
'''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)
__UpperCAmelCase												=   1e-1_0
__UpperCAmelCase												=   'This is incredible! I love it, this is the best chicken I have ever had.'
__UpperCAmelCase												=   100


class 		_a     (							nn.Module  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE="gpt2-medium"    ,__SCREAMING_SNAKE_CASE=False    ,__SCREAMING_SNAKE_CASE="cpu"      ):
    super().__init__()
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		GPTaTokenizer.from_pretrained(A_      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		GPTaLMHeadModel.from_pretrained(A_      )
    SCREAMING_SNAKE_CASE  :  Dict								=		self.encoder.transformer.config.hidden_size
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		ClassificationHead(class_size=A_    ,embed_size=self.embed_size      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		cached_mode
    SCREAMING_SNAKE_CASE  :  int								=		device






   def 					__a			(       self      ):
    return self.classifier_head






   def 					__a			(       self      ):
    for param in self.encoder.parameters():
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		False
    self.classifier_head.train()






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






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    if self.cached_mode:
     SCREAMING_SNAKE_CASE  :  Any								=		x.to(self.device      )
    else:
     SCREAMING_SNAKE_CASE  :  List[Any]								=		self.avg_representation(x.to(self.device      )      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		self.classifier_head(A_      )
    SCREAMING_SNAKE_CASE  :  str								=		nn.functional.log_softmax(A_    ,dim=-1      )

    return probs



class 		_a     (							data.Dataset  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		X
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		y






   def __len__(       self      ):
    return len(self.X      )






   def __getitem__(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		{}
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.X[index]
    SCREAMING_SNAKE_CASE  :  int								=		self.y[index]
    return data



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Any							)       ->	str:

 def pad_sequences(snake_case_				:	Tuple							):
  SCREAMING_SNAKE_CASE  :  Any								=		[len(__UpperCAmelCase							) for seq in sequences]

  SCREAMING_SNAKE_CASE  :  str								=		torch.zeros(len(__UpperCAmelCase							)   ,     max(__UpperCAmelCase							)							).long()  # padding value = 0

  for i, seq in enumerate(__UpperCAmelCase							):
   SCREAMING_SNAKE_CASE  :  str								=		lengths[i]
   SCREAMING_SNAKE_CASE  :  Any								=		seq[:end]

  return padded_sequences, lengths

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

 SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  str								=		pad_sequences(item_info['X']							)
 SCREAMING_SNAKE_CASE  :  Tuple								=		torch.tensor(item_info['y']   ,     dtype=torch.long							)

 return x_batch, y_batch



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[str]							)       ->	Dict:
 SCREAMING_SNAKE_CASE  :  str								=		{}
 for key in data[0].keys():
  SCREAMING_SNAKE_CASE  :  Tuple								=		[d[key] for d in data]

 SCREAMING_SNAKE_CASE  :  Any								=		torch.cat(item_info['X']   ,     0							)
 SCREAMING_SNAKE_CASE  :  Tuple								=		torch.tensor(item_info['y']   ,     dtype=torch.long							)

 return x_batch, y_batch



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Any   ,     snake_case_				:	Optional[Any]   ,     snake_case_				:	Optional[Any]   ,     snake_case_				:	Tuple=0   ,     snake_case_				:	int=10   ,     snake_case_				:	Tuple="cpu"							)       ->	str:
 SCREAMING_SNAKE_CASE  :  Dict								=		0
 discriminator.train_custom()
 for batch_idx, (input_t, target_t) in enumerate(__UpperCAmelCase							):
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Tuple								=		input_t.to(__UpperCAmelCase							), target_t.to(__UpperCAmelCase							)

  optimizer.zero_grad()

  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		discriminator(__UpperCAmelCase							)
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		nn.functional.nll_loss(__UpperCAmelCase   ,     __UpperCAmelCase							)
  loss.backward(retain_graph=__UpperCAmelCase							)
  optimizer.step()

  samples_so_far += len(__UpperCAmelCase							)

  if batch_idx % log_interval == 0:
   print(
       'Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
           epoch + 1   ,     __UpperCAmelCase   ,     len(data_loader.dataset							)   ,     100 * samples_so_far / len(data_loader.dataset							)   ,     loss.item()   ,     )							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[Any]   ,     snake_case_				:	Any   ,     snake_case_				:	Union[str, Any]="cpu"							)       ->	List[Any]:
 discriminator.eval()
 SCREAMING_SNAKE_CASE  :  Dict								=		0
 SCREAMING_SNAKE_CASE  :  int								=		0
 with torch.no_grad():
  for input_t, target_t in data_loader:
   SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  int								=		input_t.to(__UpperCAmelCase							), target_t.to(__UpperCAmelCase							)
   SCREAMING_SNAKE_CASE  :  Optional[int]								=		discriminator(__UpperCAmelCase							)
   # sum up batch loss
   test_loss += nn.functional.nll_loss(__UpperCAmelCase   ,     __UpperCAmelCase   ,     reduction='sum'							).item()
   # get the index of the max log-probability
   SCREAMING_SNAKE_CASE  :  Optional[int]								=		output_t.argmax(dim=1   ,     keepdim=__UpperCAmelCase							)
   correct += pred_t.eq(target_t.view_as(__UpperCAmelCase							)							).sum().item()

 test_loss /= len(data_loader.dataset							)

 print(
     'Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
         __UpperCAmelCase   ,     __UpperCAmelCase   ,     len(data_loader.dataset							)   ,     100.0 * correct / len(data_loader.dataset							)							)							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Any   ,     snake_case_				:	Optional[int]   ,     snake_case_				:	Union[str, Any]   ,     snake_case_				:	Optional[Any]=False   ,     snake_case_				:	str="cpu"							)       ->	str:
 SCREAMING_SNAKE_CASE  :  List[str]								=		model.tokenizer.encode(__UpperCAmelCase							)
 SCREAMING_SNAKE_CASE  :  Tuple								=		torch.tensor([input_t]   ,     dtype=torch.long   ,     device=__UpperCAmelCase							)
 if cached:
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model.avg_representation(__UpperCAmelCase							)

 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model(__UpperCAmelCase							).data.cpu().numpy().flatten().tolist()
 print('Input sentence:'   ,     __UpperCAmelCase							)
 print(
     'Predictions:'   ,     ', '.join('{}: {:.4f}'.format(__UpperCAmelCase   ,     math.exp(__UpperCAmelCase							)							) for c, log_prob in zip(__UpperCAmelCase   ,     __UpperCAmelCase							)							)   ,     )



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str   ,     snake_case_				:	Any   ,     snake_case_				:	Optional[Any]=False   ,     snake_case_				:	Any="cpu"							)       ->	Optional[Any]:
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.utils.data.DataLoader(dataset=__UpperCAmelCase   ,     batch_size=__UpperCAmelCase   ,     collate_fn=__UpperCAmelCase							)

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

 SCREAMING_SNAKE_CASE  :  str								=		torch.utils.data.DataLoader(
     dataset=Dataset(__UpperCAmelCase   ,     __UpperCAmelCase							)   ,     batch_size=__UpperCAmelCase   ,     shuffle=__UpperCAmelCase   ,     collate_fn=__UpperCAmelCase							)

 return data_loader



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[str]   ,     snake_case_				:	str=None   ,     snake_case_				:	List[str]="gpt2-medium"   ,     snake_case_				:	int=10   ,     snake_case_				:	str=64   ,     snake_case_				:	Optional[int]=10   ,     snake_case_				:	Tuple=False   ,     snake_case_				:	Dict=False   ,     snake_case_				:	Optional[int]=False   ,     )       ->	str:
 SCREAMING_SNAKE_CASE  :  Dict								=		'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

 print('Preprocessing {} dataset...'.format(__UpperCAmelCase							)							)
 SCREAMING_SNAKE_CASE  :  Dict								=		time.time()

 if dataset == "SST":
  SCREAMING_SNAKE_CASE  :  Any								=		['positive', 'negative', 'very positive', 'very negative', 'neutral']
  SCREAMING_SNAKE_CASE  :  Dict								=		{c: i for i, c in enumerate(__UpperCAmelCase							)}

  SCREAMING_SNAKE_CASE  :  Any								=		Discriminator(
      class_size=len(__UpperCAmelCase							)   ,     pretrained_model=__UpperCAmelCase   ,     cached_mode=__UpperCAmelCase   ,     device=__UpperCAmelCase							).to(__UpperCAmelCase							)

  SCREAMING_SNAKE_CASE  :  int								=		torchtext_data.Field()
  SCREAMING_SNAKE_CASE  :  Any								=		torchtext_data.Field(sequential=__UpperCAmelCase							)
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Tuple								=		datasets.SST.splits(
      __UpperCAmelCase   ,     __UpperCAmelCase   ,     fine_grained=__UpperCAmelCase   ,     train_subtrees=__UpperCAmelCase   ,     )

  SCREAMING_SNAKE_CASE  :  Dict								=		[]
  SCREAMING_SNAKE_CASE  :  int								=		[]
  for i in trange(len(__UpperCAmelCase							)   ,     ascii=__UpperCAmelCase							):
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		TreebankWordDetokenizer().detokenize(vars(train_data[i]							)['text']							)
   SCREAMING_SNAKE_CASE  :  Optional[int]								=		discriminator.tokenizer.encode(__UpperCAmelCase							)
   SCREAMING_SNAKE_CASE  :  Optional[int]								=		torch.tensor([5_0256] + seq   ,     device=__UpperCAmelCase   ,     dtype=torch.long							)
   x.append(__UpperCAmelCase							)
   y.append(classaidx[vars(train_data[i]							)['label']]							)
  SCREAMING_SNAKE_CASE  :  str								=		Dataset(__UpperCAmelCase   ,     __UpperCAmelCase							)

  SCREAMING_SNAKE_CASE  :  List[str]								=		[]
  SCREAMING_SNAKE_CASE  :  int								=		[]
  for i in trange(len(__UpperCAmelCase							)   ,     ascii=__UpperCAmelCase							):
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		TreebankWordDetokenizer().detokenize(vars(test_data[i]							)['text']							)
   SCREAMING_SNAKE_CASE  :  str								=		discriminator.tokenizer.encode(__UpperCAmelCase							)
   SCREAMING_SNAKE_CASE  :  Optional[int]								=		torch.tensor([5_0256] + seq   ,     device=__UpperCAmelCase   ,     dtype=torch.long							)
   test_x.append(__UpperCAmelCase							)
   test_y.append(classaidx[vars(test_data[i]							)['label']]							)
  SCREAMING_SNAKE_CASE  :  List[str]								=		Dataset(__UpperCAmelCase   ,     __UpperCAmelCase							)

  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{
      'class_size': len(__UpperCAmelCase							),
      'embed_size': discriminator.embed_size,
      'pretrained_model': pretrained_model,
      'class_vocab': classaidx,
      'default_class': 2,
  }

 elif dataset == "clickbait":
  SCREAMING_SNAKE_CASE  :  List[Any]								=		['non_clickbait', 'clickbait']
  SCREAMING_SNAKE_CASE  :  Dict								=		{c: i for i, c in enumerate(__UpperCAmelCase							)}

  SCREAMING_SNAKE_CASE  :  List[str]								=		Discriminator(
      class_size=len(__UpperCAmelCase							)   ,     pretrained_model=__UpperCAmelCase   ,     cached_mode=__UpperCAmelCase   ,     device=__UpperCAmelCase							).to(__UpperCAmelCase							)

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

     if len(__UpperCAmelCase							) < max_length_seq:
      SCREAMING_SNAKE_CASE  :  str								=		torch.tensor([5_0256] + seq   ,     device=__UpperCAmelCase   ,     dtype=torch.long							)
     else:
      print('Line {} is longer than maximum length {}'.format(__UpperCAmelCase   ,     __UpperCAmelCase							)							)
      continue
     x.append(__UpperCAmelCase							)
     y.append(d['label']							)
    except Exception:
     print('Error evaluating / tokenizing line {}, skipping it'.format(__UpperCAmelCase							)							)
     pass

  SCREAMING_SNAKE_CASE  :  Tuple								=		Dataset(__UpperCAmelCase   ,     __UpperCAmelCase							)
  SCREAMING_SNAKE_CASE  :  Any								=		int(0.9 * len(__UpperCAmelCase							)							)
  SCREAMING_SNAKE_CASE  :  Any								=		len(__UpperCAmelCase							) - train_size
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.utils.data.random_split(__UpperCAmelCase   ,     [train_size, test_size]							)

  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{
      'class_size': len(__UpperCAmelCase							),
      'embed_size': discriminator.embed_size,
      'pretrained_model': pretrained_model,
      'class_vocab': classaidx,
      'default_class': 1,
  }

 elif dataset == "toxic":
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		['non_toxic', 'toxic']
  SCREAMING_SNAKE_CASE  :  str								=		{c: i for i, c in enumerate(__UpperCAmelCase							)}

  SCREAMING_SNAKE_CASE  :  Any								=		Discriminator(
      class_size=len(__UpperCAmelCase							)   ,     pretrained_model=__UpperCAmelCase   ,     cached_mode=__UpperCAmelCase   ,     device=__UpperCAmelCase							).to(__UpperCAmelCase							)

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

     if len(__UpperCAmelCase							) < max_length_seq:
      SCREAMING_SNAKE_CASE  :  Optional[int]								=		torch.tensor([5_0256] + seq   ,     device=__UpperCAmelCase   ,     dtype=torch.long							)
     else:
      print('Line {} is longer than maximum length {}'.format(__UpperCAmelCase   ,     __UpperCAmelCase							)							)
      continue
     x.append(__UpperCAmelCase							)
     y.append(int(np.sum(d['label']							) > 0							)							)
    except Exception:
     print('Error evaluating / tokenizing line {}, skipping it'.format(__UpperCAmelCase							)							)
     pass

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

  SCREAMING_SNAKE_CASE  :  Tuple								=		{
      'class_size': len(__UpperCAmelCase							),
      '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.'							)

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

  SCREAMING_SNAKE_CASE  :  Dict								=		sorted(__UpperCAmelCase							)
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		{c: i for i, c in enumerate(__UpperCAmelCase							)}

  SCREAMING_SNAKE_CASE  :  Any								=		Discriminator(
      class_size=len(__UpperCAmelCase							)   ,     pretrained_model=__UpperCAmelCase   ,     cached_mode=__UpperCAmelCase   ,     device=__UpperCAmelCase							).to(__UpperCAmelCase							)

  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[]
  SCREAMING_SNAKE_CASE  :  str								=		[]
  with open(__UpperCAmelCase							) as f:
   SCREAMING_SNAKE_CASE  :  List[str]								=		csv.reader(__UpperCAmelCase   ,     delimiter='\t'							)
   for i, row in enumerate(tqdm(__UpperCAmelCase   ,     ascii=__UpperCAmelCase							)							):
    if row:
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		row[0]
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		row[1]

     try:
      SCREAMING_SNAKE_CASE  :  Tuple								=		discriminator.tokenizer.encode(__UpperCAmelCase							)
      if len(__UpperCAmelCase							) < max_length_seq:
       SCREAMING_SNAKE_CASE  :  Any								=		torch.tensor([5_0256] + seq   ,     device=__UpperCAmelCase   ,     dtype=torch.long							)

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

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

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

  SCREAMING_SNAKE_CASE  :  Tuple								=		Dataset(__UpperCAmelCase   ,     __UpperCAmelCase							)
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		int(0.9 * len(__UpperCAmelCase							)							)
  SCREAMING_SNAKE_CASE  :  str								=		len(__UpperCAmelCase							) - train_size
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Tuple								=		torch.utils.data.random_split(__UpperCAmelCase   ,     [train_size, test_size]							)

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

 SCREAMING_SNAKE_CASE  :  Any								=		time.time()
 print('Preprocessed {} data points'.format(len(__UpperCAmelCase							) + len(__UpperCAmelCase							)							)							)
 print('Data preprocessing took: {:.3f}s'.format(end - start							)							)

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

  SCREAMING_SNAKE_CASE  :  Dict								=		time.time()

  SCREAMING_SNAKE_CASE  :  Any								=		get_cached_data_loader(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     shuffle=__UpperCAmelCase   ,     device=__UpperCAmelCase							)

  SCREAMING_SNAKE_CASE  :  Dict								=		get_cached_data_loader(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     device=__UpperCAmelCase							)

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

 else:
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.utils.data.DataLoader(
      dataset=__UpperCAmelCase   ,     batch_size=__UpperCAmelCase   ,     shuffle=__UpperCAmelCase   ,     collate_fn=__UpperCAmelCase							)
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.utils.data.DataLoader(dataset=__UpperCAmelCase   ,     batch_size=__UpperCAmelCase   ,     collate_fn=__UpperCAmelCase							)

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

 SCREAMING_SNAKE_CASE  :  str								=		optim.Adam(discriminator.parameters()   ,     lr=0.0001							)

 for epoch in range(__UpperCAmelCase							):
  SCREAMING_SNAKE_CASE  :  str								=		time.time()
  print('\nEpoch'   ,     epoch + 1							)

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

  SCREAMING_SNAKE_CASE  :  str								=		time.time()
  print('Epoch took: {:.3f}s'.format(end - start							)							)

  print('\nExample prediction'							)
  predict(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     cached=__UpperCAmelCase   ,     device=__UpperCAmelCase							)

  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(__UpperCAmelCase   ,     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=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')
   __UpperCAmelCase												=   parser.parse_args()

   train_discriminator(**(vars(args)))



import torch

from accelerate import Accelerator, DistributedType



class 			UpperCamelCase					:

     '''simple docstring'''

     def __enter__(       self    )			->					int:






           """simple docstring"""
           if self.enabled:
                 _lowerCamelCase           =					self.model.no_sync()
                 self.model_sync_obj.__enter__()

           return self



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






           """simple docstring"""
           if self.enabled:
                 # Average all models on exit
                 self._sync_and_avg_model_params()
                 self.model_sync_obj.__exit__(A_					,					A_					,					A_    )



     def __init__(       self					,					A_					,					A_					,					A_					,					A_ = True    )			->					List[str]:






           """simple docstring"""
           if accelerator.distributed_type not in [
               DistributedType.NO,
               DistributedType.MULTI_CPU,
               DistributedType.MULTI_GPU,
           ]:
                 raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''    )
           _lowerCamelCase           =					enabled and accelerator.distributed_type != DistributedType.NO
           _lowerCamelCase           =					0
           if self.enabled:
                 _lowerCamelCase           =					accelerator
                 _lowerCamelCase           =					model
                 _lowerCamelCase           =					local_sgd_steps



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






           """simple docstring"""
           self.num_steps += 1
           if not self.enabled:
                 return

           if self.num_steps % self.local_sgd_steps == 0:
                 self._sync_and_avg_model_params()





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






           """simple docstring"""
           self.accelerator.wait_for_everyone()
           with self.accelerator.autocast():
                 for param in self.model.parameters():
                       _lowerCamelCase           =					self.accelerator.reduce(param.data					,					reduction='''mean'''    )