





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						:




					def __init__(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=2     ,   __lowerCAmelCase=32     ,   __lowerCAmelCase=16     ,   __lowerCAmelCase=3     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=32     ,   __lowerCAmelCase=4     ,   __lowerCAmelCase=[0, 1, 2, 3]     ,   __lowerCAmelCase=4     ,   __lowerCAmelCase=37     ,   __lowerCAmelCase="gelu"     ,   __lowerCAmelCase=0.1     ,   __lowerCAmelCase=0.1     ,   __lowerCAmelCase=0.0_2     ,   __lowerCAmelCase=3     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=None     ,   )    ->	str:
							UpperCamelCase__								=						parent
							UpperCamelCase__								=						batch_size
							UpperCamelCase__								=						image_size
							UpperCamelCase__								=						patch_size
							UpperCamelCase__								=						num_channels
							UpperCamelCase__								=						is_training
							UpperCamelCase__								=						use_labels
							UpperCamelCase__								=						hidden_size
							UpperCamelCase__								=						num_hidden_layers
							UpperCamelCase__								=						backbone_out_indices
							UpperCamelCase__								=						num_attention_heads
							UpperCamelCase__								=						intermediate_size
							UpperCamelCase__								=						hidden_act
							UpperCamelCase__								=						hidden_dropout_prob
							UpperCamelCase__								=						attention_probs_dropout_prob
							UpperCamelCase__								=						initializer_range
							UpperCamelCase__								=						num_labels
							UpperCamelCase__								=						scope
							UpperCamelCase__								=						is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							UpperCamelCase__								=						(image_size // patch_size) ** 2
							UpperCamelCase__								=						num_patches + 1




					def 						snake_case_		(    self							)    ->	Dict:
							UpperCamelCase__								=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

							UpperCamelCase__								=						self.get_config()

							return config, pixel_values, labels




					def 						snake_case_		(    self							)    ->	Dict:
							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 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	Union[str, Any]:
							UpperCamelCase__								=						DPTModel(config=__lowerCAmelCase							)
							model.to(__lowerCAmelCase							)
							model.eval()
							UpperCamelCase__								=						model(__lowerCAmelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, self.seq_length, self.hidden_size)							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	str:
							UpperCamelCase__								=						self.num_labels
							UpperCamelCase__								=						DPTForDepthEstimation(__lowerCAmelCase							)
							model.to(__lowerCAmelCase							)
							model.eval()
							UpperCamelCase__								=						model(__lowerCAmelCase							)
							self.parent.assertEqual(result.predicted_depth.shape     ,   (self.batch_size, self.image_size, self.image_size)							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	int:
							UpperCamelCase__								=						self.num_labels
							UpperCamelCase__								=						DPTForSemanticSegmentation(__lowerCAmelCase							)
							model.to(__lowerCAmelCase							)
							model.eval()
							UpperCamelCase__								=						model(__lowerCAmelCase     ,   labels=__lowerCAmelCase							)
							self.parent.assertEqual(
							    result.logits.shape     ,   (self.batch_size, self.num_labels, self.image_size, self.image_size)							)




					def 						snake_case_		(    self							)    ->	List[Any]:
							UpperCamelCase__								=						self.prepare_config_and_inputs()
							UpperCamelCase__	,      UpperCamelCase__	,      UpperCamelCase__								=						config_and_inputs
							UpperCamelCase__								=						{"""pixel_values""": pixel_values}
							return config, inputs_dict




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

					__A        = False
					__A        = False
					__A        = False




					def 						snake_case_		(    self							)    ->	str:
							UpperCamelCase__								=						DPTModelTester(self							)
							UpperCamelCase__								=						ConfigTester(self     ,   config_class=__lowerCAmelCase     ,   has_text_modality=__lowerCAmelCase     ,   hidden_size=37							)




					def 						snake_case_		(    self							)    ->	List[str]:
							self.config_tester.run_common_tests()




					@unittest.skip(reason="""DPT does not use inputs_embeds"""							)
					def 						snake_case_		(    self							)    ->	Optional[Any]:
							pass




					def 						snake_case_		(    self							)    ->	str:
							UpperCamelCase__	,      UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs_for_common()

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




					def 						snake_case_		(    self							)    ->	int:
							UpperCamelCase__	,      UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs_for_common()

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

									UpperCamelCase__								=						["""pixel_values"""]
									self.assertListEqual(arg_names[:1]     ,   __lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Optional[int]:
							UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*__lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Optional[Any]:
							UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase							)




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

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

									if model_class in get_values(__lowerCAmelCase							):
											continue

									UpperCamelCase__								=						model_class(__lowerCAmelCase							)
									model.to(__lowerCAmelCase							)
									model.train()
									UpperCamelCase__								=						self._prepare_for_class(__lowerCAmelCase     ,   __lowerCAmelCase     ,   return_labels=__lowerCAmelCase							)
									UpperCamelCase__								=						model(**__lowerCAmelCase							).loss
									loss.backward()




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

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

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




					def 						snake_case_		(    self							)    ->	List[Any]:
							UpperCamelCase__	,      UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase__								=						_config_zero_init(__lowerCAmelCase							)
							for model_class in self.all_model_classes:
									UpperCamelCase__								=						model_class(config=__lowerCAmelCase							)
									# Skip the check for the backbone
									UpperCamelCase__								=						[]
									for name, module in model.named_modules():
											if module.__class__.__name__ == "DPTViTHybridEmbeddings":
													UpperCamelCase__								=						[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 						snake_case_		(    self							)    ->	Union[str, Any]:
							pass




					@slow
					def 						snake_case_		(    self							)    ->	Tuple:
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCamelCase__								=						DPTModel.from_pretrained(__lowerCAmelCase							)
									self.assertIsNotNone(__lowerCAmelCase							)



def      a    (   )  ->Dict:
		UpperCamelCase__								=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
		return image




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




					def 						snake_case_		(    self							)    ->	List[str]:
							UpperCamelCase__								=						DPTImageProcessor.from_pretrained("""Intel/dpt-large"""							)
							UpperCamelCase__								=						DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""							).to(__lowerCAmelCase							)

							UpperCamelCase__								=						prepare_img()
							UpperCamelCase__								=						image_processor(images=__lowerCAmelCase     ,   return_tensors="""pt"""							).to(__lowerCAmelCase							)

							# forward pass
							with torch.no_grad():
									UpperCamelCase__								=						model(**__lowerCAmelCase							)
									UpperCamelCase__								=						outputs.predicted_depth

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

							UpperCamelCase__								=						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(__lowerCAmelCase							)

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




					def 						snake_case_		(    self							)    ->	str:
							UpperCamelCase__								=						DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""							)
							UpperCamelCase__								=						DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""							).to(__lowerCAmelCase							)

							UpperCamelCase__								=						prepare_img()
							UpperCamelCase__								=						image_processor(images=__lowerCAmelCase     ,   return_tensors="""pt"""							).to(__lowerCAmelCase							)

							# forward pass
							with torch.no_grad():
									UpperCamelCase__								=						model(**__lowerCAmelCase							)

							# verify the logits
							UpperCamelCase__								=						torch.Size((1, 150, 480, 480)							)
							self.assertEqual(outputs.logits.shape     ,   __lowerCAmelCase							)

							UpperCamelCase__								=						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(__lowerCAmelCase							)

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




					def 						snake_case_		(    self							)    ->	Optional[int]:
							UpperCamelCase__								=						DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""							)
							UpperCamelCase__								=						DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""							).to(__lowerCAmelCase							)

							UpperCamelCase__								=						prepare_img()
							UpperCamelCase__								=						image_processor(images=__lowerCAmelCase     ,   return_tensors="""pt"""							).to(__lowerCAmelCase							)

							# forward pass
							with torch.no_grad():
									UpperCamelCase__								=						model(**__lowerCAmelCase							)

							UpperCamelCase__								=						outputs.logits.detach().cpu()

							UpperCamelCase__								=						image_processor.post_process_semantic_segmentation(outputs=__lowerCAmelCase     ,   target_sizes=[(500, 300)]							)
							UpperCamelCase__								=						torch.Size((500, 300)							)
							self.assertEqual(segmentation[0].shape     ,   __lowerCAmelCase							)

							UpperCamelCase__								=						image_processor.post_process_semantic_segmentation(outputs=__lowerCAmelCase							)
							UpperCamelCase__								=						torch.Size((480, 480)							)
							self.assertEqual(segmentation[0].shape     ,   __lowerCAmelCase							)












import itertools
from pathlib import Path

def      a    (   _lowercase :  list[str]							)  ->int:
		UpperCamelCase__								=						[tuple(_lowercase							) for login in logins]

		UpperCamelCase__								=						{char for login in split_logins for char in login}

		for permutation in itertools.permutations(_lowercase							):
				UpperCamelCase__								=						True
				for login in logins:
						if not (
						    permutation.index(login[0]							)
						    < permutation.index(login[1]							)
						    < permutation.index(login[2]							)
						):
								UpperCamelCase__								=						False
								break

				if satisfied:
						return int("""""".join(_lowercase							)							)

		raise Exception("""Unable to find the secret passcode"""							)

def      a    (   _lowercase :  str = "keylog.txt"							)  ->int:
		UpperCamelCase__								=						Path(_lowercase							).parent.joinpath(_lowercase							).read_text().splitlines()

		return find_secret_passcode(_lowercase							)


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





