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 ViltImageProcessor
  
  
  
  class        lowerCAmelCase_	(						unittest.TestCase							):
        def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=7,    SCREAMING_SNAKE_CASE_=3,    SCREAMING_SNAKE_CASE_=18,    SCREAMING_SNAKE_CASE_=30,    SCREAMING_SNAKE_CASE_=400,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=2,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    )  ->	List[Any]:
           UpperCamelCase					:						Optional[int]					=			size if size is not None else {'shortest_edge': 30}
           UpperCamelCase					:						Dict					=			parent
           UpperCamelCase					:						str					=			batch_size
           UpperCamelCase					:						str					=			num_channels
           UpperCamelCase					:						Optional[int]					=			image_size
           UpperCamelCase					:						List[Any]					=			min_resolution
           UpperCamelCase					:						Tuple					=			max_resolution
           UpperCamelCase					:						Tuple					=			do_resize
           UpperCamelCase					:						Any					=			size
           UpperCamelCase					:						int					=			size_divisor
           UpperCamelCase					:						str					=			do_normalize
           UpperCamelCase					:						Optional[int]					=			image_mean
           UpperCamelCase					:						List[str]					=			image_std
        def       snake_case_ (    self )  ->	List[Any]:
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "size_divisor": self.size_divisor,
           }
  
  
  
  
  
  
        def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=False )  ->	Union[str, Any]:
           if not batched:
              UpperCamelCase					:						Optional[Any]					=			self.size['shortest_edge']
              UpperCamelCase					:						int					=			image_inputs[0]
              if isinstance(SCREAMING_SNAKE_CASE_,    Image.Image ):
                 UpperCamelCase					:						List[Any]					=			image.size
              else:
                 UpperCamelCase					:						List[Any]					=			image.shape[1], image.shape[2]
              UpperCamelCase					:						Any					=			size / min(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
              if h < w:
                 UpperCamelCase					:						Optional[int]					=			size, scale * w
              else:
                 UpperCamelCase					:						List[str]					=			scale * h, size
  
              UpperCamelCase					:						int					=			int((1333 / 800) * size )
              if max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ) > max_size:
                 UpperCamelCase					:						Any					=			max_size / max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
                 UpperCamelCase					:						List[Any]					=			newh * scale
                 UpperCamelCase					:						Optional[int]					=			neww * scale
  
              UpperCamelCase					:						List[Any]					=			int(newh + 0.5 ), int(neww + 0.5 )
              UpperCamelCase					:						int					=			(
                  newh // self.size_divisor * self.size_divisor,
                  neww // self.size_divisor * self.size_divisor,
              )
  
           else:
              UpperCamelCase					:						Dict					=			[]
              for image in image_inputs:
                 UpperCamelCase					:						Union[str, Any]					=			self.get_expected_values([image] )
                 expected_values.append((expected_height, expected_width) )
              UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
              UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]
  
           return expected_height, expected_width
  
  
  
  
  
  
  @require_torch
  @require_vision
  class        lowerCAmelCase_	(						a__ ,					unittest.TestCase							):
        UpperCAmelCase__       :      List[Any]       =						ViltImageProcessor if is_vision_available() else None
        def       snake_case_ (    self )  ->	Union[str, Any]:
           UpperCamelCase					:						Dict					=			ViltImageProcessingTester(self )
        @property
        def       snake_case_ (    self )  ->	Dict:
           return self.image_processor_tester.prepare_image_processor_dict()
        def       snake_case_ (    self )  ->	Union[str, Any]:
           UpperCamelCase					:						List[str]					=			self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_mean' ) )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_std' ) )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_normalize' ) )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_resize' ) )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size' ) )
           self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size_divisor' ) )
        def       snake_case_ (    self )  ->	List[Any]:
           UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict )
           self.assertEqual(image_processor.size,    {'shortest_edge': 30} )
  
           UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict,    size=42 )
           self.assertEqual(image_processor.size,    {'shortest_edge': 42} )
        def       snake_case_ (    self )  ->	Dict:
           pass
        def       snake_case_ (    self )  ->	str:
           # Initialize image_processing
           UpperCamelCase					:						int					=			self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           UpperCamelCase					:						Any					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_ )
           for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_,    Image.Image )
  
           # Test not batched input
           UpperCamelCase					:						int					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
           UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
           self.assertEqual(
               encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
           # Test batched
           UpperCamelCase					:						str					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
           UpperCamelCase					:						Tuple					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
           self.assertEqual(
               encoded_images.shape,    (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               ),    )
        def       snake_case_ (    self )  ->	List[str]:
           # Initialize image_processing
           UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           UpperCamelCase					:						Optional[Any]					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    numpify=SCREAMING_SNAKE_CASE_ )
           for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_,    np.ndarray )
  
           # Test not batched input
           UpperCamelCase					:						Optional[Any]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
           UpperCamelCase					:						str					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
           self.assertEqual(
               encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
           # Test batched
           UpperCamelCase					:						Dict					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
           UpperCamelCase					:						Optional[Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
           self.assertEqual(
               encoded_images.shape,    (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               ),    )
  
  
  
  
  
  
        def       snake_case_ (    self )  ->	Optional[Any]:
           # Initialize image_processing
           UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           UpperCamelCase					:						Dict					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    torchify=SCREAMING_SNAKE_CASE_ )
           for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_,    torch.Tensor )
  
           # Test not batched input
           UpperCamelCase					:						List[str]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
           UpperCamelCase					:						Union[str, Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
           self.assertEqual(
               encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
           # Test batched
           UpperCamelCase					:						List[Any]					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
           UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
           self.assertEqual(
               encoded_images.shape,    (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               ),    )
  
  
  
  
style_context: 
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  
  __UpperCAmelCase      = {
      '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
  }
  
  
  
  class        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      Union[str, Any]       =						"mpnet"
         def __init__(    self,    SCREAMING_SNAKE_CASE_=3_0527,    SCREAMING_SNAKE_CASE_=768,    SCREAMING_SNAKE_CASE_=12,    SCREAMING_SNAKE_CASE_=12,    SCREAMING_SNAKE_CASE_=3072,    SCREAMING_SNAKE_CASE_="gelu",    SCREAMING_SNAKE_CASE_=0.1,    SCREAMING_SNAKE_CASE_=0.1,    SCREAMING_SNAKE_CASE_=512,    SCREAMING_SNAKE_CASE_=0.02,    SCREAMING_SNAKE_CASE_=1e-12,    SCREAMING_SNAKE_CASE_=32,    SCREAMING_SNAKE_CASE_=1,    SCREAMING_SNAKE_CASE_=0,    SCREAMING_SNAKE_CASE_=2,    **SCREAMING_SNAKE_CASE_,    )  ->	Optional[int]:
            super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,    bos_token_id=SCREAMING_SNAKE_CASE_,    eos_token_id=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )
  
            UpperCamelCase					:						Any					=			vocab_size
            UpperCamelCase					:						Optional[Any]					=			hidden_size
            UpperCamelCase					:						Union[str, Any]					=			num_hidden_layers
            UpperCamelCase					:						Optional[int]					=			num_attention_heads
            UpperCamelCase					:						Optional[Any]					=			hidden_act
            UpperCamelCase					:						str					=			intermediate_size
            UpperCamelCase					:						Optional[Any]					=			hidden_dropout_prob
            UpperCamelCase					:						Union[str, Any]					=			attention_probs_dropout_prob
            UpperCamelCase					:						List[Any]					=			max_position_embeddings
            UpperCamelCase					:						Union[str, Any]					=			initializer_range
            UpperCamelCase					:						List[str]					=			layer_norm_eps
            UpperCamelCase					:						str					=			relative_attention_num_buckets
  
  
  
  