
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     _SCREAMING_SNAKE_CASE							:





				def __init__(		self :		List[Any]					,      snake_case_ :		List[str]					,      snake_case_ :		Any=2					,      snake_case_ :		Tuple=32					,      snake_case_ :		str=16					,      snake_case_ :		List[str]=3					,      snake_case_ :		Dict=True					,      snake_case_ :		Optional[int]=True					,      snake_case_ :		Optional[Any]=32					,      snake_case_ :		int=4					,      snake_case_ :		Union[str, Any]=[0, 1, 2, 3]					,      snake_case_ :		Tuple=4					,      snake_case_ :		Any=37					,      snake_case_ :		Union[str, Any]="gelu"					,      snake_case_ :		str=0.1					,      snake_case_ :		Any=0.1					,      snake_case_ :		Tuple=0.02					,      snake_case_ :		Dict=3					,      snake_case_ :		Union[str, Any]=False					,      snake_case_ :		Union[str, Any]=None					,      ):


								"""simple docstring"""

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





				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								A     :  List[str]   =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

								A     :  List[str]   =				self.get_config()

								return config, pixel_values, labels





				def        _UpperCAmelCase						(		self :		List[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=lowerCAmelCase_					,      initializer_range=self.initializer_range					,      is_hybrid=self.is_hybrid					,      )





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		Optional[int]					,      snake_case_ :		Tuple					,      snake_case_ :		str							):


								"""simple docstring"""

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





				def        _UpperCAmelCase						(		self :		Tuple					,      snake_case_ :		Union[str, Any]					,      snake_case_ :		Optional[int]					,      snake_case_ :		Optional[Any]							):


								"""simple docstring"""

								A     :  Tuple   =				self.num_labels
								A     :  str   =				DPTForDepthEstimation(lowerCAmelCase_							)
								model.to(lowerCAmelCase_							)
								model.eval()
								A     :  Union[str, Any]   =				model(lowerCAmelCase_							)
								self.parent.assertEqual(result.predicted_depth.shape					,      (self.batch_size, self.image_size, self.image_size)							)





				def        _UpperCAmelCase						(		self :		Tuple					,      snake_case_ :		str					,      snake_case_ :		Any					,      snake_case_ :		Union[str, Any]							):


								"""simple docstring"""

								A     :  Optional[int]   =				self.num_labels
								A     :  Any   =				DPTForSemanticSegmentation(lowerCAmelCase_							)
								model.to(lowerCAmelCase_							)
								model.eval()
								A     :  Union[str, Any]   =				model(lowerCAmelCase_					,      labels=lowerCAmelCase_							)
								self.parent.assertEqual(
								    result.logits.shape					,      (self.batch_size, self.num_labels, self.image_size, self.image_size)							)






				def        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								A     :  int   =				self.prepare_config_and_inputs()
								A ,       A ,       A     :  Optional[Any]   =				config_and_inputs
								A     :  str   =				{'''pixel_values''': pixel_values}
								return config, inputs_dict



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

				lowerCamelCase_       =     False
				lowerCamelCase_       =     False
				lowerCamelCase_       =     False





				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

								A     :  Optional[Any]   =				DPTModelTester(self							)
								A     :  Tuple   =				ConfigTester(self					,      config_class=lowerCAmelCase_					,      has_text_modality=lowerCAmelCase_					,      hidden_size=37							)





				def        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								self.config_tester.run_common_tests()





				@unittest.skip(reason='''DPT does not use inputs_embeds'''							)
				def        _UpperCAmelCase						(		self :		Dict							):


								"""simple docstring"""

								pass





				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

								A ,       A     :  Dict   =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												A     :  List[Any]   =				model_class(lowerCAmelCase_							)
												self.assertIsInstance(model.get_input_embeddings()					,      (nn.Module)							)
												A     :  int   =				model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(lowerCAmelCase_					,      nn.Linear							)							)





				def        _UpperCAmelCase						(		self :		Union[str, Any]							):


								"""simple docstring"""

								A ,       A     :  List[str]   =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												A     :  Union[str, Any]   =				model_class(lowerCAmelCase_							)
												A     :  int   =				inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												A     :  Tuple   =				[*signature.parameters.keys()]

												A     :  List[Any]   =				['''pixel_values''']
												self.assertListEqual(arg_names[:1]					,      lowerCAmelCase_							)





				def        _UpperCAmelCase						(		self :		Dict							):


								"""simple docstring"""

								A     :  Union[str, Any]   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase_							)





				def        _UpperCAmelCase						(		self :		Optional[int]							):


								"""simple docstring"""

								A     :  List[str]   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase_							)





				def        _UpperCAmelCase						(		self :		Any							):


								"""simple docstring"""

								A     :  Any   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_							)





				def        _UpperCAmelCase						(		self :		Optional[int]							):


								"""simple docstring"""

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

												A ,       A     :  Tuple   =				self.model_tester.prepare_config_and_inputs_for_common()
												A     :  str   =				True

												if model_class in get_values(lowerCAmelCase_							):
																continue

												A     :  List[Any]   =				model_class(lowerCAmelCase_							)
												model.to(lowerCAmelCase_							)
												model.train()
												A     :  Any   =				self._prepare_for_class(lowerCAmelCase_					,      lowerCAmelCase_					,      return_labels=lowerCAmelCase_							)
												A     :  Dict   =				model(**lowerCAmelCase_							).loss
												loss.backward()





				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

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

												A ,       A     :  Tuple   =				self.model_tester.prepare_config_and_inputs_for_common()
												A     :  List[str]   =				False
												A     :  Tuple   =				True

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





				def        _UpperCAmelCase						(		self :		Dict							):


								"""simple docstring"""

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

								A     :  Union[str, Any]   =				_config_zero_init(lowerCAmelCase_							)
								for model_class in self.all_model_classes:
												A     :  Optional[Any]   =				model_class(config=lowerCAmelCase_							)
												# Skip the check for the backbone
												A     :  str   =				[]
												for name, module in model.named_modules():
																if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																				A     :  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        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								pass






				@slow
				def        _UpperCAmelCase						(		self :		Union[str, Any]							):


								"""simple docstring"""

								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A     :  Union[str, Any]   =				DPTModel.from_pretrained(lowerCAmelCase_							)
												self.assertIsNotNone(lowerCAmelCase_							)





def 							_lowerCamelCase			(					):



				'''simple docstring'''







				A     :  Dict   =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
				return image






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





				def        _UpperCAmelCase						(		self :		Union[str, Any]							):


								"""simple docstring"""

								A     :  List[str]   =				DPTImageProcessor.from_pretrained('''Intel/dpt-large'''							)
								A     :  Optional[int]   =				DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''							).to(lowerCAmelCase_							)

								A     :  Optional[Any]   =				prepare_img()
								A     :  int   =				image_processor(images=lowerCAmelCase_					,      return_tensors='''pt'''							).to(lowerCAmelCase_							)

								# forward pass
								with torch.no_grad():
												A     :  Dict   =				model(**lowerCAmelCase_							)
												A     :  Optional[int]   =				outputs.predicted_depth

								# verify the predicted depth
								A     :  Union[str, Any]   =				torch.Size((1, 384, 384)							)
								self.assertEqual(predicted_depth.shape					,      lowerCAmelCase_							)

								A     :  Dict   =				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        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								A     :  Optional[int]   =				DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''							)
								A     :  int   =				DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''							).to(lowerCAmelCase_							)

								A     :  List[str]   =				prepare_img()
								A     :  Union[str, Any]   =				image_processor(images=lowerCAmelCase_					,      return_tensors='''pt'''							).to(lowerCAmelCase_							)

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

								# verify the logits
								A     :  Tuple   =				torch.Size((1, 150, 480, 480)							)
								self.assertEqual(outputs.logits.shape					,      lowerCAmelCase_							)

								A     :  Dict   =				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        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								A     :  str   =				DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''							)
								A     :  int   =				DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''							).to(lowerCAmelCase_							)

								A     :  List[str]   =				prepare_img()
								A     :  Optional[int]   =				image_processor(images=lowerCAmelCase_					,      return_tensors='''pt'''							).to(lowerCAmelCase_							)

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

								A     :  Union[str, Any]   =				outputs.logits.detach().cpu()

								A     :  str   =				image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_					,      target_sizes=[(500, 300)]							)
								A     :  Dict   =				torch.Size((500, 300)							)
								self.assertEqual(segmentation[0].shape					,      lowerCAmelCase_							)

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

def 							_lowerCamelCase			(					lowerCamelCase_:  int		):



				'''simple docstring'''







				if not isinstance(lowerCamelCase_				,	lowerCamelCase_		):
								raise ValueError('''double_factorial() only accepts integral values'''		)
				if num < 0:
								raise ValueError('''double_factorial() not defined for negative values'''		)
				A     :  int   =				1
				for i in range(lowerCamelCase_				,	0				,	-2		):
								value *= i
				return value


if __name__ == "__main__":
					import doctest

					doctest.testmod()