


"""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 		snake_case_:






     def __init__(	self  :					Any					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					Dict=2					,				UpperCamelCase_  :					List[Any]=3_2					,				UpperCamelCase_  :					Any=1_6					,				UpperCamelCase_  :					Optional[Any]=3					,				UpperCamelCase_  :					int=True					,				UpperCamelCase_  :					Union[str, Any]=True					,				UpperCamelCase_  :					Dict=3_2					,				UpperCamelCase_  :					Union[str, Any]=4					,				UpperCamelCase_  :					Optional[Any]=[0, 1, 2, 3]					,				UpperCamelCase_  :					Optional[int]=4					,				UpperCamelCase_  :					Dict=3_7					,				UpperCamelCase_  :					int="gelu"					,				UpperCamelCase_  :					Union[str, Any]=0.1					,				UpperCamelCase_  :					str=0.1					,				UpperCamelCase_  :					List[Any]=0.02					,				UpperCamelCase_  :					Optional[int]=3					,				UpperCamelCase_  :					Dict=[1, 3_8_4, 2_4, 2_4]					,				UpperCamelCase_  :					Any=True					,				UpperCamelCase_  :					List[str]=None					,				):
           lowerCAmelCase					:	List[Any]       =   parent
           lowerCAmelCase					:	int       =   batch_size
           lowerCAmelCase					:	Optional[int]       =   image_size
           lowerCAmelCase					:	Tuple       =   patch_size
           lowerCAmelCase					:	Optional[Any]       =   num_channels
           lowerCAmelCase					:	Union[str, Any]       =   is_training
           lowerCAmelCase					:	str       =   use_labels
           lowerCAmelCase					:	Any       =   hidden_size
           lowerCAmelCase					:	int       =   num_hidden_layers
           lowerCAmelCase					:	List[Any]       =   backbone_out_indices
           lowerCAmelCase					:	Union[str, Any]       =   num_attention_heads
           lowerCAmelCase					:	Dict       =   intermediate_size
           lowerCAmelCase					:	Optional[Any]       =   hidden_act
           lowerCAmelCase					:	Tuple       =   hidden_dropout_prob
           lowerCAmelCase					:	Dict       =   attention_probs_dropout_prob
           lowerCAmelCase					:	List[Any]       =   initializer_range
           lowerCAmelCase					:	Any       =   num_labels
           lowerCAmelCase					:	Any       =   backbone_featmap_shape
           lowerCAmelCase					:	Tuple       =   scope
           lowerCAmelCase					:	Tuple       =   is_hybrid
           # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
           lowerCAmelCase					:	str       =   (image_size // patch_size) ** 2
           lowerCAmelCase					:	Optional[Any]       =   num_patches + 1






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           lowerCAmelCase					:	str       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

           lowerCAmelCase					:	int       =   self.get_config()

           return config, pixel_values, labels






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase					:	Tuple       =   {
               '''global_padding''': '''same''',
               '''layer_type''': '''bottleneck''',
               '''depths''': [3, 4, 9],
               '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
               '''embedding_dynamic_padding''': True,
               '''hidden_sizes''': [9_6, 1_9_2, 3_8_4, 7_6_8],
               '''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  :					Tuple					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Dict				):
           lowerCAmelCase					:	int       =   DPTModel(config=UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	int       =   model(UpperCamelCase_				)
           self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size)				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					Dict				):
           lowerCAmelCase					:	Optional[Any]       =   self.num_labels
           lowerCAmelCase					:	Dict       =   DPTForDepthEstimation(UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	List[Any]       =   model(UpperCamelCase_				)
           self.parent.assertEqual(result.predicted_depth.shape					,				(self.batch_size, self.image_size, self.image_size)				)






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					int					,				UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Dict				):
           lowerCAmelCase					:	Tuple       =   self.num_labels
           lowerCAmelCase					:	List[str]       =   DPTForSemanticSegmentation(UpperCamelCase_				)
           model.to(UpperCamelCase_				)
           model.eval()
           lowerCAmelCase					:	int       =   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				):
           lowerCAmelCase					:	Union[str, Any]       =   self.prepare_config_and_inputs()
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	List[Any]       =   config_and_inputs
           lowerCAmelCase					:	List[Any]       =   {'''pixel_values''': pixel_values}
           return config, inputs_dict



@require_torch
class 		snake_case_(       a__							,					a__							,					unittest.TestCase			):
     __UpperCamelCase  =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     __UpperCamelCase  =					(
         {
             '''depth-estimation''': DPTForDepthEstimation,
             '''feature-extraction''': DPTModel,
             '''image-segmentation''': DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __UpperCamelCase  =					False
     __UpperCamelCase  =					False
     __UpperCamelCase  =					False






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Any       =   DPTModelTester(self				)
           lowerCAmelCase					:	Optional[Any]       =   ConfigTester(self					,				config_class=UpperCamelCase_					,				has_text_modality=UpperCamelCase_					,				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  :					Dict				):
           pass






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase,      lowerCAmelCase					:	Tuple       =   self.model_tester.prepare_config_and_inputs_for_common()

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






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCAmelCase					:	Union[str, Any]       =   model_class(UpperCamelCase_				)
                 lowerCAmelCase					:	Tuple       =   inspect.signature(model.forward				)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCAmelCase					:	Tuple       =   [*signature.parameters.keys()]

                 lowerCAmelCase					:	Dict       =   ['''pixel_values''']
                 self.assertListEqual(arg_names[:1]					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           lowerCAmelCase					:	Union[str, Any]       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	int       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	Dict       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_				)






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

                 lowerCAmelCase,      lowerCAmelCase					:	str       =   self.model_tester.prepare_config_and_inputs_for_common()
                 lowerCAmelCase					:	Optional[int]       =   True

                 if model_class in get_values(UpperCamelCase_				):
                       continue

                 lowerCAmelCase					:	Optional[int]       =   model_class(UpperCamelCase_				)
                 model.to(UpperCamelCase_				)
                 model.train()
                 lowerCAmelCase					:	Optional[Any]       =   self._prepare_for_class(UpperCamelCase_					,				UpperCamelCase_					,				return_labels=UpperCamelCase_				)
                 lowerCAmelCase					:	Optional[int]       =   model(**UpperCamelCase_				).loss
                 loss.backward()






     def      lowerCamelCase__     (	self  :					List[str]				):
           for model_class in self.all_model_classes:
                 if model_class.__name__ == "DPTForDepthEstimation":
                       continue

                 lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self.model_tester.prepare_config_and_inputs_for_common()
                 lowerCAmelCase					:	Tuple       =   False
                 lowerCAmelCase					:	Optional[int]       =   True

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






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase,      lowerCAmelCase					:	Any       =   self.model_tester.prepare_config_and_inputs_for_common()

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






     @slow
     def      lowerCamelCase__     (	self  :					List[str]				):
           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                 lowerCAmelCase					:	str       =   DPTModel.from_pretrained(UpperCamelCase_				)
                 self.assertIsNotNone(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					str				):
           # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
           lowerCAmelCase,      lowerCAmelCase					:	Tuple       =   self.model_tester.prepare_config_and_inputs_for_common()
           lowerCAmelCase					:	List[str]       =   '''add'''
           with self.assertRaises(UpperCamelCase_				):
                 lowerCAmelCase					:	Tuple       =   DPTForDepthEstimation(UpperCamelCase_				)



def      _snake_case						(						):
      lowerCAmelCase					:	List[Any]       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
      return image






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






     def      lowerCamelCase__     (	self  :					List[Any]				):
           lowerCAmelCase					:	str       =   DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''				)
           lowerCAmelCase					:	Dict       =   DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''				).to(UpperCamelCase_				)

           lowerCAmelCase					:	Optional[int]       =   prepare_img()
           lowerCAmelCase					:	Dict       =   image_processor(images=UpperCamelCase_					,				return_tensors='''pt'''				).to(UpperCamelCase_				)

           # forward pass
           with torch.no_grad():
                 lowerCAmelCase					:	int       =   model(**UpperCamelCase_				)
                 lowerCAmelCase					:	Any       =   outputs.predicted_depth

           # verify the predicted depth
           lowerCAmelCase					:	Any       =   torch.Size((1, 3_8_4, 3_8_4)				)
           self.assertEqual(predicted_depth.shape					,				UpperCamelCase_				)

           lowerCAmelCase					:	List[str]       =   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] / 1_0_0					,				UpperCamelCase_					,				atol=1E-4				)				)






"""simple docstring"""





class 		snake_case_:






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					str				):
           lowerCAmelCase					:	Dict       =   val
           lowerCAmelCase					:	str       =   None
           lowerCAmelCase					:	Dict       =   None






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Dict				):
           if self.val:
                 if val < self.val:
                       if self.left is None:
                             lowerCAmelCase					:	int       =   Node(UpperCamelCase_				)
                       else:
                             self.left.insert(UpperCamelCase_				)
                 elif val > self.val:
                       if self.right is None:
                             lowerCAmelCase					:	Any       =   Node(UpperCamelCase_				)
                       else:
                             self.right.insert(UpperCamelCase_				)
           else:
                 lowerCAmelCase					:	Optional[Any]       =   val



def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			str			):
      # Recursive traversal
      if root:
            inorder(root.left ,   _snake_case			)
            res.append(root.val			)
            inorder(root.right ,   _snake_case			)


def      _snake_case						(						_snake_case				:			Optional[Any]			):
      # Build BST
      if len(_snake_case			) == 0:
            return arr
      lowerCAmelCase					:	Optional[Any]       =   Node(arr[0]			)
      for i in range(1 ,   len(_snake_case			)			):
            root.insert(arr[i]			)
      # Traverse BST in order.
      lowerCAmelCase					:	Optional[int]       =   []
      inorder(_snake_case ,   _snake_case			)
      return res


if __name__ == "__main__":
 print(tree_sort([10, 1, 3, 2, 9, 14, 13]))


