





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_  :							List[Any]            =	{
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_  :							Dict            =	[
					    "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "NystromformerForMaskedLM",
					    "NystromformerForMultipleChoice",
					    "NystromformerForQuestionAnswering",
					    "NystromformerForSequenceClassification",
					    "NystromformerForTokenClassification",
					    "NystromformerLayer",
					    "NystromformerModel",
					    "NystromformerPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nystromformer import (
										    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NystromformerForMaskedLM,
										    NystromformerForMultipleChoice,
										    NystromformerForQuestionAnswering,
										    NystromformerForSequenceClassification,
										    NystromformerForTokenClassification,
										    NystromformerLayer,
										    NystromformerModel,
										    NystromformerPreTrainedModel,
										)


else:
					import sys

					snake_case_  :							Optional[int]            =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












'''simple docstring'''







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        __a   :

							def __init__(				self				:  List[Any]							,  __magic_name__				:  Optional[Any]							,  __magic_name__				:  Dict=13							,  __magic_name__				:  str=7							,  __magic_name__				:  Tuple=True							,  __magic_name__				:  Union[str, Any]=True							,  __magic_name__				:  Union[str, Any]=True							,  __magic_name__				:  Dict=True							,  __magic_name__				:  Any=99							,  __magic_name__				:  str=32							,  __magic_name__				:  int=5							,  __magic_name__				:  str=4							,  __magic_name__				:  Dict=37							,  __magic_name__				:  List[str]="gelu"							,  __magic_name__				:  Optional[Any]=0.1							,  __magic_name__				:  Any=0.1							,  __magic_name__				:  Tuple=5_12							,  __magic_name__				:  Any=16							,  __magic_name__				:  str=2							,  __magic_name__				:  List[Any]=0.0_2							,  __magic_name__				:  List[str]=3							,  __magic_name__				:  Optional[int]=4							,  __magic_name__				:  Dict=None							,  __magic_name__				:  List[str]=10_00							,  )       ->							Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	parent
													UpperCAmelCase_      :   str      =	batch_size
													UpperCAmelCase_      :   str      =	seq_length
													UpperCAmelCase_      :   Optional[Any]      =	is_training
													UpperCAmelCase_      :   List[Any]      =	use_input_mask
													UpperCAmelCase_      :   Any      =	use_token_type_ids
													UpperCAmelCase_      :   Dict      =	use_labels
													UpperCAmelCase_      :   Union[str, Any]      =	vocab_size
													UpperCAmelCase_      :   Any      =	hidden_size
													UpperCAmelCase_      :   Dict      =	num_hidden_layers
													UpperCAmelCase_      :   Union[str, Any]      =	num_attention_heads
													UpperCAmelCase_      :   Dict      =	intermediate_size
													UpperCAmelCase_      :   int      =	hidden_act
													UpperCAmelCase_      :   int      =	hidden_dropout_prob
													UpperCAmelCase_      :   Union[str, Any]      =	attention_probs_dropout_prob
													UpperCAmelCase_      :   Union[str, Any]      =	max_position_embeddings
													UpperCAmelCase_      :   int      =	type_vocab_size
													UpperCAmelCase_      :   List[Any]      =	type_sequence_label_size
													UpperCAmelCase_      :   int      =	initializer_range
													UpperCAmelCase_      :   Dict      =	num_labels
													UpperCAmelCase_      :   Any      =	num_choices
													UpperCAmelCase_      :   Optional[Any]      =	scope
													UpperCAmelCase_      :   Any      =	range_bbox

							def      UpperCAmelCase__						(				self				:  Any )       ->							str:







													"""simple docstring"""
													UpperCAmelCase_      :   List[str]      =	ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size )

													UpperCAmelCase_      :   Tuple      =	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_      :   Any      =	bbox[i, j, 3]
																															UpperCAmelCase_      :   List[Any]      =	bbox[i, j, 1]
																															UpperCAmelCase_      :   Optional[Any]      =	t
																									if bbox[i, j, 2] < bbox[i, j, 0]:
																															UpperCAmelCase_      :   Union[str, Any]      =	bbox[i, j, 2]
																															UpperCAmelCase_      :   Union[str, Any]      =	bbox[i, j, 0]
																															UpperCAmelCase_      :   Any      =	t

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

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

													UpperCAmelCase_      :   Optional[int]      =	None
													UpperCAmelCase_      :   List[str]      =	None
													UpperCAmelCase_      :   List[Any]      =	None
													if self.use_labels:
																			UpperCAmelCase_      :   int      =	ids_tensor([self.batch_size]							,  self.type_sequence_label_size )
																			UpperCAmelCase_      :   int      =	ids_tensor([self.batch_size, self.seq_length]							,  self.num_labels )
																			UpperCAmelCase_      :   Tuple      =	ids_tensor([self.batch_size]							,  self.num_choices )

													UpperCAmelCase_      :   str      =	self.get_config()

													return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

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







													"""simple docstring"""
													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      UpperCAmelCase__						(				self				:  List[str]							,  __magic_name__				:  Tuple							,  __magic_name__				:  int							,  __magic_name__				:  int							,  __magic_name__				:  List[str]							,  __magic_name__				:  List[Any]							,  __magic_name__				:  List[Any]							,  __magic_name__				:  str							,  __magic_name__				:  Union[str, Any] )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	LayoutLMModel(config=__magic_name__ )
													model.to(__magic_name__ )
													model.eval()
													UpperCAmelCase_      :   List[Any]      =	model(__magic_name__							,  __magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	model(__magic_name__							,  __magic_name__							,  token_type_ids=__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	model(__magic_name__							,  __magic_name__ )
													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				:  List[str]							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Optional[Any]							,  __magic_name__				:  str							,  __magic_name__				:  int							,  __magic_name__				:  int							,  __magic_name__				:  int							,  __magic_name__				:  str )       ->							Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	LayoutLMForMaskedLM(config=__magic_name__ )
													model.to(__magic_name__ )
													model.eval()
													UpperCAmelCase_      :   Optional[Any]      =	model(__magic_name__							,  __magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  labels=__magic_name__ )
													self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.vocab_size) )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  List[str]							,  __magic_name__				:  Dict							,  __magic_name__				:  Tuple							,  __magic_name__				:  Dict							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  int							,  __magic_name__				:  Optional[int] )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	self.num_labels
													UpperCAmelCase_      :   Any      =	LayoutLMForSequenceClassification(__magic_name__ )
													model.to(__magic_name__ )
													model.eval()
													UpperCAmelCase_      :   Optional[int]      =	model(
													    __magic_name__							,  __magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  labels=__magic_name__ )
													self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels) )

							def      UpperCAmelCase__						(				self				:  Any							,  __magic_name__				:  Tuple							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  List[Any]							,  __magic_name__				:  Tuple							,  __magic_name__				:  Dict							,  __magic_name__				:  Tuple							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  List[Any] )       ->							str:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	self.num_labels
													UpperCAmelCase_      :   Optional[int]      =	LayoutLMForTokenClassification(config=__magic_name__ )
													model.to(__magic_name__ )
													model.eval()
													UpperCAmelCase_      :   int      =	model(__magic_name__							,  __magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  labels=__magic_name__ )
													self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.seq_length, self.num_labels) )

							def      UpperCAmelCase__						(				self				:  List[Any]							,  __magic_name__				:  Optional[Any]							,  __magic_name__				:  Any							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Dict							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Optional[int]							,  __magic_name__				:  Union[str, Any] )       ->							str:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	LayoutLMForQuestionAnswering(config=__magic_name__ )
													model.to(__magic_name__ )
													model.eval()
													UpperCAmelCase_      :   Union[str, Any]      =	model(
													    __magic_name__							,  bbox=__magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  start_positions=__magic_name__							,  end_positions=__magic_name__							,  )
													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				:  Tuple )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	self.prepare_config_and_inputs()
													(
													    (
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							(
													    UpperCAmelCase_
													)    ,							
													)      :   str      =	config_and_inputs
													UpperCAmelCase_      :   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        __a   (lowerCamelCase							,		lowerCamelCase							,		unittest.TestCase  ):
							__a       : Optional[Any]          =							(
							    (
							        LayoutLMModel,
							        LayoutLMForMaskedLM,
							        LayoutLMForSequenceClassification,
							        LayoutLMForTokenClassification,
							        LayoutLMForQuestionAnswering,
							    )
							    if is_torch_available()
							    else None
							)
							__a       : List[str]          =							(
							    {
							        "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       : Any          =							True

							def      UpperCAmelCase__						(				self				:  str )       ->							Dict:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	LayoutLMModelTester(self )
													UpperCAmelCase_      :   str      =	ConfigTester(self							,  config_class=__magic_name__							,  hidden_size=37 )

							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							int:







													"""simple docstring"""
													self.config_tester.run_common_tests()

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	self.model_tester.prepare_config_and_inputs()
													for type in ["absolute", "relative_key", "relative_key_query"]:
																			UpperCAmelCase_      :   Any      =	type
																			self.model_tester.create_and_check_model(*__magic_name__ )

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*__magic_name__ )

							def      UpperCAmelCase__						(				self				:  Union[str, Any] )       ->							Dict:







													"""simple docstring"""
													UpperCAmelCase_      :   Union[str, Any]      =	self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_sequence_classification(*__magic_name__ )

							def      UpperCAmelCase__						(				self				:  Optional[Any] )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_token_classification(*__magic_name__ )

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







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_question_answering(*__magic_name__ )



							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
							def      UpperCAmelCase__						(				self				:  Dict )       ->							Any:







													"""simple docstring"""
													pass





def 							lowerCamelCase_   (	)			->						List[Any]:
						# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
						# fmt: off
						UpperCAmelCase_      :   Optional[Any]      =	torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]],      device=SCREAMING_SNAKE_CASE__ )  # noqa: E231
						UpperCAmelCase_      :   Optional[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=SCREAMING_SNAKE_CASE__ )  # noqa: E231
						UpperCAmelCase_      :   Tuple      =	torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]],      device=SCREAMING_SNAKE_CASE__ )  # noqa: E231
						UpperCAmelCase_      :   int      =	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=SCREAMING_SNAKE_CASE__ )  # noqa: E231
						# these are sequence labels (i.e. at the token level)
						UpperCAmelCase_      :   Dict      =	torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]],      device=SCREAMING_SNAKE_CASE__ )  # noqa: E231
						# fmt: on

						return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class        __a   (unittest.TestCase  ):

							@slow
							def      UpperCAmelCase__						(				self				:  Dict )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   Any      =	LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased''' ).to(__magic_name__ )

													UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[int]      =	prepare_layoutlm_batch_inputs()

													# forward pass
													UpperCAmelCase_      :   Tuple      =	model(input_ids=__magic_name__							,  bbox=__magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__ )

													# test the sequence output on [0, :3, :3]
													UpperCAmelCase_      :   int      =	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=__magic_name__							,  )

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

													# test the pooled output on [1, :3]
													UpperCAmelCase_      :   List[str]      =	torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]							,  device=__magic_name__ )

													self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]							,  __magic_name__							,  atol=1E-3 ) )

							@slow
							def      UpperCAmelCase__						(				self				:  int )       ->							Tuple:







													"""simple docstring"""
													# initialize model with randomly initialized sequence classification head
													UpperCAmelCase_      :   Dict      =	LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''							,  num_labels=2 ).to(
													    __magic_name__ )

													UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[int]      =	prepare_layoutlm_batch_inputs()

													# forward pass
													UpperCAmelCase_      :   Dict      =	model(
													    input_ids=__magic_name__							,  bbox=__magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  labels=torch.tensor([1, 1]							,  device=__magic_name__ )							,  )

													# test whether we get a loss as a scalar
													UpperCAmelCase_      :   List[str]      =	outputs.loss
													UpperCAmelCase_      :   Tuple      =	torch.Size([] )
													self.assertEqual(loss.shape							,  __magic_name__ )

													# test the shape of the logits
													UpperCAmelCase_      :   Dict      =	outputs.logits
													UpperCAmelCase_      :   Optional[int]      =	torch.Size((2, 2) )
													self.assertEqual(logits.shape							,  __magic_name__ )

							@slow
							def      UpperCAmelCase__						(				self				:  str )       ->							Union[str, Any]:







													"""simple docstring"""
													# initialize model with randomly initialized token classification head
													UpperCAmelCase_      :   List[Any]      =	LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''							,  num_labels=13 ).to(
													    __magic_name__ )

													UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[Any]      =	prepare_layoutlm_batch_inputs()

													# forward pass
													UpperCAmelCase_      :   Any      =	model(
													    input_ids=__magic_name__							,  bbox=__magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__							,  labels=__magic_name__ )

													# 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_      :   Optional[Any]      =	outputs.logits
													UpperCAmelCase_      :   Dict      =	torch.Size((2, 25, 13) )
													self.assertEqual(logits.shape							,  __magic_name__ )



							@slow
							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							Dict:







													"""simple docstring"""
													# initialize model with randomly initialized token classification head
													UpperCAmelCase_      :   Any      =	LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased''' ).to(__magic_name__ )

													UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_      :   Optional[int]      =	prepare_layoutlm_batch_inputs()

													# forward pass
													UpperCAmelCase_      :   Optional[Any]      =	model(input_ids=__magic_name__							,  bbox=__magic_name__							,  attention_mask=__magic_name__							,  token_type_ids=__magic_name__ )

													# test the shape of the logits
													UpperCAmelCase_      :   Union[str, Any]      =	torch.Size((2, 25) )
													self.assertEqual(outputs.start_logits.shape							,  __magic_name__ )
													self.assertEqual(outputs.end_logits.shape							,  __magic_name__ )





