






"""simple docstring"""



from __future__ import annotations

import random
from collections.abc import Iterable




class        __lowercase:






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE)			->  None:




											"""simple docstring"""





											lowerCamelCase__				={literal: None for literal in literals}






						def __str__(					self)			->  str:




											"""simple docstring"""





											return "{" + " , ".join(self.literals) + "}"






						def __len__(					self)			->  int:




											"""simple docstring"""





											return len(self.literals)






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  None:




											"""simple docstring"""





											for literal in self.literals:
																lowerCamelCase__				=literal[:2]
																if symbol in model:
																					lowerCamelCase__				=model[symbol]
																else:
																					continue
																if value is not None:
																					# Complement assignment if literal is in complemented form
																					if literal.endswith('\''):
																										lowerCamelCase__				=not value
																lowerCamelCase__				=value






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  bool | None:




											"""simple docstring"""





											for literal in self.literals:
																lowerCamelCase__				=literal.rstrip('\'') if literal.endswith('\'') else literal + '\''
																if symbol in self.literals:
																					return True

											self.assign(_SCREAMING_SNAKE_CASE)
											for value in self.literals.values():
																if value in (True, None):
																					return value
											return any(self.literals.values())




class        __lowercase:






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE)			->  None:




											"""simple docstring"""





											lowerCamelCase__				=list(_SCREAMING_SNAKE_CASE)






						def __str__(					self)			->  str:




											"""simple docstring"""





											return "{" + " , ".join(str(_SCREAMING_SNAKE_CASE) for clause in self.clauses) + "}"




def 						lowerCamelCase_				(  ):
					lowerCamelCase__				=[]
					lowerCamelCase__				=random.randint(1			,	5			)
					lowerCamelCase__				='A'
					lowerCamelCase__				=0
					while i < no_of_literals:
										lowerCamelCase__				=random.randint(1			,	5			)
										lowerCamelCase__				=base_var + str(__A			)
										lowerCamelCase__				=random.randint(0			,	1			)
										if var_complement == 1:
															var_name += "'"
										if var_name in literals:
															i -= 1
										else:
															literals.append(__A			)
										i += 1
					return Clause(__A			)




def 						lowerCamelCase_				(  ):
					lowerCamelCase__				=set()
					lowerCamelCase__				=random.randint(1			,	10			)
					while len(__A			) < no_of_clauses:
										clauses.add(generate_clause()			)
					return Formula(__A			)




def 						lowerCamelCase_				(  __A  :		Any			):
					lowerCamelCase__				=formula.clauses
					lowerCamelCase__				=[]
					for clause in formula.clauses:
										for literal in clause.literals:
															lowerCamelCase__				=literal[:2]
															if symbol not in symbols_set:
																				symbols_set.append(__A			)
					return clauses, symbols_set




def 						lowerCamelCase_				(  __A  :		str			,	__A  :		int			,	__A  :		List[Any]			):
					lowerCamelCase__				=[]
					lowerCamelCase__				={}
					lowerCamelCase__				=[]

					for clause in clauses:
										if clause.evaluate(__A			):
															continue
										for literal in clause.literals:
															literals.append(__A			)

					for s in symbols:
										lowerCamelCase__				=s + '\''
										if (s in literals and sym not in literals) or (
										    s not in literals and sym in literals
										):
															pure_symbols.append(__A			)
					for p in pure_symbols:
										lowerCamelCase__				=None
					for s in pure_symbols:
										lowerCamelCase__				=s + '\''
										if s in literals:
															lowerCamelCase__				=True
										elif sym in literals:
															lowerCamelCase__				=False
					return pure_symbols, assignment




def 						lowerCamelCase_				(  __A  :		Any			,	__A  :		Dict			):
					lowerCamelCase__				=[]
					for clause in clauses:
										if len(__A			) == 1:
															unit_symbols.append(list(clause.literals.keys()			)[0]			)
										else:
															lowerCamelCase__				,     lowerCamelCase__				=0, 0
															for literal, value in clause.literals.items():
																				if value is False:
																									f_count += 1
																				elif value is None:
																									lowerCamelCase__				=literal
																									n_count += 1
															if f_count == len(__A			) - 1 and n_count == 1:
																				unit_symbols.append(__A			)
					lowerCamelCase__				={}
					for i in unit_symbols:
										lowerCamelCase__				=i[:2]
										lowerCamelCase__				=len(__A			) == 2
					lowerCamelCase__				=[i[:2] for i in unit_symbols]

					return unit_symbols, assignment




def 						lowerCamelCase_				(  __A  :		Union[str, Any]			,	__A  :		Dict			,	__A  :		Tuple			):
					lowerCamelCase__				=True
					for clause in clauses:
										lowerCamelCase__				=clause.evaluate(__A			)
										if clause_check is False:
															return False, None
										elif clause_check is None:
															lowerCamelCase__				=False
															continue

					if check_clause_all_true:
										return True, model

					try:
										lowerCamelCase__				,     lowerCamelCase__				=find_pure_symbols(__A			,	__A			,	__A			)
					except RecursionError:
										print('raises a RecursionError and is'			)
										return None, {}
					lowerCamelCase__				=None
					if len(__A			) > 0:
										lowerCamelCase__				,     lowerCamelCase__				=pure_symbols[0], assignment[pure_symbols[0]]

					if p:
										lowerCamelCase__				=model
										lowerCamelCase__				=value
										lowerCamelCase__				=list(__A			)
										if p in tmp_symbols:
															tmp_symbols.remove(__A			)
										return dpll_algorithm(__A			,	__A			,	__A			)

					lowerCamelCase__				,     lowerCamelCase__				=find_unit_clauses(__A			,	__A			)
					lowerCamelCase__				=None
					if len(__A			) > 0:
										lowerCamelCase__				,     lowerCamelCase__				=unit_symbols[0], assignment[unit_symbols[0]]
					if p:
										lowerCamelCase__				=model
										lowerCamelCase__				=value
										lowerCamelCase__				=list(__A			)
										if p in tmp_symbols:
															tmp_symbols.remove(__A			)
										return dpll_algorithm(__A			,	__A			,	__A			)
					lowerCamelCase__				=symbols[0]
					lowerCamelCase__				=symbols[1:]
					lowerCamelCase__				,     lowerCamelCase__				=model, model
					lowerCamelCase__				,     lowerCamelCase__				=True, False

					return dpll_algorithm(__A			,	__A			,	__A			) or dpll_algorithm(__A			,	__A			,	__A			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_snake_case       :      Optional[Any]       =					generate_formula()
			print(f"The formula {formula} is", end=" ")

			_snake_case       :      Optional[Any]       =					generate_parameters(formula)
			_snake_case       :      str       =					dpll_algorithm(clauses, symbols, {})

			if solution:
						print(f"satisfiable with the assignment {model}.")
			else:
						print("not satisfiable.")







"""simple docstring"""



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




class        __lowercase(       nn.Module  ):






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE="gpt2-medium"    ,						_SCREAMING_SNAKE_CASE=False    ,						_SCREAMING_SNAKE_CASE="cpu")			->  Tuple:




											"""simple docstring"""





											super().__init__()
											lowerCamelCase__				=GPTaTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=GPTaLMHeadModel.from_pretrained(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__				=self.encoder.transformer.config.hidden_size
											lowerCamelCase__				=ClassificationHead(class_size=_SCREAMING_SNAKE_CASE    ,						embed_size=self.embed_size)
											lowerCamelCase__				=cached_mode
											lowerCamelCase__				=device






						def 		lowerCAmelCase   (					self)			->  int:




											"""simple docstring"""





											return self.classifier_head






						def 		lowerCAmelCase   (					self)			->  Tuple:




											"""simple docstring"""





											for param in self.encoder.parameters():
																lowerCamelCase__				=False
											self.classifier_head.train()






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  Optional[Any]:




											"""simple docstring"""





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






						def 		lowerCAmelCase   (					self    ,						_SCREAMING_SNAKE_CASE)			->  Any:




											"""simple docstring"""





											if self.cached_mode:
																lowerCamelCase__				=x.to(self.device)
											else:
																lowerCamelCase__				=self.avg_representation(x.to(self.device))

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

											return probs




class        __lowercase(       data.Dataset  ):






						def __init__(					self    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE)			->  Dict:




											"""simple docstring"""





											lowerCamelCase__				=X
											lowerCamelCase__				=y






						def __len__(					self)			->  Optional[int]:




											"""simple docstring"""





											return len(self.X)






						def __getitem__(					self    ,						_SCREAMING_SNAKE_CASE)			->  Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__				={}
											lowerCamelCase__				=self.X[index]
											lowerCamelCase__				=self.y[index]
											return data




def 						lowerCamelCase_				(  __A  :		List[Any]			):
					def pad_sequences(__A  :		Any			):
										lowerCamelCase__				=[len(__A			) for seq in sequences]

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

										for i, seq in enumerate(__A			):
															lowerCamelCase__				=lengths[i]
															lowerCamelCase__				=seq[:end]

										return padded_sequences, lengths

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

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

					return x_batch, y_batch




def 						lowerCamelCase_				(  __A  :		Optional[int]			):
					lowerCamelCase__				={}
					for key in data[0].keys():
										lowerCamelCase__				=[d[key] for d in data]

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

					return x_batch, y_batch




def 						lowerCamelCase_				(  __A  :		List[Any]			,	__A  :		int			,	__A  :		str			,	__A  :		Any=0			,	__A  :		List[Any]=10			,	__A  :		Optional[Any]="cpu"			):
					lowerCamelCase__				=0
					discriminator.train_custom()
					for batch_idx, (input_t, target_t) in enumerate(__A			):
										lowerCamelCase__				,     lowerCamelCase__				=input_t.to(__A			), target_t.to(__A			)

										optimizer.zero_grad()

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

										samples_so_far += len(__A			)

										if batch_idx % log_interval == 0:
															print(
															    'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
															        epoch + 1			,	__A			,	len(data_loader.dataset			)			,	100 * samples_so_far / len(data_loader.dataset			)			,	loss.item()			,	)			)




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

					test_loss /= len(data_loader.dataset			)

					print(
					    'Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
					        __A			,	__A			,	len(data_loader.dataset			)			,	1_0_0.0 * correct / len(data_loader.dataset			)			)			)




def 						lowerCamelCase_				(  __A  :		Tuple			,	__A  :		Any			,	__A  :		int			,	__A  :		Optional[int]=False			,	__A  :		Any="cpu"			):
					lowerCamelCase__				=model.tokenizer.encode(__A			)
					lowerCamelCase__				=torch.tensor([input_t]			,	dtype=torch.long			,	device=__A			)
					if cached:
										lowerCamelCase__				=model.avg_representation(__A			)

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




def 						lowerCamelCase_				(  __A  :		List[str]			,	__A  :		str			,	__A  :		Dict			,	__A  :		List[Any]=False			,	__A  :		Any="cpu"			):
					lowerCamelCase__				=torch.utils.data.DataLoader(dataset=__A			,	batch_size=__A			,	collate_fn=__A			)

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

					lowerCamelCase__				=torch.utils.data.DataLoader(
					    dataset=Dataset(__A			,	__A			)			,	batch_size=__A			,	shuffle=__A			,	collate_fn=__A			)

					return data_loader




def 						lowerCamelCase_				(  __A  :		Union[str, Any]			,	__A  :		Any=None			,	__A  :		Union[str, Any]="gpt2-medium"			,	__A  :		Optional[int]=10			,	__A  :		Optional[Any]=64			,	__A  :		int=10			,	__A  :		Tuple=False			,	__A  :		Optional[int]=False			,	__A  :		List[Any]=False			,	):
					lowerCamelCase__				='cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

					print('Preprocessing {} dataset...'.format(__A			)			)
					lowerCamelCase__				=time.time()

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

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

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

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

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

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

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

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

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

																									if len(__A			) < max_length_seq:
																														lowerCamelCase__				=torch.tensor([5_0256] + seq			,	device=__A			,	dtype=torch.long			)
																									else:
																														print('Line {} is longer than maximum length {}'.format(__A			,	__A			)			)
																														continue
																									x.append(__A			)
																									y.append(d['label']			)
																				except Exception:
																									print('Error evaluating / tokenizing line {}, skipping it'.format(__A			)			)
																									pass

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

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

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

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

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

																									if len(__A			) < max_length_seq:
																														lowerCamelCase__				=torch.tensor([5_0256] + seq			,	device=__A			,	dtype=torch.long			)
																									else:
																														print('Line {} is longer than maximum length {}'.format(__A			,	__A			)			)
																														continue
																									x.append(__A			)
																									y.append(int(np.sum(d['label']			) > 0			)			)
																				except Exception:
																									print('Error evaluating / tokenizing line {}, skipping it'.format(__A			)			)
																									pass

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

										lowerCamelCase__				={
										    'class_size': len(__A			),
										    '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.'			)

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

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

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

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

																									try:
																														lowerCamelCase__				=discriminator.tokenizer.encode(__A			)
																														if len(__A			) < max_length_seq:
																																			lowerCamelCase__				=torch.tensor([5_0256] + seq			,	device=__A			,	dtype=torch.long			)

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

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

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

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

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

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

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

										lowerCamelCase__				=time.time()

										lowerCamelCase__				=get_cached_data_loader(__A			,	__A			,	__A			,	shuffle=__A			,	device=__A			)

										lowerCamelCase__				=get_cached_data_loader(__A			,	__A			,	__A			,	device=__A			)

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

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

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

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

					for epoch in range(__A			):
										lowerCamelCase__				=time.time()
										print('\nEpoch'			,	epoch + 1			)

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

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

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

										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(__A			,	epoch + 1			)			,	)


if __name__ == "__main__":
						_snake_case       :      Dict       =					argparse.ArgumentParser(description="Train a discriminator on top of GPT-2 representations")
						parser.add_argument(
						    "--dataset",
						    type=str,
						    default="SST",
						    choices=("SST", "clickbait", "toxic", "generic"),
						    help=(
						        "dataset to train the discriminator on."
						        "In case of generic, the dataset is expected"
						        "to be a TSBV file with structure: class \\t text"
						    ),
						)
						parser.add_argument(
						    "--dataset_fp",
						    type=str,
						    default="",
						    help="File path of the dataset to use. Needed only in case of generic datadset",
						)
						parser.add_argument(
						    "--pretrained_model", type=str, default="gpt2-medium", help="Pretrained model to use as encoder"
						)
						parser.add_argument("--epochs", type=int, default=10, metavar="N", help="Number of training epochs")
						parser.add_argument(
						    "--batch_size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
						)
						parser.add_argument(
						    "--log_interval",
						    type=int,
						    default=10,
						    metavar="N",
						    help="how many batches to wait before logging training status",
						)
						parser.add_argument("--save_model", action="store_true", help="whether to save the model")
						parser.add_argument("--cached", action="store_true", help="whether to cache the input representations")
						parser.add_argument("--no_cuda", action="store_true", help="use to turn off cuda")
						_snake_case       :      Optional[Any]       =					parser.parse_args()

						train_discriminator(**(vars(args)))