code: 
  
  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     lowerCamelCase_			:
  
  
  
  
  	def __init__(    self	:    List[Any]				,			__A	:    List[Any]				,			__A	:    List[Any]=13				,			__A	:    Dict=7				,			__A	:    Tuple=True				,			__A	:    Optional[Any]=True				,			__A	:    List[Any]=True				,			__A	:    str=True				,			__A	:    int=99				,			__A	:    Any=32				,			__A	:    str=5				,			__A	:    List[str]=4				,			__A	:    Optional[Any]=37				,			__A	:    str="gelu"				,			__A	:    Optional[int]=0.1				,			__A	:    Any=0.1				,			__A	:    Union[str, Any]=512				,			__A	:    Optional[int]=16				,			__A	:    List[Any]=2				,			__A	:    Optional[Any]=0.0_2				,			__A	:    Optional[Any]=3				,			__A	:    Tuple=None				,			__A	:    Optional[Any]=20				,			__A	:    str=30				,			__A	:    Optional[int]=2				,			__A	:    Dict=2				,			__A	:    str=10				,			):
  		__A   : Dict								= parent
  		__A   : Dict								= batch_size
  		__A   : List[str]								= seq_length
  		__A   : List[str]								= is_training
  		__A   : Tuple								= use_input_mask
  		__A   : str								= use_token_type_ids
  		__A   : str								= use_labels
  		__A   : List[Any]								= vocab_size
  		__A   : int								= hidden_size
  		__A   : Optional[Any]								= num_hidden_layers
  		__A   : int								= num_attention_heads
  		__A   : Any								= intermediate_size
  		__A   : Optional[Any]								= hidden_act
  		__A   : int								= hidden_dropout_prob
  		__A   : int								= attention_probs_dropout_prob
  		__A   : Optional[Any]								= max_position_embeddings
  		__A   : Any								= type_vocab_size
  		__A   : List[Any]								= type_sequence_label_size
  		__A   : int								= initializer_range
  		__A   : Tuple								= num_labels
  		__A   : List[str]								= scope
  		__A   : List[Any]								= max_xpath_tag_unit_embeddings
  		__A   : Union[str, Any]								= max_xpath_subs_unit_embeddings
  		__A   : Dict								= tag_pad_id
  		__A   : int								= subs_pad_id
  		__A   : Optional[int]								= max_depth
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
  		__A   : Any								= ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size       )
  
  		__A   : Union[str, Any]								= ids_tensor(
  		    [self.batch_size, self.seq_length, self.max_depth]				,			self.max_xpath_tag_unit_embeddings       )
  
  		__A   : Tuple								= ids_tensor(
  		    [self.batch_size, self.seq_length, self.max_depth]				,			self.max_xpath_subs_unit_embeddings       )
  
  		__A   : List[Any]								= None
  		if self.use_input_mask:
  			__A   : str								= ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2       )
  
  		__A   : Dict								= None
  		if self.use_token_type_ids:
  			__A   : Any								= ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size       )
  
  		__A   : str								= None
  		__A   : str								= None
  		if self.use_labels:
  			__A   : Optional[int]								= ids_tensor([self.batch_size]				,			self.type_sequence_label_size       )
  			__A   : List[str]								= ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels       )
  
  		__A   : Union[str, Any]								= self.get_config()
  
  		return (
  		    config,
  		    input_ids,
  		    xpath_tags_seq,
  		    xpath_subs_seq,
  		    token_type_ids,
  		    input_mask,
  		    sequence_labels,
  		    token_labels,
  		)
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
  		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 					lowerCAmelCase_					(    self	:    Dict				,			__A	:    Union[str, Any]				,			__A	:    Any				,			__A	:    Optional[int]				,			__A	:    List[str]				,			__A	:    Tuple				,			__A	:    List[str]				,			__A	:    Optional[Any]				,			__A	:    Union[str, Any]				,			):
  		__A   : List[Any]								= MarkupLMModel(config=__A       )
  		model.to(__A       )
  		model.eval()
  		print("""Configs:"""				,			model.config.tag_pad_id				,			model.config.subs_pad_id       )
  		__A   : Dict								= model(__A				,			attention_mask=__A				,			token_type_ids=__A       )
  		__A   : Tuple								= model(__A				,			token_type_ids=__A       )
  		__A   : Dict								= 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 					lowerCAmelCase_					(    self	:    str				,			__A	:    str				,			__A	:    Optional[Any]				,			__A	:    Optional[Any]				,			__A	:    Union[str, Any]				,			__A	:    Union[str, Any]				,			__A	:    Tuple				,			__A	:    Union[str, Any]				,			__A	:    Optional[int]				,			):
  		__A   : List[Any]								= self.num_labels
  		__A   : List[Any]								= MarkupLMForSequenceClassification(__A       )
  		model.to(__A       )
  		model.eval()
  		__A   : 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 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    Union[str, Any]				,			__A	:    Optional[int]				,			__A	:    List[Any]				,			__A	:    List[Any]				,			__A	:    str				,			__A	:    List[str]				,			__A	:    List[str]				,			__A	:    Dict				,			):
  		__A   : int								= self.num_labels
  		__A   : Dict								= MarkupLMForTokenClassification(config=__A       )
  		model.to(__A       )
  		model.eval()
  		__A   : int								= 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 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    Any				,			__A	:    List[str]				,			__A	:    Optional[Any]				,			__A	:    Tuple				,			__A	:    Dict				,			__A	:    List[Any]				,			__A	:    List[Any]				,			__A	:    Union[str, Any]				,			):
  		__A   : str								= MarkupLMForQuestionAnswering(config=__A       )
  		model.to(__A       )
  		model.eval()
  		__A   : Optional[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 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		__A   : int								= self.prepare_config_and_inputs()
  		(
  		    (
  		    __A
  		)						,							(
  		    __A
  		)						,							(
  		    __A
  		)						,							(
  		    __A
  		)						,							(
  		    __A
  		)						,							(
  		    __A
  		)						,							(
  		    __A
  		)						,							(
  		    __A
  		)						,							
  		)   : Optional[Any]								= config_and_inputs
  		__A   : 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     lowerCamelCase_			(  _lowercase	,			_lowercase	,			unittest.TestCase  ):
  	_lowercase		:      List[str]    				=							(
  	    (
  	        MarkupLMModel,
  	        MarkupLMForSequenceClassification,
  	        MarkupLMForTokenClassification,
  	        MarkupLMForQuestionAnswering,
  	    )
  	    if is_torch_available()
  	    else None
  	)
  	_lowercase		:      Union[str, Any]    				=							(
  	    {
  	        '''feature-extraction''': MarkupLMModel,
  	        '''question-answering''': MarkupLMForQuestionAnswering,
  	        '''text-classification''': MarkupLMForSequenceClassification,
  	        '''token-classification''': MarkupLMForTokenClassification,
  	        '''zero-shot''': MarkupLMForSequenceClassification,
  	    }
  	    if is_torch_available()
  	    else {}
  	)
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    str				,			__A	:    Tuple				,			__A	:    List[Any]				,			__A	:    Tuple				,			__A	:    List[Any]       ):
  		# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
  		# (batch of pretokenized examples).
  		return True
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Tuple       ):
  		__A   : List[Any]								= MarkupLMModelTester(self       )
  		__A   : Any								= ConfigTester(self				,			config_class=__A				,			hidden_size=37       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Tuple       ):
  		self.config_tester.run_common_tests()
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]       ):
  		__A   : str								= self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_model(*__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    str       ):
  		__A   : Tuple								= self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_for_sequence_classification(*__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Tuple       ):
  		__A   : Tuple								= self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_for_token_classification(*__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any       ):
  		__A   : Optional[Any]								= self.model_tester.prepare_config_and_inputs()
  		self.model_tester.create_and_check_for_question_answering(*__A       )
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      )				->							int:
  	__A   : Tuple								= """
      <!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     lowerCamelCase_			(  unittest.TestCase  ):
  
  
  
  
  	@cached_property
  	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
  		# TODO use from_pretrained here
  		__A   : Tuple								= MarkupLMFeatureExtractor()
  		__A   : List[str]								= MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""       )
  
  		return MarkupLMProcessor(__A				,			__A       )
  
  
  
  
  	@slow
  	def 					lowerCAmelCase_					(    self	:    List[str]       ):
  		__A   : Dict								= MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""       ).to(__A       )
  
  		__A   : Any								= self.default_processor
  
  		__A   : Optional[Any]								= processor(prepare_html_string()				,			return_tensors="""pt"""       )
  		__A   : List[Any]								= inputs.to(__A       )
  
  		# forward pass
  		with torch.no_grad():
  			__A   : Optional[int]								= model(**__A       )
  
  		# verify the last hidden states
  		__A   : Optional[int]								= torch.Size([1, 14, 768]       )
  		self.assertEqual(outputs.last_hidden_state.shape				,			__A       )
  
  		__A   : 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       )       )
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  UpperCAmelCase_       :				Optional[Any]		   =      {
      '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
      '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
  }
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			UpperCAmelCase_       :				List[str]		   =      [
  			    '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
  			    '''ProphetNetDecoder''',
  			    '''ProphetNetEncoder''',
  			    '''ProphetNetForCausalLM''',
  			    '''ProphetNetForConditionalGeneration''',
  			    '''ProphetNetModel''',
  			    '''ProphetNetPreTrainedModel''',
  			]
  
  
  if TYPE_CHECKING:
  			from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
  			from .tokenization_prophetnet import ProphetNetTokenizer
  
  			try:
  						if not is_torch_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .modeling_prophetnet import (
  						    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    ProphetNetDecoder,
  						    ProphetNetEncoder,
  						    ProphetNetForCausalLM,
  						    ProphetNetForConditionalGeneration,
  						    ProphetNetModel,
  						    ProphetNetPreTrainedModel,
  						)
  
  else:
  			import sys
  
  			UpperCAmelCase_       :				Any		   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  