





"""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							,					_snake_case							,					_snake_case=13							,					_snake_case=7							,					_snake_case=True							,					_snake_case=True							,					_snake_case=True							,					_snake_case=True							,					_snake_case=99							,					_snake_case=32							,					_snake_case=5							,					_snake_case=4							,					_snake_case=37							,					_snake_case="gelu"							,					_snake_case=0.1							,					_snake_case=0.1							,					_snake_case=5_12							,					_snake_case=16							,					_snake_case=2							,					_snake_case=0.02							,					_snake_case=3							,					_snake_case=4							,					_snake_case=None							,					_snake_case=10_00							,					):







  """simple docstring"""






  lowerCAmelCase													=   parent
  lowerCAmelCase													=   batch_size
  lowerCAmelCase													=   seq_length
  lowerCAmelCase													=   is_training
  lowerCAmelCase													=   use_input_mask
  lowerCAmelCase													=   use_token_type_ids
  lowerCAmelCase													=   use_labels
  lowerCAmelCase													=   vocab_size
  lowerCAmelCase													=   hidden_size
  lowerCAmelCase													=   num_hidden_layers
  lowerCAmelCase													=   num_attention_heads
  lowerCAmelCase													=   intermediate_size
  lowerCAmelCase													=   hidden_act
  lowerCAmelCase													=   hidden_dropout_prob
  lowerCAmelCase													=   attention_probs_dropout_prob
  lowerCAmelCase													=   max_position_embeddings
  lowerCAmelCase													=   type_vocab_size
  lowerCAmelCase													=   type_sequence_label_size
  lowerCAmelCase													=   initializer_range
  lowerCAmelCase													=   num_labels
  lowerCAmelCase													=   num_choices
  lowerCAmelCase													=   scope
  lowerCAmelCase													=   range_bbox



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

  lowerCAmelCase													=   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													=   bbox[i, j, 3]
     lowerCAmelCase													=   bbox[i, j, 1]
     lowerCAmelCase													=   t
    if bbox[i, j, 2] < bbox[i, j, 0]:
     lowerCAmelCase													=   bbox[i, j, 2]
     lowerCAmelCase													=   bbox[i, j, 0]
     lowerCAmelCase													=   t

  lowerCAmelCase													=   None
  if self.use_input_mask:
   lowerCAmelCase													=   ids_tensor([self.batch_size, self.seq_length]							,					vocab_size=2    )

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

  lowerCAmelCase													=   None
  lowerCAmelCase													=   None
  lowerCAmelCase													=   None
  if self.use_labels:
   lowerCAmelCase													=   ids_tensor([self.batch_size]							,					self.type_sequence_label_size    )
   lowerCAmelCase													=   ids_tensor([self.batch_size, self.seq_length]							,					self.num_labels    )
   lowerCAmelCase													=   ids_tensor([self.batch_size]							,					self.num_choices    )

  lowerCAmelCase													=   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							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMModel(config=_snake_case    )
  model.to(_snake_case    )
  model.eval()
  lowerCAmelCase													=   model(_snake_case							,					_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case    )
  lowerCAmelCase													=   model(_snake_case							,					_snake_case							,					token_type_ids=_snake_case    )
  lowerCAmelCase													=   model(_snake_case							,					_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							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMForMaskedLM(config=_snake_case    )
  model.to(_snake_case    )
  model.eval()
  lowerCAmelCase													=   model(_snake_case							,					_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					labels=_snake_case    )
  self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.num_labels
  lowerCAmelCase													=   LayoutLMForSequenceClassification(_snake_case    )
  model.to(_snake_case    )
  model.eval()
  lowerCAmelCase													=   model(
      _snake_case							,					_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					labels=_snake_case    )
  self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   self.num_labels
  lowerCAmelCase													=   LayoutLMForTokenClassification(config=_snake_case    )
  model.to(_snake_case    )
  model.eval()
  lowerCAmelCase													=   model(_snake_case							,					_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					labels=_snake_case    )
  self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.num_labels)    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMForQuestionAnswering(config=_snake_case    )
  model.to(_snake_case    )
  model.eval()
  lowerCAmelCase													=   model(
      _snake_case							,					bbox=_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					start_positions=_snake_case							,					end_positions=_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"""






  lowerCAmelCase													=   self.prepare_config_and_inputs()
  (
      (
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,(
      lowerCAmelCase
  )							,
  )													=   config_and_inputs
  lowerCAmelCase													=   {
      'input_ids': input_ids,
      'bbox': bbox,
      'token_type_ids': token_type_ids,
      'attention_mask': input_mask,
  }
  return config, inputs_dict







@require_torch
class 			a       (      a__      ,   a__      ,   unittest.TestCase     ):
 snake_case__								=					(
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 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 {}
 )
 snake_case__								=					True



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMModelTester(self    )
  lowerCAmelCase													=   ConfigTester(self							,					config_class=_snake_case							,					hidden_size=37    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  self.config_tester.run_common_tests()



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  for type in ["absolute", "relative_key", "relative_key_query"]:
   lowerCAmelCase													=   type
   self.model_tester.create_and_check_model(*_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*_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 		_SCREAMING_SNAKE_CASE ():
 # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
 # fmt: off
 lowerCAmelCase													=   torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]     ,    device=_UpperCAmelCase )  # noqa: E231
 lowerCAmelCase													=   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=_UpperCAmelCase )  # noqa: E231
 lowerCAmelCase													=   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=_UpperCAmelCase )  # noqa: E231
 lowerCAmelCase													=   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=_UpperCAmelCase )  # noqa: E231
 # these are sequence labels (i.e. at the token level)
 lowerCAmelCase													=   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=_UpperCAmelCase )  # 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"""






  lowerCAmelCase													=   LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(_snake_case    )

  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase													=   prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase													=   model(input_ids=_snake_case							,					bbox=_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case    )

  # test the sequence output on [0, :3, :3]
  lowerCAmelCase													=   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=_snake_case							,					)

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

  # test the pooled output on [1, :3]
  lowerCAmelCase													=   torch.tensor([-0.6_580, -0.0_214, 0.8_552]							,					device=_snake_case    )

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



 @slow
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'							,					num_labels=2    ).to(
      _snake_case    )

  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase													=   prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase													=   model(
      input_ids=_snake_case							,					bbox=_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					labels=torch.tensor([1, 1]							,					device=_snake_case    )							,					)

  # test whether we get a loss as a scalar
  lowerCAmelCase													=   outputs.loss
  lowerCAmelCase													=   torch.Size([]    )
  self.assertEqual(loss.shape							,					_snake_case    )

  # test the shape of the logits
  lowerCAmelCase													=   outputs.logits
  lowerCAmelCase													=   torch.Size((2, 2)    )
  self.assertEqual(logits.shape							,					_snake_case    )



 @slow
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'							,					num_labels=13    ).to(
      _snake_case    )

  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase													=   prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase													=   model(
      input_ids=_snake_case							,					bbox=_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case							,					labels=_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
  lowerCAmelCase													=   outputs.logits
  lowerCAmelCase													=   torch.Size((2, 25, 13)    )
  self.assertEqual(logits.shape							,					_snake_case    )



 @slow
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(_snake_case    )

  lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase							,lowerCAmelCase													=   prepare_layoutlm_batch_inputs()

  # forward pass
  lowerCAmelCase													=   model(input_ids=_snake_case							,					bbox=_snake_case							,					attention_mask=_snake_case							,					token_type_ids=_snake_case    )

  # test the shape of the logits
  lowerCAmelCase													=   torch.Size((2, 25)    )
  self.assertEqual(outputs.start_logits.shape							,					_snake_case    )
  self.assertEqual(outputs.end_logits.shape							,					_snake_case    )












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase    :	Dict						=     logging.get_logger(__name__)

__UpperCamelCase    :	List[Any]						=     {
    '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
}







class 			a       (      a__     ):
 snake_case__								=					'''mpnet'''



 def __init__(    self							,					_snake_case=3_05_27							,					_snake_case=7_68							,					_snake_case=12							,					_snake_case=12							,					_snake_case=30_72							,					_snake_case="gelu"							,					_snake_case=0.1							,					_snake_case=0.1							,					_snake_case=5_12							,					_snake_case=0.02							,					_snake_case=1E-12							,					_snake_case=32							,					_snake_case=1							,					_snake_case=0							,					_snake_case=2							,					**_snake_case							,					):







  """simple docstring"""






  super().__init__(pad_token_id=_snake_case							,					bos_token_id=_snake_case							,					eos_token_id=_snake_case							,					**_snake_case    )

  lowerCAmelCase													=   vocab_size
  lowerCAmelCase													=   hidden_size
  lowerCAmelCase													=   num_hidden_layers
  lowerCAmelCase													=   num_attention_heads
  lowerCAmelCase													=   hidden_act
  lowerCAmelCase													=   intermediate_size
  lowerCAmelCase													=   hidden_dropout_prob
  lowerCAmelCase													=   attention_probs_dropout_prob
  lowerCAmelCase													=   max_position_embeddings
  lowerCAmelCase													=   initializer_range
  lowerCAmelCase													=   layer_norm_eps
  lowerCAmelCase													=   relative_attention_num_buckets





