


'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase       =							logging.get_logger(__name__)


def  _snake_case       (					A	)						->   Dict:
							# initialize config
							if "resnet-50" in model_name:
														lowerCAmelCase__	       =     ResNetConfig.from_pretrained('''microsoft/resnet-50'''	)
							elif "resnet-101" in model_name:
														lowerCAmelCase__	       =     ResNetConfig.from_pretrained('''microsoft/resnet-101'''	)
							else:
														raise ValueError('''Model name should include either resnet50 or resnet101'''	)

							lowerCAmelCase__	       =     DetrConfig(use_timm_backbone=A			,   backbone_config=A	)

							# set label attributes
							lowerCAmelCase__	       =     '''panoptic''' in model_name
							if is_panoptic:
														lowerCAmelCase__	       =     250
							else:
														lowerCAmelCase__	       =     91
														lowerCAmelCase__	       =     '''huggingface/label-files'''
														lowerCAmelCase__	       =     '''coco-detection-id2label.json'''
														lowerCAmelCase__	       =     json.load(open(hf_hub_download(A			,   A			,   repo_type='''dataset'''	)			,   '''r'''	)	)
														lowerCAmelCase__	       =     {int(A	): v for k, v in idalabel.items()}
														lowerCAmelCase__	       =     idalabel
														lowerCAmelCase__	       =     {v: k for k, v in idalabel.items()}

							return config, is_panoptic


def  _snake_case       (					A	)						->   Tuple:
							# here we list all keys to be renamed (original name on the left, our name on the right)
							lowerCAmelCase__	       =     []

							# stem
							# fmt: off
							rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')	)
							rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')	)
							rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')	)
							rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')	)
							rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''')	)
							# stages
							for stage_idx in range(len(config.backbone_config.depths	)	):
														for layer_idx in range(config.backbone_config.depths[stage_idx]	):
																					# shortcut
																					if layer_idx == 0:
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
																												    )	)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
																												    )	)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
																												    )	)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
																												    )	)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
																												    )	)
																					# 3 convs
																					for i in range(3	):
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
																												    )	)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
																												    )	)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
																												    )	)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
																												    )	)
																												rename_keys.append(
																												    (
																												        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
																												        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																												    )	)
    # fmt: on

							for i in range(config.encoder_layers	):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append(
														    (
														        F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
														        F"""encoder.layers.{i}.self_attn.out_proj.weight""",
														    )	)
														rename_keys.append(
														    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")	)
														rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""")	)
														rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""")	)
														rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""")	)
														rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""")	)
														rename_keys.append(
														    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")	)
														rename_keys.append(
														    (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""")	)
														rename_keys.append(
														    (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""")	)
														rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""")	)
														# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
														rename_keys.append(
														    (
														        F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
														        F"""decoder.layers.{i}.self_attn.out_proj.weight""",
														    )	)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")	)
														rename_keys.append(
														    (
														        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
														        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
														    )	)
														rename_keys.append(
														    (
														        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
														        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
														    )	)
														rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""")	)
														rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""")	)
														rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""")	)
														rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""")	)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")	)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""")	)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")	)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")	)
														rename_keys.append(
														    (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""")	)
														rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""")	)

							# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
							rename_keys.extend(
							    [
							        ('''input_proj.weight''', '''input_projection.weight'''),
							        ('''input_proj.bias''', '''input_projection.bias'''),
							        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
							        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
							        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
							        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
							        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
							        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
							        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
							        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
							        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
							        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
							        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
							    ]	)

							return rename_keys


def  _snake_case       (					A			,   A			,   A	)						->   Optional[int]:
							lowerCAmelCase__	       =     state_dict.pop(A	)
							lowerCAmelCase__	       =     val


def  _snake_case       (					A			,   A=False	)						->   str:
							lowerCAmelCase__	       =     ''''''
							if is_panoptic:
														lowerCAmelCase__	       =     '''detr.'''

							# first: transformer encoder
							for i in range(6	):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														lowerCAmelCase__	       =     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""	)
														lowerCAmelCase__	       =     state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""	)
														# next, add query, keys and values (in that order) to the state dict
														lowerCAmelCase__	       =     in_proj_weight[:256, :]
														lowerCAmelCase__	       =     in_proj_bias[:256]
														lowerCAmelCase__	       =     in_proj_weight[256:512, :]
														lowerCAmelCase__	       =     in_proj_bias[256:512]
														lowerCAmelCase__	       =     in_proj_weight[-256:, :]
														lowerCAmelCase__	       =     in_proj_bias[-256:]
							# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
							for i in range(6	):
														# read in weights + bias of input projection layer of self-attention
														lowerCAmelCase__	       =     state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
														lowerCAmelCase__	       =     state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""	)
														# next, add query, keys and values (in that order) to the state dict
														lowerCAmelCase__	       =     in_proj_weight[:256, :]
														lowerCAmelCase__	       =     in_proj_bias[:256]
														lowerCAmelCase__	       =     in_proj_weight[256:512, :]
														lowerCAmelCase__	       =     in_proj_bias[256:512]
														lowerCAmelCase__	       =     in_proj_weight[-256:, :]
														lowerCAmelCase__	       =     in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														lowerCAmelCase__	       =     state_dict.pop(
														    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""	)
														lowerCAmelCase__	       =     state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""	)
														# next, add query, keys and values (in that order) of cross-attention to the state dict
														lowerCAmelCase__	       =     in_proj_weight_cross_attn[:256, :]
														lowerCAmelCase__	       =     in_proj_bias_cross_attn[:256]
														lowerCAmelCase__	       =     in_proj_weight_cross_attn[256:512, :]
														lowerCAmelCase__	       =     in_proj_bias_cross_attn[256:512]
														lowerCAmelCase__	       =     in_proj_weight_cross_attn[-256:, :]
														lowerCAmelCase__	       =     in_proj_bias_cross_attn[-256:]


def  _snake_case       (					)						->   List[Any]:
							lowerCAmelCase__	       =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							lowerCAmelCase__	       =     Image.open(requests.get(A			,   stream=A	).raw	)

							return im


@torch.no_grad()
def  _snake_case       (					A			,   A=None			,   A=False	)						->   Tuple:
							lowerCAmelCase__       , lowerCAmelCase__	       =     get_detr_config(A	)

							# load original model from torch hub
							lowerCAmelCase__	       =     {
							    '''detr-resnet-50''': '''detr_resnet50''',
							    '''detr-resnet-101''': '''detr_resnet101''',
							}
							logger.info(F"""Converting model {model_name}..."""	)
							lowerCAmelCase__	       =     torch.hub.load('''facebookresearch/detr'''			,   model_name_to_original_name[model_name]			,   pretrained=A	).eval()
							lowerCAmelCase__	       =     detr.state_dict()
							# rename keys
							for src, dest in create_rename_keys(A	):
														if is_panoptic:
																					lowerCAmelCase__	       =     '''detr.''' + src
														rename_key(A			,   A			,   A	)
							# query, key and value matrices need special treatment
							read_in_q_k_v(A			,   is_panoptic=A	)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							lowerCAmelCase__	       =     '''detr.model.''' if is_panoptic else '''model.'''
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith('''detr'''	)
																					    and not key.startswith('''class_labels_classifier'''	)
																					    and not key.startswith('''bbox_predictor'''	)
																					):
																												lowerCAmelCase__	       =     state_dict.pop(A	)
																												lowerCAmelCase__	       =     val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												lowerCAmelCase__	       =     state_dict.pop(A	)
																												lowerCAmelCase__	       =     val
																					elif key.startswith('''bbox_attention'''	) or key.startswith('''mask_head'''	):
																												continue
																					else:
																												lowerCAmelCase__	       =     state_dict.pop(A	)
																												lowerCAmelCase__	       =     val
														else:
																					if not key.startswith('''class_labels_classifier'''	) and not key.startswith('''bbox_predictor'''	):
																												lowerCAmelCase__	       =     state_dict.pop(A	)
																												lowerCAmelCase__	       =     val

    # finally, create HuggingFace model and load state dict
							lowerCAmelCase__	       =     DetrForSegmentation(A	) if is_panoptic else DetrForObjectDetection(A	)
							model.load_state_dict(A	)
							model.eval()

							# verify our conversion on an image
							lowerCAmelCase__	       =     '''coco_panoptic''' if is_panoptic else '''coco_detection'''
							lowerCAmelCase__	       =     DetrImageProcessor(format=A	)

							lowerCAmelCase__	       =     processor(images=prepare_img()			,   return_tensors='''pt'''	)
							lowerCAmelCase__	       =     encoding['''pixel_values''']

							lowerCAmelCase__	       =     detr(A	)
							lowerCAmelCase__	       =     model(A	)

							assert torch.allclose(outputs.logits			,   original_outputs['''pred_logits''']			,   atol=1E-3	)
							assert torch.allclose(outputs.pred_boxes			,   original_outputs['''pred_boxes''']			,   atol=1E-3	)
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks			,   original_outputs['''pred_masks''']			,   atol=1E-4	)
							print('''Looks ok!'''	)

							if pytorch_dump_folder_path is not None:
														# Save model and image processor
														logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""	)
														Path(A	).mkdir(exist_ok=A	)
														model.save_pretrained(A	)
														processor.save_pretrained(A	)

							if push_to_hub:
														# Upload model and image processor to the hub
														logger.info('''Uploading PyTorch model and image processor to the hub...'''	)
														model.push_to_hub(F"""nielsr/{model_name}"""	)
														processor.push_to_hub(F"""nielsr/{model_name}"""	)


if __name__ == "__main__":
		__UpperCAmelCase       =							argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name''',
		    default='''detr-resnet-50''',
		    type=str,
		    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
		    help='''Name of the DETR model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
		__UpperCAmelCase       =							parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



'''simple docstring'''






from collections.abc import Iterable
from typing import Any




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ = None       )	->							List[str]:
														lowerCAmelCase__	       =     value
														lowerCAmelCase__	       =     None  # Added in order to delete a node easier
														lowerCAmelCase__	       =     None
														lowerCAmelCase__	       =     None







							def __repr__(		self       )	->							str:
														from pprint import pformat

														if self.left is None and self.right is None:
																					return str(self.value       )
														return pformat({F"""{self.value}""": (self.left, self.right)} ,   indent=1       )




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ = None       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     root







							def __str__(		self       )	->							str:
														return str(self.root       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if new_children is not None:  # reset its kids
																					lowerCAmelCase__	       =     node.parent
														if node.parent is not None:  # reset its parent
																					if self.is_right(lowerCamelCase_       ):  # If it is the right children
																												lowerCAmelCase__	       =     new_children
																					else:
																												lowerCAmelCase__	       =     new_children
														else:
																					lowerCAmelCase__	       =     new_children







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							bool:
														if node.parent and node.parent.right:
																					return node == node.parent.right
														return False







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							bool:
														return self.root is None







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     Node(lowerCamelCase_       )  # create a new Node
														if self.empty():  # if Tree is empty
																					lowerCAmelCase__	       =     new_node  # set its root
														else:  # Tree is not empty
																					lowerCAmelCase__	       =     self.root  # from root
																					if parent_node is None:
																												return
																					while True:  # While we don't get to a leaf
																												if value < parent_node.value:  # We go left
																																			if parent_node.left is None:
																																										lowerCAmelCase__	       =     new_node  # We insert the new node in a leaf
																																										break
																																			else:
																																										lowerCAmelCase__	       =     parent_node.left
																												else:
																																			if parent_node.right is None:
																																										lowerCAmelCase__	       =     new_node
																																										break
																																			else:
																																										lowerCAmelCase__	       =     parent_node.right
																					lowerCAmelCase__	       =     parent_node







							def   __SCREAMING_SNAKE_CASE							(		self ,   *lowerCamelCase_       )	->							None:
														for value in values:
																					self.__insert(lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Node | None:
														if self.empty():
																					raise IndexError('''Warning: Tree is empty! please use another.'''       )
														else:
																					lowerCAmelCase__	       =     self.root
																					# use lazy evaluation here to avoid NoneType Attribute error
																					while node is not None and node.value is not value:
																												lowerCAmelCase__	       =     node.left if value < node.value else node.right
																					return node







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ = None       )	->							Node | None:
														if node is None:
																					if self.root is None:
																												return None
																					lowerCAmelCase__	       =     self.root

														if not self.empty():
																					while node.right is not None:
																												lowerCAmelCase__	       =     node.right
														return node







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ = None       )	->							Node | None:
														if node is None:
																					lowerCAmelCase__	       =     self.root
														if self.root is None:
																					return None
														if not self.empty():
																					lowerCAmelCase__	       =     self.root
																					while node.left is not None:
																												lowerCAmelCase__	       =     node.left
														return node







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     self.search(lowerCamelCase_       )  # Look for the node with that label
														if node is not None:
																					if node.left is None and node.right is None:  # If it has no children
																												self.__reassign_nodes(lowerCamelCase_ ,   lowerCamelCase_       )
																					elif node.left is None:  # Has only right children
																												self.__reassign_nodes(lowerCamelCase_ ,   node.right       )
																					elif node.right is None:  # Has only left children
																												self.__reassign_nodes(lowerCamelCase_ ,   node.left       )
																					else:
																												lowerCAmelCase__	       =     self.get_max(
																												    node.left       )  # Gets the max value of the left branch
																												self.remove(tmp_node.value       )  # type: ignore
																												lowerCAmelCase__	       =     (
																												    tmp_node.value  # type: ignore
																												)  # Assigns the value to the node to delete and keep tree structure







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Iterable:
														if node is not None:
																					yield node  # Preorder Traversal
																					yield from self.preorder_traverse(node.left       )
																					yield from self.preorder_traverse(node.right       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_=None       )	->							Any:
														if traversal_function is None:
																					return self.preorder_traverse(self.root       )
														else:
																					return traversal_function(self.root       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if node:
																					self.inorder(lowerCamelCase_ ,   node.left       )
																					arr.append(node.value       )
																					self.inorder(lowerCamelCase_ ,   node.right       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							int:
														lowerCAmelCase__	       =     []
														self.inorder(lowerCamelCase_ ,   lowerCamelCase_       )  # append all values to list using inorder traversal
														return arr[k - 1]


def  _snake_case       (					A	)						->   list[Node]:
							lowerCAmelCase__	       =     []
							if curr_node is not None:
														lowerCAmelCase__	       =     postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
							return node_list


def  _snake_case       (					)						->   None:
							lowerCAmelCase__	       =     (8, 3, 6, 1, 10, 14, 13, 4, 7)
							lowerCAmelCase__	       =     BinarySearchTree()
							for i in testlist:
														t.insert(A	)

							# Prints all the elements of the list in order traversal
							print(A	)

							if t.search(6	) is not None:
														print('''The value 6 exists'''	)
							else:
														print('''The value 6 doesn\'t exist'''	)

							if t.search(-1	) is not None:
														print('''The value -1 exists'''	)
							else:
														print('''The value -1 doesn\'t exist'''	)

							if not t.empty():
														print('''Max Value: '''			,   t.get_max().value	)  # type: ignore
														print('''Min Value: '''			,   t.get_min().value	)  # type: ignore

							for i in testlist:
														t.remove(A	)
														print(A	)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)