
'''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       ,     lowerCamelCase__       ,     lowerCamelCase__=2       ,     lowerCamelCase__=32       ,     lowerCamelCase__=16       ,     lowerCamelCase__=3       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=32       ,     lowerCamelCase__=4       ,     lowerCamelCase__=[0, 1, 2, 3]       ,     lowerCamelCase__=4       ,     lowerCamelCase__=37       ,     lowerCamelCase__="gelu"       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=3       ,     lowerCamelCase__=False       ,     lowerCamelCase__=None       ,     )			->		str:




									'''simple docstring'''



									lowercase__							 =       parent
									lowercase__							 =       batch_size
									lowercase__							 =       image_size
									lowercase__							 =       patch_size
									lowercase__							 =       num_channels
									lowercase__							 =       is_training
									lowercase__							 =       use_labels
									lowercase__							 =       hidden_size
									lowercase__							 =       num_hidden_layers
									lowercase__							 =       backbone_out_indices
									lowercase__							 =       num_attention_heads
									lowercase__							 =       intermediate_size
									lowercase__							 =       hidden_act
									lowercase__							 =       hidden_dropout_prob
									lowercase__							 =       attention_probs_dropout_prob
									lowercase__							 =       initializer_range
									lowercase__							 =       num_labels
									lowercase__							 =       scope
									lowercase__							 =       is_hybrid
									# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
									lowercase__							 =       (image_size // patch_size) ** 2
									lowercase__							 =       num_patches + 1



			def A__ (					self       )			->		int:




									'''simple docstring'''



									lowercase__							 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

									lowercase__							 =       self.get_config()

									return config, pixel_values, labels



			def A__ (					self       )			->		Dict:




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



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		str:




									'''simple docstring'''



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



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		int:




									'''simple docstring'''



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



			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		str:




									'''simple docstring'''



									lowercase__							 =       self.num_labels
									lowercase__							 =       DPTForSemanticSegmentation(lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()
									lowercase__							 =       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       )			->		str:




									'''simple docstring'''



									lowercase__							 =       self.prepare_config_and_inputs()
									lowercase__ ,						lowercase__ ,						lowercase__							 =       config_and_inputs
									lowercase__							 =       {"""pixel_values""": pixel_values}
									return config, inputs_dict





@require_torch
class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
			lowerCamelCase							:							Union[str, Any]       		=  (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
			lowerCamelCase							:							List[Any]       		=  (
			    {
			        """depth-estimation""": DPTForDepthEstimation,
			        """feature-extraction""": DPTModel,
			        """image-segmentation""": DPTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase							:							Any       		=  False
			lowerCamelCase							:							Any       		=  False
			lowerCamelCase							:							Optional[int]       		=  False



			def A__ (					self       )			->		int:




									'''simple docstring'''



									lowercase__							 =       DPTModelTester(self       )
									lowercase__							 =       ConfigTester(self       ,     config_class=lowerCamelCase__       ,     has_text_modality=lowerCamelCase__       ,     hidden_size=37       )



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




									'''simple docstring'''



									self.config_tester.run_common_tests()



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




									'''simple docstring'''



									pass



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()

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



			def A__ (					self       )			->		int:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()

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

															lowercase__							 =       ["""pixel_values"""]
															self.assertListEqual(arg_names[:1]       ,     lowerCamelCase__       )



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




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__       )



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




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__       )



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




									'''simple docstring'''



									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__       )



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




									'''simple docstring'''



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

															lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
															lowercase__							 =       True

															if model_class in get_values(lowerCamelCase__       ):
																					continue

															lowercase__							 =       model_class(lowerCamelCase__       )
															model.to(lowerCamelCase__       )
															model.train()
															lowercase__							 =       self._prepare_for_class(lowerCamelCase__       ,     lowerCamelCase__       ,     return_labels=lowerCamelCase__       )
															lowercase__							 =       model(**lowerCamelCase__       ).loss
															loss.backward()



			def A__ (					self       )			->		int:




									'''simple docstring'''



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

															lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()
															lowercase__							 =       False
															lowercase__							 =       True

															if model_class in get_values(lowerCamelCase__       ) or not model_class.supports_gradient_checkpointing:
																					continue
															lowercase__							 =       model_class(lowerCamelCase__       )
															model.to(lowerCamelCase__       )
															model.gradient_checkpointing_enable()
															model.train()
															lowercase__							 =       self._prepare_for_class(lowerCamelCase__       ,     lowerCamelCase__       ,     return_labels=lowerCamelCase__       )
															lowercase__							 =       model(**lowerCamelCase__       ).loss
															loss.backward()



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




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.model_tester.prepare_config_and_inputs_for_common()

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




									'''simple docstring'''



									pass



			@slow
			def A__ (					self       )			->		Optional[Any]:




									'''simple docstring'''



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





def  _A		(							):
						lowercase__							 =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
						return image







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



			def A__ (					self       )			->		int:




									'''simple docstring'''



									lowercase__							 =       DPTImageProcessor.from_pretrained("""Intel/dpt-large"""       )
									lowercase__							 =       DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""       ).to(lowerCamelCase__       )

									lowercase__							 =       prepare_img()
									lowercase__							 =       image_processor(images=lowerCamelCase__       ,     return_tensors="""pt"""       ).to(lowerCamelCase__       )

									# forward pass
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
															lowercase__							 =       outputs.predicted_depth

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

									lowercase__							 =       torch.tensor(
									    [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]       ).to(lowerCamelCase__       )

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



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__							 =       DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""       )
									lowercase__							 =       DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""       ).to(lowerCamelCase__       )

									lowercase__							 =       prepare_img()
									lowercase__							 =       image_processor(images=lowerCamelCase__       ,     return_tensors="""pt"""       ).to(lowerCamelCase__       )

									# forward pass
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )

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

									lowercase__							 =       torch.tensor(
									    [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]       ).to(lowerCamelCase__       )

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



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




									'''simple docstring'''



									lowercase__							 =       DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""       )
									lowercase__							 =       DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""       ).to(lowerCamelCase__       )

									lowercase__							 =       prepare_img()
									lowercase__							 =       image_processor(images=lowerCamelCase__       ,     return_tensors="""pt"""       ).to(lowerCamelCase__       )

									# forward pass
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )

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

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

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

'''simple docstring'''



def  _A		(							lowercase__	):

						# validate:
						if not isinstance(lowercase__		,		lowercase__	) or not all(
						    isinstance(lowercase__		,		lowercase__	) and item >= 0 for item in citations	):
												raise ValueError("""The citations should be a list of non negative integers."""	)

						citations.sort()
						lowercase__							 =       len(lowercase__	)

						for i in range(lowercase__	):
												if citations[len_citations - 1 - i] <= i:
																		return i

						return len_citations


if __name__ == "__main__":
				import doctest

				doctest.testmod()






