





"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import YolosImageProcessor




class 					__lowercase      (      unittest.TestCase				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase=7						,      _UpperCAmelCase=3						,      _UpperCAmelCase=30						,      _UpperCAmelCase=400						,      _UpperCAmelCase=True						,      _UpperCAmelCase=None						,      _UpperCAmelCase=True						,      _UpperCAmelCase=[0.5, 0.5, 0.5]						,      _UpperCAmelCase=[0.5, 0.5, 0.5]						,      _UpperCAmelCase=True						,      _UpperCAmelCase=1 / 255						,      _UpperCAmelCase=True						,      ):
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									__a			:    Dict      							=	size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
									__a			:    List[str]      							=	parent
									__a			:    str      							=	batch_size
									__a			:    Any      							=	num_channels
									__a			:    List[str]      							=	min_resolution
									__a			:    str      							=	max_resolution
									__a			:    Optional[int]      							=	do_resize
									__a			:    Dict      							=	size
									__a			:    Union[str, Any]      							=	do_normalize
									__a			:    Tuple      							=	image_mean
									__a			:    Dict      							=	image_std
									__a			:    str      							=	do_rescale
									__a			:    Optional[int]      							=	rescale_factor
									__a			:    Tuple      							=	do_pad







					def 			_lowerCamelCase		(    self  ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      _UpperCAmelCase=False  ):
									if not batched:
													__a			:    Dict      							=	image_inputs[0]
													if isinstance(_UpperCAmelCase						,      Image.Image  ):
																	__a							,    __a			:    Union[str, Any]      							=	image.size
													else:
																	__a							,    __a			:    Any      							=	image.shape[1], image.shape[2]
													if w < h:
																	__a			:    List[str]      							=	int(self.size['''shortest_edge'''] * h / w  )
																	__a			:    int      							=	self.size['''shortest_edge''']
													elif w > h:
																	__a			:    Dict      							=	self.size['''shortest_edge''']
																	__a			:    int      							=	int(self.size['''shortest_edge'''] * w / h  )
													else:
																	__a			:    str      							=	self.size['''shortest_edge''']
																	__a			:    str      							=	self.size['''shortest_edge''']

									else:
													__a			:    Optional[int]      							=	[]
													for image in image_inputs:
																	__a							,    __a			:    Optional[Any]      							=	self.get_expected_values([image]  )
																	expected_values.append((expected_height, expected_width)  )
													__a			:    Optional[int]      							=	max(_UpperCAmelCase						,      key=lambda _UpperCAmelCase  : item[0]  )[0]
													__a			:    str      							=	max(_UpperCAmelCase						,      key=lambda _UpperCAmelCase  : item[1]  )[1]

									return expected_height, expected_width






@require_torch
@require_vision
class 					__lowercase      (      _UpperCamelCase ,   unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	YolosImageProcessor if is_vision_available() else None







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[Any]      							=	YolosImageProcessingTester(self  )







					@property
					def 			_lowerCamelCase		(    self  ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 			_lowerCamelCase		(    self  ):
									__a			:    Any      							=	self.image_processing_class(**self.image_processor_dict  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''image_mean'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''image_std'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''do_normalize'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''do_resize'''  )  )
									self.assertTrue(hasattr(_UpperCAmelCase						,      '''size'''  )  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[int]      							=	self.image_processing_class.from_dict(self.image_processor_dict  )
									self.assertEqual(image_processor.size						,      {'''shortest_edge''': 18, '''longest_edge''': 1333}  )
									self.assertEqual(image_processor.do_pad						,      _UpperCAmelCase  )

									__a			:    Optional[Any]      							=	self.image_processing_class.from_dict(
									    self.image_processor_dict						,      size=42						,      max_size=84						,      pad_and_return_pixel_mask=_UpperCAmelCase  )
									self.assertEqual(image_processor.size						,      {'''shortest_edge''': 42, '''longest_edge''': 84}  )
									self.assertEqual(image_processor.do_pad						,      _UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									pass







					def 			_lowerCamelCase		(    self  ):
									# Initialize image_processing
									__a			:    Any      							=	self.image_processing_class(**self.image_processor_dict  )
									# create random PIL images
									__a			:    Union[str, Any]      							=	prepare_image_inputs(self.image_processor_tester						,      equal_resolution=_UpperCAmelCase  )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase						,      Image.Image  )

									# Test not batched input
									__a			:    str      							=	image_processing(image_inputs[0]						,      return_tensors='''pt'''  ).pixel_values

									__a							,    __a			:    int      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase  )

									self.assertEqual(
									    encoded_images.shape						,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,      )

									# Test batched
									__a							,    __a			:    int      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase						,      batched=_UpperCAmelCase  )

									__a			:    List[Any]      							=	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,
									        expected_height,
									        expected_width,
									    )						,      )







					def 			_lowerCamelCase		(    self  ):
									# Initialize image_processing
									__a			:    int      							=	self.image_processing_class(**self.image_processor_dict  )
									# create random numpy tensors
									__a			:    Any      							=	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
									__a			:    Optional[int]      							=	image_processing(image_inputs[0]						,      return_tensors='''pt'''  ).pixel_values

									__a							,    __a			:    Optional[int]      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase  )

									self.assertEqual(
									    encoded_images.shape						,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,      )

									# Test batched
									__a			:    Tuple      							=	image_processing(_UpperCAmelCase						,      return_tensors='''pt'''  ).pixel_values

									__a							,    __a			:    Optional[int]      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase						,      batched=_UpperCAmelCase  )

									self.assertEqual(
									    encoded_images.shape						,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )						,      )







					def 			_lowerCamelCase		(    self  ):
									# Initialize image_processing
									__a			:    Dict      							=	self.image_processing_class(**self.image_processor_dict  )
									# create random PyTorch tensors
									__a			:    Union[str, Any]      							=	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
									__a			:    Dict      							=	image_processing(image_inputs[0]						,      return_tensors='''pt'''  ).pixel_values

									__a							,    __a			:    int      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase  )

									self.assertEqual(
									    encoded_images.shape						,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,      )

									# Test batched
									__a			:    Any      							=	image_processing(_UpperCAmelCase						,      return_tensors='''pt'''  ).pixel_values

									__a							,    __a			:    List[str]      							=	self.image_processor_tester.get_expected_values(_UpperCAmelCase						,      batched=_UpperCAmelCase  )

									self.assertEqual(
									    encoded_images.shape						,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )						,      )







					def 			_lowerCamelCase		(    self  ):
									# Initialize image_processings
									__a			:    Dict      							=	self.image_processing_class(**self.image_processor_dict  )
									__a			:    Any      							=	self.image_processing_class(do_resize=_UpperCAmelCase						,      do_normalize=_UpperCAmelCase						,      do_rescale=_UpperCAmelCase  )
									# create random PyTorch tensors
									__a			:    Any      							=	prepare_image_inputs(self.image_processor_tester						,      equal_resolution=_UpperCAmelCase						,      torchify=_UpperCAmelCase  )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase						,      torch.Tensor  )

									# Test whether the method "pad" and calling the image processor return the same tensors
									__a			:    List[str]      							=	image_processing_a.pad(_UpperCAmelCase						,      return_tensors='''pt'''  )
									__a			:    Dict      							=	image_processing_a(_UpperCAmelCase						,      return_tensors='''pt'''  )

									self.assertTrue(
									    torch.allclose(encoded_images_with_method['''pixel_values''']						,      encoded_images['''pixel_values''']						,      atol=1e-4  )  )







					@slow
					def 			_lowerCamelCase		(    self  ):
									# prepare image and target
									__a			:    Dict      							=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
									with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''						,      '''r'''  ) as f:
													__a			:    Optional[Any]      							=	json.loads(f.read()  )

									__a			:    Optional[Any]      							=	{'''image_id''': 39769, '''annotations''': target}

									# encode them
									__a			:    Any      							=	YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''  )
									__a			:    List[Any]      							=	image_processing(images=_UpperCAmelCase						,      annotations=_UpperCAmelCase						,      return_tensors='''pt'''  )

									# verify pixel values
									__a			:    List[str]      							=	torch.Size([1, 3, 800, 1066]  )
									self.assertEqual(encoding['''pixel_values'''].shape						,      _UpperCAmelCase  )

									__a			:    Optional[int]      							=	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]  )
									self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,      _UpperCAmelCase						,      atol=1e-4  )  )

									# verify area
									__a			:    Union[str, Any]      							=	torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,      _UpperCAmelCase  )  )
									# verify boxes
									__a			:    Optional[Any]      							=	torch.Size([6, 4]  )
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,      _UpperCAmelCase  )
									__a			:    int      							=	torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,      _UpperCAmelCase						,      atol=1e-3  )  )
									# verify image_id
									__a			:    Union[str, Any]      							=	torch.tensor([39769]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,      _UpperCAmelCase  )  )
									# verify is_crowd
									__a			:    Dict      							=	torch.tensor([0, 0, 0, 0, 0, 0]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,      _UpperCAmelCase  )  )
									# verify class_labels
									__a			:    Tuple      							=	torch.tensor([75, 75, 63, 65, 17, 17]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,      _UpperCAmelCase  )  )
									# verify orig_size
									__a			:    Any      							=	torch.tensor([480, 640]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,      _UpperCAmelCase  )  )
									# verify size
									__a			:    List[str]      							=	torch.tensor([800, 1066]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,      _UpperCAmelCase  )  )







					@slow
					def 			_lowerCamelCase		(    self  ):
									# prepare image, target and masks_path
									__a			:    str      							=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
									with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''						,      '''r'''  ) as f:
													__a			:    Optional[Any]      							=	json.loads(f.read()  )

									__a			:    int      							=	{'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

									__a			:    str      							=	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''  )

									# encode them
									__a			:    List[str]      							=	YolosImageProcessor(format='''coco_panoptic'''  )
									__a			:    Tuple      							=	image_processing(images=_UpperCAmelCase						,      annotations=_UpperCAmelCase						,      masks_path=_UpperCAmelCase						,      return_tensors='''pt'''  )

									# verify pixel values
									__a			:    List[str]      							=	torch.Size([1, 3, 800, 1066]  )
									self.assertEqual(encoding['''pixel_values'''].shape						,      _UpperCAmelCase  )

									__a			:    Dict      							=	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]  )
									self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,      _UpperCAmelCase						,      atol=1e-4  )  )

									# verify area
									__a			:    List[str]      							=	torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,      _UpperCAmelCase  )  )
									# verify boxes
									__a			:    Optional[int]      							=	torch.Size([6, 4]  )
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,      _UpperCAmelCase  )
									__a			:    Dict      							=	torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,      _UpperCAmelCase						,      atol=1e-3  )  )
									# verify image_id
									__a			:    List[Any]      							=	torch.tensor([39769]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,      _UpperCAmelCase  )  )
									# verify is_crowd
									__a			:    List[Any]      							=	torch.tensor([0, 0, 0, 0, 0, 0]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,      _UpperCAmelCase  )  )
									# verify class_labels
									__a			:    Optional[int]      							=	torch.tensor([17, 17, 63, 75, 75, 93]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,      _UpperCAmelCase  )  )
									# verify masks
									__a			:    Any      							=	822873
									self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()						,      _UpperCAmelCase  )
									# verify orig_size
									__a			:    Tuple      							=	torch.tensor([480, 640]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,      _UpperCAmelCase  )  )
									# verify size
									__a			:    int      							=	torch.tensor([800, 1066]  )
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,      _UpperCAmelCase  )  )

from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFDebertaVaForMaskedLM,
			    TFDebertaVaForQuestionAnswering,
			    TFDebertaVaForSequenceClassification,
			    TFDebertaVaForTokenClassification,
			    TFDebertaVaModel,
			)





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=7							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=99							,							_UpperCamelCase=32							,							_UpperCamelCase=2							,							_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							,							)							-> Tuple:
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      seq_length
							lowerCAmelCase_         =      is_training
							lowerCAmelCase_         =      use_input_mask
							lowerCAmelCase_         =      use_token_type_ids
							lowerCAmelCase_         =      use_labels
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      type_sequence_label_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      num_labels
							lowerCAmelCase_         =      num_choices
							lowerCAmelCase_         =      relative_attention
							lowerCAmelCase_         =      position_biased_input
							lowerCAmelCase_         =      pos_att_type
							lowerCAmelCase_         =      scope




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size	)

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

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

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

							lowerCAmelCase_         =      DebertaVaConfig(
							    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							,							relative_attention=self.relative_attention							,							position_biased_input=self.position_biased_input							,							initializer_range=self.initializer_range							,							return_dict=_UpperCamelCase							,							)

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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaModel(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

							lowerCAmelCase_         =      [input_ids, input_mask]
							lowerCAmelCase_         =      model(_UpperCamelCase	)

							lowerCAmelCase_         =      model(_UpperCamelCase	)

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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Any:
							lowerCAmelCase_         =      TFDebertaVaForMaskedLM(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      TFDebertaVaForSequenceClassification(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      TFDebertaVaForTokenClassification(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.num_labels)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaForQuestionAnswering(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							lowerCAmelCase_         =      model(_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 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    
							)         =      config_and_inputs
							lowerCAmelCase_         =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict





@require_tf
class       _lowerCAmelCase   (      __a   , __a   , unittest.TestCase			):
				_lowercase     						=(
				    (
				        TFDebertaVaModel,
				        TFDebertaVaForMaskedLM,
				        TFDebertaVaForQuestionAnswering,
				        TFDebertaVaForSequenceClassification,
				        TFDebertaVaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowercase     						=(
				    {
				        '''feature-extraction''': TFDebertaVaModel,
				        '''fill-mask''': TFDebertaVaForMaskedLM,
				        '''question-answering''': TFDebertaVaForQuestionAnswering,
				        '''text-classification''': TFDebertaVaForSequenceClassification,
				        '''token-classification''': TFDebertaVaForTokenClassification,
				        '''zero-shot''': TFDebertaVaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      TFDebertaVaModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase							,							hidden_size=37	)




				def 	__a		(   self	)							-> str:
							self.config_tester.run_common_tests()




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase	)




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"	)
							self.assertIsNotNone(_UpperCamelCase	)





@require_tf
class       _lowerCAmelCase   (      unittest.TestCase			):




				@unittest.skip(reason="Model not available yet"	)
				def 	__a		(   self	)							-> List[str]:
							pass




				@slow
				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"	)
							lowerCAmelCase_         =      tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	)
							lowerCAmelCase_         =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
							lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase	)[0]

							lowerCAmelCase_         =      tf.constant(
							    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]	)
							tf.debugging.assert_near(output[:, 1:4, 1:4]							,							_UpperCamelCase							,							atol=1e-4	)



