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 _lowercase					:





							"""simple docstring"""




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







														'''simple docstring'''






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



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






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

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

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

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

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

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

														_lowercase				=	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     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   ):







														'''simple docstring'''






														_lowercase				=	MarkupLMModel(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														print("""Configs:"""     ,   model.config.tag_pad_id     ,   model.config.subs_pad_id  )
														_lowercase				=	model(UpperCAmelCase     ,   attention_mask=UpperCAmelCase     ,   token_type_ids=UpperCAmelCase  )
														_lowercase				=	model(UpperCAmelCase     ,   token_type_ids=UpperCAmelCase  )
														_lowercase				=	model(UpperCAmelCase  )
														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     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   ):







														'''simple docstring'''






														_lowercase				=	self.num_labels
														_lowercase				=	MarkupLMForSequenceClassification(UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_lowercase				=	model(
														    UpperCAmelCase     ,   xpath_tags_seq=UpperCAmelCase     ,   xpath_subs_seq=UpperCAmelCase     ,   attention_mask=UpperCAmelCase     ,   token_type_ids=UpperCAmelCase     ,   labels=UpperCAmelCase     ,   )
														self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   ):







														'''simple docstring'''






														_lowercase				=	self.num_labels
														_lowercase				=	MarkupLMForTokenClassification(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_lowercase				=	model(
														    UpperCAmelCase     ,   xpath_tags_seq=UpperCAmelCase     ,   xpath_subs_seq=UpperCAmelCase     ,   attention_mask=UpperCAmelCase     ,   token_type_ids=UpperCAmelCase     ,   labels=UpperCAmelCase     ,   )
														self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.seq_length, self.num_labels)  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   ):







														'''simple docstring'''






														_lowercase				=	MarkupLMForQuestionAnswering(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_lowercase				=	model(
														    UpperCAmelCase     ,   xpath_tags_seq=UpperCAmelCase     ,   xpath_subs_seq=UpperCAmelCase     ,   attention_mask=UpperCAmelCase     ,   token_type_ids=UpperCAmelCase     ,   start_positions=UpperCAmelCase     ,   end_positions=UpperCAmelCase     ,   )
														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				=	self.prepare_config_and_inputs()
														(
														    (
														    _lowercase
														)				,       (
														    _lowercase
														)				,       (
														    _lowercase
														)				,       (
														    _lowercase
														)				,       (
														    _lowercase
														)				,       (
														    _lowercase
														)				,       (
														    _lowercase
														)				,       (
														    _lowercase
														)				,       
														)				=	config_and_inputs
														_lowercase				=	{
														    """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 _lowercase					(       _UpperCAmelCase   ,				_UpperCAmelCase   ,				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     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):







														'''simple docstring'''






														return True



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														_lowercase				=	MarkupLMModelTester(self  )
														_lowercase				=	ConfigTester(self     ,   config_class=UpperCAmelCase     ,   hidden_size=37  )



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														self.config_tester.run_common_tests()



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														_lowercase				=	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase  )



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														_lowercase				=	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase  )



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														_lowercase				=	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase  )



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														_lowercase				=	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase  )






def 					__lowerCAmelCase			(  ):



							"""simple docstring"""


							_lowercase				=	"""
    <!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 _lowercase					(       unittest.TestCase						):





							"""simple docstring"""




							@cached_property
							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






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

														return MarkupLMProcessor(UpperCAmelCase     ,   UpperCAmelCase  )



							@slow
							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														_lowercase				=	MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""  ).to(UpperCAmelCase  )

														_lowercase				=	self.default_processor

														_lowercase				=	processor(prepare_html_string()     ,   return_tensors="""pt"""  )
														_lowercase				=	inputs.to(UpperCAmelCase  )

														# forward pass
														with torch.no_grad():
																					_lowercase				=	model(**UpperCAmelCase  )

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

														_lowercase				=	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(UpperCAmelCase  )

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



def 					__lowerCAmelCase			(  _A = 998_001					):



							"""simple docstring"""


							_lowercase				=	0
							for i in range(999 ,99 ,-1					):  # 3 digit numbers range from 999 down to 100
														for j in range(999 ,99 ,-1					):
																					_lowercase				=	str(i * j					)
																					if product_string == product_string[::-1] and i * j < n:
																												_lowercase				=	max(_A ,i * j					)
							return answer


if __name__ == "__main__":
				print(F'''{solution() = }''')


