



'''simple docstring'''




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
						import torch

						from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

						if is_vision_available():
												from transformers import MaskaFormerImageProcessor

if is_vision_available():
						from PIL import Image






class 		_UpperCamelCase						:


			'''simple docstring'''

			def __init__(							self  :				Dict							,  lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				Union[str, Any]=2							,  lowerCAmelCase__  :				Optional[int]=True							,  lowerCAmelCase__  :				Dict=False							,  lowerCAmelCase__  :				List[Any]=1_0							,  lowerCAmelCase__  :				Dict=3							,  lowerCAmelCase__  :				Tuple=3_2 * 8							,  lowerCAmelCase__  :				Optional[Any]=3_2 * 8							,  lowerCAmelCase__  :				List[Any]=4							,  lowerCAmelCase__  :				Optional[int]=6_4							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	parent
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	batch_size
					__SCREAMING_SNAKE_CASE :	str					 =	is_training
					__SCREAMING_SNAKE_CASE :	List[str]					 =	use_auxiliary_loss
					__SCREAMING_SNAKE_CASE :	Dict					 =	num_queries
					__SCREAMING_SNAKE_CASE :	int					 =	num_channels
					__SCREAMING_SNAKE_CASE :	int					 =	min_size
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	max_size
					__SCREAMING_SNAKE_CASE :	str					 =	num_labels
					__SCREAMING_SNAKE_CASE :	List[str]					 =	hidden_dim
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	hidden_dim

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
					    lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.ones([self.batch_size, self.min_size, self.max_size]							,  device=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Any					 =	(
					    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]							,  device=lowerCAmelCase__		) > 0.5
					).float()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	(torch.rand((self.batch_size, self.num_labels)							,  device=lowerCAmelCase__		) > 0.5).long()

					__SCREAMING_SNAKE_CASE :	Dict					 =	self.get_config()
					return config, pixel_values, pixel_mask, mask_labels, class_labels

			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	MaskaFormerConfig(
					    hidden_size=self.hidden_dim							,  )
					__SCREAMING_SNAKE_CASE :	Any					 =	self.num_queries
					__SCREAMING_SNAKE_CASE :	str					 =	self.num_labels

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[1, 1, 1, 1]
					__SCREAMING_SNAKE_CASE :	str					 =	self.num_channels

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	6_4
					__SCREAMING_SNAKE_CASE :	Any					 =	1_2_8
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.hidden_dim
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.hidden_dim
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.hidden_dim
					return config

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE :	int					 =	{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
					return config, inputs_dict

			def UpperCamelCase__      (							self  :				str							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	output.encoder_hidden_states
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	output.pixel_decoder_hidden_states
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	output.transformer_decoder_hidden_states

					self.parent.assertTrue(len(lowerCAmelCase__		)							,  len(config.backbone_config.depths		)		)
					self.parent.assertTrue(len(lowerCAmelCase__		)							,  len(config.backbone_config.depths		)		)
					self.parent.assertTrue(len(lowerCAmelCase__		)							,  config.decoder_layers		)

			def UpperCamelCase__      (							self  :				Dict							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				List[Any]							,  lowerCAmelCase__  :				Optional[Any]=False		):





					"""simple docstring"""






					with torch.no_grad():
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	MaskaFormerModel(config=lowerCAmelCase__		)
							model.to(lowerCAmelCase__		)
							model.eval()

							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	model(pixel_values=lowerCAmelCase__							,  pixel_mask=lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	model(lowerCAmelCase__							,  output_hidden_states=lowerCAmelCase__		)

					self.parent.assertEqual(
					    output.transformer_decoder_last_hidden_state.shape							,  (self.batch_size, self.num_queries, self.hidden_dim)							,  )
					# let's ensure the other two hidden state exists
					self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None		)
					self.parent.assertTrue(output.encoder_last_hidden_state is not None		)

					if output_hidden_states:
							self.check_output_hidden_state(lowerCAmelCase__							,  lowerCAmelCase__		)



			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				Optional[Any]							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				int							,  lowerCAmelCase__  :				List[str]							,  lowerCAmelCase__  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	MaskaFormerForUniversalSegmentation(config=lowerCAmelCase__		)
					model.to(lowerCAmelCase__		)
					model.eval()

					def comm_check_on_output(lowerCAmelCase__  :				List[str]		):
							# let's still check that all the required stuff is there
							self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None		)
							self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None		)
							self.parent.assertTrue(result.encoder_last_hidden_state is not None		)
							# okay, now we need to check the logits shape
							# due to the encoder compression, masks have a //4 spatial size
							self.parent.assertEqual(
							    result.masks_queries_logits.shape							,  (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)							,  )
							# + 1 for null class
							self.parent.assertEqual(
							    result.class_queries_logits.shape							,  (self.batch_size, self.num_queries, self.num_labels + 1)		)

					with torch.no_grad():
							__SCREAMING_SNAKE_CASE :	List[str]					 =	model(pixel_values=lowerCAmelCase__							,  pixel_mask=lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	model(lowerCAmelCase__		)

							comm_check_on_output(lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Tuple					 =	model(
							    pixel_values=lowerCAmelCase__							,  pixel_mask=lowerCAmelCase__							,  mask_labels=lowerCAmelCase__							,  class_labels=lowerCAmelCase__		)

					comm_check_on_output(lowerCAmelCase__		)

					self.parent.assertTrue(result.loss is not None		)
					self.parent.assertEqual(result.loss.shape							,  torch.Size([1]		)		)






@require_torch
class 		_UpperCamelCase						(							a__					,      a__					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	Optional[Any]    					=	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
			_A   :	List[str]    					=	{"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

			_A   :	int    					=	False
			_A   :	Dict    					=	False
			_A   :	Any    					=	False
			_A   :	List[Any]    					=	False

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	MaskaFormerModelTester(self		)
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	ConfigTester(self							,  config_class=lowerCAmelCase__							,  has_text_modality=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					self.config_tester.run_common_tests()

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__							,  **lowerCAmelCase__							,  output_hidden_states=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCAmelCase__		)

			@unittest.skip(reason="""Mask2Former does not use inputs_embeds"""		)
			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""		)
			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""Mask2Former is not a generative model"""		)
			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""Mask2Former does not use token embeddings"""		)
			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					pass

			@require_torch_multi_gpu
			@unittest.skip(
			    reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""		)
			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					pass

			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					pass

			def UpperCamelCase__      (							self  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model_class(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	inspect.signature(model.forward		)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__SCREAMING_SNAKE_CASE :	Dict					 =	[*signature.parameters.keys()]

							__SCREAMING_SNAKE_CASE :	Tuple					 =	["pixel_values"]
							self.assertListEqual(arg_names[:1]							,  lowerCAmelCase__		)

			@slow
			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	MaskaFormerModel.from_pretrained(lowerCAmelCase__		)
							self.assertIsNotNone(lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	(self.model_tester.min_size,) * 2
					__SCREAMING_SNAKE_CASE :	List[str]					 =	{
					    "pixel_values": torch.randn((2, 3, *size)							,  device=lowerCAmelCase__		),
					    "mask_labels": torch.randn((2, 1_0, *size)							,  device=lowerCAmelCase__		),
					    "class_labels": torch.zeros(2							,  1_0							,  device=lowerCAmelCase__		).long(),
					}
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.model_tester.get_config()

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	MaskaFormerForUniversalSegmentation(lowerCAmelCase__		).to(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	model(**lowerCAmelCase__		)
					self.assertTrue(outputs.loss is not None		)

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.create_and_check_maskaformer_model(lowerCAmelCase__							,  **lowerCAmelCase__							,  output_hidden_states=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	model_class(lowerCAmelCase__		).to(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	model(**lowerCAmelCase__							,  output_attentions=lowerCAmelCase__		)
							self.assertTrue(outputs.attentions is not None		)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					if not self.model_tester.is_training:
							return

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.all_model_classes[1]
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.model_tester.prepare_config_and_inputs()

					__SCREAMING_SNAKE_CASE :	List[str]					 =	model_class(lowerCAmelCase__		)
					model.to(lowerCAmelCase__		)
					model.train()

					__SCREAMING_SNAKE_CASE :	List[str]					 =	model(lowerCAmelCase__							,  mask_labels=lowerCAmelCase__							,  class_labels=lowerCAmelCase__		).loss
					loss.backward()



			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	self.all_model_classes[1]
					__SCREAMING_SNAKE_CASE :	int					 =	self.model_tester.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE :	Tuple					 =	True
					__SCREAMING_SNAKE_CASE :	int					 =	True

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	model_class(lowerCAmelCase__		).to(lowerCAmelCase__		)
					model.train()

					__SCREAMING_SNAKE_CASE :	int					 =	model(lowerCAmelCase__							,  mask_labels=lowerCAmelCase__							,  class_labels=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	outputs.encoder_hidden_states[0]
					encoder_hidden_states.retain_grad()

					__SCREAMING_SNAKE_CASE :	List[str]					 =	outputs.pixel_decoder_hidden_states[0]
					pixel_decoder_hidden_states.retain_grad()

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	outputs.transformer_decoder_hidden_states[0]
					transformer_decoder_hidden_states.retain_grad()

					__SCREAMING_SNAKE_CASE :	Tuple					 =	outputs.attentions[0]
					attentions.retain_grad()

					outputs.loss.backward(retain_graph=lowerCAmelCase__		)

					self.assertIsNotNone(encoder_hidden_states.grad		)
					self.assertIsNotNone(pixel_decoder_hidden_states.grad		)
					self.assertIsNotNone(transformer_decoder_hidden_states.grad		)
					self.assertIsNotNone(attentions.grad		)
UpperCamelCase__ :			Tuple												=				1E-4


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






@require_vision
@slow
class 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

			@cached_property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					return "facebook/mask2former-swin-small-coco-instance"

			@cached_property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints		) if is_vision_available() else None

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	int					 =	self.default_image_processor
					__SCREAMING_SNAKE_CASE :	Tuple					 =	prepare_img()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	image_processor(lowerCAmelCase__							,  return_tensors="""pt"""		).to(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Any					 =	inputs["pixel_values"].shape
					# check size is divisible by 32
					self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0		)
					# check size
					self.assertEqual(lowerCAmelCase__							,  (1, 3, 3_8_4, 3_8_4)		)

					with torch.no_grad():
							__SCREAMING_SNAKE_CASE :	int					 =	model(**lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	torch.tensor(
					    [[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]]		).to(lowerCAmelCase__		)
					self.assertTrue(
					    torch.allclose(
					        outputs.encoder_last_hidden_state[0, 0, :3, :3]							,  lowerCAmelCase__							,  atol=lowerCAmelCase__		)		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.tensor(
					    [[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]]		).to(lowerCAmelCase__		)
					self.assertTrue(
					    torch.allclose(
					        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]							,  lowerCAmelCase__							,  atol=lowerCAmelCase__		)		)

					__SCREAMING_SNAKE_CASE :	Dict					 =	torch.tensor(
					    [[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]]		).to(lowerCAmelCase__		)
					self.assertTrue(
					    torch.allclose(
					        outputs.transformer_decoder_last_hidden_state[0, :3, :3]							,  lowerCAmelCase__							,  atol=lowerCAmelCase__		)		)

			def UpperCamelCase__      (							self  :				Any		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Tuple					 =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(lowerCAmelCase__		).eval()
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.default_image_processor
					__SCREAMING_SNAKE_CASE :	Dict					 =	prepare_img()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	image_processor(lowerCAmelCase__							,  return_tensors="""pt"""		).to(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	int					 =	inputs["pixel_values"].shape
					# check size is divisible by 32
					self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0		)
					# check size
					self.assertEqual(lowerCAmelCase__							,  (1, 3, 3_8_4, 3_8_4)		)

					with torch.no_grad():
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model(**lowerCAmelCase__		)
					# masks_queries_logits
					__SCREAMING_SNAKE_CASE :	Dict					 =	outputs.masks_queries_logits
					self.assertEqual(
					    masks_queries_logits.shape							,  (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		)
					__SCREAMING_SNAKE_CASE :	Any					 =	[
					    [-8.78_39, -9.00_56, -8.81_21],
					    [-7.41_04, -7.03_13, -6.54_01],
					    [-6.61_05, -6.34_27, -6.46_75],
					]
					__SCREAMING_SNAKE_CASE :	Any					 =	torch.tensor(lowerCAmelCase__		).to(lowerCAmelCase__		)
					self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]							,  lowerCAmelCase__							,  atol=lowerCAmelCase__		)		)
					# class_queries_logits
					__SCREAMING_SNAKE_CASE :	Tuple					 =	outputs.class_queries_logits
					self.assertEqual(class_queries_logits.shape							,  (1, model.config.num_queries, model.config.num_labels + 1)		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	torch.tensor(
					    [
					        [1.83_24, -8.08_35, -4.19_22],
					        [0.84_50, -9.00_50, -3.60_53],
					        [0.30_45, -7.72_93, -3.02_75],
					    ]		).to(lowerCAmelCase__		)
					self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]							,  lowerCAmelCase__							,  atol=lowerCAmelCase__		)		)



			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(lowerCAmelCase__		).eval()
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.default_image_processor

					__SCREAMING_SNAKE_CASE :	Any					 =	image_processor(
					    [np.zeros((3, 8_0_0, 1_3_3_3)		), np.zeros((3, 8_0_0, 1_3_3_3)		)]							,  segmentation_maps=[np.zeros((3_8_4, 3_8_4)		).astype(np.floataa		), np.zeros((3_8_4, 3_8_4)		).astype(np.floataa		)]							,  return_tensors="""pt"""							,  )

					__SCREAMING_SNAKE_CASE :	int					 =	inputs["pixel_values"].to(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[el.to(lowerCAmelCase__		) for el in inputs["mask_labels"]]
					__SCREAMING_SNAKE_CASE :	str					 =	[el.to(lowerCAmelCase__		) for el in inputs["class_labels"]]

					with torch.no_grad():
							__SCREAMING_SNAKE_CASE :	Dict					 =	model(**lowerCAmelCase__		)

					self.assertTrue(outputs.loss is not None		)








'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase__ :			List[str]												=				logging.get_logger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Union[str, Any]							,  *lowerCAmelCase__  :				str							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






					warnings.warn(
					    """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
					    """ use DeiTImageProcessor instead."""							,  lowerCAmelCase__							,  )
					super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__		)