'''simple docstring'''

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
_A      :				Optional[int] 				=   1e-10
_A      :				Dict 				=   '''This is incredible! I love it, this is the best chicken I have ever had.'''
_A      :				Optional[int] 				=   100







class     _lowercase					(    nn.Module      ):


 '''simple docstring'''






 def __init__(				self       :     int				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     Any="gpt2-medium"				,	SCREAMING_SNAKE_CASE__       :     Any=False				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]="cpu"							)  ->   int:
  super().__init__()
  __lowerCAmelCase    							=						GPTaTokenizer.from_pretrained(_UpperCAmelCase							)
  __lowerCAmelCase    							=						GPTaLMHeadModel.from_pretrained(_UpperCAmelCase							)
  __lowerCAmelCase    							=						self.encoder.transformer.config.hidden_size
  __lowerCAmelCase    							=						ClassificationHead(class_size=_UpperCAmelCase				,	embed_size=self.embed_size							)
  __lowerCAmelCase    							=						cached_mode
  __lowerCAmelCase    							=						device
 def a (				self       :     Union[str, Any]							)  ->   str:
  return self.classifier_head
 def a (				self       :     str							)  ->   Tuple:
  for param in self.encoder.parameters():
   __lowerCAmelCase    							=						False
  self.classifier_head.train()
 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   Tuple:
  __lowerCAmelCase    							=						x.ne(0							).unsqueeze(2							).repeat(1				,	1				,	self.embed_size							).float().to(self.device							).detach()
  __lowerCAmelCase    							=						self.encoder.transformer(_UpperCAmelCase							)["last_hidden_state"]
  __lowerCAmelCase    							=						hidden * mask
  __lowerCAmelCase    							=						torch.sum(_UpperCAmelCase				,	dim=1							) / (torch.sum(_UpperCAmelCase				,	dim=1							).detach() + EPSILON)
  return avg_hidden





 def a (				self       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     List[Any]							)  ->   List[str]:
  if self.cached_mode:
   __lowerCAmelCase    							=						x.to(self.device							)
  else:
   __lowerCAmelCase    							=						self.avg_representation(x.to(self.device							)							)

  __lowerCAmelCase    							=						self.classifier_head(_UpperCAmelCase							)
  __lowerCAmelCase    							=						nn.functional.log_softmax(_UpperCAmelCase				,	dim=-1							)

  return probs







class     _lowercase					(    data.Dataset      ):


 '''simple docstring'''






 def __init__(				self       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     Dict				,	SCREAMING_SNAKE_CASE__       :     str							)  ->   int:
  __lowerCAmelCase    							=						X
  __lowerCAmelCase    							=						y
 def __len__(				self       :     Union[str, Any]							)  ->   Union[str, Any]:
  return len(self.X							)





 def __getitem__(				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     Dict							)  ->   Optional[int]:
  __lowerCAmelCase    							=						{}
  __lowerCAmelCase    							=						self.X[index]
  __lowerCAmelCase    							=						self.y[index]
  return data







def 					UpperCamelCase_						( snake_case_	:	Dict				) ->					str:






 '''simple docstring'''

 def pad_sequences(snake_case_	:	Optional[Any]				):
  __lowerCAmelCase    							=						[len(snake_case__				) for seq in sequences]

  __lowerCAmelCase    							=						torch.zeros(len(snake_case__				)						,		max(snake_case__				)				).long()  # padding value = 0

  for i, seq in enumerate(snake_case__				):
   __lowerCAmelCase    							=						lengths[i]
   __lowerCAmelCase    							=						seq[:end]

  return padded_sequences, lengths

 __lowerCAmelCase    							=						{}
 for key in data[0].keys():
  __lowerCAmelCase    							=						[d[key] for d in data]

 __lowerCAmelCase    							=						pad_sequences(item_info["""X"""]				)
 __lowerCAmelCase    							=						torch.tensor(item_info["""y"""]						,		dtype=torch.long				)

 return x_batch, y_batch







def 					UpperCamelCase_						( snake_case_	:	Optional[int]				) ->					Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase    							=						{}
 for key in data[0].keys():
  __lowerCAmelCase    							=						[d[key] for d in data]

 __lowerCAmelCase    							=						torch.cat(item_info["""X"""]						,		0				)
 __lowerCAmelCase    							=						torch.tensor(item_info["""y"""]						,		dtype=torch.long				)

 return x_batch, y_batch







def 					UpperCamelCase_						( snake_case_	:	str						,		snake_case_	:	int						,		snake_case_	:	Dict						,		snake_case_	:	List[Any]=0						,		snake_case_	:	List[Any]=10						,		snake_case_	:	Dict="cpu"				) ->					Union[str, Any]:






 '''simple docstring'''

 __lowerCAmelCase    							=						0
 discriminator.train_custom()
 for batch_idx, (input_t, target_t) in enumerate(snake_case__				):
  __lowerCAmelCase    							=						input_t.to(snake_case__				), target_t.to(snake_case__				)

  optimizer.zero_grad()

  __lowerCAmelCase    							=						discriminator(snake_case__				)
  __lowerCAmelCase    							=						nn.functional.nll_loss(snake_case__						,		snake_case__				)
  loss.backward(retain_graph=snake_case__				)
  optimizer.step()

  samples_so_far += len(snake_case__				)

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







def 					UpperCamelCase_						( snake_case_	:	Optional[int]						,		snake_case_	:	int						,		snake_case_	:	Union[str, Any]="cpu"				) ->					List[str]:






 '''simple docstring'''

 discriminator.eval()
 __lowerCAmelCase    							=						0
 __lowerCAmelCase    							=						0
 with torch.no_grad():
  for input_t, target_t in data_loader:
   __lowerCAmelCase    							=						input_t.to(snake_case__				), target_t.to(snake_case__				)
   __lowerCAmelCase    							=						discriminator(snake_case__				)
   # sum up batch loss
   test_loss += nn.functional.nll_loss(snake_case__						,		snake_case__						,		reduction="""sum"""				).item()
   # get the index of the max log-probability
   __lowerCAmelCase    							=						output_t.argmax(dim=1						,		keepdim=snake_case__				)
   correct += pred_t.eq(target_t.view_as(snake_case__				)				).sum().item()

 test_loss /= len(data_loader.dataset				)

 print(
     """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
         snake_case__						,		snake_case__						,		len(data_loader.dataset				)						,		1_0_0.0 * correct / len(data_loader.dataset				)				)				)







def 					UpperCamelCase_						( snake_case_	:	Tuple						,		snake_case_	:	List[str]						,		snake_case_	:	List[Any]						,		snake_case_	:	Optional[int]=False						,		snake_case_	:	Any="cpu"				) ->					Union[str, Any]:






 '''simple docstring'''

 __lowerCAmelCase    							=						model.tokenizer.encode(snake_case__				)
 __lowerCAmelCase    							=						torch.tensor([input_t]						,		dtype=torch.long						,		device=snake_case__				)
 if cached:
  __lowerCAmelCase    							=						model.avg_representation(snake_case__				)

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







def 					UpperCamelCase_						( snake_case_	:	Tuple						,		snake_case_	:	int						,		snake_case_	:	Any						,		snake_case_	:	Optional[int]=False						,		snake_case_	:	Any="cpu"				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						torch.utils.data.DataLoader(dataset=snake_case__						,		batch_size=snake_case__						,		collate_fn=snake_case__				)

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

 __lowerCAmelCase    							=						torch.utils.data.DataLoader(
     dataset=Dataset(snake_case__						,		snake_case__				)						,		batch_size=snake_case__						,		shuffle=snake_case__						,		collate_fn=snake_case__				)

 return data_loader







def 					UpperCamelCase_						( snake_case_	:	Optional[int]						,		snake_case_	:	Tuple=None						,		snake_case_	:	Union[str, Any]="gpt2-medium"						,		snake_case_	:	Optional[int]=10						,		snake_case_	:	str=64						,		snake_case_	:	Any=10						,		snake_case_	:	Dict=False						,		snake_case_	:	Any=False						,		snake_case_	:	Optional[Any]=False						,		) ->					Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase    							=						"cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

 print("""Preprocessing {} dataset...""".format(snake_case__				)				)
 __lowerCAmelCase    							=						time.time()

 if dataset == "SST":
  __lowerCAmelCase    							=						["positive", "negative", "very positive", "very negative", "neutral"]
  __lowerCAmelCase    							=						{c: i for i, c in enumerate(snake_case__				)}

  __lowerCAmelCase    							=						Discriminator(
      class_size=len(snake_case__				)						,		pretrained_model=snake_case__						,		cached_mode=snake_case__						,		device=snake_case__				).to(snake_case__				)

  __lowerCAmelCase    							=						torchtext_data.Field()
  __lowerCAmelCase    							=						torchtext_data.Field(sequential=snake_case__				)
  __lowerCAmelCase    							=						datasets.SST.splits(
      snake_case__						,		snake_case__						,		fine_grained=snake_case__						,		train_subtrees=snake_case__						,		)

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

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

  __lowerCAmelCase    							=						{
      "class_size": len(snake_case__				),
      "embed_size": discriminator.embed_size,
      "pretrained_model": pretrained_model,
      "class_vocab": classaidx,
      "default_class": 2,
  }

 elif dataset == "clickbait":
  __lowerCAmelCase    							=						["non_clickbait", "clickbait"]
  __lowerCAmelCase    							=						{c: i for i, c in enumerate(snake_case__				)}

  __lowerCAmelCase    							=						Discriminator(
      class_size=len(snake_case__				)						,		pretrained_model=snake_case__						,		cached_mode=snake_case__						,		device=snake_case__				).to(snake_case__				)

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

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

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

  __lowerCAmelCase    							=						{
      "class_size": len(snake_case__				),
      "embed_size": discriminator.embed_size,
      "pretrained_model": pretrained_model,
      "class_vocab": classaidx,
      "default_class": 1,
  }

 elif dataset == "toxic":
  __lowerCAmelCase    							=						["non_toxic", "toxic"]
  __lowerCAmelCase    							=						{c: i for i, c in enumerate(snake_case__				)}

  __lowerCAmelCase    							=						Discriminator(
      class_size=len(snake_case__				)						,		pretrained_model=snake_case__						,		cached_mode=snake_case__						,		device=snake_case__				).to(snake_case__				)

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

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

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

  __lowerCAmelCase    							=						{
      "class_size": len(snake_case__				),
      "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    							=						set()
  with open(snake_case__				) as f:
   __lowerCAmelCase    							=						csv.reader(snake_case__						,		delimiter="""\t"""				)
   for row in tqdm(snake_case__						,		ascii=snake_case__				):
    if row:
     classes.add(row[0]				)

  __lowerCAmelCase    							=						sorted(snake_case__				)
  __lowerCAmelCase    							=						{c: i for i, c in enumerate(snake_case__				)}

  __lowerCAmelCase    							=						Discriminator(
      class_size=len(snake_case__				)						,		pretrained_model=snake_case__						,		cached_mode=snake_case__						,		device=snake_case__				).to(snake_case__				)

  __lowerCAmelCase    							=						[]
  __lowerCAmelCase    							=						[]
  with open(snake_case__				) as f:
   __lowerCAmelCase    							=						csv.reader(snake_case__						,		delimiter="""\t"""				)
   for i, row in enumerate(tqdm(snake_case__						,		ascii=snake_case__				)				):
    if row:
     __lowerCAmelCase    							=						row[0]
     __lowerCAmelCase    							=						row[1]

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

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

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

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

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

  __lowerCAmelCase    							=						{
      "class_size": len(snake_case__				),
      "embed_size": discriminator.embed_size,
      "pretrained_model": pretrained_model,
      "class_vocab": classaidx,
      "default_class": 0,
  }

 __lowerCAmelCase    							=						time.time()
 print("""Preprocessed {} data points""".format(len(snake_case__				) + len(snake_case__				)				)				)
 print("""Data preprocessing took: {:.3f}s""".format(end - start				)				)

 if cached:
  print("""Building representation cache..."""				)

  __lowerCAmelCase    							=						time.time()

  __lowerCAmelCase    							=						get_cached_data_loader(snake_case__						,		snake_case__						,		snake_case__						,		shuffle=snake_case__						,		device=snake_case__				)

  __lowerCAmelCase    							=						get_cached_data_loader(snake_case__						,		snake_case__						,		snake_case__						,		device=snake_case__				)

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

 else:
  __lowerCAmelCase    							=						torch.utils.data.DataLoader(
      dataset=snake_case__						,		batch_size=snake_case__						,		shuffle=snake_case__						,		collate_fn=snake_case__				)
  __lowerCAmelCase    							=						torch.utils.data.DataLoader(dataset=snake_case__						,		batch_size=snake_case__						,		collate_fn=snake_case__				)

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

 __lowerCAmelCase    							=						optim.Adam(discriminator.parameters()						,		lr=0.0_0_0_1				)

 for epoch in range(snake_case__				):
  __lowerCAmelCase    							=						time.time()
  print("""\nEpoch"""						,		epoch + 1				)

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

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

  print("""\nExample prediction"""				)
  predict(snake_case__						,		snake_case__						,		snake_case__						,		cached=snake_case__						,		device=snake_case__				)

  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(snake_case__						,		epoch + 1				)						,		)


if __name__ == "__main__":
 _A      :				Dict 				=   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      :				Dict 				=   parser.parse_args()

 train_discriminator(**(vars(args)))







'''simple docstring'''

def 					UpperCamelCase_						( snake_case_	:	int				) ->					int:






 '''simple docstring'''

 if number != int(snake_case_				):
  raise ValueError("""factorial() only accepts integral values"""				)
 if number < 0:
  raise ValueError("""factorial() not defined for negative values"""				)
 __lowerCAmelCase    							=						1
 for i in range(1						,		number + 1				):
  value *= i
 return value







def 					UpperCamelCase_						( snake_case_	:	int				) ->					int:






 '''simple docstring'''

 if not isinstance(snake_case_						,		snake_case_				):
  raise ValueError("""factorial() only accepts integral values"""				)
 if n < 0:
  raise ValueError("""factorial() not defined for negative values"""				)
 return 1 if n == 0 or n == 1 else n * factorial(n - 1				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _A      :				List[Any] 				=    int(input('''Enter a positive integer: ''').strip() or 0)
 print(f'factorial{n} is {factorial(n)}')






