



'''simple docstring'''
import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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_MULTIPLE_CHOICE_MAPPING,
				    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
				    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
				    LayoutLMvaConfig,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				)
				from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import LayoutLMvaImageProcessor


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase=2  ,  UpperCAmelCase=3  ,  UpperCAmelCase=4  ,  UpperCAmelCase=2  ,  UpperCAmelCase=7  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=99  ,  UpperCAmelCase=36  ,  UpperCAmelCase=3  ,  UpperCAmelCase=4  ,  UpperCAmelCase=37  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=16  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=6  ,  UpperCAmelCase=6  ,  UpperCAmelCase=3  ,  UpperCAmelCase=4  ,  UpperCAmelCase=None  ,  UpperCAmelCase=1000  ,  )					->       Optional[Any]:
														_snake_case       =  parent
														_snake_case       =  batch_size
														_snake_case       =  num_channels
														_snake_case       =  image_size
														_snake_case       =  patch_size
														_snake_case       =  text_seq_length
														_snake_case       =  is_training
														_snake_case       =  use_input_mask
														_snake_case       =  use_token_type_ids
														_snake_case       =  use_labels
														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_act
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  type_sequence_label_size
														_snake_case       =  initializer_range
														_snake_case       =  coordinate_size
														_snake_case       =  shape_size
														_snake_case       =  num_labels
														_snake_case       =  num_choices
														_snake_case       =  scope
														_snake_case       =  range_bbox

														# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
														_snake_case       =  text_seq_length
														_snake_case       =  (image_size // patch_size) ** 2 + 1
														_snake_case       =  self.text_seq_length + self.image_seq_length

							def 	lowercase							(self  )					->       List[str]:
														_snake_case       =  ids_tensor([self.batch_size, self.text_seq_length]  ,  self.vocab_size  )

														_snake_case       =  ids_tensor([self.batch_size, self.text_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]:
																																			_snake_case       =  bbox[i, j, 3]
																																			_snake_case       =  bbox[i, j, 1]
																																			_snake_case       =  t
																												if bbox[i, j, 2] < bbox[i, j, 0]:
																																			_snake_case       =  bbox[i, j, 2]
																																			_snake_case       =  bbox[i, j, 0]
																																			_snake_case       =  t

														_snake_case       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

														_snake_case       =  None
														if self.use_input_mask:
																					_snake_case       =  random_attention_mask([self.batch_size, self.text_seq_length]  )

														_snake_case       =  None
														if self.use_token_type_ids:
																					_snake_case       =  ids_tensor([self.batch_size, self.text_seq_length]  ,  self.type_vocab_size  )

														_snake_case       =  None
														_snake_case       =  None
														if self.use_labels:
																					_snake_case       =  ids_tensor([self.batch_size]  ,  self.type_sequence_label_size  )
																					_snake_case       =  ids_tensor([self.batch_size, self.text_seq_length]  ,  self.num_labels  )

														_snake_case       =  LayoutLMvaConfig(
														    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  ,  coordinate_size=self.coordinate_size  ,  shape_size=self.shape_size  ,  input_size=self.image_size  ,  patch_size=self.patch_size  ,  )

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

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[Any]:
														_snake_case       =  LayoutLMvaModel(config=SCREAMING_SNAKE_CASE_  )
														model.to(SCREAMING_SNAKE_CASE_  )
														model.eval()

														# text + image
														_snake_case       =  model(SCREAMING_SNAKE_CASE_  ,  pixel_values=SCREAMING_SNAKE_CASE_  )
														_snake_case       =  model(
														    SCREAMING_SNAKE_CASE_  ,  bbox=SCREAMING_SNAKE_CASE_  ,  pixel_values=SCREAMING_SNAKE_CASE_  ,  attention_mask=SCREAMING_SNAKE_CASE_  ,  token_type_ids=SCREAMING_SNAKE_CASE_  )
														_snake_case       =  model(SCREAMING_SNAKE_CASE_  ,  bbox=SCREAMING_SNAKE_CASE_  ,  pixel_values=SCREAMING_SNAKE_CASE_  ,  token_type_ids=SCREAMING_SNAKE_CASE_  )
														_snake_case       =  model(SCREAMING_SNAKE_CASE_  ,  bbox=SCREAMING_SNAKE_CASE_  ,  pixel_values=SCREAMING_SNAKE_CASE_  )

														self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)  )

														# text only
														_snake_case       =  model(SCREAMING_SNAKE_CASE_  )
														self.parent.assertEqual(
														    result.last_hidden_state.shape  ,  (self.batch_size, self.text_seq_length, self.hidden_size)  )

														# image only
														_snake_case       =  model(pixel_values=SCREAMING_SNAKE_CASE_  )
														self.parent.assertEqual(
														    result.last_hidden_state.shape  ,  (self.batch_size, self.image_seq_length, self.hidden_size)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[Any]:
														_snake_case       =  self.num_labels
														_snake_case       =  LayoutLMvaForSequenceClassification(SCREAMING_SNAKE_CASE_  )
														model.to(SCREAMING_SNAKE_CASE_  )
														model.eval()
														_snake_case       =  model(
														    SCREAMING_SNAKE_CASE_  ,  bbox=SCREAMING_SNAKE_CASE_  ,  pixel_values=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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       str:
														_snake_case       =  self.num_labels
														_snake_case       =  LayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE_  )
														model.to(SCREAMING_SNAKE_CASE_  )
														model.eval()
														_snake_case       =  model(
														    SCREAMING_SNAKE_CASE_  ,  bbox=SCREAMING_SNAKE_CASE_  ,  pixel_values=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.text_seq_length, self.num_labels)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Union[str, Any]:
														_snake_case       =  LayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_  )
														model.to(SCREAMING_SNAKE_CASE_  )
														model.eval()
														_snake_case       =  model(
														    SCREAMING_SNAKE_CASE_  ,  bbox=SCREAMING_SNAKE_CASE_  ,  pixel_values=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 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  self.prepare_config_and_inputs()
														(
														    _snake_case    
														)       =  config_and_inputs
														_snake_case       =  {
														    """input_ids""": input_ids,
														    """bbox""": bbox,
														    """pixel_values""": pixel_values,
														    """token_type_ids""": token_type_ids,
														    """attention_mask""": input_mask,
														}
														return config, inputs_dict


@require_torch
class       _lowerCAmelCase   (   _lowerCAmelCase					,							_lowerCAmelCase					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False

							lowerCAmelCase_ 						=						(
							    (
							        LayoutLMvaModel,
							        LayoutLMvaForSequenceClassification,
							        LayoutLMvaForTokenClassification,
							        LayoutLMvaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase_ 						=						(
							    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
							    if is_torch_available()
							    else {}
							)

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       str:
														# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
														# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
														# the sequence dimension of the text embedding only.
														# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
														return True

							def 	lowercase							(self  )					->       int:
														_snake_case       =  LayoutLMvaModelTester(self  )
														_snake_case       =  ConfigTester(self  ,  config_class=SCREAMING_SNAKE_CASE_  ,  hidden_size=37  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False  )					->       List[str]:
														_snake_case       =  copy.deepcopy(SCREAMING_SNAKE_CASE_  )
														if model_class in get_values(SCREAMING_SNAKE_CASE_  ):
																					_snake_case       =  {
																					    k: v.unsqueeze(1  ).expand(-1  ,  self.model_tester.num_choices  ,  -1  ).contiguous()
																					    if isinstance(SCREAMING_SNAKE_CASE_  ,  torch.Tensor  ) and v.ndim > 1
																					    else v
																					    for k, v in inputs_dict.items()
																					}
														if return_labels:
																					if model_class in get_values(SCREAMING_SNAKE_CASE_  ):
																												_snake_case       =  torch.ones(self.model_tester.batch_size  ,  dtype=torch.long  ,  device=SCREAMING_SNAKE_CASE_  )
																					elif model_class in get_values(SCREAMING_SNAKE_CASE_  ):
																												_snake_case       =  torch.zeros(
																												    self.model_tester.batch_size  ,  dtype=torch.long  ,  device=SCREAMING_SNAKE_CASE_  )
																												_snake_case       =  torch.zeros(
																												    self.model_tester.batch_size  ,  dtype=torch.long  ,  device=SCREAMING_SNAKE_CASE_  )
																					elif model_class in [
																					    *get_values(SCREAMING_SNAKE_CASE_  ),
																					]:
																												_snake_case       =  torch.zeros(
																												    self.model_tester.batch_size  ,  dtype=torch.long  ,  device=SCREAMING_SNAKE_CASE_  )
																					elif model_class in [
																					    *get_values(SCREAMING_SNAKE_CASE_  ),
																					]:
																												_snake_case       =  torch.zeros(
																												    (self.model_tester.batch_size, self.model_tester.text_seq_length)  ,  dtype=torch.long  ,  device=SCREAMING_SNAKE_CASE_  ,  )

														return inputs_dict

							def 	lowercase							(self  )					->       Union[str, Any]:
														self.config_tester.run_common_tests()

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_  )

							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														for type in ["absolute", "relative_key", "relative_key_query"]:
																					_snake_case       =  type
																					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_  )

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_  )

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_  )

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_  )





							@slow
							def 	lowercase							(self  )					->       Dict:
														for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_snake_case       =  LayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE_  )
																					self.assertIsNotNone(SCREAMING_SNAKE_CASE_  )






def  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image


@require_torch
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							@cached_property
							def 	lowercase							(self  )					->       Dict:
														return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE_  ) if is_vision_available() else None





							@slow
							def 	lowercase							(self  )					->       int:
														_snake_case       =  LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""  ).to(SCREAMING_SNAKE_CASE_  )

														_snake_case       =  self.default_image_processor
														_snake_case       =  prepare_img()
														_snake_case       =  image_processor(images=SCREAMING_SNAKE_CASE_  ,  return_tensors="""pt"""  ).pixel_values.to(SCREAMING_SNAKE_CASE_  )

														_snake_case       =  torch.tensor([[1, 2]]  )
														_snake_case       =  torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]  ).unsqueeze(0  )

														# forward pass
														_snake_case       =  model(
														    input_ids=input_ids.to(SCREAMING_SNAKE_CASE_  )  ,  bbox=bbox.to(SCREAMING_SNAKE_CASE_  )  ,  pixel_values=pixel_values.to(SCREAMING_SNAKE_CASE_  )  ,  )

														# verify the logits
														_snake_case       =  torch.Size((1, 199, 768)  )
														self.assertEqual(outputs.last_hidden_state.shape  ,  SCREAMING_SNAKE_CASE_  )

														_snake_case       =  torch.tensor(
														    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]  ).to(SCREAMING_SNAKE_CASE_  )

														self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,  SCREAMING_SNAKE_CASE_  ,  atol=1e-4  )  )

'''simple docstring'''
import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
				    DebertaForMaskedLM,
				    DebertaForQuestionAnswering,
				    DebertaForSequenceClassification,
				    DebertaForTokenClassification,
				    DebertaModel,
				)
				from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase=13  ,  UpperCAmelCase=7  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=99  ,  UpperCAmelCase=32  ,  UpperCAmelCase=5  ,  UpperCAmelCase=4  ,  UpperCAmelCase=37  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=16  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=False  ,  UpperCAmelCase=True  ,  UpperCAmelCase="None"  ,  UpperCAmelCase=3  ,  UpperCAmelCase=4  ,  UpperCAmelCase=None  ,  )					->       Optional[Any]:
														_snake_case       =  parent
														_snake_case       =  batch_size
														_snake_case       =  seq_length
														_snake_case       =  is_training
														_snake_case       =  use_input_mask
														_snake_case       =  use_token_type_ids
														_snake_case       =  use_labels
														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_act
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  type_sequence_label_size
														_snake_case       =  initializer_range
														_snake_case       =  num_labels
														_snake_case       =  num_choices
														_snake_case       =  relative_attention
														_snake_case       =  position_biased_input
														_snake_case       =  pos_att_type
														_snake_case       =  scope

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size  )

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

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

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

														_snake_case       =  self.get_config()

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

							def 	lowercase							(self  )					->       int:
														return DebertaConfig(
														    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  ,  relative_attention=self.relative_attention  ,  position_biased_input=self.position_biased_input  ,  pos_att_type=self.pos_att_type  ,  )

							def 	lowercase							(self  )					->       int:
														_snake_case       =  self.get_config()
														_snake_case       =  300
														return config

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Dict:
														self.parent.assertListEqual(list(result.loss.size()  )  ,  []  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Optional[Any]:
														_snake_case       =  DebertaModel(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  )[0]
														_snake_case       =  model(UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  )[0]
														_snake_case       =  model(UpperCAmelCase  )[0]

														self.parent.assertListEqual(list(sequence_output.size()  )  ,  [self.batch_size, self.seq_length, self.hidden_size]  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       str:
														_snake_case       =  DebertaForMaskedLM(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  labels=UpperCAmelCase  )

														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														_snake_case       =  self.num_labels
														_snake_case       =  DebertaForSequenceClassification(UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertListEqual(list(result.logits.size()  )  ,  [self.batch_size, self.num_labels]  )
														self.check_loss_output(UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  self.num_labels
														_snake_case       =  DebertaForTokenClassification(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  DebertaForQuestionAnswering(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(
														    UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  token_type_ids=UpperCAmelCase  ,  start_positions=UpperCAmelCase  ,  end_positions=UpperCAmelCase  ,  )
														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 	lowercase							(self  )					->       Tuple:
														_snake_case       =  self.prepare_config_and_inputs()
														(
														    (
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							(
														    _snake_case
														),							
														)       =  config_and_inputs
														_snake_case       =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
														return config, inputs_dict


@require_torch
class       _lowerCAmelCase   (   __snake_case					,							__snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						(
							    (
							        DebertaModel,
							        DebertaForMaskedLM,
							        DebertaForSequenceClassification,
							        DebertaForTokenClassification,
							        DebertaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase_ 						=						(
							    {
							        "feature-extraction": DebertaModel,
							        "fill-mask": DebertaForMaskedLM,
							        "question-answering": DebertaForQuestionAnswering,
							        "text-classification": DebertaForSequenceClassification,
							        "token-classification": DebertaForTokenClassification,
							        "zero-shot": DebertaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase_ 						=						True
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  DebertaModelTester(self  )
														_snake_case       =  ConfigTester(self  ,  config_class=UpperCAmelCase  ,  hidden_size=37  )

							def 	lowercase							(self  )					->       List[str]:
														self.config_tester.run_common_tests()

							def 	lowercase							(self  )					->       Tuple:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_model(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       int:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_question_answering(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_deberta_for_token_classification(*UpperCAmelCase  )





							@slow
							def 	lowercase							(self  )					->       Tuple:
														for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					_snake_case       =  DebertaModel.from_pretrained(UpperCAmelCase  )
																					self.assertIsNotNone(UpperCAmelCase  )


@require_torch
@require_sentencepiece
@require_tokenizers
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							@unittest.skip(reason="""Model not available yet"""  )
							def 	lowercase							(self  )					->       Any:
														pass





							@slow
							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  DebertaModel.from_pretrained("""microsoft/deberta-base"""  )

														_snake_case       =  torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  )
														_snake_case       =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
														with torch.no_grad():
																					_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  )[0]
														# compare the actual values for a slice.
														_snake_case       =  torch.tensor(
														    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]  )
														self.assertTrue(torch.allclose(output[:, 1:4, 1:4]  ,  UpperCAmelCase  ,  atol=1e-4  )  ,  f"""{output[:, 1:4, 1:4]}"""  )