code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import unittest
  
  import numpy as np
  from datasets import load_dataset
  
  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 SegformerImageProcessor
  class 	a__				(				unittest.TestCase      ):
  
  
  
  
  
     '''simple docstring'''
  
     def __init__(     self       : Optional[Any] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Tuple=7 ,						lowerCAmelCase_       : int=3 ,						lowerCAmelCase_       : Optional[int]=30 ,						lowerCAmelCase_       : str=400 ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : List[Any]=None ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Dict=[0.5, 0.5, 0.5] ,						lowerCAmelCase_       : Tuple=[0.5, 0.5, 0.5] ,						lowerCAmelCase_       : List[str]=False ,						)			->		str:
        __A=  size if size is not None else {'height': 30, 'width': 30}
        __A=  parent
        __A=  batch_size
        __A=  num_channels
        __A=  min_resolution
        __A=  max_resolution
        __A=  do_resize
        __A=  size
        __A=  do_normalize
        __A=  image_mean
        __A=  image_std
        __A=  do_reduce_labels
  
     def 				lowerCAmelCase    (     self       : List[str]  )			->		Optional[int]:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_reduce_labels": self.do_reduce_labels,
        }
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     __A=  load_dataset('hf-internal-testing/fixtures_ade20k',split='test'   )
  
     __A=  Image.open(dataset[0]['file']   )
     __A=  Image.open(dataset[1]['file']   )
  
     return image, map
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     __A=  load_dataset('hf-internal-testing/fixtures_ade20k',split='test'   )
  
     __A=  Image.open(dataset[0]['file']   )
     __A=  Image.open(dataset[1]['file']   )
     __A=  Image.open(dataset[2]['file']   )
     __A=  Image.open(dataset[3]['file']   )
  
     return [imagea, imagea], [mapa, mapa]
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 	a__				(				a_			,     unittest.TestCase      ):
  
  
  
  
  
     '''simple docstring'''
  
     A	:							Any							= SegformerImageProcessor if is_vision_available() else None
     def 				lowerCAmelCase    (     self       : str  )			->		List[Any]:
        __A=  SegformerImageProcessingTester(self  )
     @property
     def 				lowerCAmelCase    (     self       : Any  )			->		List[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()
     def 				lowerCAmelCase    (     self       : List[str]  )			->		Tuple:
        __A=  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(lowerCAmelCase_ ,						'do_resize'  )  )
        self.assertTrue(hasattr(lowerCAmelCase_ ,						'size'  )  )
        self.assertTrue(hasattr(lowerCAmelCase_ ,						'do_normalize'  )  )
        self.assertTrue(hasattr(lowerCAmelCase_ ,						'image_mean'  )  )
        self.assertTrue(hasattr(lowerCAmelCase_ ,						'image_std'  )  )
        self.assertTrue(hasattr(lowerCAmelCase_ ,						'do_reduce_labels'  )  )
     def 				lowerCAmelCase    (     self       : List[Any]  )			->		List[Any]:
        __A=  self.image_processing_class.from_dict(self.image_processor_dict  )
        self.assertEqual(image_processor.size ,						{'height': 30, 'width': 30}  )
        self.assertEqual(image_processor.do_reduce_labels ,						lowerCAmelCase_  )
  
        __A=  self.image_processing_class.from_dict(self.image_processor_dict ,						size=42 ,						reduce_labels=lowerCAmelCase_  )
        self.assertEqual(image_processor.size ,						{'height': 42, 'width': 42}  )
        self.assertEqual(image_processor.do_reduce_labels ,						lowerCAmelCase_  )
     def 				lowerCAmelCase    (     self       : List[Any]  )			->		Optional[int]:
        pass
     def 				lowerCAmelCase    (     self       : List[Any]  )			->		List[str]:
        # Initialize image_processing
        __A=  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        __A=  prepare_image_inputs(self.image_processor_tester ,						equal_resolution=lowerCAmelCase_  )
        for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase_ ,						Image.Image  )
  
        # Test not batched input
        __A=  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.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
  
        # Test batched
        __A=  image_processing(lowerCAmelCase_ ,						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.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
     def 				lowerCAmelCase    (     self       : List[Any]  )			->		int:
        # Initialize image_processing
        __A=  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        __A=  prepare_image_inputs(self.image_processor_tester ,						equal_resolution=lowerCAmelCase_ ,						numpify=lowerCAmelCase_  )
        for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase_ ,						np.ndarray  )
  
        # Test not batched input
        __A=  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.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
  
        # Test batched
        __A=  image_processing(lowerCAmelCase_ ,						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.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
     def 				lowerCAmelCase    (     self       : Dict  )			->		Optional[int]:
        # Initialize image_processing
        __A=  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        __A=  prepare_image_inputs(self.image_processor_tester ,						equal_resolution=lowerCAmelCase_ ,						torchify=lowerCAmelCase_  )
        for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase_ ,						torch.Tensor  )
  
        # Test not batched input
        __A=  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.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
  
        # Test batched
        __A=  image_processing(lowerCAmelCase_ ,						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.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
     def 				lowerCAmelCase    (     self       : str  )			->		str:
        # Initialize image_processing
        __A=  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        __A=  prepare_image_inputs(self.image_processor_tester ,						equal_resolution=lowerCAmelCase_ ,						torchify=lowerCAmelCase_  )
        __A=  []
        for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase_ ,						torch.Tensor  )
           maps.append(torch.zeros(image.shape[-2:]  ).long()  )
  
        # Test not batched input
        __A=  image_processing(image_inputs[0] ,						maps[0] ,						return_tensors='pt'  )
        self.assertEqual(
            encoding['pixel_values'].shape ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
        self.assertEqual(
            encoding['labels'].shape ,						(
                1,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
        self.assertEqual(encoding['labels'].dtype ,						torch.long  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 255  )
  
        # Test batched
        __A=  image_processing(lowerCAmelCase_ ,						lowerCAmelCase_ ,						return_tensors='pt'  )
        self.assertEqual(
            encoding['pixel_values'].shape ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
        self.assertEqual(
            encoding['labels'].shape ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
        self.assertEqual(encoding['labels'].dtype ,						torch.long  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 255  )
  
        # Test not batched input (PIL images)
        __A,  __A=  prepare_semantic_single_inputs()
  
        __A=  image_processing(lowerCAmelCase_ ,						lowerCAmelCase_ ,						return_tensors='pt'  )
        self.assertEqual(
            encoding['pixel_values'].shape ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
        self.assertEqual(
            encoding['labels'].shape ,						(
                1,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
        self.assertEqual(encoding['labels'].dtype ,						torch.long  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 255  )
  
        # Test batched input (PIL images)
        __A,  __A=  prepare_semantic_batch_inputs()
  
        __A=  image_processing(lowerCAmelCase_ ,						lowerCAmelCase_ ,						return_tensors='pt'  )
        self.assertEqual(
            encoding['pixel_values'].shape ,						(
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
        self.assertEqual(
            encoding['labels'].shape ,						(
                2,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            ) ,						)
        self.assertEqual(encoding['labels'].dtype ,						torch.long  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 255  )
  
     def 				lowerCAmelCase    (     self       : Dict  )			->		Optional[Any]:
        # Initialize image_processing
        __A=  self.image_processing_class(**self.image_processor_dict  )
  
        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        __A,  __A=  prepare_semantic_single_inputs()
        __A=  image_processing(lowerCAmelCase_ ,						lowerCAmelCase_ ,						return_tensors='pt'  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 150  )
  
        __A=  True
        __A=  image_processing(lowerCAmelCase_ ,						lowerCAmelCase_ ,						return_tensors='pt'  )
        self.assertTrue(encoding['labels'].min().item() >= 0  )
        self.assertTrue(encoding['labels'].max().item() <= 255  )
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''dandelin/vilt-b32-mlm''': '''https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Dict							= '''vilt'''
  			def __init__(     self       : str ,						lowerCAmelCase_       : Tuple=30_522 ,						lowerCAmelCase_       : List[Any]=2 ,						lowerCAmelCase_       : Optional[Any]=2 ,						lowerCAmelCase_       : Union[str, Any]=40 ,						lowerCAmelCase_       : str=768 ,						lowerCAmelCase_       : Tuple=12 ,						lowerCAmelCase_       : List[Any]=12 ,						lowerCAmelCase_       : int=3_072 ,						lowerCAmelCase_       : List[Any]="gelu" ,						lowerCAmelCase_       : int=0.0 ,						lowerCAmelCase_       : str=0.0 ,						lowerCAmelCase_       : Optional[Any]=0.02 ,						lowerCAmelCase_       : Union[str, Any]=1E-12 ,						lowerCAmelCase_       : Dict=384 ,						lowerCAmelCase_       : Optional[int]=32 ,						lowerCAmelCase_       : Optional[Any]=3 ,						lowerCAmelCase_       : str=True ,						lowerCAmelCase_       : List[Any]=-1 ,						lowerCAmelCase_       : Union[str, Any]=False ,						lowerCAmelCase_       : str=-1 ,						**lowerCAmelCase_       : Any ,						)			->		Tuple:
  						super().__init__(tie_word_embeddings=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  type_vocab_size
  						__A=  modality_type_vocab_size
  						__A=  max_position_embeddings
  
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  intermediate_size
  						__A=  hidden_act
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  initializer_range
  						__A=  layer_norm_eps
  
  						__A=  image_size
  						__A=  patch_size
  						__A=  num_channels
  						__A=  qkv_bias
  						__A=  max_image_length
  						__A=  num_images
  
  
  
  