





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__ :







     def __init__(			self						,	A						,	A=2						,	A=32						,	A=16						,	A=3						,	A=True						,	A=True						,	A=32						,	A=4						,	A=[0, 1, 2, 3]						,	A=4						,	A=37						,	A="gelu"						,	A=0.1						,	A=0.1						,	A=0.0_2						,	A=3						,	A=False						,	A=None						,	)						->					Union[str, Any]:



          '''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				)						->					List[str]:



          '''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				)						->					Optional[Any]:



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







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A				)						->					List[Any]:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A				)						->					Any:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A				)						->					str:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self				)						->					Any:



          '''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     a__ (      UpperCamelCase__     ,      UpperCamelCase__     ,      unittest.TestCase   ):
     a	:    str	      =			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     a	:    Any	      =			(
         {
             '''depth-estimation''': DPTForDepthEstimation,
             '''feature-extraction''': DPTModel,
             '''image-segmentation''': DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     a	:    Tuple	      =			False
     a	:    Any	      =			False
     a	:    Tuple	      =			False







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          a											=   DPTModelTester(self				)
          a											=   ConfigTester(self						,	config_class=a_						,	has_text_modality=a_						,	hidden_size=37				)







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



          '''simple docstring'''



          self.config_tester.run_common_tests()







     @unittest.skip(reason="DPT does not use inputs_embeds"				)
     def     lowerCAmelCase_       (			self				)						->					Dict:



          '''simple docstring'''



          pass







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          a						,   a											=   self.model_tester.prepare_config_and_inputs_for_common()

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







     def     lowerCAmelCase_       (			self				)						->					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(a_				)
               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]						,	a_				)







     def     lowerCAmelCase_       (			self				)						->					str:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*a_				)







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



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*a_				)







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



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*a_				)







     def     lowerCAmelCase_       (			self				)						->					str:



          '''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(a_				):
                    continue

               a											=   model_class(a_				)
               model.to(a_				)
               model.train()
               a											=   self._prepare_for_class(a_						,	a_						,	return_labels=a_				)
               a											=   model(**a_				).loss
               loss.backward()







     def     lowerCAmelCase_       (			self				)						->					List[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(a_				) or not model_class.supports_gradient_checkpointing:
                    continue
               a											=   model_class(a_				)
               model.to(a_				)
               model.gradient_checkpointing_enable()
               model.train()
               a											=   self._prepare_for_class(a_						,	a_						,	return_labels=a_				)
               a											=   model(**a_				).loss
               loss.backward()







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



          '''simple docstring'''



          a						,   a											=   self.model_tester.prepare_config_and_inputs_for_common()

          a											=   _config_zero_init(a_				)
          for model_class in self.all_model_classes:
               a											=   model_class(config=a_				)
               # 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				)						->					Union[str, Any]:



          '''simple docstring'''



          pass







     @slow
     def     lowerCAmelCase_       (			self				)						->					Dict:



          '''simple docstring'''



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







def       SCREAMING_SNAKE_CASE							(     )    ->  List[Any]:
     a											=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     return image





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







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



          '''simple docstring'''



          a											=   DPTImageProcessor.from_pretrained("Intel/dpt-large"				)
          a											=   DPTForDepthEstimation.from_pretrained("Intel/dpt-large"				).to(a_				)

          a											=   prepare_img()
          a											=   image_processor(images=a_						,	return_tensors="pt"				).to(a_				)

          # forward pass
          with torch.no_grad():
               a											=   model(**a_				)
               a											=   outputs.predicted_depth

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

          a											=   torch.tensor(
              [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]				).to(a_				)

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







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          a											=   DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"				)
          a											=   DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"				).to(a_				)

          a											=   prepare_img()
          a											=   image_processor(images=a_						,	return_tensors="pt"				).to(a_				)

          # forward pass
          with torch.no_grad():
               a											=   model(**a_				)

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

          a											=   torch.tensor(
              [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]				).to(a_				)

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







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



          '''simple docstring'''



          a											=   DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"				)
          a											=   DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"				).to(a_				)

          a											=   prepare_img()
          a											=   image_processor(images=a_						,	return_tensors="pt"				).to(a_				)

          # forward pass
          with torch.no_grad():
               a											=   model(**a_				)

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

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

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







import safs




class     a__ (      safs.SaFileSystem   ):
     pass

