






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 	a_:



     """simple docstring"""



     def __init__(		self					:	str  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	Union[str, Any]=1_3  ,  lowerCAmelCase__					:	List[Any]=7  ,  lowerCAmelCase__					:	int=True  ,  lowerCAmelCase__					:	Union[str, Any]=True  ,  lowerCAmelCase__					:	List[Any]=True  ,  lowerCAmelCase__					:	Dict=True  ,  lowerCAmelCase__					:	int=9_9  ,  lowerCAmelCase__					:	Union[str, Any]=3_2  ,  lowerCAmelCase__					:	int=5  ,  lowerCAmelCase__					:	List[str]=4  ,  lowerCAmelCase__					:	Optional[int]=3_7  ,  lowerCAmelCase__					:	List[str]="gelu"  ,  lowerCAmelCase__					:	Any=0.1  ,  lowerCAmelCase__					:	int=0.1  ,  lowerCAmelCase__					:	List[str]=5_1_2  ,  lowerCAmelCase__					:	Optional[Any]=1_6  ,  lowerCAmelCase__					:	Optional[Any]=2  ,  lowerCAmelCase__					:	List[str]=0.02  ,  lowerCAmelCase__					:	Any=3  ,  lowerCAmelCase__					:	Tuple=None  ,  lowerCAmelCase__					:	str=2_0  ,  lowerCAmelCase__					:	List[Any]=3_0  ,  lowerCAmelCase__					:	Dict=2  ,  lowerCAmelCase__					:	Union[str, Any]=2  ,  lowerCAmelCase__					:	int=1_0  ,  )							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		parent
      SCREAMING_SNAKE_CASE				    =		batch_size
      SCREAMING_SNAKE_CASE				    =		seq_length
      SCREAMING_SNAKE_CASE				    =		is_training
      SCREAMING_SNAKE_CASE				    =		use_input_mask
      SCREAMING_SNAKE_CASE				    =		use_token_type_ids
      SCREAMING_SNAKE_CASE				    =		use_labels
      SCREAMING_SNAKE_CASE				    =		vocab_size
      SCREAMING_SNAKE_CASE				    =		hidden_size
      SCREAMING_SNAKE_CASE				    =		num_hidden_layers
      SCREAMING_SNAKE_CASE				    =		num_attention_heads
      SCREAMING_SNAKE_CASE				    =		intermediate_size
      SCREAMING_SNAKE_CASE				    =		hidden_act
      SCREAMING_SNAKE_CASE				    =		hidden_dropout_prob
      SCREAMING_SNAKE_CASE				    =		attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE				    =		max_position_embeddings
      SCREAMING_SNAKE_CASE				    =		type_vocab_size
      SCREAMING_SNAKE_CASE				    =		type_sequence_label_size
      SCREAMING_SNAKE_CASE				    =		initializer_range
      SCREAMING_SNAKE_CASE				    =		num_labels
      SCREAMING_SNAKE_CASE				    =		scope
      SCREAMING_SNAKE_CASE				    =		max_xpath_tag_unit_embeddings
      SCREAMING_SNAKE_CASE				    =		max_xpath_subs_unit_embeddings
      SCREAMING_SNAKE_CASE				    =		tag_pad_id
      SCREAMING_SNAKE_CASE				    =		subs_pad_id
      SCREAMING_SNAKE_CASE				    =		max_depth





     def     __UpperCamelCase						(		self					:	Tuple)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size)

      SCREAMING_SNAKE_CASE				    =		ids_tensor(
          [self.batch_size, self.seq_length, self.max_depth]  ,  self.max_xpath_tag_unit_embeddings)

      SCREAMING_SNAKE_CASE				    =		ids_tensor(
          [self.batch_size, self.seq_length, self.max_depth]  ,  self.max_xpath_subs_unit_embeddings)

      SCREAMING_SNAKE_CASE				    =		None
      if self.use_input_mask:
       SCREAMING_SNAKE_CASE				    =		ids_tensor([self.batch_size, self.seq_length]  ,  vocab_size=2)

      SCREAMING_SNAKE_CASE				    =		None
      if self.use_token_type_ids:
       SCREAMING_SNAKE_CASE				    =		ids_tensor([self.batch_size, self.seq_length]  ,  self.type_vocab_size)

      SCREAMING_SNAKE_CASE				    =		None
      SCREAMING_SNAKE_CASE				    =		None
      if self.use_labels:
       SCREAMING_SNAKE_CASE				    =		ids_tensor([self.batch_size]  ,  self.type_sequence_label_size)
       SCREAMING_SNAKE_CASE				    =		ids_tensor([self.batch_size, self.seq_length]  ,  self.num_labels)

      SCREAMING_SNAKE_CASE				    =		self.get_config()

      return (
          config,
          input_ids,
          xpath_tags_seq,
          xpath_subs_seq,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
      )





     def     __UpperCamelCase						(		self					:	List[Any])							->	int:

      """simple docstring"""
      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     __UpperCamelCase						(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	Optional[int]  ,  lowerCAmelCase__					:	Optional[Any]  ,  )							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		MarkupLMModel(config=_A)
      model.to(_A)
      model.eval()
      print('Configs:'  ,  model.config.tag_pad_id  ,  model.config.subs_pad_id)
      SCREAMING_SNAKE_CASE				    =		model(_A  ,  attention_mask=_A  ,  token_type_ids=_A)
      SCREAMING_SNAKE_CASE				    =		model(_A  ,  token_type_ids=_A)
      SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	Optional[int]  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	List[str]  ,  )							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.num_labels
      SCREAMING_SNAKE_CASE				    =		MarkupLMForSequenceClassification(_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	Union[str, Any]  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	Union[str, Any]  ,  )							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.num_labels
      SCREAMING_SNAKE_CASE				    =		MarkupLMForTokenClassification(config=_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	int  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	Tuple  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	Union[str, Any]  ,  lowerCAmelCase__					:	List[Any]  ,  )							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		MarkupLMForQuestionAnswering(config=_A)
      model.to(_A)
      model.eval()
      SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	Dict)							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.prepare_config_and_inputs()
      (
          (
          SCREAMING_SNAKE_CASE   
      )    ,   (
          SCREAMING_SNAKE_CASE   
      )    ,   (
          SCREAMING_SNAKE_CASE   
      )    ,   (
          SCREAMING_SNAKE_CASE   
      )    ,   (
          SCREAMING_SNAKE_CASE   
      )    ,   (
          SCREAMING_SNAKE_CASE   
      )    ,   (
          SCREAMING_SNAKE_CASE   
      )    ,   (
          SCREAMING_SNAKE_CASE   
      )    ,      
      )				    =		config_and_inputs
      SCREAMING_SNAKE_CASE				    =		{
          '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 	a_(		lowercase__			,							lowercase__			,							unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				List[Any]  					=(
         (
             MarkupLMModel,
             MarkupLMForSequenceClassification,
             MarkupLMForTokenClassification,
             MarkupLMForQuestionAnswering,
         )
         if is_torch_available()
         else None
     )
     __snake_case		:				Tuple  					=(
         {
             'feature-extraction': MarkupLMModel,
             'question-answering': MarkupLMForQuestionAnswering,
             'text-classification': MarkupLMForSequenceClassification,
             'token-classification': MarkupLMForTokenClassification,
             'zero-shot': MarkupLMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )





     def     __UpperCamelCase						(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	Optional[int])							->	Tuple:

      """simple docstring"""
      return True





     def     __UpperCamelCase						(		self					:	List[str])							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		MarkupLMModelTester(self)
      SCREAMING_SNAKE_CASE				    =		ConfigTester(self  ,  config_class=_A  ,  hidden_size=3_7)





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	int:

      """simple docstring"""
      self.config_tester.run_common_tests()





     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_A)





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_A)





     def     __UpperCamelCase						(		self					:	Tuple)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_A)





     def     __UpperCamelCase						(		self					:	List[Any])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_A)





def 						A_					(						)				->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		'\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    '

 return html_string
@require_torch
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     @cached_property
     def     __UpperCamelCase						(		self					:	Optional[int])							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		MarkupLMFeatureExtractor()
      SCREAMING_SNAKE_CASE				    =		MarkupLMTokenizer.from_pretrained('microsoft/markuplm-base')

      return MarkupLMProcessor(_A  ,  _A)





     @slow
     def     __UpperCamelCase						(		self					:	Any)							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		MarkupLMModel.from_pretrained('microsoft/markuplm-base').to(_A)

      SCREAMING_SNAKE_CASE				    =		self.default_processor

      SCREAMING_SNAKE_CASE				    =		processor(prepare_html_string()  ,  return_tensors='pt')
      SCREAMING_SNAKE_CASE				    =		inputs.to(_A)

      # forward pass
      with torch.no_grad():
       SCREAMING_SNAKE_CASE				    =		model(**_A)

      # verify the last hidden states
      SCREAMING_SNAKE_CASE				    =		torch.Size([1, 1_4, 7_6_8])
      self.assertEqual(outputs.last_hidden_state.shape  ,  _A)

      SCREAMING_SNAKE_CASE				    =		torch.tensor(
          [[0.06_75, -0.00_52, 0.50_01], [-0.22_81, 0.08_02, 0.21_92], [-0.05_83, -0.33_11, 0.11_85]]).to(_A)

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,  _A  ,  atol=1e-4))






'''simple docstring'''





import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

a__		: Tuple  							=							[(1,)]





def 							snake_case				(		)->      Union[str, Any]:







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"





def 							snake_case				(		)->      Tuple:







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase):






 @slow
 @require_torch_gpu
 def 						lowercase_    (      self	:Dict      )					->      List[Any]:







       '''simple docstring'''



       check_slow_torch_cuda()






 @require_torch_gpu
 @slow
 def 						lowercase_    (      self	:int      )					->      int:







       '''simple docstring'''



       check_slow_torch_cuda()






 @parameterized.expand(_A      )
 @slow
 def 						lowercase_    (      self	:Dict						,					_A	:Union[str, Any]=None      )					->      Dict:







       '''simple docstring'''



       check_slow()





@slow
@require_torch_gpu
def 							snake_case				(		)->      Optional[int]:







      """simple docstring"""
      check_slow_torch_cuda()





@require_torch_gpu
@slow
def 							snake_case				(		)->      List[str]:







      """simple docstring"""
      check_slow_torch_cuda()





@slow
@pytest.mark.parametrize('param'   ,	[1]						)
def 							snake_case				(		UpperCAmelCase						)->      int:







      """simple docstring"""
      check_slow()





@pytest.mark.parametrize('param'   ,	[1]						)
@slow
def 							snake_case				(		UpperCAmelCase						)->      Any:







      """simple docstring"""
      check_slow()






