



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)
a			:     List[str]     = 1E-1_0
a			:     Any     = 'This is incredible! I love it, this is the best chicken I have ever had.'
a			:     Any     = 100







class 							_a							(      nn.Module				):

			def __init__(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_="gpt2-medium",      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_="cpu"				)					->      Union[str, Any]:
						super().__init__()
						UpperCAmelCase_:		Union[str, Any]       			=    GPTaTokenizer.from_pretrained(UpperCamelCase__				)
						UpperCAmelCase_:		Optional[int]       			=    GPTaLMHeadModel.from_pretrained(UpperCamelCase__				)
						UpperCAmelCase_:		Dict       			=    self.encoder.transformer.config.hidden_size
						UpperCAmelCase_:		Optional[int]       			=    ClassificationHead(class_size=UpperCamelCase__,      embed_size=self.embed_size				)
						UpperCAmelCase_:		str       			=    cached_mode
						UpperCAmelCase_:		Union[str, Any]       			=    device

			def     __snake_case					(self				)					->      List[Any]:
						return self.classifier_head

			def     __snake_case					(self				)					->      List[Any]:
						for param in self.encoder.parameters():
									UpperCAmelCase_:		Union[str, Any]       			=    False
						self.classifier_head.train()

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

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Tuple:
						if self.cached_mode:
									UpperCAmelCase_:		Union[str, Any]       			=    x.to(self.device				)
						else:
									UpperCAmelCase_:		int       			=    self.avg_representation(x.to(self.device				)				)

						UpperCAmelCase_:		int       			=    self.classifier_head(UpperCamelCase__				)
						UpperCAmelCase_:		Optional[Any]       			=    nn.functional.log_softmax(UpperCamelCase__,      dim=-1				)

						return probs







class 							_a							(      data.Dataset				):

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

			def __len__(self				)					->      Union[str, Any]:
						return len(self.X				)

			def __getitem__(self,      SCREAMING_SNAKE_CASE_				)					->      Any:
						UpperCAmelCase_:		str       			=    {}
						UpperCAmelCase_:		List[Any]       			=    self.X[index]
						UpperCAmelCase_:		Dict       			=    self.y[index]
						return data







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict     ):





			"""simple docstring"""


			def pad_sequences(lowerCAmelCase__:		Union[str, Any]     ):
						UpperCAmelCase_:		List[Any]       			=    [len(lowerCAmelCase__     ) for seq in sequences]

						UpperCAmelCase_:		Dict       			=    torch.zeros(len(lowerCAmelCase__     )		,	max(lowerCAmelCase__     )     ).long()  # padding value = 0

						for i, seq in enumerate(lowerCAmelCase__     ):
									UpperCAmelCase_:		Any       			=    lengths[i]
									UpperCAmelCase_:		List[str]       			=    seq[:end]

						return padded_sequences, lengths

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

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

			return x_batch, y_batch







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict     ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]       			=    {}
			for key in data[0].keys():
						UpperCAmelCase_:		Optional[int]       			=    [d[key] for d in data]

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

			return x_batch, y_batch







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[Any]		,	lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		str		,	lowerCAmelCase__:		int=0		,	lowerCAmelCase__:		Union[str, Any]=1_0		,	lowerCAmelCase__:		Optional[Any]="cpu"     ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]       			=    0
			discriminator.train_custom()
			for batch_idx, (input_t, target_t) in enumerate(lowerCAmelCase__     ):
						UpperCAmelCase_			,       UpperCAmelCase_:		Dict       			=    input_t.to(lowerCAmelCase__     ), target_t.to(lowerCAmelCase__     )

						optimizer.zero_grad()

						UpperCAmelCase_:		Any       			=    discriminator(lowerCAmelCase__     )
						UpperCAmelCase_:		Optional[Any]       			=    nn.functional.nll_loss(lowerCAmelCase__		,	lowerCAmelCase__     )
						loss.backward(retain_graph=lowerCAmelCase__     )
						optimizer.step()

						samples_so_far += len(lowerCAmelCase__     )

						if batch_idx % log_interval == 0:
									print(
									    """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
									        epoch + 1		,	lowerCAmelCase__		,	len(data_loader.dataset     )		,	1_0_0 * samples_so_far / len(data_loader.dataset     )		,	loss.item()		,	)     )







def    lowerCAmelCase_  (lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		int="cpu"     ):





			"""simple docstring"""


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

			test_loss /= len(data_loader.dataset     )

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







def    lowerCAmelCase_  (lowerCAmelCase__:		int		,	lowerCAmelCase__:		str		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		Dict=False		,	lowerCAmelCase__:		Union[str, Any]="cpu"     ):





			"""simple docstring"""


			UpperCAmelCase_:		Any       			=    model.tokenizer.encode(lowerCAmelCase__     )
			UpperCAmelCase_:		List[Any]       			=    torch.tensor([input_t]		,	dtype=torch.long		,	device=lowerCAmelCase__     )
			if cached:
						UpperCAmelCase_:		str       			=    model.avg_representation(lowerCAmelCase__     )

			UpperCAmelCase_:		Optional[Any]       			=    model(lowerCAmelCase__     ).data.cpu().numpy().flatten().tolist()
			print("""Input sentence:"""		,	lowerCAmelCase__     )
			print(
			    """Predictions:"""		,	""", """.join("""{}: {:.4f}""".format(lowerCAmelCase__		,	math.exp(lowerCAmelCase__     )     ) for c, log_prob in zip(lowerCAmelCase__		,	lowerCAmelCase__     )     )		,	)







def    lowerCAmelCase_  (lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		str		,	lowerCAmelCase__:		Optional[Any]		,	lowerCAmelCase__:		List[Any]=False		,	lowerCAmelCase__:		str="cpu"     ):





			"""simple docstring"""


			UpperCAmelCase_:		int       			=    torch.utils.data.DataLoader(dataset=lowerCAmelCase__		,	batch_size=lowerCAmelCase__		,	collate_fn=lowerCAmelCase__     )

			UpperCAmelCase_:		Optional[Any]       			=    []
			UpperCAmelCase_:		Tuple       			=    []
			for batch_idx, (x, y) in enumerate(tqdm(lowerCAmelCase__		,	ascii=lowerCAmelCase__     )     ):
						with torch.no_grad():
									UpperCAmelCase_:		Tuple       			=    x.to(lowerCAmelCase__     )
									UpperCAmelCase_:		str       			=    discriminator.avg_representation(lowerCAmelCase__     ).cpu().detach()
									UpperCAmelCase_:		Dict       			=    torch.unbind(avg_rep.unsqueeze(1     )     )
									xs += avg_rep_list
									ys += y.cpu().numpy().tolist()

			UpperCAmelCase_:		Optional[int]       			=    torch.utils.data.DataLoader(
			    dataset=Dataset(lowerCAmelCase__		,	lowerCAmelCase__     )		,	batch_size=lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	collate_fn=lowerCAmelCase__     )

			return data_loader







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[Any]		,	lowerCAmelCase__:		Dict=None		,	lowerCAmelCase__:		List[str]="gpt2-medium"		,	lowerCAmelCase__:		Union[str, Any]=1_0		,	lowerCAmelCase__:		Tuple=6_4		,	lowerCAmelCase__:		List[Any]=1_0		,	lowerCAmelCase__:		int=False		,	lowerCAmelCase__:		Any=False		,	lowerCAmelCase__:		str=False		,	):





			"""simple docstring"""


			UpperCAmelCase_:		List[Any]       			=    """cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

			print("""Preprocessing {} dataset...""".format(lowerCAmelCase__     )     )
			UpperCAmelCase_:		Union[str, Any]       			=    time.time()

			if dataset == "SST":
						UpperCAmelCase_:		Optional[Any]       			=    ["""positive""", """negative""", """very positive""", """very negative""", """neutral"""]
						UpperCAmelCase_:		Union[str, Any]       			=    {c: i for i, c in enumerate(lowerCAmelCase__     )}

						UpperCAmelCase_:		str       			=    Discriminator(
						    class_size=len(lowerCAmelCase__     )		,	pretrained_model=lowerCAmelCase__		,	cached_mode=lowerCAmelCase__		,	device=lowerCAmelCase__     ).to(lowerCAmelCase__     )

						UpperCAmelCase_:		Union[str, Any]       			=    torchtext_data.Field()
						UpperCAmelCase_:		Union[str, Any]       			=    torchtext_data.Field(sequential=lowerCAmelCase__     )
						UpperCAmelCase_			,       UpperCAmelCase_			,       UpperCAmelCase_:		Tuple       			=    datasets.SST.splits(
						    lowerCAmelCase__		,	lowerCAmelCase__		,	fine_grained=lowerCAmelCase__		,	train_subtrees=lowerCAmelCase__		,	)

						UpperCAmelCase_:		List[Any]       			=    []
						UpperCAmelCase_:		Optional[int]       			=    []
						for i in trange(len(lowerCAmelCase__     )		,	ascii=lowerCAmelCase__     ):
									UpperCAmelCase_:		Any       			=    TreebankWordDetokenizer().detokenize(vars(train_data[i]     )["""text"""]     )
									UpperCAmelCase_:		Union[str, Any]       			=    discriminator.tokenizer.encode(lowerCAmelCase__     )
									UpperCAmelCase_:		Tuple       			=    torch.tensor([5_0_2_5_6] + seq		,	device=lowerCAmelCase__		,	dtype=torch.long     )
									x.append(lowerCAmelCase__     )
									y.append(classaidx[vars(train_data[i]     )["""label"""]]     )
						UpperCAmelCase_:		List[str]       			=    Dataset(lowerCAmelCase__		,	lowerCAmelCase__     )

						UpperCAmelCase_:		Optional[Any]       			=    []
						UpperCAmelCase_:		List[str]       			=    []
						for i in trange(len(lowerCAmelCase__     )		,	ascii=lowerCAmelCase__     ):
									UpperCAmelCase_:		List[Any]       			=    TreebankWordDetokenizer().detokenize(vars(test_data[i]     )["""text"""]     )
									UpperCAmelCase_:		int       			=    discriminator.tokenizer.encode(lowerCAmelCase__     )
									UpperCAmelCase_:		int       			=    torch.tensor([5_0_2_5_6] + seq		,	device=lowerCAmelCase__		,	dtype=torch.long     )
									test_x.append(lowerCAmelCase__     )
									test_y.append(classaidx[vars(test_data[i]     )["""label"""]]     )
						UpperCAmelCase_:		Any       			=    Dataset(lowerCAmelCase__		,	lowerCAmelCase__     )

						UpperCAmelCase_:		Any       			=    {
						    """class_size""": len(lowerCAmelCase__     ),
						    """embed_size""": discriminator.embed_size,
						    """pretrained_model""": pretrained_model,
						    """class_vocab""": classaidx,
						    """default_class""": 2,
						}

			elif dataset == "clickbait":
						UpperCAmelCase_:		Tuple       			=    ["""non_clickbait""", """clickbait"""]
						UpperCAmelCase_:		Optional[Any]       			=    {c: i for i, c in enumerate(lowerCAmelCase__     )}

						UpperCAmelCase_:		str       			=    Discriminator(
						    class_size=len(lowerCAmelCase__     )		,	pretrained_model=lowerCAmelCase__		,	cached_mode=lowerCAmelCase__		,	device=lowerCAmelCase__     ).to(lowerCAmelCase__     )

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

															if len(lowerCAmelCase__     ) < max_length_seq:
																		UpperCAmelCase_:		int       			=    torch.tensor([5_0_2_5_6] + seq		,	device=lowerCAmelCase__		,	dtype=torch.long     )
															else:
																		print("""Line {} is longer than maximum length {}""".format(lowerCAmelCase__		,	lowerCAmelCase__     )     )
																		continue
															x.append(lowerCAmelCase__     )
															y.append(d["""label"""]     )
												except Exception:
															print("""Error evaluating / tokenizing line {}, skipping it""".format(lowerCAmelCase__     )     )
															pass

						UpperCAmelCase_:		List[Any]       			=    Dataset(lowerCAmelCase__		,	lowerCAmelCase__     )
						UpperCAmelCase_:		List[str]       			=    int(0.9 * len(lowerCAmelCase__     )     )
						UpperCAmelCase_:		List[Any]       			=    len(lowerCAmelCase__     ) - train_size
						UpperCAmelCase_			,       UpperCAmelCase_:		Any       			=    torch.utils.data.random_split(lowerCAmelCase__		,	[train_size, test_size]     )

						UpperCAmelCase_:		Dict       			=    {
						    """class_size""": len(lowerCAmelCase__     ),
						    """embed_size""": discriminator.embed_size,
						    """pretrained_model""": pretrained_model,
						    """class_vocab""": classaidx,
						    """default_class""": 1,
						}

			elif dataset == "toxic":
						UpperCAmelCase_:		Dict       			=    ["""non_toxic""", """toxic"""]
						UpperCAmelCase_:		Optional[int]       			=    {c: i for i, c in enumerate(lowerCAmelCase__     )}

						UpperCAmelCase_:		List[str]       			=    Discriminator(
						    class_size=len(lowerCAmelCase__     )		,	pretrained_model=lowerCAmelCase__		,	cached_mode=lowerCAmelCase__		,	device=lowerCAmelCase__     ).to(lowerCAmelCase__     )

						UpperCAmelCase_:		Any       			=    []
						UpperCAmelCase_:		Tuple       			=    []
						with open("""datasets/toxic/toxic_train.txt"""     ) as f:
									for i, line in enumerate(tqdm(lowerCAmelCase__		,	ascii=lowerCAmelCase__     )     ):
												try:
															UpperCAmelCase_:		Union[str, Any]       			=    eval(lowerCAmelCase__     )
															UpperCAmelCase_:		Optional[int]       			=    discriminator.tokenizer.encode(d["""text"""]     )

															if len(lowerCAmelCase__     ) < max_length_seq:
																		UpperCAmelCase_:		Dict       			=    torch.tensor([5_0_2_5_6] + seq		,	device=lowerCAmelCase__		,	dtype=torch.long     )
															else:
																		print("""Line {} is longer than maximum length {}""".format(lowerCAmelCase__		,	lowerCAmelCase__     )     )
																		continue
															x.append(lowerCAmelCase__     )
															y.append(int(np.sum(d["""label"""]     ) > 0     )     )
												except Exception:
															print("""Error evaluating / tokenizing line {}, skipping it""".format(lowerCAmelCase__     )     )
															pass

						UpperCAmelCase_:		List[str]       			=    Dataset(lowerCAmelCase__		,	lowerCAmelCase__     )
						UpperCAmelCase_:		Union[str, Any]       			=    int(0.9 * len(lowerCAmelCase__     )     )
						UpperCAmelCase_:		Tuple       			=    len(lowerCAmelCase__     ) - train_size
						UpperCAmelCase_			,       UpperCAmelCase_:		Dict       			=    torch.utils.data.random_split(lowerCAmelCase__		,	[train_size, test_size]     )

						UpperCAmelCase_:		Tuple       			=    {
						    """class_size""": len(lowerCAmelCase__     ),
						    """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."""     )

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

						UpperCAmelCase_:		int       			=    sorted(lowerCAmelCase__     )
						UpperCAmelCase_:		Optional[Any]       			=    {c: i for i, c in enumerate(lowerCAmelCase__     )}

						UpperCAmelCase_:		Union[str, Any]       			=    Discriminator(
						    class_size=len(lowerCAmelCase__     )		,	pretrained_model=lowerCAmelCase__		,	cached_mode=lowerCAmelCase__		,	device=lowerCAmelCase__     ).to(lowerCAmelCase__     )

						UpperCAmelCase_:		int       			=    []
						UpperCAmelCase_:		Optional[Any]       			=    []
						with open(lowerCAmelCase__     ) as f:
									UpperCAmelCase_:		Tuple       			=    csv.reader(lowerCAmelCase__		,	delimiter="""\t"""     )
									for i, row in enumerate(tqdm(lowerCAmelCase__		,	ascii=lowerCAmelCase__     )     ):
												if row:
															UpperCAmelCase_:		Any       			=    row[0]
															UpperCAmelCase_:		Dict       			=    row[1]

															try:
																		UpperCAmelCase_:		Optional[Any]       			=    discriminator.tokenizer.encode(lowerCAmelCase__     )
																		if len(lowerCAmelCase__     ) < max_length_seq:
																					UpperCAmelCase_:		Tuple       			=    torch.tensor([5_0_2_5_6] + seq		,	device=lowerCAmelCase__		,	dtype=torch.long     )

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

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

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

						UpperCAmelCase_:		Tuple       			=    Dataset(lowerCAmelCase__		,	lowerCAmelCase__     )
						UpperCAmelCase_:		Dict       			=    int(0.9 * len(lowerCAmelCase__     )     )
						UpperCAmelCase_:		Optional[int]       			=    len(lowerCAmelCase__     ) - train_size
						UpperCAmelCase_			,       UpperCAmelCase_:		Any       			=    torch.utils.data.random_split(lowerCAmelCase__		,	[train_size, test_size]     )

						UpperCAmelCase_:		Union[str, Any]       			=    {
						    """class_size""": len(lowerCAmelCase__     ),
						    """embed_size""": discriminator.embed_size,
						    """pretrained_model""": pretrained_model,
						    """class_vocab""": classaidx,
						    """default_class""": 0,
						}

			UpperCAmelCase_:		Optional[int]       			=    time.time()
			print("""Preprocessed {} data points""".format(len(lowerCAmelCase__     ) + len(lowerCAmelCase__     )     )     )
			print("""Data preprocessing took: {:.3f}s""".format(end - start     )     )

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

						UpperCAmelCase_:		List[Any]       			=    time.time()

						UpperCAmelCase_:		Tuple       			=    get_cached_data_loader(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	device=lowerCAmelCase__     )

						UpperCAmelCase_:		str       			=    get_cached_data_loader(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	device=lowerCAmelCase__     )

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

			else:
						UpperCAmelCase_:		List[str]       			=    torch.utils.data.DataLoader(
						    dataset=lowerCAmelCase__		,	batch_size=lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	collate_fn=lowerCAmelCase__     )
						UpperCAmelCase_:		List[Any]       			=    torch.utils.data.DataLoader(dataset=lowerCAmelCase__		,	batch_size=lowerCAmelCase__		,	collate_fn=lowerCAmelCase__     )

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

			UpperCAmelCase_:		Any       			=    optim.Adam(discriminator.parameters()		,	lr=0.0001     )

			for epoch in range(lowerCAmelCase__     ):
						UpperCAmelCase_:		Optional[int]       			=    time.time()
						print("""\nEpoch"""		,	epoch + 1     )

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

						UpperCAmelCase_:		Union[str, Any]       			=    time.time()
						print("""Epoch took: {:.3f}s""".format(end - start     )     )

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

						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(lowerCAmelCase__		,	epoch + 1     )		,	)


if __name__ == "__main__":
	a			:     Any     = 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')
	a			:     Dict     = parser.parse_args()

	train_discriminator(**(vars(args)))











import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


a			:     List[str]     = logging.getLogger(__name__)







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict     ):





			"""simple docstring"""


			random.seed(lowerCAmelCase__     )
			np.random.seed(lowerCAmelCase__     )
			torch.manual_seed(lowerCAmelCase__     )
			torch.cuda.manual_seed_all(lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		Dict     ):





			"""simple docstring"""


			model.eval()
			UpperCAmelCase_:		Optional[Any]       			=    next(model.parameters()     ).device
			UpperCAmelCase_:		Tuple       			=    1
			UpperCAmelCase_:		Any       			=    torch.zeros((eval_batch_size, context_len)		,	dtype=torch.long		,	device=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[Any]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[Any]       			=    torch.zeros(1		,	device=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[int]       			=    0
			for batch in eval_dataloader:
						batch.to(lowerCAmelCase__     )
						# pad
						context.zero_()
						for i in range(lowerCAmelCase__     ):
									UpperCAmelCase_:		Optional[int]       			=    batch[i]
						UpperCAmelCase_:		Union[str, Any]       			=    model(lowerCAmelCase__		,	labels=lowerCAmelCase__     )
						eval_loss += outputs[0].sum().item()
						nb_eval_examples += batch.size(0     )
			UpperCAmelCase_:		Tuple       			=    eval_loss / nb_eval_examples
			UpperCAmelCase_:		List[Any]       			=    torch.exp(lowerCAmelCase__     )
			model.train()
			return perplexity







def    lowerCAmelCase_  (lowerCAmelCase__:		List[Any]="gpt2"     ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]       			=    GPTaLMHeadModel.from_pretrained(lowerCAmelCase__		,	output_hidden_states=lowerCAmelCase__     )
			torch.save(model.state_dict()		,	model_name + """local.pt"""     )
			return model







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		str     ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(lowerCAmelCase__     )
			model.to(lowerCAmelCase__     )

			UpperCAmelCase_:		Dict       			=    ["""bias""", """LayerNorm.weight"""]
			UpperCAmelCase_:		Optional[int]       			=    [
			    {
			        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay     )],
			        """weight_decay""": 0.0,
			    },
			    {"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay     )], """weight_decay""": 0.0},
			]
			UpperCAmelCase_:		str       			=    AdamW(lowerCAmelCase__		,	lr=5e-5		,	eps=1e-8     )
			UpperCAmelCase_:		Tuple       			=    get_linear_schedule_with_warmup(lowerCAmelCase__		,	0		,	lowerCAmelCase__     )
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler







def    lowerCAmelCase_  (lowerCAmelCase__:		int		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		str     ):





			"""simple docstring"""


			UpperCAmelCase_:		str       			=    np.array(lowerCAmelCase__     ).mean()
			UpperCAmelCase_:		Optional[Any]       			=    np.array(lowerCAmelCase__     ).std()
			UpperCAmelCase_:		Tuple       			=    (real_perps - avg) / std
			UpperCAmelCase_:		Tuple       			=    list(zip(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )     )
			joblib.dump(lowerCAmelCase__		,	lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		Any		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		Any		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		int		,	lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		Optional[Any]="dev.jbl"		,	lowerCAmelCase__:		Tuple=recopy_gpta		,	):





			"""simple docstring"""


			UpperCAmelCase_:		int       			=    []
			UpperCAmelCase_:		int       			=    []
			UpperCAmelCase_:		int       			=    []

			# Initialize the transformer model
			UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(lowerCAmelCase__     )
			orig_model.to(device="""cpu"""     )
			torch.cuda.empty_cache()

			# Compute perplexity of initial transformer model for comparison
			model.train()
			UpperCAmelCase_			,       UpperCAmelCase_			,       UpperCAmelCase_:		List[str]       			=    recopy_model(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

			for step in tqdm(range(lowerCAmelCase__     )     ):
						UpperCAmelCase_:		Union[str, Any]       			=    torch.zeros((1, context_len)		,	dtype=torch.long		,	device=lowerCAmelCase__     )
						UpperCAmelCase_:		str       			=    random.choice(lowerCAmelCase__     )
						UpperCAmelCase_:		int       			=    random.randint(0		,	len(story[0]     ) - context_len - 1     )
						UpperCAmelCase_:		Optional[int]       			=    story[0][start : start + context_len]
						lm_optimizer.zero_grad()
						UpperCAmelCase_:		Dict       			=    model(lowerCAmelCase__		,	labels=lowerCAmelCase__     )
						UpperCAmelCase_:		List[str]       			=    outputs[0]
						UpperCAmelCase_:		Optional[Any]       			=    compute_perplexity(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )
						model.train()
						lm_loss.backward()
						# Do LM backprop
						torch.nn.utils.clip_grad_norm_(model.parameters()		,	3.0     )
						lm_optimizer.step()
						lm_scheduler.step()  # Update learning rate schedule

						# Compute perplexity after back-propagating on the selected context
						UpperCAmelCase_:		int       			=    compute_perplexity(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

						# Periodically save the stored (X, IG(X)) pairs
						if step % 1_0_0_0 == 0 and step > 1:
									intermittent_save(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						UpperCAmelCase_			,       UpperCAmelCase_			,       UpperCAmelCase_:		str       			=    recopy_model(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

						past_perps.append(past_perp.item()     )
						real_perps.append(orig_perp - real_perp.item()     )
						contexts.append(np.array(context.cpu()     )     )

			intermittent_save(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[Any]		,	lowerCAmelCase__:		Any="data/tokenized_stories_train_wikitext103.jbl"		,	lowerCAmelCase__:		Any=1_0_0		,	lowerCAmelCase__:		Optional[int]=1_0_2_6		,	lowerCAmelCase__:		Optional[int]=True     ):





			"""simple docstring"""


			UpperCAmelCase_:		Union[str, Any]       			=    joblib.load(lowerCAmelCase__     )
			print("""data loaded"""     )
			UpperCAmelCase_:		List[Any]       			=    []
			if trim:
						for i, example in enumerate(lowerCAmelCase__     ):
									if len(example[0]     ) > min_len:
												UpperCAmelCase_:		Optional[int]       			=    random.randint(0		,	len(example[0]     ) - context_len - 1     )
												objective_set.append(example[0, start : start + context_len]     )
									if len(lowerCAmelCase__     ) >= number:
												break
						UpperCAmelCase_:		Union[str, Any]       			=    []
						for j in range(i + 1		,	len(lowerCAmelCase__     )     ):
									if len(data[j][0]     ) > min_len:
												train_data.append(data[j]     )
			else:
						UpperCAmelCase_:		Optional[int]       			=    data[0:number]
						UpperCAmelCase_:		Tuple       			=    data[number:]

			joblib.dump(lowerCAmelCase__		,	"""objective_set.jbl"""     )
			print("""objective set saved"""     )
			return train_data, objective_set







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		int		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		int=5_0		,	lowerCAmelCase__:		List[Any]="secondary_learner.pt"     ):





			"""simple docstring"""


			UpperCAmelCase_:		Tuple       			=    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""     )
			# We will use the first 512 pairs from our dataset as a test set for
			# our secondary learner and the rest to train
			UpperCAmelCase_:		int       			=    train_dataset[:5_1_2]
			UpperCAmelCase_:		Any       			=    train_dataset[5_1_2:]
			UpperCAmelCase_:		Union[str, Any]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[int]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )

			# secondary learner model set up
			UpperCAmelCase_:		Optional[int]       			=    nn.MSELoss()
			UpperCAmelCase_:		str       			=    nn.MSELoss(reduction="""sum"""     )
			secondary_learner.to(lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[int]       			=    torch.optim.Adam(secondary_learner.parameters()		,	lr=0.00001     )
			secondary_learner.train()

			# TODO in original code this is written as number of actual batches seen
			# not number of items seen but other places it is number of items instead.
			# improve consistency! changed this to epochs for clarity
			UpperCAmelCase_:		Any       			=    float("""inf"""     )
			# Iterate through batches until we've used max_steps batches
			for epoch in range(int(lowerCAmelCase__     )     ):
						UpperCAmelCase_:		Optional[Any]       			=    0.0
						secondary_learner.train()
						for step, batch in enumerate(lowerCAmelCase__     ):
									UpperCAmelCase_:		Union[str, Any]       			=    batch[0].to(lowerCAmelCase__     )
									UpperCAmelCase_:		List[Any]       			=    batch[1].to(lowerCAmelCase__     )
									UpperCAmelCase_:		Tuple       			=    secondary_learner(lowerCAmelCase__     )
									q_optimizer.zero_grad()
									UpperCAmelCase_:		Any       			=    loss(lowerCAmelCase__		,	real_q.float()     )
									q_loss.backward()
									q_optimizer.step()
									tr_q_loss += q_loss.item()

									# model trains fairly quickly so we won't wait for a full epoch
									# eval is triggered at eval_freq and end of epochs
									if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowerCAmelCase__     )):
												UpperCAmelCase_:		int       			=    tr_q_loss / (step + 1)

												secondary_learner.eval()
												UpperCAmelCase_:		Optional[Any]       			=    0.0
												UpperCAmelCase_:		Union[str, Any]       			=    0.0
												UpperCAmelCase_:		Any       			=    []
												UpperCAmelCase_:		Any       			=    []
												# Compute performance of the secondary learner after this batch
												for stepa, batcha in enumerate(lowerCAmelCase__     ):
															UpperCAmelCase_:		Optional[Any]       			=    batcha[0].to(lowerCAmelCase__     )
															UpperCAmelCase_:		int       			=    batcha[1].to(lowerCAmelCase__     )
															UpperCAmelCase_:		Dict       			=    secondary_learner(lowerCAmelCase__     )
															q_lossa += test_loss(lowerCAmelCase__		,	lowerCAmelCase__     ).item()
															sum_qa += torch.sum(lowerCAmelCase__     ).item()
															for ei, i in enumerate(predicted_qa.cpu().detach().numpy()     ):
																		predicted.append(i.item()     )
															for ei, i in enumerate(real_qa.cpu().detach().numpy()     ):
																		actual.append(i.item()     )

												q_lossa /= len(lowerCAmelCase__     )
												print(
												    """Epoch: """		,	lowerCAmelCase__		,	"""step: """		,	lowerCAmelCase__		,	"""Avg. q:"""		,	sum_qa / len(lowerCAmelCase__     )		,	"""Train Loss: """		,	lowerCAmelCase__		,	"""Test Loss: """		,	lowerCAmelCase__		,	)
												if q_lossa < best_test_loss:
															joblib.dump((predicted, actual)		,	"""pred_vs_actual.jbl"""     )
															torch.save(secondary_learner.state_dict()		,	lowerCAmelCase__     )
															UpperCAmelCase_:		str       			=    q_lossa

									secondary_learner.train()
			return secondary_learner







class 							_a							(      nn.Module				):

			def __init__(self,      SCREAMING_SNAKE_CASE_				)					->      str:
						super(SCREAMING_SNAKE_CASE_,      self				).__init__()
						UpperCAmelCase_:		List[str]       			=    model.transformer.wte
						UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(model.transformer.wte.weight				)
						UpperCAmelCase_:		List[str]       			=    nn.Convad(self.embeddings.weight.size(1				),      256,      3,      padding=1				)
						UpperCAmelCase_:		Union[str, Any]       			=    nn.Sequential(nn.Linear(256,      32				),      nn.Dropout(p=0.1				),      nn.Linear(32,      32				),      nn.Linear(32,      1				)				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Any:
						UpperCAmelCase_:		Dict       			=    torch.max(self.conv(self.embeddings(SCREAMING_SNAKE_CASE_				).squeeze(1				).transpose(1,      2				)				),      2				)[0]
						UpperCAmelCase_:		Dict       			=    self.fc(SCREAMING_SNAKE_CASE_				)
						return qs.squeeze(1				)

			@classmethod
			def     __snake_case					(cls,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Union[str, Any]:
						UpperCAmelCase_:		Optional[int]       			=    cls(SCREAMING_SNAKE_CASE_				)  # this calls __init__
						UpperCAmelCase_:		Any       			=    torch.load(SCREAMING_SNAKE_CASE_				)
						secondary_learner.load_state_dict(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		str       			=    model.transformer.wte
						UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(model.transformer.wte.weight				)
						return secondary_learner






