





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler



class 				_UpperCamelCase       (						unittest.TestCase  ):
       # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
       _UpperCamelCase					:       str       = (ScoreSdeVeScheduler,)
       _UpperCamelCase					:       Optional[Any]       = ()

       @property
       def lowercase			(		self:			Dict		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	3
              UpperCamelCase_ 							=	8
              UpperCamelCase_ 							=	8

              UpperCamelCase_ 							=	torch.rand((batch_size, num_channels, height, width)		)

              return sample

       @property
       def lowercase			(		self:			List[str]		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	3
              UpperCamelCase_ 							=	8
              UpperCamelCase_ 							=	8

              UpperCamelCase_ 							=	batch_size * num_channels * height * width
              UpperCamelCase_ 							=	torch.arange(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	sample.reshape(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	sample / num_elems
              UpperCamelCase_ 							=	sample.permute(3       , 0       , 1       , 2		)

              return sample

       def lowercase			(		self:			Dict		)							->			Dict:




              """simple docstring"""


              def model(_SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			int       , *_SCREAMING_SNAKE_CASE:			int		):
                     return sample * t / (t + 1)

              return model

       def lowercase			(		self:			int       , **_SCREAMING_SNAKE_CASE:			Dict		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	{
                  "num_train_timesteps": 2000,
                  "snr": 0.15,
                  "sigma_min": 0.01,
                  "sigma_max": 1348,
                  "sampling_eps": 1e-5,
              }

              config.update(**_SCREAMING_SNAKE_CASE		)
              return config

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			str=0       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	dict(self.forward_default_kwargs		)

              for scheduler_class in self.scheduler_classes:
                     UpperCamelCase_ 							=	self.dummy_sample
                     UpperCamelCase_ 							=	0.1 * sample

                     UpperCamelCase_ 							=	self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	scheduler_class(**_SCREAMING_SNAKE_CASE		)

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            scheduler.save_config(_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_ 							=	scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_ 							=	scheduler.step_pred(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample
                     UpperCamelCase_ 							=	new_scheduler.step_pred(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

                     UpperCamelCase_ 							=	scheduler.step_correct(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample
                     UpperCamelCase_ 							=	new_scheduler.step_correct(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Tuple=0       , **_SCREAMING_SNAKE_CASE:			Optional[Any]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	dict(self.forward_default_kwargs		)
              kwargs.update(_SCREAMING_SNAKE_CASE		)

              for scheduler_class in self.scheduler_classes:
                     UpperCamelCase_ 							=	self.dummy_sample
                     UpperCamelCase_ 							=	0.1 * sample

                     UpperCamelCase_ 							=	self.get_scheduler_config()
                     UpperCamelCase_ 							=	scheduler_class(**_SCREAMING_SNAKE_CASE		)

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            scheduler.save_config(_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_ 							=	scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_ 							=	scheduler.step_pred(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample
                     UpperCamelCase_ 							=	new_scheduler.step_pred(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

                     UpperCamelCase_ 							=	scheduler.step_correct(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample
                     UpperCamelCase_ 							=	new_scheduler.step_correct(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"

       def lowercase			(		self:			List[Any]		)							->			Dict:




              """simple docstring"""


              for timesteps in [10, 100, 1000]:
                     self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			List[str]		)							->			List[str]:




              """simple docstring"""


              for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]       , [1, 100, 1000]		):
                     self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE       , sigma_max=_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			str		)							->			str:




              """simple docstring"""


              for t in [0.1, 0.5, 0.75]:
                     self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Optional[int]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	dict(self.forward_default_kwargs		)

              UpperCamelCase_ 							=	self.scheduler_classes[0]
              UpperCamelCase_ 							=	self.get_scheduler_config()
              UpperCamelCase_ 							=	scheduler_class(**_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	3

              UpperCamelCase_ 							=	self.dummy_model()
              UpperCamelCase_ 							=	self.dummy_sample_deter

              scheduler.set_sigmas(_SCREAMING_SNAKE_CASE		)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	torch.manual_seed(0		)

              for i, t in enumerate(scheduler.timesteps		):
                     UpperCamelCase_ 							=	scheduler.sigmas[i]

                     for _ in range(scheduler.config.correct_steps		):
                            with torch.no_grad():
                                   UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_ 							=	scheduler.step_correct(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     with torch.no_grad():
                            UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_ 							=	scheduler.step_pred(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_							,			UpperCamelCase_ 							=	output.prev_sample, output.prev_sample_mean

              UpperCamelCase_ 							=	torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
              UpperCamelCase_ 							=	torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

              assert np.isclose(result_sum.item()       , 1_43_72_75_85_28.0		)
              assert np.isclose(result_mean.item()       , 18_71_45_30.0		)



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




              """simple docstring"""


              UpperCamelCase_ 							=	dict(self.forward_default_kwargs		)

              UpperCamelCase_ 							=	kwargs.pop("num_inference_steps"       , _SCREAMING_SNAKE_CASE		)

              for scheduler_class in self.scheduler_classes:
                     UpperCamelCase_ 							=	self.get_scheduler_config()
                     UpperCamelCase_ 							=	scheduler_class(**_SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_ 							=	self.dummy_sample
                     UpperCamelCase_ 							=	0.1 * sample

                     if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE       , "set_timesteps"		):
                            scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
                     elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE       , "set_timesteps"		):
                            UpperCamelCase_ 							=	num_inference_steps

                     UpperCamelCase_ 							=	scheduler.step_pred(_SCREAMING_SNAKE_CASE       , 0       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample
                     UpperCamelCase_ 							=	scheduler.step_pred(_SCREAMING_SNAKE_CASE       , 1       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     self.assertEqual(output_a.shape       , sample.shape		)
                     self.assertEqual(output_a.shape       , output_a.shape		)


"""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 					a					:



  def __init__(		self   :    str						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    Tuple=13						,  lowerCamelCase_   :    List[Any]=7						,  lowerCamelCase_   :    List[str]=True						,  lowerCamelCase_   :    str=True						,  lowerCamelCase_   :    Tuple=True						,  lowerCamelCase_   :    Optional[Any]=True						,  lowerCamelCase_   :    str=99						,  lowerCamelCase_   :    str=32						,  lowerCamelCase_   :    List[Any]=5						,  lowerCamelCase_   :    Dict=4						,  lowerCamelCase_   :    str=37						,  lowerCamelCase_   :    List[Any]="gelu"						,  lowerCamelCase_   :    List[str]=0.1						,  lowerCamelCase_   :    Optional[int]=0.1						,  lowerCamelCase_   :    List[str]=5_12						,  lowerCamelCase_   :    Union[str, Any]=16						,  lowerCamelCase_   :    Any=2						,  lowerCamelCase_   :    List[str]=0.02						,  lowerCamelCase_   :    List[Any]=3						,  lowerCamelCase_   :    Any=None						,  lowerCamelCase_   :    List[Any]=20						,  lowerCamelCase_   :    Tuple=30						,  lowerCamelCase_   :    Optional[Any]=2						,  lowerCamelCase_   :    List[Any]=2						,  lowerCamelCase_   :    Union[str, Any]=10						,  )      -> Any:
      __a			      =						parent
      __a			      =						batch_size
      __a			      =						seq_length
      __a			      =						is_training
      __a			      =						use_input_mask
      __a			      =						use_token_type_ids
      __a			      =						use_labels
      __a			      =						vocab_size
      __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			      =						max_position_embeddings
      __a			      =						type_vocab_size
      __a			      =						type_sequence_label_size
      __a			      =						initializer_range
      __a			      =						num_labels
      __a			      =						scope
      __a			      =						max_xpath_tag_unit_embeddings
      __a			      =						max_xpath_subs_unit_embeddings
      __a			      =						tag_pad_id
      __a			      =						subs_pad_id
      __a			      =						max_depth



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Dict:
      __a			      =						ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size     )

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

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

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

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

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

      __a			      =						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   :    List[Any]     )      -> Optional[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   :    Tuple						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    List[Any]						,  )      -> int:
      __a			      =						MarkupLMModel(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      print("""Configs:"""						,  model.config.tag_pad_id						,  model.config.subs_pad_id     )
      __a			      =						model(lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_     )
      __a			      =						model(lowerCamelCase_						,  token_type_ids=lowerCamelCase_     )
      __a			      =						model(lowerCamelCase_     )
      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   :    Optional[int]						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Tuple						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    int						,  )      -> Optional[Any]:
      __a			      =						self.num_labels
      __a			      =						MarkupLMForSequenceClassification(lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(
          lowerCamelCase_						,  xpath_tags_seq=lowerCamelCase_						,  xpath_subs_seq=lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_						,  )
      self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)     )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    Any						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    Union[str, Any]						,  )      -> int:
      __a			      =						self.num_labels
      __a			      =						MarkupLMForTokenClassification(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(
          lowerCamelCase_						,  xpath_tags_seq=lowerCamelCase_						,  xpath_subs_seq=lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  labels=lowerCamelCase_						,  )
      self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.num_labels)     )



  def        lowerCAmelCase_				(		self   :    List[Any]						,  lowerCamelCase_   :    Tuple						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    List[Any]						,  )      -> Tuple:
      __a			      =						MarkupLMForQuestionAnswering(config=lowerCamelCase_     )
      model.to(lowerCamelCase_     )
      model.eval()
      __a			      =						model(
          lowerCamelCase_						,  xpath_tags_seq=lowerCamelCase_						,  xpath_subs_seq=lowerCamelCase_						,  attention_mask=lowerCamelCase_						,  token_type_ids=lowerCamelCase_						,  start_positions=lowerCamelCase_						,  end_positions=lowerCamelCase_						,  )
      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   :    str     )      -> str:
      __a			      =						self.prepare_config_and_inputs()
      (
          (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  (
          __a
      )      ,  
      )			      =						config_and_inputs
      __a			      =						{
          """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					(				A_   ,		A_   ,		unittest.TestCase							):
  A_ :				int 			=							(
      (
          MarkupLMModel,
          MarkupLMForSequenceClassification,
          MarkupLMForTokenClassification,
          MarkupLMForQuestionAnswering,
      )
      if is_torch_available()
      else None
  )
  A_ :				Any 			=							(
      {
          '''feature-extraction''': MarkupLMModel,
          '''question-answering''': MarkupLMForQuestionAnswering,
          '''text-classification''': MarkupLMForSequenceClassification,
          '''token-classification''': MarkupLMForTokenClassification,
          '''zero-shot''': MarkupLMForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    List[str]						,  lowerCamelCase_   :    Union[str, Any]     )      -> 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     )      -> str:
      __a			      =						MarkupLMModelTester(self     )
      __a			      =						ConfigTester(self						,  config_class=lowerCamelCase_						,  hidden_size=37     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> List[str]:
      self.config_tester.run_common_tests()



  def        lowerCAmelCase_				(		self   :    str     )      -> Dict:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> Optional[int]:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Tuple     )      -> Tuple:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Union[str, Any]     )      -> int:
      __a			      =						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_     )

def 						UpperCamelCase				(	):
    __a			      =						"""
    <!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 					a					(				unittest.TestCase							):



  @cached_property
  def        lowerCAmelCase_				(		self   :    int     )      -> Any:
      # TODO use from_pretrained here
      __a			      =						MarkupLMFeatureExtractor()
      __a			      =						MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""     )

      return MarkupLMProcessor(lowerCamelCase_						,  lowerCamelCase_     )



  @slow
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Dict:
      __a			      =						MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""     ).to(lowerCamelCase_     )

      __a			      =						self.default_processor

      __a			      =						processor(prepare_html_string()						,  return_tensors="""pt"""     )
      __a			      =						inputs.to(lowerCamelCase_     )

      # forward pass
      with torch.no_grad():
          __a			      =						model(**lowerCamelCase_     )

      # verify the last hidden states
      __a			      =						torch.Size([1, 14, 7_68]     )
      self.assertEqual(outputs.last_hidden_state.shape						,  lowerCamelCase_     )

      __a			      =						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(lowerCamelCase_     )

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