




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					:


     """simple docstring"""


     def __init__(	self			: Optional[Any]				,_UpperCamelCase			: str				,_UpperCamelCase			: List[str]=2				,_UpperCamelCase			: str=3_2				,_UpperCamelCase			: Dict=1_6				,_UpperCamelCase			: List[str]=3				,_UpperCamelCase			: int=True				,_UpperCamelCase			: Union[str, Any]=True				,_UpperCamelCase			: Tuple=3_2				,_UpperCamelCase			: Union[str, Any]=4				,_UpperCamelCase			: List[Any]=[0, 1, 2, 3]				,_UpperCamelCase			: Tuple=4				,_UpperCamelCase			: Optional[int]=3_7				,_UpperCamelCase			: List[str]="gelu"				,_UpperCamelCase			: List[Any]=0.1				,_UpperCamelCase			: Dict=0.1				,_UpperCamelCase			: Tuple=0.02				,_UpperCamelCase			: List[str]=3				,_UpperCamelCase			: Union[str, Any]=False				,_UpperCamelCase			: Optional[int]=None				,)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=parent
         SCREAMING_SNAKE_CASE__								=batch_size
         SCREAMING_SNAKE_CASE__								=image_size
         SCREAMING_SNAKE_CASE__								=patch_size
         SCREAMING_SNAKE_CASE__								=num_channels
         SCREAMING_SNAKE_CASE__								=is_training
         SCREAMING_SNAKE_CASE__								=use_labels
         SCREAMING_SNAKE_CASE__								=hidden_size
         SCREAMING_SNAKE_CASE__								=num_hidden_layers
         SCREAMING_SNAKE_CASE__								=backbone_out_indices
         SCREAMING_SNAKE_CASE__								=num_attention_heads
         SCREAMING_SNAKE_CASE__								=intermediate_size
         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__								=num_labels
         SCREAMING_SNAKE_CASE__								=scope
         SCREAMING_SNAKE_CASE__								=is_hybrid
         # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
         SCREAMING_SNAKE_CASE__								=(image_size // patch_size) ** 2
         SCREAMING_SNAKE_CASE__								=num_patches + 1




     def   __A				(	self			: Dict						)		->   int:


         '''simple docstring'''





         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   __A				(	self			: Any						)		->   str:


         '''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=_UpperCamelCase				,initializer_range=self.initializer_range				,is_hybrid=self.is_hybrid				,)




     def   __A				(	self			: Any				,_UpperCamelCase			: int				,_UpperCamelCase			: Optional[Any]				,_UpperCamelCase			: Any						)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=DPTModel(config=_UpperCamelCase						)
         model.to(_UpperCamelCase						)
         model.eval()
         SCREAMING_SNAKE_CASE__								=model(_UpperCamelCase						)
         self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, self.seq_length, self.hidden_size)						)




     def   __A				(	self			: str				,_UpperCamelCase			: Union[str, Any]				,_UpperCamelCase			: Dict				,_UpperCamelCase			: str						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.num_labels
         SCREAMING_SNAKE_CASE__								=DPTForDepthEstimation(_UpperCamelCase						)
         model.to(_UpperCamelCase						)
         model.eval()
         SCREAMING_SNAKE_CASE__								=model(_UpperCamelCase						)
         self.parent.assertEqual(result.predicted_depth.shape				,(self.batch_size, self.image_size, self.image_size)						)




     def   __A				(	self			: int				,_UpperCamelCase			: str				,_UpperCamelCase			: Tuple				,_UpperCamelCase			: List[Any]						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.num_labels
         SCREAMING_SNAKE_CASE__								=DPTForSemanticSegmentation(_UpperCamelCase						)
         model.to(_UpperCamelCase						)
         model.eval()
         SCREAMING_SNAKE_CASE__								=model(_UpperCamelCase				,labels=_UpperCamelCase						)
         self.parent.assertEqual(
             result.logits.shape				,(self.batch_size, self.num_labels, self.image_size, self.image_size)						)




     def   __A				(	self			: Any						)		->   str:


         '''simple docstring'''





         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					( __lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase     ):


     """simple docstring"""


     _A				:    Any									=  (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     _A				:    int									=  (
         {
             "depth-estimation": DPTForDepthEstimation,
             "feature-extraction": DPTModel,
             "image-segmentation": DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     _A				:    int									=  False
     _A				:    List[Any]									=  False
     _A				:    Union[str, Any]									=  False




     def   __A				(	self			: Union[str, Any]						)		->   List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=DPTModelTester(self						)
         SCREAMING_SNAKE_CASE__								=ConfigTester(self				,config_class=_UpperCamelCase				,has_text_modality=_UpperCamelCase				,hidden_size=3_7						)




     def   __A				(	self			: List[str]						)		->   Union[str, Any]:


         '''simple docstring'''





         self.config_tester.run_common_tests()




     @unittest.skip(reason="""DPT does not use inputs_embeds"""						)
     def   __A				(	self			: List[Any]						)		->   Optional[int]:


         '''simple docstring'''





         pass




     def   __A				(	self			: Dict						)		->   str:


         '''simple docstring'''





         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(_UpperCamelCase						)
             self.assertIsInstance(model.get_input_embeddings()				,(nn.Module)						)
             SCREAMING_SNAKE_CASE__								=model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_UpperCamelCase				,nn.Linear						)						)




     def   __A				(	self			: Any						)		->   Any:


         '''simple docstring'''





         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(_UpperCamelCase						)
             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]				,_UpperCamelCase						)




     def   __A				(	self			: Union[str, Any]						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_UpperCamelCase						)




     def   __A				(	self			: List[Any]						)		->   Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*_UpperCamelCase						)




     def   __A				(	self			: Optional[Any]						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCamelCase						)




     def   __A				(	self			: Optional[int]						)		->   Optional[int]:


         '''simple docstring'''





         for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                 continue

             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=self.model_tester.prepare_config_and_inputs_for_common()
             SCREAMING_SNAKE_CASE__								=True

             if model_class in get_values(_UpperCamelCase						):
                 continue

             SCREAMING_SNAKE_CASE__								=model_class(_UpperCamelCase						)
             model.to(_UpperCamelCase						)
             model.train()
             SCREAMING_SNAKE_CASE__								=self._prepare_for_class(_UpperCamelCase				,_UpperCamelCase				,return_labels=_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=model(**_UpperCamelCase						).loss
             loss.backward()




     def   __A				(	self			: Dict						)		->   Union[str, Any]:


         '''simple docstring'''





         for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                 continue

             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=self.model_tester.prepare_config_and_inputs_for_common()
             SCREAMING_SNAKE_CASE__								=False
             SCREAMING_SNAKE_CASE__								=True

             if model_class in get_values(_UpperCamelCase						) or not model_class.supports_gradient_checkpointing:
                 continue
             SCREAMING_SNAKE_CASE__								=model_class(_UpperCamelCase						)
             model.to(_UpperCamelCase						)
             model.gradient_checkpointing_enable()
             model.train()
             SCREAMING_SNAKE_CASE__								=self._prepare_for_class(_UpperCamelCase				,_UpperCamelCase				,return_labels=_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=model(**_UpperCamelCase						).loss
             loss.backward()




     def   __A				(	self			: Any						)		->   Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=self.model_tester.prepare_config_and_inputs_for_common()

         SCREAMING_SNAKE_CASE__								=_config_zero_init(_UpperCamelCase						)
         for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE__								=model_class(config=_UpperCamelCase						)
             # Skip the check for the backbone
             SCREAMING_SNAKE_CASE__								=[]
             for name, module in model.named_modules():
                 if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                     SCREAMING_SNAKE_CASE__								=[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			: Optional[Any]						)		->   Dict:


         '''simple docstring'''





         pass




     @slow
     def   __A				(	self			: Union[str, Any]						)		->   Dict:


         '''simple docstring'''





         for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE__								=DPTModel.from_pretrained(_UpperCamelCase						)
             self.assertIsNotNone(_UpperCamelCase						)






def 	UpperCAmelCase_    (	):
    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"""


     def   __A				(	self			: int						)		->   Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=DPTImageProcessor.from_pretrained("""Intel/dpt-large"""						)
         SCREAMING_SNAKE_CASE__								=DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""						).to(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=prepare_img()
         SCREAMING_SNAKE_CASE__								=image_processor(images=_UpperCamelCase				,return_tensors="""pt"""						).to(_UpperCamelCase						)

         # forward pass
         with torch.no_grad():
             SCREAMING_SNAKE_CASE__								=model(**_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=outputs.predicted_depth

         # verify the predicted depth
         SCREAMING_SNAKE_CASE__								=torch.Size((1, 3_8_4, 3_8_4)						)
         self.assertEqual(predicted_depth.shape				,_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=torch.tensor(
             [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]						).to(_UpperCamelCase						)

         self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]				,_UpperCamelCase				,atol=1e-4						)						)




     def   __A				(	self			: List[str]						)		->   Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""						)
         SCREAMING_SNAKE_CASE__								=DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""						).to(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=prepare_img()
         SCREAMING_SNAKE_CASE__								=image_processor(images=_UpperCamelCase				,return_tensors="""pt"""						).to(_UpperCamelCase						)

         # forward pass
         with torch.no_grad():
             SCREAMING_SNAKE_CASE__								=model(**_UpperCamelCase						)

         # verify the logits
         SCREAMING_SNAKE_CASE__								=torch.Size((1, 1_5_0, 4_8_0, 4_8_0)						)
         self.assertEqual(outputs.logits.shape				,_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=torch.tensor(
             [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]						).to(_UpperCamelCase						)

         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]				,_UpperCamelCase				,atol=1e-4						)						)




     def   __A				(	self			: Tuple						)		->   Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""						)
         SCREAMING_SNAKE_CASE__								=DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""						).to(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=prepare_img()
         SCREAMING_SNAKE_CASE__								=image_processor(images=_UpperCamelCase				,return_tensors="""pt"""						).to(_UpperCamelCase						)

         # forward pass
         with torch.no_grad():
             SCREAMING_SNAKE_CASE__								=model(**_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=outputs.logits.detach().cpu()

         SCREAMING_SNAKE_CASE__								=image_processor.post_process_semantic_segmentation(outputs=_UpperCamelCase				,target_sizes=[(5_0_0, 3_0_0)]						)
         SCREAMING_SNAKE_CASE__								=torch.Size((5_0_0, 3_0_0)						)
         self.assertEqual(segmentation[0].shape				,_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=image_processor.post_process_semantic_segmentation(outputs=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=torch.Size((4_8_0, 4_8_0)						)
         self.assertEqual(segmentation[0].shape				,_UpperCamelCase						)












def 	UpperCAmelCase_    (	__UpperCamelCase	):
    if not isinstance(__UpperCamelCase,				__UpperCamelCase	):
        raise ValueError("""double_factorial() only accepts integral values"""	)
    if n < 0:
        raise ValueError("""double_factorial() not defined for negative values"""	)
    return 1 if n <= 1 else n * double_factorial(n - 2	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






