





"""simple docstring"""
import inspect
import unittest

import numpy as np

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

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


if is_torch_available():
  import torch

  from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

  if is_vision_available():
    from transformers import MaskFormerImageProcessor

if is_vision_available():
  from PIL import Image






class 					_SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=10		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=32 * 4		,SCREAMING_SNAKE_CASE__=32 * 6		,SCREAMING_SNAKE_CASE__=4		,SCREAMING_SNAKE_CASE__=32		,)			->      Optional[int]:





             """simple docstring"""






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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
                 SCREAMING_SNAKE_CASE__							)

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

             __SCREAMING_SNAKE_CASE   :Tuple						=  (
                 torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]		,device=SCREAMING_SNAKE_CASE__							) > 0.5
             ).float()
             __SCREAMING_SNAKE_CASE   :List[str]						=  (torch.rand((self.batch_size, self.num_labels)		,device=SCREAMING_SNAKE_CASE__							) > 0.5).long()

             __SCREAMING_SNAKE_CASE   :str						=  self.get_config()
             return config, pixel_values, pixel_mask, mask_labels, class_labels




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





             """simple docstring"""






             return MaskFormerConfig.from_backbone_and_decoder_configs(
                 backbone_config=SwinConfig(
                     depths=[1, 1, 1, 1]		,)		,decoder_config=DetrConfig(
                     decoder_ffn_dim=1_28		,num_queries=self.num_queries		,decoder_attention_heads=2		,d_model=self.mask_feature_size		,)		,mask_feature_size=self.mask_feature_size		,fpn_feature_size=self.mask_feature_size		,num_channels=self.num_channels		,num_labels=self.num_labels		,)




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  output.encoder_hidden_states
             __SCREAMING_SNAKE_CASE   :Tuple						=  output.pixel_decoder_hidden_states
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  output.transformer_decoder_hidden_states

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




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





             """simple docstring"""






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

                   __SCREAMING_SNAKE_CASE   :Tuple						=  model(pixel_values=SCREAMING_SNAKE_CASE__		,pixel_mask=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE   :Any						=  model(SCREAMING_SNAKE_CASE__		,output_hidden_states=SCREAMING_SNAKE_CASE__							)
             # the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
             # encoder and pixel decoder
             self.parent.assertEqual(
                 output.transformer_decoder_last_hidden_state.shape		,(self.batch_size, self.num_queries, self.mask_feature_size)		,)
             # let's ensure the other two hidden state exists
             self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None							)
             self.parent.assertTrue(output.encoder_last_hidden_state is not None							)

             if output_hidden_states:
                   self.check_output_hidden_state(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)			->      Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  MaskFormerForInstanceSegmentation(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()

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

             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  model(pixel_values=SCREAMING_SNAKE_CASE__		,pixel_mask=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE   :Dict						=  model(SCREAMING_SNAKE_CASE__							)

                   comm_check_on_output(SCREAMING_SNAKE_CASE__							)

                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  model(
                       pixel_values=SCREAMING_SNAKE_CASE__		,pixel_mask=SCREAMING_SNAKE_CASE__		,mask_labels=SCREAMING_SNAKE_CASE__		,class_labels=SCREAMING_SNAKE_CASE__							)

             comm_check_on_output(SCREAMING_SNAKE_CASE__							)

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






@require_torch
class 					_SCREAMING_SNAKE_CASE(   A    ,	A    ,	unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE_   :      int							=  (
           {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
           if is_torch_available()
           else {}
       )

       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  False
       SCREAMING_SNAKE_CASE_   :      Dict							=  False
       SCREAMING_SNAKE_CASE_   :      Dict							=  False
       SCREAMING_SNAKE_CASE_   :      Union[str, Any]							=  False




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[int]						=  MaskFormerModelTester(self							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  ConfigTester(self		,config_class=SCREAMING_SNAKE_CASE__		,has_text_modality=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             self.config_tester.run_common_tests()




       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.create_and_check_maskformer_model(SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,output_hidden_states=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             pass




       @unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''							)
       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             pass




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





             """simple docstring"""






             pass




       @unittest.skip(reason='''MaskFormer does not use token embeddings'''							)
       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             pass




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





             """simple docstring"""






             pass




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





             """simple docstring"""






             pass




       def     _UpperCamelCase     (						self							)			->      Dict:





             """simple docstring"""






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

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

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




       @slow
       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             for model_name in ["facebook/maskformer-swin-small-coco"]:
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  MaskFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE   :List[str]						=  MaskFormerForInstanceSegmentation(MaskFormerConfig()							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  model(**SCREAMING_SNAKE_CASE__							)
             self.assertTrue(outputs.loss is not None							)




       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Tuple						=  self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.create_and_check_maskformer_model(SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,output_hidden_states=SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.model_tester.prepare_config_and_inputs_for_common()

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




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





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE   :Tuple						=  model_class(SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.train()

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  model(SCREAMING_SNAKE_CASE__		,mask_labels=SCREAMING_SNAKE_CASE__		,class_labels=SCREAMING_SNAKE_CASE__							).loss
             loss.backward()




       def     _UpperCamelCase     (						self							)			->      Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Tuple						=  self.all_model_classes[1]
             __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Optional[int]						=  self.model_tester.prepare_config_and_inputs()
             __SCREAMING_SNAKE_CASE   :Tuple						=  True
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  True

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  model_class(SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.train()

             __SCREAMING_SNAKE_CASE   :str						=  model(SCREAMING_SNAKE_CASE__		,mask_labels=SCREAMING_SNAKE_CASE__		,class_labels=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :List[str]						=  outputs.encoder_hidden_states[0]
             encoder_hidden_states.retain_grad()

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

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  outputs.attentions[0]
             attentions.retain_grad()

             outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE__							)

             self.assertIsNotNone(encoder_hidden_states.grad							)
             self.assertIsNotNone(pixel_decoder_hidden_states.grad							)
             self.assertIsNotNone(transformer_decoder_hidden_states.grad							)
             self.assertIsNotNone(attentions.grad							)




lowerCamelCase_								=		1e-4



def      __lowerCamelCase   (							)			->      Dict:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  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							)			->      List[str]:





             """simple docstring"""






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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :List[str]						=  MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Any						=  self.default_image_processor
             __SCREAMING_SNAKE_CASE   :List[str]						=  prepare_img()
             __SCREAMING_SNAKE_CASE   :Dict						=  image_processor(SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  inputs['''pixel_values'''].shape
             # check size is divisible by 32
             self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0							)
             # check size
             self.assertEqual(SCREAMING_SNAKE_CASE__		,(1, 3, 8_00, 10_88)							)

             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE   :Dict						=  model(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  torch.tensor(
                 [[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]]							).to(SCREAMING_SNAKE_CASE__							)
             self.assertTrue(
                 torch.allclose(
                     outputs.encoder_last_hidden_state[0, 0, :3, :3]		,SCREAMING_SNAKE_CASE__		,atol=SCREAMING_SNAKE_CASE__							)							)

             __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.tensor(
                 [[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]]							).to(SCREAMING_SNAKE_CASE__							)
             self.assertTrue(
                 torch.allclose(
                     outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,SCREAMING_SNAKE_CASE__		,atol=SCREAMING_SNAKE_CASE__							)							)

             __SCREAMING_SNAKE_CASE   :List[str]						=  torch.tensor(
                 [[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]]							).to(SCREAMING_SNAKE_CASE__							)
             self.assertTrue(
                 torch.allclose(
                     outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,SCREAMING_SNAKE_CASE__		,atol=SCREAMING_SNAKE_CASE__							)							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :int						=  (
                 MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''							)
                 .to(SCREAMING_SNAKE_CASE__							)
                 .eval()
             )
             __SCREAMING_SNAKE_CASE   :List[Any]						=  self.default_image_processor
             __SCREAMING_SNAKE_CASE   :List[str]						=  prepare_img()
             __SCREAMING_SNAKE_CASE   :List[str]						=  image_processor(SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[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(SCREAMING_SNAKE_CASE__		,(1, 3, 8_00, 10_88)							)

             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE   :Dict						=  model(**SCREAMING_SNAKE_CASE__							)
             # masks_queries_logits
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  outputs.masks_queries_logits
             self.assertEqual(
                 masks_queries_logits.shape		,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		,)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [
                 [-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
                 [-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
                 [-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
             ]
             __SCREAMING_SNAKE_CASE   :str						=  torch.tensor(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)
             self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,SCREAMING_SNAKE_CASE__		,atol=SCREAMING_SNAKE_CASE__							)							)
             # class_queries_logits
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  outputs.class_queries_logits
             self.assertEqual(
                 class_queries_logits.shape		,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  torch.tensor(
                 [
                     [1.6_512E00, -5.2_572E00, -3.3_519E00],
                     [3.6_169E-02, -5.9_025E00, -2.9_313E00],
                     [1.0_766E-04, -7.7_630E00, -5.1_263E00],
                 ]							).to(SCREAMING_SNAKE_CASE__							)
             self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,SCREAMING_SNAKE_CASE__		,atol=SCREAMING_SNAKE_CASE__							)							)




       def     _UpperCamelCase     (						self							)			->      int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  (
                 MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''							)
                 .to(SCREAMING_SNAKE_CASE__							)
                 .eval()
             )
             __SCREAMING_SNAKE_CASE   :str						=  self.default_image_processor
             __SCREAMING_SNAKE_CASE   :Tuple						=  prepare_img()
             __SCREAMING_SNAKE_CASE   :int						=  image_processor(SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :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(SCREAMING_SNAKE_CASE__		,(1, 3, 8_00, 10_88)							)

             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE   :Tuple						=  model(**SCREAMING_SNAKE_CASE__							)
             # masks_queries_logits
             __SCREAMING_SNAKE_CASE   :Any						=  outputs.masks_queries_logits
             self.assertEqual(
                 masks_queries_logits.shape		,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		,)
             __SCREAMING_SNAKE_CASE   :int						=  [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
             __SCREAMING_SNAKE_CASE   :Any						=  torch.tensor(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)
             self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,SCREAMING_SNAKE_CASE__		,atol=SCREAMING_SNAKE_CASE__							)							)
             # class_queries_logits
             __SCREAMING_SNAKE_CASE   :Any						=  outputs.class_queries_logits
             self.assertEqual(
                 class_queries_logits.shape		,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)							)
             __SCREAMING_SNAKE_CASE   :int						=  torch.tensor(
                 [[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]]							).to(SCREAMING_SNAKE_CASE__							)
             self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,SCREAMING_SNAKE_CASE__		,atol=SCREAMING_SNAKE_CASE__							)							)




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





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE   :Any						=  image_processor(
                 [np.zeros((3, 8_00, 13_33)							), np.zeros((3, 8_00, 13_33)							)]		,segmentation_maps=[np.zeros((3_84, 3_84)							).astype(np.floataa							), np.zeros((3_84, 3_84)							).astype(np.floataa							)]		,return_tensors='''pt'''		,)

             __SCREAMING_SNAKE_CASE   :Any						=  inputs['''pixel_values'''].to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :str						=  [el.to(SCREAMING_SNAKE_CASE__							) for el in inputs['''mask_labels''']]
             __SCREAMING_SNAKE_CASE   :List[str]						=  [el.to(SCREAMING_SNAKE_CASE__							) for el in inputs['''class_labels''']]

             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE   :Any						=  model(**SCREAMING_SNAKE_CASE__							)

             self.assertTrue(outputs.loss is not None							)






"""simple docstring"""
from typing import Any



def      __lowerCamelCase   (							a_     :		list							)			->      list[Any]:
      if not input_list:
            return []
      __SCREAMING_SNAKE_CASE   :int						=  [input_list.count(a_							) for value in input_list]
      __SCREAMING_SNAKE_CASE   :str						=  max(a_							)  # Gets the maximum count in the input list.
      # Gets values of modes
      return sorted({input_list[i] for i, value in enumerate(a_							) if value == y}							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()