



'''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     : Optional[int]							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : Union[str, Any]=2							,     lowerCAmelCase__     : Optional[Any]=True							,     lowerCAmelCase__     : Union[str, Any]=False							,     lowerCAmelCase__     : Optional[Any]=1_0							,     lowerCAmelCase__     : Optional[int]=3							,     lowerCAmelCase__     : Tuple=3_2 * 8							,     lowerCAmelCase__     : Optional[Any]=3_2 * 8							,     lowerCAmelCase__     : Tuple=4							,     lowerCAmelCase__     : Union[str, Any]=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       :		Optional[Any]           =		use_auxiliary_loss
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		num_queries
					__SCREAMING_SNAKE_CASE       :		Any           =		num_channels
					__SCREAMING_SNAKE_CASE       :		int           =		min_size
					__SCREAMING_SNAKE_CASE       :		str           =		max_size
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		num_labels
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		hidden_dim
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		hidden_dim

			def UpperCamelCase__					(							self     : Any						):





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

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

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

			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		MaskaFormerConfig(
					    hidden_size=self.hidden_dim							,     )
					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.num_queries
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.num_labels

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

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

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		self.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
					return config, inputs_dict

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		output.encoder_hidden_states
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		output.pixel_decoder_hidden_states
					__SCREAMING_SNAKE_CASE       :		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     : Any							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Any=False						):





					"""simple docstring"""






					with torch.no_grad():
							__SCREAMING_SNAKE_CASE       :		Tuple           =		MaskaFormerModel(config=lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()

							__SCREAMING_SNAKE_CASE       :		List[str]           =		model(pixel_values=lowerCAmelCase__							,     pixel_mask=lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		Dict           =		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     : Tuple							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		MaskaFormerForUniversalSegmentation(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()

					def comm_check_on_output(lowerCAmelCase__     : int						):
							# 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       :		Tuple           =		model(pixel_values=lowerCAmelCase__							,     pixel_mask=lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		Dict           =		model(lowerCAmelCase__						)

							comm_check_on_output(lowerCAmelCase__						)

							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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  (							_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					unittest.TestCase    ):


			'''simple docstring'''

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

			_A      :		int			    =		False
			_A      :		List[Any]			    =		False
			_A      :		Tuple			    =		False
			_A      :		str			    =		False

			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					self.config_tester.run_common_tests()

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""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     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		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     : List[Any]						):





					"""simple docstring"""






					pass

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





					"""simple docstring"""






					pass

			@unittest.skip(reason="""Mask2Former is not a generative model"""						)
			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""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     : Dict						):





					"""simple docstring"""






					pass

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





					"""simple docstring"""






					pass

			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		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       :		int           =		inspect.signature(model.forward						)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__SCREAMING_SNAKE_CASE       :		Any           =		[*signature.parameters.keys()]

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

			@slow
			def UpperCamelCase__					(							self     : str						):





					"""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     : Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		(self.model_tester.min_size,) * 2
					__SCREAMING_SNAKE_CASE       :		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       :		Any           =		self.model_tester.get_config()

					__SCREAMING_SNAKE_CASE       :		int           =		MaskaFormerForUniversalSegmentation(lowerCAmelCase__						).to(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		model(**lowerCAmelCase__						)
					self.assertTrue(outputs.loss is not None						)

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		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[int]						):





					"""simple docstring"""






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

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

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					if not self.model_tester.is_training:
							return

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.all_model_classes[1]
					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.model_tester.prepare_config_and_inputs()

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

					__SCREAMING_SNAKE_CASE       :		int           =		model(lowerCAmelCase__							,     mask_labels=lowerCAmelCase__							,     class_labels=lowerCAmelCase__						).loss
					loss.backward()



			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE       :		Tuple           =		model_class(lowerCAmelCase__						).to(lowerCAmelCase__						)
					model.train()

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		model(lowerCAmelCase__							,     mask_labels=lowerCAmelCase__							,     class_labels=lowerCAmelCase__						)

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

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

					__SCREAMING_SNAKE_CASE       :		Dict           =		outputs.transformer_decoder_hidden_states[0]
					transformer_decoder_hidden_states.retain_grad()

					__SCREAMING_SNAKE_CASE       :		List[str]           =		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__       :	Optional[Any]    = 1E-4


def 				lowerCAmelCase_   (	):
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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[Any]						):





					"""simple docstring"""






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

			@cached_property
			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Any           =		self.default_image_processor
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		prepare_img()
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		image_processor(lowerCAmelCase__							,     return_tensors="""pt"""						).to(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		str           =		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       :		Any           =		model(**lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		str           =		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       :		List[Any]           =		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       :		Optional[int]           =		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     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(lowerCAmelCase__						).eval()
					__SCREAMING_SNAKE_CASE       :		str           =		self.default_image_processor
					__SCREAMING_SNAKE_CASE       :		Tuple           =		prepare_img()
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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       :		Optional[int]           =		model(**lowerCAmelCase__						)
					# masks_queries_logits
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		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       :		str           =		[
					    [-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       :		int           =		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       :		int           =		outputs.class_queries_logits
					self.assertEqual(class_queries_logits.shape							,     (1, model.config.num_queries, model.config.num_labels + 1)						)
					__SCREAMING_SNAKE_CASE       :		Optional[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       :		Dict           =		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(lowerCAmelCase__						).eval()
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.default_image_processor

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

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

					self.assertTrue(outputs.loss is not None						)



'''simple docstring'''






from math import ceil







def 				UpperCamelCase_			(    A__					:   int = 10_01					):
  '''simple docstring'''





  lowerCAmelCase_			:      List[Any]			=   1

  for i in range(1							,    int(ceil(n / 2.0					)					)					):
    lowerCAmelCase_			:      int			=   2 * i + 1
    lowerCAmelCase_			:      Tuple			=   2 * i
    lowerCAmelCase_			:      Tuple			=   total + 4 * odd**2 - 6 * even

  return total


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    print(solution())
  else:
    try:
      __A					:  str											=      int(sys.argv[1])
      print(solution(n))
    except ValueError:
      print("Invalid entry - please enter a number")



