
'''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  ,					)				->    str:
				_UpperCamelCase       : Optional[int]    =      parent
				_UpperCamelCase       : Union[str, Any]    =      batch_size
				_UpperCamelCase       : Union[str, Any]    =      seq_length
				_UpperCamelCase       : Any    =      is_training
				_UpperCamelCase       : Dict    =      use_input_mask
				_UpperCamelCase       : Any    =      use_token_type_ids
				_UpperCamelCase       : List[str]    =      use_labels
				_UpperCamelCase       : Optional[Any]    =      vocab_size
				_UpperCamelCase       : Optional[Any]    =      hidden_size
				_UpperCamelCase       : Tuple    =      num_hidden_layers
				_UpperCamelCase       : Any    =      num_attention_heads
				_UpperCamelCase       : int    =      intermediate_size
				_UpperCamelCase       : List[Any]    =      hidden_act
				_UpperCamelCase       : Optional[Any]    =      hidden_dropout_prob
				_UpperCamelCase       : List[str]    =      attention_probs_dropout_prob
				_UpperCamelCase       : Dict    =      max_position_embeddings
				_UpperCamelCase       : List[Any]    =      type_vocab_size
				_UpperCamelCase       : List[str]    =      type_sequence_label_size
				_UpperCamelCase       : Optional[Any]    =      initializer_range
				_UpperCamelCase       : Any    =      num_labels
				_UpperCamelCase       : List[Any]    =      scope
				_UpperCamelCase       : int    =      max_xpath_tag_unit_embeddings
				_UpperCamelCase       : Dict    =      max_xpath_subs_unit_embeddings
				_UpperCamelCase       : List[Any]    =      tag_pad_id
				_UpperCamelCase       : Any    =      subs_pad_id
				_UpperCamelCase       : List[Any]    =      max_depth
		def _lowercase  (    self     )				->    Union[str, Any]:
				_UpperCamelCase       : Union[str, Any]    =      ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size     )

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

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

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

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

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

				_UpperCamelCase       : Tuple    =      self.get_config()

				return (
				    config,
				    input_ids,
				    xpath_tags_seq,
				    xpath_subs_seq,
				    token_type_ids,
				    input_mask,
				    sequence_labels,
				    token_labels,
				)
		def _lowercase  (    self     )				->    Dict:
				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 _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					)				->    List[str]:
				_UpperCamelCase       : List[str]    =      MarkupLMModel(config=_snake_case     )
				model.to(_snake_case     )
				model.eval()
				print('''Configs:'''  ,					model.config.tag_pad_id  ,					model.config.subs_pad_id     )
				_UpperCamelCase       : Optional[int]    =      model(_snake_case  ,					attention_mask=_snake_case  ,					token_type_ids=_snake_case     )
				_UpperCamelCase       : str    =      model(_snake_case  ,					token_type_ids=_snake_case     )
				_UpperCamelCase       : Optional[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 _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					)				->    int:
				_UpperCamelCase       : Optional[Any]    =      self.num_labels
				_UpperCamelCase       : str    =      MarkupLMForSequenceClassification(_snake_case     )
				model.to(_snake_case     )
				model.eval()
				_UpperCamelCase       : Tuple    =      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 _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					)				->    str:
				_UpperCamelCase       : Tuple    =      self.num_labels
				_UpperCamelCase       : Dict    =      MarkupLMForTokenClassification(config=_snake_case     )
				model.to(_snake_case     )
				model.eval()
				_UpperCamelCase       : List[str]    =      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 _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					)				->    Any:
				_UpperCamelCase       : Tuple    =      MarkupLMForQuestionAnswering(config=_snake_case     )
				model.to(_snake_case     )
				model.eval()
				_UpperCamelCase       : List[str]    =      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 _lowercase  (    self     )				->    Union[str, Any]:
				_UpperCamelCase       : Union[str, Any]    =      self.prepare_config_and_inputs()
				(
				    (
				    _UpperCamelCase       
				),							(
				    _UpperCamelCase       
				),							(
				    _UpperCamelCase       
				),							(
				    _UpperCamelCase       
				),							(
				    _UpperCamelCase       
				),							(
				    _UpperCamelCase       
				),							(
				    _UpperCamelCase       
				),							(
				    _UpperCamelCase       
				),							       
				)       : Union[str, Any]    =      config_and_inputs
				_UpperCamelCase       : Tuple    =      {
				    '''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				(					_UpperCAmelCase  ,		_UpperCAmelCase  ,		unittest.TestCase ):





		"""simple docstring"""





		A__  :   Optional[int]          =    (
		    (
		        MarkupLMModel,
		        MarkupLMForSequenceClassification,
		        MarkupLMForTokenClassification,
		        MarkupLMForQuestionAnswering,
		    )
		    if is_torch_available()
		    else None
		)
		A__  :   Union[str, Any]          =    (
		    {
		        'feature-extraction': MarkupLMModel,
		        'question-answering': MarkupLMForQuestionAnswering,
		        'text-classification': MarkupLMForSequenceClassification,
		        'token-classification': MarkupLMForTokenClassification,
		        'zero-shot': MarkupLMForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Dict:
				return True
		def _lowercase  (    self     )				->    Optional[Any]:
				_UpperCamelCase       : Tuple    =      MarkupLMModelTester(self     )
				_UpperCamelCase       : Union[str, Any]    =      ConfigTester(self  ,					config_class=_snake_case  ,					hidden_size=37     )
		def _lowercase  (    self     )				->    Any:
				self.config_tester.run_common_tests()
		def _lowercase  (    self     )				->    Dict:
				_UpperCamelCase       : int    =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case     )
		def _lowercase  (    self     )				->    Optional[int]:
				_UpperCamelCase       : Tuple    =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*_snake_case     )
		def _lowercase  (    self     )				->    List[str]:
				_UpperCamelCase       : int    =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*_snake_case     )




		def _lowercase  (    self     )				->    Dict:
				_UpperCamelCase       : List[Any]    =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*_snake_case     )







def 						snake_case__	(      ) ->      int:
		_UpperCamelCase       : Dict    =      '''
    <!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 			UpperCAmelCase				(					unittest.TestCase ):





		"""simple docstring"""





		@cached_property
		def _lowercase  (    self     )				->    Optional[int]:
				_UpperCamelCase       : Any    =      MarkupLMFeatureExtractor()
				_UpperCamelCase       : Tuple    =      MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''     )

				return MarkupLMProcessor(_snake_case  ,					_snake_case     )




		@slow
		def _lowercase  (    self     )				->    Optional[Any]:
				_UpperCamelCase       : Any    =      MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''     ).to(_snake_case     )

				_UpperCamelCase       : Tuple    =      self.default_processor

				_UpperCamelCase       : Optional[int]    =      processor(prepare_html_string()  ,					return_tensors='''pt'''     )
				_UpperCamelCase       : Optional[Any]    =      inputs.to(_snake_case     )

				# forward pass
				with torch.no_grad():
						_UpperCamelCase       : Optional[int]    =      model(**_snake_case     )

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

				_UpperCamelCase       : Optional[Any]    =      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(_snake_case     )

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






'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


_UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)

class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		def __init__(    self  ,					*_snake_case  ,					**_snake_case     )				->    None:
				warnings.warn(
				    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileNetV2ImageProcessor instead.'''  ,					_snake_case  ,					)
				super().__init__(*_snake_case  ,					**_snake_case     )





