


"""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	:

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=4     ,					__UpperCAmelCase=[0, 1, 2, 3]     ,					__UpperCAmelCase=4     ,					__UpperCAmelCase=37     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.02     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=None     ,					)							->				str:
          _a     						=					parent
          _a     						=					batch_size
          _a     						=					image_size
          _a     						=					patch_size
          _a     						=					num_channels
          _a     						=					is_training
          _a     						=					use_labels
          _a     						=					hidden_size
          _a     						=					num_hidden_layers
          _a     						=					backbone_out_indices
          _a     						=					num_attention_heads
          _a     						=					intermediate_size
          _a     						=					hidden_act
          _a     						=					hidden_dropout_prob
          _a     						=					attention_probs_dropout_prob
          _a     						=					initializer_range
          _a     						=					num_labels
          _a     						=					scope
          _a     						=					is_hybrid
          # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
          _a     						=					(image_size // patch_size) ** 2
          _a     						=					num_patches + 1


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

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

          _a     						=					self.get_config()

          return config, pixel_values, labels


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


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


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





     def 				_UpperCAmelCase   (   self				)							->				List[Any]:
          _a     						=					self.prepare_config_and_inputs()
          _a						,   _a						,   _a     						=					config_and_inputs
          _a     						=					{'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class      __lowerCamelCase	(       _lowercase	,       _lowercase	,       unittest.TestCase					):

     '''simple docstring'''
     A_							:						str     =		(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     A_							:						int     =		(
         {
             'depth-estimation': DPTForDepthEstimation,
             'feature-extraction': DPTModel,
             'image-segmentation': DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     A_							:						Optional[Any]     =		False
     A_							:						int     =		False
     A_							:						Any     =		False


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


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


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


     def 				_UpperCAmelCase   (   self				)							->				str:
          _a						,   _a     						=					self.model_tester.prepare_config_and_inputs_for_common()

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


     def 				_UpperCAmelCase   (   self				)							->				int:
          _a						,   _a     						=					self.model_tester.prepare_config_and_inputs_for_common()

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

               _a     						=					['''pixel_values''']
               self.assertListEqual(arg_names[:1]     ,					__lowerCAmelCase				)


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


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a     						=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*__lowerCAmelCase				)


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


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

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

               if model_class in get_values(__lowerCAmelCase				):
                    continue

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


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

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

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


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

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





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







def    A_ ( ):






     """simple docstring"""

     _a     						=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image


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

     '''simple docstring'''


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					DPTImageProcessor.from_pretrained('''Intel/dpt-large'''				)
          _a     						=					DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''				).to(__lowerCAmelCase				)

          _a     						=					prepare_img()
          _a     						=					image_processor(images=__lowerCAmelCase     ,					return_tensors='''pt'''				).to(__lowerCAmelCase				)

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

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

          _a     						=					torch.tensor(
              [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]				).to(__lowerCAmelCase				)

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


     def 				_UpperCAmelCase   (   self				)							->				str:
          _a     						=					DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''				)
          _a     						=					DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''				).to(__lowerCAmelCase				)

          _a     						=					prepare_img()
          _a     						=					image_processor(images=__lowerCAmelCase     ,					return_tensors='''pt'''				).to(__lowerCAmelCase				)

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

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

          _a     						=					torch.tensor(
              [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]				).to(__lowerCAmelCase				)

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





     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          _a     						=					DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''				)
          _a     						=					DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''				).to(__lowerCAmelCase				)

          _a     						=					prepare_img()
          _a     						=					image_processor(images=__lowerCAmelCase     ,					return_tensors='''pt'''				).to(__lowerCAmelCase				)

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

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

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

          _a     						=					image_processor.post_process_semantic_segmentation(outputs=__lowerCAmelCase				)
          _a     						=					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() = }""")





