code: 
  '''simple docstring'''
  
  
  
  import inspect
  import unittest
  
  from transformers import DPTConfig
  from transformers.file_utils 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, _config_zero_init, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  				import torch
  				from torch import nn
  
  				from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
  				from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
  				from PIL import Image
  
  				from transformers import DPTImageProcessor
  
  
  
  
  
  class 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=2       ,     lowerCamelCase__=32       ,     lowerCamelCase__=16       ,     lowerCamelCase__=3       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=32       ,     lowerCamelCase__=4       ,     lowerCamelCase__=[0, 1, 2, 3]       ,     lowerCamelCase__=4       ,     lowerCamelCase__=37       ,     lowerCamelCase__="gelu"       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=3       ,     lowerCamelCase__=False       ,     lowerCamelCase__=None       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       parent
  									lowercase__							 =       batch_size
  									lowercase__							 =       image_size
  									lowercase__							 =       patch_size
  									lowercase__							 =       num_channels
  									lowercase__							 =       is_training
  									lowercase__							 =       use_labels
  									lowercase__							 =       hidden_size
  									lowercase__							 =       num_hidden_layers
  									lowercase__							 =       backbone_out_indices
  									lowercase__							 =       num_attention_heads
  									lowercase__							 =       intermediate_size
  									lowercase__							 =       hidden_act
  									lowercase__							 =       hidden_dropout_prob
  									lowercase__							 =       attention_probs_dropout_prob
  									lowercase__							 =       initializer_range
  									lowercase__							 =       num_labels
  									lowercase__							 =       scope
  									lowercase__							 =       is_hybrid
  									# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
  									lowercase__							 =       (image_size // patch_size) ** 2
  									lowercase__							 =       num_patches + 1
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
  
  									lowercase__							 =       None
  									if self.use_labels:
  															lowercase__							 =       ids_tensor([self.batch_size, self.image_size, self.image_size]       ,     self.num_labels       )
  
  									lowercase__							 =       self.get_config()
  
  									return config, pixel_values, labels
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return DPTConfig(
  									    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       ,     backbone_out_indices=self.backbone_out_indices       ,     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       ,     is_decoder=lowerCamelCase__       ,     initializer_range=self.initializer_range       ,     is_hybrid=self.is_hybrid       ,     )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       DPTModel(config=lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									lowercase__							 =       model(lowerCamelCase__       )
  									self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.num_labels
  									lowercase__							 =       DPTForDepthEstimation(lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									lowercase__							 =       model(lowerCamelCase__       )
  									self.parent.assertEqual(result.predicted_depth.shape       ,     (self.batch_size, self.image_size, self.image_size)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.num_labels
  									lowercase__							 =       DPTForSemanticSegmentation(lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									lowercase__							 =       model(lowerCamelCase__       ,     labels=lowerCamelCase__       )
  									self.parent.assertEqual(
  									    result.logits.shape       ,     (self.batch_size, self.num_labels, self.image_size, self.image_size)       )
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.prepare_config_and_inputs()
  									lowercase__ ,						lowercase__ ,						lowercase__							 =       config_and_inputs
  									lowercase__							 =       {"""pixel_values""": pixel_values}
  									return config, inputs_dict
  
  
  
  
  
  @require_torch
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							Union[str, Any]       		=  (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  			lowerCamelCase							:							List[Any]       		=  (
  			    {
  			        """depth-estimation""": DPTForDepthEstimation,
  			        """feature-extraction""": DPTModel,
  			        """image-segmentation""": DPTForSemanticSegmentation,
  			    }
  			    if is_torch_available()
  			    else {}
  			)
  
  			lowerCamelCase							:							Any       		=  False
  			lowerCamelCase							:							Any       		=  False
  			lowerCamelCase							:							Optional[int]       		=  False
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       DPTModelTester(self       )
  									lowercase__							 =       ConfigTester(self       ,     config_class=lowerCamelCase__       ,     has_text_modality=lowerCamelCase__       ,     hidden_size=37       )
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									self.config_tester.run_common_tests()
  
  
  
  			@unittest.skip(reason="""DPT does not use inputs_embeds"""       )
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									for model_class in self.all_model_classes:
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															self.assertIsInstance(model.get_input_embeddings()       ,     (nn.Module)       )
  															lowercase__							 =       model.get_output_embeddings()
  															self.assertTrue(x is None or isinstance(lowerCamelCase__       ,     nn.Linear       )       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									for model_class in self.all_model_classes:
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															lowercase__							 =       inspect.signature(model.forward       )
  															# signature.parameters is an OrderedDict => so arg_names order is deterministic
  															lowercase__							 =       [*signature.parameters.keys()]
  
  															lowercase__							 =       ["""pixel_values"""]
  															self.assertListEqual(arg_names[:1]       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_model(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_class in self.all_model_classes:
  															if model_class.__name__ == "DPTForDepthEstimation":
  																					continue
  
  															lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  															lowercase__							 =       True
  
  															if model_class in get_values(lowerCamelCase__       ):
  																					continue
  
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															model.to(lowerCamelCase__       )
  															model.train()
  															lowercase__							 =       self._prepare_for_class(lowerCamelCase__       ,     lowerCamelCase__       ,     return_labels=lowerCamelCase__       )
  															lowercase__							 =       model(**lowerCamelCase__       ).loss
  															loss.backward()
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_class in self.all_model_classes:
  															if model_class.__name__ == "DPTForDepthEstimation":
  																					continue
  
  															lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  															lowercase__							 =       False
  															lowercase__							 =       True
  
  															if model_class in get_values(lowerCamelCase__       ) or not model_class.supports_gradient_checkpointing:
  																					continue
  															lowercase__							 =       model_class(lowerCamelCase__       )
  															model.to(lowerCamelCase__       )
  															model.gradient_checkpointing_enable()
  															model.train()
  															lowercase__							 =       self._prepare_for_class(lowerCamelCase__       ,     lowerCamelCase__       ,     return_labels=lowerCamelCase__       )
  															lowercase__							 =       model(**lowerCamelCase__       ).loss
  															loss.backward()
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
  
  									lowercase__							 =       _config_zero_init(lowerCamelCase__       )
  									for model_class in self.all_model_classes:
  															lowercase__							 =       model_class(config=lowerCamelCase__       )
  															# Skip the check for the backbone
  															lowercase__							 =       []
  															for name, module in model.named_modules():
  																					if module.__class__.__name__ == "DPTViTHybridEmbeddings":
  																											lowercase__							 =       [F'''{name}.{key}''' for key in module.state_dict().keys()]
  																											break
  
  															for name, param in model.named_parameters():
  																					if param.requires_grad:
  																											if name in backbone_params:
  																																	continue
  																											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("""Will be fixed soon by reducing the size of the model used for common tests."""       )
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			@slow
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  															lowercase__							 =       DPTModel.from_pretrained(lowerCamelCase__       )
  															self.assertIsNotNone(lowerCamelCase__       )
  
  
  
  
  
  def  _A		(							):
  						lowercase__							 =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
  						return image
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  @slow
  class 			A							(       unittest.TestCase				):
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       DPTImageProcessor.from_pretrained("""Intel/dpt-large"""       )
  									lowercase__							 =       DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""       ).to(lowerCamelCase__       )
  
  									lowercase__							 =       prepare_img()
  									lowercase__							 =       image_processor(images=lowerCamelCase__       ,     return_tensors="""pt"""       ).to(lowerCamelCase__       )
  
  									# forward pass
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  															lowercase__							 =       outputs.predicted_depth
  
  									# verify the predicted depth
  									lowercase__							 =       torch.Size((1, 384, 384)       )
  									self.assertEqual(predicted_depth.shape       ,     lowerCamelCase__       )
  
  									lowercase__							 =       torch.tensor(
  									    [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]       ).to(lowerCamelCase__       )
  
  									self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]       ,     lowerCamelCase__       ,     atol=1e-4       )       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""       )
  									lowercase__							 =       DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""       ).to(lowerCamelCase__       )
  
  									lowercase__							 =       prepare_img()
  									lowercase__							 =       image_processor(images=lowerCamelCase__       ,     return_tensors="""pt"""       ).to(lowerCamelCase__       )
  
  									# forward pass
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  
  									# verify the logits
  									lowercase__							 =       torch.Size((1, 150, 480, 480)       )
  									self.assertEqual(outputs.logits.shape       ,     lowerCamelCase__       )
  
  									lowercase__							 =       torch.tensor(
  									    [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]       ).to(lowerCamelCase__       )
  
  									self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]       ,     lowerCamelCase__       ,     atol=1e-4       )       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""       )
  									lowercase__							 =       DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""       ).to(lowerCamelCase__       )
  
  									lowercase__							 =       prepare_img()
  									lowercase__							 =       image_processor(images=lowerCamelCase__       ,     return_tensors="""pt"""       ).to(lowerCamelCase__       )
  
  									# forward pass
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  
  									lowercase__							 =       outputs.logits.detach().cpu()
  
  									lowercase__							 =       image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__       ,     target_sizes=[(500, 300)]       )
  									lowercase__							 =       torch.Size((500, 300)       )
  									self.assertEqual(segmentation[0].shape       ,     lowerCamelCase__       )
  
  									lowercase__							 =       image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__       )
  									lowercase__							 =       torch.Size((480, 480)       )
  									self.assertEqual(segmentation[0].shape       ,     lowerCamelCase__       )
style_context: 
  '''simple docstring'''
  
  
  
  def  _A		(							lowercase__	):
  
  						# validate:
  						if not isinstance(lowercase__		,		lowercase__	) or not all(
  						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
  												raise ValueError("""The citations should be a list of non negative integers."""	)
  
  						citations.sort()
  						lowercase__							 =       len(lowercase__	)
  
  						for i in range(lowercase__	):
  												if citations[len_citations - 1 - i] <= i:
  																		return i
  
  						return len_citations
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  
  