



'''simple docstring'''
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)
snake_case_				:			List[Any]						 =						1e-10
snake_case_				:			List[str]						 =						'This is incredible! I love it, this is the best chicken I have ever had.'
snake_case_				:			str						 =						100






class 							lowercase__			(							nn.Module     ):





	def __init__(				self      :       int				,lowerCamelCase__      :       Tuple				,lowerCamelCase__      :       Dict="gpt2-medium"				,lowerCamelCase__      :       Dict=False				,lowerCamelCase__      :       int="cpu"				):







							'''simple docstring'''
							super().__init__()
							_UpperCamelCase      :	Optional[Any]				       =     GPTaTokenizer.from_pretrained(lowerCamelCase__				)
							_UpperCamelCase      :	Optional[Any]				       =     GPTaLMHeadModel.from_pretrained(lowerCamelCase__				)
							_UpperCamelCase      :	Tuple				       =     self.encoder.transformer.config.hidden_size
							_UpperCamelCase      :	Optional[int]				       =     ClassificationHead(class_size=lowerCamelCase__				,embed_size=self.embed_size				)
							_UpperCamelCase      :	Optional[Any]				       =     cached_mode
							_UpperCamelCase      :	List[str]				       =     device





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				):







							'''simple docstring'''
							return self.classifier_head





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				):







							'''simple docstring'''
							for param in self.encoder.parameters():
													_UpperCamelCase      :	str				       =     False
							self.classifier_head.train()





	def 			UpperCamelCase_						(				self      :       Optional[int]				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							_UpperCamelCase      :	str				       =     x.ne(0				).unsqueeze(2				).repeat(1				,1				,self.embed_size				).float().to(self.device				).detach()
							_UpperCamelCase      :	Union[str, Any]				       =     self.encoder.transformer(lowerCamelCase__				)['last_hidden_state']
							_UpperCamelCase      :	str				       =     hidden * mask
							_UpperCamelCase      :	Tuple				       =     torch.sum(lowerCamelCase__				,dim=1				) / (torch.sum(lowerCamelCase__				,dim=1				).detach() + EPSILON)
							return avg_hidden





	def 			UpperCamelCase_						(				self      :       int				,lowerCamelCase__      :       Optional[int]				):







							'''simple docstring'''
							if self.cached_mode:
													_UpperCamelCase      :	Any				       =     x.to(self.device				)
							else:
													_UpperCamelCase      :	List[Any]				       =     self.avg_representation(x.to(self.device				)				)

							_UpperCamelCase      :	Dict				       =     self.classifier_head(lowerCamelCase__				)
							_UpperCamelCase      :	Optional[Any]				       =     nn.functional.log_softmax(lowerCamelCase__				,dim=-1				)

							return probs






class 							lowercase__			(							data.Dataset     ):





	def __init__(				self      :       int				,lowerCamelCase__      :       int				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							_UpperCamelCase      :	List[Any]				       =     X
							_UpperCamelCase      :	List[str]				       =     y





	def __len__(				self      :       Any				):







							'''simple docstring'''
							return len(self.X				)





	def __getitem__(				self      :       str				,lowerCamelCase__      :       List[Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	Union[str, Any]				       =     {}
							_UpperCamelCase      :	str				       =     self.X[index]
							_UpperCamelCase      :	str				       =     self.y[index]
							return data






def A__					(  UpperCAmelCase_							):
						def pad_sequences(UpperCAmelCase_							):
												_UpperCamelCase      :	List[str]				       =     [len(UpperCAmelCase_							) for seq in sequences]

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

												for i, seq in enumerate(UpperCAmelCase_							):
																		_UpperCamelCase      :	Optional[int]				       =     lengths[i]
																		_UpperCamelCase      :	Union[str, Any]				       =     seq[:end]

												return padded_sequences, lengths

						_UpperCamelCase      :	str				       =     {}
						for key in data[0].keys():
												_UpperCamelCase      :	List[Any]				       =     [d[key] for d in data]

						_UpperCamelCase      :	Optional[Any]				       =     pad_sequences(item_info['X']							)
						_UpperCamelCase      :	Optional[Any]				       =     torch.tensor(item_info['y']						,						dtype=torch.long							)

						return x_batch, y_batch






def A__					(  UpperCAmelCase_							):
						_UpperCamelCase      :	str				       =     {}
						for key in data[0].keys():
												_UpperCamelCase      :	Dict				       =     [d[key] for d in data]

						_UpperCamelCase      :	Optional[int]				       =     torch.cat(item_info['X']						,						0							)
						_UpperCamelCase      :	Union[str, Any]				       =     torch.tensor(item_info['y']						,						dtype=torch.long							)

						return x_batch, y_batch






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_=0						,						UpperCAmelCase_=1_0						,						UpperCAmelCase_="cpu"							):
						_UpperCamelCase      :	List[str]				       =     0
						discriminator.train_custom()
						for batch_idx, (input_t, target_t) in enumerate(UpperCAmelCase_							):
												_UpperCamelCase      :	int				       =     input_t.to(UpperCAmelCase_							), target_t.to(UpperCAmelCase_							)

												optimizer.zero_grad()

												_UpperCamelCase      :	Any				       =     discriminator(UpperCAmelCase_							)
												_UpperCamelCase      :	Any				       =     nn.functional.nll_loss(UpperCAmelCase_						,						UpperCAmelCase_							)
												loss.backward(retain_graph=UpperCAmelCase_							)
												optimizer.step()

												samples_so_far += len(UpperCAmelCase_							)

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






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

						test_loss /= len(data_loader.dataset							)

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






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_=False						,						UpperCAmelCase_="cpu"							):
						_UpperCamelCase      :	Optional[int]				       =     model.tokenizer.encode(UpperCAmelCase_							)
						_UpperCamelCase      :	List[Any]				       =     torch.tensor([input_t]						,						dtype=torch.long						,						device=UpperCAmelCase_							)
						if cached:
												_UpperCamelCase      :	Union[str, Any]				       =     model.avg_representation(UpperCAmelCase_							)

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






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_=False						,						UpperCAmelCase_="cpu"							):
						_UpperCamelCase      :	Any				       =     torch.utils.data.DataLoader(dataset=UpperCAmelCase_						,						batch_size=UpperCAmelCase_						,						collate_fn=UpperCAmelCase_							)

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

						_UpperCamelCase      :	Tuple				       =     torch.utils.data.DataLoader(
						    dataset=Dataset(UpperCAmelCase_						,						UpperCAmelCase_							)						,						batch_size=UpperCAmelCase_						,						shuffle=UpperCAmelCase_						,						collate_fn=UpperCAmelCase_							)

						return data_loader






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_=None						,						UpperCAmelCase_="gpt2-medium"						,						UpperCAmelCase_=1_0						,						UpperCAmelCase_=6_4						,						UpperCAmelCase_=1_0						,						UpperCAmelCase_=False						,						UpperCAmelCase_=False						,						UpperCAmelCase_=False						,						):
						_UpperCamelCase      :	Any				       =     'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

						print('Preprocessing {} dataset...'.format(UpperCAmelCase_							)							)
						_UpperCamelCase      :	Optional[int]				       =     time.time()

						if dataset == "SST":
												_UpperCamelCase      :	Tuple				       =     ['positive', 'negative', 'very positive', 'very negative', 'neutral']
												_UpperCamelCase      :	Tuple				       =     {c: i for i, c in enumerate(UpperCAmelCase_							)}

												_UpperCamelCase      :	Optional[Any]				       =     Discriminator(
												    class_size=len(UpperCAmelCase_							)						,						pretrained_model=UpperCAmelCase_						,						cached_mode=UpperCAmelCase_						,						device=UpperCAmelCase_							).to(UpperCAmelCase_							)

												_UpperCamelCase      :	Tuple				       =     torchtext_data.Field()
												_UpperCamelCase      :	Dict				       =     torchtext_data.Field(sequential=UpperCAmelCase_							)
												_UpperCamelCase      :	Optional[Any]				       =     datasets.SST.splits(
												    UpperCAmelCase_						,						UpperCAmelCase_						,						fine_grained=UpperCAmelCase_						,						train_subtrees=UpperCAmelCase_						,						)

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

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

												_UpperCamelCase      :	Union[str, Any]				       =     {
												    'class_size': len(UpperCAmelCase_							),
												    'embed_size': discriminator.embed_size,
												    'pretrained_model': pretrained_model,
												    'class_vocab': classaidx,
												    'default_class': 2,
												}

						elif dataset == "clickbait":
												_UpperCamelCase      :	Optional[int]				       =     ['non_clickbait', 'clickbait']
												_UpperCamelCase      :	int				       =     {c: i for i, c in enumerate(UpperCAmelCase_							)}

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

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

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

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

												_UpperCamelCase      :	List[str]				       =     {
												    'class_size': len(UpperCAmelCase_							),
												    'embed_size': discriminator.embed_size,
												    'pretrained_model': pretrained_model,
												    'class_vocab': classaidx,
												    'default_class': 1,
												}

						elif dataset == "toxic":
												_UpperCamelCase      :	str				       =     ['non_toxic', 'toxic']
												_UpperCamelCase      :	Any				       =     {c: i for i, c in enumerate(UpperCAmelCase_							)}

												_UpperCamelCase      :	Optional[Any]				       =     Discriminator(
												    class_size=len(UpperCAmelCase_							)						,						pretrained_model=UpperCAmelCase_						,						cached_mode=UpperCAmelCase_						,						device=UpperCAmelCase_							).to(UpperCAmelCase_							)

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

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

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

												_UpperCamelCase      :	Union[str, Any]				       =     {
												    'class_size': len(UpperCAmelCase_							),
												    '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(UpperCAmelCase_							) as f:
																		_UpperCamelCase      :	Union[str, Any]				       =     csv.reader(UpperCAmelCase_						,						delimiter='\t'							)
																		for row in tqdm(UpperCAmelCase_						,						ascii=UpperCAmelCase_							):
																								if row:
																														classes.add(row[0]							)

												_UpperCamelCase      :	Union[str, Any]				       =     sorted(UpperCAmelCase_							)
												_UpperCamelCase      :	Any				       =     {c: i for i, c in enumerate(UpperCAmelCase_							)}

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

												_UpperCamelCase      :	Any				       =     []
												_UpperCamelCase      :	Optional[Any]				       =     []
												with open(UpperCAmelCase_							) as f:
																		_UpperCamelCase      :	Dict				       =     csv.reader(UpperCAmelCase_						,						delimiter='\t'							)
																		for i, row in enumerate(tqdm(UpperCAmelCase_						,						ascii=UpperCAmelCase_							)							):
																								if row:
																														_UpperCamelCase      :	Optional[int]				       =     row[0]
																														_UpperCamelCase      :	str				       =     row[1]

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

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

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

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

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

												_UpperCamelCase      :	Optional[int]				       =     {
												    'class_size': len(UpperCAmelCase_							),
												    'embed_size': discriminator.embed_size,
												    'pretrained_model': pretrained_model,
												    'class_vocab': classaidx,
												    'default_class': 0,
												}

						_UpperCamelCase      :	int				       =     time.time()
						print('Preprocessed {} data points'.format(len(UpperCAmelCase_							) + len(UpperCAmelCase_							)							)							)
						print('Data preprocessing took: {:.3f}s'.format(end - start							)							)

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

												_UpperCamelCase      :	Optional[int]				       =     time.time()

												_UpperCamelCase      :	Optional[int]				       =     get_cached_data_loader(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						shuffle=UpperCAmelCase_						,						device=UpperCAmelCase_							)

												_UpperCamelCase      :	Tuple				       =     get_cached_data_loader(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						device=UpperCAmelCase_							)

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

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

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

						_UpperCamelCase      :	Dict				       =     optim.Adam(discriminator.parameters()						,						lr=0.0_001							)

						for epoch in range(UpperCAmelCase_							):
												_UpperCamelCase      :	Tuple				       =     time.time()
												print('\nEpoch'						,						epoch + 1							)

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

												_UpperCamelCase      :	Optional[int]				       =     time.time()
												print('Epoch took: {:.3f}s'.format(end - start							)							)

												print('\nExample prediction'							)
												predict(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						cached=UpperCAmelCase_						,						device=UpperCAmelCase_							)

												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(UpperCAmelCase_						,						epoch + 1							)						,						)


if __name__ == "__main__":
					snake_case_				:			Union[str, 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')
					snake_case_				:			Optional[Any]						 =						parser.parse_args()

					train_discriminator(**(vars(args)))







'''simple docstring'''
import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


snake_case_				:			str						 =						{
    'gpt-j-6b': {'is_causal': True, 'model': 'sgugger/sharded-gpt-j-6B', 'tokenizer': 'EleutherAI/gpt-j-6B'},
    'gpt-neox': {'is_causal': True, 'model': 'EleutherAI/gpt-neox-20b'},
    'opt': {'is_causal': True, 'model': 'facebook/opt-30b'},
    'T0pp': {'is_causal': False, 'model': 'bigscience/T0pp', 'model_revision': 'sharded'},
}

snake_case_				:			int						 =						[
    'Hello, my name is',
    'Are unicorns real? Unicorns are',
    'For the first time in several years,',
    'My name is Julien and I am',
    'The goal of life is',
    'Whenever I\'m sad, I like to',
]






def A__					(  ):
						_UpperCamelCase      :	Tuple				       =     argparse.ArgumentParser(description='Run and time generations on a big model using Accelerate.'							)
						parser.add_argument('model_name'						,						type=UpperCAmelCase_						,						default=UpperCAmelCase_						,						help='The name of the model to try.'							)
						parser.add_argument(
						    '--tokenizer_name'						,						type=UpperCAmelCase_						,						default=UpperCAmelCase_						,						help='The name of the tokenizer (if different from the model.'							)
						parser.add_argument('--is_causal'						,						type=UpperCAmelCase_						,						default=UpperCAmelCase_						,						help='Whether or not the model is causal.'							)
						parser.add_argument(
						    '--model_revision'						,						type=UpperCAmelCase_						,						default=UpperCAmelCase_						,						help='The revision to use for the model checkpoint.'							)
						parser.add_argument('--torch_dtype'						,						type=UpperCAmelCase_						,						default=UpperCAmelCase_						,						help='The dtype for the model.'							)
						parser.add_argument('--disk_offload'						,						action='store_true'							)

						_UpperCamelCase      :	Dict				       =     parser.parse_args()

						# Sanitize args
						if args.model_name in DEFAULT_MODELS:
												_UpperCamelCase      :	Optional[Any]				       =     DEFAULT_MODELS[args.model_name]
												_UpperCamelCase      :	int				       =     defaults['model']
												if args.tokenizer_name is None:
																		_UpperCamelCase      :	List[str]				       =     defaults.get('tokenizer'						,						args.model_name							)
												if args.is_causal is None:
																		_UpperCamelCase      :	List[Any]				       =     defaults['is_causal']
												if args.model_revision is None:
																		_UpperCamelCase      :	Optional[int]				       =     defaults.get('model_revision'						,						'main'							)

						if args.is_causal is None:
												raise ValueError('Could not infer the default for `--is_causal`, pass either True or False for it.'							)
						if args.tokenizer_name is None:
												_UpperCamelCase      :	List[str]				       =     args.model_name
						if args.model_revision is None:
												_UpperCamelCase      :	List[str]				       =     'main'

						return args






def A__					(  ):
						transformers.utils.logging.set_verbosity_error()
						_UpperCamelCase      :	Optional[Any]				       =     parse_args()

						if args.torch_dtype is None:
												_UpperCamelCase      :	Tuple				       =     AutoConfig.from_pretrained(args.model_name							)
												_UpperCamelCase      :	str				       =     getattr(UpperCAmelCase_						,						'torch_dtype'						,						torch.floataa							)
						else:
												_UpperCamelCase      :	Union[str, Any]				       =     getattr(UpperCAmelCase_						,						args.torch_dtype							)
						_UpperCamelCase      :	int				       =     AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
						_UpperCamelCase      :	int				       =     {
						    'torch_dtype': torch_dtype,
						    'revision': args.model_revision,
						}
						if args.disk_offload:
												_UpperCamelCase      :	List[str]				       =     'tmp_offload'
												_UpperCamelCase      :	Union[str, Any]				       =     True

						_UpperCamelCase      :	Dict				       =     start_measure()
						_UpperCamelCase      :	Optional[int]				       =     model_cls.from_pretrained(args.model_name						,						device_map='auto'						,						**UpperCAmelCase_							)
						_UpperCamelCase      :	List[str]				       =     end_measure(UpperCAmelCase_							)
						log_measures(UpperCAmelCase_						,						'Model loading'							)

						_UpperCamelCase      :	Optional[int]				       =     compute_module_sizes(UpperCAmelCase_							)
						_UpperCamelCase      :	str				       =     {v: 0 for v in model.hf_device_map.values()}
						for module, device in model.hf_device_map.items():
												device_size[device] += module_sizes[module]
						_UpperCamelCase      :	Tuple				       =     '\n'.join([f'- {device}: {size // 2**2_0}MiB' for device, size in device_size.items()]							)
						print(f'\nTheoretical use:\n{message}'							)

						_UpperCamelCase      :	List[str]				       =     AutoTokenizer.from_pretrained(args.tokenizer_name							)

						_UpperCamelCase      :	List[str]				       =     start_measure()
						_UpperCamelCase      :	Optional[Any]				       =     []
						_UpperCamelCase      :	Dict				       =     []
						_UpperCamelCase      :	List[str]				       =     []
						for prompt in PROMPTS:
												_UpperCamelCase      :	List[Any]				       =     tokenizer(UpperCAmelCase_						,						return_tensors='pt'							).to(0							)
												_UpperCamelCase      :	List[str]				       =     inputs['input_ids'][0].tolist()
												_UpperCamelCase      :	int				       =     time.time()
												_UpperCamelCase      :	int				       =     model.generate(inputs['input_ids']							)
												_UpperCamelCase      :	int				       =     time.time()
												_UpperCamelCase      :	Optional[Any]				       =     outputs[0].tolist()
												_UpperCamelCase      :	List[Any]				       =     len(UpperCAmelCase_							) if outputs[: len(UpperCAmelCase_							)] != tokens else len(UpperCAmelCase_							) - len(UpperCAmelCase_							)
												_UpperCamelCase      :	Any				       =     after_generate - before_generate

												_UpperCamelCase      :	Optional[int]				       =     tokenizer.decode(UpperCAmelCase_						,						skip_special_tokens=UpperCAmelCase_							)
												texts_outs.append(UpperCAmelCase_							)
												generation_times.append(UpperCAmelCase_							)
												gen_tokens.append(UpperCAmelCase_							)
												print(f'Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'							)

						_UpperCamelCase      :	Union[str, Any]				       =     end_measure(UpperCAmelCase_							)
						log_measures(UpperCAmelCase_						,						'Model generation'							)

						_UpperCamelCase      :	str				       =     [gen / tok for gen, tok in zip(UpperCAmelCase_						,						UpperCAmelCase_							)]
						_UpperCamelCase      :	List[Any]				       =     sum(UpperCAmelCase_							) / len(UpperCAmelCase_							)
						print(f'Average time of generation per token: {avg_gen:.2f}s'							)
						print(f'First generation (avg time per token): {generation_times_per_token[0]:.2f}s'							)
						_UpperCamelCase      :	List[Any]				       =     sum(generation_times_per_token[1:]							) / (len(UpperCAmelCase_							) - 1)
						print(f'Average time of generation per token (excluding the first): {avg_gen:.2f}s'							)


if __name__ == "__main__":
					main()


