code: 
  
  
  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 : List[Any]          =    1e-1_0
  SCREAMING_SNAKE_CASE : Dict          =    "This is incredible! I love it, this is the best chicken I have ever had."
  SCREAMING_SNAKE_CASE : Optional[int]          =    1_00
  
  
  
  
  
  class 					snake_case__  ( nn.Module       ):
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_="gpt2-medium"				,    UpperCamelCase_=False				,    UpperCamelCase_="cpu"							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__()
  								a_       :   Optional[int]			=	GPTaTokenizer.from_pretrained(UpperCamelCase__							)
  								a_       :   List[Any]			=	GPTaLMHeadModel.from_pretrained(UpperCamelCase__							)
  								a_       :   Optional[Any]			=	self.encoder.transformer.config.hidden_size
  								a_       :   List[Any]			=	ClassificationHead(class_size=UpperCamelCase__				,    embed_size=self.embed_size							)
  								a_       :   Optional[Any]			=	cached_mode
  								a_       :   Union[str, Any]			=	device
  
  
  
  
  
  	def     A				(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.classifier_head
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								for param in self.encoder.parameters():
  															a_       :   List[Any]			=	False
  								self.classifier_head.train()
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	x.ne(0							).unsqueeze(2							).repeat(1				,    1				,    self.embed_size							).float().to(self.device							).detach()
  								a_       :   Tuple			=	self.encoder.transformer(UpperCamelCase__							)["""last_hidden_state"""]
  								a_       :   Optional[Any]			=	hidden * mask
  								a_       :   List[str]			=	torch.sum(UpperCamelCase__				,    dim=1							) / (torch.sum(UpperCamelCase__				,    dim=1							).detach() + EPSILON)
  								return avg_hidden
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.cached_mode:
  															a_       :   Any			=	x.to(self.device							)
  								else:
  															a_       :   Optional[Any]			=	self.avg_representation(x.to(self.device							)							)
  
  								a_       :   Optional[Any]			=	self.classifier_head(UpperCamelCase__							)
  								a_       :   Dict			=	nn.functional.log_softmax(UpperCamelCase__				,    dim=-1							)
  
  								return probs
  
  
  
  
  
  class 					snake_case__  ( data.Dataset       ):
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Tuple			=	X
  								a_       :   List[Any]			=	y
  
  
  
  
  
  	def __len__(  self							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return len(self.X							)
  
  
  
  
  
  	def __getitem__(  self				,    UpperCamelCase_							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	{}
  								a_       :   Optional[Any]			=	self.X[index]
  								a_       :   Optional[int]			=	self.y[index]
  								return data
  
  
  
  
  
  def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Tuple      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							def pad_sequences(SCREAMING_SNAKE_CASE_				: Optional[Any]      ):
  														a_       :   Union[str, Any]			=	[len(SCREAMING_SNAKE_CASE_      ) for seq in sequences]
  
  														a_       :   int			=	torch.zeros(len(SCREAMING_SNAKE_CASE_      )	,	max(SCREAMING_SNAKE_CASE_      )      ).long()  # padding value = 0
  
  														for i, seq in enumerate(SCREAMING_SNAKE_CASE_      ):
  																					a_       :   List[str]			=	lengths[i]
  																					a_       :   List[Any]			=	seq[:end]
  
  														return padded_sequences, lengths
  
  							a_       :   Optional[Any]			=	{}
  							for key in data[0].keys():
  														a_       :   List[str]			=	[d[key] for d in data]
  
  							a_		,				a_       :   Optional[Any]			=	pad_sequences(item_info["""X"""]      )
  							a_       :   List[Any]			=	torch.tensor(item_info["""y"""]	,	dtype=torch.long      )
  
  							return x_batch, y_batch
  
  
  
  
  
  def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Optional[Any]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Any			=	{}
  							for key in data[0].keys():
  														a_       :   str			=	[d[key] for d in data]
  
  							a_       :   Optional[int]			=	torch.cat(item_info["""X"""]	,	0      )
  							a_       :   Dict			=	torch.tensor(item_info["""y"""]	,	dtype=torch.long      )
  
  							return x_batch, y_batch
  
  
  
  
  
  def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Tuple	,	SCREAMING_SNAKE_CASE_				: Tuple	,	SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: int=0	,	SCREAMING_SNAKE_CASE_				: Any=10	,	SCREAMING_SNAKE_CASE_				: str="cpu"      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Tuple			=	0
  							discriminator.train_custom()
  							for batch_idx, (input_t, target_t) in enumerate(SCREAMING_SNAKE_CASE_      ):
  														a_		,				a_       :   Union[str, Any]			=	input_t.to(SCREAMING_SNAKE_CASE_      ), target_t.to(SCREAMING_SNAKE_CASE_      )
  
  														optimizer.zero_grad()
  
  														a_       :   Optional[Any]			=	discriminator(SCREAMING_SNAKE_CASE_      )
  														a_       :   Tuple			=	nn.functional.nll_loss(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  														loss.backward(retain_graph=SCREAMING_SNAKE_CASE_      )
  														optimizer.step()
  
  														samples_so_far += len(SCREAMING_SNAKE_CASE_      )
  
  														if batch_idx % log_interval == 0:
  																					print(
  																					    """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
  																					        epoch + 1	,	SCREAMING_SNAKE_CASE_	,	len(data_loader.dataset      )	,	1_00 * samples_so_far / len(data_loader.dataset      )	,	loss.item()	,	)      )
  
  
  
  
  
  def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Any	,	SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: List[Any]="cpu"      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							discriminator.eval()
  							a_       :   Union[str, Any]			=	0
  							a_       :   Any			=	0
  							with torch.no_grad():
  														for input_t, target_t in data_loader:
  																					a_		,				a_       :   int			=	input_t.to(SCREAMING_SNAKE_CASE_      ), target_t.to(SCREAMING_SNAKE_CASE_      )
  																					a_       :   List[str]			=	discriminator(SCREAMING_SNAKE_CASE_      )
  																					# sum up batch loss
  																					test_loss += nn.functional.nll_loss(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	reduction="""sum"""      ).item()
  																					# get the index of the max log-probability
  																					a_       :   str			=	output_t.argmax(dim=1	,	keepdim=SCREAMING_SNAKE_CASE_      )
  																					correct += pred_t.eq(target_t.view_as(SCREAMING_SNAKE_CASE_      )      ).sum().item()
  
  							test_loss /= len(data_loader.dataset      )
  
  							print(
  							    """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
  							        SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	len(data_loader.dataset      )	,	100.0 * correct / len(data_loader.dataset      )      )      )
  
  
  
  
  
  def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Optional[Any]	,	SCREAMING_SNAKE_CASE_				: int	,	SCREAMING_SNAKE_CASE_				: Dict	,	SCREAMING_SNAKE_CASE_				: Optional[int]=False	,	SCREAMING_SNAKE_CASE_				: int="cpu"      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   List[str]			=	model.tokenizer.encode(SCREAMING_SNAKE_CASE_      )
  							a_       :   List[str]			=	torch.tensor([input_t]	,	dtype=torch.long	,	device=SCREAMING_SNAKE_CASE_      )
  							if cached:
  														a_       :   List[str]			=	model.avg_representation(SCREAMING_SNAKE_CASE_      )
  
  							a_       :   Any			=	model(SCREAMING_SNAKE_CASE_      ).data.cpu().numpy().flatten().tolist()
  							print("""Input sentence:"""	,	SCREAMING_SNAKE_CASE_      )
  							print(
  							    """Predictions:"""	,	""", """.join("""{}: {:.4f}""".format(SCREAMING_SNAKE_CASE_	,	math.exp(SCREAMING_SNAKE_CASE_      )      ) for c, log_prob in zip(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )      )	,	)
  
  
  
  
  
  def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: Tuple	,	SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: Dict	,	SCREAMING_SNAKE_CASE_				: Dict=False	,	SCREAMING_SNAKE_CASE_				: int="cpu"      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Tuple			=	torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE_	,	batch_size=SCREAMING_SNAKE_CASE_	,	collate_fn=SCREAMING_SNAKE_CASE_      )
  
  							a_       :   Tuple			=	[]
  							a_       :   Union[str, Any]			=	[]
  							for batch_idx, (x, y) in enumerate(tqdm(SCREAMING_SNAKE_CASE_	,	ascii=SCREAMING_SNAKE_CASE_      )      ):
  														with torch.no_grad():
  																					a_       :   int			=	x.to(SCREAMING_SNAKE_CASE_      )
  																					a_       :   Tuple			=	discriminator.avg_representation(SCREAMING_SNAKE_CASE_      ).cpu().detach()
  																					a_       :   Dict			=	torch.unbind(avg_rep.unsqueeze(1      )      )
  																					xs += avg_rep_list
  																					ys += y.cpu().numpy().tolist()
  
  							a_       :   Any			=	torch.utils.data.DataLoader(
  							    dataset=Dataset(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )	,	batch_size=SCREAMING_SNAKE_CASE_	,	shuffle=SCREAMING_SNAKE_CASE_	,	collate_fn=SCREAMING_SNAKE_CASE_      )
  
  							return data_loader
  
  
  
  
  
  def 						_lowerCamelCase	(							SCREAMING_SNAKE_CASE_				: str	,	SCREAMING_SNAKE_CASE_				: List[Any]=None	,	SCREAMING_SNAKE_CASE_				: Any="gpt2-medium"	,	SCREAMING_SNAKE_CASE_				: int=10	,	SCREAMING_SNAKE_CASE_				: int=64	,	SCREAMING_SNAKE_CASE_				: Optional[int]=10	,	SCREAMING_SNAKE_CASE_				: Optional[Any]=False	,	SCREAMING_SNAKE_CASE_				: Optional[int]=False	,	SCREAMING_SNAKE_CASE_				: int=False	,	):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   List[str]			=	"""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""
  
  							print("""Preprocessing {} dataset...""".format(SCREAMING_SNAKE_CASE_      )      )
  							a_       :   Optional[Any]			=	time.time()
  
  							if dataset == "SST":
  														a_       :   Any			=	["""positive""", """negative""", """very positive""", """very negative""", """neutral"""]
  														a_       :   Union[str, Any]			=	{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_      )}
  
  														a_       :   Dict			=	Discriminator(
  														    class_size=len(SCREAMING_SNAKE_CASE_      )	,	pretrained_model=SCREAMING_SNAKE_CASE_	,	cached_mode=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )
  
  														a_       :   List[Any]			=	torchtext_data.Field()
  														a_       :   Union[str, Any]			=	torchtext_data.Field(sequential=SCREAMING_SNAKE_CASE_      )
  														a_		,				a_		,				a_       :   Tuple			=	datasets.SST.splits(
  														    SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	fine_grained=SCREAMING_SNAKE_CASE_	,	train_subtrees=SCREAMING_SNAKE_CASE_	,	)
  
  														a_       :   Dict			=	[]
  														a_       :   List[str]			=	[]
  														for i in trange(len(SCREAMING_SNAKE_CASE_      )	,	ascii=SCREAMING_SNAKE_CASE_      ):
  																					a_       :   Optional[Any]			=	TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["""text"""]      )
  																					a_       :   str			=	discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_      )
  																					a_       :   Tuple			=	torch.tensor([5_02_56] + seq	,	device=SCREAMING_SNAKE_CASE_	,	dtype=torch.long      )
  																					x.append(SCREAMING_SNAKE_CASE_      )
  																					y.append(classaidx[vars(train_data[i]      )["""label"""]]      )
  														a_       :   Optional[Any]			=	Dataset(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  
  														a_       :   int			=	[]
  														a_       :   Union[str, Any]			=	[]
  														for i in trange(len(SCREAMING_SNAKE_CASE_      )	,	ascii=SCREAMING_SNAKE_CASE_      ):
  																					a_       :   Any			=	TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["""text"""]      )
  																					a_       :   Union[str, Any]			=	discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_      )
  																					a_       :   List[str]			=	torch.tensor([5_02_56] + seq	,	device=SCREAMING_SNAKE_CASE_	,	dtype=torch.long      )
  																					test_x.append(SCREAMING_SNAKE_CASE_      )
  																					test_y.append(classaidx[vars(test_data[i]      )["""label"""]]      )
  														a_       :   Tuple			=	Dataset(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  
  														a_       :   Tuple			=	{
  														    """class_size""": len(SCREAMING_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_       :   Any			=	{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_      )}
  
  														a_       :   List[str]			=	Discriminator(
  														    class_size=len(SCREAMING_SNAKE_CASE_      )	,	pretrained_model=SCREAMING_SNAKE_CASE_	,	cached_mode=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )
  
  														with open("""datasets/clickbait/clickbait_train_prefix.txt"""      ) as f:
  																					a_       :   str			=	[]
  																					for i, line in enumerate(SCREAMING_SNAKE_CASE_      ):
  																												try:
  																																			data.append(eval(SCREAMING_SNAKE_CASE_      )      )
  																												except Exception:
  																																			print("""Error evaluating line {}: {}""".format(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )      )
  																																			continue
  														a_       :   int			=	[]
  														a_       :   Optional[int]			=	[]
  														with open("""datasets/clickbait/clickbait_train_prefix.txt"""      ) as f:
  																					for i, line in enumerate(tqdm(SCREAMING_SNAKE_CASE_	,	ascii=SCREAMING_SNAKE_CASE_      )      ):
  																												try:
  																																			a_       :   int			=	eval(SCREAMING_SNAKE_CASE_      )
  																																			a_       :   List[str]			=	discriminator.tokenizer.encode(d["""text"""]      )
  
  																																			if len(SCREAMING_SNAKE_CASE_      ) < max_length_seq:
  																																										a_       :   Any			=	torch.tensor([5_02_56] + seq	,	device=SCREAMING_SNAKE_CASE_	,	dtype=torch.long      )
  																																			else:
  																																										print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )      )
  																																										continue
  																																			x.append(SCREAMING_SNAKE_CASE_      )
  																																			y.append(d["""label"""]      )
  																												except Exception:
  																																			print("""Error evaluating / tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE_      )      )
  																																			pass
  
  														a_       :   Optional[int]			=	Dataset(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  														a_       :   Union[str, Any]			=	int(0.9 * len(SCREAMING_SNAKE_CASE_      )      )
  														a_       :   List[str]			=	len(SCREAMING_SNAKE_CASE_      ) - train_size
  														a_		,				a_       :   Dict			=	torch.utils.data.random_split(SCREAMING_SNAKE_CASE_	,	[train_size, test_size]      )
  
  														a_       :   Any			=	{
  														    """class_size""": len(SCREAMING_SNAKE_CASE_      ),
  														    """embed_size""": discriminator.embed_size,
  														    """pretrained_model""": pretrained_model,
  														    """class_vocab""": classaidx,
  														    """default_class""": 1,
  														}
  
  							elif dataset == "toxic":
  														a_       :   Optional[int]			=	["""non_toxic""", """toxic"""]
  														a_       :   Optional[Any]			=	{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_      )}
  
  														a_       :   Optional[int]			=	Discriminator(
  														    class_size=len(SCREAMING_SNAKE_CASE_      )	,	pretrained_model=SCREAMING_SNAKE_CASE_	,	cached_mode=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )
  
  														a_       :   str			=	[]
  														a_       :   Any			=	[]
  														with open("""datasets/toxic/toxic_train.txt"""      ) as f:
  																					for i, line in enumerate(tqdm(SCREAMING_SNAKE_CASE_	,	ascii=SCREAMING_SNAKE_CASE_      )      ):
  																												try:
  																																			a_       :   List[str]			=	eval(SCREAMING_SNAKE_CASE_      )
  																																			a_       :   List[Any]			=	discriminator.tokenizer.encode(d["""text"""]      )
  
  																																			if len(SCREAMING_SNAKE_CASE_      ) < max_length_seq:
  																																										a_       :   Tuple			=	torch.tensor([5_02_56] + seq	,	device=SCREAMING_SNAKE_CASE_	,	dtype=torch.long      )
  																																			else:
  																																										print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )      )
  																																										continue
  																																			x.append(SCREAMING_SNAKE_CASE_      )
  																																			y.append(int(np.sum(d["""label"""]      ) > 0      )      )
  																												except Exception:
  																																			print("""Error evaluating / tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE_      )      )
  																																			pass
  
  														a_       :   Optional[int]			=	Dataset(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  														a_       :   Optional[Any]			=	int(0.9 * len(SCREAMING_SNAKE_CASE_      )      )
  														a_       :   List[Any]			=	len(SCREAMING_SNAKE_CASE_      ) - train_size
  														a_		,				a_       :   Tuple			=	torch.utils.data.random_split(SCREAMING_SNAKE_CASE_	,	[train_size, test_size]      )
  
  														a_       :   Union[str, Any]			=	{
  														    """class_size""": len(SCREAMING_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_       :   List[str]			=	set()
  														with open(SCREAMING_SNAKE_CASE_      ) as f:
  																					a_       :   List[Any]			=	csv.reader(SCREAMING_SNAKE_CASE_	,	delimiter="""\t"""      )
  																					for row in tqdm(SCREAMING_SNAKE_CASE_	,	ascii=SCREAMING_SNAKE_CASE_      ):
  																												if row:
  																																			classes.add(row[0]      )
  
  														a_       :   Tuple			=	sorted(SCREAMING_SNAKE_CASE_      )
  														a_       :   Any			=	{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_      )}
  
  														a_       :   Optional[Any]			=	Discriminator(
  														    class_size=len(SCREAMING_SNAKE_CASE_      )	,	pretrained_model=SCREAMING_SNAKE_CASE_	,	cached_mode=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )
  
  														a_       :   List[Any]			=	[]
  														a_       :   Optional[Any]			=	[]
  														with open(SCREAMING_SNAKE_CASE_      ) as f:
  																					a_       :   List[Any]			=	csv.reader(SCREAMING_SNAKE_CASE_	,	delimiter="""\t"""      )
  																					for i, row in enumerate(tqdm(SCREAMING_SNAKE_CASE_	,	ascii=SCREAMING_SNAKE_CASE_      )      ):
  																												if row:
  																																			a_       :   Optional[int]			=	row[0]
  																																			a_       :   List[str]			=	row[1]
  
  																																			try:
  																																										a_       :   Any			=	discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_      )
  																																										if len(SCREAMING_SNAKE_CASE_      ) < max_length_seq:
  																																																	a_       :   Dict			=	torch.tensor([5_02_56] + seq	,	device=SCREAMING_SNAKE_CASE_	,	dtype=torch.long      )
  
  																																										else:
  																																																	print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )      )
  																																																	continue
  
  																																										x.append(SCREAMING_SNAKE_CASE_      )
  																																										y.append(classaidx[label]      )
  
  																																			except Exception:
  																																										print("""Error tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE_      )      )
  																																										pass
  
  														a_       :   Any			=	Dataset(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  														a_       :   Dict			=	int(0.9 * len(SCREAMING_SNAKE_CASE_      )      )
  														a_       :   int			=	len(SCREAMING_SNAKE_CASE_      ) - train_size
  														a_		,				a_       :   Union[str, Any]			=	torch.utils.data.random_split(SCREAMING_SNAKE_CASE_	,	[train_size, test_size]      )
  
  														a_       :   List[str]			=	{
  														    """class_size""": len(SCREAMING_SNAKE_CASE_      ),
  														    """embed_size""": discriminator.embed_size,
  														    """pretrained_model""": pretrained_model,
  														    """class_vocab""": classaidx,
  														    """default_class""": 0,
  														}
  
  							a_       :   List[Any]			=	time.time()
  							print("""Preprocessed {} data points""".format(len(SCREAMING_SNAKE_CASE_      ) + len(SCREAMING_SNAKE_CASE_      )      )      )
  							print("""Data preprocessing took: {:.3f}s""".format(end - start      )      )
  
  							if cached:
  														print("""Building representation cache..."""      )
  
  														a_       :   str			=	time.time()
  
  														a_       :   str			=	get_cached_data_loader(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	shuffle=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      )
  
  														a_       :   Dict			=	get_cached_data_loader(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      )
  
  														a_       :   Tuple			=	time.time()
  														print("""Building representation cache took: {:.3f}s""".format(end - start      )      )
  
  							else:
  														a_       :   Tuple			=	torch.utils.data.DataLoader(
  														    dataset=SCREAMING_SNAKE_CASE_	,	batch_size=SCREAMING_SNAKE_CASE_	,	shuffle=SCREAMING_SNAKE_CASE_	,	collate_fn=SCREAMING_SNAKE_CASE_      )
  														a_       :   Optional[Any]			=	torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE_	,	batch_size=SCREAMING_SNAKE_CASE_	,	collate_fn=SCREAMING_SNAKE_CASE_      )
  
  							if save_model:
  														with open("""{}_classifier_head_meta.json""".format(SCREAMING_SNAKE_CASE_      )	,	"""w"""      ) as meta_file:
  																					json.dump(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_      )
  
  							a_       :   Optional[Any]			=	optim.Adam(discriminator.parameters()	,	lr=0.0_001      )
  
  							for epoch in range(SCREAMING_SNAKE_CASE_      ):
  														a_       :   Union[str, Any]			=	time.time()
  														print("""\nEpoch"""	,	epoch + 1      )
  
  														train_epoch(
  														    discriminator=SCREAMING_SNAKE_CASE_	,	data_loader=SCREAMING_SNAKE_CASE_	,	optimizer=SCREAMING_SNAKE_CASE_	,	epoch=SCREAMING_SNAKE_CASE_	,	log_interval=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_	,	)
  														evaluate_performance(data_loader=SCREAMING_SNAKE_CASE_	,	discriminator=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_SNAKE_CASE_      )
  
  														a_       :   Any			=	time.time()
  														print("""Epoch took: {:.3f}s""".format(end - start      )      )
  
  														print("""\nExample prediction"""      )
  														predict(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	cached=SCREAMING_SNAKE_CASE_	,	device=SCREAMING_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(SCREAMING_SNAKE_CASE_	,	epoch + 1      )	,	)
  
  
  if __name__ == "__main__":
  	SCREAMING_SNAKE_CASE : 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")
  	SCREAMING_SNAKE_CASE : Dict          =    parser.parse_args()
  
  	train_discriminator(**(vars(args)))
  
  
  
style_context: 
  
  
  import copy
  from typing import Dict, Optional
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
  
  
  SCREAMING_SNAKE_CASE : List[str]          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : List[Any]          =    {
      "shi-labs/oneformer_ade20k_swin_tiny": (
          "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
      ),
      # See all OneFormer models at https://huggingface.co/models?filter=oneformer
  }
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Tuple         = """oneformer"""
  	UpperCAmelCase   :							Union[str, Any]         = {"""hidden_size""": """hidden_dim"""}
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 255				,    UpperCamelCase_ = 150				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 0.5				,    UpperCamelCase_ = 0.07				,    UpperCamelCase_ = 12544				,    UpperCamelCase_ = 3.0				,    UpperCamelCase_ = 0.75				,    UpperCamelCase_ = 0.02				,    UpperCamelCase_ = 1.0				,    UpperCamelCase_ = 1e-05				,    UpperCamelCase_ = False				,    UpperCamelCase_ = True				,    UpperCamelCase_ = True				,    UpperCamelCase_ = [4, 8, 16, 32]				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 49408				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 16				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 1024				,    UpperCamelCase_ = "GN"				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 10				,    UpperCamelCase_ = True				,    UpperCamelCase_ = 8				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2048				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 4				,    **UpperCamelCase_				,    )		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if backbone_config is None:
  															logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""							)
  															a_       :   Tuple			=	CONFIG_MAPPING["""swin"""](
  															    image_size=224				,    in_channels=3				,    patch_size=4				,    embed_dim=96				,    depths=[2, 2, 6, 2]				,    num_heads=[3, 6, 12, 24]				,    window_size=7				,    drop_path_rate=0.3				,    use_absolute_embeddings=UpperCamelCase_				,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,    )
  								elif isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  															a_       :   str			=	backbone_config.get("""model_type"""							)
  															a_       :   Optional[Any]			=	CONFIG_MAPPING[backbone_model_type]
  															a_       :   List[Any]			=	config_class.from_dict(UpperCamelCase_							)
  
  								a_       :   str			=	backbone_config
  
  								a_       :   List[str]			=	ignore_value
  								a_       :   Optional[int]			=	num_queries
  								a_       :   Optional[Any]			=	no_object_weight
  								a_       :   List[Any]			=	class_weight
  								a_       :   Union[str, Any]			=	mask_weight
  								a_       :   Union[str, Any]			=	dice_weight
  								a_       :   Optional[Any]			=	contrastive_weight
  								a_       :   Any			=	contrastive_temperature
  								a_       :   Optional[int]			=	train_num_points
  								a_       :   Tuple			=	oversample_ratio
  								a_       :   str			=	importance_sample_ratio
  								a_       :   List[Any]			=	init_std
  								a_       :   int			=	init_xavier_std
  								a_       :   List[Any]			=	layer_norm_eps
  								a_       :   Optional[int]			=	is_training
  								a_       :   List[Any]			=	use_auxiliary_loss
  								a_       :   str			=	output_auxiliary_logits
  								a_       :   Dict			=	strides
  								a_       :   List[Any]			=	task_seq_len
  								a_       :   int			=	text_encoder_width
  								a_       :   List[Any]			=	text_encoder_context_length
  								a_       :   List[str]			=	text_encoder_num_layers
  								a_       :   Union[str, Any]			=	text_encoder_vocab_size
  								a_       :   Optional[Any]			=	text_encoder_proj_layers
  								a_       :   Optional[int]			=	text_encoder_n_ctx
  								a_       :   Optional[Any]			=	conv_dim
  								a_       :   str			=	mask_dim
  								a_       :   Optional[Any]			=	hidden_dim
  								a_       :   Any			=	encoder_feedforward_dim
  								a_       :   Dict			=	norm
  								a_       :   List[Any]			=	encoder_layers
  								a_       :   int			=	decoder_layers
  								a_       :   Any			=	use_task_norm
  								a_       :   Union[str, Any]			=	num_attention_heads
  								a_       :   List[str]			=	dropout
  								a_       :   List[Any]			=	dim_feedforward
  								a_       :   Union[str, Any]			=	pre_norm
  								a_       :   Dict			=	enforce_input_proj
  								a_       :   Tuple			=	query_dec_layers
  								a_       :   Any			=	common_stride
  								a_       :   List[str]			=	decoder_layers
  
  								super().__init__(**UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Dict[str, any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	copy.deepcopy(self.__dict__							)
  								a_       :   Any			=	self.backbone_config.to_dict()
  								a_       :   Union[str, Any]			=	self.__class__.model_type
  								return output
  
  
  