'''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       __lowercase		:







     """simple docstring"""






     def __init__(		self  ,      _lowerCamelCase  ,      _lowerCamelCase=1_3  ,      _lowerCamelCase=7  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=9_9  ,      _lowerCamelCase=3_2  ,      _lowerCamelCase=5  ,      _lowerCamelCase=4  ,      _lowerCamelCase=3_7  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=4  ,      _lowerCamelCase=None  ,      _lowerCamelCase=1_0_0_0  ,      ):
         __UpperCamelCase						:						Dict		     =	parent
         __UpperCamelCase						:						Dict		     =	batch_size
         __UpperCamelCase						:						Dict		     =	seq_length
         __UpperCamelCase						:						Union[str, Any]		     =	is_training
         __UpperCamelCase						:						Dict		     =	use_input_mask
         __UpperCamelCase						:						List[Any]		     =	use_token_type_ids
         __UpperCamelCase						:						List[str]		     =	use_labels
         __UpperCamelCase						:						Dict		     =	vocab_size
         __UpperCamelCase						:						Optional[Any]		     =	hidden_size
         __UpperCamelCase						:						Dict		     =	num_hidden_layers
         __UpperCamelCase						:						Optional[Any]		     =	num_attention_heads
         __UpperCamelCase						:						Any		     =	intermediate_size
         __UpperCamelCase						:						List[str]		     =	hidden_act
         __UpperCamelCase						:						Any		     =	hidden_dropout_prob
         __UpperCamelCase						:						List[Any]		     =	attention_probs_dropout_prob
         __UpperCamelCase						:						Tuple		     =	max_position_embeddings
         __UpperCamelCase						:						Dict		     =	type_vocab_size
         __UpperCamelCase						:						str		     =	type_sequence_label_size
         __UpperCamelCase						:						Optional[int]		     =	initializer_range
         __UpperCamelCase						:						Optional[Any]		     =	num_labels
         __UpperCamelCase						:						Union[str, Any]		     =	num_choices
         __UpperCamelCase						:						int		     =	scope
         __UpperCamelCase						:						Dict		     =	range_bbox







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size     )

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

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

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

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

         __UpperCamelCase						:						Tuple		     =	self.get_config()

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







     def lowerCAmelCase  (		self     ):
         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 lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Optional[int]		     =	LayoutLMModel(config=_lowerCamelCase     )
         model.to(_lowerCamelCase     )
         model.eval()
         __UpperCamelCase						:						Optional[Any]		     =	model(_lowerCamelCase  ,      _lowerCamelCase  ,      attention_mask=_lowerCamelCase  ,      token_type_ids=_lowerCamelCase     )
         __UpperCamelCase						:						Dict		     =	model(_lowerCamelCase  ,      _lowerCamelCase  ,      token_type_ids=_lowerCamelCase     )
         __UpperCamelCase						:						List[str]		     =	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 lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						str		     =	LayoutLMForMaskedLM(config=_lowerCamelCase     )
         model.to(_lowerCamelCase     )
         model.eval()
         __UpperCamelCase						:						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 lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						str		     =	self.num_labels
         __UpperCamelCase						:						Optional[Any]		     =	LayoutLMForSequenceClassification(_lowerCamelCase     )
         model.to(_lowerCamelCase     )
         model.eval()
         __UpperCamelCase						:						List[str]		     =	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 lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						List[str]		     =	self.num_labels
         __UpperCamelCase						:						Dict		     =	LayoutLMForTokenClassification(config=_lowerCamelCase     )
         model.to(_lowerCamelCase     )
         model.eval()
         __UpperCamelCase						:						Dict		     =	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 lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						Any		     =	LayoutLMForQuestionAnswering(config=_lowerCamelCase     )
         model.to(_lowerCamelCase     )
         model.eval()
         __UpperCamelCase						:						Any		     =	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 lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	self.prepare_config_and_inputs()
         (
             (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       
         )						:						Tuple		     =	config_and_inputs
         __UpperCamelCase						:						Any		     =	{
             'input_ids': input_ids,
             'bbox': bbox,
             'token_type_ids': token_type_ids,
             'attention_mask': input_mask,
         }
         return config, inputs_dict






@require_torch
class       __lowercase		(    _lowerCamelCase      ,				_lowerCamelCase      ,				unittest.TestCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    (
         (
             LayoutLMModel,
             LayoutLMForMaskedLM,
             LayoutLMForSequenceClassification,
             LayoutLMForTokenClassification,
             LayoutLMForQuestionAnswering,
         )
         if is_torch_available()
         else None
     )
     SCREAMING_SNAKE_CASE__		 =    (
         {
             '''document-question-answering''': LayoutLMForQuestionAnswering,
             '''feature-extraction''': LayoutLMModel,
             '''fill-mask''': LayoutLMForMaskedLM,
             '''text-classification''': LayoutLMForSequenceClassification,
             '''token-classification''': LayoutLMForTokenClassification,
             '''zero-shot''': LayoutLMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__		 =    True







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	LayoutLMModelTester(self     )
         __UpperCamelCase						:						Tuple		     =	ConfigTester(self  ,      config_class=_lowerCamelCase  ,      hidden_size=3_7     )







     def lowerCAmelCase  (		self     ):
         self.config_tester.run_common_tests()







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[int]		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             __UpperCamelCase						:						List[str]		     =	type
             self.model_tester.create_and_check_model(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Any		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						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 lowerCAmelCase  (		self     ):
         pass







def 			_UpperCamelCase			(  ):




    """simple docstring"""



    __UpperCamelCase						:						int		     =	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=_a      )  # 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=_a      )  # noqa: E231
    __UpperCamelCase						:						Tuple		     =	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=_a      )  # noqa: E231
    __UpperCamelCase						:						str		     =	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=_a      )  # noqa: E231
    # these are sequence labels (i.e. at the token level)
    __UpperCamelCase						:						Optional[Any]		     =	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=_a      )  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     @slow
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'     ).to(_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	prepare_layoutlm_batch_inputs()

         # forward pass
         __UpperCamelCase						:						List[Any]		     =	model(input_ids=_lowerCamelCase  ,      bbox=_lowerCamelCase  ,      attention_mask=_lowerCamelCase  ,      token_type_ids=_lowerCamelCase     )

         # test the sequence output on [0, :3, :3]
         __UpperCamelCase						:						Optional[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=_lowerCamelCase  ,      )

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

         # test the pooled output on [1, :3]
         __UpperCamelCase						:						List[Any]		     =	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 lowerCAmelCase  (		self     ):
         # initialize model with randomly initialized sequence classification head
         __UpperCamelCase						:						Optional[Any]		     =	LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'  ,      num_labels=2     ).to(
             _lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						List[str]		     =	prepare_layoutlm_batch_inputs()

         # forward pass
         __UpperCamelCase						:						Optional[int]		     =	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						:						Optional[Any]		     =	outputs.loss
         __UpperCamelCase						:						str		     =	torch.Size([]     )
         self.assertEqual(loss.shape  ,      _lowerCamelCase     )

         # test the shape of the logits
         __UpperCamelCase						:						Dict		     =	outputs.logits
         __UpperCamelCase						:						List[str]		     =	torch.Size((2, 2)     )
         self.assertEqual(logits.shape  ,      _lowerCamelCase     )







     @slow
     def lowerCAmelCase  (		self     ):
         # initialize model with randomly initialized token classification head
         __UpperCamelCase						:						int		     =	LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'  ,      num_labels=1_3     ).to(
             _lowerCamelCase     )

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

         # forward pass
         __UpperCamelCase						:						Tuple		     =	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						:						Tuple		     =	outputs.logits
         __UpperCamelCase						:						Optional[Any]		     =	torch.Size((2, 2_5, 1_3)     )
         self.assertEqual(logits.shape  ,      _lowerCamelCase     )







     @slow
     def lowerCAmelCase  (		self     ):
         # initialize model with randomly initialized token classification head
         __UpperCamelCase						:						Dict		     =	LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'     ).to(_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						List[Any]		     =	prepare_layoutlm_batch_inputs()

         # forward pass
         __UpperCamelCase						:						List[str]		     =	model(input_ids=_lowerCamelCase  ,      bbox=_lowerCamelCase  ,      attention_mask=_lowerCamelCase  ,      token_type_ids=_lowerCamelCase     )

         # test the shape of the logits
         __UpperCamelCase						:						str		     =	torch.Size((2, 2_5)     )
         self.assertEqual(outputs.start_logits.shape  ,      _lowerCamelCase     )
         self.assertEqual(outputs.end_logits.shape  ,      _lowerCamelCase     )




'''simple docstring'''
from .stack import Stack







def 			_UpperCamelCase			(  _a						:						str      ):




    """simple docstring"""



    __UpperCamelCase						:						Stack[str]		     =	Stack()
    __UpperCamelCase						:						Any		     =	{'(': ')', '[': ']', '{': '}'}
    for bracket in parentheses:
        if bracket in bracket_pairs:
            stack.push(_a      )
        elif bracket in (")", "]", "}"):
            if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                return False
    return stack.is_empty()


if __name__ == "__main__":
  from doctest import testmod

  testmod()

  a=	['''((()))''', '''((())''', '''(()))''']
  print('''Balanced parentheses demonstration:\n''')
  for example in examples:
    a=	'''''' if balanced_parentheses(example) else '''not '''
    print(F"""{example} is {not_str}balanced""")



