code: 
  
  
  
  
  
  '''simple docstring'''
  
  import argparse
  import csv
  import json
  import math
  import time
  
  import numpy as np
  import torch
  import torch.optim as optim
  import torch.utils.data as data
  from nltk.tokenize.treebank import TreebankWordDetokenizer
  from pplm_classification_head import ClassificationHead
  from torch import nn
  from torchtext import data as torchtext_data
  from torchtext import datasets
  from tqdm import tqdm, trange
  
  from transformers import GPTaLMHeadModel, GPTaTokenizer
  
  
  torch.manual_seed(0)
  np.random.seed(0)
  __lowerCAmelCase							:			Optional[Any]				=1E-10
  __lowerCAmelCase							:			List[Any]				="This is incredible! I love it, this is the best chicken I have ever had."
  __lowerCAmelCase							:			str				=100
  
  
  
  
  
  
  class      UpperCAmelCase			( nn.Module       ):
   def __init__(       self   :Union[str, Any]		, lowercase_   :Dict		, lowercase_   :Union[str, Any]="gpt2-medium"		, lowercase_   :Dict=False		, lowercase_   :Any="cpu"							)->							Optional[int]:
       super().__init__()
       A__       =    GPTaTokenizer.from_pretrained(UpperCamelCase__							)
       A__       =    GPTaLMHeadModel.from_pretrained(UpperCamelCase__							)
       A__       =    self.encoder.transformer.config.hidden_size
       A__       =    ClassificationHead(class_size=UpperCamelCase__		, embed_size=self.embed_size							)
       A__       =    cached_mode
       A__       =    device
   def   UpperCAmelCase_							(       self   :str							)->							Any:
       return self.classifier_head
   def   UpperCAmelCase_							(       self   :Tuple							)->							Optional[Any]:
       for param in self.encoder.parameters():
           A__       =    False
       self.classifier_head.train()
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :List[str]							)->							Dict:
       A__       =    x.ne(0							).unsqueeze(2							).repeat(1		, 1		, self.embed_size							).float().to(self.device							).detach()
       A__       =    self.encoder.transformer(UpperCamelCase__							)["last_hidden_state"]
       A__       =    hidden * mask
       A__       =    torch.sum(UpperCamelCase__		, dim=1							) / (torch.sum(UpperCamelCase__		, dim=1							).detach() + EPSILON)
       return avg_hidden
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :str							)->							Dict:
       if self.cached_mode:
           A__       =    x.to(self.device							)
       else:
           A__       =    self.avg_representation(x.to(self.device							)							)
  
       A__       =    self.classifier_head(UpperCamelCase__							)
       A__       =    nn.functional.log_softmax(UpperCamelCase__		, dim=-1							)
  
       return probs
  
  
  
  
  
  
  class      UpperCAmelCase			( data.Dataset       ):
   def __init__(       self   :str		, lowercase_   :Union[str, Any]		, lowercase_   :Union[str, Any]							)->							Optional[Any]:
       A__       =    X
       A__       =    y
   def __len__(       self   :Tuple							)->							Optional[int]:
       return len(self.X							)
  
  
  
   def __getitem__(       self   :List[str]		, lowercase_   :List[str]							)->							Optional[Any]:
       A__       =    {}
       A__       =    self.X[index]
       A__       =    self.y[index]
       return data
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[str] ):
  
  
  
  
  
  
  
      def pad_sequences(_lowerCamelCase      :      str ):
          A__       =    [len(_lowerCamelCase ) for seq in sequences]
  
          A__       =    torch.zeros(len(_lowerCamelCase )		, max(_lowerCamelCase ) ).long()  # padding value = 0
  
          for i, seq in enumerate(_lowerCamelCase ):
              A__       =    lengths[i]
              A__       =    seq[:end]
  
          return padded_sequences, lengths
  
      A__       =    {}
      for key in data[0].keys():
          A__       =    [d[key] for d in data]
  
      A__,			A__       =    pad_sequences(item_info["X"] )
      A__       =    torch.tensor(item_info["y"]		, dtype=torch.long )
  
      return x_batch, y_batch
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Union[str, Any] ):
      A__       =    {}
      for key in data[0].keys():
          A__       =    [d[key] for d in data]
  
      A__       =    torch.cat(item_info["X"]		, 0 )
      A__       =    torch.tensor(item_info["y"]		, dtype=torch.long )
  
      return x_batch, y_batch
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Union[str, Any]		, _lowerCamelCase      :      Tuple		, _lowerCamelCase      :      Union[str, Any]		, _lowerCamelCase      :      Any=0		, _lowerCamelCase      :      int=10		, _lowerCamelCase      :      Optional[Any]="cpu" ):
      A__       =    0
      discriminator.train_custom()
      for batch_idx, (input_t, target_t) in enumerate(_lowerCamelCase ):
          A__,			A__       =    input_t.to(_lowerCamelCase ), target_t.to(_lowerCamelCase )
  
          optimizer.zero_grad()
  
          A__       =    discriminator(_lowerCamelCase )
          A__       =    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 		UpperCamelCase       (							_lowerCamelCase      :      Any		, _lowerCamelCase      :      Tuple		, _lowerCamelCase      :      List[str]="cpu" ):
      discriminator.eval()
      A__       =    0
      A__       =    0
      with torch.no_grad():
          for input_t, target_t in data_loader:
              A__,			A__       =    input_t.to(_lowerCamelCase ), target_t.to(_lowerCamelCase )
              A__       =    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
              A__       =    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_0_0.0 * correct / len(data_loader.dataset ) ) )
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Any		, _lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      str		, _lowerCamelCase      :      int=False		, _lowerCamelCase      :      Optional[Any]="cpu" ):
      A__       =    model.tokenizer.encode(_lowerCamelCase )
      A__       =    torch.tensor([input_t]		, dtype=torch.long		, device=_lowerCamelCase )
      if cached:
          A__       =    model.avg_representation(_lowerCamelCase )
  
      A__       =    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 		UpperCamelCase       (							_lowerCamelCase      :      Dict		, _lowerCamelCase      :      str		, _lowerCamelCase      :      Any		, _lowerCamelCase      :      str=False		, _lowerCamelCase      :      Any="cpu" ):
      A__       =    torch.utils.data.DataLoader(dataset=_lowerCamelCase		, batch_size=_lowerCamelCase		, collate_fn=_lowerCamelCase )
  
      A__       =    []
      A__       =    []
      for batch_idx, (x, y) in enumerate(tqdm(_lowerCamelCase		, ascii=_lowerCamelCase ) ):
          with torch.no_grad():
              A__       =    x.to(_lowerCamelCase )
              A__       =    discriminator.avg_representation(_lowerCamelCase ).cpu().detach()
              A__       =    torch.unbind(avg_rep.unsqueeze(1 ) )
              xs += avg_rep_list
              ys += y.cpu().numpy().tolist()
  
      A__       =    torch.utils.data.DataLoader(
          dataset=Dataset(_lowerCamelCase		, _lowerCamelCase )		, batch_size=_lowerCamelCase		, shuffle=_lowerCamelCase		, collate_fn=_lowerCamelCase )
  
      return data_loader
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Tuple		, _lowerCamelCase      :      Dict=None		, _lowerCamelCase      :      Any="gpt2-medium"		, _lowerCamelCase      :      Tuple=10		, _lowerCamelCase      :      List[str]=64		, _lowerCamelCase      :      Union[str, Any]=10		, _lowerCamelCase      :      Dict=False		, _lowerCamelCase      :      str=False		, _lowerCamelCase      :      List[str]=False		, ):
      A__       =    "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"
  
      print("Preprocessing {} dataset...".format(_lowerCamelCase ) )
      A__       =    time.time()
  
      if dataset == "SST":
          A__       =    ["positive", "negative", "very positive", "very negative", "neutral"]
          A__       =    {c: i for i, c in enumerate(_lowerCamelCase )}
  
          A__       =    Discriminator(
              class_size=len(_lowerCamelCase )		, pretrained_model=_lowerCamelCase		, cached_mode=_lowerCamelCase		, device=_lowerCamelCase ).to(_lowerCamelCase )
  
          A__       =    torchtext_data.Field()
          A__       =    torchtext_data.Field(sequential=_lowerCamelCase )
          A__,			A__,			A__       =    datasets.SST.splits(
              _lowerCamelCase		, _lowerCamelCase		, fine_grained=_lowerCamelCase		, train_subtrees=_lowerCamelCase		, )
  
          A__       =    []
          A__       =    []
          for i in trange(len(_lowerCamelCase )		, ascii=_lowerCamelCase ):
              A__       =    TreebankWordDetokenizer().detokenize(vars(train_data[i] )["text"] )
              A__       =    discriminator.tokenizer.encode(_lowerCamelCase )
              A__       =    torch.tensor([5_02_56] + seq		, device=_lowerCamelCase		, dtype=torch.long )
              x.append(_lowerCamelCase )
              y.append(classaidx[vars(train_data[i] )["label"]] )
          A__       =    Dataset(_lowerCamelCase		, _lowerCamelCase )
  
          A__       =    []
          A__       =    []
          for i in trange(len(_lowerCamelCase )		, ascii=_lowerCamelCase ):
              A__       =    TreebankWordDetokenizer().detokenize(vars(test_data[i] )["text"] )
              A__       =    discriminator.tokenizer.encode(_lowerCamelCase )
              A__       =    torch.tensor([5_02_56] + seq		, device=_lowerCamelCase		, dtype=torch.long )
              test_x.append(_lowerCamelCase )
              test_y.append(classaidx[vars(test_data[i] )["label"]] )
          A__       =    Dataset(_lowerCamelCase		, _lowerCamelCase )
  
          A__       =    {
              "class_size": len(_lowerCamelCase ),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 2,
          }
  
      elif dataset == "clickbait":
          A__       =    ["non_clickbait", "clickbait"]
          A__       =    {c: i for i, c in enumerate(_lowerCamelCase )}
  
          A__       =    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:
              A__       =    []
              for i, line in enumerate(_lowerCamelCase ):
                  try:
                      data.append(eval(_lowerCamelCase ) )
                  except Exception:
                      print("Error evaluating line {}: {}".format(_lowerCamelCase		, _lowerCamelCase ) )
                      continue
          A__       =    []
          A__       =    []
          with open("datasets/clickbait/clickbait_train_prefix.txt" ) as f:
              for i, line in enumerate(tqdm(_lowerCamelCase		, ascii=_lowerCamelCase ) ):
                  try:
                      A__       =    eval(_lowerCamelCase )
                      A__       =    discriminator.tokenizer.encode(d["text"] )
  
                      if len(_lowerCamelCase ) < max_length_seq:
                          A__       =    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
  
          A__       =    Dataset(_lowerCamelCase		, _lowerCamelCase )
          A__       =    int(0.9 * len(_lowerCamelCase ) )
          A__       =    len(_lowerCamelCase ) - train_size
          A__,			A__       =    torch.utils.data.random_split(_lowerCamelCase		, [train_size, test_size] )
  
          A__       =    {
              "class_size": len(_lowerCamelCase ),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 1,
          }
  
      elif dataset == "toxic":
          A__       =    ["non_toxic", "toxic"]
          A__       =    {c: i for i, c in enumerate(_lowerCamelCase )}
  
          A__       =    Discriminator(
              class_size=len(_lowerCamelCase )		, pretrained_model=_lowerCamelCase		, cached_mode=_lowerCamelCase		, device=_lowerCamelCase ).to(_lowerCamelCase )
  
          A__       =    []
          A__       =    []
          with open("datasets/toxic/toxic_train.txt" ) as f:
              for i, line in enumerate(tqdm(_lowerCamelCase		, ascii=_lowerCamelCase ) ):
                  try:
                      A__       =    eval(_lowerCamelCase )
                      A__       =    discriminator.tokenizer.encode(d["text"] )
  
                      if len(_lowerCamelCase ) < max_length_seq:
                          A__       =    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
  
          A__       =    Dataset(_lowerCamelCase		, _lowerCamelCase )
          A__       =    int(0.9 * len(_lowerCamelCase ) )
          A__       =    len(_lowerCamelCase ) - train_size
          A__,			A__       =    torch.utils.data.random_split(_lowerCamelCase		, [train_size, test_size] )
  
          A__       =    {
              "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." )
  
          A__       =    set()
          with open(_lowerCamelCase ) as f:
              A__       =    csv.reader(_lowerCamelCase		, delimiter="\t" )
              for row in tqdm(_lowerCamelCase		, ascii=_lowerCamelCase ):
                  if row:
                      classes.add(row[0] )
  
          A__       =    sorted(_lowerCamelCase )
          A__       =    {c: i for i, c in enumerate(_lowerCamelCase )}
  
          A__       =    Discriminator(
              class_size=len(_lowerCamelCase )		, pretrained_model=_lowerCamelCase		, cached_mode=_lowerCamelCase		, device=_lowerCamelCase ).to(_lowerCamelCase )
  
          A__       =    []
          A__       =    []
          with open(_lowerCamelCase ) as f:
              A__       =    csv.reader(_lowerCamelCase		, delimiter="\t" )
              for i, row in enumerate(tqdm(_lowerCamelCase		, ascii=_lowerCamelCase ) ):
                  if row:
                      A__       =    row[0]
                      A__       =    row[1]
  
                      try:
                          A__       =    discriminator.tokenizer.encode(_lowerCamelCase )
                          if len(_lowerCamelCase ) < max_length_seq:
                              A__       =    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
  
          A__       =    Dataset(_lowerCamelCase		, _lowerCamelCase )
          A__       =    int(0.9 * len(_lowerCamelCase ) )
          A__       =    len(_lowerCamelCase ) - train_size
          A__,			A__       =    torch.utils.data.random_split(_lowerCamelCase		, [train_size, test_size] )
  
          A__       =    {
              "class_size": len(_lowerCamelCase ),
              "embed_size": discriminator.embed_size,
              "pretrained_model": pretrained_model,
              "class_vocab": classaidx,
              "default_class": 0,
          }
  
      A__       =    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..." )
  
          A__       =    time.time()
  
          A__       =    get_cached_data_loader(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, shuffle=_lowerCamelCase		, device=_lowerCamelCase )
  
          A__       =    get_cached_data_loader(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, device=_lowerCamelCase )
  
          A__       =    time.time()
          print("Building representation cache took: {:.3f}s".format(end - start ) )
  
      else:
          A__       =    torch.utils.data.DataLoader(
              dataset=_lowerCamelCase		, batch_size=_lowerCamelCase		, shuffle=_lowerCamelCase		, collate_fn=_lowerCamelCase )
          A__       =    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 )
  
      A__       =    optim.Adam(discriminator.parameters()		, lr=0.0_0_0_1 )
  
      for epoch in range(_lowerCamelCase ):
          A__       =    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 )
  
          A__       =    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__":
    __lowerCAmelCase							:			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")
    __lowerCAmelCase							:			Optional[Any]				=parser.parse_args()
  
    train_discriminator(**(vars(args)))
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  __lowerCAmelCase							:			Dict				=logging.get_logger(__name__)
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :List[str]		, *lowercase_   :Dict		, **lowercase_   :int							)->							None:
       warnings.warn(
           "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use ViltImageProcessor instead."		, lowercase_		, )
       super().__init__(*lowercase_		, **lowercase_							)
  
  
  
  