




# 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)
__A    :				List[str]    						=	1e-10
__A    :				List[str]    						=	"""This is incredible! I love it, this is the best chicken I have ever had."""
__A    :				List[str]    						=	1_0_0




class       UpperCAmelCase_   ( nn.Module							):




							'''simple docstring'''



							def __init__(       self							:						List[Any]							,  a							:						Union[str, Any]							,  a							:						Any="gpt2-medium"							,  a							:						Tuple=False							,  a							:						int="cpu"      )      ->       Optional[Any]:
										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  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       List[str]:
										return self.classifier_head





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





							def  _UpperCAmelCase  (       self							:						int							,  a							:						int      )      ->       Optional[int]:
										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  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						List[Any]      )      ->       List[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       UpperCAmelCase_   ( data.Dataset							):




							'''simple docstring'''



							def __init__(       self							:						Optional[Any]							,  a							:						str							,  a							:						Tuple      )      ->       Any:
										SCREAMING_SNAKE_CASE     =       X
										SCREAMING_SNAKE_CASE     =       y





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







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





def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			def pad_sequences(SCREAMING_SNAKE_CASE ):
						SCREAMING_SNAKE_CASE     =       [len(SCREAMING_SNAKE_CASE ) for seq in sequences]

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

						for i, seq in enumerate(SCREAMING_SNAKE_CASE ):
									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        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''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        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE=0       ,						SCREAMING_SNAKE_CASE=10       ,						SCREAMING_SNAKE_CASE="cpu" ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       0
			discriminator.train_custom()
			for batch_idx, (input_t, target_t) in enumerate(SCREAMING_SNAKE_CASE ):
						SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       input_t.to(SCREAMING_SNAKE_CASE ), target_t.to(SCREAMING_SNAKE_CASE )

						optimizer.zero_grad()

						SCREAMING_SNAKE_CASE     =       discriminator(SCREAMING_SNAKE_CASE )
						SCREAMING_SNAKE_CASE     =       nn.functional.nll_loss(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )
						loss.backward(retain_graph=SCREAMING_SNAKE_CASE )
						optimizer.step()

						samples_so_far += len(SCREAMING_SNAKE_CASE )

						if batch_idx % log_interval == 0:
									print(
									    """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
									        epoch + 1       ,						SCREAMING_SNAKE_CASE       ,						len(data_loader.dataset )       ,						1_00 * samples_so_far / len(data_loader.dataset )       ,						loss.item()       ,						) )
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE="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(SCREAMING_SNAKE_CASE ), target_t.to(SCREAMING_SNAKE_CASE )
									SCREAMING_SNAKE_CASE     =       discriminator(SCREAMING_SNAKE_CASE )
									# sum up batch loss
									test_loss += nn.functional.nll_loss(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						reduction="""sum""" ).item()
									# get the index of the max log-probability
									SCREAMING_SNAKE_CASE     =       output_t.argmax(dim=1       ,						keepdim=SCREAMING_SNAKE_CASE )
									correct += pred_t.eq(target_t.view_as(SCREAMING_SNAKE_CASE ) ).sum().item()

			test_loss /= len(data_loader.dataset )

			print(
			    """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
			        SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						len(data_loader.dataset )       ,						100.0 * correct / len(data_loader.dataset ) ) )
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE=False       ,						SCREAMING_SNAKE_CASE="cpu" ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       model.tokenizer.encode(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       torch.tensor([input_t]       ,						dtype=torch.long       ,						device=SCREAMING_SNAKE_CASE )
			if cached:
						SCREAMING_SNAKE_CASE     =       model.avg_representation(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       model(SCREAMING_SNAKE_CASE ).data.cpu().numpy().flatten().tolist()
			print("""Input sentence:"""       ,						SCREAMING_SNAKE_CASE )
			print(
			    """Predictions:"""       ,						""", """.join("""{}: {:.4f}""".format(SCREAMING_SNAKE_CASE       ,						math.exp(SCREAMING_SNAKE_CASE ) ) for c, log_prob in zip(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ) )       ,						)
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE=False       ,						SCREAMING_SNAKE_CASE="cpu" ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE       ,						batch_size=SCREAMING_SNAKE_CASE       ,						collate_fn=SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       []
			SCREAMING_SNAKE_CASE     =       []
			for batch_idx, (x, y) in enumerate(tqdm(SCREAMING_SNAKE_CASE       ,						ascii=SCREAMING_SNAKE_CASE ) ):
						with torch.no_grad():
									SCREAMING_SNAKE_CASE     =       x.to(SCREAMING_SNAKE_CASE )
									SCREAMING_SNAKE_CASE     =       discriminator.avg_representation(SCREAMING_SNAKE_CASE ).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(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )       ,						batch_size=SCREAMING_SNAKE_CASE       ,						shuffle=SCREAMING_SNAKE_CASE       ,						collate_fn=SCREAMING_SNAKE_CASE )

			return data_loader
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE=None       ,						SCREAMING_SNAKE_CASE="gpt2-medium"       ,						SCREAMING_SNAKE_CASE=10       ,						SCREAMING_SNAKE_CASE=64       ,						SCREAMING_SNAKE_CASE=10       ,						SCREAMING_SNAKE_CASE=False       ,						SCREAMING_SNAKE_CASE=False       ,						SCREAMING_SNAKE_CASE=False       ,						):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       """cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

			print("""Preprocessing {} dataset...""".format(SCREAMING_SNAKE_CASE ) )
			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(SCREAMING_SNAKE_CASE )}

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

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

						SCREAMING_SNAKE_CASE     =       []
						SCREAMING_SNAKE_CASE     =       []
						for i in trange(len(SCREAMING_SNAKE_CASE )       ,						ascii=SCREAMING_SNAKE_CASE ):
									SCREAMING_SNAKE_CASE     =       TreebankWordDetokenizer().detokenize(vars(train_data[i] )["""text"""] )
									SCREAMING_SNAKE_CASE     =       discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE )
									SCREAMING_SNAKE_CASE     =       torch.tensor([5_02_56] + seq       ,						device=SCREAMING_SNAKE_CASE       ,						dtype=torch.long )
									x.append(SCREAMING_SNAKE_CASE )
									y.append(classaidx[vars(train_data[i] )["""label"""]] )
						SCREAMING_SNAKE_CASE     =       Dataset(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )

						SCREAMING_SNAKE_CASE     =       []
						SCREAMING_SNAKE_CASE     =       []
						for i in trange(len(SCREAMING_SNAKE_CASE )       ,						ascii=SCREAMING_SNAKE_CASE ):
									SCREAMING_SNAKE_CASE     =       TreebankWordDetokenizer().detokenize(vars(test_data[i] )["""text"""] )
									SCREAMING_SNAKE_CASE     =       discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE )
									SCREAMING_SNAKE_CASE     =       torch.tensor([5_02_56] + seq       ,						device=SCREAMING_SNAKE_CASE       ,						dtype=torch.long )
									test_x.append(SCREAMING_SNAKE_CASE )
									test_y.append(classaidx[vars(test_data[i] )["""label"""]] )
						SCREAMING_SNAKE_CASE     =       Dataset(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )

						SCREAMING_SNAKE_CASE     =       {
						    """class_size""": len(SCREAMING_SNAKE_CASE ),
						    """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(SCREAMING_SNAKE_CASE )}

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

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

															if len(SCREAMING_SNAKE_CASE ) < max_length_seq:
																		SCREAMING_SNAKE_CASE     =       torch.tensor([5_02_56] + seq       ,						device=SCREAMING_SNAKE_CASE       ,						dtype=torch.long )
															else:
																		print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ) )
																		continue
															x.append(SCREAMING_SNAKE_CASE )
															y.append(d["""label"""] )
												except Exception:
															print("""Error evaluating / tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE ) )
															pass

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

						SCREAMING_SNAKE_CASE     =       {
						    """class_size""": len(SCREAMING_SNAKE_CASE ),
						    """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(SCREAMING_SNAKE_CASE )}

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

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

															if len(SCREAMING_SNAKE_CASE ) < max_length_seq:
																		SCREAMING_SNAKE_CASE     =       torch.tensor([5_02_56] + seq       ,						device=SCREAMING_SNAKE_CASE       ,						dtype=torch.long )
															else:
																		print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ) )
																		continue
															x.append(SCREAMING_SNAKE_CASE )
															y.append(int(np.sum(d["""label"""] ) > 0 ) )
												except Exception:
															print("""Error evaluating / tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE ) )
															pass

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

						SCREAMING_SNAKE_CASE     =       {
						    """class_size""": len(SCREAMING_SNAKE_CASE ),
						    """embed_size""": discriminator.embed_size,
						    """pretrained_model""": pretrained_model,
						    """class_vocab""": classaidx,
						    """default_class""": 0,
						}

			else:  # if dataset == "generic":
						# This assumes the input dataset is a TSV with the following structure:
						# class \t text

						if dataset_fp is None:
									raise ValueError("""When generic dataset is selected, dataset_fp needs to be specified aswell.""" )

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

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

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

						SCREAMING_SNAKE_CASE     =       []
						SCREAMING_SNAKE_CASE     =       []
						with open(SCREAMING_SNAKE_CASE ) as f:
									SCREAMING_SNAKE_CASE     =       csv.reader(SCREAMING_SNAKE_CASE       ,						delimiter="""\t""" )
									for i, row in enumerate(tqdm(SCREAMING_SNAKE_CASE       ,						ascii=SCREAMING_SNAKE_CASE ) ):
												if row:
															SCREAMING_SNAKE_CASE     =       row[0]
															SCREAMING_SNAKE_CASE     =       row[1]

															try:
																		SCREAMING_SNAKE_CASE     =       discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE )
																		if len(SCREAMING_SNAKE_CASE ) < max_length_seq:
																					SCREAMING_SNAKE_CASE     =       torch.tensor([5_02_56] + seq       ,						device=SCREAMING_SNAKE_CASE       ,						dtype=torch.long )

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

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

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

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

						SCREAMING_SNAKE_CASE     =       {
						    """class_size""": len(SCREAMING_SNAKE_CASE ),
						    """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(SCREAMING_SNAKE_CASE ) + len(SCREAMING_SNAKE_CASE ) ) )
			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(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						shuffle=SCREAMING_SNAKE_CASE       ,						device=SCREAMING_SNAKE_CASE )

						SCREAMING_SNAKE_CASE     =       get_cached_data_loader(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						device=SCREAMING_SNAKE_CASE )

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

			else:
						SCREAMING_SNAKE_CASE     =       torch.utils.data.DataLoader(
						    dataset=SCREAMING_SNAKE_CASE       ,						batch_size=SCREAMING_SNAKE_CASE       ,						shuffle=SCREAMING_SNAKE_CASE       ,						collate_fn=SCREAMING_SNAKE_CASE )
						SCREAMING_SNAKE_CASE     =       torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE       ,						batch_size=SCREAMING_SNAKE_CASE       ,						collate_fn=SCREAMING_SNAKE_CASE )

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

			SCREAMING_SNAKE_CASE     =       optim.Adam(discriminator.parameters()       ,						lr=0.0_001 )

			for epoch in range(SCREAMING_SNAKE_CASE ):
						SCREAMING_SNAKE_CASE     =       time.time()
						print("""\nEpoch"""       ,						epoch + 1 )

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

						SCREAMING_SNAKE_CASE     =       time.time()
						print("""Epoch took: {:.3f}s""".format(end - start ) )

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

						if save_model:
									# torch.save(discriminator.state_dict(),
									#           "{}_discriminator_{}.pt".format(
									#               args.dataset, epoch + 1
									#               ))
									torch.save(
									    discriminator.get_classifier().state_dict()       ,						"""{}_classifier_head_epoch_{}.pt""".format(SCREAMING_SNAKE_CASE       ,						epoch + 1 )       ,						)


if __name__ == "__main__":
		__A    :				Tuple    						=	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=1_0, metavar="""N""", help="""Number of training epochs""")
		parser.add_argument(
		    """--batch_size""", type=int, default=6_4, metavar="""N""", help="""input batch size for training (default: 64)"""
		)
		parser.add_argument(
		    """--log_interval""",
		    type=int,
		    default=1_0,
		    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    :				Optional[int]    						=	parser.parse_args()

		train_discriminator(**(vars(args)))












from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A    :				List[str]    						=	logging.get_logger(__name__)

__A    :				List[Any]    						=	{
    """mnaylor/mega-base-wikitext""": """https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json""",
}




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							a__    	=  '''mega'''





							def __init__(       self							:						Any							,  a							:						List[str]=30_522							,  a							:						Dict=128							,  a							:						Dict=4							,  a							:						Union[str, Any]=256							,  a							:						Tuple=16							,  a							:						Any=True							,  a							:						Dict=64							,  a							:						Union[str, Any]=False							,  a							:						Tuple=-1							,  a							:						str=None							,  a							:						int=True							,  a							:						Optional[int]="scalenorm"							,  a							:						Optional[int]=True							,  a							:						Dict="silu"							,  a							:						Optional[Any]="softmax"							,  a							:						Any=0.1							,  a							:						List[Any]=0.1							,  a							:						Tuple=0.1							,  a							:						Optional[int]=False							,  a							:						List[str]=True							,  a							:						Optional[int]=256							,  a							:						List[str]=True							,  a							:						int=0.1							,  a							:						str=2_048							,  a							:						Tuple=False							,  a							:						Optional[int]=2							,  a							:						Optional[int]=0.02							,  a							:						Optional[int]=0.2							,  a							:						Union[str, Any]=0.02							,  a							:						str=1.0							,  a							:						Dict=1							,  a							:						Union[str, Any]=0							,  a							:						Dict=2							,  a							:						List[str]="rotary"							,  a							:						Union[str, Any]=None							,  a							:						List[Any]=True							,  a							:						Tuple=True							,  **a							:						Optional[int]							,  )      ->       Dict:
										super().__init__(pad_token_id=a							,  bos_token_id=a							,  eos_token_id=a							,  **a      )

										SCREAMING_SNAKE_CASE     =       vocab_size
										SCREAMING_SNAKE_CASE     =       hidden_size
										SCREAMING_SNAKE_CASE     =       num_hidden_layers
										SCREAMING_SNAKE_CASE     =       activation
										SCREAMING_SNAKE_CASE     =       attention_activation
										SCREAMING_SNAKE_CASE     =       intermediate_size
										SCREAMING_SNAKE_CASE     =       ema_projection_size
										SCREAMING_SNAKE_CASE     =       bidirectional
										SCREAMING_SNAKE_CASE     =       shared_representation_size
										SCREAMING_SNAKE_CASE     =       use_chunking
										SCREAMING_SNAKE_CASE     =       chunk_size
										SCREAMING_SNAKE_CASE     =       truncation
										SCREAMING_SNAKE_CASE     =       normalize_before_mega
										SCREAMING_SNAKE_CASE     =       normalization_type
										SCREAMING_SNAKE_CASE     =       norm_affine
										SCREAMING_SNAKE_CASE     =       dropout_prob
										SCREAMING_SNAKE_CASE     =       hidden_dropout_prob
										SCREAMING_SNAKE_CASE     =       attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE     =       use_feature_dropout
										SCREAMING_SNAKE_CASE     =       use_normalized_ffn
										SCREAMING_SNAKE_CASE     =       nffn_hidden_size
										SCREAMING_SNAKE_CASE     =       normalize_before_ffn
										SCREAMING_SNAKE_CASE     =       nffn_activation_dropout_prob
										SCREAMING_SNAKE_CASE     =       max_positions
										SCREAMING_SNAKE_CASE     =       add_token_type_embeddings
										SCREAMING_SNAKE_CASE     =       type_vocab_size
										SCREAMING_SNAKE_CASE     =       initializer_range
										SCREAMING_SNAKE_CASE     =       ema_delta_alpha_range
										SCREAMING_SNAKE_CASE     =       ema_beta_range
										SCREAMING_SNAKE_CASE     =       ema_gamma_omega_range
										SCREAMING_SNAKE_CASE     =       relative_positional_bias
										SCREAMING_SNAKE_CASE     =       use_cache
										SCREAMING_SNAKE_CASE     =       classifier_dropout
										SCREAMING_SNAKE_CASE     =       add_lm_hidden_dense_layer
										SCREAMING_SNAKE_CASE     =       1  # not used but required by Hugging Face





class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							@property
							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
													SCREAMING_SNAKE_CASE     =       {0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
													SCREAMING_SNAKE_CASE     =       {0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]      )






