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    _lowerCAmelCase						:
  
  
  
  							def __init__(      self ,						a_ ,						a_=2 ,						a_=32 ,						a_=16 ,						a_=3 ,						a_=True ,						a_=True ,						a_=32 ,						a_=4 ,						a_=[0, 1, 2, 3] ,						a_=4 ,						a_=37 ,						a_="gelu" ,						a_=0.1 ,						a_=0.1 ,						a_=0.02 ,						a_=3 ,						a_=False ,						a_=None ,						)							->							Union[str, Any]:
  								_UpperCAmelCase =						parent
  								_UpperCAmelCase =						batch_size
  								_UpperCAmelCase =						image_size
  								_UpperCAmelCase =						patch_size
  								_UpperCAmelCase =						num_channels
  								_UpperCAmelCase =						is_training
  								_UpperCAmelCase =						use_labels
  								_UpperCAmelCase =						hidden_size
  								_UpperCAmelCase =						num_hidden_layers
  								_UpperCAmelCase =						backbone_out_indices
  								_UpperCAmelCase =						num_attention_heads
  								_UpperCAmelCase =						intermediate_size
  								_UpperCAmelCase =						hidden_act
  								_UpperCAmelCase =						hidden_dropout_prob
  								_UpperCAmelCase =						attention_probs_dropout_prob
  								_UpperCAmelCase =						initializer_range
  								_UpperCAmelCase =						num_labels
  								_UpperCAmelCase =						scope
  								_UpperCAmelCase =						is_hybrid
  								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
  								_UpperCAmelCase =						(image_size // patch_size) ** 2
  								_UpperCAmelCase =						num_patches + 1
  
  
  
  							def       _a   (      self   )							->							List[str]:
  								_UpperCAmelCase =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
  
  								_UpperCAmelCase =						None
  								if self.use_labels:
  									_UpperCAmelCase =						ids_tensor([self.batch_size, self.image_size, self.image_size] ,						self.num_labels   )
  
  								_UpperCAmelCase =						self.get_config()
  
  								return config, pixel_values, labels
  
  
  
  							def       _a   (      self   )							->							Optional[Any]:
  								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=a_ ,						initializer_range=self.initializer_range ,						is_hybrid=self.is_hybrid ,						)
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_   )							->							List[Any]:
  								_UpperCAmelCase =						DPTModel(config=a_   )
  								model.to(a_   )
  								model.eval()
  								_UpperCAmelCase =						model(a_   )
  								self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_   )							->							Any:
  								_UpperCAmelCase =						self.num_labels
  								_UpperCAmelCase =						DPTForDepthEstimation(a_   )
  								model.to(a_   )
  								model.eval()
  								_UpperCAmelCase =						model(a_   )
  								self.parent.assertEqual(result.predicted_depth.shape ,						(self.batch_size, self.image_size, self.image_size)   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_   )							->							str:
  								_UpperCAmelCase =						self.num_labels
  								_UpperCAmelCase =						DPTForSemanticSegmentation(a_   )
  								model.to(a_   )
  								model.eval()
  								_UpperCAmelCase =						model(a_ ,						labels=a_   )
  								self.parent.assertEqual(
  								    result.logits.shape ,						(self.batch_size, self.num_labels, self.image_size, self.image_size)   )
  
  
  
  							def       _a   (      self   )							->							Any:
  								_UpperCAmelCase =						self.prepare_config_and_inputs()
  								_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						config_and_inputs
  								_UpperCAmelCase =						{"pixel_values": pixel_values}
  								return config, inputs_dict
  
  
  
  
  
  @require_torch
  class    _lowerCAmelCase						(						lowerCamelCase    ,       lowerCamelCase    ,       unittest.TestCase						):
  							lowercase_     :     str       =							(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  							lowercase_     :     Any       =							(
  							    {
  							        '''depth-estimation''': DPTForDepthEstimation,
  							        '''feature-extraction''': DPTModel,
  							        '''image-segmentation''': DPTForSemanticSegmentation,
  							    }
  							    if is_torch_available()
  							    else {}
  							)
  
  							lowercase_     :     Tuple       =							False
  							lowercase_     :     Any       =							False
  							lowercase_     :     Tuple       =							False
  
  
  
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase =						DPTModelTester(self   )
  								_UpperCAmelCase =						ConfigTester(self ,						config_class=a_ ,						has_text_modality=a_ ,						hidden_size=37   )
  
  
  
  							def       _a   (      self   )							->							Union[str, Any]:
  								self.config_tester.run_common_tests()
  
  
  
  							@unittest.skip(reason="DPT does not use inputs_embeds"   )
  							def       _a   (      self   )							->							Dict:
  								pass
  
  
  
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  									_UpperCAmelCase =						model_class(a_   )
  									self.assertIsInstance(model.get_input_embeddings() ,						(nn.Module)   )
  									_UpperCAmelCase =						model.get_output_embeddings()
  									self.assertTrue(x is None or isinstance(a_ ,						nn.Linear   )   )
  
  
  
  							def       _a   (      self   )							->							Any:
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  									_UpperCAmelCase =						model_class(a_   )
  									_UpperCAmelCase =						inspect.signature(model.forward   )
  									# signature.parameters is an OrderedDict => so arg_names order is deterministic
  									_UpperCAmelCase =						[*signature.parameters.keys()]
  
  									_UpperCAmelCase =						["pixel_values"]
  									self.assertListEqual(arg_names[:1] ,						a_   )
  
  
  
  							def       _a   (      self   )							->							str:
  								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_model(*a_   )
  
  
  
  							def       _a   (      self   )							->							Optional[Any]:
  								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_for_depth_estimation(*a_   )
  
  
  
  							def       _a   (      self   )							->							Optional[Any]:
  								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_for_semantic_segmentation(*a_   )
  
  
  
  							def       _a   (      self   )							->							str:
  								for model_class in self.all_model_classes:
  									if model_class.__name__ == "DPTForDepthEstimation":
  										continue
  
  									_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  									_UpperCAmelCase =						True
  
  									if model_class in get_values(a_   ):
  										continue
  
  									_UpperCAmelCase =						model_class(a_   )
  									model.to(a_   )
  									model.train()
  									_UpperCAmelCase =						self._prepare_for_class(a_ ,						a_ ,						return_labels=a_   )
  									_UpperCAmelCase =						model(**a_   ).loss
  									loss.backward()
  
  
  
  							def       _a   (      self   )							->							List[Any]:
  								for model_class in self.all_model_classes:
  									if model_class.__name__ == "DPTForDepthEstimation":
  										continue
  
  									_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  									_UpperCAmelCase =						False
  									_UpperCAmelCase =						True
  
  									if model_class in get_values(a_   ) or not model_class.supports_gradient_checkpointing:
  										continue
  									_UpperCAmelCase =						model_class(a_   )
  									model.to(a_   )
  									model.gradient_checkpointing_enable()
  									model.train()
  									_UpperCAmelCase =						self._prepare_for_class(a_ ,						a_ ,						return_labels=a_   )
  									_UpperCAmelCase =						model(**a_   ).loss
  									loss.backward()
  
  
  
  							def       _a   (      self   )							->							Optional[Any]:
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  
  								_UpperCAmelCase =						_config_zero_init(a_   )
  								for model_class in self.all_model_classes:
  									_UpperCAmelCase =						model_class(config=a_   )
  									# Skip the check for the backbone
  									_UpperCAmelCase =						[]
  									for name, module in model.named_modules():
  										if module.__class__.__name__ == "DPTViTHybridEmbeddings":
  											_UpperCAmelCase =						[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   )							->							Union[str, Any]:
  								pass
  
  
  
  							@slow
  							def       _a   (      self   )							->							Dict:
  								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  									_UpperCAmelCase =						DPTModel.from_pretrained(a_   )
  									self.assertIsNotNone(a_   )
  
  
  
  
  
  def     __lowerCamelCase  (		):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
  	return image
  
  
  
  @require_torch
  @require_vision
  @slow
  class    _lowerCAmelCase						(						unittest.TestCase						):
  
  
  
  							def       _a   (      self   )							->							Optional[int]:
  								_UpperCAmelCase =						DPTImageProcessor.from_pretrained("Intel/dpt-large"   )
  								_UpperCAmelCase =						DPTForDepthEstimation.from_pretrained("Intel/dpt-large"   ).to(a_   )
  
  								_UpperCAmelCase =						prepare_img()
  								_UpperCAmelCase =						image_processor(images=a_ ,						return_tensors="pt"   ).to(a_   )
  
  								# forward pass
  								with torch.no_grad():
  									_UpperCAmelCase =						model(**a_   )
  									_UpperCAmelCase =						outputs.predicted_depth
  
  								# verify the predicted depth
  								_UpperCAmelCase =						torch.Size((1, 384, 384)   )
  								self.assertEqual(predicted_depth.shape ,						a_   )
  
  								_UpperCAmelCase =						torch.tensor(
  								    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]   ).to(a_   )
  
  								self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3] ,						a_ ,						atol=1e-4   )   )
  
  
  
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase =						DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"   )
  								_UpperCAmelCase =						DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"   ).to(a_   )
  
  								_UpperCAmelCase =						prepare_img()
  								_UpperCAmelCase =						image_processor(images=a_ ,						return_tensors="pt"   ).to(a_   )
  
  								# forward pass
  								with torch.no_grad():
  									_UpperCAmelCase =						model(**a_   )
  
  								# verify the logits
  								_UpperCAmelCase =						torch.Size((1, 150, 480, 480)   )
  								self.assertEqual(outputs.logits.shape ,						a_   )
  
  								_UpperCAmelCase =						torch.tensor(
  								    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]   ).to(a_   )
  
  								self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] ,						a_ ,						atol=1e-4   )   )
  
  
  
  							def       _a   (      self   )							->							List[str]:
  								_UpperCAmelCase =						DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"   )
  								_UpperCAmelCase =						DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"   ).to(a_   )
  
  								_UpperCAmelCase =						prepare_img()
  								_UpperCAmelCase =						image_processor(images=a_ ,						return_tensors="pt"   ).to(a_   )
  
  								# forward pass
  								with torch.no_grad():
  									_UpperCAmelCase =						model(**a_   )
  
  								_UpperCAmelCase =						outputs.logits.detach().cpu()
  
  								_UpperCAmelCase =						image_processor.post_process_semantic_segmentation(outputs=a_ ,						target_sizes=[(500, 300)]   )
  								_UpperCAmelCase =						torch.Size((500, 300)   )
  								self.assertEqual(segmentation[0].shape ,						a_   )
  
  								_UpperCAmelCase =						image_processor.post_process_semantic_segmentation(outputs=a_   )
  								_UpperCAmelCase =						torch.Size((480, 480)   )
  								self.assertEqual(segmentation[0].shape ,						a_   )
  
  
  
  
  
style_context: 
  """simple docstring"""
  def     __lowerCamelCase  (		UpperCamelCase__ = 400_0000      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						1
  	_UpperCAmelCase =						2
  	_UpperCAmelCase =						0
  	while j <= n:
  		if j % 2 == 0:
  			total += j
  		_UpperCAmelCase ,      _UpperCAmelCase =						j, i + j
  
  	return total
  
  
  if __name__ == "__main__":
  			print(f'''{solution() = }''')
  
  
  
  
  