


import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

 from transformers import ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor





class   __a			:
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__=13				, lowerCAmelCase__=30				, lowerCAmelCase__=2				, lowerCAmelCase__=3				, lowerCAmelCase__=True				, lowerCAmelCase__=True				, lowerCAmelCase__=32				, lowerCAmelCase__=5				, lowerCAmelCase__=4				, lowerCAmelCase__=37				, lowerCAmelCase__="gelu"				, lowerCAmelCase__=0.1				, lowerCAmelCase__=0.1				, lowerCAmelCase__=10				, lowerCAmelCase__=0.0_2				, lowerCAmelCase__=None				, )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   parent
        lowercase__:   List[str]  			=   batch_size
        lowercase__:   List[str]  			=   image_size
        lowercase__:   Dict  			=   patch_size
        lowercase__:   Optional[Any]  			=   num_channels
        lowercase__:   List[Any]  			=   is_training
        lowercase__:   str  			=   use_labels
        lowercase__:   Dict  			=   hidden_size
        lowercase__:   Tuple  			=   num_hidden_layers
        lowercase__:   Tuple  			=   num_attention_heads
        lowercase__:   Union[str, Any]  			=   intermediate_size
        lowercase__:   List[str]  			=   hidden_act
        lowercase__:   List[str]  			=   hidden_dropout_prob
        lowercase__:   Any  			=   attention_probs_dropout_prob
        lowercase__:   Dict  			=   type_sequence_label_size
        lowercase__:   Tuple  			=   initializer_range
        lowercase__:   Optional[int]  			=   scope

        # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase__:   List[str]  			=   (image_size // patch_size) ** 2
        lowercase__:   Any  			=   num_patches + 1
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        lowercase__:   Tuple  			=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        lowercase__:   Tuple  			=   None
        if self.use_labels:
               lowercase__:   List[Any]  			=   ids_tensor([self.batch_size]				, self.type_sequence_label_size )

        lowercase__:   List[str]  			=   self.get_config()

        return config, pixel_values, labels
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        return ViTMSNConfig(
            image_size=self.image_size				, patch_size=self.patch_size				, num_channels=self.num_channels				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, 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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        lowercase__:   int  			=   ViTMSNModel(config=lowerCAmelCase__ )
        model.to(lowerCAmelCase__ )
        model.eval()
        lowercase__:   int  			=   model(lowerCAmelCase__ )
        self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size) )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   self.type_sequence_label_size
        lowercase__:   List[str]  			=   ViTMSNForImageClassification(lowerCAmelCase__ )
        model.to(lowerCAmelCase__ )
        model.eval()
        lowercase__:   int  			=   model(lowerCAmelCase__				, labels=lowerCAmelCase__ )
        print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}' )
        print('Labels: {labels}' )
        self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size) )

        # test greyscale images
        lowercase__:   List[str]  			=   1
        lowercase__:   Optional[int]  			=   ViTMSNForImageClassification(lowerCAmelCase__ )
        model.to(lowerCAmelCase__ )
        model.eval()

        lowercase__:   Dict  			=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
        lowercase__:   Dict  			=   model(lowerCAmelCase__ )
        self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size) )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




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





@require_torch
class   __a			(    __UpperCAmelCase       ,       __UpperCAmelCase       ,       unittest.TestCase      ):
 __lowercase :							Any        = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
 __lowercase :							Tuple        = (
     {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
     if is_torch_available()
     else {}
 )

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




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   ViTMSNModelTester(self )
        lowercase__:   Union[str, Any]  			=   ConfigTester(self				, config_class=lowerCAmelCase__				, has_text_modality=lowerCAmelCase__				, hidden_size=37 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        self.config_tester.run_common_tests()
 @unittest.skip(reason='ViTMSN does not use inputs_embeds' )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Tuple  			=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase__:   Union[str, Any]  			=   model_class(lowerCAmelCase__ )
               self.assertIsInstance(model.get_input_embeddings()				, (nn.Module) )
               lowercase__:   str  			=   model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase__				, nn.Linear ) )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase__:   Optional[Any]  			=   ['pixel_values']
               self.assertListEqual(arg_names[:1]				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        lowercase__:   List[str]  			=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        lowercase__:   Any  			=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ )
 @slow
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''

        for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__:   Tuple  			=   ViTMSNModel.from_pretrained(lowerCAmelCase__ )
               self.assertIsNotNone(lowerCAmelCase__ )





def    snake_case_     (  )  ->				Dict:
       lowercase__:   List[str]  			=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image





@require_torch
@require_vision
class   __a			(    unittest.TestCase      ):
 @cached_property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        return ViTImageProcessor.from_pretrained('facebook/vit-msn-small' ) if is_vision_available() else None
 @slow
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        torch.manual_seed(2 )
        lowercase__:   List[str]  			=   ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small' ).to(lowerCAmelCase__ )

        lowercase__:   Any  			=   self.default_image_processor
        lowercase__:   List[Any]  			=   prepare_img()
        lowercase__:   Optional[Any]  			=   image_processor(images=lowerCAmelCase__				, return_tensors='pt' ).to(lowerCAmelCase__ )

        # forward pass
        with torch.no_grad():
               lowercase__:   Tuple  			=   model(**lowerCAmelCase__ )

        # verify the logits
        lowercase__:   Union[str, Any]  			=   torch.Size((1, 1_000) )
        self.assertEqual(outputs.logits.shape				, lowerCAmelCase__ )

        lowercase__:   Any  			=   torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5] ).to(lowerCAmelCase__ )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]				, lowerCAmelCase__				, 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)


