



'''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 				__lowerCAmelCase:






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




					"""simple docstring"""







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






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




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

					SCREAMING_SNAKE_CASE_						:str										=      None
					if self.use_labels:
							SCREAMING_SNAKE_CASE_						:List[Any]										=      ids_tensor([self.batch_size, self.image_size, self.image_size]      ,       self.num_labels	)

					SCREAMING_SNAKE_CASE_						:Any										=      self.get_config()

					return config, pixel_values, labels






			def 				_lowercase	(   self  :							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=SCREAMING_SNAKE_CASE      ,       initializer_range=self.initializer_range      ,       is_hybrid=self.is_hybrid      ,       )






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]      ,       SCREAMING_SNAKE_CASE  :							Tuple	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							str	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							int	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      config_and_inputs
					SCREAMING_SNAKE_CASE_						:Dict										=      {'pixel_values': pixel_values}
					return config, inputs_dict




@require_torch
class 				__lowerCAmelCase(							lowerCAmelCase__		,			lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		int		=      (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
			__snake_case  :		Dict		=      (
			    {
			        'depth-estimation': DPTForDepthEstimation,
			        'feature-extraction': DPTModel,
			        'image-segmentation': DPTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__snake_case  :		Tuple		=      False
			__snake_case  :		Optional[int]		=      False
			__snake_case  :		Any		=      False






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      DPTModelTester(self	)
					SCREAMING_SNAKE_CASE_						:Any										=      ConfigTester(self      ,       config_class=SCREAMING_SNAKE_CASE      ,       has_text_modality=SCREAMING_SNAKE_CASE      ,       hidden_size=37	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					self.config_tester.run_common_tests()






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




					"""simple docstring"""







					pass






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Any										=      self.model_tester.prepare_config_and_inputs_for_common()

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






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







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

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

							SCREAMING_SNAKE_CASE_						:int										=      ['pixel_values']
							self.assertListEqual(arg_names[:1]      ,       SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:str										=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







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

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

							if model_class in get_values(SCREAMING_SNAKE_CASE	):
									continue

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






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







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

							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.model_tester.prepare_config_and_inputs_for_common()
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      False
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      True

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






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.model_tester.prepare_config_and_inputs_for_common()

					SCREAMING_SNAKE_CASE_						:Tuple										=      _config_zero_init(SCREAMING_SNAKE_CASE	)
					for model_class in self.all_model_classes:
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      model_class(config=SCREAMING_SNAKE_CASE	)
							# Skip the check for the backbone
							SCREAMING_SNAKE_CASE_						:Tuple										=      []
							for name, module in model.named_modules():
									if module.__class__.__name__ == "DPTViTHybridEmbeddings":
											SCREAMING_SNAKE_CASE_						:Dict										=      [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 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					pass






			@slow
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							SCREAMING_SNAKE_CASE_						:Tuple										=      DPTModel.from_pretrained(SCREAMING_SNAKE_CASE	)
							self.assertIsNotNone(SCREAMING_SNAKE_CASE	)







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
		return image





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






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      DPTImageProcessor.from_pretrained('Intel/dpt-large'	)
					SCREAMING_SNAKE_CASE_						:Any										=      DPTForDepthEstimation.from_pretrained('Intel/dpt-large'	).to(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:int										=      prepare_img()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      image_processor(images=SCREAMING_SNAKE_CASE      ,       return_tensors='pt'	).to(SCREAMING_SNAKE_CASE	)

					# forward pass
					with torch.no_grad():
							SCREAMING_SNAKE_CASE_						:List[str]										=      model(**SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:str										=      outputs.predicted_depth

					# verify the predicted depth
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      torch.Size((1, 384, 384)	)
					self.assertEqual(predicted_depth.shape      ,       SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      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(SCREAMING_SNAKE_CASE	)

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






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      prepare_img()
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      image_processor(images=SCREAMING_SNAKE_CASE      ,       return_tensors='pt'	).to(SCREAMING_SNAKE_CASE	)

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

					# verify the logits
					SCREAMING_SNAKE_CASE_						:int										=      torch.Size((1, 150, 480, 480)	)
					self.assertEqual(outputs.logits.shape      ,       SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:int										=      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(SCREAMING_SNAKE_CASE	)

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






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      prepare_img()
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      image_processor(images=SCREAMING_SNAKE_CASE      ,       return_tensors='pt'	).to(SCREAMING_SNAKE_CASE	)

					# forward pass
					with torch.no_grad():
							SCREAMING_SNAKE_CASE_						:List[str]										=      model(**SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      outputs.logits.detach().cpu()

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE      ,       target_sizes=[(500, 300)]	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      torch.Size((500, 300)	)
					self.assertEqual(segmentation[0].shape      ,       SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      torch.Size((480, 480)	)
					self.assertEqual(segmentation[0].shape      ,       SCREAMING_SNAKE_CASE	)



"""simple docstring"""




import sys

sys.set_int_max_str_digits(0)  # type: ignore
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   [0] * 11
					A     =   [0] * 11

					# mark last 9 numbers
					for _ in range(9 ):
										A     =   1
										A     =   number // 10
					# flag
					A     =   True

					# check last 9 numbers for pandigitality

					for x in range(9 ):
										if not check_last[x + 1]:
															A     =   False
					if not f:
										return f

					# mark first 9 numbers
					A     =   int(str(lowercase__ )[:9] )

					for _ in range(9 ):
										A     =   1
										A     =   number // 10

					# check first 9 numbers for pandigitality

					for x in range(9 ):
										if not check_front[x + 1]:
															A     =   False
					return f
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   [0] * 11

					# mark last 9 numbers
					for _ in range(9 ):
										A     =   1
										A     =   number // 10
					# flag
					A     =   True

					# check last 9 numbers for pandigitality

					for x in range(9 ):
										if not check_last[x + 1]:
															A     =   False
					return f
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   1
					A     =   1
					A     =   2
					# temporary Fibonacci numbers

					A     =   1
					A     =   1
					A     =   2
					# temporary Fibonacci numbers mod 1e9

					# mod m=1e9, done for fast optimisation
					A     =   [0] * 1_000_000
					A     =   1_000_000_000

					for x in range(1_000_000 ):
										A     =   (aa + ba) % m
										A     =   ba % m
										A     =   ca % m
										if checka(lowercase__ ):
															A     =   1

					for x in range(1_000_000 ):
										A     =   a + b
										A     =   b
										A     =   c
										# perform check only if in tocheck
										if tocheck[x + 3] and check(lowercase__ ):
															return x + 3  # first 2 already done
					return -1


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





