






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[int] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     List[str]=2 ,     __SCREAMING_SNAKE_CASE      :     Tuple=32 ,     __SCREAMING_SNAKE_CASE      :     Tuple=16 ,     __SCREAMING_SNAKE_CASE      :     Any=3 ,     __SCREAMING_SNAKE_CASE      :     str=True ,     __SCREAMING_SNAKE_CASE      :     List[str]=True ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=32 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=4 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=[0, 1, 2, 3] ,     __SCREAMING_SNAKE_CASE      :     List[str]=4 ,     __SCREAMING_SNAKE_CASE      :     str=37 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]="gelu" ,     __SCREAMING_SNAKE_CASE      :     List[Any]=0.1 ,     __SCREAMING_SNAKE_CASE      :     Any=0.1 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=0.02 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=3 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=[1, 384, 24, 24] ,     __SCREAMING_SNAKE_CASE      :     Tuple=True ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=None ,     )      ->							List[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     				=backbone_featmap_shape
           __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      :     str      )      ->							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      :     Any      )      ->							int:
           __UpperCAmelCase     				={
               """global_padding""": """same""",
               """layer_type""": """bottleneck""",
               """depths""": [3, 4, 9],
               """out_features""": ["""stage1""", """stage2""", """stage3"""],
               """embedding_dynamic_padding""": True,
               """hidden_sizes""": [96, 192, 384, 768],
               """num_groups""": 2,
           }

           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 ,     backbone_config=__lowerCAmelCase ,     backbone_featmap_shape=self.backbone_featmap_shape ,     )



    def _a							(				self      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     str ,     __SCREAMING_SNAKE_CASE      :     int      )      ->							List[str]:
           __UpperCAmelCase     				=DPTModel(config=__lowerCAmelCase      )
           model.to(__lowerCAmelCase      )
           model.eval()
           __UpperCAmelCase     				=model(__lowerCAmelCase      )
           self.parent.assertEqual(result.last_hidden_state.shape ,     (self.batch_size, self.seq_length, self.hidden_size)      )



    def _a							(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     Dict      )      ->							Optional[Any]:
           __UpperCAmelCase     				=self.num_labels
           __UpperCAmelCase     				=DPTForDepthEstimation(__lowerCAmelCase      )
           model.to(__lowerCAmelCase      )
           model.eval()
           __UpperCAmelCase     				=model(__lowerCAmelCase      )
           self.parent.assertEqual(result.predicted_depth.shape ,     (self.batch_size, self.image_size, self.image_size)      )



    def _a							(				self      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Optional[Any] ,     __SCREAMING_SNAKE_CASE      :     Any      )      ->							Tuple:
           __UpperCAmelCase     				=self.num_labels
           __UpperCAmelCase     				=DPTForSemanticSegmentation(__lowerCAmelCase      )
           model.to(__lowerCAmelCase      )
           model.eval()
           __UpperCAmelCase     				=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      :     Dict      )      ->							List[str]:
           __UpperCAmelCase     				=self.prepare_config_and_inputs()
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=config_and_inputs
           __UpperCAmelCase     				={"""pixel_values""": pixel_values}
           return config, inputs_dict




@require_torch
class 				_A			(     _A					,					_A					,					unittest.TestCase				):
    """simple docstring"""

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

    lowerCamelCase	:							Optional[Any]				= False
    lowerCamelCase	:							Any				= False
    lowerCamelCase	:							List[str]				= False



    def _a							(				self      :     List[Any]      )      ->							Any:
           __UpperCAmelCase     				=DPTModelTester(self      )
           __UpperCAmelCase     				=ConfigTester(self ,     config_class=__lowerCAmelCase ,     has_text_modality=__lowerCAmelCase ,     hidden_size=37      )



    def _a							(				self      :     Optional[int]      )      ->							Dict:
           self.config_tester.run_common_tests()



    @unittest.skip(reason="""DPT does not use inputs_embeds"""      )
    def _a							(				self      :     Any      )      ->							str:
           pass



    def _a							(				self      :     str      )      ->							Any:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()

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



    def _a							(				self      :     Dict      )      ->							Union[str, Any]:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  __UpperCAmelCase     				=model_class(__lowerCAmelCase      )
                  __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] ,     __lowerCAmelCase      )



    def _a							(				self      :     str      )      ->							List[Any]:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCAmelCase      )



    def _a							(				self      :     Any      )      ->							str:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*__lowerCAmelCase      )



    def _a							(				self      :     Dict      )      ->							Optional[Any]:
           __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase      )



    def _a							(				self      :     Optional[int]      )      ->							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     				=True

                  if model_class in get_values(__lowerCAmelCase      ):
                         continue

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



    def _a							(				self      :     List[str]      )      ->							Optional[int]:
           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(__lowerCAmelCase      ) or not model_class.supports_gradient_checkpointing:
                         continue
                  __UpperCAmelCase     				=model_class(__lowerCAmelCase      )
                  model.to(__lowerCAmelCase      )
                  model.gradient_checkpointing_enable()
                  model.train()
                  __UpperCAmelCase     				=self._prepare_for_class(__lowerCAmelCase ,     __lowerCAmelCase ,     return_labels=__lowerCAmelCase      )
                  __UpperCAmelCase     				=model(**__lowerCAmelCase      ).loss
                  loss.backward()



    def _a							(				self      :     Tuple      )      ->							Optional[int]:
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()

           __UpperCAmelCase     				=_config_zero_init(__lowerCAmelCase      )
           for model_class in self.all_model_classes:
                  __UpperCAmelCase     				=model_class(config=__lowerCAmelCase      )
                  # 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      :     Any      )      ->							Optional[int]:
           pass



    @slow
    def _a							(				self      :     Optional[int]      )      ->							Union[str, Any]:
           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                  __UpperCAmelCase     				=DPTModel.from_pretrained(__lowerCAmelCase      )
                  self.assertIsNotNone(__lowerCAmelCase      )



    def _a							(				self      :     str      )      ->							Optional[int]:
           # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
           __UpperCAmelCase				, __UpperCAmelCase     				=self.model_tester.prepare_config_and_inputs_for_common()
           __UpperCAmelCase     				="""add"""
           with self.assertRaises(__lowerCAmelCase      ):
                  __UpperCAmelCase     				=DPTForDepthEstimation(__lowerCAmelCase      )







def       lowercase__	(					)	->				List[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=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      :     Dict      )      ->							str:
           __UpperCAmelCase     				=DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""      )
           __UpperCAmelCase     				=DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""      ).to(__lowerCAmelCase      )

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

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

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

           __UpperCAmelCase     				=torch.tensor(
               [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]      ).to(__lowerCAmelCase      )

           self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100 ,     __lowerCAmelCase ,     atol=1e-4      )      )









def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   if not isinstance(_snake_case	,   _snake_case				):
      raise ValueError('''Input must be an integer'''				)
   if input_num <= 0:
      raise ValueError('''Input must be positive'''				)
   return sum(
       divisor for divisor in range(1	,   input_num // 2 + 1				) if input_num % divisor == 0				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



