




import heapq as hq
import math
from collections.abc import Iterator





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Dict    ,    _lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      str(id_						)
       SCREAMING_SNAKE_CASE_     						=      None
       SCREAMING_SNAKE_CASE_     						=      None
       SCREAMING_SNAKE_CASE_     						=      []
       SCREAMING_SNAKE_CASE_     						=      {}  # {vertex:distance}



 def __lt__(							self  :		List[Any]    ,    _lowerCAmelCase  :		Union[str, Any]						):
       return self.key < other.key



 def __repr__(							self  :		str						):
       return self.id



 def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    _lowerCAmelCase  :		Dict						):
       self.neighbors.append(_lowerCAmelCase						)




 def 				lowerCAmelCase_  (							self  :		Tuple    ,    _lowerCAmelCase  :		List[Any]    ,    _lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      weight


def  UpperCAmelCase_							(				__UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Optional[int]      ,      __UpperCAmelCase						:     Dict							) ->			Dict:
      # add the neighbors:
      graph[a - 1].add_neighbor(graph[b - 1]							)
      graph[b - 1].add_neighbor(graph[a - 1]							)
      # add the edges:
      graph[a - 1].add_edge(graph[b - 1]      ,      __UpperCAmelCase							)
      graph[b - 1].add_edge(graph[a - 1]      ,      __UpperCAmelCase							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     list      ,      __UpperCAmelCase						:     Vertex							) ->			list:
      SCREAMING_SNAKE_CASE_     						=      []
      for u in graph:
            SCREAMING_SNAKE_CASE_     						=      math.inf
            SCREAMING_SNAKE_CASE_     						=      None
      SCREAMING_SNAKE_CASE_     						=      0
      SCREAMING_SNAKE_CASE_     						=      graph[:]
      while q:
            SCREAMING_SNAKE_CASE_     						=      min(__UpperCAmelCase							)
            q.remove(__UpperCAmelCase							)
            for v in u.neighbors:
                  if (v in q) and (u.edges[v.id] < v.key):
                        SCREAMING_SNAKE_CASE_     						=      u
                        SCREAMING_SNAKE_CASE_     						=      u.edges[v.id]
      for i in range(1      ,      len(__UpperCAmelCase							)							):
            a.append((int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)							)
      return a

def  UpperCAmelCase_							(				__UpperCAmelCase						:     list      ,      __UpperCAmelCase						:     Vertex							) ->			Iterator[tuple]:
      for u in graph:
            SCREAMING_SNAKE_CASE_     						=      math.inf
            SCREAMING_SNAKE_CASE_     						=      None
      SCREAMING_SNAKE_CASE_     						=      0

      SCREAMING_SNAKE_CASE_     						=      list(__UpperCAmelCase							)
      hq.heapify(__UpperCAmelCase							)

      while h:
            SCREAMING_SNAKE_CASE_     						=      hq.heappop(__UpperCAmelCase							)
            for v in u.neighbors:
                  if (v in h) and (u.edges[v.id] < v.key):
                        SCREAMING_SNAKE_CASE_     						=      u
                        SCREAMING_SNAKE_CASE_     						=      u.edges[v.id]
                        hq.heapify(__UpperCAmelCase							)

      for i in range(1      ,      len(__UpperCAmelCase							)							):
            yield (int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)

def  UpperCAmelCase_							(				) ->			None:
      pass


if __name__ == "__main__":
   import doctest

   doctest.testmod()





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 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Any    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		List[Any]=2    ,    _lowerCAmelCase  :		List[str]=True    ,    _lowerCAmelCase  :		Any=False    ,    _lowerCAmelCase  :		Tuple=10    ,    _lowerCAmelCase  :		Optional[int]=3    ,    _lowerCAmelCase  :		Dict=32 * 8    ,    _lowerCAmelCase  :		List[str]=32 * 8    ,    _lowerCAmelCase  :		List[Any]=4    ,    _lowerCAmelCase  :		Optional[Any]=64    ,    ):
       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 				lowerCAmelCase_  (							self  :		str						):
       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 				lowerCAmelCase_  (							self  :		List[Any]						):
       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 				lowerCAmelCase_  (							self  :		Optional[int]						):
       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 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		Tuple						):
       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 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Any=False						):
       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 				lowerCAmelCase_  (							self  :		str    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerForUniversalSegmentation(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()

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

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      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 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 lowercase_       =   {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

 lowercase_       =   False
 lowercase_       =   False
 lowercase_       =   False
 lowercase_       =   False



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerModelTester(self						)
       SCREAMING_SNAKE_CASE_     						=      ConfigTester(self    ,    config_class=_lowerCAmelCase    ,    has_text_modality=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       self.config_tester.run_common_tests()



 def 				lowerCAmelCase_  (							self  :		Tuple						):
       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 				lowerCAmelCase_  (							self  :		Any						):
       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 				lowerCAmelCase_  (							self  :		Optional[int]						):
       pass



 @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'						)
 def 				lowerCAmelCase_  (							self  :		Tuple						):
       pass



 @unittest.skip(reason='Mask2Former is not a generative model'						)
 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       pass



 @unittest.skip(reason='Mask2Former does not use token embeddings'						)
 def 				lowerCAmelCase_  (							self  :		Tuple						):
       pass



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



 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
 def 				lowerCAmelCase_  (							self  :		int						):
       pass



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       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 				lowerCAmelCase_  (							self  :		Any						):
       for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
             SCREAMING_SNAKE_CASE_     						=      MaskaFormerModel.from_pretrained(_lowerCAmelCase						)
             self.assertIsNotNone(_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       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 				lowerCAmelCase_  (							self  :		List[Any]						):
       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 				lowerCAmelCase_  (							self  :		str						):
       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 				lowerCAmelCase_  (							self  :		List[str]						):
       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 				lowerCAmelCase_  (							self  :		str						):
       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						)


lowerCamelCase__			:       Tuple         =							1E-4

def  UpperCAmelCase_							(				) ->			List[Any]:
      SCREAMING_SNAKE_CASE_     						=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image





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


 '''simple docstring'''







 @cached_property
 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       return "facebook/mask2former-swin-small-coco-instance"



 @cached_property
 def 				lowerCAmelCase_  (							self  :		int						):
       return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints						) if is_vision_available() else None



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       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 				lowerCAmelCase_  (							self  :		List[Any]						):
       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 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       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, 1_333)						), np.zeros((3, 800, 1_333)						)]    ,    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						)