
"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      str   			=  [tensor.shape for tensor in tensor_list]
  return all(shape == shapes[0] for shape in shapes[1:]    )


class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		StableDiffusionLatentUpscalePipeline
       UpperCamelCase_ =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           '''height''',
           '''width''',
           '''cross_attention_kwargs''',
           '''negative_prompt_embeds''',
           '''prompt_embeds''',
       }
       UpperCamelCase_ =		PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
       UpperCamelCase_ =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       UpperCamelCase_ =		frozenset(
           []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       UpperCamelCase_ =		frozenset([]			)

       UpperCamelCase_ =		True
       @property
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  1
         _lowercase:      Optional[Any]   			=  4
         _lowercase:      Dict   			=  (16, 16)

         _lowercase:      Union[str, Any]   			=  floats_tensor((batch_size, num_channels) + sizes			,					rng=random.Random(0							)							).to(A_							)
         return image
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         torch.manual_seed(0							)
         _lowercase:      List[str]   			=  UNetaDConditionModel(
             act_fn='''gelu'''			,					attention_head_dim=8			,					norm_num_groups=A_			,					block_out_channels=[32, 32, 64, 64]			,					time_cond_proj_dim=160			,					conv_in_kernel=1			,					conv_out_kernel=1			,					cross_attention_dim=32			,					down_block_types=(
                 '''KDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
             )			,					in_channels=8			,					mid_block_type=A_			,					only_cross_attention=A_			,					out_channels=5			,					resnet_time_scale_shift='''scale_shift'''			,					time_embedding_type='''fourier'''			,					timestep_post_act='''gelu'''			,					up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')			,					)
         _lowercase:      Optional[Any]   			=  AutoencoderKL(
             block_out_channels=[32, 32, 64, 64]			,					in_channels=3			,					out_channels=3			,					down_block_types=[
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
             ]			,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,					latent_channels=4			,					)
         _lowercase:      int   			=  EulerDiscreteScheduler(prediction_type='''sample'''							)
         _lowercase:      List[Any]   			=  CLIPTextConfig(
             bos_token_id=0			,					eos_token_id=2			,					hidden_size=32			,					intermediate_size=37			,					layer_norm_eps=1E-05			,					num_attention_heads=4			,					num_hidden_layers=5			,					pad_token_id=1			,					vocab_size=1000			,					hidden_act='''quick_gelu'''			,					projection_dim=512			,					)
         _lowercase:      Tuple   			=  CLIPTextModel(A_							)
         _lowercase:      List[Any]   			=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

         _lowercase:      Any   			=  {
             '''unet''': model.eval(),
             '''vae''': vae.eval(),
             '''scheduler''': scheduler,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
         }

         return components
       def 				lowercase_				(  self			,					A_			,					A_=0							)  ->						Optional[Any]:





         """simple docstring"""




         if str(A_							).startswith('''mps'''							):
           _lowercase:      str   			=  torch.manual_seed(A_							)
         else:
           _lowercase:      Dict   			=  torch.Generator(device=A_							).manual_seed(A_							)
         _lowercase:      List[Any]   			=  {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': self.dummy_image.cpu(),
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[str]   			=  '''cpu'''

         _lowercase:      str   			=  self.get_dummy_components()
         _lowercase:      List[Any]   			=  self.pipeline_class(**A_							)
         pipe.to(A_							)
         pipe.set_progress_bar_config(disable=A_							)

         _lowercase:      Optional[int]   			=  self.get_dummy_inputs(A_							)
         _lowercase:      Optional[Any]   			=  pipe(**A_							).images
         _lowercase:      Union[str, Any]   			=  image[0, -3:, -3:, -1]

         self.assertEqual(image.shape			,					(1, 256, 256, 3)							)
         _lowercase:      Union[str, Any]   			=  np.array(
             [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]							)
         _lowercase:      Union[str, Any]   			=  np.abs(image_slice.flatten() - expected_slice							).max()
         self.assertLessEqual(A_			,					1E-3							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         super().test_attention_slicing_forward_pass(expected_max_diff=7E-3							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         super().test_cpu_offload_forward_pass(expected_max_diff=3E-3							)
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3							)
       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         super().test_inference_batch_single_identical(expected_max_diff=7E-3							)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3							)
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         super().test_save_load_local(expected_max_difference=3E-3							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         super().test_save_load_optional_components(expected_max_difference=3E-3							)



       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      int   			=  [
             '''DDIMScheduler''',
             '''DDPMScheduler''',
             '''PNDMScheduler''',
             '''HeunDiscreteScheduler''',
             '''EulerAncestralDiscreteScheduler''',
             '''KDPM2DiscreteScheduler''',
             '''KDPM2AncestralDiscreteScheduler''',
             '''DPMSolverSDEScheduler''',
         ]
         _lowercase:      List[Any]   			=  self.get_dummy_components()
         _lowercase:      Tuple   			=  self.pipeline_class(**A_							)

         # make sure that PNDM does not need warm-up
         pipe.scheduler.register_to_config(skip_prk_steps=A_							)

         pipe.to(A_							)
         pipe.set_progress_bar_config(disable=A_							)
         _lowercase:      List[str]   			=  self.get_dummy_inputs(A_							)
         _lowercase:      Tuple   			=  2

         _lowercase:      Optional[Any]   			=  []
         for scheduler_enum in KarrasDiffusionSchedulers:
           if scheduler_enum.name in skip_schedulers:
             # no sigma schedulers are not supported
             # no schedulers
             continue

           _lowercase:      List[Any]   			=  getattr(A_			,					scheduler_enum.name							)
           _lowercase:      str   			=  scheduler_cls.from_config(pipe.scheduler.config							)
           _lowercase:      Dict   			=  pipe(**A_							)[0]
           outputs.append(A_							)

         assert check_same_shape(A_							)


@require_torch_gpu
@slow
class       __magic_name__							(							unittest.TestCase			):
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Any   			=  torch.manual_seed(33							)

         _lowercase:      str   			=  StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''			,					torch_dtype=torch.floataa							)
         pipe.to('''cuda'''							)

         _lowercase:      Union[str, Any]   			=  StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,					torch_dtype=torch.floataa							)
         upscaler.to('''cuda'''							)

         _lowercase:      Any   			=  '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

         _lowercase:      Any   			=  pipe(A_			,					generator=A_			,					output_type='''latent'''							).images

         _lowercase:      Union[str, Any]   			=  upscaler(
             prompt=A_			,					image=A_			,					num_inference_steps=20			,					guidance_scale=0			,					generator=A_			,					output_type='''np'''			,					).images[0]

         _lowercase:      List[Any]   			=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''							)
         assert np.abs((expected_image - image).mean()							) < 5E-2



       def 				lowercase_				(  self							)  ->						Any:





         """simple docstring"""




         _lowercase:      Any   			=  torch.manual_seed(33							)

         _lowercase:      List[Any]   			=  StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,					torch_dtype=torch.floataa							)
         upscaler.to('''cuda'''							)

         _lowercase:      Dict   			=  '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

         _lowercase:      Union[str, Any]   			=  load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''							)

         _lowercase:      Optional[int]   			=  upscaler(
             prompt=A_			,					image=A_			,					num_inference_steps=20			,					guidance_scale=0			,					generator=A_			,					output_type='''np'''			,					).images[0]

         _lowercase:      List[Any]   			=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''							)
         assert np.abs((expected_image - image).max()							) < 5E-2


"""simple docstring"""




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





         """simple docstring"""




         _lowercase:      str   			=  parent
         _lowercase:      Union[str, Any]   			=  batch_size
         _lowercase:      Tuple   			=  image_size
         _lowercase:      Tuple   			=  num_channels
         _lowercase:      Optional[int]   			=  num_stages
         _lowercase:      List[Any]   			=  hidden_sizes
         _lowercase:      Dict   			=  depths
         _lowercase:      int   			=  is_training
         _lowercase:      Tuple   			=  use_labels
         _lowercase:      Any   			=  intermediate_size
         _lowercase:      Union[str, Any]   			=  hidden_act
         _lowercase:      str   			=  type_sequence_label_size
         _lowercase:      int   			=  initializer_range
         _lowercase:      List[str]   			=  out_features
         _lowercase:      Any   			=  num_labels
         _lowercase:      Any   			=  scope
         _lowercase:      str   			=  num_stages
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _lowercase:      Any   			=  None
         if self.use_labels:
           _lowercase:      Dict   			=  ids_tensor([self.batch_size]			,					self.type_sequence_label_size							)

         _lowercase:      Tuple   			=  self.get_config()

         return config, pixel_values, labels
       def 				lowercase_				(  self							)  ->						List[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							)  ->						Dict:





         """simple docstring"""




         return UperNetConfig(
             backbone_config=self.get_backbone_config()			,					hidden_size=512			,					pool_scales=[1, 2, 3, 6]			,					use_auxiliary_head=A_			,					auxiliary_loss_weight=0.4			,					auxiliary_in_channels=40			,					auxiliary_channels=256			,					auxiliary_num_convs=1			,					auxiliary_concat_input=A_			,					loss_ignore_index=255			,					num_labels=self.num_labels			,					)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_							)  ->						int:





         """simple docstring"""




         _lowercase:      List[str]   			=  UperNetForSemanticSegmentation(config=A_							)
         model.to(A_							)
         model.eval()
         _lowercase:      int   			=  model(A_							)
         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"""




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


@require_torch
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		(UperNetForSemanticSegmentation,) if is_torch_available() else ()
       UpperCamelCase_ =		{'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  UperNetModelTester(self							)
         _lowercase:      Optional[Any]   			=  ConfigTester(self			,					config_class=A_			,					has_text_modality=A_			,					hidden_size=37							)
       def 				lowercase_				(  self							)  ->						List[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							)  ->						Dict:





         """simple docstring"""




         return
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




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

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

           _lowercase:      int   			=  ['''pixel_values''']
           self.assertListEqual(arg_names[:1]			,					A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[str]   			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*A_							)
       @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							)  ->						List[Any]:





         """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							)  ->						Tuple:





         """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[Any]:





         """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							)  ->						str:





         """simple docstring"""




         def check_hidden_states_output(A_			,					A_			,					A_							):
           _lowercase:      Dict   			=  model_class(A_							)
           model.to(A_							)
           model.eval()

           with torch.no_grad():
             _lowercase:      List[str]   			=  model(**self._prepare_for_class(A_			,					A_							)							)

           _lowercase:      Tuple   			=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           _lowercase:      Union[str, Any]   			=  self.model_tester.num_stages
           self.assertEqual(len(A_							)			,					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]			,					)

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

         for model_class in self.all_model_classes:
           _lowercase:      Optional[int]   			=  True
           check_hidden_states_output(A_			,					A_			,					A_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowercase:      Union[str, Any]   			=  True

           check_hidden_states_output(A_			,					A_			,					A_							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




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

         _lowercase:      int   			=  _config_zero_init(A_							)
         _lowercase:      Optional[Any]   			=  _config_zero_init(configs_no_init.backbone_config							)
         for model_class in self.all_model_classes:
           _lowercase:      Any   			=  model_class(config=A_							)
           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							)  ->						List[str]:





         """simple docstring"""




         pass



       @slow
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




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




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  hf_hub_download(
      repo_id='''hf-internal-testing/fixtures_ade20k'''			,					repo_type='''dataset'''			,					filename='''ADE_val_00000001.jpg'''    )
  _lowercase:      Optional[int]   			=  Image.open(_UpperCamelCase    ).convert('''RGB'''    )
  return image







@require_torch
@require_vision
@slow
class       __magic_name__							(							unittest.TestCase			):
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny'''							)
         _lowercase:      Optional[Any]   			=  UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny'''							).to(A_							)

         _lowercase:      List[Any]   			=  prepare_img()
         _lowercase:      Any   			=  processor(images=A_			,					return_tensors='''pt'''							).to(A_							)

         with torch.no_grad():
           _lowercase:      str   			=  model(**A_							)

         _lowercase:      Union[str, Any]   			=  torch.Size((1, model.config.num_labels, 512, 512)							)
         self.assertEqual(outputs.logits.shape			,					A_							)

         _lowercase:      Optional[Any]   			=  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(A_							)
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					A_			,					atol=1E-4							)							)



       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Tuple   			=  AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny'''							)
         _lowercase:      str   			=  UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny'''							).to(A_							)

         _lowercase:      Union[str, Any]   			=  prepare_img()
         _lowercase:      Optional[Any]   			=  processor(images=A_			,					return_tensors='''pt'''							).to(A_							)

         with torch.no_grad():
           _lowercase:      str   			=  model(**A_							)

         _lowercase:      Optional[Any]   			=  torch.Size((1, model.config.num_labels, 512, 512)							)
         self.assertEqual(outputs.logits.shape			,					A_							)

         _lowercase:      Tuple   			=  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(A_							)
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					A_			,					atol=1E-4							)							)
