


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

import argparse
import csv
import json
import math
import time

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

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
snake_case			:      Optional[int] 							=   1e-10
snake_case			:      List[str] 							=   'This is incredible! I love it, this is the best chicken I have ever had.'
snake_case			:      str 							=   1_00



class    __lowercase  (       nn.Module							):

							"""simple docstring"""


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





							def 		__magic_name__			(    self	)->	int:
											return self.classifier_head





							def 		__magic_name__			(    self	)->	List[Any]:
											for param in self.encoder.parameters():
															_SCREAMING_SNAKE_CASE      = False
											self.classifier_head.train()





							def 		__magic_name__			(    self				, A_	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = x.ne(0	).unsqueeze(2	).repeat(1				, 1				, self.embed_size	).float().to(self.device	).detach()
											_SCREAMING_SNAKE_CASE      = self.encoder.transformer(A_	)['last_hidden_state']
											_SCREAMING_SNAKE_CASE      = hidden * mask
											_SCREAMING_SNAKE_CASE      = torch.sum(A_				, dim=1	) / (torch.sum(A_				, dim=1	).detach() + EPSILON)
											return avg_hidden





							def 		__magic_name__			(    self				, A_	)->	Any:
											if self.cached_mode:
															_SCREAMING_SNAKE_CASE      = x.to(self.device	)
											else:
															_SCREAMING_SNAKE_CASE      = self.avg_representation(x.to(self.device	)	)

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

											return probs







class    __lowercase  (       data.Dataset							):

							"""simple docstring"""


							def __init__(    self				, A_				, A_	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = X
											_SCREAMING_SNAKE_CASE      = y





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





							def __getitem__(    self				, A_	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = {}
											_SCREAMING_SNAKE_CASE      = self.X[index]
											_SCREAMING_SNAKE_CASE      = self.y[index]
											return data







def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				def pad_sequences(UpperCAmelCase__							):
								_SCREAMING_SNAKE_CASE      = [len(UpperCAmelCase__							) for seq in sequences]

								_SCREAMING_SNAKE_CASE      = torch.zeros(len(UpperCAmelCase__							)   ,max(UpperCAmelCase__							)							).long()  # padding value = 0

								for i, seq in enumerate(UpperCAmelCase__							):
												_SCREAMING_SNAKE_CASE      = lengths[i]
												_SCREAMING_SNAKE_CASE      = seq[:end]

								return padded_sequences, lengths

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

				_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = pad_sequences(item_info['X']							)
				_SCREAMING_SNAKE_CASE      = torch.tensor(item_info['y']   ,dtype=torch.long							)

				return x_batch, y_batch


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






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

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

				return x_batch, y_batch


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__=0   ,UpperCAmelCase__=10   ,UpperCAmelCase__="cpu"							):

				"""simple docstring"""






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

								optimizer.zero_grad()

								_SCREAMING_SNAKE_CASE      = discriminator(UpperCAmelCase__							)
								_SCREAMING_SNAKE_CASE      = 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							)   ,100 * samples_so_far / len(data_loader.dataset							)   ,loss.item()   ,)							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__="cpu"							):

				"""simple docstring"""






				discriminator.eval()
				_SCREAMING_SNAKE_CASE      = 0
				_SCREAMING_SNAKE_CASE      = 0
				with torch.no_grad():
								for input_t, target_t in data_loader:
												_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = input_t.to(UpperCAmelCase__							), target_t.to(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = 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
												_SCREAMING_SNAKE_CASE      = 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 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__=False   ,UpperCAmelCase__="cpu"							):

				"""simple docstring"""






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

				_SCREAMING_SNAKE_CASE      = 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 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__=False   ,UpperCAmelCase__="cpu"							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = torch.utils.data.DataLoader(dataset=UpperCAmelCase__   ,batch_size=UpperCAmelCase__   ,collate_fn=UpperCAmelCase__							)

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

				_SCREAMING_SNAKE_CASE      = torch.utils.data.DataLoader(
				    dataset=Dataset(UpperCAmelCase__   ,UpperCAmelCase__							)   ,batch_size=UpperCAmelCase__   ,shuffle=UpperCAmelCase__   ,collate_fn=UpperCAmelCase__							)

				return data_loader


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=None   ,UpperCAmelCase__="gpt2-medium"   ,UpperCAmelCase__=10   ,UpperCAmelCase__=64   ,UpperCAmelCase__=10   ,UpperCAmelCase__=False   ,UpperCAmelCase__=False   ,UpperCAmelCase__=False   ,):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = 'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

				print('Preprocessing {} dataset...'.format(UpperCAmelCase__							)							)
				_SCREAMING_SNAKE_CASE      = time.time()

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

								_SCREAMING_SNAKE_CASE      = Discriminator(
								    class_size=len(UpperCAmelCase__							)   ,pretrained_model=UpperCAmelCase__   ,cached_mode=UpperCAmelCase__   ,device=UpperCAmelCase__							).to(UpperCAmelCase__							)

								_SCREAMING_SNAKE_CASE      = torchtext_data.Field()
								_SCREAMING_SNAKE_CASE      = torchtext_data.Field(sequential=UpperCAmelCase__							)
								_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = datasets.SST.splits(
								    UpperCAmelCase__   ,UpperCAmelCase__   ,fine_grained=UpperCAmelCase__   ,train_subtrees=UpperCAmelCase__   ,)

								_SCREAMING_SNAKE_CASE      = []
								_SCREAMING_SNAKE_CASE      = []
								for i in trange(len(UpperCAmelCase__							)   ,ascii=UpperCAmelCase__							):
												_SCREAMING_SNAKE_CASE      = TreebankWordDetokenizer().detokenize(vars(train_data[i]							)['text']							)
												_SCREAMING_SNAKE_CASE      = discriminator.tokenizer.encode(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = torch.tensor([5_0256] + seq   ,device=UpperCAmelCase__   ,dtype=torch.long							)
												x.append(UpperCAmelCase__							)
												y.append(classaidx[vars(train_data[i]							)['label']]							)
								_SCREAMING_SNAKE_CASE      = Dataset(UpperCAmelCase__   ,UpperCAmelCase__							)

								_SCREAMING_SNAKE_CASE      = []
								_SCREAMING_SNAKE_CASE      = []
								for i in trange(len(UpperCAmelCase__							)   ,ascii=UpperCAmelCase__							):
												_SCREAMING_SNAKE_CASE      = TreebankWordDetokenizer().detokenize(vars(test_data[i]							)['text']							)
												_SCREAMING_SNAKE_CASE      = discriminator.tokenizer.encode(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = torch.tensor([5_0256] + seq   ,device=UpperCAmelCase__   ,dtype=torch.long							)
												test_x.append(UpperCAmelCase__							)
												test_y.append(classaidx[vars(test_data[i]							)['label']]							)
								_SCREAMING_SNAKE_CASE      = Dataset(UpperCAmelCase__   ,UpperCAmelCase__							)

								_SCREAMING_SNAKE_CASE      = {
								    'class_size': len(UpperCAmelCase__							),
								    'embed_size': discriminator.embed_size,
								    'pretrained_model': pretrained_model,
								    'class_vocab': classaidx,
								    'default_class': 2,
								}

				elif dataset == "clickbait":
								_SCREAMING_SNAKE_CASE      = ['non_clickbait', 'clickbait']
								_SCREAMING_SNAKE_CASE      = {c: i for i, c in enumerate(UpperCAmelCase__							)}

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

																				if len(UpperCAmelCase__							) < max_length_seq:
																								_SCREAMING_SNAKE_CASE      = torch.tensor([5_0256] + 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

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

								_SCREAMING_SNAKE_CASE      = {
								    'class_size': len(UpperCAmelCase__							),
								    'embed_size': discriminator.embed_size,
								    'pretrained_model': pretrained_model,
								    'class_vocab': classaidx,
								    'default_class': 1,
								}

				elif dataset == "toxic":
								_SCREAMING_SNAKE_CASE      = ['non_toxic', 'toxic']
								_SCREAMING_SNAKE_CASE      = {c: i for i, c in enumerate(UpperCAmelCase__							)}

								_SCREAMING_SNAKE_CASE      = Discriminator(
								    class_size=len(UpperCAmelCase__							)   ,pretrained_model=UpperCAmelCase__   ,cached_mode=UpperCAmelCase__   ,device=UpperCAmelCase__							).to(UpperCAmelCase__							)

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

																				if len(UpperCAmelCase__							) < max_length_seq:
																								_SCREAMING_SNAKE_CASE      = torch.tensor([5_0256] + 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

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

								_SCREAMING_SNAKE_CASE      = {
								    '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.'							)

								_SCREAMING_SNAKE_CASE      = set()
								with open(UpperCAmelCase__							) as f:
												_SCREAMING_SNAKE_CASE      = csv.reader(UpperCAmelCase__   ,delimiter='\t'							)
												for row in tqdm(UpperCAmelCase__   ,ascii=UpperCAmelCase__							):
																if row:
																				classes.add(row[0]							)

								_SCREAMING_SNAKE_CASE      = sorted(UpperCAmelCase__							)
								_SCREAMING_SNAKE_CASE      = {c: i for i, c in enumerate(UpperCAmelCase__							)}

								_SCREAMING_SNAKE_CASE      = Discriminator(
								    class_size=len(UpperCAmelCase__							)   ,pretrained_model=UpperCAmelCase__   ,cached_mode=UpperCAmelCase__   ,device=UpperCAmelCase__							).to(UpperCAmelCase__							)

								_SCREAMING_SNAKE_CASE      = []
								_SCREAMING_SNAKE_CASE      = []
								with open(UpperCAmelCase__							) as f:
												_SCREAMING_SNAKE_CASE      = csv.reader(UpperCAmelCase__   ,delimiter='\t'							)
												for i, row in enumerate(tqdm(UpperCAmelCase__   ,ascii=UpperCAmelCase__							)							):
																if row:
																				_SCREAMING_SNAKE_CASE      = row[0]
																				_SCREAMING_SNAKE_CASE      = row[1]

																				try:
																								_SCREAMING_SNAKE_CASE      = discriminator.tokenizer.encode(UpperCAmelCase__							)
																								if len(UpperCAmelCase__							) < max_length_seq:
																												_SCREAMING_SNAKE_CASE      = torch.tensor([5_0256] + 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

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

								_SCREAMING_SNAKE_CASE      = {
								    'class_size': len(UpperCAmelCase__							),
								    'embed_size': discriminator.embed_size,
								    'pretrained_model': pretrained_model,
								    'class_vocab': classaidx,
								    'default_class': 0,
								}

				_SCREAMING_SNAKE_CASE      = 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...'							)

								_SCREAMING_SNAKE_CASE      = time.time()

								_SCREAMING_SNAKE_CASE      = get_cached_data_loader(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,shuffle=UpperCAmelCase__   ,device=UpperCAmelCase__							)

								_SCREAMING_SNAKE_CASE      = get_cached_data_loader(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,device=UpperCAmelCase__							)

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

				else:
								_SCREAMING_SNAKE_CASE      = torch.utils.data.DataLoader(
								    dataset=UpperCAmelCase__   ,batch_size=UpperCAmelCase__   ,shuffle=UpperCAmelCase__   ,collate_fn=UpperCAmelCase__							)
								_SCREAMING_SNAKE_CASE      = 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__							)

				_SCREAMING_SNAKE_CASE      = optim.Adam(discriminator.parameters()   ,lr=0.0_0_0_1							)

				for epoch in range(UpperCAmelCase__							):
								_SCREAMING_SNAKE_CASE      = 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__							)

								_SCREAMING_SNAKE_CASE      = 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			:      List[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			:      Tuple 							=   parser.parse_args()

							train_discriminator(**(vars(args)))





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


snake_case			:      Dict 							=   logging.get_logger(__name__)

snake_case			:      List[str] 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case			:      Any 							=   {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case			:      Optional[int] 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Union[str, Any] 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[int]        =						VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE   :					List[str]        =						PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE   :					List[Any]        =						["input_ids", "attention_mask", "token_type_ids"]
							SCREAMING_SNAKE_CASE   :					Any        =						DebertaTokenizer





							def __init__(    self				, A_=None				, A_=None				, A_=None				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, **A_				, )->	List[str]:
											super().__init__(
											    A_				, A_				, tokenizer_file=A_				, errors=A_				, bos_token=A_				, eos_token=A_				, unk_token=A_				, sep_token=A_				, cls_token=A_				, pad_token=A_				, mask_token=A_				, add_prefix_space=A_				, **A_				, )
											_SCREAMING_SNAKE_CASE      = kwargs.pop('add_bos_token'				, A_	)

											_SCREAMING_SNAKE_CASE      = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
											if pre_tok_state.get('add_prefix_space'				, A_	) != add_prefix_space:
															_SCREAMING_SNAKE_CASE      = getattr(A_				, pre_tok_state.pop('type'	)	)
															_SCREAMING_SNAKE_CASE      = add_prefix_space
															_SCREAMING_SNAKE_CASE      = pre_tok_class(**A_	)

											_SCREAMING_SNAKE_CASE      = add_prefix_space





							@property
							def 		__magic_name__			(    self	)->	str:
											if self._mask_token is None:
															if self.verbose:
																			logger.error('Using mask_token, but it is not set yet.'	)
															return None
											return str(self._mask_token	)





							@mask_token.setter
							def 		__magic_name__			(    self				, A_	)->	int:
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else value
											_SCREAMING_SNAKE_CASE      = value





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep	) * [0]
											return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





							def 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
											_SCREAMING_SNAKE_CASE      = kwargs.get('is_split_into_words'				, A_	)
											assert self.add_prefix_space or not is_split_into_words, (
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    "to use it with pretokenized inputs."
											)

											return super()._batch_encode_plus(*A_				, **A_	)





							def 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
											_SCREAMING_SNAKE_CASE      = kwargs.get('is_split_into_words'				, A_	)

											assert self.add_prefix_space or not is_split_into_words, (
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
											    "to use it with pretokenized inputs."
											)

											return super()._encode_plus(*A_				, **A_	)





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
											_SCREAMING_SNAKE_CASE      = self._tokenizer.model.save(A_				, name=A_	)
											return tuple(A_	)





							def 		__magic_name__			(    self				, A_	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = []
											for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(A_				, add_special_tokens=A_	) + [self.eos_token_id]	)

											if len(A_	) > self.model_max_length:
															_SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
											return input_ids
