

'''simple docstring'''







import argparse
import json
from pathlib import Path

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

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_=False							,       snake_case_=False	):
	_lowercase							=     """backbone.""" if is_semantic else """"""

	_lowercase							=     []
	for i in range(config.num_hidden_layers	):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F"""{prefix}blocks.{i}.norm1.weight""", F"""beit.encoder.layer.{i}.layernorm_before.weight""")	)
		rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""")	)
		rename_keys.append(
		    (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""")	)
		rename_keys.append(
		    (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""")	)
		rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""")	)
		rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""")	)
		rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""")	)
		rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""")	)
		rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""")	)
		rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""")	)

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        (F"""{prefix}cls_token""", """beit.embeddings.cls_token"""),
	        (F"""{prefix}patch_embed.proj.weight""", """beit.embeddings.patch_embeddings.projection.weight"""),
	        (F"""{prefix}patch_embed.proj.bias""", """beit.embeddings.patch_embeddings.projection.bias"""),
	    ]	)

	if has_lm_head:
		# mask token + shared relative position bias + layernorm
		rename_keys.extend(
		    [
		        ("""mask_token""", """beit.embeddings.mask_token"""),
		        (
		            """rel_pos_bias.relative_position_bias_table""",
		            """beit.encoder.relative_position_bias.relative_position_bias_table""",
		        ),
		        (
		            """rel_pos_bias.relative_position_index""",
		            """beit.encoder.relative_position_bias.relative_position_index""",
		        ),
		        ("""norm.weight""", """layernorm.weight"""),
		        ("""norm.bias""", """layernorm.bias"""),
		    ]	)
	elif is_semantic:
		# semantic segmentation classification heads
		rename_keys.extend(
		    [
		        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
		        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
		        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
		        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
		    ]	)
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
		        ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
		        ("""head.weight""", """classifier.weight"""),
		        ("""head.bias""", """classifier.bias"""),
		    ]	)

	return rename_keys






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_=False							,       snake_case_=False	):
	for i in range(config.num_hidden_layers	):
		_lowercase							=     """backbone.""" if is_semantic else """"""
		# queries, keys and values
		_lowercase							=     state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""	)
		_lowercase							=     state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""	)
		_lowercase							=     state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""	)

		_lowercase							=     in_proj_weight[
		    : config.hidden_size, :
		]
		_lowercase							=     q_bias
		_lowercase							=     in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		_lowercase							=     in_proj_weight[
		    -config.hidden_size :, :
		]
		_lowercase							=     v_bias

		# gamma_1 and gamma_2
		# we call them lambda because otherwise they are renamed when using .from_pretrained
		_lowercase							=     state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""	)
		_lowercase							=     state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""	)

		_lowercase							=     gamma_a
		_lowercase							=     gamma_a

		# relative_position bias table + index
		if not has_lm_head:
			# each layer has its own relative position bias
			_lowercase							=     state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""	)
			_lowercase							=     state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""	)

			_lowercase							=     table
			_lowercase							=     index






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     dct.pop(snake_case_	)
	_lowercase							=     val






def 					_SCREAMING_SNAKE_CASE		(						):
	_lowercase							=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
	_lowercase							=     Image.open(requests.get(snake_case_							,       stream=snake_case_	).raw	)
	return im






@torch.no_grad()
def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	_lowercase							=     BeitConfig()
	_lowercase							=     False
	_lowercase							=     False
	_lowercase							=     """huggingface/label-files"""
	# set config parameters based on URL
	if checkpoint_url[-9:-4] == "pt22k":
		# masked image modeling
		_lowercase							=     True
		_lowercase							=     True
		_lowercase							=     True
	elif checkpoint_url[-9:-4] == "ft22k":
		# intermediate fine-tuning on ImageNet-22k
		_lowercase							=     True
		_lowercase							=     21841
		_lowercase							=     """imagenet-22k-id2label.json"""
		_lowercase							=     json.load(open(hf_hub_download(snake_case_							,       snake_case_							,       repo_type="""dataset"""	)							,       """r"""	)	)
		_lowercase							=     {int(snake_case_	): v for k, v in idalabel.items()}
		# this dataset contains 21843 labels but the model only has 21841
		# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
		del idalabel[9205]
		del idalabel[15027]
		_lowercase							=     idalabel
		_lowercase							=     {v: k for k, v in idalabel.items()}
	elif checkpoint_url[-8:-4] == "to1k":
		# fine-tuning on ImageNet-1k
		_lowercase							=     True
		_lowercase							=     1000
		_lowercase							=     """imagenet-1k-id2label.json"""
		_lowercase							=     json.load(open(hf_hub_download(snake_case_							,       snake_case_							,       repo_type="""dataset"""	)							,       """r"""	)	)
		_lowercase							=     {int(snake_case_	): v for k, v in idalabel.items()}
		_lowercase							=     idalabel
		_lowercase							=     {v: k for k, v in idalabel.items()}
		if "384" in checkpoint_url:
			_lowercase							=     384
		if "512" in checkpoint_url:
			_lowercase							=     512
	elif "ade20k" in checkpoint_url:
		# fine-tuning
		_lowercase							=     True
		_lowercase							=     150
		_lowercase							=     """ade20k-id2label.json"""
		_lowercase							=     json.load(open(hf_hub_download(snake_case_							,       snake_case_							,       repo_type="""dataset"""	)							,       """r"""	)	)
		_lowercase							=     {int(snake_case_	): v for k, v in idalabel.items()}
		_lowercase							=     idalabel
		_lowercase							=     {v: k for k, v in idalabel.items()}
		_lowercase							=     640
		_lowercase							=     True
	else:
		raise ValueError("""Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"""	)

	# size of the architecture
	if "base" in checkpoint_url:
		pass
	elif "large" in checkpoint_url:
		_lowercase							=     1024
		_lowercase							=     4096
		_lowercase							=     24
		_lowercase							=     16
		if "ade20k" in checkpoint_url:
			_lowercase							=     640
			_lowercase							=     [7, 11, 15, 23]
	else:
		raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""	)

	# load state_dict of original model, remove and rename some keys
	_lowercase							=     torch.hub.load_state_dict_from_url(snake_case_							,       map_location="""cpu"""							,       check_hash=snake_case_	)
	_lowercase							=     state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

	_lowercase							=     create_rename_keys(snake_case_							,       has_lm_head=snake_case_							,       is_semantic=snake_case_	)
	for src, dest in rename_keys:
		rename_key(snake_case_							,       snake_case_							,       snake_case_	)
	read_in_q_k_v(snake_case_							,       snake_case_							,       has_lm_head=snake_case_							,       is_semantic=snake_case_	)
	if is_semantic:
		# add prefix to decoder keys
		for key, val in state_dict.copy().items():
			_lowercase							=     state_dict.pop(snake_case_	)
			if key.startswith("""backbone.fpn"""	):
				_lowercase							=     key.replace("""backbone.fpn"""							,       """fpn"""	)
			_lowercase							=     val

    # load HuggingFace model
	if checkpoint_url[-9:-4] == "pt22k":
		_lowercase							=     BeitForMaskedImageModeling(snake_case_	)
	elif "ade20k" in checkpoint_url:
		_lowercase							=     BeitForSemanticSegmentation(snake_case_	)
	else:
		_lowercase							=     BeitForImageClassification(snake_case_	)
	model.eval()
	model.load_state_dict(snake_case_	)

	# Check outputs on an image
	if is_semantic:
		_lowercase							=     BeitImageProcessor(size=config.image_size							,       do_center_crop=snake_case_	)
		_lowercase							=     load_dataset("""hf-internal-testing/fixtures_ade20k"""							,       split="""test"""	)
		_lowercase							=     Image.open(ds[0]["""file"""]	)
	else:
		_lowercase							=     BeitImageProcessor(
		    size=config.image_size							,       resample=PILImageResampling.BILINEAR							,       do_center_crop=snake_case_	)
		_lowercase							=     prepare_img()

	_lowercase							=     image_processor(images=snake_case_							,       return_tensors="""pt"""	)
	_lowercase							=     encoding["""pixel_values"""]

	_lowercase							=     model(snake_case_	)
	_lowercase							=     outputs.logits

	# verify logits
	_lowercase							=     torch.Size([1, 1000]	)
	if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""	):
		_lowercase							=     torch.Size([1, 196, 8192]	)
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""	):
		_lowercase							=     torch.Size([1, 196, 8192]	)
	elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""	):
		_lowercase							=     torch.Size([1, 21841]	)
		_lowercase							=     torch.tensor([2.2288, 2.4671, 0.7395]	)
		_lowercase							=     2397
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""	):
		_lowercase							=     torch.Size([1, 21841]	)
		_lowercase							=     torch.tensor([1.6881, -0.2787, 0.5901]	)
		_lowercase							=     2396
	elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""	):
		_lowercase							=     torch.tensor([0.1241, 0.0798, -0.6569]	)
		_lowercase							=     285
	elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""	):
		_lowercase							=     torch.tensor([-1.2385, -1.0987, -1.0108]	)
		_lowercase							=     281
	elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""	):
		_lowercase							=     torch.tensor([-1.5303, -0.9484, -0.3147]	)
		_lowercase							=     761
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""	):
		_lowercase							=     torch.tensor([0.4610, -0.0928, 0.2086]	)
		_lowercase							=     761
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""	):
		_lowercase							=     torch.tensor([-0.4804, 0.6257, -0.1837]	)
		_lowercase							=     761
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""	):
		_lowercase							=     torch.tensor([[-0.5122, 0.5117, -0.2113]]	)
		_lowercase							=     761
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""	):
		_lowercase							=     torch.tensor([-0.3062, 0.7261, 0.4852]	)
		_lowercase							=     761
	elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""	):
		_lowercase							=     (1, 150, 160, 160)
		_lowercase							=     torch.tensor(
		    [
		        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
		        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
		        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
		    ]	)
	elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""	):
		_lowercase							=     (1, 150, 160, 160)
		_lowercase							=     torch.tensor(
		    [
		        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
		        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
		        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
		    ]	)
	else:
		raise ValueError("""Can't verify logits as model is not supported"""	)

	assert logits.shape == expected_shape, "Shape of logits not as expected"
	if not has_lm_head:
		if is_semantic:
			assert torch.allclose(
			    logits[0, :3, :3, :3]							,       snake_case_							,       atol=1e-3	), "First elements of logits not as expected"
		else:
			print("""Predicted class idx:"""							,       logits.argmax(-1	).item()	)
			assert torch.allclose(
			    logits[0, :3]							,       snake_case_							,       atol=1e-3	), "First elements of logits not as expected"
			assert logits.argmax(-1	).item() == expected_class_idx, "Predicted class index not as expected"

	Path(snake_case_	).mkdir(exist_ok=snake_case_	)
	print(F"""Saving model to {pytorch_dump_folder_path}"""	)
	model.save_pretrained(snake_case_	)
	print(F"""Saving image processor to {pytorch_dump_folder_path}"""	)
	image_processor.save_pretrained(snake_case_	)


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

							parser.add_argument(
							    '--checkpoint_url',
							    default='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth',
							    type=str,
							    help='URL to the original PyTorch checkpoint (.pth file).',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
							)
							_lowerCamelCase    =  parser.parse_args()
							convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''







class 					__a   :

						def __init__(		self	:						str    ,			lowercase__	:						List[str])						->List[str]:

							"""simple docstring"""



							_lowercase							=     val
							# Number of nodes in left subtree
							_lowercase							=     0
							_lowercase							=     None
							_lowercase							=     None
							_lowercase							=     None


						def 	_UpperCAmelCase							(		self	:						List[Any]    ,			lowercase__	:						Union[str, Any])						->str:

							"""simple docstring"""



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

							if self.val < other.val:
								_lowercase							=     self.right
								_lowercase							=     None
								if self.right:
									_lowercase							=     other
								_lowercase							=     other
								_lowercase							=     self.left_tree_size * 2 + 1
								return self
							else:
								_lowercase							=     other.right
								_lowercase							=     None
								if other.right:
									_lowercase							=     self
								_lowercase							=     self
								_lowercase							=     other.left_tree_size * 2 + 1
								return other






class 					__a   :

						def __init__(		self	:						Optional[Any]    ,			lowercase__	:						List[Any]=None    ,			lowercase__	:						Any=None    ,			lowercase__	:						Any=0)						->Optional[int]:

							"""simple docstring"""



							_lowercase							=     heap_size
							_lowercase							=     bottom_root
							_lowercase							=     min_node

						def 	_UpperCAmelCase							(		self	:						Dict    ,			lowercase__	:						Union[str, Any])						->Any:

							"""simple docstring"""



							if other.size == 0:
								return None
							if self.size == 0:
								_lowercase							=     other.size
								_lowercase							=     other.bottom_root
								_lowercase							=     other.min_node
								return None
							# Update size
							_lowercase							=     self.size + other.size

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

							# Order roots by left_subtree_size
							_lowercase							=     []
							_lowercase		,		_lowercase							=     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))
									_lowercase							=     i.parent
								else:
									combined_roots_list.append((j, False))
									_lowercase							=     j.parent
        # Insert links between them
							for i in range(len(lowercase__) - 1):
								if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
									_lowercase							=     combined_roots_list[i + 1][0]
									_lowercase							=     combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
							_lowercase							=     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
									_lowercase							=     i.left
									_lowercase							=     i.parent.parent

									# Merging trees
									_lowercase							=     i.merge_trees(i.parent)

									# Updating links
									_lowercase							=     previous_node
									_lowercase							=     next_node
									if previous_node:
										_lowercase							=     i
									if next_node:
										_lowercase							=     i
								else:
									_lowercase							=     i.parent
        # Updating self.bottom_root
							while i.left:
								_lowercase							=     i.left
							_lowercase							=     i

							# Update other
							_lowercase							=     self.size
							_lowercase							=     self.bottom_root
							_lowercase							=     self.min_node

							# Return the merged heap
							return self

						def 	_UpperCAmelCase							(		self	:						Optional[Any]    ,			lowercase__	:						List[Any])						->List[str]:

							"""simple docstring"""



							if self.size == 0:
								_lowercase							=     Node(lowercase__)
								_lowercase							=     1
								_lowercase							=     self.bottom_root
							else:
								# Create new node
								_lowercase							=     Node(lowercase__)

								# Update size
								self.size += 1

								# update min_node
								if val < self.min_node.val:
									_lowercase							=     new_node
								# Put new_node as a bottom_root in heap
								_lowercase							=     new_node
								_lowercase							=     self.bottom_root
								_lowercase							=     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
									_lowercase							=     self.bottom_root.parent.parent

									# Merge
									_lowercase							=     self.bottom_root.merge_trees(self.bottom_root.parent)

									# Update Links
									_lowercase							=     next_node
									_lowercase							=     None
									if next_node:
										_lowercase							=     self.bottom_root

						def 	_UpperCAmelCase							(		self	:						Optional[Any])						->List[str]:

							"""simple docstring"""



							return self.min_node.val

						def 	_UpperCAmelCase							(		self	:						Optional[int])						->Tuple:

							"""simple docstring"""



							return self.size == 0

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

							"""simple docstring"""



							_lowercase							=     self.min_node.val

							# Last element in heap corner case
							if self.size == 1:
								# Update size
								_lowercase							=     0

								# Update bottom root
								_lowercase							=     None

								# Update min_node
								_lowercase							=     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
								_lowercase							=     self.bottom_root.parent
								_lowercase							=     None

								# Update min_node
								_lowercase							=     self.bottom_root
								_lowercase							=     self.bottom_root.parent
								while i:
									if i.val < self.min_node.val:
										_lowercase							=     i
									_lowercase							=     i.parent
								return min_value
							# General case
							# Find the BinomialHeap of the right subtree of min_node
							_lowercase							=     self.min_node.right
							_lowercase							=     None
							_lowercase							=     bottom_of_new
							_lowercase							=     1

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

							# Update size
							_lowercase							=     self.size - 1 - size_of_new

							# Neighbour nodes
							_lowercase							=     self.min_node.left
							_lowercase							=     self.min_node.parent

							# Initialize new bottom_root and min_node
							_lowercase							=     previous_node or next_node
							_lowercase							=     next_node

							# Update links of previous_node and search below for new min_node and
							# bottom_root
							if previous_node:
								_lowercase							=     next_node

								# Update bottom_root and search for min_node below
								_lowercase							=     previous_node
								_lowercase							=     previous_node
								while self.bottom_root.left:
									_lowercase							=     self.bottom_root.left
									if self.bottom_root.val < self.min_node.val:
										_lowercase							=     self.bottom_root
							if next_node:
								_lowercase							=     previous_node

								# Search for new min_node above min_node
								_lowercase							=     next_node
								while i:
									if i.val < self.min_node.val:
										_lowercase							=     i
									_lowercase							=     i.parent
        # Merge heaps
							self.merge_heaps(lowercase__)

							return min_value

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

							"""simple docstring"""



							_lowercase							=     self.bottom_root
							while top_root.parent:
								_lowercase							=     top_root.parent
							# preorder
							_lowercase							=     []
							self.__traversal(lowercase__    ,			lowercase__)
							return heap_pre_order

						def 	_UpperCAmelCase							(		self	:						Dict    ,			lowercase__	:						Union[str, Any]    ,			lowercase__	:						List[Any]    ,			lowercase__	:						Optional[int]=0)						->int:

							"""simple docstring"""



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


						def __str__(		self	:						Optional[Any])						->str:

							"""simple docstring"""



							if self.is_empty():
								return ""
							_lowercase							=     self.pre_order()

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


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

							doctest.testmod()




