code: 
  '''simple docstring'''
  
  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 			UpperCAmelCase				:
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					_snake_case  ,					_snake_case=13  ,					_snake_case=7  ,					_snake_case=True  ,					_snake_case=True  ,					_snake_case=True  ,					_snake_case=True  ,					_snake_case=99  ,					_snake_case=32  ,					_snake_case=5  ,					_snake_case=4  ,					_snake_case=37  ,					_snake_case="gelu"  ,					_snake_case=0.1  ,					_snake_case=0.1  ,					_snake_case=512  ,					_snake_case=16  ,					_snake_case=2  ,					_snake_case=0.02  ,					_snake_case=3  ,					_snake_case=None  ,					_snake_case=20  ,					_snake_case=30  ,					_snake_case=2  ,					_snake_case=2  ,					_snake_case=10  ,					)				->    str:
  				_UpperCamelCase       : Optional[int]    =      parent
  				_UpperCamelCase       : Union[str, Any]    =      batch_size
  				_UpperCamelCase       : Union[str, Any]    =      seq_length
  				_UpperCamelCase       : Any    =      is_training
  				_UpperCamelCase       : Dict    =      use_input_mask
  				_UpperCamelCase       : Any    =      use_token_type_ids
  				_UpperCamelCase       : List[str]    =      use_labels
  				_UpperCamelCase       : Optional[Any]    =      vocab_size
  				_UpperCamelCase       : Optional[Any]    =      hidden_size
  				_UpperCamelCase       : Tuple    =      num_hidden_layers
  				_UpperCamelCase       : Any    =      num_attention_heads
  				_UpperCamelCase       : int    =      intermediate_size
  				_UpperCamelCase       : List[Any]    =      hidden_act
  				_UpperCamelCase       : Optional[Any]    =      hidden_dropout_prob
  				_UpperCamelCase       : List[str]    =      attention_probs_dropout_prob
  				_UpperCamelCase       : Dict    =      max_position_embeddings
  				_UpperCamelCase       : List[Any]    =      type_vocab_size
  				_UpperCamelCase       : List[str]    =      type_sequence_label_size
  				_UpperCamelCase       : Optional[Any]    =      initializer_range
  				_UpperCamelCase       : Any    =      num_labels
  				_UpperCamelCase       : List[Any]    =      scope
  				_UpperCamelCase       : int    =      max_xpath_tag_unit_embeddings
  				_UpperCamelCase       : Dict    =      max_xpath_subs_unit_embeddings
  				_UpperCamelCase       : List[Any]    =      tag_pad_id
  				_UpperCamelCase       : Any    =      subs_pad_id
  				_UpperCamelCase       : List[Any]    =      max_depth
  		def _lowercase  (    self     )				->    Union[str, Any]:
  				_UpperCamelCase       : Union[str, Any]    =      ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size     )
  
  				_UpperCamelCase       : Optional[int]    =      ids_tensor(
  				    [self.batch_size, self.seq_length, self.max_depth]  ,					self.max_xpath_tag_unit_embeddings     )
  
  				_UpperCamelCase       : str    =      ids_tensor(
  				    [self.batch_size, self.seq_length, self.max_depth]  ,					self.max_xpath_subs_unit_embeddings     )
  
  				_UpperCamelCase       : str    =      None
  				if self.use_input_mask:
  						_UpperCamelCase       : Dict    =      ids_tensor([self.batch_size, self.seq_length]  ,					vocab_size=2     )
  
  				_UpperCamelCase       : Optional[Any]    =      None
  				if self.use_token_type_ids:
  						_UpperCamelCase       : str    =      ids_tensor([self.batch_size, self.seq_length]  ,					self.type_vocab_size     )
  
  				_UpperCamelCase       : str    =      None
  				_UpperCamelCase       : List[str]    =      None
  				if self.use_labels:
  						_UpperCamelCase       : List[Any]    =      ids_tensor([self.batch_size]  ,					self.type_sequence_label_size     )
  						_UpperCamelCase       : Union[str, Any]    =      ids_tensor([self.batch_size, self.seq_length]  ,					self.num_labels     )
  
  				_UpperCamelCase       : Tuple    =      self.get_config()
  
  				return (
  				    config,
  				    input_ids,
  				    xpath_tags_seq,
  				    xpath_subs_seq,
  				    token_type_ids,
  				    input_mask,
  				    sequence_labels,
  				    token_labels,
  				)
  		def _lowercase  (    self     )				->    Dict:
  				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 _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					)				->    List[str]:
  				_UpperCamelCase       : List[str]    =      MarkupLMModel(config=_snake_case     )
  				model.to(_snake_case     )
  				model.eval()
  				print('''Configs:'''  ,					model.config.tag_pad_id  ,					model.config.subs_pad_id     )
  				_UpperCamelCase       : Optional[int]    =      model(_snake_case  ,					attention_mask=_snake_case  ,					token_type_ids=_snake_case     )
  				_UpperCamelCase       : str    =      model(_snake_case  ,					token_type_ids=_snake_case     )
  				_UpperCamelCase       : Optional[Any]    =      model(_snake_case     )
  				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 _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					)				->    int:
  				_UpperCamelCase       : Optional[Any]    =      self.num_labels
  				_UpperCamelCase       : str    =      MarkupLMForSequenceClassification(_snake_case     )
  				model.to(_snake_case     )
  				model.eval()
  				_UpperCamelCase       : Tuple    =      model(
  				    _snake_case  ,					xpath_tags_seq=_snake_case  ,					xpath_subs_seq=_snake_case  ,					attention_mask=_snake_case  ,					token_type_ids=_snake_case  ,					labels=_snake_case  ,					)
  				self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)     )
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					)				->    str:
  				_UpperCamelCase       : Tuple    =      self.num_labels
  				_UpperCamelCase       : Dict    =      MarkupLMForTokenClassification(config=_snake_case     )
  				model.to(_snake_case     )
  				model.eval()
  				_UpperCamelCase       : List[str]    =      model(
  				    _snake_case  ,					xpath_tags_seq=_snake_case  ,					xpath_subs_seq=_snake_case  ,					attention_mask=_snake_case  ,					token_type_ids=_snake_case  ,					labels=_snake_case  ,					)
  				self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.num_labels)     )
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					)				->    Any:
  				_UpperCamelCase       : Tuple    =      MarkupLMForQuestionAnswering(config=_snake_case     )
  				model.to(_snake_case     )
  				model.eval()
  				_UpperCamelCase       : List[str]    =      model(
  				    _snake_case  ,					xpath_tags_seq=_snake_case  ,					xpath_subs_seq=_snake_case  ,					attention_mask=_snake_case  ,					token_type_ids=_snake_case  ,					start_positions=_snake_case  ,					end_positions=_snake_case  ,					)
  				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 _lowercase  (    self     )				->    Union[str, Any]:
  				_UpperCamelCase       : Union[str, Any]    =      self.prepare_config_and_inputs()
  				(
  				    (
  				    _UpperCamelCase       
  				),							(
  				    _UpperCamelCase       
  				),							(
  				    _UpperCamelCase       
  				),							(
  				    _UpperCamelCase       
  				),							(
  				    _UpperCamelCase       
  				),							(
  				    _UpperCamelCase       
  				),							(
  				    _UpperCamelCase       
  				),							(
  				    _UpperCamelCase       
  				),							       
  				)       : Union[str, Any]    =      config_and_inputs
  				_UpperCamelCase       : Tuple    =      {
  				    '''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 			UpperCAmelCase				(					_UpperCAmelCase  ,		_UpperCAmelCase  ,		unittest.TestCase ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   Optional[int]          =    (
  		    (
  		        MarkupLMModel,
  		        MarkupLMForSequenceClassification,
  		        MarkupLMForTokenClassification,
  		        MarkupLMForQuestionAnswering,
  		    )
  		    if is_torch_available()
  		    else None
  		)
  		A__  :   Union[str, Any]          =    (
  		    {
  		        'feature-extraction': MarkupLMModel,
  		        'question-answering': MarkupLMForQuestionAnswering,
  		        'text-classification': MarkupLMForSequenceClassification,
  		        'token-classification': MarkupLMForTokenClassification,
  		        'zero-shot': MarkupLMForSequenceClassification,
  		    }
  		    if is_torch_available()
  		    else {}
  		)
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Dict:
  				return True
  		def _lowercase  (    self     )				->    Optional[Any]:
  				_UpperCamelCase       : Tuple    =      MarkupLMModelTester(self     )
  				_UpperCamelCase       : Union[str, Any]    =      ConfigTester(self  ,					config_class=_snake_case  ,					hidden_size=37     )
  		def _lowercase  (    self     )				->    Any:
  				self.config_tester.run_common_tests()
  		def _lowercase  (    self     )				->    Dict:
  				_UpperCamelCase       : int    =      self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*_snake_case     )
  		def _lowercase  (    self     )				->    Optional[int]:
  				_UpperCamelCase       : Tuple    =      self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_sequence_classification(*_snake_case     )
  		def _lowercase  (    self     )				->    List[str]:
  				_UpperCamelCase       : int    =      self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_token_classification(*_snake_case     )
  
  
  
  
  		def _lowercase  (    self     )				->    Dict:
  				_UpperCamelCase       : List[Any]    =      self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_question_answering(*_snake_case     )
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      int:
  		_UpperCamelCase       : Dict    =      '''
      <!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 			UpperCAmelCase				(					unittest.TestCase ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		@cached_property
  		def _lowercase  (    self     )				->    Optional[int]:
  				_UpperCamelCase       : Any    =      MarkupLMFeatureExtractor()
  				_UpperCamelCase       : Tuple    =      MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''     )
  
  				return MarkupLMProcessor(_snake_case  ,					_snake_case     )
  
  
  
  
  		@slow
  		def _lowercase  (    self     )				->    Optional[Any]:
  				_UpperCamelCase       : Any    =      MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''     ).to(_snake_case     )
  
  				_UpperCamelCase       : Tuple    =      self.default_processor
  
  				_UpperCamelCase       : Optional[int]    =      processor(prepare_html_string()  ,					return_tensors='''pt'''     )
  				_UpperCamelCase       : Optional[Any]    =      inputs.to(_snake_case     )
  
  				# forward pass
  				with torch.no_grad():
  						_UpperCamelCase       : Optional[int]    =      model(**_snake_case     )
  
  				# verify the last hidden states
  				_UpperCamelCase       : Optional[Any]    =      torch.Size([1, 14, 768]     )
  				self.assertEqual(outputs.last_hidden_state.shape  ,					_snake_case     )
  
  				_UpperCamelCase       : Optional[Any]    =      torch.tensor(
  				    [[0.0_675, -0.0_052, 0.5_001], [-0.2_281, 0.0_802, 0.2_192], [-0.0_583, -0.3_311, 0.1_185]]     ).to(_snake_case     )
  
  				self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,					_snake_case  ,					atol=1E-4     )     )
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  import warnings
  
  from ...utils import logging
  from .image_processing_mobilenet_va import MobileNetVaImageProcessor
  
  
  _UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					*_snake_case  ,					**_snake_case     )				->    None:
  				warnings.warn(
  				    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
  				    ''' Please use MobileNetV2ImageProcessor instead.'''  ,					_snake_case  ,					)
  				super().__init__(*_snake_case  ,					**_snake_case     )
  
  
  
  
  
  