		None:
  
  
  
          '''simple docstring'''
  
  
  
  
          warnings.warn(
              "The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use DetrImageProcessor instead."	,   lowerCAmelCase__	,   )
          super().__init__(*lowerCAmelCase__	,   **lowerCAmelCase__							)
  
  
  
  
  
  
  