code: 
  
  
  # 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)
  __lowerCamelCase       :       List[str]				   =			1E-10
  __lowerCamelCase       :       str				   =			"This is incredible! I love it, this is the best chicken I have ever had."
  __lowerCamelCase       :       Tuple				   =			100
  
  
  
  class 			a      (      nn.Module   ):
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase="gpt2-medium"	,     __UpperCamelCase=False	,     __UpperCamelCase="cpu"							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						super().__init__()
  						A__     :							Optional[Any]   				=GPTaTokenizer.from_pretrained(__UpperCamelCase							)
  						A__     :							str   				=GPTaLMHeadModel.from_pretrained(__UpperCamelCase							)
  						A__     :							Union[str, Any]   				=self.encoder.transformer.config.hidden_size
  						A__     :							Union[str, Any]   				=ClassificationHead(class_size=__UpperCamelCase	,     embed_size=self.embed_size							)
  						A__     :							Tuple   				=cached_mode
  						A__     :							int   				=device
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						return self.classifier_head
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						for param in self.encoder.parameters():
  										A__     :							str   				=False
  						self.classifier_head.train()
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Dict:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=x.ne(0							).unsqueeze(2							).repeat(1	,     1	,     self.embed_size							).float().to(self.device							).detach()
  						A__     :							List[Any]   				=self.encoder.transformer(__UpperCamelCase							)['''last_hidden_state''']
  						A__     :							Optional[int]   				=hidden * mask
  						A__     :							Optional[int]   				=torch.sum(__UpperCamelCase	,     dim=1							) / (torch.sum(__UpperCamelCase	,     dim=1							).detach() + EPSILON)
  						return avg_hidden
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						if self.cached_mode:
  										A__     :							Dict   				=x.to(self.device							)
  						else:
  										A__     :							Union[str, Any]   				=self.avg_representation(x.to(self.device							)							)
  
  						A__     :							Optional[int]   				=self.classifier_head(__UpperCamelCase							)
  						A__     :							str   				=nn.functional.log_softmax(__UpperCamelCase	,     dim=-1							)
  
  						return probs
  
  
  
  
  
  
  
  class 			a      (      data.Dataset   ):
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=X
  						A__     :							Optional[Any]   				=y
  
  
  
  
  
  
  		def __len__(  self							)->			Any:
  						'''simple docstring'''
  
  
  						return len(self.X							)
  
  
  
  
  
  
  		def __getitem__(  self	,     __UpperCamelCase							)->			Any:
  						'''simple docstring'''
  
  
  						A__     :							str   				={}
  						A__     :							Tuple   				=self.X[index]
  						A__     :							Any   				=self.y[index]
  						return data
  
  
  
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Tuple:
  				def pad_sequences(snake_case_	):
  								A__     :							List[Any]   				=[len(snake_case_	) for seq in sequences]
  
  								A__     :							Optional[Any]   				=torch.zeros(len(snake_case_	),	max(snake_case_	)	).long()  # padding value = 0
  
  								for i, seq in enumerate(snake_case_	):
  												A__     :							Any   				=lengths[i]
  												A__     :							Tuple   				=seq[:end]
  
  								return padded_sequences, lengths
  
  				A__     :							Tuple   				={}
  				for key in data[0].keys():
  								A__     :							List[str]   				=[d[key] for d in data]
  
  				A__				,  A__     :							Any   				=pad_sequences(item_info['''X''']	)
  				A__     :							int   				=torch.tensor(item_info['''y'''],	dtype=torch.long	)
  
  				return x_batch, y_batch
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							List[str]:
  				A__     :							int   				={}
  				for key in data[0].keys():
  								A__     :							int   				=[d[key] for d in data]
  
  				A__     :							Union[str, Any]   				=torch.cat(item_info['''X'''],	0	)
  				A__     :							List[Any]   				=torch.tensor(item_info['''y'''],	dtype=torch.long	)
  
  				return x_batch, y_batch
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_=0,	snake_case_=1_0,	snake_case_="cpu"	)			->							Optional[int]:
  				A__     :							int   				=0
  				discriminator.train_custom()
  				for batch_idx, (input_t, target_t) in enumerate(snake_case_	):
  								A__				,  A__     :							str   				=input_t.to(snake_case_	), target_t.to(snake_case_	)
  
  								optimizer.zero_grad()
  
  								A__     :							Dict   				=discriminator(snake_case_	)
  								A__     :							List[str]   				=nn.functional.nll_loss(snake_case_,	snake_case_	)
  								loss.backward(retain_graph=snake_case_	)
  								optimizer.step()
  
  								samples_so_far += len(snake_case_	)
  
  								if batch_idx % log_interval == 0:
  												print(
  												    '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
  												        epoch + 1,	snake_case_,	len(data_loader.dataset	),	1_0_0 * samples_so_far / len(data_loader.dataset	),	loss.item(),	)	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_="cpu"	)			->							Tuple:
  				discriminator.eval()
  				A__     :							int   				=0
  				A__     :							Tuple   				=0
  				with torch.no_grad():
  								for input_t, target_t in data_loader:
  												A__				,  A__     :							Optional[int]   				=input_t.to(snake_case_	), target_t.to(snake_case_	)
  												A__     :							Tuple   				=discriminator(snake_case_	)
  												# sum up batch loss
  												test_loss += nn.functional.nll_loss(snake_case_,	snake_case_,	reduction='''sum'''	).item()
  												# get the index of the max log-probability
  												A__     :							Dict   				=output_t.argmax(dim=1,	keepdim=snake_case_	)
  												correct += pred_t.eq(target_t.view_as(snake_case_	)	).sum().item()
  
  				test_loss /= len(data_loader.dataset	)
  
  				print(
  				    '''Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'''.format(
  				        snake_case_,	snake_case_,	len(data_loader.dataset	),	1_0_0.0 * correct / len(data_loader.dataset	)	)	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_=False,	snake_case_="cpu"	)			->							Optional[int]:
  				A__     :							Any   				=model.tokenizer.encode(snake_case_	)
  				A__     :							Optional[Any]   				=torch.tensor([input_t],	dtype=torch.long,	device=snake_case_	)
  				if cached:
  								A__     :							List[str]   				=model.avg_representation(snake_case_	)
  
  				A__     :							Optional[Any]   				=model(snake_case_	).data.cpu().numpy().flatten().tolist()
  				print('''Input sentence:''',	snake_case_	)
  				print(
  				    '''Predictions:''',	''', '''.join('''{}: {:.4f}'''.format(snake_case_,	math.exp(snake_case_	)	) for c, log_prob in zip(snake_case_,	snake_case_	)	),	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_=False,	snake_case_="cpu"	)			->							Tuple:
  				A__     :							int   				=torch.utils.data.DataLoader(dataset=snake_case_,	batch_size=snake_case_,	collate_fn=snake_case_	)
  
  				A__     :							Optional[int]   				=[]
  				A__     :							Any   				=[]
  				for batch_idx, (x, y) in enumerate(tqdm(snake_case_,	ascii=snake_case_	)	):
  								with torch.no_grad():
  												A__     :							Union[str, Any]   				=x.to(snake_case_	)
  												A__     :							Dict   				=discriminator.avg_representation(snake_case_	).cpu().detach()
  												A__     :							List[Any]   				=torch.unbind(avg_rep.unsqueeze(1	)	)
  												xs += avg_rep_list
  												ys += y.cpu().numpy().tolist()
  
  				A__     :							Any   				=torch.utils.data.DataLoader(
  				    dataset=Dataset(snake_case_,	snake_case_	),	batch_size=snake_case_,	shuffle=snake_case_,	collate_fn=snake_case_	)
  
  				return data_loader
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_=None,	snake_case_="gpt2-medium",	snake_case_=1_0,	snake_case_=6_4,	snake_case_=1_0,	snake_case_=False,	snake_case_=False,	snake_case_=False,	)			->							Dict:
  				A__     :							Optional[int]   				='''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''
  
  				print('''Preprocessing {} dataset...'''.format(snake_case_	)	)
  				A__     :							List[str]   				=time.time()
  
  				if dataset == "SST":
  								A__     :							Tuple   				=['''positive''', '''negative''', '''very positive''', '''very negative''', '''neutral''']
  								A__     :							Dict   				={c: i for i, c in enumerate(snake_case_	)}
  
  								A__     :							Any   				=Discriminator(
  								    class_size=len(snake_case_	),	pretrained_model=snake_case_,	cached_mode=snake_case_,	device=snake_case_	).to(snake_case_	)
  
  								A__     :							int   				=torchtext_data.Field()
  								A__     :							int   				=torchtext_data.Field(sequential=snake_case_	)
  								A__				,  A__				,  A__     :							int   				=datasets.SST.splits(
  								    snake_case_,	snake_case_,	fine_grained=snake_case_,	train_subtrees=snake_case_,	)
  
  								A__     :							int   				=[]
  								A__     :							Any   				=[]
  								for i in trange(len(snake_case_	),	ascii=snake_case_	):
  												A__     :							Tuple   				=TreebankWordDetokenizer().detokenize(vars(train_data[i]	)['''text''']	)
  												A__     :							str   				=discriminator.tokenizer.encode(snake_case_	)
  												A__     :							Optional[int]   				=torch.tensor([5_0_2_5_6] + seq,	device=snake_case_,	dtype=torch.long	)
  												x.append(snake_case_	)
  												y.append(classaidx[vars(train_data[i]	)['''label''']]	)
  								A__     :							Union[str, Any]   				=Dataset(snake_case_,	snake_case_	)
  
  								A__     :							List[str]   				=[]
  								A__     :							Dict   				=[]
  								for i in trange(len(snake_case_	),	ascii=snake_case_	):
  												A__     :							str   				=TreebankWordDetokenizer().detokenize(vars(test_data[i]	)['''text''']	)
  												A__     :							List[str]   				=discriminator.tokenizer.encode(snake_case_	)
  												A__     :							Optional[int]   				=torch.tensor([5_0_2_5_6] + seq,	device=snake_case_,	dtype=torch.long	)
  												test_x.append(snake_case_	)
  												test_y.append(classaidx[vars(test_data[i]	)['''label''']]	)
  								A__     :							str   				=Dataset(snake_case_,	snake_case_	)
  
  								A__     :							Optional[Any]   				={
  								    '''class_size''': len(snake_case_	),
  								    '''embed_size''': discriminator.embed_size,
  								    '''pretrained_model''': pretrained_model,
  								    '''class_vocab''': classaidx,
  								    '''default_class''': 2,
  								}
  
  				elif dataset == "clickbait":
  								A__     :							List[Any]   				=['''non_clickbait''', '''clickbait''']
  								A__     :							str   				={c: i for i, c in enumerate(snake_case_	)}
  
  								A__     :							Union[str, Any]   				=Discriminator(
  								    class_size=len(snake_case_	),	pretrained_model=snake_case_,	cached_mode=snake_case_,	device=snake_case_	).to(snake_case_	)
  
  								with open('''datasets/clickbait/clickbait_train_prefix.txt'''	) as f:
  												A__     :							Dict   				=[]
  												for i, line in enumerate(snake_case_	):
  																try:
  																				data.append(eval(snake_case_	)	)
  																except Exception:
  																				print('''Error evaluating line {}: {}'''.format(snake_case_,	snake_case_	)	)
  																				continue
  								A__     :							int   				=[]
  								A__     :							List[Any]   				=[]
  								with open('''datasets/clickbait/clickbait_train_prefix.txt'''	) as f:
  												for i, line in enumerate(tqdm(snake_case_,	ascii=snake_case_	)	):
  																try:
  																				A__     :							Union[str, Any]   				=eval(snake_case_	)
  																				A__     :							List[Any]   				=discriminator.tokenizer.encode(d['''text''']	)
  
  																				if len(snake_case_	) < max_length_seq:
  																								A__     :							List[Any]   				=torch.tensor([5_0_2_5_6] + seq,	device=snake_case_,	dtype=torch.long	)
  																				else:
  																								print('''Line {} is longer than maximum length {}'''.format(snake_case_,	snake_case_	)	)
  																								continue
  																				x.append(snake_case_	)
  																				y.append(d['''label''']	)
  																except Exception:
  																				print('''Error evaluating / tokenizing line {}, skipping it'''.format(snake_case_	)	)
  																				pass
  
  								A__     :							Optional[int]   				=Dataset(snake_case_,	snake_case_	)
  								A__     :							List[str]   				=int(0.9 * len(snake_case_	)	)
  								A__     :							str   				=len(snake_case_	) - train_size
  								A__				,  A__     :							Any   				=torch.utils.data.random_split(snake_case_,	[train_size, test_size]	)
  
  								A__     :							int   				={
  								    '''class_size''': len(snake_case_	),
  								    '''embed_size''': discriminator.embed_size,
  								    '''pretrained_model''': pretrained_model,
  								    '''class_vocab''': classaidx,
  								    '''default_class''': 1,
  								}
  
  				elif dataset == "toxic":
  								A__     :							int   				=['''non_toxic''', '''toxic''']
  								A__     :							List[str]   				={c: i for i, c in enumerate(snake_case_	)}
  
  								A__     :							Optional[int]   				=Discriminator(
  								    class_size=len(snake_case_	),	pretrained_model=snake_case_,	cached_mode=snake_case_,	device=snake_case_	).to(snake_case_	)
  
  								A__     :							int   				=[]
  								A__     :							List[str]   				=[]
  								with open('''datasets/toxic/toxic_train.txt'''	) as f:
  												for i, line in enumerate(tqdm(snake_case_,	ascii=snake_case_	)	):
  																try:
  																				A__     :							Union[str, Any]   				=eval(snake_case_	)
  																				A__     :							Tuple   				=discriminator.tokenizer.encode(d['''text''']	)
  
  																				if len(snake_case_	) < max_length_seq:
  																								A__     :							List[str]   				=torch.tensor([5_0_2_5_6] + seq,	device=snake_case_,	dtype=torch.long	)
  																				else:
  																								print('''Line {} is longer than maximum length {}'''.format(snake_case_,	snake_case_	)	)
  																								continue
  																				x.append(snake_case_	)
  																				y.append(int(np.sum(d['''label''']	) > 0	)	)
  																except Exception:
  																				print('''Error evaluating / tokenizing line {}, skipping it'''.format(snake_case_	)	)
  																				pass
  
  								A__     :							Dict   				=Dataset(snake_case_,	snake_case_	)
  								A__     :							List[Any]   				=int(0.9 * len(snake_case_	)	)
  								A__     :							Tuple   				=len(snake_case_	) - train_size
  								A__				,  A__     :							Optional[Any]   				=torch.utils.data.random_split(snake_case_,	[train_size, test_size]	)
  
  								A__     :							Dict   				={
  								    '''class_size''': len(snake_case_	),
  								    '''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.'''	)
  
  								A__     :							Any   				=set()
  								with open(snake_case_	) as f:
  												A__     :							Union[str, Any]   				=csv.reader(snake_case_,	delimiter='''\t'''	)
  												for row in tqdm(snake_case_,	ascii=snake_case_	):
  																if row:
  																				classes.add(row[0]	)
  
  								A__     :							int   				=sorted(snake_case_	)
  								A__     :							int   				={c: i for i, c in enumerate(snake_case_	)}
  
  								A__     :							Any   				=Discriminator(
  								    class_size=len(snake_case_	),	pretrained_model=snake_case_,	cached_mode=snake_case_,	device=snake_case_	).to(snake_case_	)
  
  								A__     :							List[Any]   				=[]
  								A__     :							Any   				=[]
  								with open(snake_case_	) as f:
  												A__     :							str   				=csv.reader(snake_case_,	delimiter='''\t'''	)
  												for i, row in enumerate(tqdm(snake_case_,	ascii=snake_case_	)	):
  																if row:
  																				A__     :							int   				=row[0]
  																				A__     :							str   				=row[1]
  
  																				try:
  																								A__     :							str   				=discriminator.tokenizer.encode(snake_case_	)
  																								if len(snake_case_	) < max_length_seq:
  																												A__     :							List[str]   				=torch.tensor([5_0_2_5_6] + seq,	device=snake_case_,	dtype=torch.long	)
  
  																								else:
  																												print('''Line {} is longer than maximum length {}'''.format(snake_case_,	snake_case_	)	)
  																												continue
  
  																								x.append(snake_case_	)
  																								y.append(classaidx[label]	)
  
  																				except Exception:
  																								print('''Error tokenizing line {}, skipping it'''.format(snake_case_	)	)
  																								pass
  
  								A__     :							int   				=Dataset(snake_case_,	snake_case_	)
  								A__     :							Union[str, Any]   				=int(0.9 * len(snake_case_	)	)
  								A__     :							Any   				=len(snake_case_	) - train_size
  								A__				,  A__     :							Union[str, Any]   				=torch.utils.data.random_split(snake_case_,	[train_size, test_size]	)
  
  								A__     :							Union[str, Any]   				={
  								    '''class_size''': len(snake_case_	),
  								    '''embed_size''': discriminator.embed_size,
  								    '''pretrained_model''': pretrained_model,
  								    '''class_vocab''': classaidx,
  								    '''default_class''': 0,
  								}
  
  				A__     :							Union[str, Any]   				=time.time()
  				print('''Preprocessed {} data points'''.format(len(snake_case_	) + len(snake_case_	)	)	)
  				print('''Data preprocessing took: {:.3f}s'''.format(end - start	)	)
  
  				if cached:
  								print('''Building representation cache...'''	)
  
  								A__     :							Optional[Any]   				=time.time()
  
  								A__     :							Any   				=get_cached_data_loader(snake_case_,	snake_case_,	snake_case_,	shuffle=snake_case_,	device=snake_case_	)
  
  								A__     :							str   				=get_cached_data_loader(snake_case_,	snake_case_,	snake_case_,	device=snake_case_	)
  
  								A__     :							int   				=time.time()
  								print('''Building representation cache took: {:.3f}s'''.format(end - start	)	)
  
  				else:
  								A__     :							Dict   				=torch.utils.data.DataLoader(
  								    dataset=snake_case_,	batch_size=snake_case_,	shuffle=snake_case_,	collate_fn=snake_case_	)
  								A__     :							List[str]   				=torch.utils.data.DataLoader(dataset=snake_case_,	batch_size=snake_case_,	collate_fn=snake_case_	)
  
  				if save_model:
  								with open('''{}_classifier_head_meta.json'''.format(snake_case_	),	'''w'''	) as meta_file:
  												json.dump(snake_case_,	snake_case_	)
  
  				A__     :							Union[str, Any]   				=optim.Adam(discriminator.parameters(),	lr=0.0_0_0_1	)
  
  				for epoch in range(snake_case_	):
  								A__     :							str   				=time.time()
  								print('''\nEpoch''',	epoch + 1	)
  
  								train_epoch(
  								    discriminator=snake_case_,	data_loader=snake_case_,	optimizer=snake_case_,	epoch=snake_case_,	log_interval=snake_case_,	device=snake_case_,	)
  								evaluate_performance(data_loader=snake_case_,	discriminator=snake_case_,	device=snake_case_	)
  
  								A__     :							Optional[int]   				=time.time()
  								print('''Epoch took: {:.3f}s'''.format(end - start	)	)
  
  								print('''\nExample prediction'''	)
  								predict(snake_case_,	snake_case_,	snake_case_,	cached=snake_case_,	device=snake_case_	)
  
  								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(snake_case_,	epoch + 1	),	)
  
  
  if __name__ == "__main__":
  						__lowerCamelCase       :       Dict				   =			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")
  						__lowerCamelCase       :       Tuple				   =			parser.parse_args()
  
  						train_discriminator(**(vars(args)))
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							list[int]:
  				A__     :							Optional[int]   				=[0] * len(snake_case_	)
  				for i in range(1,	len(snake_case_	)	):
  								A__     :							List[Any]   				=duration_times[i - 1] + waiting_times[i - 1]
  				return waiting_times
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							list[int]:
  				return [
  				    duration_time + waiting_times[i]
  				    for i, duration_time in enumerate(snake_case_	)
  				]
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
  				return sum(snake_case_	) / len(snake_case_	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
  				return sum(snake_case_	) / len(snake_case_	)
  
  
  if __name__ == "__main__":
  						# process id's
  						__lowerCamelCase       :       List[str]				   =			[1, 2, 3]
  
  						# ensure that we actually have processes
  						if len(processes) == 0:
  												print("Zero amount of processes")
  												raise SystemExit(0)
  
  						# duration time of all processes
  						__lowerCamelCase       :       Dict				   =			[19, 8, 9]
  
  						# ensure we can match each id to a duration time
  						if len(duration_times) != len(processes):
  												print("Unable to match all id's with their duration time")
  												raise SystemExit(0)
  
  						# get the waiting times and the turnaround times
  						__lowerCamelCase       :       int				   =			calculate_waiting_times(duration_times)
  						__lowerCamelCase       :       Optional[int]				   =			calculate_turnaround_times(duration_times, waiting_times)
  
  						# get the average times
  						__lowerCamelCase       :       List[str]				   =			calculate_average_waiting_time(waiting_times)
  						__lowerCamelCase       :       Dict				   =			calculate_average_turnaround_time(turnaround_times)
  
  						# print all the results
  						print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
  						for i, process in enumerate(processes):
  												print(
  												    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
  												    F"{turnaround_times[i]}"
  												)
  						print(F"Average waiting time = {average_waiting_time}")
  						print(F"Average turn around time = {average_turnaround_time}")
  
  
  
  