






'''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      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			str   ,   __UpperCAmelCase				:			Optional[Any]   ,   __UpperCAmelCase				:			Tuple=2   ,   __UpperCAmelCase				:			Any=32   ,   __UpperCAmelCase				:			Optional[Any]=16   ,   __UpperCAmelCase				:			int=3   ,   __UpperCAmelCase				:			Optional[int]=True   ,   __UpperCAmelCase				:			Any=True   ,   __UpperCAmelCase				:			List[str]=32   ,   __UpperCAmelCase				:			List[str]=4   ,   __UpperCAmelCase				:			Optional[Any]=[0, 1, 2, 3]   ,   __UpperCAmelCase				:			Optional[Any]=4   ,   __UpperCAmelCase				:			Union[str, Any]=37   ,   __UpperCAmelCase				:			str="gelu"   ,   __UpperCAmelCase				:			Union[str, Any]=0.1   ,   __UpperCAmelCase				:			Any=0.1   ,   __UpperCAmelCase				:			Tuple=0.02   ,   __UpperCAmelCase				:			Tuple=3   ,   __UpperCAmelCase				:			Optional[int]=False   ,   __UpperCAmelCase				:			Union[str, Any]=None   ,   ):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			Any	):





         '''simple docstring'''





         _A      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

         _A      =			None
         if self.use_labels:
               _A      =			ids_tensor([self.batch_size, self.image_size, self.image_size]   ,   self.num_labels	)

         _A      =			self.get_config()

         return config, pixel_values, labels

   def 		lowerCAmelCase				(	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=__UpperCAmelCase   ,   initializer_range=self.initializer_range   ,   is_hybrid=self.is_hybrid   ,   )

   def 		lowerCAmelCase				(	self				:			List[str]   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			Optional[int]   ,   __UpperCAmelCase				:			Optional[Any]	):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			str   ,   __UpperCAmelCase				:			Union[str, Any]   ,   __UpperCAmelCase				:			Union[str, Any]   ,   __UpperCAmelCase				:			Optional[int]	):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			Tuple   ,   __UpperCAmelCase				:			Optional[Any]   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			List[str]	):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





         _A      =			self.prepare_config_and_inputs()
         _A  ,			_A  ,			_A      =			config_and_inputs
         _A      =			{"pixel_values": pixel_values}
         return config, inputs_dict





@require_torch
class      _UpperCAmelCase    (  snake_case_							,					snake_case_							,					unittest.TestCase		):




   """simple docstring"""

   snake_case					       =		(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
   snake_case					       =		(
       {
           '''depth-estimation''': DPTForDepthEstimation,
           '''feature-extraction''': DPTModel,
           '''image-segmentation''': DPTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   snake_case					       =		False
   snake_case					       =		False
   snake_case					       =		False

   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			DPTModelTester(self	)
         _A      =			ConfigTester(self   ,   config_class=__UpperCAmelCase   ,   has_text_modality=__UpperCAmelCase   ,   hidden_size=37	)

   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         self.config_tester.run_common_tests()

   @unittest.skip(reason="DPT does not use inputs_embeds"	)
   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         pass

   def 		lowerCAmelCase				(	self				:			Dict	):





         '''simple docstring'''





         _A  ,			_A      =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               _A      =			model_class(__UpperCAmelCase	)
               self.assertIsInstance(model.get_input_embeddings()   ,   (nn.Module)	)
               _A      =			model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(__UpperCAmelCase   ,   nn.Linear	)	)

   def 		lowerCAmelCase				(	self				:			Union[str, Any]	):





         '''simple docstring'''





         _A  ,			_A      =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               _A      =			model_class(__UpperCAmelCase	)
               _A      =			inspect.signature(model.forward	)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _A      =			[*signature.parameters.keys()]

               _A      =			["pixel_values"]
               self.assertListEqual(arg_names[:1]   ,   __UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Union[str, Any]	):





         '''simple docstring'''





         _A      =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





         _A      =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         _A      =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase	)

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





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

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

               if model_class in get_values(__UpperCAmelCase	):
                     continue

               _A      =			model_class(__UpperCAmelCase	)
               model.to(__UpperCAmelCase	)
               model.train()
               _A      =			self._prepare_for_class(__UpperCAmelCase   ,   __UpperCAmelCase   ,   return_labels=__UpperCAmelCase	)
               _A      =			model(**__UpperCAmelCase	).loss
               loss.backward()

   def 		lowerCAmelCase				(	self				:			Union[str, Any]	):





         '''simple docstring'''





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

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

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

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         _A  ,			_A      =			self.model_tester.prepare_config_and_inputs_for_common()

         _A      =			_config_zero_init(__UpperCAmelCase	)
         for model_class in self.all_model_classes:
               _A      =			model_class(config=__UpperCAmelCase	)
               # Skip the check for the backbone
               _A      =			[]
               for name, module in model.named_modules():
                     if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                           _A      =			[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 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         pass

   @slow
   def 		lowerCAmelCase				(	self				:			Tuple	):





         '''simple docstring'''





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





def       __lowercase		(    ) ->		Optional[int]:

      '''simple docstring'''


      _A      =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
      return image


@require_torch
@require_vision
@slow
class      _UpperCAmelCase    (  unittest.TestCase		):




   """simple docstring"""

   def 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			DPTImageProcessor.from_pretrained("Intel/dpt-large"	)
         _A      =			DPTForDepthEstimation.from_pretrained("Intel/dpt-large"	).to(__UpperCAmelCase	)

         _A      =			prepare_img()
         _A      =			image_processor(images=__UpperCAmelCase   ,   return_tensors="pt"	).to(__UpperCAmelCase	)

         # forward pass
         with torch.no_grad():
               _A      =			model(**__UpperCAmelCase	)
               _A      =			outputs.predicted_depth

         # verify the predicted depth
         _A      =			torch.Size((1, 384, 384)	)
         self.assertEqual(predicted_depth.shape   ,   __UpperCAmelCase	)

         _A      =			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 		lowerCAmelCase				(	self				:			str	):





         '''simple docstring'''





         _A      =			DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"	)
         _A      =			DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"	).to(__UpperCAmelCase	)

         _A      =			prepare_img()
         _A      =			image_processor(images=__UpperCAmelCase   ,   return_tensors="pt"	).to(__UpperCAmelCase	)

         # forward pass
         with torch.no_grad():
               _A      =			model(**__UpperCAmelCase	)

         # verify the logits
         _A      =			torch.Size((1, 150, 480, 480)	)
         self.assertEqual(outputs.logits.shape   ,   __UpperCAmelCase	)

         _A      =			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 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"	)
         _A      =			DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"	).to(__UpperCAmelCase	)

         _A      =			prepare_img()
         _A      =			image_processor(images=__UpperCAmelCase   ,   return_tensors="pt"	).to(__UpperCAmelCase	)

         # forward pass
         with torch.no_grad():
               _A      =			model(**__UpperCAmelCase	)

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

         _A      =			image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase   ,   target_sizes=[(500, 300)]	)
         _A      =			torch.Size((500, 300)	)
         self.assertEqual(segmentation[0].shape   ,   __UpperCAmelCase	)

         _A      =			image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase	)
         _A      =			torch.Size((480, 480)	)
         self.assertEqual(segmentation[0].shape   ,   __UpperCAmelCase	)













'''simple docstring'''




def       __lowercase		(    __lowercase ) ->		int:

      '''simple docstring'''


      _A      =			str(__lowercase ).strip()
      if not bin_string:
            raise ValueError("Empty string was passed to the function" )
      _A      =			bin_string[0] == "-"
      if is_negative:
            _A      =			bin_string[1:]
      if not all(char in "01" for char in bin_string ):
            raise ValueError("Non-binary value was passed to the function" )
      _A      =			0
      for char in bin_string:
            _A      =			2 * decimal_number + int(__lowercase )
      return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()





