

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	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE=13	,   _SCREAMING_SNAKE_CASE=7	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=99	,   _SCREAMING_SNAKE_CASE=32	,   _SCREAMING_SNAKE_CASE=5	,   _SCREAMING_SNAKE_CASE=4	,   _SCREAMING_SNAKE_CASE=37	,   _SCREAMING_SNAKE_CASE="gelu"	,   _SCREAMING_SNAKE_CASE=0.1	,   _SCREAMING_SNAKE_CASE=0.1	,   _SCREAMING_SNAKE_CASE=512	,   _SCREAMING_SNAKE_CASE=16	,   _SCREAMING_SNAKE_CASE=2	,   _SCREAMING_SNAKE_CASE=0.02	,   _SCREAMING_SNAKE_CASE=3	,   _SCREAMING_SNAKE_CASE=4	,   _SCREAMING_SNAKE_CASE=None	,   _SCREAMING_SNAKE_CASE=1000	,   ):





        """simple docstring"""







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

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size   )

        SCREAMING_SNAKE_CASE_			:					Dict          =       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]:
                          SCREAMING_SNAKE_CASE_			:					int          =       bbox[i, j, 3]
                          SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       bbox[i, j, 1]
                          SCREAMING_SNAKE_CASE_			:					Any          =       t
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                          SCREAMING_SNAKE_CASE_			:					str          =       bbox[i, j, 2]
                          SCREAMING_SNAKE_CASE_			:					Dict          =       bbox[i, j, 0]
                          SCREAMING_SNAKE_CASE_			:					int          =       t

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

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

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

        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       self.get_config()

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

  def 	UpperCAmelCase      (			self   ):





        """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	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       LayoutLMModel(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)   )
        self.parent.assertEqual(result.pooler_output.shape	,   (self.batch_size, self.hidden_size)   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Dict          =       LayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE	,   labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.vocab_size)   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       self.num_labels
        SCREAMING_SNAKE_CASE_			:					Dict          =       LayoutLMForSequenceClassification(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       model(
            _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE	,   labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels)   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       self.num_labels
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       LayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE	,   labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.num_labels)   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					int          =       LayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       model(
            _SCREAMING_SNAKE_CASE	,   bbox=_SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE	,   start_positions=_SCREAMING_SNAKE_CASE	,   end_positions=_SCREAMING_SNAKE_CASE	,   )
        self.parent.assertEqual(result.start_logits.shape	,   (self.batch_size, self.seq_length)   )
        self.parent.assertEqual(result.end_logits.shape	,   (self.batch_size, self.seq_length)   )



  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       self.prepare_config_and_inputs()
        (
            (
            SCREAMING_SNAKE_CASE_
        )    ,  (
            SCREAMING_SNAKE_CASE_
        )    ,  (
            SCREAMING_SNAKE_CASE_
        )    ,  (
            SCREAMING_SNAKE_CASE_
        )    ,  (
            SCREAMING_SNAKE_CASE_
        )    ,  (
            SCREAMING_SNAKE_CASE_
        )    ,  (
            SCREAMING_SNAKE_CASE_
        )    ,  (
            SCREAMING_SNAKE_CASE_
        )    ,  
        )			:					Optional[int]          =       config_and_inputs
        SCREAMING_SNAKE_CASE_			:					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						(					__magic_name__				,       __magic_name__				,       unittest.TestCase):
  SCREAMING_SNAKE_CASE      : Dict    =					(
      (
          LayoutLMModel,
          LayoutLMForMaskedLM,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMForQuestionAnswering,
      )
      if is_torch_available()
      else None
  )
  SCREAMING_SNAKE_CASE      : 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 {}
  )
  SCREAMING_SNAKE_CASE      : Any    =					True

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[str]          =       LayoutLMModelTester(self   )
        SCREAMING_SNAKE_CASE_			:					List[str]          =       ConfigTester(self	,   config_class=_SCREAMING_SNAKE_CASE	,   hidden_size=37   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        self.config_tester.run_common_tests()

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					int          =       self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
              SCREAMING_SNAKE_CASE_			:					Dict          =       type
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Dict          =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE   )



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





        """simple docstring"""







        pass

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       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
      SCREAMING_SNAKE_CASE_			:					Dict          =       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
      SCREAMING_SNAKE_CASE_			:					Dict          =       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
      SCREAMING_SNAKE_CASE_			:					Optional[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=a					)  # noqa: E231
      # these are sequence labels (i.e. at the token level)
      SCREAMING_SNAKE_CASE_			:					List[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 	_A						(					unittest.TestCase):

  @slow
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'   ).to(_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       prepare_layoutlm_batch_inputs()

        # forward pass
        SCREAMING_SNAKE_CASE_			:					str          =       model(input_ids=_SCREAMING_SNAKE_CASE	,   bbox=_SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE   )

        # test the sequence output on [0, :3, :3]
        SCREAMING_SNAKE_CASE_			:					Tuple          =       torch.tensor(
            [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]	,   device=_SCREAMING_SNAKE_CASE	,   )

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

        # test the pooled output on [1, :3]
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       torch.tensor([-0.6580, -0.0214, 0.8552]	,   device=_SCREAMING_SNAKE_CASE   )

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

  @slow
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					int          =       LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'	,   num_labels=2   ).to(
            _SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					Any          =       prepare_layoutlm_batch_inputs()

        # forward pass
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       model(
            input_ids=_SCREAMING_SNAKE_CASE	,   bbox=_SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE	,   labels=torch.tensor([1, 1]	,   device=_SCREAMING_SNAKE_CASE   )	,   )

        # test whether we get a loss as a scalar
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       outputs.loss
        SCREAMING_SNAKE_CASE_			:					str          =       torch.Size([]   )
        self.assertEqual(loss.shape	,   _SCREAMING_SNAKE_CASE   )

        # test the shape of the logits
        SCREAMING_SNAKE_CASE_			:					List[str]          =       outputs.logits
        SCREAMING_SNAKE_CASE_			:					Tuple          =       torch.Size((2, 2)   )
        self.assertEqual(logits.shape	,   _SCREAMING_SNAKE_CASE   )

  @slow
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[str]          =       LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'	,   num_labels=13   ).to(
            _SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					int          =       prepare_layoutlm_batch_inputs()

        # forward pass
        SCREAMING_SNAKE_CASE_			:					str          =       model(
            input_ids=_SCREAMING_SNAKE_CASE	,   bbox=_SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE	,   labels=_SCREAMING_SNAKE_CASE   )

        # 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
        SCREAMING_SNAKE_CASE_			:					Dict          =       outputs.logits
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       torch.Size((2, 25, 13)   )
        self.assertEqual(logits.shape	,   _SCREAMING_SNAKE_CASE   )



  @slow
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					int          =       LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'   ).to(_SCREAMING_SNAKE_CASE   )

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

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

        # test the shape of the logits
        SCREAMING_SNAKE_CASE_			:					Dict          =       torch.Size((2, 25)   )
        self.assertEqual(outputs.start_logits.shape	,   _SCREAMING_SNAKE_CASE   )
        self.assertEqual(outputs.end_logits.shape	,   _SCREAMING_SNAKE_CASE   )









from math import sqrt

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       [1]
      for i in range(2   ,				int(sqrt(a					)					) + 1   ,				1					):
            if number % i == 0:
                  values.append(a					)
                  if int(number // i					) != i:
                        values.append(int(number // i					)					)
      return sorted(a					)

def 				A_					(							a					):

      """simple docstring"""



      return sum(factors(a					)					) > n

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       factors(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       len(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       [[0 for i in range(number + 1					)] for j in range(r + 1					)]
      for i in range(r + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       True

      for i in range(1   ,				number + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       False

      for i in range(1   ,				r + 1					):
            for j in range(1   ,				number + 1					):
                  if j < values[i - 1]:
                        SCREAMING_SNAKE_CASE_			:					Dict          =       subset[i - 1][j]
                  else:
                        SCREAMING_SNAKE_CASE_			:					List[str]          =       subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

      return subset[r][number] != 0

def 				A_					(							a					):

      """simple docstring"""



      return abundant(a					) and not semi_perfect(a					)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    for number in (69, 70, 71):
        print(F'{number} is {"" if weird(number) else "not "}weird.')






