'''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__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=13	,   __lowerCAmelCase=7	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase=99	,   __lowerCAmelCase=32	,   __lowerCAmelCase=5	,   __lowerCAmelCase=4	,   __lowerCAmelCase=37	,   __lowerCAmelCase="gelu"	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=512	,   __lowerCAmelCase=16	,   __lowerCAmelCase=2	,   __lowerCAmelCase=0.02	,   __lowerCAmelCase=3	,   __lowerCAmelCase=None	,   __lowerCAmelCase=20	,   __lowerCAmelCase=30	,   __lowerCAmelCase=2	,   __lowerCAmelCase=2	,   __lowerCAmelCase=10	,   ):




					"""simple docstring"""


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



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size)

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

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

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

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

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

					lowerCAmelCase        =  self.get_config()

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



			def 							a_	(       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 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  MarkupLMModel(config=__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					print("""Configs:"""	,   model.config.tag_pad_id	,   model.config.subs_pad_id)
					lowerCAmelCase        =  model(__lowerCAmelCase	,   attention_mask=__lowerCAmelCase	,   token_type_ids=__lowerCAmelCase)
					lowerCAmelCase        =  model(__lowerCAmelCase	,   token_type_ids=__lowerCAmelCase)
					lowerCAmelCase        =  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 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  self.num_labels
					lowerCAmelCase        =  MarkupLMForSequenceClassification(__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase        =  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 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  self.num_labels
					lowerCAmelCase        =  MarkupLMForTokenClassification(config=__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase        =  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 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  MarkupLMForQuestionAnswering(config=__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase        =  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 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.prepare_config_and_inputs()
					(
					    (
					    lowerCAmelCase
					) ,					(
					    lowerCAmelCase
					) ,					(
					    lowerCAmelCase
					) ,					(
					    lowerCAmelCase
					) ,					(
					    lowerCAmelCase
					) ,					(
					    lowerCAmelCase
					) ,					(
					    lowerCAmelCase
					) ,					(
					    lowerCAmelCase
					) ,					
					)        =  config_and_inputs
					lowerCAmelCase        =  {
					    """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__( lowerCAmelCase__			,						lowerCAmelCase__			,						unittest.TestCase     ):


			'''simple docstring'''






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



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					return True



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  MarkupLMModelTester(self)
					lowerCAmelCase        =  ConfigTester(self	,   config_class=__lowerCAmelCase	,   hidden_size=37)



			def 							a_	(       self):




					"""simple docstring"""


					self.config_tester.run_common_tests()



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase)







			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase)







def     snake_case__							(      )   ->     Tuple:

		'''simple docstring'''


		lowerCAmelCase        =  """
    <!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     ):


			'''simple docstring'''






			@cached_property
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  MarkupLMFeatureExtractor()
					lowerCAmelCase        =  MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base""")

					return MarkupLMProcessor(__lowerCAmelCase	,   __lowerCAmelCase)







			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  MarkupLMModel.from_pretrained("""microsoft/markuplm-base""").to(__lowerCAmelCase)

					lowerCAmelCase        =  self.default_processor

					lowerCAmelCase        =  processor(prepare_html_string()	,   return_tensors="""pt""")
					lowerCAmelCase        =  inputs.to(__lowerCAmelCase)

					# forward pass
					with torch.no_grad():
							lowerCAmelCase        =  model(**__lowerCAmelCase)

					# verify the last hidden states
					lowerCAmelCase        =  torch.Size([1, 14, 768])
					self.assertEqual(outputs.last_hidden_state.shape	,   __lowerCAmelCase)

					lowerCAmelCase        =  torch.tensor(
					    [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]).to(__lowerCAmelCase)

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





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase				   =  {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  ['''BartTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BartForCausalLM''',
						    '''BartForConditionalGeneration''',
						    '''BartForQuestionAnswering''',
						    '''BartForSequenceClassification''',
						    '''BartModel''',
						    '''BartPretrainedModel''',
						    '''PretrainedBartModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''TFBartForConditionalGeneration''',
						    '''TFBartForSequenceClassification''',
						    '''TFBartModel''',
						    '''TFBartPretrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase				   =  [
						    '''FlaxBartDecoderPreTrainedModel''',
						    '''FlaxBartForCausalLM''',
						    '''FlaxBartForConditionalGeneration''',
						    '''FlaxBartForQuestionAnswering''',
						    '''FlaxBartForSequenceClassification''',
						    '''FlaxBartModel''',
						    '''FlaxBartPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowercase				   =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




