






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




class 							lowerCAmelCase					(							nn.Module   ):







   """simple docstring"""







   def __init__( self   ,   _A   ,   _A="gpt2-medium"   ,   _A=False   ,   _A="cpu"			)    ->   Any:
    super().__init__()
    __a    :	Optional[int]									=						GPTaTokenizer.from_pretrained(_A			)
    __a    :	Optional[int]									=						GPTaLMHeadModel.from_pretrained(_A			)
    __a    :	List[str]									=						self.encoder.transformer.config.hidden_size
    __a    :	Tuple									=						ClassificationHead(class_size=_A   ,   embed_size=self.embed_size			)
    __a    :	int									=						cached_mode
    __a    :	Optional[Any]									=						device



   def    __magic_name__    ( self			)    ->   List[str]:
    return self.classifier_head



   def    __magic_name__    ( self			)    ->   List[Any]:
    for param in self.encoder.parameters():
     __a    :	Tuple									=						False
    self.classifier_head.train()



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



   def    __magic_name__    ( self   ,   _A			)    ->   int:
    if self.cached_mode:
     __a    :	Dict									=						x.to(self.device			)
    else:
     __a    :	Optional[int]									=						self.avg_representation(x.to(self.device			)			)

    __a    :	Optional[int]									=						self.classifier_head(_A			)
    __a    :	List[Any]									=						nn.functional.log_softmax(_A   ,   dim=-1			)

    return probs




class 							lowerCAmelCase					(							data.Dataset   ):







   """simple docstring"""







   def __init__( self   ,   _A   ,   _A			)    ->   str:
    __a    :	List[str]									=						X
    __a    :	str									=						y



   def __len__( self			)    ->   Union[str, Any]:
    return len(self.X			)



   def __getitem__( self   ,   _A			)    ->   Dict:
    __a    :	Any									=						{}
    __a    :	List[str]									=						self.X[index]
    __a    :	Tuple									=						self.y[index]
    return data






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 def pad_sequences(SCREAMING_SNAKE_CASE__		):
  __a    :	Tuple									=						[len(SCREAMING_SNAKE_CASE__		) for seq in sequences]

  __a    :	Optional[Any]									=						torch.zeros(len(SCREAMING_SNAKE_CASE__		)       ,		max(SCREAMING_SNAKE_CASE__		)		).long()  # padding value = 0

  for i, seq in enumerate(SCREAMING_SNAKE_CASE__		):
   __a    :	str									=						lengths[i]
   __a    :	str									=						seq[:end]

  return padded_sequences, lengths

 __a    :	List[str]									=						{}
 for key in data[0].keys():
  __a    :	Optional[int]									=						[d[key] for d in data]

 __a			,    __a    :	Dict									=						pad_sequences(item_info['X']		)
 __a    :	List[Any]									=						torch.tensor(item_info['y']       ,		dtype=torch.long		)

 return x_batch, y_batch






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 __a    :	Optional[Any]									=						{}
 for key in data[0].keys():
  __a    :	Any									=						[d[key] for d in data]

 __a    :	List[Any]									=						torch.cat(item_info['X']       ,		0		)
 __a    :	str									=						torch.tensor(item_info['y']       ,		dtype=torch.long		)

 return x_batch, y_batch






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__=0       ,		SCREAMING_SNAKE_CASE__=10       ,		SCREAMING_SNAKE_CASE__="cpu"		):
 __a    :	Any									=						0
 discriminator.train_custom()
 for batch_idx, (input_t, target_t) in enumerate(SCREAMING_SNAKE_CASE__		):
  __a			,    __a    :	Any									=						input_t.to(SCREAMING_SNAKE_CASE__		), target_t.to(SCREAMING_SNAKE_CASE__		)

  optimizer.zero_grad()

  __a    :	Tuple									=						discriminator(SCREAMING_SNAKE_CASE__		)
  __a    :	Any									=						nn.functional.nll_loss(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
  loss.backward(retain_graph=SCREAMING_SNAKE_CASE__		)
  optimizer.step()

  samples_so_far += len(SCREAMING_SNAKE_CASE__		)

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






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

 test_loss /= len(data_loader.dataset		)

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






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__=False       ,		SCREAMING_SNAKE_CASE__="cpu"		):
 __a    :	List[str]									=						model.tokenizer.encode(SCREAMING_SNAKE_CASE__		)
 __a    :	Optional[Any]									=						torch.tensor([input_t]       ,		dtype=torch.long       ,		device=SCREAMING_SNAKE_CASE__		)
 if cached:
  __a    :	Dict									=						model.avg_representation(SCREAMING_SNAKE_CASE__		)

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






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__=False       ,		SCREAMING_SNAKE_CASE__="cpu"		):
 __a    :	Optional[Any]									=						torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE__       ,		batch_size=SCREAMING_SNAKE_CASE__       ,		collate_fn=SCREAMING_SNAKE_CASE__		)

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

 __a    :	List[Any]									=						torch.utils.data.DataLoader(
     dataset=Dataset(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)       ,		batch_size=SCREAMING_SNAKE_CASE__       ,		shuffle=SCREAMING_SNAKE_CASE__       ,		collate_fn=SCREAMING_SNAKE_CASE__		)

 return data_loader






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__=None       ,		SCREAMING_SNAKE_CASE__="gpt2-medium"       ,		SCREAMING_SNAKE_CASE__=10       ,		SCREAMING_SNAKE_CASE__=64       ,		SCREAMING_SNAKE_CASE__=10       ,		SCREAMING_SNAKE_CASE__=False       ,		SCREAMING_SNAKE_CASE__=False       ,		SCREAMING_SNAKE_CASE__=False       ,		):
 __a    :	Any									=						'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

 print('Preprocessing {} dataset...'.format(SCREAMING_SNAKE_CASE__		)		)
 __a    :	str									=						time.time()

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

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

  __a    :	Any									=						torchtext_data.Field()
  __a    :	Union[str, Any]									=						torchtext_data.Field(sequential=SCREAMING_SNAKE_CASE__		)
  __a			,    __a			,    __a    :	Dict									=						datasets.SST.splits(
      SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		fine_grained=SCREAMING_SNAKE_CASE__       ,		train_subtrees=SCREAMING_SNAKE_CASE__       ,		)

  __a    :	Any									=						[]
  __a    :	Any									=						[]
  for i in trange(len(SCREAMING_SNAKE_CASE__		)       ,		ascii=SCREAMING_SNAKE_CASE__		):
   __a    :	Tuple									=						TreebankWordDetokenizer().detokenize(vars(train_data[i]		)['text']		)
   __a    :	Tuple									=						discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE__		)
   __a    :	Tuple									=						torch.tensor([50256] + seq       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=torch.long		)
   x.append(SCREAMING_SNAKE_CASE__		)
   y.append(classaidx[vars(train_data[i]		)['label']]		)
  __a    :	Any									=						Dataset(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)

  __a    :	Any									=						[]
  __a    :	int									=						[]
  for i in trange(len(SCREAMING_SNAKE_CASE__		)       ,		ascii=SCREAMING_SNAKE_CASE__		):
   __a    :	Any									=						TreebankWordDetokenizer().detokenize(vars(test_data[i]		)['text']		)
   __a    :	Tuple									=						discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE__		)
   __a    :	List[Any]									=						torch.tensor([50256] + seq       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=torch.long		)
   test_x.append(SCREAMING_SNAKE_CASE__		)
   test_y.append(classaidx[vars(test_data[i]		)['label']]		)
  __a    :	List[str]									=						Dataset(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)

  __a    :	List[str]									=						{
      'class_size': len(SCREAMING_SNAKE_CASE__		),
      'embed_size': discriminator.embed_size,
      'pretrained_model': pretrained_model,
      'class_vocab': classaidx,
      'default_class': 2,
  }

 elif dataset == "clickbait":
  __a    :	Tuple									=						['non_clickbait', 'clickbait']
  __a    :	str									=						{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE__		)}

  __a    :	List[Any]									=						Discriminator(
      class_size=len(SCREAMING_SNAKE_CASE__		)       ,		pretrained_model=SCREAMING_SNAKE_CASE__       ,		cached_mode=SCREAMING_SNAKE_CASE__       ,		device=SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)

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

     if len(SCREAMING_SNAKE_CASE__		) < max_length_seq:
      __a    :	Union[str, Any]									=						torch.tensor([50256] + seq       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=torch.long		)
     else:
      print('Line {} is longer than maximum length {}'.format(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)		)
      continue
     x.append(SCREAMING_SNAKE_CASE__		)
     y.append(d['label']		)
    except Exception:
     print('Error evaluating / tokenizing line {}, skipping it'.format(SCREAMING_SNAKE_CASE__		)		)
     pass

  __a    :	Any									=						Dataset(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
  __a    :	Dict									=						int(0.9 * len(SCREAMING_SNAKE_CASE__		)		)
  __a    :	Optional[int]									=						len(SCREAMING_SNAKE_CASE__		) - train_size
  __a			,    __a    :	Any									=						torch.utils.data.random_split(SCREAMING_SNAKE_CASE__       ,		[train_size, test_size]		)

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

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

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

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

     if len(SCREAMING_SNAKE_CASE__		) < max_length_seq:
      __a    :	str									=						torch.tensor([50256] + seq       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=torch.long		)
     else:
      print('Line {} is longer than maximum length {}'.format(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)		)
      continue
     x.append(SCREAMING_SNAKE_CASE__		)
     y.append(int(np.sum(d['label']		) > 0		)		)
    except Exception:
     print('Error evaluating / tokenizing line {}, skipping it'.format(SCREAMING_SNAKE_CASE__		)		)
     pass

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

  __a    :	int									=						{
      'class_size': len(SCREAMING_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.'		)

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

  __a    :	List[str]									=						sorted(SCREAMING_SNAKE_CASE__		)
  __a    :	int									=						{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE__		)}

  __a    :	Union[str, Any]									=						Discriminator(
      class_size=len(SCREAMING_SNAKE_CASE__		)       ,		pretrained_model=SCREAMING_SNAKE_CASE__       ,		cached_mode=SCREAMING_SNAKE_CASE__       ,		device=SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)

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

     try:
      __a    :	int									=						discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE__		)
      if len(SCREAMING_SNAKE_CASE__		) < max_length_seq:
       __a    :	Any									=						torch.tensor([50256] + seq       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=torch.long		)

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

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

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

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

  __a    :	Union[str, Any]									=						{
      'class_size': len(SCREAMING_SNAKE_CASE__		),
      'embed_size': discriminator.embed_size,
      'pretrained_model': pretrained_model,
      'class_vocab': classaidx,
      'default_class': 0,
  }

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

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

  __a    :	Union[str, Any]									=						time.time()

  __a    :	Dict									=						get_cached_data_loader(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		shuffle=SCREAMING_SNAKE_CASE__       ,		device=SCREAMING_SNAKE_CASE__		)

  __a    :	List[str]									=						get_cached_data_loader(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		device=SCREAMING_SNAKE_CASE__		)

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

 else:
  __a    :	Tuple									=						torch.utils.data.DataLoader(
      dataset=SCREAMING_SNAKE_CASE__       ,		batch_size=SCREAMING_SNAKE_CASE__       ,		shuffle=SCREAMING_SNAKE_CASE__       ,		collate_fn=SCREAMING_SNAKE_CASE__		)
  __a    :	List[Any]									=						torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE__       ,		batch_size=SCREAMING_SNAKE_CASE__       ,		collate_fn=SCREAMING_SNAKE_CASE__		)

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

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

 for epoch in range(SCREAMING_SNAKE_CASE__		):
  __a    :	Tuple									=						time.time()
  print('\nEpoch'       ,		epoch + 1		)

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

  __a    :	List[Any]									=						time.time()
  print('Epoch took: {:.3f}s'.format(end - start		)		)

  print('\nExample prediction'		)
  predict(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		cached=SCREAMING_SNAKE_CASE__       ,		device=SCREAMING_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(SCREAMING_SNAKE_CASE__       ,		epoch + 1		)       ,		)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_				      =							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")
  SCREAMING_SNAKE_CASE_				      =							parser.parse_args()

  train_discriminator(**(vars(args)))












'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Any:
    torch.manual_seed(0			)
    __a    :	Optional[Any]									=						UNetaDModel(
        block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,   )
    return model



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Union[str, Any]									=						'cpu'
    __a    :	Tuple									=						self.dummy_uncond_unet
    __a    :	str									=						DDPMScheduler()

    __a    :	int									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	str									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	Optional[int]									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'   ,   return_dict=_A			)[0]

    __a    :	List[str]									=						image[0, -3:, -3:, -1]
    __a    :	List[str]									=						image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	List[Any]									=						np.array(
        [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]			)

    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Optional[Any]									=						self.dummy_uncond_unet
    __a    :	List[str]									=						DDPMScheduler(prediction_type='sample'			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	Optional[Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			)[0]

    __a    :	Dict									=						image[0, -3:, -3:, -1]
    __a    :	int									=						image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	int									=						1E-2 if torch_device != 'mps' else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   List[str]:
    __a    :	Union[str, Any]									=						'google/ddpm-cifar10-32'

    __a    :	str									=						UNetaDModel.from_pretrained(_A			)
    __a    :	List[str]									=						DDPMScheduler.from_pretrained(_A			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	int									=						torch.manual_seed(0			)
    __a    :	Optional[Any]									=						ddpm(generator=_A   ,   output_type='numpy'			).images

    __a    :	int									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	Optional[Any]									=						np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2




