



'''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  :





		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 ,       )       ->     List[Any]:

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





		def 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

					__UpperCAmelCase   :      str										=     self.get_config()

					return config, pixel_values, labels





		def 							__A       (     self				)       ->     int:

					'''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 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     DPTModel(config=__UpperCAmelCase				)
					model.to(__UpperCAmelCase				)
					model.eval()
					__UpperCAmelCase   :      List[Any]										=     model(__UpperCAmelCase				)
					self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size)				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     self.num_labels
					__UpperCAmelCase   :      Optional[int]										=     DPTForDepthEstimation(__UpperCAmelCase				)
					model.to(__UpperCAmelCase				)
					model.eval()
					__UpperCAmelCase   :      List[Any]										=     model(__UpperCAmelCase				)
					self.parent.assertEqual(result.predicted_depth.shape ,       (self.batch_size, self.image_size, self.image_size)				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     self.num_labels
					__UpperCAmelCase   :      Optional[int]										=     DPTForSemanticSegmentation(__UpperCAmelCase				)
					model.to(__UpperCAmelCase				)
					model.eval()
					__UpperCAmelCase   :      List[str]										=     model(__UpperCAmelCase ,       labels=__UpperCAmelCase				)
					self.parent.assertEqual(
					    result.logits.shape ,       (self.batch_size, self.num_labels, self.image_size, self.image_size)				)





		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     self.prepare_config_and_inputs()
					__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      int										=     config_and_inputs
					__UpperCAmelCase   :      Any										=     {"""pixel_values""": pixel_values}
					return config, inputs_dict



@require_torch
class _A  (    __SCREAMING_SNAKE_CASE	,				__SCREAMING_SNAKE_CASE	,				unittest.TestCase    ):
		_SCREAMING_SNAKE_CASE					:    int						      =			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
		_SCREAMING_SNAKE_CASE					:    Any						      =			(
		    {
		        "depth-estimation": DPTForDepthEstimation,
		        "feature-extraction": DPTModel,
		        "image-segmentation": DPTForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		_SCREAMING_SNAKE_CASE					:    int						      =			False
		_SCREAMING_SNAKE_CASE					:    Optional[Any]						      =			False
		_SCREAMING_SNAKE_CASE					:    Dict						      =			False





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

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     DPTModelTester(self				)
					__UpperCAmelCase   :      Optional[Any]										=     ConfigTester(self ,       config_class=__UpperCAmelCase ,       has_text_modality=__UpperCAmelCase ,       hidden_size=37				)





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					self.config_tester.run_common_tests()





		@unittest.skip(reason="""DPT does not use inputs_embeds"""				)
		def 							__A       (     self				)       ->     List[Any]:

					'''simple docstring'''
					pass





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

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      List[str]										=     self.model_tester.prepare_config_and_inputs_for_common()

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





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

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      Any										=     self.model_tester.prepare_config_and_inputs_for_common()

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

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





		def 							__A       (     self				)       ->     str:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__UpperCAmelCase				)





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

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_depth_estimation(*__UpperCAmelCase				)





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

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase				)





		def 							__A       (     self				)       ->     Tuple:

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

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

								if model_class in get_values(__UpperCAmelCase				):
											continue

								__UpperCAmelCase   :      Tuple										=     model_class(__UpperCAmelCase				)
								model.to(__UpperCAmelCase				)
								model.train()
								__UpperCAmelCase   :      Optional[Any]										=     self._prepare_for_class(__UpperCAmelCase ,       __UpperCAmelCase ,       return_labels=__UpperCAmelCase				)
								__UpperCAmelCase   :      Optional[int]										=     model(**__UpperCAmelCase				).loss
								loss.backward()





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

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

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

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





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

					'''simple docstring'''
					__UpperCAmelCase				,  __UpperCAmelCase   :      List[str]										=     self.model_tester.prepare_config_and_inputs_for_common()

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

					'''simple docstring'''
					pass





		@slow
		def 							__A       (     self				)       ->     Union[str, Any]:

					'''simple docstring'''
					for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__UpperCAmelCase   :      Union[str, Any]										=     DPTModel.from_pretrained(__UpperCAmelCase				)
								self.assertIsNotNone(__UpperCAmelCase				)


def   lowercase_	(   ):

			"""simple docstring"""





			__UpperCAmelCase   :      List[Any]										=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
			return image






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





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

					'''simple docstring'''
					__UpperCAmelCase   :      Dict										=     DPTImageProcessor.from_pretrained("""Intel/dpt-large"""				)
					__UpperCAmelCase   :      str										=     DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""				).to(__UpperCAmelCase				)

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

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

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

					__UpperCAmelCase   :      int										=     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 							__A       (     self				)       ->     Any:

					'''simple docstring'''
					__UpperCAmelCase   :      Tuple										=     DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""				)
					__UpperCAmelCase   :      Any										=     DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""				).to(__UpperCAmelCase				)

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

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

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

					__UpperCAmelCase   :      Any										=     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 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""				)
					__UpperCAmelCase   :      Dict										=     DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""				).to(__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[Any]										=     prepare_img()
					__UpperCAmelCase   :      Dict										=     image_processor(images=__UpperCAmelCase ,       return_tensors="""pt"""				).to(__UpperCAmelCase				)

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

					__UpperCAmelCase   :      Optional[Any]										=     outputs.logits.detach().cpu()

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

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









'''simple docstring'''
from math import pi


def   lowercase_	(   lowerCAmelCase__	:  float ):

			"""simple docstring"""





			return degree / (180 / pi)


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

					testmod()




