
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)
__UpperCAmelCase   						=     1E-10
__UpperCAmelCase   						=     """This is incredible! I love it, this is the best chicken I have ever had."""
__UpperCAmelCase   						=     1_00





class 				UpperCamelCase__		(	nn.Module	):






       """simple docstring"""


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



       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          return self.classifier_head



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



       def     _UpperCamelCase	(							self ,     _A	)				->				List[str]:
          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(__lowercase	)['''last_hidden_state''']
          SCREAMING_SNAKE_CASE_	=      hidden * mask
          SCREAMING_SNAKE_CASE_	=      torch.sum(__lowercase ,     dim=1	) / (torch.sum(__lowercase ,     dim=1	).detach() + EPSILON)
          return avg_hidden




       def     _UpperCamelCase	(							self ,     _A	)				->				str:
          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(__lowercase	)
          SCREAMING_SNAKE_CASE_	=      nn.functional.log_softmax(__lowercase ,     dim=-1	)

          return probs







class 				UpperCamelCase__		(	data.Dataset	):






       """simple docstring"""


       def __init__(							self ,     _A ,     _A	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      X
          SCREAMING_SNAKE_CASE_	=      y



       def __len__(							self	)				->				List[str]:
          return len(self.X	)




       def __getitem__(							self ,     _A	)				->				int:
          SCREAMING_SNAKE_CASE_	=      {}
          SCREAMING_SNAKE_CASE_	=      self.X[index]
          SCREAMING_SNAKE_CASE_	=      self.y[index]
          return data







def  A__ (       __lowerCamelCase						):
   def pad_sequences(__lowerCamelCase						):
      SCREAMING_SNAKE_CASE_	=      [len(__lowerCamelCase						) for seq in sequences]

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

      for i, seq in enumerate(__lowerCamelCase						):
         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  A__ (       __lowerCamelCase						):
   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  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase=0,  __lowerCamelCase=10,  __lowerCamelCase="cpu"						):
   SCREAMING_SNAKE_CASE_	=      0
   discriminator.train_custom()
   for batch_idx, (input_t, target_t) in enumerate(__lowerCamelCase						):
      SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      input_t.to(__lowerCamelCase						), target_t.to(__lowerCamelCase						)

      optimizer.zero_grad()

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

      samples_so_far += len(__lowerCamelCase						)

      if batch_idx % log_interval == 0:
         print(
             '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
                 epoch + 1,  __lowerCamelCase,  len(data_loader.dataset						),  1_00 * samples_so_far / len(data_loader.dataset						),  loss.item(),  )						)


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase="cpu"						):
   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(__lowerCamelCase						), target_t.to(__lowerCamelCase						)
         SCREAMING_SNAKE_CASE_	=      discriminator(__lowerCamelCase						)
         # sum up batch loss
         test_loss += nn.functional.nll_loss(__lowerCamelCase,  __lowerCamelCase,  reduction='''sum'''						).item()
         # get the index of the max log-probability
         SCREAMING_SNAKE_CASE_	=      output_t.argmax(dim=1,  keepdim=__lowerCamelCase						)
         correct += pred_t.eq(target_t.view_as(__lowerCamelCase						)						).sum().item()

   test_loss /= len(data_loader.dataset						)

   print(
       '''Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'''.format(
           __lowerCamelCase,  __lowerCamelCase,  len(data_loader.dataset						),  1_00.0 * correct / len(data_loader.dataset						)						)						)


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase=False,  __lowerCamelCase="cpu"						):
   SCREAMING_SNAKE_CASE_	=      model.tokenizer.encode(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      torch.tensor([input_t],  dtype=torch.long,  device=__lowerCamelCase						)
   if cached:
      SCREAMING_SNAKE_CASE_	=      model.avg_representation(__lowerCamelCase						)

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


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase=False,  __lowerCamelCase="cpu"						):
   SCREAMING_SNAKE_CASE_	=      torch.utils.data.DataLoader(dataset=__lowerCamelCase,  batch_size=__lowerCamelCase,  collate_fn=__lowerCamelCase						)

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

   return data_loader


def  A__ (       __lowerCamelCase,  __lowerCamelCase=None,  __lowerCamelCase="gpt2-medium",  __lowerCamelCase=10,  __lowerCamelCase=64,  __lowerCamelCase=10,  __lowerCamelCase=False,  __lowerCamelCase=False,  __lowerCamelCase=False,  ):
   SCREAMING_SNAKE_CASE_	=      '''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

   print('''Preprocessing {} dataset...'''.format(__lowerCamelCase						)						)
   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(__lowerCamelCase						)}

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

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

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

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

      SCREAMING_SNAKE_CASE_	=      {
          '''class_size''': len(__lowerCamelCase						),
          '''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(__lowerCamelCase						)}

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

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

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

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

      SCREAMING_SNAKE_CASE_	=      {
          '''class_size''': len(__lowerCamelCase						),
          '''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(__lowerCamelCase						)}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      SCREAMING_SNAKE_CASE_	=      {
          '''class_size''': len(__lowerCamelCase						),
          '''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(__lowerCamelCase						) + len(__lowerCamelCase						)						)						)
   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(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  shuffle=__lowerCamelCase,  device=__lowerCamelCase						)

      SCREAMING_SNAKE_CASE_	=      get_cached_data_loader(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  device=__lowerCamelCase						)

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

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

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

   SCREAMING_SNAKE_CASE_	=      optim.Adam(discriminator.parameters(),  lr=0.00_01						)

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

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

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

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

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


if __name__ == "__main__":
   __UpperCAmelCase   						=     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")
   __UpperCAmelCase   						=     parser.parse_args()

   train_discriminator(**(vars(args)))






'''simple docstring'''

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCAmelCase					:			Any				     =					TypeVar("""T""", bound=bool)


class    a__					(			Generic[T]   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase		):
										__lowerCAmelCase											=					value
										__lowerCAmelCase											=					None
										__lowerCAmelCase											=					None




				@property
				def        _snake_case	(self		):
										return self._value




				@staticmethod
				def        _snake_case	(__lowercase							,      __lowercase		):
										if not roota:
																return roota

										if not roota:
																return roota

										if roota.value > roota.value:
																__lowerCAmelCase							,       __lowerCAmelCase											=					roota, roota

										if random.choice([True, False]		):
																__lowerCAmelCase							,       __lowerCAmelCase											=					roota.right, roota.left

										__lowerCAmelCase											=					RandomizedHeapNode.merge(roota.left							,      __lowercase		)

										return roota


class    a__					(			Generic[T]   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase = ()		):
										__lowerCAmelCase											=					None

										if data:
																for item in data:
																						self.insert(__lowercase		)




				def        _snake_case	(self							,      __lowercase		):
										__lowerCAmelCase											=					RandomizedHeapNode.merge(self._root							,      RandomizedHeapNode(__lowercase		)		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.top()

										if self._root is None:
																return None

										__lowerCAmelCase											=					RandomizedHeapNode.merge(self._root.left							,      self._root.right		)

										return result




				def        _snake_case	(self		):
										if not self._root:
																raise IndexError('''Can\'t get top element for the empty heap.'''		)
										return self._root.value




				def        _snake_case	(self		):
										__lowerCAmelCase											=					None




				def        _snake_case	(self		):
										__lowerCAmelCase											=					[]
										while self:
																result.append(self.pop()		)

										return result




				def __bool__(self		):
										return self._root is not None


if __name__ == "__main__":
	import doctest

	doctest.testmod()

