





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




										'''simple docstring'''



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






				def        snake_case			(				self	:					Tuple							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					ids_tensor([self.batch_size, self.seq_length],				self.vocab_size							)

										_UpperCamelCase    :      str						     =					ids_tensor(
										    [self.batch_size, self.seq_length, self.max_depth],				self.max_xpath_tag_unit_embeddings							)

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

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

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

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

										_UpperCamelCase    :      str						     =					self.get_config()

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






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




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




										'''simple docstring'''



										_UpperCamelCase    :      Tuple						     =					MarkupLMModel(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										print('''Configs:''',				model.config.tag_pad_id,				model.config.subs_pad_id							)
										_UpperCamelCase    :      List[str]						     =					model(lowerCAmelCase__,				attention_mask=lowerCAmelCase__,				token_type_ids=lowerCAmelCase__							)
										_UpperCamelCase    :      List[str]						     =					model(lowerCAmelCase__,				token_type_ids=lowerCAmelCase__							)
										_UpperCamelCase    :      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        snake_case			(				self	:					Optional[int],				lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					Dict,				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					Tuple,				)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					self.num_labels
										_UpperCamelCase    :      str						     =					MarkupLMForSequenceClassification(lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      Any						     =					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        snake_case			(				self	:					Dict,				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					Optional[int],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					List[str],				)			->   List[str]:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					self.num_labels
										_UpperCamelCase    :      Any						     =					MarkupLMForTokenClassification(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      List[Any]						     =					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        snake_case			(				self	:					Optional[Any],				lowerCAmelCase__	:					Optional[Any],				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					List[str],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					str,				lowerCAmelCase__	:					Optional[Any],				)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					MarkupLMForQuestionAnswering(config=lowerCAmelCase__							)
										model.to(lowerCAmelCase__							)
										model.eval()
										_UpperCamelCase    :      Union[str, Any]						     =					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        snake_case			(				self	:					str							)			->   int:




										'''simple docstring'''



										_UpperCamelCase    :      int						     =					self.prepare_config_and_inputs()
										(
										    _UpperCamelCase							
										)    :      List[str]						     =					config_and_inputs
										_UpperCamelCase    :      List[Any]						     =					{
										    '''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       ):
				UpperCamelCase			=					(
				    (
				        MarkupLMModel,
				        MarkupLMForSequenceClassification,
				        MarkupLMForTokenClassification,
				        MarkupLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				UpperCamelCase			=					(
				    {
				        '''feature-extraction''': MarkupLMModel,
				        '''question-answering''': MarkupLMForQuestionAnswering,
				        '''text-classification''': MarkupLMForSequenceClassification,
				        '''token-classification''': MarkupLMForTokenClassification,
				        '''zero-shot''': MarkupLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)






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




										'''simple docstring'''



										return True






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




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					MarkupLMModelTester(self							)
										_UpperCamelCase    :      List[str]						     =					ConfigTester(self,				config_class=lowerCAmelCase__,				hidden_size=3_7							)






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




										'''simple docstring'''



										self.config_tester.run_common_tests()






				def        snake_case			(				self	:					int							)			->   List[str]:




										'''simple docstring'''



										_UpperCamelCase    :      Tuple						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__							)





def  a_       (							):
						_UpperCamelCase    :      List[Any]						     =					'''
    <!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        snake_case			(				self	:					Optional[int]							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					MarkupLMFeatureExtractor()
										_UpperCamelCase    :      Tuple						     =					MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''							)

										return MarkupLMProcessor(lowerCAmelCase__,				lowerCAmelCase__							)






				@slow
				def        snake_case			(				self	:					Optional[Any]							)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      List[Any]						     =					MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''							).to(lowerCAmelCase__							)

										_UpperCamelCase    :      Optional[Any]						     =					self.default_processor

										_UpperCamelCase    :      Any						     =					processor(prepare_html_string(),				return_tensors='''pt'''							)
										_UpperCamelCase    :      int						     =					inputs.to(lowerCAmelCase__							)

										# forward pass
										with torch.no_grad():
																_UpperCamelCase    :      int						     =					model(**lowerCAmelCase__							)

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

										_UpperCamelCase    :      List[str]						     =					torch.tensor(
										    [[0.0_675, -0.0_052, 0.5_001], [-0.2_281, 0.0_802, 0.2_192], [-0.0_583, -0.3_311, 0.1_185]]							).to(lowerCAmelCase__							)

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










"""simple docstring"""




def  a_       (							_lowercase ,		_lowercase     ):
						sequence.append(_lowercase     )

						_UpperCamelCase    :      int						     =					0
						while sequence[index] != target:
												index += 1

						sequence.pop()

						if index == len(_lowercase     ):
												return None

						return index


if __name__ == "__main__":
						UpperCamelCase_     =input("""Enter numbers separated by comma:\n""").strip()
						UpperCamelCase_     =[int(item) for item in user_input.split(""",""")]

						UpperCamelCase_     =input("""Enter a single number to be found in the list:\n""")
						UpperCamelCase_     =int(target_input)
						UpperCamelCase_     =sentinel_linear_search(sequence, target)
						if result is not None:
												print(F"{target} found at positions: {result}")
						else:
												print("""Not found""")



