code: 
  
  """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)
  _A						=     1E-10
  _A						=     """This is incredible! I love it, this is the best chicken I have ever had."""
  _A						=     1_00
  
  class 			lowerCamelCase  (					nn.Module      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase="gpt2-medium"       ,					_lowerCamelCase=False       ,					_lowerCamelCase="cpu"  ):
  
  
          """simple docstring"""
  
          super().__init__()
          UpperCAmelCase__ :       List[str]						=						GPTaTokenizer.from_pretrained(_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						GPTaLMHeadModel.from_pretrained(_lowerCamelCase  )
          UpperCAmelCase__ :       Optional[Any]						=						self.encoder.transformer.config.hidden_size
          UpperCAmelCase__ :       Dict						=						ClassificationHead(class_size=_lowerCamelCase       ,					embed_size=self.embed_size  )
          UpperCAmelCase__ :       Union[str, Any]						=						cached_mode
          UpperCAmelCase__ :       Dict						=						device
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return self.classifier_head
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for param in self.encoder.parameters():
                UpperCAmelCase__ :       List[Any]						=						False
          self.classifier_head.train()
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						x.ne(0  ).unsqueeze(2  ).repeat(1       ,					1       ,					self.embed_size  ).float().to(self.device  ).detach()
          UpperCAmelCase__ :       List[Any]						=						self.encoder.transformer(_lowerCamelCase  )["""last_hidden_state"""]
          UpperCAmelCase__ :       Optional[Any]						=						hidden * mask
          UpperCAmelCase__ :       List[Any]						=						torch.sum(_lowerCamelCase       ,					dim=1  ) / (torch.sum(_lowerCamelCase       ,					dim=1  ).detach() + EPSILON)
          return avg_hidden
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          if self.cached_mode:
                UpperCAmelCase__ :       Tuple						=						x.to(self.device  )
          else:
                UpperCAmelCase__ :       Any						=						self.avg_representation(x.to(self.device  )  )
  
          UpperCAmelCase__ :       Dict						=						self.classifier_head(_lowerCamelCase  )
          UpperCAmelCase__ :       Union[str, Any]						=						nn.functional.log_softmax(_lowerCamelCase       ,					dim=-1  )
  
          return probs
  
  
  
  
  class 			lowerCamelCase  (					data.Dataset      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						X
          UpperCAmelCase__ :       Optional[Any]						=						y
  
  
  
    def __len__(self  ):
  
  
          """simple docstring"""
  
          return len(self.X  )
  
  
  
  
    def __getitem__(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						{}
          UpperCAmelCase__ :       Optional[int]						=						self.X[index]
          UpperCAmelCase__ :       Union[str, Any]						=						self.y[index]
          return data
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   str:
        def pad_sequences(lowerCAmelCase						):
              UpperCAmelCase__ :       Union[str, Any]						=						[len(lowerCAmelCase						) for seq in sequences]
  
              UpperCAmelCase__ :       Tuple						=						torch.zeros(len(lowerCAmelCase						) ,  max(lowerCAmelCase						)						).long()  # padding value = 0
  
              for i, seq in enumerate(lowerCAmelCase						):
                    UpperCAmelCase__ :       Optional[int]						=						lengths[i]
                    UpperCAmelCase__ :       Optional[int]						=						seq[:end]
  
              return padded_sequences, lengths
  
        UpperCAmelCase__ :       Union[str, Any]						=						{}
        for key in data[0].keys():
              UpperCAmelCase__ :       Union[str, Any]						=						[d[key] for d in data]
  
        UpperCAmelCase__				,		UpperCAmelCase__ :       Dict						=						pad_sequences(item_info["""X"""]						)
        UpperCAmelCase__ :       int						=						torch.tensor(item_info["""y"""] ,  dtype=torch.long						)
  
        return x_batch, y_batch
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   List[str]:
        UpperCAmelCase__ :       List[str]						=						{}
        for key in data[0].keys():
              UpperCAmelCase__ :       Tuple						=						[d[key] for d in data]
  
        UpperCAmelCase__ :       int						=						torch.cat(item_info["""X"""] ,  0						)
        UpperCAmelCase__ :       List[Any]						=						torch.tensor(item_info["""y"""] ,  dtype=torch.long						)
  
        return x_batch, y_batch
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase=0 ,  lowerCAmelCase=10 ,  lowerCAmelCase="cpu"						)  ->   Tuple:
        UpperCAmelCase__ :       List[Any]						=						0
        discriminator.train_custom()
        for batch_idx, (input_t, target_t) in enumerate(lowerCAmelCase						):
              UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						input_t.to(lowerCAmelCase						), target_t.to(lowerCAmelCase						)
  
              optimizer.zero_grad()
  
              UpperCAmelCase__ :       Tuple						=						discriminator(lowerCAmelCase						)
              UpperCAmelCase__ :       List[str]						=						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"						)  ->   List[Any]:
        discriminator.eval()
        UpperCAmelCase__ :       str						=						0
        UpperCAmelCase__ :       int						=						0
        with torch.no_grad():
              for input_t, target_t in data_loader:
                    UpperCAmelCase__				,		UpperCAmelCase__ :       Dict						=						input_t.to(lowerCAmelCase						), target_t.to(lowerCAmelCase						)
                    UpperCAmelCase__ :       Optional[Any]						=						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
                    UpperCAmelCase__ :       Optional[Any]						=						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						) ,  100.0 * correct / len(data_loader.dataset						)						)						)
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase=False ,  lowerCAmelCase="cpu"						)  ->   Union[str, Any]:
        UpperCAmelCase__ :       int						=						model.tokenizer.encode(lowerCAmelCase						)
        UpperCAmelCase__ :       Union[str, Any]						=						torch.tensor([input_t] ,  dtype=torch.long ,  device=lowerCAmelCase						)
        if cached:
              UpperCAmelCase__ :       Tuple						=						model.avg_representation(lowerCAmelCase						)
  
        UpperCAmelCase__ :       Any						=						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"						)  ->   List[Any]:
        UpperCAmelCase__ :       Union[str, Any]						=						torch.utils.data.DataLoader(dataset=lowerCAmelCase ,  batch_size=lowerCAmelCase ,  collate_fn=lowerCAmelCase						)
  
        UpperCAmelCase__ :       str						=						[]
        UpperCAmelCase__ :       Union[str, Any]						=						[]
        for batch_idx, (x, y) in enumerate(tqdm(lowerCAmelCase ,  ascii=lowerCAmelCase						)						):
              with torch.no_grad():
                    UpperCAmelCase__ :       Any						=						x.to(lowerCAmelCase						)
                    UpperCAmelCase__ :       int						=						discriminator.avg_representation(lowerCAmelCase						).cpu().detach()
                    UpperCAmelCase__ :       Optional[int]						=						torch.unbind(avg_rep.unsqueeze(1						)						)
                    xs += avg_rep_list
                    ys += y.cpu().numpy().tolist()
  
        UpperCAmelCase__ :       Optional[int]						=						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 ,  )  ->   Dict:
        UpperCAmelCase__ :       List[str]						=						"""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""
  
        print("""Preprocessing {} dataset...""".format(lowerCAmelCase						)						)
        UpperCAmelCase__ :       str						=						time.time()
  
        if dataset == "SST":
              UpperCAmelCase__ :       Tuple						=						["""positive""", """negative""", """very positive""", """very negative""", """neutral"""]
              UpperCAmelCase__ :       int						=						{c: i for i, c in enumerate(lowerCAmelCase						)}
  
              UpperCAmelCase__ :       List[str]						=						Discriminator(
                  class_size=len(lowerCAmelCase						) ,  pretrained_model=lowerCAmelCase ,  cached_mode=lowerCAmelCase ,  device=lowerCAmelCase						).to(lowerCAmelCase						)
  
              UpperCAmelCase__ :       Optional[int]						=						torchtext_data.Field()
              UpperCAmelCase__ :       str						=						torchtext_data.Field(sequential=lowerCAmelCase						)
              UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__ :       Optional[int]						=						datasets.SST.splits(
                  lowerCAmelCase ,  lowerCAmelCase ,  fine_grained=lowerCAmelCase ,  train_subtrees=lowerCAmelCase ,  )
  
              UpperCAmelCase__ :       Optional[Any]						=						[]
              UpperCAmelCase__ :       Dict						=						[]
              for i in trange(len(lowerCAmelCase						) ,  ascii=lowerCAmelCase						):
                    UpperCAmelCase__ :       List[str]						=						TreebankWordDetokenizer().detokenize(vars(train_data[i]						)["""text"""]						)
                    UpperCAmelCase__ :       List[str]						=						discriminator.tokenizer.encode(lowerCAmelCase						)
                    UpperCAmelCase__ :       int						=						torch.tensor([5_02_56] + seq ,  device=lowerCAmelCase ,  dtype=torch.long						)
                    x.append(lowerCAmelCase						)
                    y.append(classaidx[vars(train_data[i]						)["""label"""]]						)
              UpperCAmelCase__ :       int						=						Dataset(lowerCAmelCase ,  lowerCAmelCase						)
  
              UpperCAmelCase__ :       Union[str, Any]						=						[]
              UpperCAmelCase__ :       Optional[int]						=						[]
              for i in trange(len(lowerCAmelCase						) ,  ascii=lowerCAmelCase						):
                    UpperCAmelCase__ :       Any						=						TreebankWordDetokenizer().detokenize(vars(test_data[i]						)["""text"""]						)
                    UpperCAmelCase__ :       Optional[int]						=						discriminator.tokenizer.encode(lowerCAmelCase						)
                    UpperCAmelCase__ :       str						=						torch.tensor([5_02_56] + seq ,  device=lowerCAmelCase ,  dtype=torch.long						)
                    test_x.append(lowerCAmelCase						)
                    test_y.append(classaidx[vars(test_data[i]						)["""label"""]]						)
              UpperCAmelCase__ :       Optional[int]						=						Dataset(lowerCAmelCase ,  lowerCAmelCase						)
  
              UpperCAmelCase__ :       List[str]						=						{
                  """class_size""": len(lowerCAmelCase						),
                  """embed_size""": discriminator.embed_size,
                  """pretrained_model""": pretrained_model,
                  """class_vocab""": classaidx,
                  """default_class""": 2,
              }
  
        elif dataset == "clickbait":
              UpperCAmelCase__ :       Dict						=						["""non_clickbait""", """clickbait"""]
              UpperCAmelCase__ :       Any						=						{c: i for i, c in enumerate(lowerCAmelCase						)}
  
              UpperCAmelCase__ :       Any						=						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:
                    UpperCAmelCase__ :       str						=						[]
                    for i, line in enumerate(lowerCAmelCase						):
                          try:
                                data.append(eval(lowerCAmelCase						)						)
                          except Exception:
                                print("""Error evaluating line {}: {}""".format(lowerCAmelCase ,  lowerCAmelCase						)						)
                                continue
              UpperCAmelCase__ :       Tuple						=						[]
              UpperCAmelCase__ :       Union[str, Any]						=						[]
              with open("""datasets/clickbait/clickbait_train_prefix.txt"""						) as f:
                    for i, line in enumerate(tqdm(lowerCAmelCase ,  ascii=lowerCAmelCase						)						):
                          try:
                                UpperCAmelCase__ :       Tuple						=						eval(lowerCAmelCase						)
                                UpperCAmelCase__ :       Any						=						discriminator.tokenizer.encode(d["""text"""]						)
  
                                if len(lowerCAmelCase						) < max_length_seq:
                                      UpperCAmelCase__ :       List[str]						=						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
  
              UpperCAmelCase__ :       Dict						=						Dataset(lowerCAmelCase ,  lowerCAmelCase						)
              UpperCAmelCase__ :       Optional[Any]						=						int(0.9 * len(lowerCAmelCase						)						)
              UpperCAmelCase__ :       Optional[int]						=						len(lowerCAmelCase						) - train_size
              UpperCAmelCase__				,		UpperCAmelCase__ :       Dict						=						torch.utils.data.random_split(lowerCAmelCase ,  [train_size, test_size]						)
  
              UpperCAmelCase__ :       Tuple						=						{
                  """class_size""": len(lowerCAmelCase						),
                  """embed_size""": discriminator.embed_size,
                  """pretrained_model""": pretrained_model,
                  """class_vocab""": classaidx,
                  """default_class""": 1,
              }
  
        elif dataset == "toxic":
              UpperCAmelCase__ :       int						=						["""non_toxic""", """toxic"""]
              UpperCAmelCase__ :       str						=						{c: i for i, c in enumerate(lowerCAmelCase						)}
  
              UpperCAmelCase__ :       List[Any]						=						Discriminator(
                  class_size=len(lowerCAmelCase						) ,  pretrained_model=lowerCAmelCase ,  cached_mode=lowerCAmelCase ,  device=lowerCAmelCase						).to(lowerCAmelCase						)
  
              UpperCAmelCase__ :       int						=						[]
              UpperCAmelCase__ :       Optional[Any]						=						[]
              with open("""datasets/toxic/toxic_train.txt"""						) as f:
                    for i, line in enumerate(tqdm(lowerCAmelCase ,  ascii=lowerCAmelCase						)						):
                          try:
                                UpperCAmelCase__ :       str						=						eval(lowerCAmelCase						)
                                UpperCAmelCase__ :       Optional[Any]						=						discriminator.tokenizer.encode(d["""text"""]						)
  
                                if len(lowerCAmelCase						) < max_length_seq:
                                      UpperCAmelCase__ :       Union[str, Any]						=						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
  
              UpperCAmelCase__ :       Optional[Any]						=						Dataset(lowerCAmelCase ,  lowerCAmelCase						)
              UpperCAmelCase__ :       List[str]						=						int(0.9 * len(lowerCAmelCase						)						)
              UpperCAmelCase__ :       Tuple						=						len(lowerCAmelCase						) - train_size
              UpperCAmelCase__				,		UpperCAmelCase__ :       Optional[Any]						=						torch.utils.data.random_split(lowerCAmelCase ,  [train_size, test_size]						)
  
              UpperCAmelCase__ :       Optional[Any]						=						{
                  """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."""						)
  
              UpperCAmelCase__ :       Optional[Any]						=						set()
              with open(lowerCAmelCase						) as f:
                    UpperCAmelCase__ :       Optional[Any]						=						csv.reader(lowerCAmelCase ,  delimiter="""\t"""						)
                    for row in tqdm(lowerCAmelCase ,  ascii=lowerCAmelCase						):
                          if row:
                                classes.add(row[0]						)
  
              UpperCAmelCase__ :       Any						=						sorted(lowerCAmelCase						)
              UpperCAmelCase__ :       Union[str, Any]						=						{c: i for i, c in enumerate(lowerCAmelCase						)}
  
              UpperCAmelCase__ :       List[str]						=						Discriminator(
                  class_size=len(lowerCAmelCase						) ,  pretrained_model=lowerCAmelCase ,  cached_mode=lowerCAmelCase ,  device=lowerCAmelCase						).to(lowerCAmelCase						)
  
              UpperCAmelCase__ :       Optional[int]						=						[]
              UpperCAmelCase__ :       List[Any]						=						[]
              with open(lowerCAmelCase						) as f:
                    UpperCAmelCase__ :       str						=						csv.reader(lowerCAmelCase ,  delimiter="""\t"""						)
                    for i, row in enumerate(tqdm(lowerCAmelCase ,  ascii=lowerCAmelCase						)						):
                          if row:
                                UpperCAmelCase__ :       str						=						row[0]
                                UpperCAmelCase__ :       str						=						row[1]
  
                                try:
                                      UpperCAmelCase__ :       Optional[int]						=						discriminator.tokenizer.encode(lowerCAmelCase						)
                                      if len(lowerCAmelCase						) < max_length_seq:
                                            UpperCAmelCase__ :       Optional[Any]						=						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
  
              UpperCAmelCase__ :       Tuple						=						Dataset(lowerCAmelCase ,  lowerCAmelCase						)
              UpperCAmelCase__ :       str						=						int(0.9 * len(lowerCAmelCase						)						)
              UpperCAmelCase__ :       Optional[Any]						=						len(lowerCAmelCase						) - train_size
              UpperCAmelCase__				,		UpperCAmelCase__ :       Union[str, Any]						=						torch.utils.data.random_split(lowerCAmelCase ,  [train_size, test_size]						)
  
              UpperCAmelCase__ :       Any						=						{
                  """class_size""": len(lowerCAmelCase						),
                  """embed_size""": discriminator.embed_size,
                  """pretrained_model""": pretrained_model,
                  """class_vocab""": classaidx,
                  """default_class""": 0,
              }
  
        UpperCAmelCase__ :       List[Any]						=						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..."""						)
  
              UpperCAmelCase__ :       Optional[int]						=						time.time()
  
              UpperCAmelCase__ :       Union[str, Any]						=						get_cached_data_loader(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  shuffle=lowerCAmelCase ,  device=lowerCAmelCase						)
  
              UpperCAmelCase__ :       Optional[int]						=						get_cached_data_loader(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  device=lowerCAmelCase						)
  
              UpperCAmelCase__ :       int						=						time.time()
              print("""Building representation cache took: {:.3f}s""".format(end - start						)						)
  
        else:
              UpperCAmelCase__ :       Union[str, Any]						=						torch.utils.data.DataLoader(
                  dataset=lowerCAmelCase ,  batch_size=lowerCAmelCase ,  shuffle=lowerCAmelCase ,  collate_fn=lowerCAmelCase						)
              UpperCAmelCase__ :       Optional[int]						=						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						)
  
        UpperCAmelCase__ :       Tuple						=						optim.Adam(discriminator.parameters() ,  lr=0.0001						)
  
        for epoch in range(lowerCAmelCase						):
              UpperCAmelCase__ :       Optional[Any]						=						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						)
  
              UpperCAmelCase__ :       Any						=						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__":
      _A						=     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""")
      _A						=     parser.parse_args()
  
      train_discriminator(**(vars(args)))
  
  
style_context: 
  
  """simple docstring"""
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (ScoreSdeVeScheduler,)
    SCREAMING_SNAKE_CASE							=     ()
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						4
          UpperCAmelCase__ :       Tuple						=						3
          UpperCAmelCase__ :       Optional[Any]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[Any]						=						torch.rand((batch_size, num_channels, height, width)  )
  
          return sample
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						4
          UpperCAmelCase__ :       Optional[Any]						=						3
          UpperCAmelCase__ :       List[str]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[str]						=						batch_size * num_channels * height * width
          UpperCAmelCase__ :       Any						=						torch.arange(_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						sample.reshape(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						sample / num_elems
          UpperCAmelCase__ :       Optional[int]						=						sample.permute(3       ,					0       ,					1       ,					2  )
  
          return sample
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          def model(_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
                return sample * t / (t + 1)
  
          return model
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						{
              """num_train_timesteps""": 2000,
              """snr""": 0.15,
              """sigma_min""": 0.01,
              """sigma_max""": 1348,
              """sampling_eps""": 1e-5,
          }
  
          config.update(**_lowerCamelCase  )
          return config
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						dict(self.forward_default_kwargs  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       int						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                UpperCAmelCase__ :       int						=						self.get_scheduler_config(**_lowerCamelCase  )
                UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Tuple						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Optional[int]						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Union[str, Any]						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
          kwargs.update(_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       List[str]						=						self.dummy_sample
                UpperCAmelCase__ :       List[str]						=						0.1 * sample
  
                UpperCAmelCase__ :       Optional[Any]						=						self.get_scheduler_config()
                UpperCAmelCase__ :       Optional[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Any						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[Any]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Dict						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for timesteps in [10, 100, 1000]:
                self.check_over_configs(num_train_timesteps=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,					[1, 100, 1000]  ):
                self.check_over_configs(sigma_min=_lowerCamelCase       ,					sigma_max=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for t in [0.1, 0.5, 0.75]:
                self.check_over_forward(time_step=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       int						=						self.scheduler_classes[0]
          UpperCAmelCase__ :       int						=						self.get_scheduler_config()
          UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						3
  
          UpperCAmelCase__ :       List[Any]						=						self.dummy_model()
          UpperCAmelCase__ :       int						=						self.dummy_sample_deter
  
          scheduler.set_sigmas(_lowerCamelCase  )
          scheduler.set_timesteps(_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						torch.manual_seed(0  )
  
          for i, t in enumerate(scheduler.timesteps  ):
                UpperCAmelCase__ :       Tuple						=						scheduler.sigmas[i]
  
                for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                            UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
                      UpperCAmelCase__ :       List[str]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  ).prev_sample
  
                with torch.no_grad():
                      UpperCAmelCase__ :       Tuple						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  )
                UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						output.prev_sample, output.prev_sample_mean
  
          UpperCAmelCase__ :       int						=						torch.sum(torch.abs(_lowerCamelCase  )  )
          UpperCAmelCase__ :       str						=						torch.mean(torch.abs(_lowerCamelCase  )  )
  
          assert np.isclose(result_sum.item()       ,					14_372_758_528.0  )
          assert np.isclose(result_mean.item()       ,					18_714_530.0  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						kwargs.pop("""num_inference_steps"""       ,					_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       Dict						=						self.get_scheduler_config()
                UpperCAmelCase__ :       List[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                if num_inference_steps is not None and hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      scheduler.set_timesteps(_lowerCamelCase  )
                elif num_inference_steps is not None and not hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      UpperCAmelCase__ :       Dict						=						num_inference_steps
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					0       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       int						=						scheduler.step_pred(_lowerCamelCase       ,					1       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                self.assertEqual(output_a.shape       ,					sample.shape  )
                self.assertEqual(output_a.shape       ,					output_a.shape  )
  
  