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    :    List[Any]					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    Union[str, Any]=13					,			lowerCamelCase    :    str=7					,			lowerCamelCase    :    Optional[Any]=True					,			lowerCamelCase    :    Any=True					,			lowerCamelCase    :    Tuple=True					,			lowerCamelCase    :    Optional[Any]=True					,			lowerCamelCase    :    str=99					,			lowerCamelCase    :    Optional[Any]=32					,			lowerCamelCase    :    int=5					,			lowerCamelCase    :    Tuple=4					,			lowerCamelCase    :    Optional[Any]=37					,			lowerCamelCase    :    Tuple="gelu"					,			lowerCamelCase    :    int=0.1					,			lowerCamelCase    :    Any=0.1					,			lowerCamelCase    :    int=5_12					,			lowerCamelCase    :    Dict=16					,			lowerCamelCase    :    int=2					,			lowerCamelCase    :    List[Any]=0.02					,			lowerCamelCase    :    Any=3					,			lowerCamelCase    :    int=4					,			lowerCamelCase    :    Dict=None					,			lowerCamelCase    :    int=10_00					,			):
  							lowerCamelCase_  :    Any   	=			parent
  							lowerCamelCase_  :    Optional[int]   	=			batch_size
  							lowerCamelCase_  :    int   	=			seq_length
  							lowerCamelCase_  :    List[Any]   	=			is_training
  							lowerCamelCase_  :    int   	=			use_input_mask
  							lowerCamelCase_  :    List[Any]   	=			use_token_type_ids
  							lowerCamelCase_  :    List[str]   	=			use_labels
  							lowerCamelCase_  :    Tuple   	=			vocab_size
  							lowerCamelCase_  :    Dict   	=			hidden_size
  							lowerCamelCase_  :    Optional[int]   	=			num_hidden_layers
  							lowerCamelCase_  :    Any   	=			num_attention_heads
  							lowerCamelCase_  :    int   	=			intermediate_size
  							lowerCamelCase_  :    Dict   	=			hidden_act
  							lowerCamelCase_  :    Optional[int]   	=			hidden_dropout_prob
  							lowerCamelCase_  :    str   	=			attention_probs_dropout_prob
  							lowerCamelCase_  :    Optional[Any]   	=			max_position_embeddings
  							lowerCamelCase_  :    List[str]   	=			type_vocab_size
  							lowerCamelCase_  :    Dict   	=			type_sequence_label_size
  							lowerCamelCase_  :    Dict   	=			initializer_range
  							lowerCamelCase_  :    Optional[Any]   	=			num_labels
  							lowerCamelCase_  :    Any   	=			num_choices
  							lowerCamelCase_  :    Tuple   	=			scope
  							lowerCamelCase_  :    str   	=			range_bbox
  
  
  
  					def      __a    (		self    :    Any	):
  							lowerCamelCase_  :    Any   	=			ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size	)
  
  							lowerCamelCase_  :    Optional[Any]   	=			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]:
  													lowerCamelCase_  :    List[Any]   	=			bbox[i, j, 3]
  													lowerCamelCase_  :    int   	=			bbox[i, j, 1]
  													lowerCamelCase_  :    List[str]   	=			t
  											if bbox[i, j, 2] < bbox[i, j, 0]:
  													lowerCamelCase_  :    str   	=			bbox[i, j, 2]
  													lowerCamelCase_  :    Tuple   	=			bbox[i, j, 0]
  													lowerCamelCase_  :    List[Any]   	=			t
  
  							lowerCamelCase_  :    Dict   	=			None
  							if self.use_input_mask:
  									lowerCamelCase_  :    Union[str, Any]   	=			ids_tensor([self.batch_size, self.seq_length]					,			vocab_size=2	)
  
  							lowerCamelCase_  :    List[str]   	=			None
  							if self.use_token_type_ids:
  									lowerCamelCase_  :    int   	=			ids_tensor([self.batch_size, self.seq_length]					,			self.type_vocab_size	)
  
  							lowerCamelCase_  :    Tuple   	=			None
  							lowerCamelCase_  :    Optional[int]   	=			None
  							lowerCamelCase_  :    List[str]   	=			None
  							if self.use_labels:
  									lowerCamelCase_  :    Optional[Any]   	=			ids_tensor([self.batch_size]					,			self.type_sequence_label_size	)
  									lowerCamelCase_  :    List[str]   	=			ids_tensor([self.batch_size, self.seq_length]					,			self.num_labels	)
  									lowerCamelCase_  :    List[Any]   	=			ids_tensor([self.batch_size]					,			self.num_choices	)
  
  							lowerCamelCase_  :    List[str]   	=			self.get_config()
  
  							return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  					def      __a    (		self    :    Optional[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      __a    (		self    :    Optional[int]					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    str					,			lowerCamelCase    :    Optional[int]	):
  							lowerCamelCase_  :    Optional[int]   	=			LayoutLMModel(config=lowerCamelCase	)
  							model.to(lowerCamelCase	)
  							model.eval()
  							lowerCamelCase_  :    Optional[Any]   	=			model(lowerCamelCase					,			lowerCamelCase					,			attention_mask=lowerCamelCase					,			token_type_ids=lowerCamelCase	)
  							lowerCamelCase_  :    Optional[Any]   	=			model(lowerCamelCase					,			lowerCamelCase					,			token_type_ids=lowerCamelCase	)
  							lowerCamelCase_  :    Any   	=			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      __a    (		self    :    Any					,			lowerCamelCase    :    str					,			lowerCamelCase    :    Union[str, Any]					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    List[Any]					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    str					,			lowerCamelCase    :    Any	):
  							lowerCamelCase_  :    int   	=			LayoutLMForMaskedLM(config=lowerCamelCase	)
  							model.to(lowerCamelCase	)
  							model.eval()
  							lowerCamelCase_  :    List[Any]   	=			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      __a    (		self    :    List[str]					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    str					,			lowerCamelCase    :    str					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    Optional[Any]					,			lowerCamelCase    :    Any	):
  							lowerCamelCase_  :    Optional[Any]   	=			self.num_labels
  							lowerCamelCase_  :    Union[str, Any]   	=			LayoutLMForSequenceClassification(lowerCamelCase	)
  							model.to(lowerCamelCase	)
  							model.eval()
  							lowerCamelCase_  :    Tuple   	=			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      __a    (		self    :    Any					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    List[Any]					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    str	):
  							lowerCamelCase_  :    Union[str, Any]   	=			self.num_labels
  							lowerCamelCase_  :    List[str]   	=			LayoutLMForTokenClassification(config=lowerCamelCase	)
  							model.to(lowerCamelCase	)
  							model.eval()
  							lowerCamelCase_  :    str   	=			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      __a    (		self    :    int					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    str					,			lowerCamelCase    :    Union[str, Any]					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    int	):
  							lowerCamelCase_  :    Optional[Any]   	=			LayoutLMForQuestionAnswering(config=lowerCamelCase	)
  							model.to(lowerCamelCase	)
  							model.eval()
  							lowerCamelCase_  :    Dict   	=			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      __a    (		self    :    int	):
  							lowerCamelCase_  :    Any   	=			self.prepare_config_and_inputs()
  							(
  							    lowerCamelCase_			
  							)  :    Union[str, Any]   	=			config_and_inputs
  							lowerCamelCase_  :    Optional[Any]   	=			{
  							    'input_ids': input_ids,
  							    'bbox': bbox,
  							    'token_type_ids': token_type_ids,
  							    'attention_mask': input_mask,
  							}
  							return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class 	UpperCamelCase_     (     snake_case__     ,    snake_case__     ,    unittest.TestCase       ):
  					_a			:	Optional[Any]							=				(
  					    (
  					        LayoutLMModel,
  					        LayoutLMForMaskedLM,
  					        LayoutLMForSequenceClassification,
  					        LayoutLMForTokenClassification,
  					        LayoutLMForQuestionAnswering,
  					    )
  					    if is_torch_available()
  					    else None
  					)
  					_a			:	int							=				(
  					    {
  					        '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			:	Dict							=				True
  
  
  
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    Optional[int]   	=			LayoutLMModelTester(self	)
  							lowerCamelCase_  :    List[Any]   	=			ConfigTester(self					,			config_class=lowerCamelCase					,			hidden_size=37	)
  
  
  
  					def      __a    (		self    :    Union[str, Any]	):
  							self.config_tester.run_common_tests()
  
  
  
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    Tuple   	=			self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_model(*lowerCamelCase	)
  
  
  
  					def      __a    (		self    :    str	):
  							lowerCamelCase_  :    List[str]   	=			self.model_tester.prepare_config_and_inputs()
  							for type in ["absolute", "relative_key", "relative_key_query"]:
  									lowerCamelCase_  :    Optional[Any]   	=			type
  									self.model_tester.create_and_check_model(*lowerCamelCase	)
  
  
  
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    Tuple   	=			self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase	)
  
  
  
  					def      __a    (		self    :    Union[str, Any]	):
  							lowerCamelCase_  :    Union[str, Any]   	=			self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase	)
  
  
  
  					def      __a    (		self    :    int	):
  							lowerCamelCase_  :    Tuple   	=			self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_token_classification(*lowerCamelCase	)
  
  
  
  					def      __a    (		self    :    Any	):
  							lowerCamelCase_  :    Optional[int]   	=			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      __a    (		self    :    Tuple	):
  							pass
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
  		# fmt: off
  		lowerCamelCase_  :    int   	=			torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]       ,device=lowerCAmelCase__	)  # noqa: E231
  		lowerCamelCase_  :    Union[str, Any]   	=			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=lowerCAmelCase__	)  # noqa: E231
  		lowerCamelCase_  :    int   	=			torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]       ,device=lowerCAmelCase__	)  # noqa: E231
  		lowerCamelCase_  :    Any   	=			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=lowerCAmelCase__	)  # noqa: E231
  		# these are sequence labels (i.e. at the token level)
  		lowerCamelCase_  :    Tuple   	=			torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]       ,device=lowerCAmelCase__	)  # noqa: E231
  		# fmt: on
  
  		return input_ids, attention_mask, bbox, token_type_ids, labels
  
  
  
  
  
  
  @require_torch
  class 	UpperCamelCase_     (     unittest.TestCase       ):
  
  
  
  					@slow
  					def      __a    (		self    :    Optional[Any]	):
  							lowerCamelCase_  :    str   	=			LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'	).to(lowerCamelCase	)
  
  							lowerCamelCase_  :    Union[str, Any]   	=			prepare_layoutlm_batch_inputs()
  
  							# forward pass
  							lowerCamelCase_  :    Dict   	=			model(input_ids=lowerCamelCase					,			bbox=lowerCamelCase					,			attention_mask=lowerCamelCase					,			token_type_ids=lowerCamelCase	)
  
  							# test the sequence output on [0, :3, :3]
  							lowerCamelCase_  :    Union[str, Any]   	=			torch.tensor(
  							    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]					,			device=lowerCamelCase					,			)
  
  							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]					,			lowerCamelCase					,			atol=1E-3	)	)
  
  							# test the pooled output on [1, :3]
  							lowerCamelCase_  :    List[Any]   	=			torch.tensor([-0.6_580, -0.0_214, 0.8_552]					,			device=lowerCamelCase	)
  
  							self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]					,			lowerCamelCase					,			atol=1E-3	)	)
  
  
  
  					@slow
  					def      __a    (		self    :    Union[str, Any]	):
  							# initialize model with randomly initialized sequence classification head
  							lowerCamelCase_  :    Dict   	=			LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'					,			num_labels=2	).to(
  							    lowerCamelCase	)
  
  							lowerCamelCase_  :    int   	=			prepare_layoutlm_batch_inputs()
  
  							# forward pass
  							lowerCamelCase_  :    Union[str, Any]   	=			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
  							lowerCamelCase_  :    Any   	=			outputs.loss
  							lowerCamelCase_  :    Union[str, Any]   	=			torch.Size([]	)
  							self.assertEqual(loss.shape					,			lowerCamelCase	)
  
  							# test the shape of the logits
  							lowerCamelCase_  :    List[Any]   	=			outputs.logits
  							lowerCamelCase_  :    Dict   	=			torch.Size((2, 2)	)
  							self.assertEqual(logits.shape					,			lowerCamelCase	)
  
  
  
  					@slow
  					def      __a    (		self    :    Optional[Any]	):
  							# initialize model with randomly initialized token classification head
  							lowerCamelCase_  :    List[Any]   	=			LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'					,			num_labels=13	).to(
  							    lowerCamelCase	)
  
  							lowerCamelCase_  :    Tuple   	=			prepare_layoutlm_batch_inputs()
  
  							# forward pass
  							lowerCamelCase_  :    Union[str, Any]   	=			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
  							lowerCamelCase_  :    Any   	=			outputs.logits
  							lowerCamelCase_  :    Optional[int]   	=			torch.Size((2, 25, 13)	)
  							self.assertEqual(logits.shape					,			lowerCamelCase	)
  
  
  
  
  
  					@slow
  					def      __a    (		self    :    Tuple	):
  							# initialize model with randomly initialized token classification head
  							lowerCamelCase_  :    Any   	=			LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'	).to(lowerCamelCase	)
  
  							lowerCamelCase_  :    Union[str, Any]   	=			prepare_layoutlm_batch_inputs()
  
  							# forward pass
  							lowerCamelCase_  :    Any   	=			model(input_ids=lowerCamelCase					,			bbox=lowerCamelCase					,			attention_mask=lowerCamelCase					,			token_type_ids=lowerCamelCase	)
  
  							# test the shape of the logits
  							lowerCamelCase_  :    int   	=			torch.Size((2, 25)	)
  							self.assertEqual(outputs.start_logits.shape					,			lowerCamelCase	)
  							self.assertEqual(outputs.end_logits.shape					,			lowerCamelCase	)
  
  
  
style_context: 
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  _lowercase		:     Dict 					=logging.get_logger(__name__)
  
  _lowercase		:     Any 					={
      """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
  }
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  					_a			:	List[str]							=				'vilt'
  
  
  
  					def __init__(		self    :    int					,			lowerCamelCase    :    Any=3_05_22					,			lowerCamelCase    :    Union[str, Any]=2					,			lowerCamelCase    :    Optional[Any]=2					,			lowerCamelCase    :    str=40					,			lowerCamelCase    :    List[str]=7_68					,			lowerCamelCase    :    Optional[int]=12					,			lowerCamelCase    :    List[Any]=12					,			lowerCamelCase    :    List[str]=30_72					,			lowerCamelCase    :    Union[str, Any]="gelu"					,			lowerCamelCase    :    int=0.0					,			lowerCamelCase    :    str=0.0					,			lowerCamelCase    :    int=0.02					,			lowerCamelCase    :    Union[str, Any]=1E-12					,			lowerCamelCase    :    Dict=3_84					,			lowerCamelCase    :    List[str]=32					,			lowerCamelCase    :    Dict=3					,			lowerCamelCase    :    List[str]=True					,			lowerCamelCase    :    int=-1					,			lowerCamelCase    :    List[Any]=False					,			lowerCamelCase    :    Any=-1					,			**lowerCamelCase    :    str					,			):
  							super().__init__(tie_word_embeddings=lowerCamelCase					,			**lowerCamelCase	)
  
  							lowerCamelCase_  :    str   	=			vocab_size
  							lowerCamelCase_  :    Dict   	=			type_vocab_size
  							lowerCamelCase_  :    str   	=			modality_type_vocab_size
  							lowerCamelCase_  :    Optional[Any]   	=			max_position_embeddings
  
  							lowerCamelCase_  :    List[Any]   	=			hidden_size
  							lowerCamelCase_  :    Union[str, Any]   	=			num_hidden_layers
  							lowerCamelCase_  :    Any   	=			num_attention_heads
  							lowerCamelCase_  :    List[Any]   	=			intermediate_size
  							lowerCamelCase_  :    Union[str, Any]   	=			hidden_act
  							lowerCamelCase_  :    Any   	=			hidden_dropout_prob
  							lowerCamelCase_  :    Optional[int]   	=			attention_probs_dropout_prob
  							lowerCamelCase_  :    Any   	=			initializer_range
  							lowerCamelCase_  :    str   	=			layer_norm_eps
  
  							lowerCamelCase_  :    Optional[Any]   	=			image_size
  							lowerCamelCase_  :    List[Any]   	=			patch_size
  							lowerCamelCase_  :    Tuple   	=			num_channels
  							lowerCamelCase_  :    Optional[Any]   	=			qkv_bias
  							lowerCamelCase_  :    Optional[int]   	=			max_image_length
  							lowerCamelCase_  :    Optional[Any]   	=			num_images
  