




'''simple docstring'''



import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel

     if is_vision_available():
          from transformers import MaskFormerImageProcessor

if is_vision_available():
     from PIL import Image





class      lowerCAmelCase :


       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 * 4     ,	__SCREAMING_SNAKE_CASE=32 * 6     ,	__SCREAMING_SNAKE_CASE=4     ,	__SCREAMING_SNAKE_CASE=32     ,	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	is_training
            __snake_case            =	use_auxiliary_loss
            __snake_case            =	num_queries
            __snake_case            =	num_channels
            __snake_case            =	min_size
            __snake_case            =	max_size
            __snake_case            =	num_labels
            __snake_case            =	mask_feature_size


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]	).to(
                __SCREAMING_SNAKE_CASE	)

            __snake_case            =	torch.ones([self.batch_size, self.min_size, self.max_size]     ,	device=__SCREAMING_SNAKE_CASE	)

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

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


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            return MaskFormerConfig.from_backbone_and_decoder_configs(
                backbone_config=SwinConfig(
                    depths=[1, 1, 1, 1]     ,	)     ,	decoder_config=DetrConfig(
                    decoder_ffn_dim=128     ,	num_queries=self.num_queries     ,	decoder_attention_heads=2     ,	d_model=self.mask_feature_size     ,	)     ,	mask_feature_size=self.mask_feature_size     ,	fpn_feature_size=self.mask_feature_size     ,	num_channels=self.num_channels     ,	num_labels=self.num_labels     ,	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	output.encoder_hidden_states
            __snake_case            =	output.pixel_decoder_hidden_states
            __snake_case            =	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_config.decoder_layers	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False	)   ->	Any:



            '''simple docstring'''




            with torch.no_grad():
                 __snake_case            =	MaskFormerModel(config=__SCREAMING_SNAKE_CASE	)
                 model.to(__SCREAMING_SNAKE_CASE	)
                 model.eval()

                 __snake_case            =	model(pixel_values=__SCREAMING_SNAKE_CASE     ,	pixel_mask=__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	output_hidden_states=__SCREAMING_SNAKE_CASE	)
            # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
            # encoder and pixel decoder
            self.parent.assertEqual(
                output.transformer_decoder_last_hidden_state.shape     ,	(self.batch_size, self.num_queries, self.mask_feature_size)     ,	)
            # 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    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	MaskFormerForInstanceSegmentation(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():
                 __snake_case            =	model(pixel_values=__SCREAMING_SNAKE_CASE     ,	pixel_mask=__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	model(__SCREAMING_SNAKE_CASE	)

                 comm_check_on_output(__SCREAMING_SNAKE_CASE	)

                 __snake_case            =	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      lowerCAmelCase (			__lowerCAmelCase					, __lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Union[str, Any]										=      (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
       __lowercase	:   Union[str, Any]										=      (
           {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
           if is_torch_available()
           else {}
       )

       __lowercase	:   Any										=      False
       __lowercase	:   Any										=      False
       __lowercase	:   Optional[Any]										=      False
       __lowercase	:   Optional[Any]										=      False


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	MaskFormerModelTester(self	)
            __snake_case            =	ConfigTester(self     ,	config_class=__SCREAMING_SNAKE_CASE     ,	has_text_modality=__SCREAMING_SNAKE_CASE	)


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



            '''simple docstring'''




            self.config_tester.run_common_tests()


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskformer_model(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE     ,	output_hidden_states=__SCREAMING_SNAKE_CASE	)


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



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__SCREAMING_SNAKE_CASE	)


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



            '''simple docstring'''




            pass


       @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''	)
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            pass


       @unittest.skip(reason='''MaskFormer is not a generative model'''	)
       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            pass


       @unittest.skip(reason='''MaskFormer does not use token embeddings'''	)
       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            pass


       @require_torch_multi_gpu
       @unittest.skip(
           reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''	)
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            pass


       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            pass


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



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()

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

                 __snake_case            =	['''pixel_values''']
                 self.assertListEqual(arg_names[:1]     ,	__SCREAMING_SNAKE_CASE	)


       @slow
       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            for model_name in ["facebook/maskformer-swin-small-coco"]:
                 __snake_case            =	MaskFormerModel.from_pretrained(__SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	(self.model_tester.min_size,) * 2
            __snake_case            =	{
                '''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(),
            }

            __snake_case            =	MaskFormerForInstanceSegmentation(MaskFormerConfig()	).to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	model(**__SCREAMING_SNAKE_CASE	)
            self.assertTrue(outputs.loss is not None	)


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.create_and_check_maskformer_model(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE     ,	output_hidden_states=__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()

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


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



            '''simple docstring'''




            if not self.model_tester.is_training:
                 return
            # only MaskFormerForInstanceSegmentation has the loss
            __snake_case            =	self.all_model_classes[1]
            __snake_case       ,     __snake_case       ,     __snake_case       ,     __snake_case       ,     __snake_case            =	self.model_tester.prepare_config_and_inputs()

            __snake_case            =	model_class(__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.train()

            __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	mask_labels=__SCREAMING_SNAKE_CASE     ,	class_labels=__SCREAMING_SNAKE_CASE	).loss
            loss.backward()




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



            '''simple docstring'''




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

            __snake_case            =	model_class(__SCREAMING_SNAKE_CASE	)
            model.to(__SCREAMING_SNAKE_CASE	)
            model.train()

            __snake_case            =	model(__SCREAMING_SNAKE_CASE     ,	mask_labels=__SCREAMING_SNAKE_CASE     ,	class_labels=__SCREAMING_SNAKE_CASE	)

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

            __snake_case            =	outputs.pixel_decoder_hidden_states[0]
            pixel_decoder_hidden_states.retain_grad()
            # we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
            __snake_case            =	outputs.transformer_decoder_hidden_states[0]
            transformer_decoder_hidden_states.retain_grad()

            __snake_case            =	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	)





UpperCAmelCase_							:	List[str]	     =		1E-4







def       _UpperCamelCase			()-> str:




     '''simple docstring'''





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







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


       @cached_property
       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            return (
                MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''	)
                if is_vision_available()
                else None
            )


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''	).to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.default_image_processor
            __snake_case            =	prepare_img()
            __snake_case            =	image_processor(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	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, 800, 1088)	)

            with torch.no_grad():
                 __snake_case            =	model(**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	torch.tensor(
                [[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]]	).to(__SCREAMING_SNAKE_CASE	)
            self.assertTrue(
                torch.allclose(
                    outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=__SCREAMING_SNAKE_CASE	)	)

            __snake_case            =	torch.tensor(
                [[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]]	).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	)	)

            __snake_case            =	torch.tensor(
                [[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]]	).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    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	(
                MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''	)
                .to(__SCREAMING_SNAKE_CASE	)
                .eval()
            )
            __snake_case            =	self.default_image_processor
            __snake_case            =	prepare_img()
            __snake_case            =	image_processor(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	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, 800, 1088)	)

            with torch.no_grad():
                 __snake_case            =	model(**__SCREAMING_SNAKE_CASE	)
            # masks_queries_logits
            __snake_case            =	outputs.masks_queries_logits
            self.assertEqual(
                masks_queries_logits.shape     ,	(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)     ,	)
            __snake_case            =	[
                [-1.3_737_124, -1.7_724_937, -1.9_364_233],
                [-1.5_977_281, -1.9_867_939, -2.1_523_695],
                [-1.5_795_398, -1.9_269_832, -2.093_942],
            ]
            __snake_case            =	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
            __snake_case            =	outputs.class_queries_logits
            self.assertEqual(
                class_queries_logits.shape     ,	(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)	)
            __snake_case            =	torch.tensor(
                [
                    [1.65_12E00, -5.25_72E00, -3.35_19E00],
                    [3.61_69E-02, -5.90_25E00, -2.93_13E00],
                    [1.07_66E-04, -7.76_30E00, -5.12_63E00],
                ]	).to(__SCREAMING_SNAKE_CASE	)
            self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=__SCREAMING_SNAKE_CASE	)	)


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	(
                MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''	)
                .to(__SCREAMING_SNAKE_CASE	)
                .eval()
            )
            __snake_case            =	self.default_image_processor
            __snake_case            =	prepare_img()
            __snake_case            =	image_processor(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	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, 800, 1088)	)

            with torch.no_grad():
                 __snake_case            =	model(**__SCREAMING_SNAKE_CASE	)
            # masks_queries_logits
            __snake_case            =	outputs.masks_queries_logits
            self.assertEqual(
                masks_queries_logits.shape     ,	(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)     ,	)
            __snake_case            =	[[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
            __snake_case            =	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
            __snake_case            =	outputs.class_queries_logits
            self.assertEqual(
                class_queries_logits.shape     ,	(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)	)
            __snake_case            =	torch.tensor(
                [[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]]	).to(__SCREAMING_SNAKE_CASE	)
            self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,	__SCREAMING_SNAKE_CASE     ,	atol=__SCREAMING_SNAKE_CASE	)	)




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



            '''simple docstring'''




            __snake_case            =	(
                MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''	)
                .to(__SCREAMING_SNAKE_CASE	)
                .eval()
            )
            __snake_case            =	self.default_image_processor

            __snake_case            =	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'''     ,	)

            __snake_case            =	inputs['''pixel_values'''].to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	[el.to(__SCREAMING_SNAKE_CASE	) for el in inputs['''mask_labels''']]
            __snake_case            =	[el.to(__SCREAMING_SNAKE_CASE	) for el in inputs['''class_labels''']]

            with torch.no_grad():
                 __snake_case            =	model(**__SCREAMING_SNAKE_CASE	)

            self.assertTrue(outputs.loss is not None	)








from __future__ import annotations





class     lowerCamelCase      :







	def __init__(     self      :List[Any]   ,     lowercase      :list[list[int]]			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= TypeError(
				    '''Matrices must be formed from a list of zero or more lists containing at '''
				    '''least one and the same number of values, each of which must be of type '''
				    '''int or float.'''			)
				if len(lowercase			) != 0:
							SCREAMING_SNAKE_CASE     						= len(rows[0]			)
							if cols == 0:
										raise error
							for row in rows:
										if len(lowercase			) != cols:
													raise error
										for value in row:
													if not isinstance(lowercase   ,     (int, float)			):
																raise error
							SCREAMING_SNAKE_CASE     						= rows
				else:
							SCREAMING_SNAKE_CASE     						= []







	def  snake_case__     (     self      :List[str]			)		->      list[list[int]]:







				"""simple docstring"""






				return [[row[i] for row in self.rows] for i in range(len(self.rows[0]			)			)]







	@property
	def  snake_case__     (     self      :Any			)		->      int:







				"""simple docstring"""






				return len(self.rows			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      int:







				"""simple docstring"""






				return len(self.rows[0]			)







	@property
	def  snake_case__     (     self      :Dict			)		->      tuple[int, int]:







				"""simple docstring"""






				return (self.num_rows, self.num_columns)







	@property
	def  snake_case__     (     self      :Any			)		->      bool:







				"""simple docstring"""






				return self.order[0] == self.order[1]







	def  snake_case__     (     self      :Tuple			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [0 if column_num != row_num else 1 for column_num in range(self.num_rows			)]
				    for row_num in range(self.num_rows			)
				]
				return Matrix(lowercase			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      int:







				"""simple docstring"""






				if not self.is_square:
							return 0
				if self.order == (0, 0):
							return 1
				if self.order == (1, 1):
							return int(self.rows[0][0]			)
				if self.order == (2, 2):
							return int(
							    (self.rows[0][0] * self.rows[1][1])
							    - (self.rows[0][1] * self.rows[1][0])			)
				else:
							return sum(
							    self.rows[0][column] * self.cofactors().rows[0][column]
							    for column in range(self.num_columns			)			)







	def  snake_case__     (     self      :int			)		->      bool:







				"""simple docstring"""






				return bool(self.determinant()			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [
				        self.rows[other_row][other_column]
				        for other_column in range(self.num_columns			)
				        if other_column != column
				    ]
				    for other_row in range(self.num_rows			)
				    if other_row != row
				]
				return Matrix(lowercase			).determinant()







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :int   ,     lowercase      :int			)		->      int:







				"""simple docstring"""






				if (row + column) % 2 == 0:
							return self.get_minor(lowercase   ,     lowercase			)
				return -1 * self.get_minor(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Matrix:







				"""simple docstring"""






				return Matrix(
				    [
				        [self.get_minor(lowercase   ,     lowercase			) for column in range(self.num_columns			)]
				        for row in range(self.num_rows			)
				    ]			)







	def  snake_case__     (     self      :Dict			)		->      Matrix:







				"""simple docstring"""






				return Matrix(
				    [
				        [
				            self.minors().rows[row][column]
				            if (row + column) % 2 == 0
				            else self.minors().rows[row][column] * -1
				            for column in range(self.minors().num_columns			)
				        ]
				        for row in range(self.minors().num_rows			)
				    ]			)







	def  snake_case__     (     self      :str			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [self.cofactors().rows[column][row] for column in range(self.num_columns			)]
				    for row in range(self.num_rows			)
				]
				return Matrix(lowercase			)







	def  snake_case__     (     self      :Any			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.determinant()
				if not determinant:
							raise TypeError('''Only matrices with a non-zero determinant have an inverse'''			)
				return self.adjugate() * (1 / determinant)







	def __repr__(     self      :Union[str, Any]			)		->      str:







				"""simple docstring"""






				return str(self.rows			)







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







				"""simple docstring"""






				if self.num_rows == 0:
							return "[]"
				if self.num_rows == 1:
							return "[[" + ". ".join(str(self.rows[0]			)			) + "]]"
				return (
				    "["
				    + "\n ".join(
				        [
				            '''[''' + '''. '''.join([str(lowercase			) for value in row]			) + '''.]'''
				            for row in self.rows
				        ]			)
				    + "]"
				)







	def  snake_case__     (     self      :int   ,     lowercase      :list[int]   ,     lowercase      :int | None = None			)		->      None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= TypeError('''Row must be a list containing all ints and/or floats'''			)
				if not isinstance(lowercase   ,     lowercase			):
							raise type_error
				for value in row:
							if not isinstance(lowercase   ,     (int, float)			):
										raise type_error
				if len(lowercase			) != self.num_columns:
							raise ValueError(
							    '''Row must be equal in length to the other rows in the matrix'''			)
				if position is None:
							self.rows.append(lowercase			)
				else:
							SCREAMING_SNAKE_CASE     						= self.rows[0:position] + [row] + self.rows[position:]







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :list[int]   ,     lowercase      :int | None = None			)		->      None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= TypeError(
				    '''Column must be a list containing all ints and/or floats'''			)
				if not isinstance(lowercase   ,     lowercase			):
							raise type_error
				for value in column:
							if not isinstance(lowercase   ,     (int, float)			):
										raise type_error
				if len(lowercase			) != self.num_rows:
							raise ValueError(
							    '''Column must be equal in length to the other columns in the matrix'''			)
				if position is None:
							SCREAMING_SNAKE_CASE     						= [self.rows[i] + [column[i]] for i in range(self.num_rows			)]
				else:
							SCREAMING_SNAKE_CASE     						= [
							    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
							    for i in range(self.num_rows			)
							]







	def __eq__(     self      :Union[str, Any]   ,     lowercase      :object			)		->      bool:







				"""simple docstring"""






				if not isinstance(lowercase   ,     lowercase			):
							return NotImplemented
				return self.rows == other.rows







	def __ne__(     self      :int   ,     lowercase      :object			)		->      bool:







				"""simple docstring"""






				return not self == other







	def __neg__(     self      :int			)		->      Matrix:







				"""simple docstring"""






				return self * -1







	def __add__(     self      :Optional[Any]   ,     lowercase      :Matrix			)		->      Matrix:







				"""simple docstring"""






				if self.order != other.order:
							raise ValueError('''Addition requires matrices of the same order'''			)
				return Matrix(
				    [
				        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns			)]
				        for i in range(self.num_rows			)
				    ]			)







	def __sub__(     self      :Optional[Any]   ,     lowercase      :Matrix			)		->      Matrix:







				"""simple docstring"""






				if self.order != other.order:
							raise ValueError('''Subtraction requires matrices of the same order'''			)
				return Matrix(
				    [
				        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns			)]
				        for i in range(self.num_rows			)
				    ]			)







	def __mul__(     self      :Optional[int]   ,     lowercase      :Matrix | int | float			)		->      Matrix:







				"""simple docstring"""






				if isinstance(lowercase   ,     (int, float)			):
							return Matrix(
							    [[int(element * other			) for element in row] for row in self.rows]			)
				elif isinstance(lowercase   ,     lowercase			):
							if self.num_columns != other.num_rows:
										raise ValueError(
										    '''The number of columns in the first matrix must '''
										    '''be equal to the number of rows in the second'''			)
							return Matrix(
							    [
							        [Matrix.dot_product(lowercase   ,     lowercase			) for column in other.columns()]
							        for row in self.rows
							    ]			)
				else:
							raise TypeError(
							    '''A Matrix can only be multiplied by an int, float, or another matrix'''			)







	def __pow__(     self      :str   ,     lowercase      :int			)		->      Matrix:







				"""simple docstring"""






				if not isinstance(lowercase   ,     lowercase			):
							raise TypeError('''A Matrix can only be raised to the power of an int'''			)
				if not self.is_square:
							raise ValueError('''Only square matrices can be raised to a power'''			)
				if other == 0:
							return self.identity()
				if other < 0:
							if self.is_invertable():
										return self.inverse() ** (-other)
							raise ValueError(
							    '''Only invertable matrices can be raised to a negative power'''			)
				SCREAMING_SNAKE_CASE     						= self
				for _ in range(other - 1			):
							result *= self
				return result







	@classmethod
	def  snake_case__     (     cls      :List[Any]   ,     lowercase      :list[int]   ,     lowercase      :list[int]			)		->      int:







				"""simple docstring"""






				return sum(row[i] * column[i] for i in range(len(lowercase			)			)			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()