
"""simple docstring"""





_a									=				10**8







def       __a	(						__lowerCamelCase    ):
   if n % 10 == 0:
      return False
   UpperCAmelCase_   :   Union[str, Any]     	=       str(__lowerCamelCase    )
   return s == s[::-1]







def       __a	(						):
   UpperCAmelCase_   :   List[str]     	=       set()
   UpperCAmelCase_   :   Union[str, Any]     	=       1
   UpperCAmelCase_   :   str     	=       5
   while sum_squares < LIMIT:
      UpperCAmelCase_   :   List[str]     	=       first_square + 1
      while sum_squares < LIMIT:
         if is_palindrome(__lowerCamelCase    ):
            answer.add(__lowerCamelCase    )
         last_square += 1
         sum_squares += last_square**2
      first_square += 1
      UpperCAmelCase_   :   Any     	=       first_square**2 + (first_square + 1) ** 2

   return sum(__lowerCamelCase    )


if __name__ == "__main__":
    print(solution())







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 						lowerCamelCase__			:


						def __init__(			self    :	List[str]   ,							_a    :	Any   ,							_a    :	str=2   ,							_a    :	List[str]=3_2   ,							_a    :	str=1_6   ,							_a    :	Tuple=3   ,							_a    :	Any=True   ,							_a    :	Optional[Any]=True   ,							_a    :	Optional[Any]=3_2   ,							_a    :	Optional[Any]=4   ,							_a    :	str=[0, 1, 2, 3]   ,							_a    :	Any=4   ,							_a    :	List[Any]=3_7   ,							_a    :	Optional[Any]="gelu"   ,							_a    :	str=0.1   ,							_a    :	Optional[Any]=0.1   ,							_a    :	Optional[Any]=0.0_2   ,							_a    :	Optional[int]=3   ,							_a    :	List[str]=False   ,							_a    :	Dict=None   ,							):
													a__:							List[Any]									=parent
													a__:							List[str]									=batch_size
													a__:							Optional[Any]									=image_size
													a__:							Optional[Any]									=patch_size
													a__:							Dict									=num_channels
													a__:							Optional[int]									=is_training
													a__:							List[Any]									=use_labels
													a__:							List[str]									=hidden_size
													a__:							int									=num_hidden_layers
													a__:							List[Any]									=backbone_out_indices
													a__:							int									=num_attention_heads
													a__:							List[Any]									=intermediate_size
													a__:							int									=hidden_act
													a__:							int									=hidden_dropout_prob
													a__:							Union[str, Any]									=attention_probs_dropout_prob
													a__:							Tuple									=initializer_range
													a__:							Dict									=num_labels
													a__:							Tuple									=scope
													a__:							List[Any]									=is_hybrid
													# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
													a__:							Any									=(image_size // patch_size) ** 2
													a__:							Tuple									=num_patches + 1


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							str									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

													a__:							Union[str, Any]									=self.get_config()

													return config, pixel_values, labels


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													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    :	Optional[int]   ,							_a    :	Dict   ,							_a    :	List[str]   ,							_a    :	int      ):
													a__:							Union[str, Any]									=DPTModel(config=_a      )
													model.to(_a      )
													model.eval()
													a__:							List[Any]									=model(_a      )
													self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)      )


						def 	_lowerCamelCase      (			self    :	int   ,							_a    :	Tuple   ,							_a    :	Union[str, Any]   ,							_a    :	List[Any]      ):
													a__:							Union[str, Any]									=self.num_labels
													a__:							List[str]									=DPTForDepthEstimation(_a      )
													model.to(_a      )
													model.eval()
													a__:							Optional[int]									=model(_a      )
													self.parent.assertEqual(result.predicted_depth.shape   ,							(self.batch_size, self.image_size, self.image_size)      )


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	str   ,							_a    :	Optional[int]   ,							_a    :	int      ):
													a__:							Any									=self.num_labels
													a__:							List[str]									=DPTForSemanticSegmentation(_a      )
													model.to(_a      )
													model.eval()
													a__:							Union[str, Any]									=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    :	Optional[Any]      ):
													a__:							List[str]									=self.prepare_config_and_inputs()
													a__				,      a__				,      a__:							Union[str, Any]									=config_and_inputs
													a__:							Tuple									={"pixel_values": pixel_values}
													return config, inputs_dict


@require_torch
class 						lowerCamelCase__			(		_a					,       _a					,       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 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__:							Dict									=DPTModelTester(self      )
													a__:							int									=ConfigTester(self   ,							config_class=_a   ,							has_text_modality=_a   ,							hidden_size=3_7      )


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													self.config_tester.run_common_tests()


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


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													a__				,      a__:							Optional[Any]									=self.model_tester.prepare_config_and_inputs_for_common()

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


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__				,      a__:							int									=self.model_tester.prepare_config_and_inputs_for_common()

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

																				a__:							List[str]									=["pixel_values"]
																				self.assertListEqual(arg_names[:1]   ,							_a      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													a__:							List[str]									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_a      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Any									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_depth_estimation(*_a      )


						def 	_lowerCamelCase      (			self    :	Dict      ):
													a__:							Any									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_semantic_segmentation(*_a      )


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													for model_class in self.all_model_classes:
																				if model_class.__name__ == "DPTForDepthEstimation":
																											continue

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

																				if model_class in get_values(_a      ):
																											continue

																				a__:							Union[str, Any]									=model_class(_a      )
																				model.to(_a      )
																				model.train()
																				a__:							Optional[int]									=self._prepare_for_class(_a   ,							_a   ,							return_labels=_a      )
																				a__:							List[Any]									=model(**_a      ).loss
																				loss.backward()


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													for model_class in self.all_model_classes:
																				if model_class.__name__ == "DPTForDepthEstimation":
																											continue

																				a__				,      a__:							Optional[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
																				a__:							Union[str, Any]									=False
																				a__:							Any									=True

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


						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__				,      a__:							str									=self.model_tester.prepare_config_and_inputs_for_common()

													a__:							Tuple									=_config_zero_init(_a      )
													for model_class in self.all_model_classes:
																				a__:							List[Any]									=model_class(config=_a      )
																				# Skip the check for the backbone
																				a__:							List[Any]									=[]
																				for name, module in model.named_modules():
																											if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																																		a__:							Optional[Any]									=[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    :	Dict      ):
													pass







						@slow
						def 	_lowerCamelCase      (			self    :	str      ):
													for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				a__:							List[Any]									=DPTModel.from_pretrained(_a      )
																				self.assertIsNotNone(_a      )





def     __lowerCamelCase							(   ):
							a__:							Optional[int]									=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
							return image







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


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__:							Any									=DPTImageProcessor.from_pretrained("Intel/dpt-large"      )
													a__:							str									=DPTForDepthEstimation.from_pretrained("Intel/dpt-large"      ).to(_a      )

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

													# forward pass
													with torch.no_grad():
																				a__:							int									=model(**_a      )
																				a__:							List[str]									=outputs.predicted_depth

													# verify the predicted depth
													a__:							Optional[int]									=torch.Size((1, 3_8_4, 3_8_4)      )
													self.assertEqual(predicted_depth.shape   ,							_a      )

													a__:							Union[str, Any]									=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    :	Optional[int]      ):
													a__:							Optional[Any]									=DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
													a__:							Union[str, Any]									=DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(_a      )

													a__:							Dict									=prepare_img()
													a__:							Optional[int]									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							int									=model(**_a      )

													# verify the logits
													a__:							int									=torch.Size((1, 1_5_0, 4_8_0, 4_8_0)      )
													self.assertEqual(outputs.logits.shape   ,							_a      )

													a__:							Any									=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    :	int      ):
													a__:							Dict									=DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
													a__:							List[Any]									=DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(_a      )

													a__:							int									=prepare_img()
													a__:							Any									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							Optional[int]									=model(**_a      )

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

													a__:							List[Any]									=image_processor.post_process_semantic_segmentation(outputs=_a   ,							target_sizes=[(5_0_0, 3_0_0)]      )
													a__:							Dict									=torch.Size((5_0_0, 3_0_0)      )
													self.assertEqual(segmentation[0].shape   ,							_a      )

													a__:							Optional[Any]									=image_processor.post_process_semantic_segmentation(outputs=_a      )
													a__:							List[str]									=torch.Size((4_8_0, 4_8_0)      )
													self.assertEqual(segmentation[0].shape   ,							_a      )





