






# 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)
UpperCamelCase											=      1e-10
UpperCamelCase											=      '''This is incredible! I love it, this is the best chicken I have ever had.'''
UpperCamelCase											=      1_0_0




class 	lowerCamelCase__				(							nn.Module ):






      def __init__(self					:    Optional[Any]		,		_snake_case					:    int		,		_snake_case					:    int="gpt2-medium"		,		_snake_case					:    Tuple=False		,		_snake_case					:    Dict="cpu"		)       ->   Optional[Any]:


       """simple docstring"""






       super().__init__()
       lowerCamelCase_							:    Any							=     GPTaTokenizer.from_pretrained(_snake_case		)
       lowerCamelCase_							:    Optional[Any]							=     GPTaLMHeadModel.from_pretrained(_snake_case		)
       lowerCamelCase_							:    Any							=     self.encoder.transformer.config.hidden_size
       lowerCamelCase_							:    Any							=     ClassificationHead(class_size=_snake_case		,		embed_size=self.embed_size		)
       lowerCamelCase_							:    List[str]							=     cached_mode
       lowerCamelCase_							:    List[str]							=     device






      def      UpperCAmelCase_     (self					:    str		)       ->   Union[str, Any]:


       """simple docstring"""






       return self.classifier_head






      def      UpperCAmelCase_     (self					:    Union[str, Any]		)       ->   Tuple:


       """simple docstring"""






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






      def      UpperCAmelCase_     (self					:    Optional[Any]		,		_snake_case					:    List[Any]		)       ->   List[str]:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     x.ne(0		).unsqueeze(2		).repeat(1		,		1		,		self.embed_size		).float().to(self.device		).detach()
       lowerCamelCase_							:    Optional[Any]							=     self.encoder.transformer(_snake_case		)['last_hidden_state']
       lowerCamelCase_							:    List[str]							=     hidden * mask
       lowerCamelCase_							:    Dict							=     torch.sum(_snake_case		,		dim=1		) / (torch.sum(_snake_case		,		dim=1		).detach() + EPSILON)
       return avg_hidden






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    Optional[Any]		)       ->   List[Any]:


       """simple docstring"""






       if self.cached_mode:
        lowerCamelCase_							:    Optional[Any]							=     x.to(self.device		)
       else:
        lowerCamelCase_							:    str							=     self.avg_representation(x.to(self.device		)		)

       lowerCamelCase_							:    Dict							=     self.classifier_head(_snake_case		)
       lowerCamelCase_							:    str							=     nn.functional.log_softmax(_snake_case		,		dim=-1		)

       return probs




class 	lowerCamelCase__				(							data.Dataset ):






      def __init__(self					:    Optional[Any]		,		_snake_case					:    str		,		_snake_case					:    Any		)       ->   Optional[int]:


       """simple docstring"""






       lowerCamelCase_							:    str							=     X
       lowerCamelCase_							:    Dict							=     y






      def __len__(self					:    int		)       ->   List[str]:


       """simple docstring"""






       return len(self.X		)






      def __getitem__(self					:    Any		,		_snake_case					:    Dict		)       ->   List[str]:


       """simple docstring"""






       lowerCamelCase_							:    Union[str, Any]							=     {}
       lowerCamelCase_							:    Union[str, Any]							=     self.X[index]
       lowerCamelCase_							:    List[Any]							=     self.y[index]
       return data




def       _a   (							lowerCamelCase__				)  ->      Any:
 def pad_sequences(lowerCamelCase__				):
  lowerCamelCase_							:    Union[str, Any]							=     [len(lowerCamelCase__				) for seq in sequences]

  lowerCamelCase_							:    Dict							=     torch.zeros(len(lowerCamelCase__				) ,			max(lowerCamelCase__				)				).long()  # padding value = 0

  for i, seq in enumerate(lowerCamelCase__				):
   lowerCamelCase_							:    Optional[int]							=     lengths[i]
   lowerCamelCase_							:    str							=     seq[:end]

  return padded_sequences, lengths

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

 lowerCamelCase_		,      lowerCamelCase_							:    Optional[Any]							=     pad_sequences(item_info['X']				)
 lowerCamelCase_							:    Optional[Any]							=     torch.tensor(item_info['y'] ,			dtype=torch.long				)

 return x_batch, y_batch




def       _a   (							lowerCamelCase__				)  ->      Tuple:
 lowerCamelCase_							:    List[str]							=     {}
 for key in data[0].keys():
  lowerCamelCase_							:    str							=     [d[key] for d in data]

 lowerCamelCase_							:    Optional[Any]							=     torch.cat(item_info['X'] ,			0				)
 lowerCamelCase_							:    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"				)  ->      Any:
 lowerCamelCase_							:    str							=     0
 discriminator.train_custom()
 for batch_idx, (input_t, target_t) in enumerate(lowerCamelCase__				):
  lowerCamelCase_		,      lowerCamelCase_							:    Optional[int]							=     input_t.to(lowerCamelCase__				), target_t.to(lowerCamelCase__				)

  optimizer.zero_grad()

  lowerCamelCase_							:    int							=     discriminator(lowerCamelCase__				)
  lowerCamelCase_							:    Union[str, Any]							=     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"				)  ->      Union[str, Any]:
 discriminator.eval()
 lowerCamelCase_							:    List[str]							=     0
 lowerCamelCase_							:    Optional[Any]							=     0
 with torch.no_grad():
  for input_t, target_t in data_loader:
   lowerCamelCase_		,      lowerCamelCase_							:    str							=     input_t.to(lowerCamelCase__				), target_t.to(lowerCamelCase__				)
   lowerCamelCase_							:    Dict							=     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
   lowerCamelCase_							:    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"				)  ->      str:
 lowerCamelCase_							:    int							=     model.tokenizer.encode(lowerCamelCase__				)
 lowerCamelCase_							:    Dict							=     torch.tensor([input_t] ,			dtype=torch.long ,			device=lowerCamelCase__				)
 if cached:
  lowerCamelCase_							:    int							=     model.avg_representation(lowerCamelCase__				)

 lowerCamelCase_							:    Tuple							=     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[str]:
 lowerCamelCase_							:    Any							=     torch.utils.data.DataLoader(dataset=lowerCamelCase__ ,			batch_size=lowerCamelCase__ ,			collate_fn=lowerCamelCase__				)

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

 lowerCamelCase_							:    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 ,			)  ->      Optional[Any]:
 lowerCamelCase_							:    Optional[int]							=     'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

 print('Preprocessing {} dataset...'.format(lowerCamelCase__				)				)
 lowerCamelCase_							:    Tuple							=     time.time()

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

  lowerCamelCase_							:    str							=     Discriminator(
      class_size=len(lowerCamelCase__				) ,			pretrained_model=lowerCamelCase__ ,			cached_mode=lowerCamelCase__ ,			device=lowerCamelCase__				).to(lowerCamelCase__				)

  lowerCamelCase_							:    Dict							=     torchtext_data.Field()
  lowerCamelCase_							:    List[Any]							=     torchtext_data.Field(sequential=lowerCamelCase__				)
  lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_							:    int							=     datasets.SST.splits(
      lowerCamelCase__ ,			lowerCamelCase__ ,			fine_grained=lowerCamelCase__ ,			train_subtrees=lowerCamelCase__ ,			)

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

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

  lowerCamelCase_							:    Union[str, Any]							=     {
      'class_size': len(lowerCamelCase__				),
      'embed_size': discriminator.embed_size,
      'pretrained_model': pretrained_model,
      'class_vocab': classaidx,
      'default_class': 2,
  }

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

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

     if len(lowerCamelCase__				) < max_length_seq:
      lowerCamelCase_							:    List[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(d['label']				)
    except Exception:
     print('Error evaluating / tokenizing line {}, skipping it'.format(lowerCamelCase__				)				)
     pass

  lowerCamelCase_							:    Optional[Any]							=     Dataset(lowerCamelCase__ ,			lowerCamelCase__				)
  lowerCamelCase_							:    List[str]							=     int(0.9 * len(lowerCamelCase__				)				)
  lowerCamelCase_							:    str							=     len(lowerCamelCase__				) - train_size
  lowerCamelCase_		,      lowerCamelCase_							:    int							=     torch.utils.data.random_split(lowerCamelCase__ ,			[train_size, test_size]				)

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

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

  lowerCamelCase_							:    Any							=     Discriminator(
      class_size=len(lowerCamelCase__				) ,			pretrained_model=lowerCamelCase__ ,			cached_mode=lowerCamelCase__ ,			device=lowerCamelCase__				).to(lowerCamelCase__				)

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

     if len(lowerCamelCase__				) < max_length_seq:
      lowerCamelCase_							:    Tuple							=     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

  lowerCamelCase_							:    List[str]							=     Dataset(lowerCamelCase__ ,			lowerCamelCase__				)
  lowerCamelCase_							:    List[str]							=     int(0.9 * len(lowerCamelCase__				)				)
  lowerCamelCase_							:    Tuple							=     len(lowerCamelCase__				) - train_size
  lowerCamelCase_		,      lowerCamelCase_							:    Optional[int]							=     torch.utils.data.random_split(lowerCamelCase__ ,			[train_size, test_size]				)

  lowerCamelCase_							:    Union[str, 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.'				)

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

  lowerCamelCase_							:    List[Any]							=     sorted(lowerCamelCase__				)
  lowerCamelCase_							:    Optional[int]							=     {c: i for i, c in enumerate(lowerCamelCase__				)}

  lowerCamelCase_							:    Optional[int]							=     Discriminator(
      class_size=len(lowerCamelCase__				) ,			pretrained_model=lowerCamelCase__ ,			cached_mode=lowerCamelCase__ ,			device=lowerCamelCase__				).to(lowerCamelCase__				)

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

     try:
      lowerCamelCase_							:    Tuple							=     discriminator.tokenizer.encode(lowerCamelCase__				)
      if len(lowerCamelCase__				) < max_length_seq:
       lowerCamelCase_							:    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(classaidx[label]				)

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

  lowerCamelCase_							:    List[str]							=     Dataset(lowerCamelCase__ ,			lowerCamelCase__				)
  lowerCamelCase_							:    Union[str, Any]							=     int(0.9 * len(lowerCamelCase__				)				)
  lowerCamelCase_							:    Any							=     len(lowerCamelCase__				) - train_size
  lowerCamelCase_		,      lowerCamelCase_							:    Dict							=     torch.utils.data.random_split(lowerCamelCase__ ,			[train_size, test_size]				)

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

 lowerCamelCase_							:    Optional[int]							=     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...'				)

  lowerCamelCase_							:    Tuple							=     time.time()

  lowerCamelCase_							:    Dict							=     get_cached_data_loader(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			shuffle=lowerCamelCase__ ,			device=lowerCamelCase__				)

  lowerCamelCase_							:    List[str]							=     get_cached_data_loader(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			device=lowerCamelCase__				)

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

 else:
  lowerCamelCase_							:    Union[str, Any]							=     torch.utils.data.DataLoader(
      dataset=lowerCamelCase__ ,			batch_size=lowerCamelCase__ ,			shuffle=lowerCamelCase__ ,			collate_fn=lowerCamelCase__				)
  lowerCamelCase_							:    Tuple							=     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__				)

 lowerCamelCase_							:    Any							=     optim.Adam(discriminator.parameters() ,			lr=0.0001				)

 for epoch in range(lowerCamelCase__				):
  lowerCamelCase_							:    str							=     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__				)

  lowerCamelCase_							:    Optional[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__":
       UpperCamelCase											=      argparse.ArgumentParser(description='''Train a discriminator on top of GPT-2 representations''')
       parser.add_argument(
           '''--dataset''',
           type=str,
           default='''SST''',
           choices=('''SST''', '''clickbait''', '''toxic''', '''generic'''),
           help=(
               '''dataset to train the discriminator on.'''
               '''In case of generic, the dataset is expected'''
               '''to be a TSBV file with structure: class \\t text'''
           ),
       )
       parser.add_argument(
           '''--dataset_fp''',
           type=str,
           default='''''',
           help='''File path of the dataset to use. Needed only in case of generic datadset''',
       )
       parser.add_argument(
           '''--pretrained_model''', type=str, default='''gpt2-medium''', help='''Pretrained model to use as encoder'''
       )
       parser.add_argument('''--epochs''', type=int, default=1_0, metavar='''N''', help='''Number of training epochs''')
       parser.add_argument(
           '''--batch_size''', type=int, default=6_4, metavar='''N''', help='''input batch size for training (default: 64)'''
       )
       parser.add_argument(
           '''--log_interval''',
           type=int,
           default=1_0,
           metavar='''N''',
           help='''how many batches to wait before logging training status''',
       )
       parser.add_argument('''--save_model''', action='''store_true''', help='''whether to save the model''')
       parser.add_argument('''--cached''', action='''store_true''', help='''whether to cache the input representations''')
       parser.add_argument('''--no_cuda''', action='''store_true''', help='''use to turn off cuda''')
       UpperCamelCase											=      parser.parse_args()

       train_discriminator(**(vars(args)))








import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types




class 	lowerCamelCase__				(							unittest.TestCase,   UpperCAmelCase ):






      def      UpperCAmelCase_     (self					:    Any		)       ->   Union[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    str							=     load_tool('translation'		)
       self.tool.setup()
       lowerCamelCase_							:    Optional[int]							=     load_tool('translation'		,		remote=_snake_case		)






      def      UpperCAmelCase_     (self					:    Any		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     self.tool('Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    Optional[int]		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     self.remote_tool('Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    List[str]		)       ->   Optional[int]:


       """simple docstring"""






       lowerCamelCase_							:    Union[str, Any]							=     self.tool(text='Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    Any		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     self.remote_tool(text='Hey, what\'s up?'		,		src_lang='English'		,		tgt_lang='French'		)
       self.assertEqual(_snake_case		,		'- Hé, comment ça va?'		)






      def      UpperCAmelCase_     (self					:    Union[str, Any]		)       ->   Any:


       """simple docstring"""






       lowerCamelCase_							:    str							=     ['Hey, what\'s up?', 'English', 'Spanish']
       lowerCamelCase_							:    Any							=     self.tool(*_snake_case		)

       # There is a single output
       if len(self.tool.outputs		) == 1:
        lowerCamelCase_							:    Any							=     [outputs]

       self.assertListEqual(output_types(_snake_case		)		,		self.tool.outputs		)






      def      UpperCAmelCase_     (self					:    Tuple		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    List[str]							=     ['Hey, what\'s up?', 'English', 'Spanish']
       lowerCamelCase_							:    Optional[Any]							=     self.tool(*_snake_case		)

       if not isinstance(_snake_case		,		_snake_case		):
        lowerCamelCase_							:    List[str]							=     [outputs]

       self.assertEqual(len(_snake_case		)		,		len(self.tool.outputs		)		)

       for output, output_type in zip(_snake_case		,		self.tool.outputs		):
        lowerCamelCase_							:    Optional[int]							=     AGENT_TYPE_MAPPING[output_type]
        self.assertTrue(isinstance(_snake_case		,		_snake_case		)		)






      def      UpperCAmelCase_     (self					:    Any		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    Dict							=     ['Hey, what\'s up?', 'English', 'Spanish']

       lowerCamelCase_							:    List[str]							=     []

       for _input, input_type in zip(_snake_case		,		self.tool.inputs		):
        if isinstance(_snake_case		,		_snake_case		):
         _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input		) for _input_type in input_type]		)
        else:
         _inputs.append(AGENT_TYPE_MAPPING[input_type](_input		)		)

        # Should not raise an error
       lowerCamelCase_							:    Any							=     self.tool(*_snake_case		)

       if not isinstance(_snake_case		,		_snake_case		):
        lowerCamelCase_							:    Optional[Any]							=     [outputs]

       self.assertEqual(len(_snake_case		)		,		len(self.tool.outputs		)		)
