






"""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       ,  ):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       parent
												lowercase__				:      int=       batch_size
												lowercase__				:      Optional[int]=       seq_length
												lowercase__				:      Tuple=       is_training
												lowercase__				:      List[Any]=       use_input_mask
												lowercase__				:      List[Any]=       use_token_type_ids
												lowercase__				:      Optional[int]=       use_labels
												lowercase__				:      str=       vocab_size
												lowercase__				:      Optional[int]=       hidden_size
												lowercase__				:      Any=       num_hidden_layers
												lowercase__				:      Tuple=       num_attention_heads
												lowercase__				:      int=       intermediate_size
												lowercase__				:      List[str]=       hidden_act
												lowercase__				:      Dict=       hidden_dropout_prob
												lowercase__				:      int=       attention_probs_dropout_prob
												lowercase__				:      str=       max_position_embeddings
												lowercase__				:      Optional[Any]=       type_vocab_size
												lowercase__				:      Union[str, Any]=       type_sequence_label_size
												lowercase__				:      Dict=       initializer_range
												lowercase__				:      Any=       num_labels
												lowercase__				:      Optional[Any]=       scope
												lowercase__				:      List[str]=       max_xpath_tag_unit_embeddings
												lowercase__				:      str=       max_xpath_subs_unit_embeddings
												lowercase__				:      List[Any]=       tag_pad_id
												lowercase__				:      int=       subs_pad_id
												lowercase__				:      int=       max_depth




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      int=       ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size			)

												lowercase__				:      Union[str, Any]=       ids_tensor(
												    [self.batch_size, self.seq_length, self.max_depth]       ,  self.max_xpath_tag_unit_embeddings			)

												lowercase__				:      List[Any]=       ids_tensor(
												    [self.batch_size, self.seq_length, self.max_depth]       ,  self.max_xpath_subs_unit_embeddings			)

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

												lowercase__				:      str=       None
												if self.use_token_type_ids:
																		lowercase__				:      Union[str, Any]=       ids_tensor([self.batch_size, self.seq_length]       ,  self.type_vocab_size			)

												lowercase__				:      Union[str, Any]=       None
												lowercase__				:      int=       None
												if self.use_labels:
																		lowercase__				:      List[str]=       ids_tensor([self.batch_size]       ,  self.type_sequence_label_size			)
																		lowercase__				:      Union[str, Any]=       ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels			)

												lowercase__				:      Any=       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			):
												'''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       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  ):
												'''simple docstring'''
												lowercase__				:      Any=       MarkupLMModel(config=snake_case__			)
												model.to(snake_case__			)
												model.eval()
												print("Configs:"       ,  model.config.tag_pad_id       ,  model.config.subs_pad_id			)
												lowercase__				:      List[str]=       model(snake_case__       ,  attention_mask=snake_case__       ,  token_type_ids=snake_case__			)
												lowercase__				:      Union[str, Any]=       model(snake_case__       ,  token_type_ids=snake_case__			)
												lowercase__				:      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 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  ):
												'''simple docstring'''
												lowercase__				:      int=       self.num_labels
												lowercase__				:      Optional[int]=       MarkupLMForSequenceClassification(snake_case__			)
												model.to(snake_case__			)
												model.eval()
												lowercase__				:      int=       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 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  ):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       self.num_labels
												lowercase__				:      int=       MarkupLMForTokenClassification(config=snake_case__			)
												model.to(snake_case__			)
												model.eval()
												lowercase__				:      Dict=       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 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  ):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       MarkupLMForQuestionAnswering(config=snake_case__			)
												model.to(snake_case__			)
												model.eval()
												lowercase__				:      int=       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 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      int=       self.prepare_config_and_inputs()
												(
												    (
												    lowercase__
												),	(
												    lowercase__
												),	(
												    lowercase__
												),	(
												    lowercase__
												),	(
												    lowercase__
												),	(
												    lowercase__
												),	(
												    lowercase__
												),	(
												    lowercase__
												),	
												)				:      Union[str, Any]=       config_and_inputs
												lowercase__				:      List[str]=       {
												    "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(   SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					(
						    (
						        MarkupLMModel,
						        MarkupLMForSequenceClassification,
						        MarkupLMForTokenClassification,
						        MarkupLMForQuestionAnswering,
						    )
						    if is_torch_available()
						    else None
						)
						__lowerCamelCase				       =					(
						    {
						        "feature-extraction": MarkupLMModel,
						        "question-answering": MarkupLMForQuestionAnswering,
						        "text-classification": MarkupLMForSequenceClassification,
						        "token-classification": MarkupLMForTokenClassification,
						        "zero-shot": MarkupLMForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
												# (batch of pretokenized examples).
												return True




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      str=       MarkupLMModelTester(self			)
												lowercase__				:      Optional[int]=       ConfigTester(self       ,  config_class=snake_case__       ,  hidden_size=37			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												self.config_tester.run_common_tests()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Tuple=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Any=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Any=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*snake_case__			)





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




						"""simple docstring"""



						lowercase__				:      Optional[int]=       "\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     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						@cached_property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# TODO use from_pretrained here
												lowercase__				:      Tuple=       MarkupLMFeatureExtractor()
												lowercase__				:      int=       MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"			)

												return MarkupLMProcessor(snake_case__       ,  snake_case__			)




						@slow
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Tuple=       MarkupLMModel.from_pretrained("microsoft/markuplm-base"			).to(snake_case__			)

												lowercase__				:      List[Any]=       self.default_processor

												lowercase__				:      Tuple=       processor(prepare_html_string()       ,  return_tensors="pt"			)
												lowercase__				:      List[Any]=       inputs.to(snake_case__			)

												# forward pass
												with torch.no_grad():
																		lowercase__				:      Optional[Any]=       model(**snake_case__			)

												# verify the last hidden states
												lowercase__				:      Optional[Any]=       torch.Size([1, 14, 768]			)
												self.assertEqual(outputs.last_hidden_state.shape       ,  snake_case__			)

												lowercase__				:      Tuple=       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(snake_case__			)

												self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]       ,  snake_case__       ,  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

_lowerCamelCase	 =			[(1,)]


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[int]      =   bool(os.getenv("RUN_SLOW"     ,							0							)							)
	if run_slow:
		assert True
	else:
		assert False, "should have been skipped"


def       a__    (  )			-> str:


	"""simple docstring"""





	UpperCAmelCase_			:	Optional[Any]      =   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  _snake_case						(unittest.TestCase):






			@slow
			@require_torch_gpu
			def 						UpperCamelCase__				(   self			):
				check_slow_torch_cuda()






			@require_torch_gpu
			@slow
			def 						UpperCamelCase__				(   self			):
				check_slow_torch_cuda()






			@parameterized.expand(_snake_case			)
			@slow
			def 						UpperCamelCase__				(   self       ,_snake_case=None			):
				check_slow()




@slow
@require_torch_gpu
def       a__    (  )			-> List[Any]:


	"""simple docstring"""





	check_slow_torch_cuda()


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


	"""simple docstring"""





	check_slow_torch_cuda()


@slow
@pytest.mark.parametrize("param"     ,							[1]							)
def       a__    (  _SCREAMING_SNAKE_CASE					:    int							)			-> List[Any]:


	"""simple docstring"""





	check_slow()





@pytest.mark.parametrize("param"     ,							[1]							)
@slow
def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Any:


	"""simple docstring"""





	check_slow()




