
'''simple docstring'''







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_		(__a      :    int ):






   """simple docstring"""

   random.seed(__a )
   np.random.seed(__a )
   torch.manual_seed(__a )
   torch.cuda.manual_seed_all(__a )
   # ^^ safe to call this function even if cuda is not available







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Optional[Any]							,_a      :     Iterable[torch.nn.Parameter]							,_a      :     float = 0.9999							,_a      :     float = 0.0							,_a      :     int = 0							,_a      :     bool = False							,_a      :     Union[float, int] = 1.0							,_a      :     Union[float, int] = 2 / 3							,_a      :     Optional[Any] = None							,_a      :     Dict[str, Any] = None							,**_a      :     Tuple							,):


     '''simple docstring'''






     if isinstance(_a							,torch.nn.Module       ):
        _a				:	str			=   (
            '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'							,_a							,standard_warn=_a							,)
        _a				:	Optional[int]			=   parameters.parameters()

        # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
        _a				:	List[str]			=   True

     if kwargs.get('max_value'							,_a       ) is not None:
        _a				:	Optional[int]			=   'The `max_value` argument is deprecated. Please use `decay` instead.'
        deprecate('max_value'							,'1.0.0'							,_a							,standard_warn=_a       )
        _a				:	Union[str, Any]			=   kwargs['max_value']

     if kwargs.get('min_value'							,_a       ) is not None:
        _a				:	List[str]			=   'The `min_value` argument is deprecated. Please use `min_decay` instead.'
        deprecate('min_value'							,'1.0.0'							,_a							,standard_warn=_a       )
        _a				:	Optional[int]			=   kwargs['min_value']

     _a				:	Tuple			=   list(_a       )
     _a				:	List[str]			=   [p.clone().detach() for p in parameters]

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

     _a				:	str			=   None

     _a				:	Optional[Any]			=   decay
     _a				:	Union[str, Any]			=   min_decay
     _a				:	Tuple			=   update_after_step
     _a				:	Union[str, Any]			=   use_ema_warmup
     _a				:	str			=   inv_gamma
     _a				:	int			=   power
     _a				:	Tuple			=   0
     _a				:	Any			=   None  # set in `step()`

     _a				:	List[str]			=   model_cls
     _a				:	Tuple			=   model_config


  @classmethod
  def   __lowercase		(		cls      :     Union[str, Any]							,_a      :     List[Any]							,_a      :     List[Any]       ):


     '''simple docstring'''






     _a,  _a				:	Union[str, Any]			=   model_cls.load_config(_a							,return_unused_kwargs=_a       )
     _a				:	Dict			=   model_cls.from_pretrained(_a       )

     _a				:	List[str]			=   cls(model.parameters()							,model_cls=_a							,model_config=model.config       )

     ema_model.load_state_dict(_a       )
     return ema_model


  def   __lowercase		(		self      :     Dict							,_a      :     Any       ):


     '''simple docstring'''






     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				:	str			=   self.model_cls.from_config(self.model_config       )
     _a				:	List[str]			=   self.state_dict()
     state_dict.pop('shadow_params'							,_a       )

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


  def   __lowercase		(		self      :     Dict							,_a      :     int       ):


     '''simple docstring'''






     _a				:	Tuple			=   max(0							,optimization_step - self.update_after_step - 1       )

     if step <= 0:
        return 0.0

     if self.use_ema_warmup:
        _a				:	Optional[int]			=   1 - (1 + step / self.inv_gamma) ** -self.power
     else:
        _a				:	List[Any]			=   (1 + step) / (10 + step)

     _a				:	Tuple			=   min(_a							,self.decay       )
     # make sure decay is not smaller than min_decay
     _a				:	str			=   max(_a							,self.min_decay       )
     return cur_decay_value


  @torch.no_grad()
  def   __lowercase		(		self      :     Any							,_a      :     Iterable[torch.nn.Parameter]       ):


     '''simple docstring'''






     if isinstance(_a							,torch.nn.Module       ):
        _a				:	str			=   (
            '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'							,_a							,standard_warn=_a							,)
        _a				:	Tuple			=   parameters.parameters()

     _a				:	Optional[Any]			=   list(_a       )

     self.optimization_step += 1

     # Compute the decay factor for the exponential moving average.
     _a				:	int			=   self.get_decay(self.optimization_step       )
     _a				:	Dict			=   decay
     _a				:	int			=   1 - decay

     _a				:	Union[str, Any]			=   contextlib.nullcontext
     if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
        import deepspeed

     for s_param, param in zip(self.shadow_params							,_a       ):
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
           _a				:	Union[str, Any]			=   deepspeed.zero.GatheredParameters(_a							,modifier_rank=_a       )

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


  def   __lowercase		(		self      :     Optional[int]							,_a      :     Iterable[torch.nn.Parameter]       ):


     '''simple docstring'''






     _a				:	str			=   list(_a       )
     for s_param, param in zip(self.shadow_params							,_a       ):
        param.data.copy_(s_param.to(param.device       ).data       )


  def   __lowercase		(		self      :     int							,_a      :     Optional[Any]=None							,_a      :     int=None       ):


     '''simple docstring'''






     _a				:	str			=   [
         p.to(device=_a							,dtype=_a       ) if p.is_floating_point() else p.to(device=_a       )
         for p in self.shadow_params
     ]


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     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   __lowercase		(		self      :     int							,_a      :     Iterable[torch.nn.Parameter]       ):


     '''simple docstring'''






     _a				:	List[Any]			=   [param.detach().cpu().clone() for param in parameters]


  def   __lowercase		(		self      :     Optional[int]							,_a      :     Iterable[torch.nn.Parameter]       ):


     '''simple docstring'''






     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							,_a       ):
        param.data.copy_(c_param.data       )

     # Better memory-wise.
     _a				:	Union[str, Any]			=   None


  def   __lowercase		(		self      :     List[Any]							,_a      :     dict       ):


     '''simple docstring'''






     _a				:	List[Any]			=   copy.deepcopy(_a       )

     _a				:	Optional[int]			=   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				:	List[Any]			=   state_dict.get('min_decay'							,self.min_decay       )
     if not isinstance(self.min_decay							,_a       ):
        raise ValueError('Invalid min_decay'       )

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

     _a				:	Optional[int]			=   state_dict.get('update_after_step'							,self.update_after_step       )
     if not isinstance(self.update_after_step							,_a       ):
        raise ValueError('Invalid update_after_step'       )

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

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

     _a				:	Optional[Any]			=   state_dict.get('power'							,self.power       )
     if not isinstance(self.power							,(float, int)       ):
        raise ValueError('Invalid power'       )

     _a				:	Union[str, Any]			=   state_dict.get('shadow_params'							,_a       )
     if shadow_params is not None:
        _a				:	Union[str, Any]			=   shadow_params
        if not isinstance(self.shadow_params							,_a       ):
           raise ValueError('shadow_params must be a list'       )
        if not all(isinstance(_a							,torch.Tensor       ) for p in self.shadow_params       ):
           raise ValueError('shadow_params must all be Tensors'       )





'''simple docstring'''







import inspect
import unittest

import numpy as np

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

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


if is_torch_available():
  import torch

  from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

  if is_vision_available():
    from transformers import MaskaFormerImageProcessor

if is_vision_available():
  from PIL import Image







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     int							,_a      :     Any							,_a      :     Optional[int]=2							,_a      :     Optional[Any]=True							,_a      :     Dict=False							,_a      :     Dict=10							,_a      :     Any=3							,_a      :     str=32 * 8							,_a      :     Optional[int]=32 * 8							,_a      :     int=4							,_a      :     str=64							,):


     '''simple docstring'''






     _a				:	Dict			=   parent
     _a				:	Union[str, Any]			=   batch_size
     _a				:	Tuple			=   is_training
     _a				:	List[str]			=   use_auxiliary_loss
     _a				:	Optional[Any]			=   num_queries
     _a				:	str			=   num_channels
     _a				:	List[str]			=   min_size
     _a				:	int			=   max_size
     _a				:	Optional[int]			=   num_labels
     _a				:	List[str]			=   hidden_dim
     _a				:	int			=   hidden_dim


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Tuple			=   floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
         _a       )

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

     _a				:	Union[str, Any]			=   (
         torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]							,device=_a       ) > 0.5
     ).float()
     _a				:	Tuple			=   (torch.rand((self.batch_size, self.num_labels)							,device=_a       ) > 0.5).long()

     _a				:	Dict			=   self.get_config()
     return config, pixel_values, pixel_mask, mask_labels, class_labels


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	int			=   MaskaFormerConfig(
         hidden_size=self.hidden_dim							,)
     _a				:	str			=   self.num_queries
     _a				:	Union[str, Any]			=   self.num_labels

     _a				:	Tuple			=   [1, 1, 1, 1]
     _a				:	Dict			=   self.num_channels

     _a				:	str			=   64
     _a				:	Tuple			=   128
     _a				:	Optional[Any]			=   self.hidden_dim
     _a				:	Union[str, Any]			=   self.hidden_dim
     _a				:	List[Any]			=   self.hidden_dim
     return config


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a,  _a,  _a,  _a,  _a				:	Optional[Any]			=   self.prepare_config_and_inputs()
     _a				:	str			=   {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
     return config, inputs_dict


  def   __lowercase		(		self      :     List[str]							,_a      :     Optional[Any]							,_a      :     str       ):


     '''simple docstring'''






     _a				:	str			=   output.encoder_hidden_states
     _a				:	Any			=   output.pixel_decoder_hidden_states
     _a				:	Optional[Any]			=   output.transformer_decoder_hidden_states

     self.parent.assertTrue(len(_a       )							,len(config.backbone_config.depths       )       )
     self.parent.assertTrue(len(_a       )							,len(config.backbone_config.depths       )       )
     self.parent.assertTrue(len(_a       )							,config.decoder_layers       )


  def   __lowercase		(		self      :     List[str]							,_a      :     str							,_a      :     List[Any]							,_a      :     Any							,_a      :     Union[str, Any]=False       ):


     '''simple docstring'''






     with torch.no_grad():
        _a				:	str			=   MaskaFormerModel(config=_a       )
        model.to(_a       )
        model.eval()

        _a				:	Any			=   model(pixel_values=_a							,pixel_mask=_a       )
        _a				:	Optional[Any]			=   model(_a							,output_hidden_states=_a       )

     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(_a							,_a       )


  def   __lowercase		(		self      :     Tuple							,_a      :     List[Any]							,_a      :     Union[str, Any]							,_a      :     Tuple							,_a      :     List[str]							,_a      :     Any       ):


     '''simple docstring'''






     _a				:	int			=   MaskaFormerForUniversalSegmentation(config=_a       )
     model.to(_a       )
     model.eval()

     def comm_check_on_output(_a      :     Any       ):
        # 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				:	Any			=   model(pixel_values=_a							,pixel_mask=_a       )
        _a				:	Optional[int]			=   model(_a       )

        comm_check_on_output(_a       )

        _a				:	List[str]			=   model(
            pixel_values=_a							,pixel_mask=_a							,mask_labels=_a							,class_labels=_a       )

     comm_check_on_output(_a       )

     self.parent.assertTrue(result.loss is not None       )
     self.parent.assertEqual(result.loss.shape							,torch.Size([1]       )       )







@require_torch
class UpperCAmelCase__	( lowercase__    ,				lowercase__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[int]   =					(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
  __UpperCAmelCase				:				Dict   =					{'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

  __UpperCAmelCase				:				Dict   =					False
  __UpperCAmelCase				:				Tuple   =					False
  __UpperCAmelCase				:				Dict   =					False
  __UpperCAmelCase				:				List[Any]   =					False


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   MaskaFormerModelTester(self       )
     _a				:	Dict			=   ConfigTester(self							,config_class=_a							,has_text_modality=_a       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     self.config_tester.run_common_tests()


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a,  _a				:	List[str]			=   self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(_a							,**_a							,output_hidden_states=_a       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	Dict			=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_a       )


  @unittest.skip(reason='Mask2Former does not use inputs_embeds'       )
  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     pass


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


     '''simple docstring'''






     pass


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


     '''simple docstring'''






     pass


  @unittest.skip(reason='Mask2Former does not use token embeddings'       )
  def   __lowercase		(		self      :     Optional[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   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     pass


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


     '''simple docstring'''






     pass


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a,  _a				:	Any			=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        _a				:	Union[str, Any]			=   model_class(_a       )
        _a				:	List[str]			=   inspect.signature(model.forward       )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        _a				:	Optional[Any]			=   [*signature.parameters.keys()]

        _a				:	List[Any]			=   ['pixel_values']
        self.assertListEqual(arg_names[:1]							,_a       )


  @slow
  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	int			=   (self.model_tester.min_size,) * 2
     _a				:	Any			=   {
         'pixel_values': torch.randn((2, 3, *size)							,device=_a       ),
         'mask_labels': torch.randn((2, 10, *size)							,device=_a       ),
         'class_labels': torch.zeros(2							,10							,device=_a       ).long(),
     }
     _a				:	List[Any]			=   self.model_tester.get_config()

     _a				:	int			=   MaskaFormerForUniversalSegmentation(_a       ).to(_a       )
     _a				:	str			=   model(**_a       )
     self.assertTrue(outputs.loss is not None       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a,  _a				:	List[Any]			=   self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.create_and_check_maskaformer_model(_a							,**_a							,output_hidden_states=_a       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a,  _a				:	Dict			=   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        _a				:	Any			=   model_class(_a       ).to(_a       )
        _a				:	Optional[int]			=   model(**_a							,output_attentions=_a       )
        self.assertTrue(outputs.attentions is not None       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     if not self.model_tester.is_training:
        return

     _a				:	List[str]			=   self.all_model_classes[1]
     _a,  _a,  _a,  _a,  _a				:	List[str]			=   self.model_tester.prepare_config_and_inputs()

     _a				:	Any			=   model_class(_a       )
     model.to(_a       )
     model.train()

     _a				:	Union[str, Any]			=   model(_a							,mask_labels=_a							,class_labels=_a       ).loss
     loss.backward()


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	int			=   self.all_model_classes[1]
     _a,  _a,  _a,  _a,  _a				:	List[Any]			=   self.model_tester.prepare_config_and_inputs()
     _a				:	str			=   True
     _a				:	str			=   True

     _a				:	List[str]			=   model_class(_a       ).to(_a       )
     model.train()

     _a				:	Optional[int]			=   model(_a							,mask_labels=_a							,class_labels=_a       )

     _a				:	Tuple			=   outputs.encoder_hidden_states[0]
     encoder_hidden_states.retain_grad()

     _a				:	str			=   outputs.pixel_decoder_hidden_states[0]
     pixel_decoder_hidden_states.retain_grad()

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

     _a				:	List[str]			=   outputs.attentions[0]
     attentions.retain_grad()

     outputs.loss.backward(retain_graph=_a       )

     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         =    1e-4





def 					UpperCAmelCase_		():






   """simple docstring"""

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







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





  """simple docstring"""

  @cached_property
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






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


  @cached_property
  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	List[str]			=   MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(_a       )
     _a				:	int			=   self.default_image_processor
     _a				:	Tuple			=   prepare_img()
     _a				:	Any			=   image_processor(_a							,return_tensors='pt'       ).to(_a       )
     _a				:	Union[str, Any]			=   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(_a							,(1, 3, 384, 384)       )

     with torch.no_grad():
        _a				:	Optional[Any]			=   model(**_a       )

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

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

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


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[Any]			=   MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(_a       ).eval()
     _a				:	Optional[Any]			=   self.default_image_processor
     _a				:	List[Any]			=   prepare_img()
     _a				:	str			=   image_processor(_a							,return_tensors='pt'       ).to(_a       )
     _a				:	Any			=   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(_a							,(1, 3, 384, 384)       )

     with torch.no_grad():
        _a				:	Optional[int]			=   model(**_a       )
     # masks_queries_logits
     _a				:	Dict			=   outputs.masks_queries_logits
     self.assertEqual(
         masks_queries_logits.shape							,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
     _a				:	Dict			=   [
         [-8.7839, -9.0056, -8.8121],
         [-7.4104, -7.0313, -6.5401],
         [-6.6105, -6.3427, -6.4675],
     ]
     _a				:	Optional[Any]			=   torch.tensor(_a       ).to(_a       )
     self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]							,_a							,atol=_a       )       )
     # class_queries_logits
     _a				:	str			=   outputs.class_queries_logits
     self.assertEqual(class_queries_logits.shape							,(1, model.config.num_queries, model.config.num_labels + 1)       )
     _a				:	str			=   torch.tensor(
         [
             [1.8324, -8.0835, -4.1922],
             [0.8450, -9.0050, -3.6053],
             [0.3045, -7.7293, -3.0275],
         ]       ).to(_a       )
     self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]							,_a							,atol=_a       )       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	Any			=   MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(_a       ).eval()
     _a				:	Tuple			=   self.default_image_processor

     _a				:	Tuple			=   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				:	str			=   inputs['pixel_values'].to(_a       )
     _a				:	str			=   [el.to(_a       ) for el in inputs['mask_labels']]
     _a				:	Dict			=   [el.to(_a       ) for el in inputs['class_labels']]

     with torch.no_grad():
        _a				:	List[str]			=   model(**_a       )

     self.assertTrue(outputs.loss is not None       )



