





'''simple docstring'''


# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
_SCREAMING_SNAKE_CASE			=							1e-10
_SCREAMING_SNAKE_CASE			=							"This is incredible! I love it, this is the best chicken I have ever had."
_SCREAMING_SNAKE_CASE			=							1_00

class 				lowerCAmelCase_						(						nn.Module						):




	def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase="gpt2-medium"     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase="cpu"						)      ->							int:
					super().__init__()
					_lowerCAmelCase		     = GPTaTokenizer.from_pretrained(_lowerCAmelCase						)
					_lowerCAmelCase		     = GPTaLMHeadModel.from_pretrained(_lowerCAmelCase						)
					_lowerCAmelCase		     = self.encoder.transformer.config.hidden_size
					_lowerCAmelCase		     = ClassificationHead(class_size=_lowerCAmelCase     ,       embed_size=self.embed_size						)
					_lowerCAmelCase		     = cached_mode
					_lowerCAmelCase		     = device




	def 							_snake_case				(		self						)      ->							Optional[Any]:
					return self.classifier_head




	def 							_snake_case				(		self						)      ->							Any:

					for param in self.encoder.parameters():
									_lowerCAmelCase		     = False
					self.classifier_head.train()




	def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Dict:
					_lowerCAmelCase		     = x.ne(0						).unsqueeze(2						).repeat(1     ,       1     ,       self.embed_size						).float().to(self.device						).detach()
					_lowerCAmelCase		     = self.encoder.transformer(_lowerCAmelCase						)["last_hidden_state"]
					_lowerCAmelCase		     = hidden * mask
					_lowerCAmelCase		     = torch.sum(_lowerCAmelCase     ,       dim=1						) / (torch.sum(_lowerCAmelCase     ,       dim=1						).detach() + EPSILON)
					return avg_hidden





	def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Any:

					if self.cached_mode:
									_lowerCAmelCase		     = x.to(self.device						)
					else:
									_lowerCAmelCase		     = self.avg_representation(x.to(self.device						)						)

					_lowerCAmelCase		     = self.classifier_head(_lowerCAmelCase						)
					_lowerCAmelCase		     = nn.functional.log_softmax(_lowerCAmelCase     ,       dim=-1						)

					return probs







class 				lowerCAmelCase_						(						data.Dataset						):




	def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Optional[int]:
					_lowerCAmelCase		     = X
					_lowerCAmelCase		     = y




	def __len__(		self						)      ->							Dict:
					return len(self.X						)





	def __getitem__(		self     ,       _lowerCAmelCase						)      ->							Any:
					_lowerCAmelCase		     = {}
					_lowerCAmelCase		     = self.X[index]
					_lowerCAmelCase		     = self.y[index]
					return data







def 		__a(SCREAMING_SNAKE_CASE_					:   List[str]      ):



				'''simple docstring'''





				def pad_sequences(SCREAMING_SNAKE_CASE_					:   Dict      ):
								_lowerCAmelCase		     = [len(_a      ) for seq in sequences]

								_lowerCAmelCase		     = torch.zeros(len(_a      )						,    max(_a      )      ).long()  # padding value = 0

								for i, seq in enumerate(_a      ):
												_lowerCAmelCase		     = lengths[i]
												_lowerCAmelCase		     = seq[:end]

								return padded_sequences, lengths

				_lowerCAmelCase		     = {}
				for key in data[0].keys():
								_lowerCAmelCase		     = [d[key] for d in data]

				_lowerCAmelCase		     = pad_sequences(item_info["X"]      )
				_lowerCAmelCase		     = torch.tensor(item_info["y"]						,    dtype=torch.long      )

				return x_batch, y_batch


def 		__a(SCREAMING_SNAKE_CASE_					:   Optional[int]      ):



				'''simple docstring'''





				_lowerCAmelCase		     = {}
				for key in data[0].keys():
								_lowerCAmelCase		     = [d[key] for d in data]

				_lowerCAmelCase		     = torch.cat(item_info["X"]						,    0      )
				_lowerCAmelCase		     = torch.tensor(item_info["y"]						,    dtype=torch.long      )

				return x_batch, y_batch


def 		__a(SCREAMING_SNAKE_CASE_					:   Optional[int]						,    SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]=0						,    SCREAMING_SNAKE_CASE_					:   Dict=10						,    SCREAMING_SNAKE_CASE_					:   List[str]="cpu"      ):



				'''simple docstring'''





				_lowerCAmelCase		     = 0
				discriminator.train_custom()
				for batch_idx, (input_t, target_t) in enumerate(_a      ):
								_lowerCAmelCase		     = input_t.to(_a      ), target_t.to(_a      )

								optimizer.zero_grad()

								_lowerCAmelCase		     = discriminator(_a      )
								_lowerCAmelCase		     = nn.functional.nll_loss(_a						,    _a      )
								loss.backward(retain_graph=_a      )
								optimizer.step()

								samples_so_far += len(_a      )

								if batch_idx % log_interval == 0:
												print(
												    "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
												        epoch + 1						,    _a						,    len(data_loader.dataset      )						,    100 * samples_so_far / len(data_loader.dataset      )						,    loss.item()						,    )      )


def 		__a(SCREAMING_SNAKE_CASE_					:   List[str]						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]="cpu"      ):



				'''simple docstring'''





				discriminator.eval()
				_lowerCAmelCase		     = 0
				_lowerCAmelCase		     = 0
				with torch.no_grad():
								for input_t, target_t in data_loader:
												_lowerCAmelCase		     = input_t.to(_a      ), target_t.to(_a      )
												_lowerCAmelCase		     = discriminator(_a      )
												# sum up batch loss
												test_loss += nn.functional.nll_loss(_a						,    _a						,    reduction="sum"      ).item()
												# get the index of the max log-probability
												_lowerCAmelCase		     = output_t.argmax(dim=1						,    keepdim=_a      )
												correct += pred_t.eq(target_t.view_as(_a      )      ).sum().item()

				test_loss /= len(data_loader.dataset      )

				print(
				    "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
				        _a						,    _a						,    len(data_loader.dataset      )						,    100.0 * correct / len(data_loader.dataset      )      )      )


def 		__a(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   Any=False						,    SCREAMING_SNAKE_CASE_					:   Optional[int]="cpu"      ):



				'''simple docstring'''





				_lowerCAmelCase		     = model.tokenizer.encode(_a      )
				_lowerCAmelCase		     = torch.tensor([input_t]						,    dtype=torch.long						,    device=_a      )
				if cached:
								_lowerCAmelCase		     = model.avg_representation(_a      )

				_lowerCAmelCase		     = model(_a      ).data.cpu().numpy().flatten().tolist()
				print("Input sentence:"						,    _a      )
				print(
				    "Predictions:"						,    ", ".join("{}: {:.4f}".format(_a						,    math.exp(_a      )      ) for c, log_prob in zip(_a						,    _a      )      )						,    )


def 		__a(SCREAMING_SNAKE_CASE_					:   List[Any]						,    SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   List[str]						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]=False						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]="cpu"      ):



				'''simple docstring'''





				_lowerCAmelCase		     = torch.utils.data.DataLoader(dataset=_a						,    batch_size=_a						,    collate_fn=_a      )

				_lowerCAmelCase		     = []
				_lowerCAmelCase		     = []
				for batch_idx, (x, y) in enumerate(tqdm(_a						,    ascii=_a      )      ):
								with torch.no_grad():
												_lowerCAmelCase		     = x.to(_a      )
												_lowerCAmelCase		     = discriminator.avg_representation(_a      ).cpu().detach()
												_lowerCAmelCase		     = torch.unbind(avg_rep.unsqueeze(1      )      )
												xs += avg_rep_list
												ys += y.cpu().numpy().tolist()

				_lowerCAmelCase		     = torch.utils.data.DataLoader(
				    dataset=Dataset(_a						,    _a      )						,    batch_size=_a						,    shuffle=_a						,    collate_fn=_a      )

				return data_loader


def 		__a(SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   Optional[int]=None						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]="gpt2-medium"						,    SCREAMING_SNAKE_CASE_					:   Tuple=10						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]=64						,    SCREAMING_SNAKE_CASE_					:   Optional[int]=10						,    SCREAMING_SNAKE_CASE_					:   str=False						,    SCREAMING_SNAKE_CASE_					:   Any=False						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]=False						,    ):



				'''simple docstring'''





				_lowerCAmelCase		     = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

				print("Preprocessing {} dataset...".format(_a      )      )
				_lowerCAmelCase		     = time.time()

				if dataset == "SST":
								_lowerCAmelCase		     = ["positive", "negative", "very positive", "very negative", "neutral"]
								_lowerCAmelCase		     = {c: i for i, c in enumerate(_a      )}

								_lowerCAmelCase		     = Discriminator(
								    class_size=len(_a      )						,    pretrained_model=_a						,    cached_mode=_a						,    device=_a      ).to(_a      )

								_lowerCAmelCase		     = torchtext_data.Field()
								_lowerCAmelCase		     = torchtext_data.Field(sequential=_a      )
								_lowerCAmelCase		     = datasets.SST.splits(
								    _a						,    _a						,    fine_grained=_a						,    train_subtrees=_a						,    )

								_lowerCAmelCase		     = []
								_lowerCAmelCase		     = []
								for i in trange(len(_a      )						,    ascii=_a      ):
												_lowerCAmelCase		     = TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["text"]      )
												_lowerCAmelCase		     = discriminator.tokenizer.encode(_a      )
												_lowerCAmelCase		     = torch.tensor([50256] + seq						,    device=_a						,    dtype=torch.long      )
												x.append(_a      )
												y.append(classaidx[vars(train_data[i]      )["label"]]      )
								_lowerCAmelCase		     = Dataset(_a						,    _a      )

								_lowerCAmelCase		     = []
								_lowerCAmelCase		     = []
								for i in trange(len(_a      )						,    ascii=_a      ):
												_lowerCAmelCase		     = TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["text"]      )
												_lowerCAmelCase		     = discriminator.tokenizer.encode(_a      )
												_lowerCAmelCase		     = torch.tensor([50256] + seq						,    device=_a						,    dtype=torch.long      )
												test_x.append(_a      )
												test_y.append(classaidx[vars(test_data[i]      )["label"]]      )
								_lowerCAmelCase		     = Dataset(_a						,    _a      )

								_lowerCAmelCase		     = {
								    "class_size": len(_a      ),
								    "embed_size": discriminator.embed_size,
								    "pretrained_model": pretrained_model,
								    "class_vocab": classaidx,
								    "default_class": 2,
								}

				elif dataset == "clickbait":
								_lowerCAmelCase		     = ["non_clickbait", "clickbait"]
								_lowerCAmelCase		     = {c: i for i, c in enumerate(_a      )}

								_lowerCAmelCase		     = Discriminator(
								    class_size=len(_a      )						,    pretrained_model=_a						,    cached_mode=_a						,    device=_a      ).to(_a      )

								with open("datasets/clickbait/clickbait_train_prefix.txt"      ) as f:
												_lowerCAmelCase		     = []
												for i, line in enumerate(_a      ):
																try:
																				data.append(eval(_a      )      )
																except Exception:
																				print("Error evaluating line {}: {}".format(_a						,    _a      )      )
																				continue
								_lowerCAmelCase		     = []
								_lowerCAmelCase		     = []
								with open("datasets/clickbait/clickbait_train_prefix.txt"      ) as f:
												for i, line in enumerate(tqdm(_a						,    ascii=_a      )      ):
																try:
																				_lowerCAmelCase		     = eval(_a      )
																				_lowerCAmelCase		     = discriminator.tokenizer.encode(d["text"]      )

																				if len(_a      ) < max_length_seq:
																								_lowerCAmelCase		     = torch.tensor([50256] + seq						,    device=_a						,    dtype=torch.long      )
																				else:
																								print("Line {} is longer than maximum length {}".format(_a						,    _a      )      )
																								continue
																				x.append(_a      )
																				y.append(d["label"]      )
																except Exception:
																				print("Error evaluating / tokenizing line {}, skipping it".format(_a      )      )
																				pass

								_lowerCAmelCase		     = Dataset(_a						,    _a      )
								_lowerCAmelCase		     = int(0.9 * len(_a      )      )
								_lowerCAmelCase		     = len(_a      ) - train_size
								_lowerCAmelCase		     = torch.utils.data.random_split(_a						,    [train_size, test_size]      )

								_lowerCAmelCase		     = {
								    "class_size": len(_a      ),
								    "embed_size": discriminator.embed_size,
								    "pretrained_model": pretrained_model,
								    "class_vocab": classaidx,
								    "default_class": 1,
								}

				elif dataset == "toxic":
								_lowerCAmelCase		     = ["non_toxic", "toxic"]
								_lowerCAmelCase		     = {c: i for i, c in enumerate(_a      )}

								_lowerCAmelCase		     = Discriminator(
								    class_size=len(_a      )						,    pretrained_model=_a						,    cached_mode=_a						,    device=_a      ).to(_a      )

								_lowerCAmelCase		     = []
								_lowerCAmelCase		     = []
								with open("datasets/toxic/toxic_train.txt"      ) as f:
												for i, line in enumerate(tqdm(_a						,    ascii=_a      )      ):
																try:
																				_lowerCAmelCase		     = eval(_a      )
																				_lowerCAmelCase		     = discriminator.tokenizer.encode(d["text"]      )

																				if len(_a      ) < max_length_seq:
																								_lowerCAmelCase		     = torch.tensor([50256] + seq						,    device=_a						,    dtype=torch.long      )
																				else:
																								print("Line {} is longer than maximum length {}".format(_a						,    _a      )      )
																								continue
																				x.append(_a      )
																				y.append(int(np.sum(d["label"]      ) > 0      )      )
																except Exception:
																				print("Error evaluating / tokenizing line {}, skipping it".format(_a      )      )
																				pass

								_lowerCAmelCase		     = Dataset(_a						,    _a      )
								_lowerCAmelCase		     = int(0.9 * len(_a      )      )
								_lowerCAmelCase		     = len(_a      ) - train_size
								_lowerCAmelCase		     = torch.utils.data.random_split(_a						,    [train_size, test_size]      )

								_lowerCAmelCase		     = {
								    "class_size": len(_a      ),
								    "embed_size": discriminator.embed_size,
								    "pretrained_model": pretrained_model,
								    "class_vocab": classaidx,
								    "default_class": 0,
								}

				else:  # if dataset == "generic":
								# This assumes the input dataset is a TSV with the following structure:
								# class \t text

								if dataset_fp is None:
												raise ValueError("When generic dataset is selected, dataset_fp needs to be specified aswell."      )

								_lowerCAmelCase		     = set()
								with open(_a      ) as f:
												_lowerCAmelCase		     = csv.reader(_a						,    delimiter="\t"      )
												for row in tqdm(_a						,    ascii=_a      ):
																if row:
																				classes.add(row[0]      )

								_lowerCAmelCase		     = sorted(_a      )
								_lowerCAmelCase		     = {c: i for i, c in enumerate(_a      )}

								_lowerCAmelCase		     = Discriminator(
								    class_size=len(_a      )						,    pretrained_model=_a						,    cached_mode=_a						,    device=_a      ).to(_a      )

								_lowerCAmelCase		     = []
								_lowerCAmelCase		     = []
								with open(_a      ) as f:
												_lowerCAmelCase		     = csv.reader(_a						,    delimiter="\t"      )
												for i, row in enumerate(tqdm(_a						,    ascii=_a      )      ):
																if row:
																				_lowerCAmelCase		     = row[0]
																				_lowerCAmelCase		     = row[1]

																				try:
																								_lowerCAmelCase		     = discriminator.tokenizer.encode(_a      )
																								if len(_a      ) < max_length_seq:
																												_lowerCAmelCase		     = torch.tensor([50256] + seq						,    device=_a						,    dtype=torch.long      )

																								else:
																												print("Line {} is longer than maximum length {}".format(_a						,    _a      )      )
																												continue

																								x.append(_a      )
																								y.append(classaidx[label]      )

																				except Exception:
																								print("Error tokenizing line {}, skipping it".format(_a      )      )
																								pass

								_lowerCAmelCase		     = Dataset(_a						,    _a      )
								_lowerCAmelCase		     = int(0.9 * len(_a      )      )
								_lowerCAmelCase		     = len(_a      ) - train_size
								_lowerCAmelCase		     = torch.utils.data.random_split(_a						,    [train_size, test_size]      )

								_lowerCAmelCase		     = {
								    "class_size": len(_a      ),
								    "embed_size": discriminator.embed_size,
								    "pretrained_model": pretrained_model,
								    "class_vocab": classaidx,
								    "default_class": 0,
								}

				_lowerCAmelCase		     = time.time()
				print("Preprocessed {} data points".format(len(_a      ) + len(_a      )      )      )
				print("Data preprocessing took: {:.3f}s".format(end - start      )      )

				if cached:
								print("Building representation cache..."      )

								_lowerCAmelCase		     = time.time()

								_lowerCAmelCase		     = get_cached_data_loader(_a						,    _a						,    _a						,    shuffle=_a						,    device=_a      )

								_lowerCAmelCase		     = get_cached_data_loader(_a						,    _a						,    _a						,    device=_a      )

								_lowerCAmelCase		     = time.time()
								print("Building representation cache took: {:.3f}s".format(end - start      )      )

				else:
								_lowerCAmelCase		     = torch.utils.data.DataLoader(
								    dataset=_a						,    batch_size=_a						,    shuffle=_a						,    collate_fn=_a      )
								_lowerCAmelCase		     = torch.utils.data.DataLoader(dataset=_a						,    batch_size=_a						,    collate_fn=_a      )

				if save_model:
								with open("{}_classifier_head_meta.json".format(_a      )						,    "w"      ) as meta_file:
												json.dump(_a						,    _a      )

				_lowerCAmelCase		     = optim.Adam(discriminator.parameters()						,    lr=0.0001      )

				for epoch in range(_a      ):
								_lowerCAmelCase		     = time.time()
								print("\nEpoch"						,    epoch + 1      )

								train_epoch(
								    discriminator=_a						,    data_loader=_a						,    optimizer=_a						,    epoch=_a						,    log_interval=_a						,    device=_a						,    )
								evaluate_performance(data_loader=_a						,    discriminator=_a						,    device=_a      )

								_lowerCAmelCase		     = time.time()
								print("Epoch took: {:.3f}s".format(end - start      )      )

								print("\nExample prediction"      )
								predict(_a						,    _a						,    _a						,    cached=_a						,    device=_a      )

								if save_model:
												# torch.save(discriminator.state_dict(),
												#           "{}_discriminator_{}.pt".format(
												#               args.dataset, epoch + 1
												#               ))
												torch.save(
												    discriminator.get_classifier().state_dict()						,    "{}_classifier_head_epoch_{}.pt".format(_a						,    epoch + 1      )						,    )


if __name__ == "__main__":
							_SCREAMING_SNAKE_CASE			=							argparse.ArgumentParser(description="Train a discriminator on top of GPT-2 representations")
							parser.add_argument(
							    "--dataset",
							    type=str,
							    default="SST",
							    choices=("SST", "clickbait", "toxic", "generic"),
							    help=(
							        "dataset to train the discriminator on."
							        "In case of generic, the dataset is expected"
							        "to be a TSBV file with structure: class \\t text"
							    ),
							)
							parser.add_argument(
							    "--dataset_fp",
							    type=str,
							    default="",
							    help="File path of the dataset to use. Needed only in case of generic datadset",
							)
							parser.add_argument(
							    "--pretrained_model", type=str, default="gpt2-medium", help="Pretrained model to use as encoder"
							)
							parser.add_argument("--epochs", type=int, default=10, metavar="N", help="Number of training epochs")
							parser.add_argument(
							    "--batch_size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
							)
							parser.add_argument(
							    "--log_interval",
							    type=int,
							    default=10,
							    metavar="N",
							    help="how many batches to wait before logging training status",
							)
							parser.add_argument("--save_model", action="store_true", help="whether to save the model")
							parser.add_argument("--cached", action="store_true", help="whether to cache the input representations")
							parser.add_argument("--no_cuda", action="store_true", help="use to turn off cuda")
							_SCREAMING_SNAKE_CASE			=							parser.parse_args()

							train_discriminator(**(vars(args)))









'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_SCREAMING_SNAKE_CASE			=							TypeVar("T", bound=bool)

class 				lowerCAmelCase_						(						Generic[T]						):




 def __init__(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = value
     _lowerCAmelCase		     = None
     _lowerCAmelCase		     = None




 @property
 def 							_snake_case				(		self						)      ->							T:
     return self._value





 @staticmethod
 def 							_snake_case				(		_lowerCAmelCase     ,       _lowerCAmelCase						)      ->							SkewNode[T] | None:
     if not roota:
         return roota

     if not roota:
         return roota

     if roota.value > roota.value:
         _lowerCAmelCase     ,     _lowerCAmelCase		     = roota, roota

     _lowerCAmelCase		     = roota
     _lowerCAmelCase		     = roota.right
     _lowerCAmelCase		     = roota.left
     _lowerCAmelCase		     = SkewNode.merge(_lowerCAmelCase     ,       _lowerCAmelCase						)

     return result







class 				lowerCAmelCase_						(						Generic[T]						):




 def __init__(		self     ,       _lowerCAmelCase = ()						)      ->							None:
     _lowerCAmelCase		     = None
     if data:
         for item in data:
             self.insert(_lowerCAmelCase						)




 def __bool__(		self						)      ->							bool:
     return self._root is not None




 def __iter__(		self						)      ->							Iterator[T]:
     _lowerCAmelCase		     = []
     while self:
         result.append(self.pop()						)

     # Pushing items back to the heap not to clear it.
     for item in result:
         self.insert(_lowerCAmelCase						)

     return iter(_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							None:
     _lowerCAmelCase		     = SkewNode.merge(self._root     ,       SkewNode(_lowerCAmelCase						)						)




 def 							_snake_case				(		self						)      ->							T | None:
     _lowerCAmelCase		     = self.top()
     _lowerCAmelCase		     = (
         SkewNode.merge(self._root.left     ,       self._root.right						) if self._root else None
     )

     return result




 def 							_snake_case				(		self						)      ->							T:
     if not self._root:
         raise IndexError("Can't get top element for the empty heap."						)
     return self._root.value





 def 							_snake_case				(		self						)      ->							None:
     _lowerCAmelCase		     = None


if __name__ == "__main__":
       import doctest

       doctest.testmod()


