code: 
  
  
  
  
  
  '''simple docstring'''
  
  import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig
  
  from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
  from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
  from diffusers.utils import floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  class       a_     (       lowerCAmelCase_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				PaintByExamplePipeline
      UpperCamelCase									=				IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCamelCase									=				IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCamelCase									=				frozenset([] )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
      def     snake_case_(	self )	->     str:
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    UNetaDConditionModel(
              block_out_channels=(32, 64)				,	layers_per_block=2				,	sample_size=32				,	in_channels=9				,	out_channels=4				,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,	cross_attention_dim=32				,	)
          _SCREAMING_SNAKE_CASE             =    PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE )
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    AutoencoderKL(
              block_out_channels=[32, 64]				,	in_channels=3				,	out_channels=3				,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,	latent_channels=4				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    CLIPVisionConfig(
              hidden_size=32				,	projection_dim=32				,	intermediate_size=37				,	layer_norm_eps=1e-05				,	num_attention_heads=4				,	num_hidden_layers=5				,	image_size=32				,	patch_size=4				,	)
          _SCREAMING_SNAKE_CASE             =    PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE				,	proj_size=32 )
          _SCREAMING_SNAKE_CASE             =    CLIPImageProcessor(crop_size=32				,	size=32 )
  
          _SCREAMING_SNAKE_CASE             =    {
              """unet""": unet,
              """scheduler""": scheduler,
              """vae""": vae,
              """image_encoder""": image_encoder,
              """safety_checker""": None,
              """feature_extractor""": feature_extractor,
          }
          return components
  
  
      def     snake_case_(	self				,	A )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    np.array(image.convert("""RGB""" ) )
          _SCREAMING_SNAKE_CASE             =    image[None].transpose(0				,	3				,	1				,	2 )
          _SCREAMING_SNAKE_CASE             =    torch.from_numpy(__SCREAMING_SNAKE_CASE ).to(dtype=torch.floataa ) / 127.5 - 1.0
          return image
  
  
      def     snake_case_(	self				,	A="cpu"				,	A=0 )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    floats_tensor((1, 3, 32, 32)				,	rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
          _SCREAMING_SNAKE_CASE             =    image.cpu().permute(0				,	2				,	3				,	1 )[0]
          _SCREAMING_SNAKE_CASE             =    Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert("""RGB""" ).resize((64, 64) )
          _SCREAMING_SNAKE_CASE             =    Image.fromarray(np.uinta(image + 4 ) ).convert("""RGB""" ).resize((64, 64) )
          _SCREAMING_SNAKE_CASE             =    Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert("""RGB""" ).resize((32, 32) )
  
          if str(__SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
              _SCREAMING_SNAKE_CASE             =    torch.manual_seed(__SCREAMING_SNAKE_CASE )
          else:
              _SCREAMING_SNAKE_CASE             =    torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
          _SCREAMING_SNAKE_CASE             =    {
              """example_image""": example_image,
              """image""": init_image,
              """mask_image""": mask_image,
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
          }
          return inputs
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_components()
  
          # make sure here that pndm scheduler skips prk
          _SCREAMING_SNAKE_CASE             =    PaintByExamplePipeline(**__SCREAMING_SNAKE_CASE )
          _SCREAMING_SNAKE_CASE             =    pipe.to("""cpu""" )
          pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs()
          _SCREAMING_SNAKE_CASE             =    pipe(**__SCREAMING_SNAKE_CASE )
          _SCREAMING_SNAKE_CASE             =    output.images
  
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1]
  
          assert image.shape == (1, 64, 64, 3)
          _SCREAMING_SNAKE_CASE             =    np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503] )
  
          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    """cpu"""
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs()
          inputs.pop("""mask_image""" )
          _SCREAMING_SNAKE_CASE             =    self.convert_to_pt(inputs.pop("""image""" ) )
          _SCREAMING_SNAKE_CASE             =    image.clamp(0				,	1 ) / 2
  
          # make sure here that pndm scheduler skips prk
          _SCREAMING_SNAKE_CASE             =    PaintByExamplePipeline(**self.get_dummy_components() )
          _SCREAMING_SNAKE_CASE             =    pipe.to(__SCREAMING_SNAKE_CASE )
          pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
  
          _SCREAMING_SNAKE_CASE             =    pipe(image=__SCREAMING_SNAKE_CASE				,	mask_image=mask_image[:, 0]				,	**__SCREAMING_SNAKE_CASE )
          _SCREAMING_SNAKE_CASE             =    output.images
  
          _SCREAMING_SNAKE_CASE             =    image.cpu().permute(0				,	2				,	3				,	1 )[0]
          _SCREAMING_SNAKE_CASE             =    mask_image.cpu().permute(0				,	2				,	3				,	1 )[0]
  
          _SCREAMING_SNAKE_CASE             =    Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert("""RGB""" )
          _SCREAMING_SNAKE_CASE             =    Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert("""RGB""" )
  
          _SCREAMING_SNAKE_CASE             =    pipe(**self.get_dummy_inputs() )
          _SCREAMING_SNAKE_CASE             =    output.images
  
          assert out_a.shape == (1, 64, 64, 3)
          assert np.abs(out_a.flatten() - out_a.flatten() ).max() < 5e-2
  
  
  
  
  
      def     snake_case_(	self )	->     List[str]:
          super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
  
  
  
  
  @slow
  @require_torch_gpu
  class       a_     (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     Tuple:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
  
  
  
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/paint_by_example/dog_in_bucket.png""" )
          _SCREAMING_SNAKE_CASE             =    load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/paint_by_example/mask.png""" )
          _SCREAMING_SNAKE_CASE             =    load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/paint_by_example/panda.jpg""" )
  
          _SCREAMING_SNAKE_CASE             =    PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example""" )
          _SCREAMING_SNAKE_CASE             =    pipe.to(__SCREAMING_SNAKE_CASE )
          pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
  
          _SCREAMING_SNAKE_CASE             =    torch.manual_seed(321 )
          _SCREAMING_SNAKE_CASE             =    pipe(
              image=__SCREAMING_SNAKE_CASE				,	mask_image=__SCREAMING_SNAKE_CASE				,	example_image=__SCREAMING_SNAKE_CASE				,	generator=__SCREAMING_SNAKE_CASE				,	guidance_scale=5.0				,	num_inference_steps=50				,	output_type="""np"""				,	)
  
          _SCREAMING_SNAKE_CASE             =    output.images
  
          _SCREAMING_SNAKE_CASE             =    image[0, -3:, -3:, -1]
  
          assert image.shape == (1, 512, 512, 3)
          _SCREAMING_SNAKE_CASE             =    np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374] )
  
          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import inspect
  import unittest
  
  from transformers import is_torch_available, is_vision_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, 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, GLPNConfig, GLPNForDepthEstimation, GLPNModel
        from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
        from PIL import Image
  
        from transformers import GLPNImageProcessor
  
  
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     int:
          _SCREAMING_SNAKE_CASE             =    self.config_class(**self.inputs_dict )
          self.parent.assertTrue(hasattr(A				,	"""hidden_sizes""" ) )
          self.parent.assertTrue(hasattr(A				,	"""num_attention_heads""" ) )
          self.parent.assertTrue(hasattr(A				,	"""num_encoder_blocks""" ) )
  
  
  
  
  class       a_  :
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A=13				,	A=64				,	A=3				,	A=4				,	A=[2, 2, 2, 2]				,	A=[8, 4, 2, 1]				,	A=[16, 32, 64, 128]				,	A=[1, 4, 8, 16]				,	A=[1, 2, 4, 8]				,	A=True				,	A=True				,	A="gelu"				,	A=0.1				,	A=0.1				,	A=0.02				,	A=16				,	A=3				,	A=None				,	)	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    parent
          _SCREAMING_SNAKE_CASE             =    batch_size
          _SCREAMING_SNAKE_CASE             =    image_size
          _SCREAMING_SNAKE_CASE             =    num_channels
          _SCREAMING_SNAKE_CASE             =    num_encoder_blocks
          _SCREAMING_SNAKE_CASE             =    sr_ratios
          _SCREAMING_SNAKE_CASE             =    depths
          _SCREAMING_SNAKE_CASE             =    hidden_sizes
          _SCREAMING_SNAKE_CASE             =    downsampling_rates
          _SCREAMING_SNAKE_CASE             =    num_attention_heads
          _SCREAMING_SNAKE_CASE             =    is_training
          _SCREAMING_SNAKE_CASE             =    use_labels
          _SCREAMING_SNAKE_CASE             =    hidden_act
          _SCREAMING_SNAKE_CASE             =    hidden_dropout_prob
          _SCREAMING_SNAKE_CASE             =    attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE             =    initializer_range
          _SCREAMING_SNAKE_CASE             =    decoder_hidden_size
          _SCREAMING_SNAKE_CASE             =    num_labels
          _SCREAMING_SNAKE_CASE             =    scope
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
  
          _SCREAMING_SNAKE_CASE             =    None
          if self.use_labels:
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.image_size, self.image_size]				,	self.num_labels )
  
          _SCREAMING_SNAKE_CASE             =    self.get_config()
          return config, pixel_values, labels
  
  
      def     snake_case_(	self )	->     Optional[int]:
          return GLPNConfig(
              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				,	decoder_hidden_size=self.decoder_hidden_size				,	)
  
  
      def     snake_case_(	self				,	A				,	A				,	A )	->     str:
          _SCREAMING_SNAKE_CASE             =    GLPNModel(config=A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(A )
          _SCREAMING_SNAKE_CASE             =    _SCREAMING_SNAKE_CASE             =    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     snake_case_(	self				,	A				,	A				,	A )	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.num_labels
          _SCREAMING_SNAKE_CASE             =    GLPNForDepthEstimation(A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(A )
          self.parent.assertEqual(result.predicted_depth.shape				,	(self.batch_size, self.image_size, self.image_size) )
          _SCREAMING_SNAKE_CASE             =    model(A				,	labels=A )
          self.parent.assertEqual(result.predicted_depth.shape				,	(self.batch_size, self.image_size, self.image_size) )
  
  
  
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.prepare_config_and_inputs()
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    config_and_inputs
          _SCREAMING_SNAKE_CASE             =    {"""pixel_values""": pixel_values}
          return config, inputs_dict
  
  
  
  
  @require_torch
  class       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
      UpperCamelCase									=				(
          {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
      )
  
      UpperCamelCase									=				False
      UpperCamelCase									=				False
      UpperCamelCase									=				False
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE             =    GLPNModelTester(self )
          _SCREAMING_SNAKE_CASE             =    GLPNConfigTester(self				,	config_class=A )
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          self.config_tester.run_common_tests()
  
  
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A )
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*A )
  
  
      @unittest.skip("""GLPN does not use inputs_embeds""" )
      def     snake_case_(	self )	->     List[Any]:
          pass
  
  
      @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods""" )
      def     snake_case_(	self )	->     Any:
          pass
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              _SCREAMING_SNAKE_CASE             =    inspect.signature(model.forward )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              _SCREAMING_SNAKE_CASE             =    [*signature.parameters.keys()]
  
              _SCREAMING_SNAKE_CASE             =    ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]				,	A )
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE             =    True
  
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    False
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.eval()
              with torch.no_grad():
                  _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
              _SCREAMING_SNAKE_CASE             =    outputs.attentions
  
              _SCREAMING_SNAKE_CASE             =    sum(self.model_tester.depths )
              self.assertEqual(len(A )				,	A )
  
              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.eval()
              with torch.no_grad():
                  _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
              _SCREAMING_SNAKE_CASE             =    outputs.attentions
  
              self.assertEqual(len(A )				,	A )
  
              # verify the first attentions (first block, first layer)
              _SCREAMING_SNAKE_CASE             =    (self.model_tester.image_size // 4) ** 2
              _SCREAMING_SNAKE_CASE             =    (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)
              _SCREAMING_SNAKE_CASE             =    (self.model_tester.image_size // 32) ** 2
              _SCREAMING_SNAKE_CASE             =    (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]				,	)
              _SCREAMING_SNAKE_CASE             =    len(A )
  
              # Check attention is always last and order is fine
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.eval()
              with torch.no_grad():
                  _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
  
              self.assertEqual(out_len + 1				,	len(A ) )
  
              _SCREAMING_SNAKE_CASE             =    outputs.attentions
  
              self.assertEqual(len(A )				,	A )
              # verify the first attentions (first block, first layer)
              _SCREAMING_SNAKE_CASE             =    (self.model_tester.image_size // 4) ** 2
              _SCREAMING_SNAKE_CASE             =    (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     snake_case_(	self )	->     List[Any]:
          def check_hidden_states_output(A				,	A				,	A ):
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.eval()
  
              with torch.no_grad():
                  _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
  
              _SCREAMING_SNAKE_CASE             =    outputs.hidden_states
  
              _SCREAMING_SNAKE_CASE             =    self.model_tester.num_encoder_blocks
              self.assertEqual(len(A )				,	A )
  
              # 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,
                  ]				,	)
  
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    True
              check_hidden_states_output(A				,	A				,	A )
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _SCREAMING_SNAKE_CASE             =    True
  
              check_hidden_states_output(A				,	A				,	A )
  
  
      def     snake_case_(	self )	->     int:
          if not self.model_tester.is_training:
              return
  
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE             =    True
  
          for model_class in self.all_model_classes:
              if model_class in get_values(A ):
                  continue
              # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
              # this can then be incorporated into _prepare_for_class in test_modeling_common.py
              if model_class.__name__ == "GLPNForDepthEstimation":
                  _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    inputs_dict["""pixel_values"""].shape
                  _SCREAMING_SNAKE_CASE             =    torch.zeros(
                      [self.model_tester.batch_size, height, width]				,	device=A ).long()
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.train()
              _SCREAMING_SNAKE_CASE             =    self._prepare_for_class(A				,	A				,	return_labels=A )
              _SCREAMING_SNAKE_CASE             =    model(**A ).loss
              loss.backward()
  
  
  
  
  
      @slow
      def     snake_case_(	self )	->     str:
          for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _SCREAMING_SNAKE_CASE             =    GLPNModel.from_pretrained(A )
              self.assertIsNotNone(A )
  
  
  
  
  
  def lowerCamelCase		(  )		->Union[str, Any]:
      _SCREAMING_SNAKE_CASE             =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
      return image
  
  
  
  
  
  
  @require_torch
  @require_vision
  @slow
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      @slow
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
          _SCREAMING_SNAKE_CASE             =    GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(A )
  
          _SCREAMING_SNAKE_CASE             =    prepare_img()
          _SCREAMING_SNAKE_CASE             =    image_processor(images=A				,	return_tensors="""pt""" ).to(A )
  
          # forward pass
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A )
  
          # verify the predicted depth
          _SCREAMING_SNAKE_CASE             =    torch.Size([1, 480, 640] )
          self.assertEqual(outputs.predicted_depth.shape				,	A )
  
          _SCREAMING_SNAKE_CASE             =    torch.tensor(
              [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] ).to(A )
  
          self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]				,	A				,	atol=1e-4 ) )
  
  
  
  