





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					,  lowercase					,  lowercase=2					,  lowercase=32					,  lowercase=16					,  lowercase=3					,  lowercase=True					,  lowercase=True					,  lowercase=32					,  lowercase=4					,  lowercase=[0, 1, 2, 3]					,  lowercase=4					,  lowercase=37					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=0.02					,  lowercase=3					,  lowercase=[1, 384, 24, 24]					,  lowercase=True					,  lowercase=None					,  )			->						Dict:







          '''simple docstring'''

          a__						:	List[str] =				parent
          a__						:	Optional[Any] =				batch_size
          a__						:	Optional[int] =				image_size
          a__						:	List[Any] =				patch_size
          a__						:	List[Any] =				num_channels
          a__						:	Any =				is_training
          a__						:	Dict =				use_labels
          a__						:	Dict =				hidden_size
          a__						:	Tuple =				num_hidden_layers
          a__						:	int =				backbone_out_indices
          a__						:	int =				num_attention_heads
          a__						:	Any =				intermediate_size
          a__						:	Optional[Any] =				hidden_act
          a__						:	Tuple =				hidden_dropout_prob
          a__						:	Any =				attention_probs_dropout_prob
          a__						:	int =				initializer_range
          a__						:	Optional[int] =				num_labels
          a__						:	int =				backbone_featmap_shape
          a__						:	Dict =				scope
          a__						:	Dict =				is_hybrid
          # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
          a__						:	List[str] =				(image_size // patch_size) ** 2
          a__						:	str =				num_patches + 1

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Optional[Any] =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	int =				None
          if self.use_labels:
                 a__						:	Union[str, Any] =				ids_tensor([self.batch_size, self.image_size, self.image_size]					,  self.num_labels)

          a__						:	str =				self.get_config()

          return config, pixel_values, labels

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







          '''simple docstring'''

          a__						:	List[str] =				{
              '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=lowercase					,  initializer_range=self.initializer_range					,  is_hybrid=self.is_hybrid					,  backbone_config=lowercase					,  backbone_featmap_shape=self.backbone_featmap_shape					,  )

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







          '''simple docstring'''

          a__						:	Union[str, Any] =				DPTModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	str =				model(lowercase)
          self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						str:







          '''simple docstring'''

          a__						:	str =				self.num_labels
          a__						:	Dict =				DPTForDepthEstimation(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Dict =				model(lowercase)
          self.parent.assertEqual(result.predicted_depth.shape					,  (self.batch_size, self.image_size, self.image_size))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	List[Any] =				self.num_labels
          a__						:	str =				DPTForSemanticSegmentation(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase					,  labels=lowercase)
          self.parent.assertEqual(
              result.logits.shape					,  (self.batch_size, self.num_labels, self.image_size, self.image_size))

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







          '''simple docstring'''

          a__						:	Any =				self.prepare_config_and_inputs()
          a__ ,							a__ ,							a__						:	Any =				config_and_inputs
          a__						:	Tuple =				{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



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

   __A   :				str         =  False
   __A   :				Optional[int]         =  False
   __A   :				int         =  False

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[Any] =				DPTModelTester(self)
          a__						:	Optional[int] =				ConfigTester(self					,  config_class=lowercase					,  has_text_modality=lowercase					,  hidden_size=37)

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







          '''simple docstring'''

          self.config_tester.run_common_tests()

   @unittest.skip(reason='DPT does not use inputs_embeds')
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__ ,							a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	Tuple =				model_class(lowercase)
                 self.assertIsInstance(model.get_input_embeddings()					,  (nn.Module))
                 a__						:	Optional[int] =				model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(lowercase					,  nn.Linear))

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







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				self.model_tester.prepare_config_and_inputs_for_common()

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

                 a__						:	Optional[Any] =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

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







          '''simple docstring'''

          a__						:	List[Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	int =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Tuple =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*lowercase)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

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

                 a__ ,							a__						:	Optional[Any] =				self.model_tester.prepare_config_and_inputs_for_common()
                 a__						:	Tuple =				True

                 if model_class in get_values(lowercase):
                        continue

                 a__						:	Optional[int] =				model_class(lowercase)
                 model.to(lowercase)
                 model.train()
                 a__						:	List[str] =				self._prepare_for_class(lowercase					,  lowercase					,  return_labels=lowercase)
                 a__						:	Optional[Any] =				model(**lowercase).loss
                 loss.backward()

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

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

                 a__ ,							a__						:	List[Any] =				self.model_tester.prepare_config_and_inputs_for_common()
                 a__						:	Optional[Any] =				False
                 a__						:	Any =				True

                 if model_class in get_values(lowercase) or not model_class.supports_gradient_checkpointing:
                        continue
                 a__						:	Tuple =				model_class(lowercase)
                 model.to(lowercase)
                 model.gradient_checkpointing_enable()
                 model.train()
                 a__						:	List[Any] =				self._prepare_for_class(lowercase					,  lowercase					,  return_labels=lowercase)
                 a__						:	Any =				model(**lowercase).loss
                 loss.backward()

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				self.model_tester.prepare_config_and_inputs_for_common()

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







          '''simple docstring'''

          pass

   @slow
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                 a__						:	Dict =				DPTModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__ ,							a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs_for_common()
          a__						:	Optional[int] =				'add'
          with self.assertRaises(lowercase):
                 a__						:	str =				DPTForDepthEstimation(lowercase)



def A_	(		)  ->		Any:
       a__						:	Tuple =				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)			->						List[str]:







          '''simple docstring'''

          a__						:	str =				DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas')
          a__						:	List[Any] =				DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas').to(lowercase)

          a__						:	Dict =				prepare_img()
          a__						:	Any =				image_processor(images=lowercase					,  return_tensors='pt').to(lowercase)

          # forward pass
          with torch.no_grad():
                 a__						:	str =				model(**lowercase)
                 a__						:	List[Any] =				outputs.predicted_depth

          # verify the predicted depth
          a__						:	Optional[int] =				torch.Size((1, 384, 384))
          self.assertEqual(predicted_depth.shape					,  lowercase)

          a__						:	Optional[Any] =				torch.tensor(
              [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]).to(lowercase)

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









from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase      :       Any									=  TypeVar("""_T""")





class  A__    (							Generic[_T]	):




   """simple docstring"""



   def __init__(					self					,  lowercase = None)			->						None:







          '''simple docstring'''

          a__						:	list[_T] =				list(iterable or [])
          a__						:	list[_T] =				[]

   def __len__(					self)			->						int:







          '''simple docstring'''

          return len(self._stacka) + len(self._stacka)

   def __repr__(					self)			->						str:







          '''simple docstring'''

          return F'Queue({tuple(self._stacka[::-1] + self._stacka)})'

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          self._stacka.append(lowercase)

   def 							__lowercase		(					self)			->						_T:







          '''simple docstring'''

          a__						:	List[str] =				self._stacka.pop
          a__						:	Optional[int] =				self._stacka.append

          if not self._stacka:
                 while self._stacka:
                        stacka_append(stacka_pop())

          if not self._stacka:
                 raise IndexError('Queue is empty')
          return self._stacka.pop()


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

    testmod()


