





import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import UperNetForSemanticSegmentation
    from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=32					,  lowercase=3					,  lowercase=4					,  lowercase=[10, 20, 30, 40]					,  lowercase=[2, 2, 3, 2]					,  lowercase=True					,  lowercase=True					,  lowercase=37					,  lowercase="gelu"					,  lowercase=10					,  lowercase=0.02					,  lowercase=["stage2", "stage3", "stage4"]					,  lowercase=3					,  lowercase=None					,  )			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[Any] =				parent
          a__						:	List[str] =				batch_size
          a__						:	str =				image_size
          a__						:	int =				num_channels
          a__						:	List[str] =				num_stages
          a__						:	Tuple =				hidden_sizes
          a__						:	List[str] =				depths
          a__						:	str =				is_training
          a__						:	Any =				use_labels
          a__						:	Optional[int] =				intermediate_size
          a__						:	Union[str, Any] =				hidden_act
          a__						:	List[str] =				type_sequence_label_size
          a__						:	List[str] =				initializer_range
          a__						:	Optional[Any] =				out_features
          a__						:	Union[str, Any] =				num_labels
          a__						:	Dict =				scope
          a__						:	List[str] =				num_stages

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







          '''simple docstring'''

          a__						:	List[str] =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	int =				None
          if self.use_labels:
                 a__						:	Union[str, Any] =				ids_tensor([self.batch_size]					,  self.type_sequence_label_size)

          a__						:	Tuple =				self.get_config()

          return config, pixel_values, labels

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







          '''simple docstring'''

          return ConvNextConfig(
              num_channels=self.num_channels					,  num_stages=self.num_stages					,  hidden_sizes=self.hidden_sizes					,  depths=self.depths					,  is_training=self.is_training					,  intermediate_size=self.intermediate_size					,  hidden_act=self.hidden_act					,  out_features=self.out_features					,  )

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







          '''simple docstring'''

          return UperNetConfig(
              backbone_config=self.get_backbone_config()					,  hidden_size=512					,  pool_scales=[1, 2, 3, 6]					,  use_auxiliary_head=lowercase					,  auxiliary_loss_weight=0.4					,  auxiliary_in_channels=40					,  auxiliary_channels=256					,  auxiliary_num_convs=1					,  auxiliary_concat_input=lowercase					,  loss_ignore_index=255					,  num_labels=self.num_labels					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	Tuple =				UperNetForSemanticSegmentation(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Optional[Any] =				model(lowercase)
          self.parent.assertEqual(
              result.logits.shape					,  (self.batch_size, self.num_labels, self.image_size, self.image_size))

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







          '''simple docstring'''

          a__						:	Dict =				self.prepare_config_and_inputs()
          (
              (
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							
          )						:	List[Any] =				config_and_inputs
          a__						:	Dict =				{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Tuple         =  (UperNetForSemanticSegmentation,) if is_torch_available() else ()
   __A   :				Union[str, Any]         =  {'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
   __A   :				Optional[Any]         =  False
   __A   :				Union[str, Any]         =  False
   __A   :				List[str]         =  False
   __A   :				Union[str, Any]         =  False
   __A   :				Any         =  False
   __A   :				List[Any]         =  False

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Tuple =				UperNetModelTester(self)
          a__						:	List[str] =				ConfigTester(self					,  config_class=lowercase					,  has_text_modality=lowercase					,  hidden_size=37)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()

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







          '''simple docstring'''

          return

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

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

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

                 a__						:	str =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Optional[int] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*lowercase)

   @unittest.skip(reason='UperNet does not use inputs_embeds')
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          pass

   @unittest.skip(reason='UperNet does not support input and output embeddings')
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          pass

   @unittest.skip(reason='UperNet does not have a base model')
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          pass

   @unittest.skip(reason='UperNet does not have a base model')
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          pass

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







          '''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)			->						Tuple:







          '''simple docstring'''

          def check_hidden_states_output(lowercase					,  lowercase					,  lowercase):
                 a__						:	Optional[int] =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()

                 with torch.no_grad():
                        a__						:	Dict =				model(**self._prepare_for_class(lowercase					,  lowercase))

                 a__						:	str =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 a__						:	Tuple =				self.model_tester.num_stages
                 self.assertEqual(len(lowercase)					,  expected_num_stages + 1)

                 # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:])					,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,  )

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

          for model_class in self.all_model_classes:
                 a__						:	List[Any] =				True
                 check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 a__						:	Tuple =				True

                 check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

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

          a__						:	List[Any] =				_config_zero_init(lowercase)
          a__						:	Dict =				_config_zero_init(configs_no_init.backbone_config)
          for model_class in self.all_model_classes:
                 a__						:	Any =				model_class(config=lowercase)
                 for name, param in model.named_parameters():
                        if param.requires_grad:
                               self.assertIn(
                                   ((param.data.mean() * 1e9).round() / 1e9).item()					,  [0.0, 1.0]					,  msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,  )

   @unittest.skip(reason='UperNet does not have tied weights')
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          pass

   @slow
   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Union[str, Any] =				UperNetForSemanticSegmentation.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		Optional[Any]:
       a__						:	Dict =				hf_hub_download(
           repo_id='hf-internal-testing/fixtures_ade20k'		,      repo_type='dataset'		,      filename='ADE_val_00000001.jpg'				)
       a__						:	Tuple =				Image.open(A__				).convert('RGB'				)
       return image





@require_torch
@require_vision
@slow
class  A__    (							unittest.TestCase	):




   """simple docstring"""



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







          '''simple docstring'''

          a__						:	Any =				AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny')
          a__						:	List[str] =				UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny').to(lowercase)

          a__						:	str =				prepare_img()
          a__						:	List[str] =				processor(images=lowercase					,  return_tensors='pt').to(lowercase)

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

          a__						:	Optional[int] =				torch.Size((1, model.config.num_labels, 512, 512))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Dict =				torch.tensor(
              [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]					,  lowercase					,  atol=1e-4))

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Union[str, Any] =				AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny')
          a__						:	Tuple =				UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny').to(lowercase)

          a__						:	Optional[int] =				prepare_img()
          a__						:	Any =				processor(images=lowercase					,  return_tensors='pt').to(lowercase)

          with torch.no_grad():
                 a__						:	Any =				model(**lowercase)

          a__						:	Tuple =				torch.Size((1, model.config.num_labels, 512, 512))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	List[Any] =				torch.tensor(
              [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]					,  lowercase					,  atol=1e-4))









import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_MAPPING,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerModel,
    )
    from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import SegformerImageProcessor





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Dict =				self.config_class(**self.inputs_dict)
          self.parent.assertTrue(hasattr(lowercase					,  'hidden_sizes'))
          self.parent.assertTrue(hasattr(lowercase					,  'num_attention_heads'))
          self.parent.assertTrue(hasattr(lowercase					,  'num_encoder_blocks'))





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=64					,  lowercase=3					,  lowercase=4					,  lowercase=[2, 2, 2, 2]					,  lowercase=[8, 4, 2, 1]					,  lowercase=[16, 32, 64, 128]					,  lowercase=[1, 4, 8, 16]					,  lowercase=[1, 2, 4, 8]					,  lowercase=True					,  lowercase=True					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=0.02					,  lowercase=3					,  lowercase=None					,  )			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[Any] =				parent
          a__						:	int =				batch_size
          a__						:	Tuple =				image_size
          a__						:	Union[str, Any] =				num_channels
          a__						:	str =				num_encoder_blocks
          a__						:	Dict =				sr_ratios
          a__						:	Dict =				depths
          a__						:	Union[str, Any] =				hidden_sizes
          a__						:	str =				downsampling_rates
          a__						:	Tuple =				num_attention_heads
          a__						:	Optional[Any] =				is_training
          a__						:	Union[str, Any] =				use_labels
          a__						:	Any =				hidden_act
          a__						:	Optional[int] =				hidden_dropout_prob
          a__						:	int =				attention_probs_dropout_prob
          a__						:	Optional[Any] =				initializer_range
          a__						:	Tuple =				num_labels
          a__						:	Union[str, Any] =				scope

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Optional[int] =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	str =				None
          if self.use_labels:
                 a__						:	Dict =				ids_tensor([self.batch_size, self.image_size, self.image_size]					,  self.num_labels)

          a__						:	Any =				self.get_config()
          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          return SegformerConfig(
              image_size=self.image_size					,  num_channels=self.num_channels					,  num_encoder_blocks=self.num_encoder_blocks					,  depths=self.depths					,  hidden_sizes=self.hidden_sizes					,  num_attention_heads=self.num_attention_heads					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  initializer_range=self.initializer_range					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	Dict =				SegformerModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Optional[Any] =				model(lowercase)
          a__						:	Optional[Any] =				self.image_size // (self.downsampling_rates[-1] * 2)
          self.parent.assertEqual(
              result.last_hidden_state.shape					,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						str:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.num_labels
          a__						:	List[str] =				SegformerForSemanticSegmentation(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[str] =				model(lowercase)
          self.parent.assertEqual(
              result.logits.shape					,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
          a__						:	int =				model(lowercase					,  labels=lowercase)
          self.parent.assertEqual(
              result.logits.shape					,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
          self.parent.assertGreater(result.loss					,  0.0)

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				1
          a__						:	Optional[int] =				SegformerForSemanticSegmentation(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Union[str, Any] =				torch.randint(0					,  1					,  (self.batch_size, self.image_size, self.image_size)).to(lowercase)
          a__						:	Optional[Any] =				model(lowercase					,  labels=lowercase)
          self.parent.assertGreater(result.loss					,  0.0)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

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





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Any         =  (
       (
           SegformerModel,
           SegformerForSemanticSegmentation,
           SegformerForImageClassification,
       )
       if is_torch_available()
       else ()
   )
   __A   :				List[str]         =  (
       {
           '''feature-extraction''': SegformerModel,
           '''image-classification''': SegformerForImageClassification,
           '''image-segmentation''': SegformerForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __A   :				List[str]         =  True
   __A   :				Any         =  False
   __A   :				Any         =  False
   __A   :				str         =  False

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Union[str, Any] =				SegformerModelTester(self)
          a__						:	Optional[Any] =				SegformerConfigTester(self					,  config_class=lowercase)

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







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Dict =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_binary_image_segmentation(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	int =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_segmentation(*lowercase)

   @unittest.skip('SegFormer does not use inputs_embeds')
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          pass

   @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods')
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          pass

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







          '''simple docstring'''

          a__ ,							a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	List[str] =				model_class(lowercase)
                 a__						:	Dict =				inspect.signature(model.forward)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 a__						:	Optional[int] =				[*signature.parameters.keys()]

                 a__						:	Union[str, Any] =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__ ,							a__						:	Dict =				self.model_tester.prepare_config_and_inputs_for_common()
          a__						:	Tuple =				True

          for model_class in self.all_model_classes:
                 a__						:	str =				True
                 a__						:	List[str] =				False
                 a__						:	int =				True
                 a__						:	List[Any] =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	Optional[Any] =				model(**self._prepare_for_class(lowercase					,  lowercase))
                 a__						:	Optional[Any] =				outputs.attentions

                 a__						:	Dict =				sum(self.model_tester.depths)
                 self.assertEqual(len(lowercase)					,  lowercase)

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 a__						:	Dict =				True
                 a__						:	int =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	Optional[int] =				model(**self._prepare_for_class(lowercase					,  lowercase))
                 a__						:	Optional[Any] =				outputs.attentions

                 self.assertEqual(len(lowercase)					,  lowercase)

                 # verify the first attentions (first block, first layer)
                 a__						:	Tuple =				(self.model_tester.image_size // 4) ** 2
                 a__						:	List[str] =				(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                 self.assertListEqual(
                     list(attentions[0].shape[-3:])					,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,  )

                 # verify the last attentions (last block, last layer)
                 a__						:	str =				(self.model_tester.image_size // 32) ** 2
                 a__						:	Optional[int] =				(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
                 self.assertListEqual(
                     list(attentions[-1].shape[-3:])					,  [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]					,  )
                 a__						:	Dict =				len(lowercase)

                 # Check attention is always last and order is fine
                 a__						:	List[Any] =				True
                 a__						:	Any =				True
                 a__						:	Dict =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	int =				model(**self._prepare_for_class(lowercase					,  lowercase))

                 self.assertEqual(out_len + 1					,  len(lowercase))

                 a__						:	int =				outputs.attentions

                 self.assertEqual(len(lowercase)					,  lowercase)
                 # verify the first attentions (first block, first layer)
                 a__						:	List[Any] =				(self.model_tester.image_size // 4) ** 2
                 a__						:	Union[str, Any] =				(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:])					,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,  )

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







          '''simple docstring'''

          def check_hidden_states_output(lowercase					,  lowercase					,  lowercase):
                 a__						:	Optional[Any] =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()

                 with torch.no_grad():
                        a__						:	int =				model(**self._prepare_for_class(lowercase					,  lowercase))

                 a__						:	Union[str, Any] =				outputs.hidden_states

                 a__						:	Any =				self.model_tester.num_encoder_blocks
                 self.assertEqual(len(lowercase)					,  lowercase)

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:])					,  [
                         self.model_tester.hidden_sizes[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]					,  )

          a__ ,							a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	List[str] =				True
                 check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 a__						:	int =				True

                 check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          if not self.model_tester.is_training:
                 return

          a__ ,							a__						:	Optional[Any] =				self.model_tester.prepare_config_and_inputs_for_common()
          a__						:	Tuple =				True

          for model_class in self.all_model_classes:
                 if model_class in get_values(lowercase):
                        continue

                 a__						:	Dict =				model_class(lowercase)
                 model.to(lowercase)
                 model.train()
                 a__						:	str =				self._prepare_for_class(lowercase					,  lowercase					,  return_labels=lowercase)
                 a__						:	Optional[int] =				model(**lowercase).loss
                 loss.backward()

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







          '''simple docstring'''

          pass

   @slow
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Optional[Any] =				SegformerModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		int:
       a__						:	str =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





@require_torch
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   @slow
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Dict =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	int =				SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
              lowercase)

          a__						:	Optional[int] =				prepare_img()
          a__						:	Optional[int] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	List[str] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Optional[int] =				model(lowercase)

          a__						:	Union[str, Any] =				torch.Size((1, model.config.num_labels, 128, 128))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Dict =				torch.tensor(
              [
                  [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
                  [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
                  [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
              ]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,  lowercase					,  atol=1e-4))

   @slow
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Dict =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	List[str] =				SegformerForSemanticSegmentation.from_pretrained(
              'nvidia/segformer-b1-finetuned-cityscapes-1024-1024').to(lowercase)

          a__						:	Dict =				prepare_img()
          a__						:	Optional[int] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	List[str] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Optional[Any] =				model(lowercase)

          a__						:	List[Any] =				torch.Size((1, model.config.num_labels, 128, 128))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Optional[Any] =				torch.tensor(
              [
                  [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
                  [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
                  [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
              ]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,  lowercase					,  atol=1e-1))

   @slow
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	List[str] =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	List[str] =				SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
              lowercase)

          a__						:	Any =				prepare_img()
          a__						:	Optional[Any] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	Optional[int] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Union[str, Any] =				model(lowercase)

          a__						:	int =				outputs.logits.detach().cpu()

          a__						:	List[str] =				image_processor.post_process_semantic_segmentation(outputs=lowercase					,  target_sizes=[(500, 300)])
          a__						:	Optional[Any] =				torch.Size((500, 300))
          self.assertEqual(segmentation[0].shape					,  lowercase)

          a__						:	Any =				image_processor.post_process_semantic_segmentation(outputs=lowercase)
          a__						:	Union[str, Any] =				torch.Size((128, 128))
          self.assertEqual(segmentation[0].shape					,  lowercase)


