


"""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     :





							'''simple docstring'''

							def __init__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      int=2		,       UpperCAmelCase_							:      str=32		,       UpperCAmelCase_							:      Any=16		,       UpperCAmelCase_							:      Optional[int]=3		,       UpperCAmelCase_							:      Optional[int]=True		,       UpperCAmelCase_							:      Optional[Any]=True		,       UpperCAmelCase_							:      Dict=32		,       UpperCAmelCase_							:      Dict=4		,       UpperCAmelCase_							:      Any=[0, 1, 2, 3]		,       UpperCAmelCase_							:      Optional[int]=4		,       UpperCAmelCase_							:      Tuple=37		,       UpperCAmelCase_							:      List[str]="gelu"		,       UpperCAmelCase_							:      List[str]=0.1		,       UpperCAmelCase_							:      Dict=0.1		,       UpperCAmelCase_							:      Union[str, Any]=0.02		,       UpperCAmelCase_							:      Union[str, Any]=3		,       UpperCAmelCase_							:      Any=[1, 384, 24, 24]		,       UpperCAmelCase_							:      List[Any]=True		,       UpperCAmelCase_							:      str=None		,       )					->Any:



														"""simple docstring"""



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



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



														"""simple docstring"""



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

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

														snake_case_           =		self.get_config()

														return config, pixel_values, labels



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		{
														    """global_padding""": """same""",
														    """layer_type""": """bottleneck""",
														    """depths""": [3, 4, 9],
														    """out_features""": ["""stage1""", """stage2""", """stage3"""],
														    """embedding_dynamic_padding""": True,
														    """hidden_sizes""": [96, 192, 384, 768],
														    """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 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      str       )					->Union[str, Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Union[str, Any]       )					->Union[str, Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      int       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.num_labels
														snake_case_           =		DPTForSemanticSegmentation(UpperCAmelCase_       )
														model.to(UpperCAmelCase_       )
														model.eval()
														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 		lowerCAmelCase       (				self							:      Any       )					->List[Any]:



														"""simple docstring"""



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





@require_torch
class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

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

							__lowercase:		Union[str, Any]							      =    False
							__lowercase:		Tuple							      =    False
							__lowercase:		Tuple							      =    False



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		DPTModelTester(self       )
														snake_case_           =		ConfigTester(self		,       config_class=UpperCAmelCase_		,       has_text_modality=UpperCAmelCase_		,       hidden_size=37       )



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



														"""simple docstring"""



														self.config_tester.run_common_tests()



							@unittest.skip(reason="""DPT does not use inputs_embeds"""       )
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->str:



														"""simple docstring"""



														pass



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



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

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



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



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

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

																					snake_case_           =		["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_depth_estimation(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Tuple:



														"""simple docstring"""



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

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

																					if model_class in get_values(UpperCAmelCase_       ):
																												continue

																					snake_case_           =		model_class(UpperCAmelCase_       )
																					model.to(UpperCAmelCase_       )
																					model.train()
																					snake_case_           =		self._prepare_for_class(UpperCAmelCase_		,       UpperCAmelCase_		,       return_labels=UpperCAmelCase_       )
																					snake_case_           =		model(**UpperCAmelCase_       ).loss
																					loss.backward()



							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[str]:



														"""simple docstring"""



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

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

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



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()

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



														"""simple docstring"""



														pass



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



														"""simple docstring"""



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



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



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		self.model_tester.prepare_config_and_inputs_for_common()
														snake_case_           =		"""add"""
														with self.assertRaises(UpperCAmelCase_       ):
																					snake_case_           =		DPTForDepthEstimation(UpperCAmelCase_       )







def 						_a				(      )    ->				Any:
							snake_case_           =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image





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





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Dict       )					->str:



														"""simple docstring"""



														snake_case_           =		DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""       )
														snake_case_           =		DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""       ).to(UpperCAmelCase_       )

														snake_case_           =		prepare_img()
														snake_case_           =		image_processor(images=UpperCAmelCase_		,       return_tensors="""pt"""       ).to(UpperCAmelCase_       )

														# forward pass
														with torch.no_grad():
																					snake_case_           =		model(**UpperCAmelCase_       )
																					snake_case_           =		outputs.predicted_depth

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

														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] / 100		,       UpperCAmelCase_		,       atol=1E-4       )       )







"""simple docstring"""
from math import factorial







def 						_a				(      _SCREAMING_SNAKE_CASE = 100				)    ->				int:
							return sum(int(_SCREAMING_SNAKE_CASE				) for x in str(factorial(_SCREAMING_SNAKE_CASE				)				)				)


if __name__ == "__main__":
				print(solution(int(input('Enter the Number: ').strip())))



