


"""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  UpperCamelCase_	:

	"""simple docstring"""





	def __init__( self			:				Union[str, Any]			,			UpperCAmelCase__			:				Optional[Any]			,			UpperCAmelCase__			:				Union[str, Any]=2			,			UpperCAmelCase__			:				Optional[Any]=3_2			,			UpperCAmelCase__			:				Dict=1_6			,			UpperCAmelCase__			:				Tuple=3			,			UpperCAmelCase__			:				str=True			,			UpperCAmelCase__			:				Union[str, Any]=True			,			UpperCAmelCase__			:				Any=3_2			,			UpperCAmelCase__			:				Tuple=4			,			UpperCAmelCase__			:				List[str]=[0, 1, 2, 3]			,			UpperCAmelCase__			:				Optional[Any]=4			,			UpperCAmelCase__			:				str=3_7			,			UpperCAmelCase__			:				Any="gelu"			,			UpperCAmelCase__			:				str=0.1			,			UpperCAmelCase__			:				Dict=0.1			,			UpperCAmelCase__			:				List[Any]=0.02			,			UpperCAmelCase__			:				int=3			,			UpperCAmelCase__			:				List[Any]=[1, 3_8_4, 2_4, 2_4]			,			UpperCAmelCase__			:				Dict=True			,			UpperCAmelCase__			:				Optional[int]=None			,			)       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				parent
						__SCREAMING_SNAKE_CASE						   =				batch_size
						__SCREAMING_SNAKE_CASE						   =				image_size
						__SCREAMING_SNAKE_CASE						   =				patch_size
						__SCREAMING_SNAKE_CASE						   =				num_channels
						__SCREAMING_SNAKE_CASE						   =				is_training
						__SCREAMING_SNAKE_CASE						   =				use_labels
						__SCREAMING_SNAKE_CASE						   =				hidden_size
						__SCREAMING_SNAKE_CASE						   =				num_hidden_layers
						__SCREAMING_SNAKE_CASE						   =				backbone_out_indices
						__SCREAMING_SNAKE_CASE						   =				num_attention_heads
						__SCREAMING_SNAKE_CASE						   =				intermediate_size
						__SCREAMING_SNAKE_CASE						   =				hidden_act
						__SCREAMING_SNAKE_CASE						   =				hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						   =				attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE						   =				initializer_range
						__SCREAMING_SNAKE_CASE						   =				num_labels
						__SCREAMING_SNAKE_CASE						   =				backbone_featmap_shape
						__SCREAMING_SNAKE_CASE						   =				scope
						__SCREAMING_SNAKE_CASE						   =				is_hybrid
						# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
						__SCREAMING_SNAKE_CASE						   =				(image_size // patch_size) ** 2
						__SCREAMING_SNAKE_CASE						   =				num_patches + 1




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

						__SCREAMING_SNAKE_CASE						   =				self.get_config()

						return config, pixel_values, labels




	def   UpperCAmelCase_      ( self			:				str   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				{
						    "global_padding": "same",
						    "layer_type": "bottleneck",
						    "depths": [3, 4, 9],
						    "out_features": ["stage1", "stage2", "stage3"],
						    "embedding_dynamic_padding": True,
						    "hidden_sizes": [9_6, 1_9_2, 3_8_4, 7_6_8],
						    "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=UpperCAmelCase__			,			initializer_range=self.initializer_range			,			is_hybrid=self.is_hybrid			,			backbone_config=UpperCAmelCase__			,			backbone_featmap_shape=self.backbone_featmap_shape			,			)




	def   UpperCAmelCase_      ( self			:				List[str]			,			UpperCAmelCase__			:				Optional[int]			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				Tuple   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				DPTModel(config=UpperCAmelCase__   )
						model.to(UpperCAmelCase__   )
						model.eval()
						__SCREAMING_SNAKE_CASE						   =				model(UpperCAmelCase__   )
						self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)   )




	def   UpperCAmelCase_      ( self			:				Any			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Dict   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				self.num_labels
						__SCREAMING_SNAKE_CASE						   =				DPTForDepthEstimation(UpperCAmelCase__   )
						model.to(UpperCAmelCase__   )
						model.eval()
						__SCREAMING_SNAKE_CASE						   =				model(UpperCAmelCase__   )
						self.parent.assertEqual(result.predicted_depth.shape			,			(self.batch_size, self.image_size, self.image_size)   )




	def   UpperCAmelCase_      ( self			:				int			,			UpperCAmelCase__			:				Any			,			UpperCAmelCase__			:				Optional[int]			,			UpperCAmelCase__			:				Union[str, Any]   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				self.num_labels
						__SCREAMING_SNAKE_CASE						   =				DPTForSemanticSegmentation(UpperCAmelCase__   )
						model.to(UpperCAmelCase__   )
						model.eval()
						__SCREAMING_SNAKE_CASE						   =				model(UpperCAmelCase__			,			labels=UpperCAmelCase__   )
						self.parent.assertEqual(
						    result.logits.shape			,			(self.batch_size, self.num_labels, self.image_size, self.image_size)   )




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				self.prepare_config_and_inputs()
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				config_and_inputs
						__SCREAMING_SNAKE_CASE						   =				{"pixel_values": pixel_values}
						return config, inputs_dict




@require_torch
class  UpperCamelCase_	( UpperCamelCase			,  UpperCamelCase			,  unittest.TestCase):

	"""simple docstring"""





	snake_case__				:  Union[str, Any]    					=			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	snake_case__				:  Any    					=			(
	    {
	        "depth-estimation": DPTForDepthEstimation,
	        "feature-extraction": DPTModel,
	        "image-segmentation": DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	snake_case__				:  Union[str, Any]    					=			False
	snake_case__				:  List[Any]    					=			False
	snake_case__				:  Optional[int]    					=			False




	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				DPTModelTester(self   )
						__SCREAMING_SNAKE_CASE						   =				ConfigTester(self			,			config_class=UpperCAmelCase__			,			has_text_modality=UpperCAmelCase__			,			hidden_size=3_7   )




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	List[Any]:
						self.config_tester.run_common_tests()




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




	def   UpperCAmelCase_      ( self			:				Any   )       ->	Dict:
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				self.model_tester.prepare_config_and_inputs_for_common()

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




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				self.model_tester.prepare_config_and_inputs_for_common()

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

											__SCREAMING_SNAKE_CASE						   =				["pixel_values"]
											self.assertListEqual(arg_names[:1]			,			UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_depth_estimation(*UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				int   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Any:
						for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
																continue

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

											if model_class in get_values(UpperCAmelCase__   ):
																continue

											__SCREAMING_SNAKE_CASE						   =				model_class(UpperCAmelCase__   )
											model.to(UpperCAmelCase__   )
											model.train()
											__SCREAMING_SNAKE_CASE						   =				self._prepare_for_class(UpperCAmelCase__			,			UpperCAmelCase__			,			return_labels=UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				model(**UpperCAmelCase__   ).loss
											loss.backward()




	def   UpperCAmelCase_      ( self			:				Dict   )       ->	str:
						for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
																continue

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

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




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	Dict:
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				self.model_tester.prepare_config_and_inputs_for_common()

						__SCREAMING_SNAKE_CASE						   =				_config_zero_init(UpperCAmelCase__   )
						for model_class in self.all_model_classes:
											__SCREAMING_SNAKE_CASE						   =				model_class(config=UpperCAmelCase__   )
											# Skip the check for the backbone
											__SCREAMING_SNAKE_CASE						   =				[]
											for name, module in model.named_modules():
																if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																					__SCREAMING_SNAKE_CASE						   =				[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			:				Tuple   )       ->	Dict:
						pass




	@slow
	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	str:
						for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											__SCREAMING_SNAKE_CASE						   =				DPTModel.from_pretrained(UpperCAmelCase__   )
											self.assertIsNotNone(UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	Dict:
						# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				self.model_tester.prepare_config_and_inputs_for_common()
						__SCREAMING_SNAKE_CASE						   =				"add"
						with self.assertRaises(UpperCAmelCase__   ):
											__SCREAMING_SNAKE_CASE						   =				DPTForDepthEstimation(UpperCAmelCase__   )




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
					return image




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

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"   )
						__SCREAMING_SNAKE_CASE						   =				DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"   ).to(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				prepare_img()
						__SCREAMING_SNAKE_CASE						   =				image_processor(images=UpperCAmelCase__			,			return_tensors="pt"   ).to(UpperCAmelCase__   )

						# forward pass
						with torch.no_grad():
											__SCREAMING_SNAKE_CASE						   =				model(**UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				outputs.predicted_depth

						# verify the predicted depth
						__SCREAMING_SNAKE_CASE						   =				torch.Size((1, 3_8_4, 3_8_4)   )
						self.assertEqual(predicted_depth.shape			,			UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				torch.tensor(
						    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]   ).to(UpperCAmelCase__   )

						self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0			,			UpperCAmelCase__			,			atol=1E-4   )   )




"""simple docstring"""







def    UpperCAmelCase__       (lowerCAmelCase_ = 100_0000   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				set(range(3  ,						lowerCAmelCase_  ,						2   )   )
					primes.add(2   )
					for p in range(3  ,						lowerCAmelCase_  ,						2   ):
										if p not in primes:
															continue
										primes.difference_update(set(range(p * p  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )   )   )

					__SCREAMING_SNAKE_CASE						   =				[float(lowerCAmelCase_   ) for n in range(limit + 1   )]

					for p in primes:
										for n in range(lowerCAmelCase_  ,						limit + 1  ,						lowerCAmelCase_   ):
															phi[n] *= 1 - 1 / p

					return int(sum(phi[2:]   )   )


if __name__ == "__main__":
		print(F"{solution() = }")
