code: 
  
  
  
  
  
  import unittest
  
  from transformers import LayoutLMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  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 (
  				    LayoutLMForMaskedLM,
  				    LayoutLMForQuestionAnswering,
  				    LayoutLMForSequenceClassification,
  				    LayoutLMForTokenClassification,
  				    LayoutLMModel,
  				)
  
  
  
  
  class   _UpperCAmelCase	:
  
  
  
  
  					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=0.0_2    ,      __lowerCAmelCase=3    ,      __lowerCAmelCase=4    ,      __lowerCAmelCase=None    ,      __lowerCAmelCase=1_000    ,      )						->  Dict:
  							UpperCamelCase__         =	parent
  							UpperCamelCase__         =	batch_size
  							UpperCamelCase__         =	seq_length
  							UpperCamelCase__         =	is_training
  							UpperCamelCase__         =	use_input_mask
  							UpperCamelCase__         =	use_token_type_ids
  							UpperCamelCase__         =	use_labels
  							UpperCamelCase__         =	vocab_size
  							UpperCamelCase__         =	hidden_size
  							UpperCamelCase__         =	num_hidden_layers
  							UpperCamelCase__         =	num_attention_heads
  							UpperCamelCase__         =	intermediate_size
  							UpperCamelCase__         =	hidden_act
  							UpperCamelCase__         =	hidden_dropout_prob
  							UpperCamelCase__         =	attention_probs_dropout_prob
  							UpperCamelCase__         =	max_position_embeddings
  							UpperCamelCase__         =	type_vocab_size
  							UpperCamelCase__         =	type_sequence_label_size
  							UpperCamelCase__         =	initializer_range
  							UpperCamelCase__         =	num_labels
  							UpperCamelCase__         =	num_choices
  							UpperCamelCase__         =	scope
  							UpperCamelCase__         =	range_bbox
  
  
  
  
  					def 	snake_case_		(						self       )						->  Any:
  							UpperCamelCase__         =	ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size       )
  
  							UpperCamelCase__         =	ids_tensor([self.batch_size, self.seq_length, 4]    ,      self.range_bbox       )
  							# Ensure that bbox is legal
  							for i in range(bbox.shape[0]       ):
  									for j in range(bbox.shape[1]       ):
  											if bbox[i, j, 3] < bbox[i, j, 1]:
  													UpperCamelCase__         =	bbox[i, j, 3]
  													UpperCamelCase__         =	bbox[i, j, 1]
  													UpperCamelCase__         =	t
  											if bbox[i, j, 2] < bbox[i, j, 0]:
  													UpperCamelCase__         =	bbox[i, j, 2]
  													UpperCamelCase__         =	bbox[i, j, 0]
  													UpperCamelCase__         =	t
  
  							UpperCamelCase__         =	None
  							if self.use_input_mask:
  									UpperCamelCase__         =	ids_tensor([self.batch_size, self.seq_length]    ,      vocab_size=2       )
  
  							UpperCamelCase__         =	None
  							if self.use_token_type_ids:
  									UpperCamelCase__         =	ids_tensor([self.batch_size, self.seq_length]    ,      self.type_vocab_size       )
  
  							UpperCamelCase__         =	None
  							UpperCamelCase__         =	None
  							UpperCamelCase__         =	None
  							if self.use_labels:
  									UpperCamelCase__         =	ids_tensor([self.batch_size]    ,      self.type_sequence_label_size       )
  									UpperCamelCase__         =	ids_tensor([self.batch_size, self.seq_length]    ,      self.num_labels       )
  									UpperCamelCase__         =	ids_tensor([self.batch_size]    ,      self.num_choices       )
  
  							UpperCamelCase__         =	self.get_config()
  
  							return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  					def 	snake_case_		(						self       )						->  List[Any]:
  							return LayoutLMConfig(
  							    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    ,      )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  str:
  							UpperCamelCase__         =	LayoutLMModel(config=__lowerCAmelCase       )
  							model.to(__lowerCAmelCase       )
  							model.eval()
  							UpperCamelCase__         =	model(__lowerCAmelCase    ,      __lowerCAmelCase    ,      attention_mask=__lowerCAmelCase    ,      token_type_ids=__lowerCAmelCase       )
  							UpperCamelCase__         =	model(__lowerCAmelCase    ,      __lowerCAmelCase    ,      token_type_ids=__lowerCAmelCase       )
  							UpperCamelCase__         =	model(__lowerCAmelCase    ,      __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    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  List[str]:
  							UpperCamelCase__         =	LayoutLMForMaskedLM(config=__lowerCAmelCase       )
  							model.to(__lowerCAmelCase       )
  							model.eval()
  							UpperCamelCase__         =	model(__lowerCAmelCase    ,      __lowerCAmelCase    ,      attention_mask=__lowerCAmelCase    ,      token_type_ids=__lowerCAmelCase    ,      labels=__lowerCAmelCase       )
  							self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.seq_length, self.vocab_size)       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  int:
  							UpperCamelCase__         =	self.num_labels
  							UpperCamelCase__         =	LayoutLMForSequenceClassification(__lowerCAmelCase       )
  							model.to(__lowerCAmelCase       )
  							model.eval()
  							UpperCamelCase__         =	model(
  							    __lowerCAmelCase    ,      __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    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  str:
  							UpperCamelCase__         =	self.num_labels
  							UpperCamelCase__         =	LayoutLMForTokenClassification(config=__lowerCAmelCase       )
  							model.to(__lowerCAmelCase       )
  							model.eval()
  							UpperCamelCase__         =	model(__lowerCAmelCase    ,      __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    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Tuple:
  							UpperCamelCase__         =	LayoutLMForQuestionAnswering(config=__lowerCAmelCase       )
  							model.to(__lowerCAmelCase       )
  							model.eval()
  							UpperCamelCase__         =	model(
  							    __lowerCAmelCase    ,      bbox=__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       )						->  Optional[Any]:
  							UpperCamelCase__         =	self.prepare_config_and_inputs()
  							(
  							    (
  							    UpperCamelCase__
  							)  ,			(
  							    UpperCamelCase__
  							)  ,			(
  							    UpperCamelCase__
  							)  ,			(
  							    UpperCamelCase__
  							)  ,			(
  							    UpperCamelCase__
  							)  ,			(
  							    UpperCamelCase__
  							)  ,			(
  							    UpperCamelCase__
  							)  ,			(
  							    UpperCamelCase__
  							)  ,			
  							)         =	config_and_inputs
  							UpperCamelCase__         =	{
  							    """input_ids""": input_ids,
  							    """bbox""": bbox,
  							    """token_type_ids""": token_type_ids,
  							    """attention_mask""": input_mask,
  							}
  							return config, inputs_dict
  
  
  
  
  @require_torch
  class   _UpperCAmelCase	(   _lowercase ,       _lowercase ,       unittest.TestCase					):
  					__A          =   (
  					    (
  					        LayoutLMModel,
  					        LayoutLMForMaskedLM,
  					        LayoutLMForSequenceClassification,
  					        LayoutLMForTokenClassification,
  					        LayoutLMForQuestionAnswering,
  					    )
  					    if is_torch_available()
  					    else None
  					)
  					__A          =   (
  					    {
  					        "document-question-answering": LayoutLMForQuestionAnswering,
  					        "feature-extraction": LayoutLMModel,
  					        "fill-mask": LayoutLMForMaskedLM,
  					        "text-classification": LayoutLMForSequenceClassification,
  					        "token-classification": LayoutLMForTokenClassification,
  					        "zero-shot": LayoutLMForSequenceClassification,
  					    }
  					    if is_torch_available()
  					    else {}
  					)
  					__A          =   True
  
  
  
  
  					def 	snake_case_		(						self       )						->  List[Any]:
  							UpperCamelCase__         =	LayoutLMModelTester(self       )
  							UpperCamelCase__         =	ConfigTester(self    ,      config_class=__lowerCAmelCase    ,      hidden_size=37       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  Optional[Any]:
  							self.config_tester.run_common_tests()
  
  
  
  
  					def 	snake_case_		(						self       )						->  Any:
  							UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_model(*__lowerCAmelCase       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  Union[str, Any]:
  							UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs()
  							for type in ["absolute", "relative_key", "relative_key_query"]:
  									UpperCamelCase__         =	type
  									self.model_tester.create_and_check_model(*__lowerCAmelCase       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  int:
  							UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  List[Any]:
  							UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  Optional[Any]:
  							UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase       )
  
  
  
  
  					def 	snake_case_		(						self       )						->  str:
  							UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase       )
  
  
  
  
  					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
  					def 	snake_case_		(						self       )						->  List[Any]:
  							pass
  
  
  
  def     a						(      )     ->Dict:
  		# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
  		# fmt: off
  		UpperCamelCase__         =	torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]] ,						device=_lowercase       )  # noqa: E231
  		UpperCamelCase__         =	torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ,						device=_lowercase       )  # noqa: E231
  		UpperCamelCase__         =	torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]] ,						device=_lowercase       )  # noqa: E231
  		UpperCamelCase__         =	torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ,						device=_lowercase       )  # noqa: E231
  		# these are sequence labels (i.e. at the token level)
  		UpperCamelCase__         =	torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]] ,						device=_lowercase       )  # noqa: E231
  		# fmt: on
  
  		return input_ids, attention_mask, bbox, token_type_ids, labels
  
  
  
  
  @require_torch
  class   _UpperCAmelCase	(   unittest.TestCase					):
  
  
  
  
  					@slow
  					def 	snake_case_		(						self       )						->  Optional[Any]:
  							UpperCamelCase__         =	LayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""       ).to(__lowerCAmelCase       )
  
  							UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__         =	prepare_layoutlm_batch_inputs()
  
  							# forward pass
  							UpperCamelCase__         =	model(input_ids=__lowerCAmelCase    ,      bbox=__lowerCAmelCase    ,      attention_mask=__lowerCAmelCase    ,      token_type_ids=__lowerCAmelCase       )
  
  							# test the sequence output on [0, :3, :3]
  							UpperCamelCase__         =	torch.tensor(
  							    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]    ,      device=__lowerCAmelCase    ,      )
  
  							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,      __lowerCAmelCase    ,      atol=1E-3       )       )
  
  							# test the pooled output on [1, :3]
  							UpperCamelCase__         =	torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]    ,      device=__lowerCAmelCase       )
  
  							self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]    ,      __lowerCAmelCase    ,      atol=1E-3       )       )
  
  
  
  
  					@slow
  					def 	snake_case_		(						self       )						->  int:
  							# initialize model with randomly initialized sequence classification head
  							UpperCamelCase__         =	LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""    ,      num_labels=2       ).to(
  							    __lowerCAmelCase       )
  
  							UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__         =	prepare_layoutlm_batch_inputs()
  
  							# forward pass
  							UpperCamelCase__         =	model(
  							    input_ids=__lowerCAmelCase    ,      bbox=__lowerCAmelCase    ,      attention_mask=__lowerCAmelCase    ,      token_type_ids=__lowerCAmelCase    ,      labels=torch.tensor([1, 1]    ,      device=__lowerCAmelCase       )    ,      )
  
  							# test whether we get a loss as a scalar
  							UpperCamelCase__         =	outputs.loss
  							UpperCamelCase__         =	torch.Size([]       )
  							self.assertEqual(loss.shape    ,      __lowerCAmelCase       )
  
  							# test the shape of the logits
  							UpperCamelCase__         =	outputs.logits
  							UpperCamelCase__         =	torch.Size((2, 2)       )
  							self.assertEqual(logits.shape    ,      __lowerCAmelCase       )
  
  
  
  
  					@slow
  					def 	snake_case_		(						self       )						->  Union[str, Any]:
  							# initialize model with randomly initialized token classification head
  							UpperCamelCase__         =	LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""    ,      num_labels=13       ).to(
  							    __lowerCAmelCase       )
  
  							UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__         =	prepare_layoutlm_batch_inputs()
  
  							# forward pass
  							UpperCamelCase__         =	model(
  							    input_ids=__lowerCAmelCase    ,      bbox=__lowerCAmelCase    ,      attention_mask=__lowerCAmelCase    ,      token_type_ids=__lowerCAmelCase    ,      labels=__lowerCAmelCase       )
  
  							# test the loss calculation to be around 2.65
  							# expected_loss = torch.tensor(2.65, device=torch_device)
  
  							# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
  							# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))
  
  							# test the shape of the logits
  							UpperCamelCase__         =	outputs.logits
  							UpperCamelCase__         =	torch.Size((2, 25, 13)       )
  							self.assertEqual(logits.shape    ,      __lowerCAmelCase       )
  
  
  
  
  					@slow
  					def 	snake_case_		(						self       )						->  List[str]:
  							# initialize model with randomly initialized token classification head
  							UpperCamelCase__         =	LayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""       ).to(__lowerCAmelCase       )
  
  							UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__         =	prepare_layoutlm_batch_inputs()
  
  							# forward pass
  							UpperCamelCase__         =	model(input_ids=__lowerCAmelCase    ,      bbox=__lowerCAmelCase    ,      attention_mask=__lowerCAmelCase    ,      token_type_ids=__lowerCAmelCase       )
  
  							# test the shape of the logits
  							UpperCamelCase__         =	torch.Size((2, 25)       )
  							self.assertEqual(outputs.start_logits.shape    ,      __lowerCAmelCase       )
  							self.assertEqual(outputs.end_logits.shape    ,      __lowerCAmelCase       )
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from dataclasses import dataclass
  
  __lowerCAmelCase     =			"0.1"
  __lowerCAmelCase     =			"Lucia Harcekova"
  
  
  
  
  @dataclass
  class   _UpperCAmelCase	:
  					__A          =   42
  					__A          =   42
  					__A          =   42
  
  
  
  
  					def __repr__(						self       )						->  str:
  							return F"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class   _UpperCAmelCase	:
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase = 13    ,      __lowerCAmelCase = 6       )						->  None:
  							UpperCamelCase__         =	window_size
  							UpperCamelCase__         =	lookahead_buffer_size
  							UpperCamelCase__         =	self.window_size - self.lookahead_buffer_size
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  list[Token]:
  							UpperCamelCase__         =	[]
  							UpperCamelCase__         =	""""""
  
  							# while there are still characters in text to compress
  							while text:
  									# find the next encoding phrase
  									# - triplet with offset, length, indicator (the next encoding character)
  									UpperCamelCase__         =	self._find_encoding_token(__lowerCAmelCase    ,      __lowerCAmelCase       )
  
  									# update the search buffer:
  									# - add new characters from text into it
  									# - check if size exceed the max search buffer size, if so, drop the
  									#   oldest elements
  									search_buffer += text[: token.length + 1]
  									if len(__lowerCAmelCase       ) > self.search_buffer_size:
  											UpperCamelCase__         =	search_buffer[-self.search_buffer_size :]
  
  									# update the text
  									UpperCamelCase__         =	text[token.length + 1 :]
  
  									# append the token to output
  									output.append(__lowerCAmelCase       )
  
  							return output
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  str:
  							UpperCamelCase__         =	""""""
  
  							for token in tokens:
  									for _ in range(token.length       ):
  											output += output[-token.offset]
  									output += token.indicator
  
  							return output
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Token:
  
  							if not text:
  									raise ValueError("""We need some text to work with."""       )
  
  							# Initialise result parameters to default values
  							UpperCamelCase__  ,			UpperCamelCase__         =	0, 0
  
  							if not search_buffer:
  									return Token(__lowerCAmelCase    ,      __lowerCAmelCase    ,      text[length]       )
  
  							for i, character in enumerate(__lowerCAmelCase       ):
  									UpperCamelCase__         =	len(__lowerCAmelCase       ) - i
  									if character == text[0]:
  											UpperCamelCase__         =	self._match_length_from_index(__lowerCAmelCase    ,      __lowerCAmelCase    ,      0    ,      __lowerCAmelCase       )
  											# if the found length is bigger than the current or if it's equal,
  											# which means it's offset is smaller: update offset and length
  											if found_length >= length:
  													UpperCamelCase__  ,			UpperCamelCase__         =	found_offset, found_length
  
  							return Token(__lowerCAmelCase    ,      __lowerCAmelCase    ,      text[length]       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  int:
  							if not text or text[text_index] != window[window_index]:
  									return 0
  							return 1 + self._match_length_from_index(
  							    __lowerCAmelCase    ,      window + text[text_index]    ,      text_index + 1    ,      window_index + 1       )
  
  
  if __name__ == "__main__":
  				from doctest import testmod
  
  				testmod()
  				# Initialize compressor class
  				__lowerCAmelCase     =			LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  				# Example
  				__lowerCAmelCase     =			"cabracadabrarrarrad"
  				__lowerCAmelCase     =			lzaa_compressor.compress(TEXT)
  				print(lzaa_compressor.compress("ababcbababaa"))
  				__lowerCAmelCase     =			lzaa_compressor.decompress(compressed_text)
  				assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
  
  