





import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
       import transformers


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->List[str]:
       random.seed(UpperCamelCase__  )
       np.random.seed(UpperCamelCase__  )
       torch.manual_seed(UpperCamelCase__  )
       torch.cuda.manual_seed_all(UpperCamelCase__  )
       # ^^ safe to call this function even if cuda is not available




class     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase,__lowerCamelCase = 0.9999,__lowerCamelCase = 0.0,__lowerCamelCase = 0,__lowerCamelCase = False,__lowerCamelCase = 1.0,__lowerCamelCase = 2 / 3,__lowerCamelCase = None,__lowerCamelCase = None,**__lowerCamelCase,):

        if isinstance(__lowerCamelCase,torch.nn.Module ):
               A__											=						(
                   '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
                   '''Please pass the parameters of the module instead.'''
               )
               deprecate(
                   '''passing a `torch.nn.Module` to `ExponentialMovingAverage`''','''1.0.0''',__lowerCamelCase,standard_warn=__lowerCamelCase,)
               A__											=						parameters.parameters()

               # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
               A__											=						True

        if kwargs.get('''max_value''',__lowerCamelCase ) is not None:
               A__											=						'''The `max_value` argument is deprecated. Please use `decay` instead.'''
               deprecate('''max_value''','''1.0.0''',__lowerCamelCase,standard_warn=__lowerCamelCase )
               A__											=						kwargs['''max_value''']

        if kwargs.get('''min_value''',__lowerCamelCase ) is not None:
               A__											=						'''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
               deprecate('''min_value''','''1.0.0''',__lowerCamelCase,standard_warn=__lowerCamelCase )
               A__											=						kwargs['''min_value''']

        A__											=						list(__lowerCamelCase )
        A__											=						[p.clone().detach() for p in parameters]

        if kwargs.get('''device''',__lowerCamelCase ) is not None:
               A__											=						'''The `device` argument is deprecated. Please use `to` instead.'''
               deprecate('''device''','''1.0.0''',__lowerCamelCase,standard_warn=__lowerCamelCase )
               self.to(device=kwargs['''device'''] )

        A__											=						None

        A__											=						decay
        A__											=						min_decay
        A__											=						update_after_step
        A__											=						use_ema_warmup
        A__											=						inv_gamma
        A__											=						power
        A__											=						0
        A__											=						None  # set in `step()`

        A__											=						model_cls
        A__											=						model_config



 @classmethod
 def 				UpperCamelCase			(							cls,__lowerCamelCase,__lowerCamelCase ):
        A__					,	A__											=						model_cls.load_config(__lowerCamelCase,return_unused_kwargs=__lowerCamelCase )
        A__											=						model_cls.from_pretrained(__lowerCamelCase )

        A__											=						cls(model.parameters(),model_cls=__lowerCamelCase,model_config=model.config )

        ema_model.load_state_dict(__lowerCamelCase )
        return ema_model



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        if self.model_cls is None:
               raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.''' )

        if self.model_config is None:
               raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.''' )

        A__											=						self.model_cls.from_config(self.model_config )
        A__											=						self.state_dict()
        state_dict.pop('''shadow_params''',__lowerCamelCase )

        model.register_to_config(**__lowerCamelCase )
        self.copy_to(model.parameters() )
        model.save_pretrained(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						max(0,optimization_step - self.update_after_step - 1 )

        if step <= 0:
               return 0.0

        if self.use_ema_warmup:
               A__											=						1 - (1 + step / self.inv_gamma) ** -self.power
        else:
               A__											=						(1 + step) / (10 + step)

        A__											=						min(__lowerCamelCase,self.decay )
        # make sure decay is not smaller than min_decay
        A__											=						max(__lowerCamelCase,self.min_decay )
        return cur_decay_value



 @torch.no_grad()
 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        if isinstance(__lowerCamelCase,torch.nn.Module ):
               A__											=						(
                   '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
                   '''Please pass the parameters of the module instead.'''
               )
               deprecate(
                   '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`''','''1.0.0''',__lowerCamelCase,standard_warn=__lowerCamelCase,)
               A__											=						parameters.parameters()

        A__											=						list(__lowerCamelCase )

        self.optimization_step += 1

        # Compute the decay factor for the exponential moving average.
        A__											=						self.get_decay(self.optimization_step )
        A__											=						decay
        A__											=						1 - decay

        A__											=						contextlib.nullcontext
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
               import deepspeed

        for s_param, param in zip(self.shadow_params,__lowerCamelCase ):
               if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                      A__											=						deepspeed.zero.GatheredParameters(__lowerCamelCase,modifier_rank=__lowerCamelCase )

               with context_manager():
                      if param.requires_grad:
                             s_param.sub_(one_minus_decay * (s_param - param) )
                      else:
                             s_param.copy_(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						list(__lowerCamelCase )
        for s_param, param in zip(self.shadow_params,__lowerCamelCase ):
               param.data.copy_(s_param.to(param.device ).data )



 def 				UpperCamelCase			(							self,__lowerCamelCase=None,__lowerCamelCase=None ):
        A__											=						[
            p.to(device=__lowerCamelCase,dtype=__lowerCamelCase ) if p.is_floating_point() else p.to(device=__lowerCamelCase )
            for p in self.shadow_params
        ]



 def 				UpperCamelCase			(							self ):
        return {
            "decay": self.decay,
            "min_decay": self.min_decay,
            "optimization_step": self.optimization_step,
            "update_after_step": self.update_after_step,
            "use_ema_warmup": self.use_ema_warmup,
            "inv_gamma": self.inv_gamma,
            "power": self.power,
            "shadow_params": self.shadow_params,
        }



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						[param.detach().cpu().clone() for param in parameters]



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        if self.temp_stored_params is None:
               raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`''' )
        for c_param, param in zip(self.temp_stored_params,__lowerCamelCase ):
               param.data.copy_(c_param.data )

        # Better memory-wise.
        A__											=						None




 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						copy.deepcopy(__lowerCamelCase )

        A__											=						state_dict.get('''decay''',self.decay )
        if self.decay < 0.0 or self.decay > 1.0:
               raise ValueError('''Decay must be between 0 and 1''' )

        A__											=						state_dict.get('''min_decay''',self.min_decay )
        if not isinstance(self.min_decay,__lowerCamelCase ):
               raise ValueError('''Invalid min_decay''' )

        A__											=						state_dict.get('''optimization_step''',self.optimization_step )
        if not isinstance(self.optimization_step,__lowerCamelCase ):
               raise ValueError('''Invalid optimization_step''' )

        A__											=						state_dict.get('''update_after_step''',self.update_after_step )
        if not isinstance(self.update_after_step,__lowerCamelCase ):
               raise ValueError('''Invalid update_after_step''' )

        A__											=						state_dict.get('''use_ema_warmup''',self.use_ema_warmup )
        if not isinstance(self.use_ema_warmup,__lowerCamelCase ):
               raise ValueError('''Invalid use_ema_warmup''' )

        A__											=						state_dict.get('''inv_gamma''',self.inv_gamma )
        if not isinstance(self.inv_gamma,(float, int) ):
               raise ValueError('''Invalid inv_gamma''' )

        A__											=						state_dict.get('''power''',self.power )
        if not isinstance(self.power,(float, int) ):
               raise ValueError('''Invalid power''' )

        A__											=						state_dict.get('''shadow_params''',__lowerCamelCase )
        if shadow_params is not None:
               A__											=						shadow_params
               if not isinstance(self.shadow_params,__lowerCamelCase ):
                      raise ValueError('''shadow_params must be a list''' )
               if not all(isinstance(__lowerCamelCase,torch.Tensor ) for p in self.shadow_params ):
                      raise ValueError('''shadow_params must all be Tensors''' )












import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

       if is_vision_available():
              from transformers import MaskaFormerImageProcessor

if is_vision_available():
       from PIL import Image




class     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase,__lowerCamelCase=2,__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=10,__lowerCamelCase=3,__lowerCamelCase=32 * 8,__lowerCamelCase=32 * 8,__lowerCamelCase=4,__lowerCamelCase=64,):
        A__											=						parent
        A__											=						batch_size
        A__											=						is_training
        A__											=						use_auxiliary_loss
        A__											=						num_queries
        A__											=						num_channels
        A__											=						min_size
        A__											=						max_size
        A__											=						num_labels
        A__											=						hidden_dim
        A__											=						hidden_dim



 def 				UpperCamelCase			(							self ):
        A__											=						floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
            __lowerCamelCase )

        A__											=						torch.ones([self.batch_size, self.min_size, self.max_size],device=__lowerCamelCase )

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

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



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerConfig(
            hidden_size=self.hidden_dim,)
        A__											=						self.num_queries
        A__											=						self.num_labels

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

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



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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						output.encoder_hidden_states
        A__											=						output.pixel_decoder_hidden_states
        A__											=						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,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=False ):
        with torch.no_grad():
               A__											=						MaskaFormerModel(config=__lowerCamelCase )
               model.to(__lowerCamelCase )
               model.eval()

               A__											=						model(pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase )
               A__											=						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,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						MaskaFormerForUniversalSegmentation(config=__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.eval()

        def comm_check_on_output(__lowerCamelCase ):
               # 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():
               A__											=						model(pixel_values=__lowerCamelCase,pixel_mask=__lowerCamelCase )
               A__											=						model(__lowerCamelCase )

               comm_check_on_output(__lowerCamelCase )

               A__											=						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     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__				,					unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 __SCREAMING_SNAKE_CASE							       =	{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False
 __SCREAMING_SNAKE_CASE							       =	False



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerModelTester(self )
        A__											=						ConfigTester(self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.config_tester.run_common_tests()



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						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 ):
        A__											=						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 ):
        pass



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



 @unittest.skip(reason='''Mask2Former is not a generative model''' )
 def 				UpperCamelCase			(							self ):
        pass



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



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



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

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

               A__											=						['''pixel_values''']
               self.assertListEqual(arg_names[:1],__lowerCamelCase )



 @slow
 def 				UpperCamelCase			(							self ):
        for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
               A__											=						MaskaFormerModel.from_pretrained(__lowerCamelCase )
               self.assertIsNotNone(__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						(self.model_tester.min_size,) * 2
        A__											=						{
            '''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(),
        }
        A__											=						self.model_tester.get_config()

        A__											=						MaskaFormerForUniversalSegmentation(__lowerCamelCase ).to(__lowerCamelCase )
        A__											=						model(**__lowerCamelCase )
        self.assertTrue(outputs.loss is not None )



 def 				UpperCamelCase			(							self ):
        A__					,	A__											=						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 ):
        A__					,	A__											=						self.model_tester.prepare_config_and_inputs_for_common()

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



 def 				UpperCamelCase			(							self ):
        if not self.model_tester.is_training:
               return

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

        A__											=						model_class(__lowerCamelCase )
        model.to(__lowerCamelCase )
        model.train()

        A__											=						model(__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase ).loss
        loss.backward()




 def 				UpperCamelCase			(							self ):
        A__											=						self.all_model_classes[1]
        A__					,	A__					,	A__					,	A__					,	A__											=						self.model_tester.prepare_config_and_inputs()
        A__											=						True
        A__											=						True

        A__											=						model_class(__lowerCamelCase ).to(__lowerCamelCase )
        model.train()

        A__											=						model(__lowerCamelCase,mask_labels=__lowerCamelCase,class_labels=__lowerCamelCase )

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

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

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

        A__											=						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 )
a__:      str									=		1e-4


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
       return image




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



 @cached_property
 def 				UpperCamelCase			(							self ):
        return "facebook/mask2former-swin-small-coco-instance"



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



 def 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase )
        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )
        A__											=						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():
               A__											=						model(**__lowerCamelCase )

        A__											=						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 ) )

        A__											=						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 ) )

        A__											=						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 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase ).eval()
        A__											=						self.default_image_processor
        A__											=						prepare_img()
        A__											=						image_processor(__lowerCamelCase,return_tensors='''pt''' ).to(__lowerCamelCase )
        A__											=						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():
               A__											=						model(**__lowerCamelCase )
        # masks_queries_logits
        A__											=						outputs.masks_queries_logits
        self.assertEqual(
            masks_queries_logits.shape,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
        A__											=						[
            [-8.7839, -9.0056, -8.8121],
            [-7.4104, -7.0313, -6.5401],
            [-6.6105, -6.3427, -6.4675],
        ]
        A__											=						torch.tensor(__lowerCamelCase ).to(__lowerCamelCase )
        self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],__lowerCamelCase,atol=__lowerCamelCase ) )
        # class_queries_logits
        A__											=						outputs.class_queries_logits
        self.assertEqual(class_queries_logits.shape,(1, model.config.num_queries, model.config.num_labels + 1) )
        A__											=						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 				UpperCamelCase			(							self ):
        A__											=						MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__lowerCamelCase ).eval()
        A__											=						self.default_image_processor

        A__											=						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''',)

        A__											=						inputs['''pixel_values'''].to(__lowerCamelCase )
        A__											=						[el.to(__lowerCamelCase ) for el in inputs['''mask_labels''']]
        A__											=						[el.to(__lowerCamelCase ) for el in inputs['''class_labels''']]

        with torch.no_grad():
               A__											=						model(**__lowerCamelCase )

        self.assertTrue(outputs.loss is not None )





