


"""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_       :





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=2  ,					__UpperCAmelCase=32  ,					__UpperCAmelCase=16  ,					__UpperCAmelCase=3  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=True  ,					__UpperCAmelCase=32  ,					__UpperCAmelCase=4  ,					__UpperCAmelCase=[0, 1, 2, 3]  ,					__UpperCAmelCase=4  ,					__UpperCAmelCase=37  ,					__UpperCAmelCase="gelu"  ,					__UpperCAmelCase=0.1  ,					__UpperCAmelCase=0.1  ,					__UpperCAmelCase=0.02  ,					__UpperCAmelCase=3  ,					__UpperCAmelCase=False  ,					__UpperCAmelCase=None  ,					)				->					Optional[Any]:
     a   :	Dict						=    parent
     a   :	Tuple						=    batch_size
     a   :	List[Any]						=    image_size
     a   :	int						=    patch_size
     a   :	Any						=    num_channels
     a   :	str						=    is_training
     a   :	int						=    use_labels
     a   :	List[Any]						=    hidden_size
     a   :	Tuple						=    num_hidden_layers
     a   :	Any						=    backbone_out_indices
     a   :	int						=    num_attention_heads
     a   :	int						=    intermediate_size
     a   :	Optional[int]						=    hidden_act
     a   :	str						=    hidden_dropout_prob
     a   :	str						=    attention_probs_dropout_prob
     a   :	int						=    initializer_range
     a   :	Any						=    num_labels
     a   :	str						=    scope
     a   :	Optional[Any]						=    is_hybrid
     # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
     a   :	Optional[Any]						=    (image_size // patch_size) ** 2
     a   :	Optional[int]						=    num_patches + 1
  def 							lowercase_     (						self						)				->					Tuple:
     a   :	List[Any]						=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

     a   :	str						=    None
     if self.use_labels:
        a   :	List[Any]						=    ids_tensor([self.batch_size, self.image_size, self.image_size]  ,					self.num_labels						)

     a   :	Any						=    self.get_config()

     return config, pixel_values, labels
  def 							lowercase_     (						self						)				->					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=__UpperCAmelCase  ,					initializer_range=self.initializer_range  ,					is_hybrid=self.is_hybrid  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					str:
     a   :	Union[str, Any]						=    DPTModel(config=__UpperCAmelCase						)
     model.to(__UpperCAmelCase						)
     model.eval()
     a   :	str						=    model(__UpperCAmelCase						)
     self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size)						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					List[Any]:
     a   :	Union[str, Any]						=    self.num_labels
     a   :	Dict						=    DPTForDepthEstimation(__UpperCAmelCase						)
     model.to(__UpperCAmelCase						)
     model.eval()
     a   :	Union[str, Any]						=    model(__UpperCAmelCase						)
     self.parent.assertEqual(result.predicted_depth.shape  ,					(self.batch_size, self.image_size, self.image_size)						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					List[str]:
     a   :	str						=    self.num_labels
     a   :	Optional[Any]						=    DPTForSemanticSegmentation(__UpperCAmelCase						)
     model.to(__UpperCAmelCase						)
     model.eval()
     a   :	str						=    model(__UpperCAmelCase  ,					labels=__UpperCAmelCase						)
     self.parent.assertEqual(
         result.logits.shape  ,					(self.batch_size, self.num_labels, self.image_size, self.image_size)						)






  def 							lowercase_     (						self						)				->					str:
     a   :	List[Any]						=    self.prepare_config_and_inputs()
     a   :	Optional[Any]						=    config_and_inputs
     a   :	Dict						=    {'pixel_values': pixel_values}
     return config, inputs_dict



@require_torch
class 	A_       (     lowerCAmelCase__      ,  lowerCAmelCase__      ,  unittest.TestCase   ):





  """simple docstring"""

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

  lowercase		:	Tuple   =						False
  lowercase		:	Optional[int]   =						False
  lowercase		:	Any   =						False
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	int						=    DPTModelTester(self						)
     a   :	Any						=    ConfigTester(self  ,					config_class=__UpperCAmelCase  ,					has_text_modality=__UpperCAmelCase  ,					hidden_size=37						)
  def 							lowercase_     (						self						)				->					Tuple:
     self.config_tester.run_common_tests()
  @unittest.skip(reason='DPT does not use inputs_embeds'						)
  def 							lowercase_     (						self						)				->					Optional[int]:
     pass
  def 							lowercase_     (						self						)				->					Tuple:
     a   :	Any						=    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        a   :	List[Any]						=    model_class(__UpperCAmelCase						)
        self.assertIsInstance(model.get_input_embeddings()  ,					(nn.Module)						)
        a   :	Optional[Any]						=    model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__UpperCAmelCase  ,					nn.Linear						)						)
  def 							lowercase_     (						self						)				->					Optional[Any]:
     a   :	Union[str, Any]						=    self.model_tester.prepare_config_and_inputs_for_common()

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

        a   :	int						=    ['pixel_values']
        self.assertListEqual(arg_names[:1]  ,					__UpperCAmelCase						)
  def 							lowercase_     (						self						)				->					List[Any]:
     a   :	Union[str, Any]						=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__UpperCAmelCase						)
  def 							lowercase_     (						self						)				->					Any:
     a   :	str						=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*__UpperCAmelCase						)
  def 							lowercase_     (						self						)				->					str:
     a   :	Dict						=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase						)
  def 							lowercase_     (						self						)				->					Union[str, Any]:







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

        a   :	int						=    self.model_tester.prepare_config_and_inputs_for_common()
        a   :	List[Any]						=    True

        if model_class in get_values(__UpperCAmelCase						):
           continue

        a   :	str						=    model_class(__UpperCAmelCase						)
        model.to(__UpperCAmelCase						)
        model.train()
        a   :	Optional[int]						=    self._prepare_for_class(__UpperCAmelCase  ,					__UpperCAmelCase  ,					return_labels=__UpperCAmelCase						)
        a   :	List[Any]						=    model(**__UpperCAmelCase						).loss
        loss.backward()
  def 							lowercase_     (						self						)				->					Dict:







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

        a   :	Optional[int]						=    self.model_tester.prepare_config_and_inputs_for_common()
        a   :	Optional[int]						=    False
        a   :	Optional[Any]						=    True

        if model_class in get_values(__UpperCAmelCase						) or not model_class.supports_gradient_checkpointing:
           continue
        a   :	int						=    model_class(__UpperCAmelCase						)
        model.to(__UpperCAmelCase						)
        model.gradient_checkpointing_enable()
        model.train()
        a   :	Optional[Any]						=    self._prepare_for_class(__UpperCAmelCase  ,					__UpperCAmelCase  ,					return_labels=__UpperCAmelCase						)
        a   :	Dict						=    model(**__UpperCAmelCase						).loss
        loss.backward()
  def 							lowercase_     (						self						)				->					List[Any]:
     a   :	Optional[Any]						=    self.model_tester.prepare_config_and_inputs_for_common()

     a   :	Tuple						=    _config_zero_init(__UpperCAmelCase						)
     for model_class in self.all_model_classes:
        a   :	Union[str, Any]						=    model_class(config=__UpperCAmelCase						)
        # Skip the check for the backbone
        a   :	Tuple						=    []
        for name, module in model.named_modules():
           if module.__class__.__name__ == "DPTViTHybridEmbeddings":
              a   :	Dict						=    [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 							lowercase_     (						self						)				->					str:
     pass






  @slow
  def 							lowercase_     (						self						)				->					int:







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




def        A_					(					)     ->     Dict:
   a   :	Optional[Any]						=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
   return image


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





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Optional[int]:
     a   :	List[Any]						=    DPTImageProcessor.from_pretrained('Intel/dpt-large'						)
     a   :	Any						=    DPTForDepthEstimation.from_pretrained('Intel/dpt-large'						).to(__UpperCAmelCase						)

     a   :	int						=    prepare_img()
     a   :	Optional[int]						=    image_processor(images=__UpperCAmelCase  ,					return_tensors='pt'						).to(__UpperCAmelCase						)

     # forward pass
     with torch.no_grad():
        a   :	List[str]						=    model(**__UpperCAmelCase						)
        a   :	str						=    outputs.predicted_depth

     # verify the predicted depth
     a   :	Optional[Any]						=    torch.Size((1, 3_84, 3_84)						)
     self.assertEqual(predicted_depth.shape  ,					__UpperCAmelCase						)

     a   :	List[Any]						=    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 							lowercase_     (						self						)				->					str:
     a   :	Any						=    DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'						)
     a   :	List[Any]						=    DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'						).to(__UpperCAmelCase						)

     a   :	Tuple						=    prepare_img()
     a   :	Optional[Any]						=    image_processor(images=__UpperCAmelCase  ,					return_tensors='pt'						).to(__UpperCAmelCase						)

     # forward pass
     with torch.no_grad():
        a   :	Union[str, Any]						=    model(**__UpperCAmelCase						)

     # verify the logits
     a   :	int						=    torch.Size((1, 1_50, 4_80, 4_80)						)
     self.assertEqual(outputs.logits.shape  ,					__UpperCAmelCase						)

     a   :	int						=    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 							lowercase_     (						self						)				->					List[Any]:
     a   :	Union[str, Any]						=    DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'						)
     a   :	List[Any]						=    DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'						).to(__UpperCAmelCase						)

     a   :	Any						=    prepare_img()
     a   :	Union[str, Any]						=    image_processor(images=__UpperCAmelCase  ,					return_tensors='pt'						).to(__UpperCAmelCase						)

     # forward pass
     with torch.no_grad():
        a   :	List[str]						=    model(**__UpperCAmelCase						)

     a   :	Any						=    outputs.logits.detach().cpu()

     a   :	Optional[Any]						=    image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase  ,					target_sizes=[(5_00, 3_00)]						)
     a   :	List[Any]						=    torch.Size((5_00, 3_00)						)
     self.assertEqual(segmentation[0].shape  ,					__UpperCAmelCase						)

     a   :	Optional[Any]						=    image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase						)
     a   :	Optional[Any]						=    torch.Size((4_80, 4_80)						)
     self.assertEqual(segmentation[0].shape  ,					__UpperCAmelCase						)










'''simple docstring'''







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE = 1000     ):
		return sum(i for i in range(SCREAMING_SNAKE_CASE     ) if i % 3 == 0 or i % 5 == 0     )


if __name__ == "__main__":
							print(f"""{solution() = }""")
