code: 
  
  
  
  
  
  '''simple docstring'''
  
  import unittest
  import warnings
  
  from datasets.utils import experimental
  
  
  
  
  
  @experimental
  def lowerCamelCase		(  )		->Union[str, Any]:
      return "success"
  
  
  
  
  
  
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     Any:
          with warnings.catch_warnings(record=A ) as w:
              warnings.simplefilter("""always""" )
              self.assertEqual(dummy_function()				,	"""success""" )
          self.assertEqual(len(A )				,	1 )
  
  
  
  
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 ) )
  
  
  
  