



'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_pytesseract_available():
				from PIL import Image

				from transformers import LayoutLMvaImageProcessor


class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase=7  ,  UpperCAmelCase=3  ,  UpperCAmelCase=18  ,  UpperCAmelCase=30  ,  UpperCAmelCase=400  ,  UpperCAmelCase=True  ,  UpperCAmelCase=None  ,  UpperCAmelCase=True  ,  )					->       Any:
														_snake_case       =  size if size is not None else {"""height""": 18, """width""": 18}
														_snake_case       =  parent
														_snake_case       =  batch_size
														_snake_case       =  num_channels
														_snake_case       =  image_size
														_snake_case       =  min_resolution
														_snake_case       =  max_resolution
														_snake_case       =  do_resize
														_snake_case       =  size
														_snake_case       =  apply_ocr





							def 	lowercase							(self  )					->       List[Any]:
														return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}


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







							'''simple docstring'''

							lowerCAmelCase_ 						=						LayoutLMvaImageProcessor if is_pytesseract_available() else None

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  LayoutLMvaImageProcessingTester(self  )

							@property
							def 	lowercase							(self  )					->       Tuple:
														return self.image_processor_tester.prepare_image_processor_dict()

							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case       =  self.image_processing_class(**self.image_processor_dict  )
														self.assertTrue(hasattr(__UpperCAmelCase  ,  """do_resize"""  )  )
														self.assertTrue(hasattr(__UpperCAmelCase  ,  """size"""  )  )
														self.assertTrue(hasattr(__UpperCAmelCase  ,  """apply_ocr"""  )  )

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  self.image_processing_class.from_dict(self.image_processor_dict  )
														self.assertEqual(image_processor.size  ,  {"""height""": 18, """width""": 18}  )

														_snake_case       =  self.image_processing_class.from_dict(self.image_processor_dict  ,  size=42  )
														self.assertEqual(image_processor.size  ,  {"""height""": 42, """width""": 42}  )

							def 	lowercase							(self  )					->       str:
														pass

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  self.image_processing_class(**self.image_processor_dict  )
														# create random PIL images
														_snake_case       =  prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=__UpperCAmelCase  )
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase  ,  Image.Image  )

														# Test not batched input
														_snake_case       =  image_processing(image_inputs[0]  ,  return_tensors="""pt"""  )
														self.assertEqual(
														    encoding.pixel_values.shape  ,  (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )  ,  )

														self.assertIsInstance(encoding.words  ,  __UpperCAmelCase  )
														self.assertIsInstance(encoding.boxes  ,  __UpperCAmelCase  )

														# Test batched
														_snake_case       =  image_processing(__UpperCAmelCase  ,  return_tensors="""pt"""  ).pixel_values
														self.assertEqual(
														    encoded_images.shape  ,  (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )  ,  )

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  self.image_processing_class(**self.image_processor_dict  )
														# create random numpy tensors
														_snake_case       =  prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=__UpperCAmelCase  ,  numpify=__UpperCAmelCase  )
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase  ,  np.ndarray  )

														# Test not batched input
														_snake_case       =  image_processing(image_inputs[0]  ,  return_tensors="""pt"""  ).pixel_values
														self.assertEqual(
														    encoded_images.shape  ,  (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )  ,  )

														# Test batched
														_snake_case       =  image_processing(__UpperCAmelCase  ,  return_tensors="""pt"""  ).pixel_values
														self.assertEqual(
														    encoded_images.shape  ,  (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )  ,  )

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  self.image_processing_class(**self.image_processor_dict  )
														# create random PyTorch tensors
														_snake_case       =  prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=__UpperCAmelCase  ,  torchify=__UpperCAmelCase  )
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase  ,  torch.Tensor  )

														# Test not batched input
														_snake_case       =  image_processing(image_inputs[0]  ,  return_tensors="""pt"""  ).pixel_values
														self.assertEqual(
														    encoded_images.shape  ,  (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )  ,  )

														# Test batched
														_snake_case       =  image_processing(__UpperCAmelCase  ,  return_tensors="""pt"""  ).pixel_values
														self.assertEqual(
														    encoded_images.shape  ,  (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )  ,  )





							def 	lowercase							(self  )					->       int:
														_snake_case       =  LayoutLMvaImageProcessor()

														from datasets import load_dataset

														_snake_case       =  load_dataset("""hf-internal-testing/fixtures_docvqa"""  ,  split="""test"""  )

														_snake_case       =  Image.open(ds[0]["""file"""]  ).convert("""RGB"""  )

														_snake_case       =  image_processing(__UpperCAmelCase  ,  return_tensors="""pt"""  )

														self.assertEqual(encoding.pixel_values.shape  ,  (1, 3, 224, 224)  )
														self.assertEqual(len(encoding.words  )  ,  len(encoding.boxes  )  )

														# fmt: off
														# the words and boxes were obtained with Tesseract 4.1.1
														_snake_case       =  [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
														_snake_case       =  [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
														# fmt: on

														self.assertListEqual(encoding.words  ,  __UpperCAmelCase  )
														self.assertListEqual(encoding.boxes  ,  __UpperCAmelCase  )

														# with apply_OCR = False
														_snake_case       =  LayoutLMvaImageProcessor(apply_ocr=__UpperCAmelCase  )

														_snake_case       =  image_processing(__UpperCAmelCase  ,  return_tensors="""pt"""  )

														self.assertEqual(encoding.pixel_values.shape  ,  (1, 3, 224, 224)  )

'''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]}"""  )