code: 
  
  
  
  
  
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import List, Optional
  
  import torch
  from datasets import Dataset
  from torch import nn
  from tqdm.auto import tqdm
  
  from transformers import (
      AutoModelForSequenceClassification,
      AutoTokenizer,
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      set_seed,
      utils,
  )
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
  
  
  _lowercase		:     Optional[int] 					="""
  Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
  names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
  when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
  dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
  class names.
  """
  
  _lowercase		:     List[str] 					=logging.getLogger(__name__)
  
  
  
  
  
  
  @dataclass
  class 	UpperCamelCase_     :
  					_a			:	Optional[str]							=				field(
  					    default='roberta-large-mnli'     ,    metadata={'help': 'The NLI/zero-shot teacher model to be distilled.'}       )
  					_a			:	Optional[str]							=				field(
  					    default='This example is {}.'     ,    metadata={
  					        'help': (
  					            'Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'
  					            'where class name is inserted.'
  					        )
  					    }     ,    )
  					_a			:	Optional[int]							=				field(
  					    default=3_2     ,    metadata={'help': 'Batch size for generating teacher predictions.'}       )
  					_a			:	Optional[bool]							=				field(
  					    default=snake_case__     ,    metadata={
  					        'help': (
  					            'Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'
  					            'multi-class multi-label classification).'
  					        )
  					    }     ,    )
  					_a			:	Optional[float]							=				field(
  					    default=1.0     ,    metadata={'help': 'Temperature applied to teacher softmax for distillation.'}       )
  
  
  
  
  
  
  @dataclass
  class 	UpperCamelCase_     :
  					_a			:	Optional[str]							=				field(
  					    default='distilbert-base-uncased'     ,    metadata={'help': 'The NLI/zero-shot teacher model to be distilled.'}       )
  
  
  
  
  
  
  @dataclass
  class 	UpperCamelCase_     :
  					_a			:	str							=				field(metadata={'help': 'Text file with one unlabeled instance per line.'}       )
  					_a			:	str							=				field(metadata={'help': 'Text file with one class name per line.'}       )
  					_a			:	bool							=				field(
  					    default=snake_case__     ,    metadata={'help': 'Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'}     ,    )
  
  
  
  
  
  
  @dataclass
  class 	UpperCamelCase_     (     snake_case__       ):
  					_a			:	Optional[str]							=				field(
  					    default=snake_case__     ,    metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}     ,    )
  					_a			:	int							=				field(
  					    default=3_2     ,    metadata={'help': 'Batch size per GPU/TPU core/CPU for training.'}       )
  					_a			:	int							=				field(
  					    default=1_2_8     ,    metadata={'help': 'Batch size per GPU/TPU core/CPU for evaluation.'}       )
  					_a			:	float							=				field(default=1.0     ,    metadata={'help': 'Total number of training epochs to perform.'}       )
  					_a			:	bool							=				field(default=snake_case__     ,    metadata={'help': 'Whether to run training of student model.'}       )
  					_a			:	bool							=				field(
  					    default=snake_case__     ,    metadata={
  					        'help': (
  					            'Whether to evaluate the agreement of the final student predictions and the teacher predictions'
  					            'after training.'
  					        )
  					    }     ,    )
  					_a			:	Optional[int]							=				field(
  					    default=0     ,    metadata={
  					        'help': (
  					            'Limit the total amount of checkpoints.'
  					            'Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'
  					        )
  					    }     ,    )
  
  
  
  
  
  
  class 	UpperCamelCase_     (     snake_case__       ):
  
  
  
  					def      __a    (		self    :    Any					,			lowerCamelCase    :    List[Any]					,			lowerCamelCase    :    str					,			lowerCamelCase    :    Optional[int]=False	):
  							lowerCamelCase_  :    Dict   	=			inputs['labels']
  							lowerCamelCase_  :    Dict   	=			model(inputs['input_ids']					,			attention_mask=inputs['attention_mask']	)
  							lowerCamelCase_  :    List[str]   	=			outputs[0]
  
  							lowerCamelCase_  :    Tuple   	=			-torch.sum(target_p * logits.log_softmax(dim=-1	)					,			axis=-1	).mean()
  
  							if return_outputs:
  									return loss, outputs
  
  							return loss
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		lowerCamelCase_  :    str   	=			[]
  		with open(lowerCAmelCase__       ,'r'	) as f:
  				for line in f:
  						lowerCamelCase_  :    Any   	=			line.strip()
  						if len(lowerCAmelCase__	) > 0:
  								lines.append(lowerCAmelCase__	)
  		return lines
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	):
  		lowerCamelCase_  :    List[Any]   	=			[]
  		lowerCamelCase_  :    Optional[Any]   	=			[]
  		for example in examples:
  				for name in class_names:
  						premises.append(lowerCAmelCase__	)
  						hypotheses.append(hypothesis_template.format(lowerCAmelCase__	)	)
  		return premises, hypotheses
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		for label, ind in config.labelaid.items():
  				if label.lower().startswith('entail'	):
  						return ind
  		logger.warning('Could not identify entailment dimension from teacher config label2id. Setting to -1.'	)
  		return -1
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,):
  		lowerCamelCase_  :    Optional[Any]   	=			AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__	)
  		lowerCamelCase_  :    Optional[Any]   	=			model.config
  		if not no_cuda and torch.cuda.is_available():
  				lowerCamelCase_  :    Dict   	=			nn.DataParallel(model.cuda()	)
  				batch_size *= len(model.device_ids	)
  		lowerCamelCase_  :    Any   	=			AutoTokenizer.from_pretrained(lowerCAmelCase__       ,use_fast=lowerCAmelCase__	)
  
  		lowerCamelCase_	,			lowerCamelCase_  :    Dict   	=			get_premise_hypothesis_pairs(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__	)
  		lowerCamelCase_  :    str   	=			[]
  
  		for i in tqdm(range(0       ,len(lowerCAmelCase__	)       ,lowerCAmelCase__	)	):
  				lowerCamelCase_  :    Optional[int]   	=			premises[i : i + batch_size]
  				lowerCamelCase_  :    Dict   	=			hypotheses[i : i + batch_size]
  
  				lowerCamelCase_  :    Any   	=			tokenizer(
  				    lowerCAmelCase__       ,lowerCAmelCase__       ,padding=lowerCAmelCase__       ,truncation='only_first'       ,return_tensors='pt'       ,)
  
  				with torch.cuda.amp.autocast(enabled=lowerCAmelCase__	):
  						with torch.no_grad():
  								lowerCamelCase_  :    List[Any]   	=			model(**lowerCAmelCase__	)
  				logits.append(outputs.logits.detach().cpu().float()	)
  
  		lowerCamelCase_  :    int   	=			get_entailment_id(lowerCAmelCase__	)
  		lowerCamelCase_  :    Optional[int]   	=			-1 if entail_id == 0 else 0
  		lowerCamelCase_  :    List[Any]   	=			torch.cat(lowerCAmelCase__       ,dim=0	)  # N*K x 3
  		lowerCamelCase_  :    Dict   	=			logits.reshape(len(lowerCAmelCase__	)       ,len(lowerCAmelCase__	)       ,-1	)[..., [contr_id, entail_id]]  # N x K x 2
  
  		if multi_label:
  				# softmax over (contr, entail) logits for each class independently
  				lowerCamelCase_  :    Tuple   	=			(nli_logits / temperature).softmax(-1	)
  		else:
  				# softmax over entail logits across classes s.t. class probabilities sum to 1.
  				lowerCamelCase_  :    Dict   	=			(nli_logits / temperature).softmax(1	)
  
  		return nli_prob[..., 1]  # N x K
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		lowerCamelCase_  :    Any   	=			HfArgumentParser(
  		    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)       ,description=lowerCAmelCase__       ,)
  
  		if len(sys.argv	) == 2 and sys.argv[1].endswith('.json'	):
  				# If we pass only one argument to the script and it's the path to a json file,
  				# let's parse it to get our arguments.
  				lowerCamelCase_	,			lowerCamelCase_	,			lowerCamelCase_	,			lowerCamelCase_  :    Union[str, Any]   	=			parser.parse_json_file(
  				    json_file=os.path.abspath(sys.argv[1]	)	)
  		else:
  				lowerCamelCase_	,			lowerCamelCase_	,			lowerCamelCase_	,			lowerCamelCase_  :    List[Any]   	=			parser.parse_args_into_dataclasses()
  
  		# Detecting last checkpoint.
  		lowerCamelCase_  :    str   	=			None
  		if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
  				lowerCamelCase_  :    Optional[int]   	=			get_last_checkpoint(training_args.output_dir	)
  				if last_checkpoint is None and len(os.listdir(training_args.output_dir	)	) > 0:
  						raise ValueError(
  						    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
  						    'Use --overwrite_output_dir to overcome.'	)
  				elif last_checkpoint is not None:
  						logger.info(
  						    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
  						    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'	)
  
      # Setup logging
  		logging.basicConfig(
  		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'       ,datefmt='%m/%d/%Y %H:%M:%S'       ,handlers=[logging.StreamHandler(sys.stdout	)]       ,)
  		logger.setLevel(logging.INFO if is_main_process(training_args.local_rank	) else logging.WARN	)
  
  		# Log on each process the small summary:
  		logger.warning(
  		    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
  		    + F"distributed training: {bool(training_args.local_rank != -1	)}, 16-bits training: {training_args.fpaa}"	)
  		# Set the verbosity to info of the Transformers logger (on main process only):
  		if is_main_process(training_args.local_rank	):
  				utils.logging.set_verbosity_info()
  				utils.logging.enable_default_handler()
  				utils.logging.enable_explicit_format()
  
  		if training_args.local_rank != -1:
  				raise ValueError('Distributed training is not currently supported.'	)
  		if training_args.tpu_num_cores is not None:
  				raise ValueError('TPU acceleration is not currently supported.'	)
  
  		logger.info(F"Training/evaluation parameters {training_args}"	)
  
  		# Set seed before initializing model.
  		set_seed(training_args.seed	)
  
  		# 1. read in data
  		lowerCamelCase_  :    Tuple   	=			read_lines(data_args.data_file	)
  		lowerCamelCase_  :    Union[str, Any]   	=			read_lines(data_args.class_names_file	)
  
  		# 2. get teacher predictions and load into dataset
  		logger.info('Generating predictions from zero-shot teacher model'	)
  		lowerCamelCase_  :    List[str]   	=			get_teacher_predictions(
  		    teacher_args.teacher_name_or_path       ,lowerCAmelCase__       ,lowerCAmelCase__       ,teacher_args.hypothesis_template       ,teacher_args.teacher_batch_size       ,teacher_args.temperature       ,teacher_args.multi_label       ,data_args.use_fast_tokenizer       ,training_args.no_cuda       ,training_args.fpaa       ,)
  		lowerCamelCase_  :    List[Any]   	=			Dataset.from_dict(
  		    {
  		        'text': examples,
  		        'labels': teacher_soft_preds,
  		    }	)
  
  		# 3. create student
  		logger.info('Initializing student model'	)
  		lowerCamelCase_  :    Optional[int]   	=			AutoModelForSequenceClassification.from_pretrained(
  		    student_args.student_name_or_path       ,num_labels=len(lowerCAmelCase__	)	)
  		lowerCamelCase_  :    List[Any]   	=			AutoTokenizer.from_pretrained(student_args.student_name_or_path       ,use_fast=data_args.use_fast_tokenizer	)
  		lowerCamelCase_  :    Tuple   	=			dict(enumerate(lowerCAmelCase__	)	)
  		lowerCamelCase_  :    Any   	=			{label: i for i, label in enumerate(lowerCAmelCase__	)}
  
  		# 4. train student on teacher predictions
  		lowerCamelCase_  :    List[Any]   	=			dataset.map(lowerCAmelCase__       ,input_columns='text'	)
  		dataset.set_format('torch'	)
  
  		def compute_metrics(lowerCAmelCase__       ,lowerCAmelCase__=False	):
  				lowerCamelCase_  :    Tuple   	=			p.predictions.argmax(-1	)
  				lowerCamelCase_  :    Tuple   	=			p.label_ids.argmax(-1	)  # "label_ids" are actually distributions
  				return {"agreement": (preds == proxy_labels).mean().item()}
  
  		lowerCamelCase_  :    str   	=			DistillationTrainer(
  		    model=lowerCAmelCase__       ,tokenizer=lowerCAmelCase__       ,args=lowerCAmelCase__       ,train_dataset=lowerCAmelCase__       ,compute_metrics=lowerCAmelCase__       ,)
  
  		if training_args.do_train:
  				logger.info('Training student model on teacher predictions'	)
  				trainer.train()
  
  		if training_args.do_eval:
  				lowerCamelCase_  :    Optional[int]   	=			trainer.evaluate(eval_dataset=lowerCAmelCase__	)['eval_agreement']
  				logger.info(F"Agreement of student and teacher predictions: {agreement * 1_00:0.2f}%"	)
  
  		trainer.save_model()
  
  
  if __name__ == "__main__":
  		main()
  
style_context: 
  
  
  
  
  
  class 	UpperCamelCase_     :
  
  
  
  					def __init__(		self    :    List[Any]					,			lowerCamelCase    :    int	):
  							lowerCamelCase_  :    Union[str, Any]   	=			val
  							# Number of nodes in left subtree
  							lowerCamelCase_  :    Dict   	=			0
  							lowerCamelCase_  :    str   	=			None
  							lowerCamelCase_  :    int   	=			None
  							lowerCamelCase_  :    Dict   	=			None
  
  
  
  
  
  					def      __a    (		self    :    Optional[int]					,			lowerCamelCase    :    List[Any]	):
  							assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"
  
  							if self.val < other.val:
  									lowerCamelCase_  :    int   	=			self.right
  									lowerCamelCase_  :    Any   	=			None
  									if self.right:
  											lowerCamelCase_  :    Optional[Any]   	=			other
  									lowerCamelCase_  :    Union[str, Any]   	=			other
  									lowerCamelCase_  :    List[Any]   	=			self.left_tree_size * 2 + 1
  									return self
  							else:
  									lowerCamelCase_  :    str   	=			other.right
  									lowerCamelCase_  :    List[Any]   	=			None
  									if other.right:
  											lowerCamelCase_  :    Union[str, Any]   	=			self
  									lowerCamelCase_  :    Optional[int]   	=			self
  									lowerCamelCase_  :    Optional[int]   	=			other.left_tree_size * 2 + 1
  									return other
  
  
  
  
  
  
  class 	UpperCamelCase_     :
  
  
  
  					def __init__(		self    :    List[str]					,			lowerCamelCase    :    Optional[int]=None					,			lowerCamelCase    :    str=None					,			lowerCamelCase    :    List[Any]=0	):
  							lowerCamelCase_  :    Any   	=			heap_size
  							lowerCamelCase_  :    int   	=			bottom_root
  							lowerCamelCase_  :    Any   	=			min_node
  
  
  
  					def      __a    (		self    :    int					,			lowerCamelCase    :    int	):
  
  							# Empty heaps corner cases
  							if other.size == 0:
  									return None
  							if self.size == 0:
  									lowerCamelCase_  :    List[str]   	=			other.size
  									lowerCamelCase_  :    Tuple   	=			other.bottom_root
  									lowerCamelCase_  :    Tuple   	=			other.min_node
  									return None
  							# Update size
  							lowerCamelCase_  :    Optional[Any]   	=			self.size + other.size
  
  							# Update min.node
  							if self.min_node.val > other.min_node.val:
  									lowerCamelCase_  :    Optional[Any]   	=			other.min_node
  							# Merge
  
  							# Order roots by left_subtree_size
  							lowerCamelCase_  :    Dict   	=			[]
  							lowerCamelCase_	,			lowerCamelCase_  :    Optional[Any]   	=			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)	)
  											lowerCamelCase_  :    Tuple   	=			i.parent
  									else:
  											combined_roots_list.append((j, False)	)
  											lowerCamelCase_  :    Optional[Any]   	=			j.parent
          # Insert links between them
  							for i in range(len(lowerCamelCase	) - 1	):
  									if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
  											lowerCamelCase_  :    Dict   	=			combined_roots_list[i + 1][0]
  											lowerCamelCase_  :    Optional[int]   	=			combined_roots_list[i][0]
          # Consecutively merge roots with same left_tree_size
  							lowerCamelCase_  :    Any   	=			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
  											lowerCamelCase_  :    Dict   	=			i.left
  											lowerCamelCase_  :    int   	=			i.parent.parent
  
  											# Merging trees
  											lowerCamelCase_  :    str   	=			i.merge_trees(i.parent	)
  
  											# Updating links
  											lowerCamelCase_  :    Union[str, Any]   	=			previous_node
  											lowerCamelCase_  :    List[Any]   	=			next_node
  											if previous_node:
  													lowerCamelCase_  :    List[str]   	=			i
  											if next_node:
  													lowerCamelCase_  :    Dict   	=			i
  									else:
  											lowerCamelCase_  :    List[str]   	=			i.parent
          # Updating self.bottom_root
  							while i.left:
  									lowerCamelCase_  :    Union[str, Any]   	=			i.left
  							lowerCamelCase_  :    Any   	=			i
  
  							# Update other
  							lowerCamelCase_  :    Optional[int]   	=			self.size
  							lowerCamelCase_  :    Optional[Any]   	=			self.bottom_root
  							lowerCamelCase_  :    str   	=			self.min_node
  
  							# Return the merged heap
  							return self
  
  
  
  					def      __a    (		self    :    Any					,			lowerCamelCase    :    Dict	):
  							if self.size == 0:
  									lowerCamelCase_  :    Tuple   	=			Node(lowerCamelCase	)
  									lowerCamelCase_  :    Optional[int]   	=			1
  									lowerCamelCase_  :    Optional[int]   	=			self.bottom_root
  							else:
  									# Create new node
  									lowerCamelCase_  :    List[Any]   	=			Node(lowerCamelCase	)
  
  									# Update size
  									self.size += 1
  
  									# update min_node
  									if val < self.min_node.val:
  											lowerCamelCase_  :    List[str]   	=			new_node
  									# Put new_node as a bottom_root in heap
  									lowerCamelCase_  :    int   	=			new_node
  									lowerCamelCase_  :    Tuple   	=			self.bottom_root
  									lowerCamelCase_  :    Any   	=			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
  											lowerCamelCase_  :    Any   	=			self.bottom_root.parent.parent
  
  											# Merge
  											lowerCamelCase_  :    int   	=			self.bottom_root.merge_trees(self.bottom_root.parent	)
  
  											# Update Links
  											lowerCamelCase_  :    Dict   	=			next_node
  											lowerCamelCase_  :    Optional[Any]   	=			None
  											if next_node:
  													lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root
  
  
  
  					def      __a    (		self    :    Dict	):
  							return self.min_node.val
  
  
  
  					def      __a    (		self    :    int	):
  							return self.size == 0
  
  
  
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    Tuple   	=			self.min_node.val
  
  							# Last element in heap corner case
  							if self.size == 1:
  									# Update size
  									lowerCamelCase_  :    Optional[Any]   	=			0
  
  									# Update bottom root
  									lowerCamelCase_  :    Any   	=			None
  
  									# Update min_node
  									lowerCamelCase_  :    List[str]   	=			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
  									lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root.parent
  									lowerCamelCase_  :    Union[str, Any]   	=			None
  
  									# Update min_node
  									lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root
  									lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root.parent
  									while i:
  											if i.val < self.min_node.val:
  													lowerCamelCase_  :    Union[str, Any]   	=			i
  											lowerCamelCase_  :    Optional[int]   	=			i.parent
  									return min_value
  							# General case
  							# Find the BinomialHeap of the right subtree of min_node
  							lowerCamelCase_  :    Dict   	=			self.min_node.right
  							lowerCamelCase_  :    Dict   	=			None
  							lowerCamelCase_  :    Tuple   	=			bottom_of_new
  							lowerCamelCase_  :    Dict   	=			1
  
  							# Size, min_node and bottom_root
  							while bottom_of_new.left:
  									lowerCamelCase_  :    Any   	=			size_of_new * 2 + 1
  									lowerCamelCase_  :    Tuple   	=			bottom_of_new.left
  									if bottom_of_new.val < min_of_new.val:
  											lowerCamelCase_  :    int   	=			bottom_of_new
          # Corner case of single root on top left path
  							if (not self.min_node.left) and (not self.min_node.parent):
  									lowerCamelCase_  :    Union[str, Any]   	=			size_of_new
  									lowerCamelCase_  :    Union[str, Any]   	=			bottom_of_new
  									lowerCamelCase_  :    Tuple   	=			min_of_new
  									# print("Single root, multiple nodes case")
  									return min_value
  							# Remaining cases
  							# Construct heap of right subtree
  							lowerCamelCase_  :    Optional[Any]   	=			BinomialHeap(
  							    bottom_root=lowerCamelCase					,			min_node=lowerCamelCase					,			heap_size=lowerCamelCase	)
  
  							# Update size
  							lowerCamelCase_  :    Union[str, Any]   	=			self.size - 1 - size_of_new
  
  							# Neighbour nodes
  							lowerCamelCase_  :    Union[str, Any]   	=			self.min_node.left
  							lowerCamelCase_  :    Optional[Any]   	=			self.min_node.parent
  
  							# Initialize new bottom_root and min_node
  							lowerCamelCase_  :    Optional[int]   	=			previous_node or next_node
  							lowerCamelCase_  :    Tuple   	=			next_node
  
  							# Update links of previous_node and search below for new min_node and
  							# bottom_root
  							if previous_node:
  									lowerCamelCase_  :    Dict   	=			next_node
  
  									# Update bottom_root and search for min_node below
  									lowerCamelCase_  :    int   	=			previous_node
  									lowerCamelCase_  :    Tuple   	=			previous_node
  									while self.bottom_root.left:
  											lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root.left
  											if self.bottom_root.val < self.min_node.val:
  													lowerCamelCase_  :    Any   	=			self.bottom_root
  							if next_node:
  									lowerCamelCase_  :    Any   	=			previous_node
  
  									# Search for new min_node above min_node
  									lowerCamelCase_  :    List[str]   	=			next_node
  									while i:
  											if i.val < self.min_node.val:
  													lowerCamelCase_  :    List[Any]   	=			i
  											lowerCamelCase_  :    Dict   	=			i.parent
          # Merge heaps
  							self.merge_heaps(lowerCamelCase	)
  
  							return min_value
  
  
  
  					def      __a    (		self    :    Optional[int]	):
  							lowerCamelCase_  :    List[Any]   	=			self.bottom_root
  							while top_root.parent:
  									lowerCamelCase_  :    Tuple   	=			top_root.parent
  							# preorder
  							lowerCamelCase_  :    str   	=			[]
  							self.__traversal(lowerCamelCase					,			lowerCamelCase	)
  							return heap_pre_order
  
  
  
  					def      __a    (		self    :    Dict					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    List[str]=0	):
  							if curr_node:
  									preorder.append((curr_node.val, level)	)
  									self.__traversal(curr_node.left					,			lowerCamelCase					,			level + 1	)
  									self.__traversal(curr_node.right					,			lowerCamelCase					,			level + 1	)
  							else:
  									preorder.append(('#', level)	)
  
  
  
  
  
  					def __str__(		self    :    List[Any]	):
  							if self.is_empty():
  									return ""
  							lowerCamelCase_  :    str   	=			self.pre_order()
  
  							return "\n".join(('-' * level + str(lowerCamelCase	)) for value, level in preorder_heap	)
  
  
  # Unit Tests
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  