

import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_PRETRAINING_MAPPING,
      ElectraForCausalLM,
      ElectraForMaskedLM,
      ElectraForMultipleChoice,
      ElectraForPreTraining,
      ElectraForQuestionAnswering,
      ElectraForSequenceClassification,
      ElectraForTokenClassification,
      ElectraModel,
  )
  from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST







class  UpperCamelCase_      :


  """simple docstring"""



  def __init__(						self     :					List[str]  ,	A_     :					Any  ,	A_     :					List[str]=13  ,	A_     :					Any=7  ,	A_     :					Dict=True  ,	A_     :					str=True  ,	A_     :					int=True  ,	A_     :					int=True  ,	A_     :					Union[str, Any]=99  ,	A_     :					List[Any]=32  ,	A_     :					List[Any]=5  ,	A_     :					Tuple=4  ,	A_     :					int=37  ,	A_     :					Dict="gelu"  ,	A_     :					Optional[int]=0.1  ,	A_     :					Tuple=0.1  ,	A_     :					Dict=512  ,	A_     :					Optional[int]=16  ,	A_     :					Optional[int]=2  ,	A_     :					List[str]=0.02  ,	A_     :					Optional[int]=3  ,	A_     :					Union[str, Any]=4  ,	A_     :					Tuple=None  ,	)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    parent
      SCREAMING_SNAKE_CASE__         =    batch_size
      SCREAMING_SNAKE_CASE__         =    seq_length
      SCREAMING_SNAKE_CASE__         =    is_training
      SCREAMING_SNAKE_CASE__         =    use_input_mask
      SCREAMING_SNAKE_CASE__         =    use_token_type_ids
      SCREAMING_SNAKE_CASE__         =    use_labels
      SCREAMING_SNAKE_CASE__         =    vocab_size
      SCREAMING_SNAKE_CASE__         =    hidden_size
      SCREAMING_SNAKE_CASE__         =    num_hidden_layers
      SCREAMING_SNAKE_CASE__         =    num_attention_heads
      SCREAMING_SNAKE_CASE__         =    intermediate_size
      SCREAMING_SNAKE_CASE__         =    hidden_act
      SCREAMING_SNAKE_CASE__         =    hidden_dropout_prob
      SCREAMING_SNAKE_CASE__         =    attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE__         =    max_position_embeddings
      SCREAMING_SNAKE_CASE__         =    type_vocab_size
      SCREAMING_SNAKE_CASE__         =    type_sequence_label_size
      SCREAMING_SNAKE_CASE__         =    initializer_range
      SCREAMING_SNAKE_CASE__         =    num_labels
      SCREAMING_SNAKE_CASE__         =    num_choices
      SCREAMING_SNAKE_CASE__         =    scope



  def   _UpperCamelCase     (						self     :					Dict						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    ids_tensor([self.batch_size, self.seq_length]  ,	self.vocab_size						)

      SCREAMING_SNAKE_CASE__         =    None
      if self.use_input_mask:
          SCREAMING_SNAKE_CASE__         =    random_attention_mask([self.batch_size, self.seq_length]						)

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

      SCREAMING_SNAKE_CASE__         =    None
      SCREAMING_SNAKE_CASE__         =    None
      SCREAMING_SNAKE_CASE__         =    None
      if self.use_labels:
          SCREAMING_SNAKE_CASE__         =    ids_tensor([self.batch_size]  ,	self.type_sequence_label_size						)
          SCREAMING_SNAKE_CASE__         =    ids_tensor([self.batch_size, self.seq_length]  ,	self.num_labels						)
          SCREAMING_SNAKE_CASE__         =    ids_tensor([self.batch_size]  ,	self.num_choices						)
          SCREAMING_SNAKE_CASE__         =    ids_tensor([self.batch_size, self.seq_length]  ,	1						)

      SCREAMING_SNAKE_CASE__         =    self.get_config()

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          fake_token_labels,
      )



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			Tuple:
      return ElectraConfig(
          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  ,	is_decoder=A_  ,	initializer_range=self.initializer_range  ,	)



  def   _UpperCamelCase     (						self     :					str						)       ->			Dict:
      (
          (
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		(
          SCREAMING_SNAKE_CASE__
      )  ,		
      )         =    self.prepare_config_and_inputs()

      SCREAMING_SNAKE_CASE__         =    True
      SCREAMING_SNAKE_CASE__         =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
      SCREAMING_SNAKE_CASE__         =    ids_tensor([self.batch_size, self.seq_length]  ,	vocab_size=2						)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )



  def   _UpperCamelCase     (						self     :					int  ,	A_     :					List[Any]  ,	A_     :					Optional[int]  ,	A_     :					Union[str, Any]  ,	A_     :					List[Any]  ,	A_     :					Optional[Any]  ,	A_     :					Optional[int]  ,	A_     :					str  ,	A_     :					str  ,	)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    ElectraModel(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	attention_mask=A_  ,	token_type_ids=A_						)
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	token_type_ids=A_						)
      SCREAMING_SNAKE_CASE__         =    model(A_						)
      self.parent.assertEqual(result.last_hidden_state.shape  ,	(self.batch_size, self.seq_length, self.hidden_size)						)



  def   _UpperCamelCase     (						self     :					List[Any]  ,	A_     :					Optional[int]  ,	A_     :					Optional[Any]  ,	A_     :					int  ,	A_     :					List[Any]  ,	A_     :					Tuple  ,	A_     :					Dict  ,	A_     :					Dict  ,	A_     :					List[str]  ,	A_     :					int  ,	)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    True
      SCREAMING_SNAKE_CASE__         =    ElectraModel(A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(
          A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	encoder_hidden_states=A_  ,	encoder_attention_mask=A_  ,	)
      SCREAMING_SNAKE_CASE__         =    model(
          A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	encoder_hidden_states=A_  ,	)
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	attention_mask=A_  ,	token_type_ids=A_						)
      self.parent.assertEqual(result.last_hidden_state.shape  ,	(self.batch_size, self.seq_length, self.hidden_size)						)



  def   _UpperCamelCase     (						self     :					int  ,	A_     :					Optional[Any]  ,	A_     :					Optional[int]  ,	A_     :					Optional[int]  ,	A_     :					List[str]  ,	A_     :					Any  ,	A_     :					Any  ,	A_     :					int  ,	A_     :					List[Any]  ,	)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    ElectraForMaskedLM(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.vocab_size)						)



  def   _UpperCamelCase     (						self     :					Tuple  ,	A_     :					List[str]  ,	A_     :					List[str]  ,	A_     :					int  ,	A_     :					Union[str, Any]  ,	A_     :					Tuple  ,	A_     :					List[Any]  ,	A_     :					Tuple  ,	A_     :					List[str]  ,	A_     :					Optional[int]  ,	)       ->			int:
      SCREAMING_SNAKE_CASE__         =    ElectraForCausalLM(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.vocab_size)						)



  def   _UpperCamelCase     (						self     :					List[Any]  ,	A_     :					Any  ,	A_     :					List[Any]  ,	A_     :					Optional[Any]  ,	A_     :					Any  ,	A_     :					List[Any]  ,	A_     :					Optional[int]  ,	A_     :					Tuple  ,	A_     :					Union[str, Any]  ,	)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    self.num_labels
      SCREAMING_SNAKE_CASE__         =    ElectraForTokenClassification(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.num_labels)						)



  def   _UpperCamelCase     (						self     :					List[str]  ,	A_     :					str  ,	A_     :					int  ,	A_     :					str  ,	A_     :					Optional[Any]  ,	A_     :					Union[str, Any]  ,	A_     :					Tuple  ,	A_     :					Tuple  ,	A_     :					Dict  ,	)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    self.num_labels
      SCREAMING_SNAKE_CASE__         =    ElectraForPreTraining(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length)						)



  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					Dict  ,	A_     :					Dict  ,	A_     :					Any  ,	A_     :					List[str]  ,	A_     :					Optional[int]  ,	A_     :					List[str]  ,	A_     :					Union[str, Any]  ,	A_     :					int  ,	)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    self.num_labels
      SCREAMING_SNAKE_CASE__         =    ElectraForSequenceClassification(A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.num_labels)						)



  def   _UpperCamelCase     (						self     :					str  ,	A_     :					Union[str, Any]  ,	A_     :					Dict  ,	A_     :					Optional[int]  ,	A_     :					Optional[Any]  ,	A_     :					List[str]  ,	A_     :					Union[str, Any]  ,	A_     :					int  ,	A_     :					List[str]  ,	)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    ElectraForQuestionAnswering(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(
          A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	start_positions=A_  ,	end_positions=A_  ,	)
      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  ,	A_     :					List[Any]  ,	A_     :					List[str]  ,	A_     :					Any  ,	A_     :					Any  ,	A_     :					List[Any]  ,	A_     :					List[str]  ,	A_     :					Optional[Any]  ,	A_     :					Any  ,	)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    self.num_choices
      SCREAMING_SNAKE_CASE__         =    ElectraForMultipleChoice(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    input_ids.unsqueeze(1						).expand(-1  ,	self.num_choices  ,	-1						).contiguous()
      SCREAMING_SNAKE_CASE__         =    token_type_ids.unsqueeze(1						).expand(-1  ,	self.num_choices  ,	-1						).contiguous()
      SCREAMING_SNAKE_CASE__         =    input_mask.unsqueeze(1						).expand(-1  ,	self.num_choices  ,	-1						).contiguous()
      SCREAMING_SNAKE_CASE__         =    model(
          A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_  ,	)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.num_choices)						)







  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    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__
      )  ,		
      )         =    config_and_inputs
      SCREAMING_SNAKE_CASE__         =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_torch
class  UpperCamelCase_      (	_snake_case		,  _snake_case		,  unittest.TestCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			int					    =   (
      (
          ElectraModel,
          ElectraForPreTraining,
          ElectraForMaskedLM,
          ElectraForCausalLM,
          ElectraForMultipleChoice,
          ElectraForTokenClassification,
          ElectraForSequenceClassification,
          ElectraForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__					:			str					    =   (
      {
          """feature-extraction""": ElectraModel,
          """fill-mask""": ElectraForMaskedLM,
          """question-answering""": ElectraForQuestionAnswering,
          """text-classification""": ElectraForSequenceClassification,
          """text-generation""": ElectraForCausalLM,
          """token-classification""": ElectraForTokenClassification,
          """zero-shot""": ElectraForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   True



  def   _UpperCamelCase     (						self     :					Optional[Any]  ,	A_     :					Tuple  ,	A_     :					int  ,	A_     :					List[str]=False						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    super()._prepare_for_class(A_  ,	A_  ,	return_labels=A_						)

      if return_labels:
          if model_class in get_values(A_						):
              SCREAMING_SNAKE_CASE__         =    torch.zeros(
                  (self.model_tester.batch_size, self.model_tester.seq_length)  ,	dtype=torch.long  ,	device=A_						)
      return inputs_dict



  def   _UpperCamelCase     (						self     :					Optional[int]						)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    ElectraModelTester(self						)
      SCREAMING_SNAKE_CASE__         =    ConfigTester(self  ,	config_class=A_  ,	hidden_size=37						)



  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			Any:
      self.config_tester.run_common_tests()



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_model(*A_						)



  def   _UpperCamelCase     (						self     :					str						)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_electra_model_as_decoder(*A_						)



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			int:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
          SCREAMING_SNAKE_CASE__         =    type
          self.model_tester.create_and_check_electra_model(*A_						)



  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_masked_lm(*A_						)



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			int:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_token_classification(*A_						)



  def   _UpperCamelCase     (						self     :					str						)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_pretraining(*A_						)



  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_sequence_classification(*A_						)



  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_question_answering(*A_						)



  def   _UpperCamelCase     (						self     :					Optional[int]						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_electra_for_multiple_choice(*A_						)



  @slow
  def   _UpperCamelCase     (						self     :					Any						)       ->			int:
      for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          SCREAMING_SNAKE_CASE__         =    ElectraModel.from_pretrained(A_						)
          self.assertIsNotNone(A_						)







  def   _UpperCamelCase     (						self     :					List[str]						)       ->			str:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_electra_for_causal_lm(*A_						)







@require_torch
class  UpperCamelCase_      (	unittest.TestCase      ):


  """simple docstring"""



  @slow
  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    ElectraModel.from_pretrained("google/electra-small-discriminator"						)
      SCREAMING_SNAKE_CASE__         =    torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
      SCREAMING_SNAKE_CASE__         =    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	attention_mask=A_						)[0]
      SCREAMING_SNAKE_CASE__         =    torch.Size((1, 11, 256)						)
      self.assertEqual(output.shape  ,	A_						)
      SCREAMING_SNAKE_CASE__         =    torch.tensor(
          [[[0.4_471, 0.6_821, -0.3_265], [0.4_627, 0.5_255, -0.3_668], [0.4_532, 0.3_313, -0.4_344]]]						)

      self.assertTrue(torch.allclose(output[:, 1:4, 1:4]  ,	A_  ,	atol=1E-4						)						)





import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
  )







class  UpperCamelCase_      :


  """simple docstring"""



  def __init__(						self     :					Tuple  ,	A_     :					List[str]  ,	A_     :					List[Any]=13  ,	A_     :					Optional[int]=7  ,	A_     :					List[Any]=True  ,	A_     :					str=True  ,	A_     :					str=True  ,	A_     :					str=True  ,	A_     :					List[Any]=99  ,	A_     :					str=32  ,	A_     :					Dict=5  ,	A_     :					Tuple=4  ,	A_     :					Optional[Any]=37  ,	A_     :					List[Any]="gelu"  ,	A_     :					Any=0.1  ,	A_     :					Union[str, Any]=0.1  ,	A_     :					List[Any]=512  ,	A_     :					Dict=16  ,	A_     :					List[Any]=2  ,	A_     :					Optional[Any]=0.02  ,	A_     :					List[Any]=3  ,	A_     :					Optional[Any]=4  ,	A_     :					Dict=None  ,	A_     :					str=1000  ,	)       ->			str:
      SCREAMING_SNAKE_CASE__         =    parent
      SCREAMING_SNAKE_CASE__         =    batch_size
      SCREAMING_SNAKE_CASE__         =    seq_length
      SCREAMING_SNAKE_CASE__         =    is_training
      SCREAMING_SNAKE_CASE__         =    use_input_mask
      SCREAMING_SNAKE_CASE__         =    use_token_type_ids
      SCREAMING_SNAKE_CASE__         =    use_labels
      SCREAMING_SNAKE_CASE__         =    vocab_size
      SCREAMING_SNAKE_CASE__         =    hidden_size
      SCREAMING_SNAKE_CASE__         =    num_hidden_layers
      SCREAMING_SNAKE_CASE__         =    num_attention_heads
      SCREAMING_SNAKE_CASE__         =    intermediate_size
      SCREAMING_SNAKE_CASE__         =    hidden_act
      SCREAMING_SNAKE_CASE__         =    hidden_dropout_prob
      SCREAMING_SNAKE_CASE__         =    attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE__         =    max_position_embeddings
      SCREAMING_SNAKE_CASE__         =    type_vocab_size
      SCREAMING_SNAKE_CASE__         =    type_sequence_label_size
      SCREAMING_SNAKE_CASE__         =    initializer_range
      SCREAMING_SNAKE_CASE__         =    num_labels
      SCREAMING_SNAKE_CASE__         =    num_choices
      SCREAMING_SNAKE_CASE__         =    scope
      SCREAMING_SNAKE_CASE__         =    range_bbox



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			int:
      SCREAMING_SNAKE_CASE__         =    ids_tensor([self.batch_size, self.seq_length]  ,	self.vocab_size						)

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

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

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

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

      SCREAMING_SNAKE_CASE__         =    self.get_config()

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



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			int:
      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[Any]  ,	A_     :					Any  ,	A_     :					List[str]  ,	A_     :					Optional[int]  ,	A_     :					Dict  ,	A_     :					int  ,	A_     :					List[str]  ,	A_     :					Any  ,	A_     :					Tuple						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    LayoutLMModel(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_  ,	attention_mask=A_  ,	token_type_ids=A_						)
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_  ,	token_type_ids=A_						)
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_						)
      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     :					int  ,	A_     :					int  ,	A_     :					Any  ,	A_     :					Tuple  ,	A_     :					Dict  ,	A_     :					int  ,	A_     :					Any  ,	A_     :					int  ,	A_     :					Union[str, Any]						)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    LayoutLMForMaskedLM(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.vocab_size)						)



  def   _UpperCamelCase     (						self     :					Dict  ,	A_     :					Union[str, Any]  ,	A_     :					Union[str, Any]  ,	A_     :					Dict  ,	A_     :					Optional[int]  ,	A_     :					Tuple  ,	A_     :					Optional[int]  ,	A_     :					Optional[int]  ,	A_     :					Optional[Any]						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    self.num_labels
      SCREAMING_SNAKE_CASE__         =    LayoutLMForSequenceClassification(A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(
          A_  ,	A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.num_labels)						)



  def   _UpperCamelCase     (						self     :					Any  ,	A_     :					Any  ,	A_     :					Any  ,	A_     :					str  ,	A_     :					Any  ,	A_     :					Optional[Any]  ,	A_     :					Tuple  ,	A_     :					Tuple  ,	A_     :					Any						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    self.num_labels
      SCREAMING_SNAKE_CASE__         =    LayoutLMForTokenClassification(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(A_  ,	A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)
      self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.seq_length, self.num_labels)						)



  def   _UpperCamelCase     (						self     :					Optional[int]  ,	A_     :					str  ,	A_     :					Optional[Any]  ,	A_     :					int  ,	A_     :					str  ,	A_     :					Optional[Any]  ,	A_     :					List[str]  ,	A_     :					Union[str, Any]  ,	A_     :					Optional[Any]						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    LayoutLMForQuestionAnswering(config=A_						)
      model.to(A_						)
      model.eval()
      SCREAMING_SNAKE_CASE__         =    model(
          A_  ,	bbox=A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	start_positions=A_  ,	end_positions=A_  ,	)
      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     :					Optional[Any]						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    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__
      )  ,		
      )         =    config_and_inputs
      SCREAMING_SNAKE_CASE__         =    {
          "input_ids": input_ids,
          "bbox": bbox,
          "token_type_ids": token_type_ids,
          "attention_mask": input_mask,
      }
      return config, inputs_dict







@require_torch
class  UpperCamelCase_      (	_snake_case		,  _snake_case		,  unittest.TestCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   (
      (
          LayoutLMModel,
          LayoutLMForMaskedLM,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMForQuestionAnswering,
      )
      if is_torch_available()
      else None
  )
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   (
      {
          """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     :					int						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    LayoutLMModelTester(self						)
      SCREAMING_SNAKE_CASE__         =    ConfigTester(self  ,	config_class=A_  ,	hidden_size=37						)



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Tuple:
      self.config_tester.run_common_tests()



  def   _UpperCamelCase     (						self     :					Dict						)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A_						)



  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
          SCREAMING_SNAKE_CASE__         =    type
          self.model_tester.create_and_check_model(*A_						)



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*A_						)



  def   _UpperCamelCase     (						self     :					Dict						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*A_						)



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*A_						)



  def   _UpperCamelCase     (						self     :					int						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*A_						)







  @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			str:
      pass





def 				__a							(       ):
    # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
    # fmt: off
    SCREAMING_SNAKE_CASE__         =    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=__A						)  # noqa: E231
    SCREAMING_SNAKE_CASE__         =    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__         =    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=__A						)  # noqa: E231
    SCREAMING_SNAKE_CASE__         =    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__         =    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=__A						)  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class  UpperCamelCase_      (	unittest.TestCase      ):


  """simple docstring"""



  @slow
  def   _UpperCamelCase     (						self     :					List[str]						)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"						).to(A_						)

      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    prepare_layoutlm_batch_inputs()

      # forward pass
      SCREAMING_SNAKE_CASE__         =    model(input_ids=A_  ,	bbox=A_  ,	attention_mask=A_  ,	token_type_ids=A_						)

      # test the sequence output on [0, :3, :3]
      SCREAMING_SNAKE_CASE__         =    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=A_  ,	)

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

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

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



  @slow
  def   _UpperCamelCase     (						self     :					str						)       ->			str:
      # initialize model with randomly initialized sequence classification head
      SCREAMING_SNAKE_CASE__         =    LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"  ,	num_labels=2						).to(
          A_						)

      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    prepare_layoutlm_batch_inputs()

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

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

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



  @slow
  def   _UpperCamelCase     (						self     :					List[str]						)       ->			List[Any]:
      # initialize model with randomly initialized token classification head
      SCREAMING_SNAKE_CASE__         =    LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"  ,	num_labels=13						).to(
          A_						)

      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    prepare_layoutlm_batch_inputs()

      # forward pass
      SCREAMING_SNAKE_CASE__         =    model(
          input_ids=A_  ,	bbox=A_  ,	attention_mask=A_  ,	token_type_ids=A_  ,	labels=A_						)

      # 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__         =    outputs.logits
      SCREAMING_SNAKE_CASE__         =    torch.Size((2, 25, 13)						)
      self.assertEqual(logits.shape  ,	A_						)







  @slow
  def   _UpperCamelCase     (						self     :					Dict						)       ->			List[Any]:
      # initialize model with randomly initialized token classification head
      SCREAMING_SNAKE_CASE__         =    LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"						).to(A_						)

      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    prepare_layoutlm_batch_inputs()

      # forward pass
      SCREAMING_SNAKE_CASE__         =    model(input_ids=A_  ,	bbox=A_  ,	attention_mask=A_  ,	token_type_ids=A_						)

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


