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 CLIPImageProcessor
  
  
  class 		_SCREAMING_SNAKE_CASE    (	unittest.TestCase    ):
  
  
       def __init__(     self       ,				A_       ,				A_=7       ,				A_=3       ,				A_=18       ,				A_=30       ,				A_=4_00       ,				A_=True       ,				A_=None       ,				A_=True       ,				A_=None       ,				A_=True       ,				A_=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]       ,				A_=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]       ,				A_=True       ,				):
         _UpperCAmelCase		:       int       =    size if size is not None else {"""shortest_edge""": 20}
         _UpperCAmelCase		:       Dict       =    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
         _UpperCAmelCase		:       List[str]       =    parent
         _UpperCAmelCase		:       Optional[int]       =    batch_size
         _UpperCAmelCase		:       Tuple       =    num_channels
         _UpperCAmelCase		:       List[Any]       =    image_size
         _UpperCAmelCase		:       Union[str, Any]       =    min_resolution
         _UpperCAmelCase		:       Optional[Any]       =    max_resolution
         _UpperCAmelCase		:       List[Any]       =    do_resize
         _UpperCAmelCase		:       Any       =    size
         _UpperCAmelCase		:       Dict       =    do_center_crop
         _UpperCAmelCase		:       Any       =    crop_size
         _UpperCAmelCase		:       str       =    do_normalize
         _UpperCAmelCase		:       Any       =    image_mean
         _UpperCAmelCase		:       Optional[int]       =    image_std
         _UpperCAmelCase		:       Dict       =    do_convert_rgb
  
  
       def 					__snake_case(     self						):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_convert_rgb": self.do_convert_rgb,
         }
  
  
  
  
       def 					__snake_case(     self       ,				A_=False       ,				A_=False       ,				A_=False						):
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
         if equal_resolution:
           _UpperCAmelCase		:       Optional[int]       =    []
           for i in range(self.batch_size						):
             image_inputs.append(
                 np.random.randint(
                     2_55       ,				size=(self.num_channels, self.max_resolution, self.max_resolution)       ,				dtype=np.uinta						)						)
         else:
           _UpperCAmelCase		:       Union[str, Any]       =    []
           for i in range(self.batch_size						):
             _UpperCAmelCase,_UpperCAmelCase		:       str       =    np.random.choice(np.arange(self.min_resolution       ,				self.max_resolution						)       ,				2						)
             image_inputs.append(np.random.randint(2_55       ,				size=(self.num_channels, width, height)       ,				dtype=np.uinta						)						)
  
         if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           _UpperCAmelCase		:       int       =    [Image.fromarray(np.moveaxis(A_       ,				0       ,				-1						)						) for x in image_inputs]
  
         if torchify:
           _UpperCAmelCase		:       Union[str, Any]       =    [torch.from_numpy(A_						) for x in image_inputs]
  
         return image_inputs
  
  
  @require_torch
  @require_vision
  class 		_SCREAMING_SNAKE_CASE    (	A       ,  unittest.TestCase    ):
       __SCREAMING_SNAKE_CASE    				=						CLIPImageProcessor if is_vision_available() else None
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       str       =    CLIPImageProcessingTester(self						)
  
  
       @property
       def 					__snake_case(     self						):
         return self.image_processor_tester.prepare_image_processor_dict()
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       List[str]       =    self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(A_       ,				"""do_resize"""						)						)
         self.assertTrue(hasattr(A_       ,				"""size"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_center_crop"""						)						)
         self.assertTrue(hasattr(A_       ,				"""center_crop"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_normalize"""						)						)
         self.assertTrue(hasattr(A_       ,				"""image_mean"""						)						)
         self.assertTrue(hasattr(A_       ,				"""image_std"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_convert_rgb"""						)						)
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Tuple       =    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}						)
  
         _UpperCAmelCase		:       List[str]       =    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 					__snake_case(     self						):
         pass
  
  
       def 					__snake_case(     self						):
         # Initialize image_processing
         _UpperCAmelCase		:       str       =    self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         _UpperCAmelCase		:       List[str]       =    self.image_processor_tester.prepare_inputs(equal_resolution=A_						)
         for image in image_inputs:
           self.assertIsInstance(A_       ,				Image.Image						)
  
         # Test not batched input
         _UpperCAmelCase		:       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
         _UpperCAmelCase		:       Optional[int]       =    image_processing(A_       ,				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 					__snake_case(     self						):
         # Initialize image_processing
         _UpperCAmelCase		:       List[str]       =    self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         _UpperCAmelCase		:       Optional[int]       =    self.image_processor_tester.prepare_inputs(equal_resolution=A_       ,				numpify=A_						)
         for image in image_inputs:
           self.assertIsInstance(A_       ,				np.ndarray						)
  
         # Test not batched input
         _UpperCAmelCase		:       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
         _UpperCAmelCase		:       int       =    image_processing(A_       ,				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 					__snake_case(     self						):
         # Initialize image_processing
         _UpperCAmelCase		:       Union[str, Any]       =    self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         _UpperCAmelCase		:       Optional[Any]       =    self.image_processor_tester.prepare_inputs(equal_resolution=A_       ,				torchify=A_						)
         for image in image_inputs:
           self.assertIsInstance(A_       ,				torch.Tensor						)
  
         # Test not batched input
         _UpperCAmelCase		:       Optional[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
         _UpperCAmelCase		:       Dict       =    image_processing(A_       ,				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"""],
             )       ,				)
  
  
  @require_torch
  @require_vision
  class 		_SCREAMING_SNAKE_CASE    (	A       ,  unittest.TestCase    ):
       __SCREAMING_SNAKE_CASE    				=						CLIPImageProcessor if is_vision_available() else None
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       List[str]       =    CLIPImageProcessingTester(self       ,				num_channels=4						)
         _UpperCAmelCase		:       Optional[int]       =    3
  
  
       @property
       def 					__snake_case(     self						):
         return self.image_processor_tester.prepare_image_processor_dict()
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Any       =    self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(A_       ,				"""do_resize"""						)						)
         self.assertTrue(hasattr(A_       ,				"""size"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_center_crop"""						)						)
         self.assertTrue(hasattr(A_       ,				"""center_crop"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_normalize"""						)						)
         self.assertTrue(hasattr(A_       ,				"""image_mean"""						)						)
         self.assertTrue(hasattr(A_       ,				"""image_std"""						)						)
         self.assertTrue(hasattr(A_       ,				"""do_convert_rgb"""						)						)
  
  
       def 					__snake_case(     self						):
         pass
  
  
  
  
       def 					__snake_case(     self						):
         # Initialize image_processing
         _UpperCAmelCase		:       Union[str, Any]       =    self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         _UpperCAmelCase		:       str       =    self.image_processor_tester.prepare_inputs(equal_resolution=A_						)
         for image in image_inputs:
           self.assertIsInstance(A_       ,				Image.Image						)
  
         # Test not batched input
         _UpperCAmelCase		:       List[str]       =    image_processing(image_inputs[0]       ,				return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 1,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)
  
         # Test batched
         _UpperCAmelCase		:       Optional[int]       =    image_processing(A_       ,				return_tensors="""pt"""						).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )       ,				)
  
  
  
  
  
  
style_context: 
  
  
  import unittest
  
  from transformers import MarkupLMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import cached_property
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
   import torch
  
   from transformers import (
       MarkupLMForQuestionAnswering,
       MarkupLMForSequenceClassification,
       MarkupLMForTokenClassification,
       MarkupLMModel,
   )
  
  # TODO check dependencies
  from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer
  
  
  class 		_SCREAMING_SNAKE_CASE    :
  
  
       def __init__(     self       ,				A_       ,				A_=13       ,				A_=7       ,				A_=True       ,				A_=True       ,				A_=True       ,				A_=True       ,				A_=99       ,				A_=32       ,				A_=5       ,				A_=4       ,				A_=37       ,				A_="gelu"       ,				A_=0.1       ,				A_=0.1       ,				A_=5_12       ,				A_=16       ,				A_=2       ,				A_=0.0_2       ,				A_=3       ,				A_=None       ,				A_=20       ,				A_=30       ,				A_=2       ,				A_=2       ,				A_=10       ,				):
         _UpperCAmelCase		:       str       =    parent
         _UpperCAmelCase		:       Dict       =    batch_size
         _UpperCAmelCase		:       Optional[int]       =    seq_length
         _UpperCAmelCase		:       Union[str, Any]       =    is_training
         _UpperCAmelCase		:       Tuple       =    use_input_mask
         _UpperCAmelCase		:       Dict       =    use_token_type_ids
         _UpperCAmelCase		:       Dict       =    use_labels
         _UpperCAmelCase		:       Optional[int]       =    vocab_size
         _UpperCAmelCase		:       List[str]       =    hidden_size
         _UpperCAmelCase		:       Tuple       =    num_hidden_layers
         _UpperCAmelCase		:       Tuple       =    num_attention_heads
         _UpperCAmelCase		:       Optional[int]       =    intermediate_size
         _UpperCAmelCase		:       str       =    hidden_act
         _UpperCAmelCase		:       Optional[Any]       =    hidden_dropout_prob
         _UpperCAmelCase		:       List[str]       =    attention_probs_dropout_prob
         _UpperCAmelCase		:       Union[str, Any]       =    max_position_embeddings
         _UpperCAmelCase		:       List[Any]       =    type_vocab_size
         _UpperCAmelCase		:       Union[str, Any]       =    type_sequence_label_size
         _UpperCAmelCase		:       List[Any]       =    initializer_range
         _UpperCAmelCase		:       Union[str, Any]       =    num_labels
         _UpperCAmelCase		:       Any       =    scope
         _UpperCAmelCase		:       List[str]       =    max_xpath_tag_unit_embeddings
         _UpperCAmelCase		:       Tuple       =    max_xpath_subs_unit_embeddings
         _UpperCAmelCase		:       Optional[Any]       =    tag_pad_id
         _UpperCAmelCase		:       Union[str, Any]       =    subs_pad_id
         _UpperCAmelCase		:       Optional[int]       =    max_depth
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Dict       =    ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size						)
  
         _UpperCAmelCase		:       List[str]       =    ids_tensor(
             [self.batch_size, self.seq_length, self.max_depth]       ,				self.max_xpath_tag_unit_embeddings						)
  
         _UpperCAmelCase		:       Tuple       =    ids_tensor(
             [self.batch_size, self.seq_length, self.max_depth]       ,				self.max_xpath_subs_unit_embeddings						)
  
         _UpperCAmelCase		:       int       =    None
         if self.use_input_mask:
           _UpperCAmelCase		:       Any       =    ids_tensor([self.batch_size, self.seq_length]       ,				vocab_size=2						)
  
         _UpperCAmelCase		:       Tuple       =    None
         if self.use_token_type_ids:
           _UpperCAmelCase		:       Dict       =    ids_tensor([self.batch_size, self.seq_length]       ,				self.type_vocab_size						)
  
         _UpperCAmelCase		:       str       =    None
         _UpperCAmelCase		:       Union[str, Any]       =    None
         if self.use_labels:
           _UpperCAmelCase		:       int       =    ids_tensor([self.batch_size]       ,				self.type_sequence_label_size						)
           _UpperCAmelCase		:       int       =    ids_tensor([self.batch_size, self.seq_length]       ,				self.num_labels						)
  
         _UpperCAmelCase		:       List[Any]       =    self.get_config()
  
         return (
             config,
             input_ids,
             xpath_tags_seq,
             xpath_subs_seq,
             token_type_ids,
             input_mask,
             sequence_labels,
             token_labels,
         )
  
  
       def 					__snake_case(     self						):
         return MarkupLMConfig(
             vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				hidden_act=self.hidden_act       ,				hidden_dropout_prob=self.hidden_dropout_prob       ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,				max_position_embeddings=self.max_position_embeddings       ,				type_vocab_size=self.type_vocab_size       ,				initializer_range=self.initializer_range       ,				max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings       ,				max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings       ,				tag_pad_id=self.tag_pad_id       ,				subs_pad_id=self.subs_pad_id       ,				max_depth=self.max_depth       ,				)
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				):
         _UpperCAmelCase		:       Tuple       =    MarkupLMModel(config=A_						)
         model.to(A_						)
         model.eval()
         print("""Configs:"""       ,				model.config.tag_pad_id       ,				model.config.subs_pad_id						)
         _UpperCAmelCase		:       List[Any]       =    model(A_       ,				attention_mask=A_       ,				token_type_ids=A_						)
         _UpperCAmelCase		:       Tuple       =    model(A_       ,				token_type_ids=A_						)
         _UpperCAmelCase		:       Union[str, Any]       =    model(A_						)
         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)						)
         self.parent.assertEqual(result.pooler_output.shape       ,				(self.batch_size, self.hidden_size)						)
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				):
         _UpperCAmelCase		:       Optional[int]       =    self.num_labels
         _UpperCAmelCase		:       List[Any]       =    MarkupLMForSequenceClassification(A_						)
         model.to(A_						)
         model.eval()
         _UpperCAmelCase		:       Any       =    model(
             A_       ,				xpath_tags_seq=A_       ,				xpath_subs_seq=A_       ,				attention_mask=A_       ,				token_type_ids=A_       ,				labels=A_       ,				)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)						)
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				):
         _UpperCAmelCase		:       List[Any]       =    self.num_labels
         _UpperCAmelCase		:       Any       =    MarkupLMForTokenClassification(config=A_						)
         model.to(A_						)
         model.eval()
         _UpperCAmelCase		:       Tuple       =    model(
             A_       ,				xpath_tags_seq=A_       ,				xpath_subs_seq=A_       ,				attention_mask=A_       ,				token_type_ids=A_       ,				labels=A_       ,				)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length, self.num_labels)						)
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				):
         _UpperCAmelCase		:       List[Any]       =    MarkupLMForQuestionAnswering(config=A_						)
         model.to(A_						)
         model.eval()
         _UpperCAmelCase		:       List[Any]       =    model(
             A_       ,				xpath_tags_seq=A_       ,				xpath_subs_seq=A_       ,				attention_mask=A_       ,				token_type_ids=A_       ,				start_positions=A_       ,				end_positions=A_       ,				)
         self.parent.assertEqual(result.start_logits.shape       ,				(self.batch_size, self.seq_length)						)
         self.parent.assertEqual(result.end_logits.shape       ,				(self.batch_size, self.seq_length)						)
  
  
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Union[str, Any]       =    self.prepare_config_and_inputs()
         (
             (
             _UpperCAmelCase
         ),(
             _UpperCAmelCase
         ),(
             _UpperCAmelCase
         ),(
             _UpperCAmelCase
         ),(
             _UpperCAmelCase
         ),(
             _UpperCAmelCase
         ),(
             _UpperCAmelCase
         ),(
             _UpperCAmelCase
         ),
         )		:       List[str]       =    config_and_inputs
         _UpperCAmelCase		:       Optional[Any]       =    {
             """input_ids""": input_ids,
             """xpath_tags_seq""": xpath_tags_seq,
             """xpath_subs_seq""": xpath_subs_seq,
             """token_type_ids""": token_type_ids,
             """attention_mask""": input_mask,
         }
         return config, inputs_dict
  
  
  @require_torch
  class 		_SCREAMING_SNAKE_CASE    (	A       ,  A       ,  unittest.TestCase    ):
       __SCREAMING_SNAKE_CASE    				=						(
           (
               MarkupLMModel,
               MarkupLMForSequenceClassification,
               MarkupLMForTokenClassification,
               MarkupLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       __SCREAMING_SNAKE_CASE    				=						(
           {
               '''feature-extraction''': MarkupLMModel,
               '''question-answering''': MarkupLMForQuestionAnswering,
               '''text-classification''': MarkupLMForSequenceClassification,
               '''token-classification''': MarkupLMForTokenClassification,
               '''zero-shot''': MarkupLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_						):
         # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
         # (batch of pretokenized examples).
         return True
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Union[str, Any]       =    MarkupLMModelTester(self						)
         _UpperCAmelCase		:       str       =    ConfigTester(self       ,				config_class=A_       ,				hidden_size=37						)
  
  
       def 					__snake_case(     self						):
         self.config_tester.run_common_tests()
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       List[Any]       =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_						)
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Optional[int]       =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A_						)
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Union[str, Any]       =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A_						)
  
  
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Any       =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A_						)
  
  
  
  
  def       a__						(		):
    _UpperCAmelCase		:       Any       =    """
      <!DOCTYPE html>
      <html>
      <head>
      <title>Page Title</title>
      </head>
      <body>
  
      <h1>This is a Heading</h1>
      <p>This is a paragraph.</p>
  
      </body>
      </html>
      """
  
    return html_string
  
  
  
  
  
  @require_torch
  class 		_SCREAMING_SNAKE_CASE    (	unittest.TestCase    ):
  
  
       @cached_property
       def 					__snake_case(     self						):
         # TODO use from_pretrained here
         _UpperCAmelCase		:       int       =    MarkupLMFeatureExtractor()
         _UpperCAmelCase		:       int       =    MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""						)
  
         return MarkupLMProcessor(A_       ,				A_						)
  
  
  
  
       @slow
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       List[Any]       =    MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""						).to(A_						)
  
         _UpperCAmelCase		:       Optional[Any]       =    self.default_processor
  
         _UpperCAmelCase		:       str       =    processor(prepare_html_string()       ,				return_tensors="""pt"""						)
         _UpperCAmelCase		:       List[str]       =    inputs.to(A_						)
  
         # forward pass
         with torch.no_grad():
           _UpperCAmelCase		:       Optional[Any]       =    model(**A_						)
  
         # verify the last hidden states
         _UpperCAmelCase		:       Tuple       =    torch.Size([1, 14, 7_68]						)
         self.assertEqual(outputs.last_hidden_state.shape       ,				A_						)
  
         _UpperCAmelCase		:       List[Any]       =    torch.tensor(
             [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]]						).to(A_						)
  
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]       ,				A_       ,				atol=1e-4						)						)
  
  
  
  
  
  