
def 	A_ (							snake_case							:   int ,      snake_case							:   int )      ->    int:



						'''simple docstring'''

						__UpperCamelCase       =			1  # To kept the Calculated Value
						# Since C(n, k) = C(n, n-k)
						if k > (n - k):
												__UpperCamelCase       =			n - k
						# Calculate C(n,k)
						for i in range(snake_case ):
												result *= n - i
												result //= i + 1
						return result




def 	A_ (							snake_case							:   int )      ->    int:



						'''simple docstring'''

						return binomial_coefficient(2 * node_count ,      snake_case ) // (node_count + 1)




def 	A_ (							snake_case							:   int )      ->    int:



						'''simple docstring'''

						if n < 0:
												raise ValueError('''factorial() not defined for negative values''' )
						__UpperCamelCase       =			1
						for i in range(1 ,      n + 1 ):
												result *= i
						return result




def 	A_ (							snake_case							:   int )      ->    int:



						'''simple docstring'''

						return catalan_number(snake_case ) * factorial(snake_case )


if __name__ == "__main__":
				lowercase__							:							List[str]									=			int(input("Enter the number of nodes: ").strip() or 0)
				if node_count <= 0:
								raise ValueError("We need some nodes to work with.")
				print(
				    F"Given {node_count} nodes, there are {binary_tree_count(node_count)} "
				    F"binary trees and {catalan_number(node_count)} binary search trees."
				)






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        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=32 * 8     ,	SCREAMING_SNAKE_CASE_=32 * 8     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=64     ,	)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			parent
													__UpperCamelCase       =			batch_size
													__UpperCamelCase       =			is_training
													__UpperCamelCase       =			use_auxiliary_loss
													__UpperCamelCase       =			num_queries
													__UpperCamelCase       =			num_channels
													__UpperCamelCase       =			min_size
													__UpperCamelCase       =			max_size
													__UpperCamelCase       =			num_labels
													__UpperCamelCase       =			hidden_dim
													__UpperCamelCase       =			hidden_dim



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]			).to(
													    SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			torch.ones([self.batch_size, self.min_size, self.max_size]     ,	device=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			(
													    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,	device=SCREAMING_SNAKE_CASE_			) > 0.5
													).float()
													__UpperCamelCase       =			(torch.rand((self.batch_size, self.num_labels)     ,	device=SCREAMING_SNAKE_CASE_			) > 0.5).long()

													__UpperCamelCase       =			self.get_config()
													return config, pixel_values, pixel_mask, mask_labels, class_labels



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerConfig(
													    hidden_size=self.hidden_dim     ,	)
													__UpperCamelCase       =			self.num_queries
													__UpperCamelCase       =			self.num_labels

													__UpperCamelCase       =			[1, 1, 1, 1]
													__UpperCamelCase       =			self.num_channels

													__UpperCamelCase       =			64
													__UpperCamelCase       =			128
													__UpperCamelCase       =			self.hidden_dim
													__UpperCamelCase       =			self.hidden_dim
													__UpperCamelCase       =			self.hidden_dim
													return config



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			self.prepare_config_and_inputs()
													__UpperCamelCase       =			{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
													return config, inputs_dict



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Any:





													'''simple docstring'''






													__UpperCamelCase       =			output.encoder_hidden_states
													__UpperCamelCase       =			output.pixel_decoder_hidden_states
													__UpperCamelCase       =			output.transformer_decoder_hidden_states

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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=False			)->   Tuple:





													'''simple docstring'''






													with torch.no_grad():
																			__UpperCamelCase       =			MaskaFormerModel(config=SCREAMING_SNAKE_CASE_			)
																			model.to(SCREAMING_SNAKE_CASE_			)
																			model.eval()

																			__UpperCamelCase       =			model(pixel_values=SCREAMING_SNAKE_CASE_     ,	pixel_mask=SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	output_hidden_states=SCREAMING_SNAKE_CASE_			)

													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(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.eval()

													def comm_check_on_output(SCREAMING_SNAKE_CASE_			):
																			# 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():
																			__UpperCamelCase       =			model(pixel_values=SCREAMING_SNAKE_CASE_     ,	pixel_mask=SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_			)

																			comm_check_on_output(SCREAMING_SNAKE_CASE_			)

																			__UpperCamelCase       =			model(
																			    pixel_values=SCREAMING_SNAKE_CASE_     ,	pixel_mask=SCREAMING_SNAKE_CASE_     ,	mask_labels=SCREAMING_SNAKE_CASE_     ,	class_labels=SCREAMING_SNAKE_CASE_			)

													comm_check_on_output(SCREAMING_SNAKE_CASE_			)

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






@require_torch
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							_snake_case     		=  {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False
							_snake_case     		=  False



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerModelTester(self			)
													__UpperCamelCase       =			ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	output_hidden_states=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE_			)



							@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''			)
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													pass



							@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''			)
							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													pass



							@unittest.skip(reason='''Mask2Former is not a generative model'''			)
							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													pass



							@unittest.skip(reason='''Mask2Former does not use token embeddings'''			)
							def     A__		(  self			)->   List[str]:





													'''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     A__		(  self			)->   Dict:





													'''simple docstring'''






													pass



							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''			)
							def     A__		(  self			)->   str:





													'''simple docstring'''






													pass



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			inspect.signature(model.forward			)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__UpperCamelCase       =			[*signature.parameters.keys()]

																			__UpperCamelCase       =			['''pixel_values''']
																			self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_			)



							@slow
							def     A__		(  self			)->   Any:





													'''simple docstring'''






													for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																			__UpperCamelCase       =			MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			(self.model_tester.min_size,) * 2
													__UpperCamelCase       =			{
													    '''pixel_values''': torch.randn((2, 3, *size)     ,	device=SCREAMING_SNAKE_CASE_			),
													    '''mask_labels''': torch.randn((2, 10, *size)     ,	device=SCREAMING_SNAKE_CASE_			),
													    '''class_labels''': torch.zeros(2     ,	10     ,	device=SCREAMING_SNAKE_CASE_			).long(),
													}
													__UpperCamelCase       =			self.model_tester.get_config()

													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)
													self.assertTrue(outputs.loss is not None			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	output_hidden_states=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_     ,	output_attentions=SCREAMING_SNAKE_CASE_			)
																			self.assertTrue(outputs.attentions is not None			)



							def     A__		(  self			)->   Any:





													'''simple docstring'''






													if not self.model_tester.is_training:
																			return

													__UpperCamelCase       =			self.all_model_classes[1]
													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()

													__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			)
													model.to(SCREAMING_SNAKE_CASE_			)
													model.train()

													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	mask_labels=SCREAMING_SNAKE_CASE_     ,	class_labels=SCREAMING_SNAKE_CASE_			).loss
													loss.backward()



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			self.all_model_classes[1]
													__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase       =			self.model_tester.prepare_config_and_inputs()
													__UpperCamelCase       =			True
													__UpperCamelCase       =			True

													__UpperCamelCase       =			model_class(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
													model.train()

													__UpperCamelCase       =			model(SCREAMING_SNAKE_CASE_     ,	mask_labels=SCREAMING_SNAKE_CASE_     ,	class_labels=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			outputs.encoder_hidden_states[0]
													encoder_hidden_states.retain_grad()

													__UpperCamelCase       =			outputs.pixel_decoder_hidden_states[0]
													pixel_decoder_hidden_states.retain_grad()

													__UpperCamelCase       =			outputs.transformer_decoder_hidden_states[0]
													transformer_decoder_hidden_states.retain_grad()

													__UpperCamelCase       =			outputs.attentions[0]
													attentions.retain_grad()

													outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE_			)

													self.assertIsNotNone(encoder_hidden_states.grad			)
													self.assertIsNotNone(pixel_decoder_hidden_states.grad			)
													self.assertIsNotNone(transformer_decoder_hidden_states.grad			)
													self.assertIsNotNone(attentions.grad			)





lowercase__							:							Any									=			1e-4




def 	A_ (							)      ->    List[Any]:



						'''simple docstring'''

						__UpperCamelCase       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
						return image






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

							"""simple docstring"""






							@cached_property
							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






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



							@cached_property
							def     A__		(  self			)->   Any:





													'''simple docstring'''






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



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerModel.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			inputs['''pixel_values'''].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
													# check size
													self.assertEqual(SCREAMING_SNAKE_CASE_     ,	(1, 3, 384, 384)			)

													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			torch.tensor(
													    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(
													    torch.allclose(
													        outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)

													__UpperCamelCase       =			torch.tensor(
													    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(
													    torch.allclose(
													        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)

													__UpperCamelCase       =			torch.tensor(
													    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(
													    torch.allclose(
													        outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			).eval()
													__UpperCamelCase       =			self.default_image_processor
													__UpperCamelCase       =			prepare_img()
													__UpperCamelCase       =			image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='''pt'''			).to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			inputs['''pixel_values'''].shape
													# check size is divisible by 32
													self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0			)
													# check size
													self.assertEqual(SCREAMING_SNAKE_CASE_     ,	(1, 3, 384, 384)			)

													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)
													# masks_queries_logits
													__UpperCamelCase       =			outputs.masks_queries_logits
													self.assertEqual(
													    masks_queries_logits.shape     ,	(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)			)
													__UpperCamelCase       =			[
													    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
													    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
													    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
													]
													__UpperCamelCase       =			torch.tensor(SCREAMING_SNAKE_CASE_			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)
													# class_queries_logits
													__UpperCamelCase       =			outputs.class_queries_logits
													self.assertEqual(class_queries_logits.shape     ,	(1, model.config.num_queries, model.config.num_labels + 1)			)
													__UpperCamelCase       =			torch.tensor(
													    [
													        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
													        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
													        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
													    ]			).to(SCREAMING_SNAKE_CASE_			)
													self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints			).to(SCREAMING_SNAKE_CASE_			).eval()
													__UpperCamelCase       =			self.default_image_processor

													__UpperCamelCase       =			image_processor(
													    [np.zeros((3, 800, 1333)			), np.zeros((3, 800, 1333)			)]     ,	segmentation_maps=[np.zeros((384, 384)			).astype(np.floataa			), np.zeros((384, 384)			).astype(np.floataa			)]     ,	return_tensors='''pt'''     ,	)

													__UpperCamelCase       =			inputs['''pixel_values'''].to(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			[el.to(SCREAMING_SNAKE_CASE_			) for el in inputs['''mask_labels''']]
													__UpperCamelCase       =			[el.to(SCREAMING_SNAKE_CASE_			) for el in inputs['''class_labels''']]

													with torch.no_grad():
																			__UpperCamelCase       =			model(**SCREAMING_SNAKE_CASE_			)

													self.assertTrue(outputs.loss is not None			)




