


"""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 			SCREAMING_SNAKE_CASE_  :




							'''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	,					)   ->	Optional[Any]:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Dict		=      ids_tensor([self.batch_size, self.seq_length]	,					self.vocab_size)

								snake_case__  :						List[Any]		=      ids_tensor(
								    [self.batch_size, self.seq_length, self.max_depth]	,					self.max_xpath_tag_unit_embeddings)

								snake_case__  :						int		=      ids_tensor(
								    [self.batch_size, self.seq_length, self.max_depth]	,					self.max_xpath_subs_unit_embeddings)

								snake_case__  :						Dict		=      None
								if self.use_input_mask:
									snake_case__  :						Optional[int]		=      ids_tensor([self.batch_size, self.seq_length]	,					vocab_size=2)

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

								snake_case__  :						Any		=      None
								snake_case__  :						str		=      None
								if self.use_labels:
									snake_case__  :						Tuple		=      ids_tensor([self.batch_size]	,					self.type_sequence_label_size)
									snake_case__  :						Tuple		=      ids_tensor([self.batch_size, self.seq_length]	,					self.num_labels)

								snake_case__  :						List[str]		=      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)   ->	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	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      MarkupLMModel(config=lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								print("Configs:"	,					model.config.tag_pad_id	,					model.config.subs_pad_id)
								snake_case__  :						Any		=      model(lowerCamelCase__	,					attention_mask=lowerCamelCase__	,					token_type_ids=lowerCamelCase__)
								snake_case__  :						Optional[Any]		=      model(lowerCamelCase__	,					token_type_ids=lowerCamelCase__)
								snake_case__  :						Tuple		=      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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	int:







								'''simple docstring'''




								snake_case__  :						List[str]		=      self.num_labels
								snake_case__  :						int		=      MarkupLMForSequenceClassification(lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								snake_case__  :						Tuple		=      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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.num_labels
								snake_case__  :						List[str]		=      MarkupLMForTokenClassification(config=lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								snake_case__  :						Tuple		=      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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      MarkupLMForQuestionAnswering(config=lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								snake_case__  :						List[str]		=      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        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						Any		=      self.prepare_config_and_inputs()
								(
								    (
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					(
								    snake_case__
								),					
								)  :						str		=      config_and_inputs
								snake_case__  :						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 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      _lowercase	,      unittest.TestCase):




							'''simple docstring'''





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




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Any:







								'''simple docstring'''




								return True




							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						str		=      MarkupLMModelTester(self)
								snake_case__  :						List[str]		=      ConfigTester(self	,					config_class=lowerCamelCase__	,					hidden_size=37)




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







								'''simple docstring'''




								self.config_tester.run_common_tests()




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						Any		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__)







							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						List[str]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__)



def 			A__		(					)							->	Tuple:

	'''simple docstring'''


	snake_case__  :						Tuple		=      "\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 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							@cached_property
							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      MarkupLMFeatureExtractor()
								snake_case__  :						List[str]		=      MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")

								return MarkupLMProcessor(lowerCamelCase__	,					lowerCamelCase__)







							@slow
							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




								snake_case__  :						str		=      MarkupLMModel.from_pretrained("microsoft/markuplm-base").to(lowerCamelCase__)

								snake_case__  :						List[Any]		=      self.default_processor

								snake_case__  :						Optional[Any]		=      processor(prepare_html_string()	,					return_tensors="pt")
								snake_case__  :						Any		=      inputs.to(lowerCamelCase__)

								# forward pass
								with torch.no_grad():
									snake_case__  :						Dict		=      model(**lowerCamelCase__)

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

								snake_case__  :						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(lowerCamelCase__)

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









import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


lowerCamelCase		=						logging.get_logger(__name__)





class    _a				(							_lowercase):




	def __init__(   self		:						Any						,   *_SCREAMING_SNAKE_CASE		:						int						,   **_SCREAMING_SNAKE_CASE		:						Dict						)->			None:
			warnings.warn(
			    '''The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
			    ''' Please use MaskFormerImageProcessor instead.'''						,   _SCREAMING_SNAKE_CASE						,   )
			super().__init__(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




