





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE_		     = datasets.load_iris()

SCREAMING_SNAKE_CASE_		     = np.array(data["""data"""])
SCREAMING_SNAKE_CASE_		     = np.array(data["""target"""])
SCREAMING_SNAKE_CASE_		     = data["""target_names"""]

SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_		     = train_test_split(X, y)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Any:


      '''simple docstring'''


      return np.linalg.norm(np.array(__snake_case			) - np.array(__snake_case			)			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=5			)				->     Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		zip(__snake_case				,		__snake_case			)
      # List of distances of all points from the point to be classified
      SCREAMING_SNAKE_CASE	=		[]
      for data_point in data:
            SCREAMING_SNAKE_CASE	=		euclidean_distance(data_point[0]				,		__snake_case			)
            distances.append((distance, data_point[1])			)
      # Choosing 'k' points with the least distances.
      SCREAMING_SNAKE_CASE	=		[i[1] for i in sorted(__snake_case			)[:k]]
      # Most commonly occurring class among them
      # is the class into which the point is classified
      SCREAMING_SNAKE_CASE	=		Counter(__snake_case			).most_common(1			)[0][0]
      return classes[result]


if __name__ == "__main__":
      print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))









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		:					Tuple		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Tuple=2		,lowerCamelCase__		:					Optional[int]=True		,lowerCamelCase__		:					int=False		,lowerCamelCase__		:					List[str]=10		,lowerCamelCase__		:					int=3		,lowerCamelCase__		:					Optional[Any]=32 * 8		,lowerCamelCase__		:					Tuple=32 * 8		,lowerCamelCase__		:					Tuple=4		,lowerCamelCase__		:					int=64		,)  ->	List[Any]:




           '''simple docstring'''


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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
               lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		torch.ones([self.batch_size, self.min_size, self.max_size]		,device=lowerCamelCase__ )

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

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






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




           '''simple docstring'''


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

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

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE	=		{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
           return config, inputs_dict






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					Dict )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		output.encoder_hidden_states
           SCREAMING_SNAKE_CASE	=		output.pixel_decoder_hidden_states
           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[int]=False )  ->	int:




           '''simple docstring'''


           with torch.no_grad():
                 SCREAMING_SNAKE_CASE	=		MaskaFormerModel(config=lowerCamelCase__ )
                 model.to(lowerCamelCase__ )
                 model.eval()

                 SCREAMING_SNAKE_CASE	=		model(pixel_values=lowerCamelCase__		,pixel_mask=lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Any )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		MaskaFormerForUniversalSegmentation(config=lowerCamelCase__ )
           model.to(lowerCamelCase__ )
           model.eval()

           def comm_check_on_output(lowerCamelCase__		:					Optional[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	=		model(pixel_values=lowerCamelCase__		,pixel_mask=lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__ )

                 comm_check_on_output(lowerCamelCase__ )

                 SCREAMING_SNAKE_CASE	=		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__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

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

     __snake_case					:			Dict				=    False
     __snake_case					:			Tuple				=    False
     __snake_case					:			Union[str, Any]				=    False
     __snake_case					:			Optional[Any]				=    False






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		MaskaFormerModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,has_text_modality=lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Dict:




           '''simple docstring'''


           self.config_tester.run_common_tests()






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__		,**lowerCamelCase__		,output_hidden_states=lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	int:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Tuple:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""Mask2Former is not a generative model""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Dict:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""Mask2Former does not use token embeddings""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	List[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        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Any:




           '''simple docstring'''


           pass






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




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE	=		["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]		,lowerCamelCase__ )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Any:




           '''simple docstring'''


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






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




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE	=		MaskaFormerForUniversalSegmentation(lowerCamelCase__ ).to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(**lowerCamelCase__ )
           self.assertTrue(outputs.loss is not None )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__		,**lowerCamelCase__		,output_hidden_states=lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           if not self.model_tester.is_training:
                 return

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

           SCREAMING_SNAKE_CASE	=		model_class(lowerCamelCase__ )
           model.to(lowerCamelCase__ )
           model.train()

           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__		,mask_labels=lowerCamelCase__		,class_labels=lowerCamelCase__ ).loss
           loss.backward()






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




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE	=		model_class(lowerCamelCase__ ).to(lowerCamelCase__ )
           model.train()

           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__		,mask_labels=lowerCamelCase__		,class_labels=lowerCamelCase__ )

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

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

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

           SCREAMING_SNAKE_CASE	=		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 )
SCREAMING_SNAKE_CASE_		     = 1e-4



def      __lowercase     (   )				->     Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
      return image



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



     '''simple docstring'''

     @cached_property
     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	Optional[int]:




           '''simple docstring'''


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






     @cached_property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[int]:




           '''simple docstring'''


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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		self.default_image_processor
           SCREAMING_SNAKE_CASE	=		prepare_img()
           SCREAMING_SNAKE_CASE	=		image_processor(lowerCamelCase__		,return_tensors="""pt""" ).to(lowerCamelCase__ )
           SCREAMING_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(lowerCamelCase__		,(1, 3, 384, 384) )

           with torch.no_grad():
                 SCREAMING_SNAKE_CASE	=		model(**lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		torch.tensor(
               [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(lowerCamelCase__ )
           self.assertTrue(
               torch.allclose(
                   outputs.encoder_last_hidden_state[0, 0, :3, :3]		,lowerCamelCase__		,atol=lowerCamelCase__ ) )

           SCREAMING_SNAKE_CASE	=		torch.tensor(
               [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(lowerCamelCase__ )
           self.assertTrue(
               torch.allclose(
                   outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,lowerCamelCase__		,atol=lowerCamelCase__ ) )

           SCREAMING_SNAKE_CASE	=		torch.tensor(
               [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(lowerCamelCase__ )
           self.assertTrue(
               torch.allclose(
                   outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,lowerCamelCase__		,atol=lowerCamelCase__ ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
           SCREAMING_SNAKE_CASE	=		self.default_image_processor
           SCREAMING_SNAKE_CASE	=		prepare_img()
           SCREAMING_SNAKE_CASE	=		image_processor(lowerCamelCase__		,return_tensors="""pt""" ).to(lowerCamelCase__ )
           SCREAMING_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(lowerCamelCase__		,(1, 3, 384, 384) )

           with torch.no_grad():
                 SCREAMING_SNAKE_CASE	=		model(**lowerCamelCase__ )
           # masks_queries_logits
           SCREAMING_SNAKE_CASE	=		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	=		[
               [-8.7839, -9.0056, -8.8121],
               [-7.4104, -7.0313, -6.5401],
               [-6.6105, -6.3427, -6.4675],
           ]
           SCREAMING_SNAKE_CASE	=		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	=		outputs.class_queries_logits
           self.assertEqual(class_queries_logits.shape		,(1, model.config.num_queries, model.config.num_labels + 1) )
           SCREAMING_SNAKE_CASE	=		torch.tensor(
               [
                   [1.8324, -8.0835, -4.1922],
                   [0.8450, -9.0050, -3.6053],
                   [0.3045, -7.7293, -3.0275],
               ] ).to(lowerCamelCase__ )
           self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,lowerCamelCase__		,atol=lowerCamelCase__ ) )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowerCamelCase__ ).eval()
           SCREAMING_SNAKE_CASE	=		self.default_image_processor

           SCREAMING_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"""		,)

           SCREAMING_SNAKE_CASE	=		inputs["""pixel_values"""].to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		[el.to(lowerCamelCase__ ) for el in inputs["""mask_labels"""]]
           SCREAMING_SNAKE_CASE	=		[el.to(lowerCamelCase__ ) for el in inputs["""class_labels"""]]

           with torch.no_grad():
                 SCREAMING_SNAKE_CASE	=		model(**lowerCamelCase__ )

           self.assertTrue(outputs.loss is not None )


