

'''simple docstring'''

import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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,
						    DataaVecVisionForImageClassification,
						    DataaVecVisionForSemanticSegmentation,
						    DataaVecVisionModel,
						)
						from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import BeitImageProcessor







class lowerCamelCase   :

				def __init__(       self,						lowercase_,						lowercase_=100,						lowercase_=13,						lowercase_=30,						lowercase_=2,						lowercase_=3,						lowercase_=True,						lowercase_=True,						lowercase_=32,						lowercase_=4,						lowercase_=4,						lowercase_=37,						lowercase_="gelu",						lowercase_=0.1,						lowercase_=0.1,						lowercase_=10,						lowercase_=0.02,						lowercase_=3,						lowercase_=None,						lowercase_=[0, 1, 2, 3],						)    ->							Dict:
					snake_case							=					parent
					snake_case							=					100
					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							=					num_attention_heads
					snake_case							=					intermediate_size
					snake_case							=					hidden_act
					snake_case							=					hidden_dropout_prob
					snake_case							=					attention_probs_dropout_prob
					snake_case							=					type_sequence_label_size
					snake_case							=					initializer_range
					snake_case							=					scope
					snake_case							=					out_indices
					snake_case							=					num_labels

					# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
					snake_case							=					(image_size // patch_size) ** 2
					snake_case							=					num_patches + 1

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

					snake_case							=					None
					snake_case							=					None
					if self.use_labels:
						snake_case							=					ids_tensor([self.batch_size],						self.type_sequence_label_size )
						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, pixel_labels

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					return DataaVecVisionConfig(
					    vocab_size=self.vocab_size,						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,						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=lowercase_,						initializer_range=self.initializer_range,						out_indices=self.out_indices,						)

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Tuple:
					snake_case							=					DataaVecVisionModel(config=lowercase_ )
					model.to(lowercase_ )
					model.eval()
					snake_case							=					model(lowercase_ )
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
					snake_case							=					(self.image_size // self.patch_size) ** 2
					self.parent.assertEqual(result.last_hidden_state.shape,						(self.batch_size, num_patches + 1, self.hidden_size) )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Any:
					snake_case							=					self.type_sequence_label_size
					snake_case							=					DataaVecVisionForImageClassification(lowercase_ )
					model.to(lowercase_ )
					model.eval()
					snake_case							=					model(lowercase_,						labels=lowercase_ )
					self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.type_sequence_label_size) )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							str:
					snake_case							=					self.num_labels
					snake_case							=					DataaVecVisionForSemanticSegmentation(lowercase_ )
					model.to(lowercase_ )
					model.eval()
					snake_case							=					model(lowercase_ )
					self.parent.assertEqual(
					    result.logits.shape,						(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
					snake_case							=					model(lowercase_,						labels=lowercase_ )
					self.parent.assertEqual(
					    result.logits.shape,						(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )






				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					self.prepare_config_and_inputs()
					snake_case			,			snake_case			,			snake_case			,			snake_case							=					config_and_inputs
					snake_case							=					{'pixel_values': pixel_values}
					return config, inputs_dict







@require_torch
class lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = (
				    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				snake_case_							     = (
				    {
				        '''feature-extraction''': DataaVecVisionModel,
				        '''image-classification''': DataaVecVisionForImageClassification,
				        '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case							=					DataaVecVisionModelTester(self )
					snake_case							=					ConfigTester(
					    self,						config_class=lowercase_,						has_text_modality=lowercase_,						hidden_size=37 )

				def 					_lowerCamelCase    (       self )    ->							Any:
					self.config_tester.run_common_tests()

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					# Data2VecVision does not use inputs_embeds
					pass

				@require_torch_multi_gpu
				@unittest.skip(
				    reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					pass

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					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(lowercase_ )
						self.assertIsInstance(model.get_input_embeddings(),						(nn.Module) )
						snake_case							=					model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(lowercase_,						nn.Linear ) )

				def 					_lowerCamelCase    (       self )    ->							Dict:
					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(lowercase_ )
						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],						lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_segmentation(*lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					if not self.model_tester.is_training:
						return

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

					for model_class in self.all_model_classes:
						if model_class in [*get_values(lowercase_ )]:
							continue

						snake_case							=					model_class(lowercase_ )
						model.to(lowercase_ )
						model.train()
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						snake_case							=					model(**lowercase_ ).loss
						loss.backward()

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()
					if not self.model_tester.is_training:
						return

					snake_case							=					False
					snake_case							=					True

					for model_class in self.all_model_classes:
						if model_class in [*get_values(lowercase_ )] or not model_class.supports_gradient_checkpointing:
							continue
						# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
						# this can then be incorporated into _prepare_for_class in test_modeling_common.py
						elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
							snake_case			,			snake_case			,			snake_case			,			snake_case							=					inputs_dict['pixel_values'].shape
							snake_case							=					torch.zeros(
							    [self.model_tester.batch_size, height, width],						device=lowercase_ ).long()
						snake_case							=					model_class(lowercase_ )
						model.gradient_checkpointing_enable()
						model.to(lowercase_ )
						model.train()
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						snake_case							=					model(**lowercase_ ).loss
						loss.backward()

				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

					snake_case							=					_config_zero_init(lowercase_ )
					for model_class in self.all_model_classes:
						snake_case							=					model_class(config=lowercase_ )
						for name, param in model.named_parameters():
							# we skip lambda parameters as these require special initial values
							# determined by config.layer_scale_init_value
							if "lambda" in name:
								continue
							if param.requires_grad:
								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''',						)

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_=2E-4,						lowercase_="outputs",						lowercase_=None )    ->							Tuple:
					# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
					super().check_pt_tf_outputs(lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowercase_ )






				@slow
				def 					_lowerCamelCase    (       self )    ->							Dict:
					for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case							=					DataaVecVisionModel.from_pretrained(lowercase_ )
						self.assertIsNotNone(lowercase_ )

def      __magic_name__							(			)      ->		Tuple:
	snake_case							=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image







@require_torch
@require_vision
class lowerCamelCase   (    unittest.TestCase				):

				@cached_property
				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					return (
					    BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k' ) if is_vision_available() else None
					)






				@slow
				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k' ).to(
					    lowercase_ )

					snake_case							=					self.default_image_processor
					snake_case							=					prepare_img()
					snake_case							=					image_processor(images=lowercase_,						return_tensors='pt' ).to(lowercase_ )

					# forward pass
					with torch.no_grad():
						snake_case							=					model(**lowercase_ )
					snake_case							=					outputs.logits

					# verify the logits
					snake_case							=					torch.Size((1, 1000) )
					self.assertEqual(logits.shape,						lowercase_ )

					snake_case							=					torch.tensor([0.3_277, -0.1_395, 0.0_911] ).to(lowercase_ )

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

					snake_case							=					[model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
					self.assertEqual(logits[0].topk(2 ).indices.cpu().tolist(),						lowercase_ )












'''simple docstring'''

class      SCREAMING_SNAKE_CASE__      :







					"""simple docstring"""






					def __init__(   self       :      Tuple		,	UpperCAmelCase_       :      Dict		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     val
							# Number of nodes in left subtree
							__UpperCAmelCase	:							Dict     =     0
							__UpperCAmelCase	:							int     =     None
							__UpperCAmelCase	:							Optional[int]     =     None
							__UpperCAmelCase	:							List[str]     =     None






					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      Optional[Any]		):







							"""simple docstring"""
							assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

							if self.val < other.val:
									__UpperCAmelCase	:							str     =     self.right
									__UpperCAmelCase	:							Tuple     =     None
									if self.right:
											__UpperCAmelCase	:							int     =     other
									__UpperCAmelCase	:							List[str]     =     other
									__UpperCAmelCase	:							Any     =     self.left_tree_size * 2 + 1
									return self
							else:
									__UpperCAmelCase	:							List[Any]     =     other.right
									__UpperCAmelCase	:							Union[str, Any]     =     None
									if other.right:
											__UpperCAmelCase	:							List[str]     =     self
									__UpperCAmelCase	:							Optional[Any]     =     self
									__UpperCAmelCase	:							Any     =     other.left_tree_size * 2 + 1
									return other






class      SCREAMING_SNAKE_CASE__      :







					"""simple docstring"""






					def __init__(   self       :      Optional[int]		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Optional[int]=0		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     heap_size
							__UpperCAmelCase	:							Any     =     bottom_root
							__UpperCAmelCase	:							List[str]     =     min_node
					def 		lowerCamelCase_   (   self       :      List[str]		,	UpperCAmelCase_       :      Dict		):







							"""simple docstring"""

							# Empty heaps corner cases
							if other.size == 0:
									return None
							if self.size == 0:
									__UpperCAmelCase	:							Dict     =     other.size
									__UpperCAmelCase	:							Optional[Any]     =     other.bottom_root
									__UpperCAmelCase	:							str     =     other.min_node
									return None
							# Update size
							__UpperCAmelCase	:							str     =     self.size + other.size

							# Update min.node
							if self.min_node.val > other.min_node.val:
									__UpperCAmelCase	:							Tuple     =     other.min_node
							# Merge

							# Order roots by left_subtree_size
							__UpperCAmelCase	:							int     =     []
							__UpperCAmelCase  ,			__UpperCAmelCase	:							List[str]     =     self.bottom_root, other.bottom_root
							while i or j:
									if i and ((not j) or i.left_tree_size < j.left_tree_size):
											combined_roots_list.append((i, True)		)
											__UpperCAmelCase	:							Optional[Any]     =     i.parent
									else:
											combined_roots_list.append((j, False)		)
											__UpperCAmelCase	:							Tuple     =     j.parent
        # Insert links between them
							for i in range(len(UpperCAmelCase_		) - 1		):
									if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
											__UpperCAmelCase	:							Optional[Any]     =     combined_roots_list[i + 1][0]
											__UpperCAmelCase	:							Any     =     combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
							__UpperCAmelCase	:							List[str]     =     combined_roots_list[0][0]
							while i.parent:
									if (
									    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
									) or (
									    i.left_tree_size == i.parent.left_tree_size
									    and i.left_tree_size != i.parent.parent.left_tree_size
									):
											# Neighbouring Nodes
											__UpperCAmelCase	:							List[str]     =     i.left
											__UpperCAmelCase	:							Dict     =     i.parent.parent

											# Merging trees
											__UpperCAmelCase	:							Optional[int]     =     i.merge_trees(i.parent		)

											# Updating links
											__UpperCAmelCase	:							Dict     =     previous_node
											__UpperCAmelCase	:							Union[str, Any]     =     next_node
											if previous_node:
													__UpperCAmelCase	:							List[str]     =     i
											if next_node:
													__UpperCAmelCase	:							Dict     =     i
									else:
											__UpperCAmelCase	:							Optional[int]     =     i.parent
        # Updating self.bottom_root
							while i.left:
									__UpperCAmelCase	:							str     =     i.left
							__UpperCAmelCase	:							List[Any]     =     i

							# Update other
							__UpperCAmelCase	:							int     =     self.size
							__UpperCAmelCase	:							Dict     =     self.bottom_root
							__UpperCAmelCase	:							Union[str, Any]     =     self.min_node

							# Return the merged heap
							return self
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      Optional[int]		):







							"""simple docstring"""
							if self.size == 0:
									__UpperCAmelCase	:							Optional[Any]     =     Node(UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[Any]     =     1
									__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root
							else:
									# Create new node
									__UpperCAmelCase	:							Optional[Any]     =     Node(UpperCAmelCase_		)

									# Update size
									self.size += 1

									# update min_node
									if val < self.min_node.val:
											__UpperCAmelCase	:							Optional[int]     =     new_node
									# Put new_node as a bottom_root in heap
									__UpperCAmelCase	:							Union[str, Any]     =     new_node
									__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root
									__UpperCAmelCase	:							List[str]     =     new_node

									# Consecutively merge roots with same left_tree_size
									while (
									    self.bottom_root.parent
									    and self.bottom_root.left_tree_size
									    == self.bottom_root.parent.left_tree_size
									):
											# Next node
											__UpperCAmelCase	:							Optional[Any]     =     self.bottom_root.parent.parent

											# Merge
											__UpperCAmelCase	:							str     =     self.bottom_root.merge_trees(self.bottom_root.parent		)

											# Update Links
											__UpperCAmelCase	:							int     =     next_node
											__UpperCAmelCase	:							Any     =     None
											if next_node:
													__UpperCAmelCase	:							Any     =     self.bottom_root
					def 		lowerCamelCase_   (   self       :      Any		):







							"""simple docstring"""
							return self.min_node.val
					def 		lowerCamelCase_   (   self       :      str		):







							"""simple docstring"""
							return self.size == 0
					def 		lowerCamelCase_   (   self       :      int		):







							"""simple docstring"""
							__UpperCAmelCase	:							str     =     self.min_node.val

							# Last element in heap corner case
							if self.size == 1:
									# Update size
									__UpperCAmelCase	:							Optional[int]     =     0

									# Update bottom root
									__UpperCAmelCase	:							List[str]     =     None

									# Update min_node
									__UpperCAmelCase	:							int     =     None

									return min_value
							# No right subtree corner case
							# The structure of the tree implies that this should be the bottom root
							# and there is at least one other root
							if self.min_node.right is None:
									# Update size
									self.size -= 1

									# Update bottom root
									__UpperCAmelCase	:							Union[str, Any]     =     self.bottom_root.parent
									__UpperCAmelCase	:							str     =     None

									# Update min_node
									__UpperCAmelCase	:							int     =     self.bottom_root
									__UpperCAmelCase	:							int     =     self.bottom_root.parent
									while i:
											if i.val < self.min_node.val:
													__UpperCAmelCase	:							Union[str, Any]     =     i
											__UpperCAmelCase	:							Optional[int]     =     i.parent
									return min_value
							# General case
							# Find the BinomialHeap of the right subtree of min_node
							__UpperCAmelCase	:							Optional[int]     =     self.min_node.right
							__UpperCAmelCase	:							List[Any]     =     None
							__UpperCAmelCase	:							Optional[int]     =     bottom_of_new
							__UpperCAmelCase	:							Union[str, Any]     =     1

							# Size, min_node and bottom_root
							while bottom_of_new.left:
									__UpperCAmelCase	:							str     =     size_of_new * 2 + 1
									__UpperCAmelCase	:							List[str]     =     bottom_of_new.left
									if bottom_of_new.val < min_of_new.val:
											__UpperCAmelCase	:							Union[str, Any]     =     bottom_of_new
        # Corner case of single root on top left path
							if (not self.min_node.left) and (not self.min_node.parent):
									__UpperCAmelCase	:							Optional[Any]     =     size_of_new
									__UpperCAmelCase	:							List[str]     =     bottom_of_new
									__UpperCAmelCase	:							List[Any]     =     min_of_new
									# print("Single root, multiple nodes case")
									return min_value
							# Remaining cases
							# Construct heap of right subtree
							__UpperCAmelCase	:							Union[str, Any]     =     BinomialHeap(
							    bottom_root=UpperCAmelCase_		,	min_node=UpperCAmelCase_		,	heap_size=UpperCAmelCase_		)

							# Update size
							__UpperCAmelCase	:							Any     =     self.size - 1 - size_of_new

							# Neighbour nodes
							__UpperCAmelCase	:							Union[str, Any]     =     self.min_node.left
							__UpperCAmelCase	:							List[str]     =     self.min_node.parent

							# Initialize new bottom_root and min_node
							__UpperCAmelCase	:							Optional[Any]     =     previous_node or next_node
							__UpperCAmelCase	:							Optional[Any]     =     next_node

							# Update links of previous_node and search below for new min_node and
							# bottom_root
							if previous_node:
									__UpperCAmelCase	:							List[str]     =     next_node

									# Update bottom_root and search for min_node below
									__UpperCAmelCase	:							List[str]     =     previous_node
									__UpperCAmelCase	:							int     =     previous_node
									while self.bottom_root.left:
											__UpperCAmelCase	:							Dict     =     self.bottom_root.left
											if self.bottom_root.val < self.min_node.val:
													__UpperCAmelCase	:							Union[str, Any]     =     self.bottom_root
							if next_node:
									__UpperCAmelCase	:							Tuple     =     previous_node

									# Search for new min_node above min_node
									__UpperCAmelCase	:							Optional[int]     =     next_node
									while i:
											if i.val < self.min_node.val:
													__UpperCAmelCase	:							List[Any]     =     i
											__UpperCAmelCase	:							List[Any]     =     i.parent
        # Merge heaps
							self.merge_heaps(UpperCAmelCase_		)

							return min_value
					def 		lowerCamelCase_   (   self       :      Union[str, Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Tuple     =     self.bottom_root
							while top_root.parent:
									__UpperCAmelCase	:							Optional[int]     =     top_root.parent
							# preorder
							__UpperCAmelCase	:							Union[str, Any]     =     []
							self.__traversal(UpperCAmelCase_		,	UpperCAmelCase_		)
							return heap_pre_order
					def 		lowerCamelCase_   (   self       :      Optional[int]		,	UpperCAmelCase_       :      Optional[Any]		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      Dict=0		):







							"""simple docstring"""
							if curr_node:
									preorder.append((curr_node.val, level)		)
									self.__traversal(curr_node.left		,	UpperCAmelCase_		,	level + 1		)
									self.__traversal(curr_node.right		,	UpperCAmelCase_		,	level + 1		)
							else:
									preorder.append(("#", level)		)






					def __str__(   self       :      Optional[int]		):







							"""simple docstring"""
							if self.is_empty():
									return ""
							__UpperCAmelCase	:							Optional[Any]     =     self.pre_order()

							return "\n".join(("-" * level + str(UpperCAmelCase_		)) for value, level in preorder_heap		)


# Unit Tests
if __name__ == "__main__":
			import doctest

			doctest.testmod()




