

"""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)))




"""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  )

