code: 
  
  
  import copy
  import logging
  import random
  
  import joblib
  import numpy as np
  import torch
  import torch.nn as nn
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  
  from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup
  
  
  lowerCAmelCase					 =	logging.getLogger(__name__)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			Tuple:
  
    '''simple docstring'''
  
  
  
  
  
  
  
    random.seed(lowercase__			)
    np.random.seed(lowercase__			)
    torch.manual_seed(lowercase__			)
    torch.cuda.manual_seed_all(lowercase__			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__			)		->			List[str]:
  
    '''simple docstring'''
  
  
  
  
  
  
  
    model.eval()
    __lowercase=    next(model.parameters()			).device
    __lowercase=    1
    __lowercase=    torch.zeros((eval_batch_size, context_len)     ,				dtype=torch.long     ,				device=lowercase__			)
    __lowercase=    DataLoader(lowercase__     ,				shuffle=lowercase__     ,				batch_size=lowercase__			)
    __lowercase=    torch.zeros(1     ,				device=lowercase__			)
    __lowercase=    0
    for batch in eval_dataloader:
      batch.to(lowercase__			)
      # pad
      context.zero_()
      for i in range(lowercase__			):
        __lowercase=    batch[i]
      __lowercase=    model(lowercase__     ,				labels=lowercase__			)
      eval_loss += outputs[0].sum().item()
      nb_eval_examples += batch.size(0			)
    __lowercase=    eval_loss / nb_eval_examples
    __lowercase=    torch.exp(lowercase__			)
    model.train()
    return perplexity
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__="gpt2"			)		->			str:
  
    '''simple docstring'''
  
  
  
  
  
  
  
    __lowercase=    GPTaLMHeadModel.from_pretrained(lowercase__     ,				output_hidden_states=lowercase__			)
    torch.save(model.state_dict()     ,				model_name + 'local.pt'			)
    return model
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__			)		->			List[str]:
  
    '''simple docstring'''
  
  
  
  
  
  
  
    __lowercase=    copy.deepcopy(lowercase__			)
    model.to(lowercase__			)
  
    __lowercase=    ['bias', 'LayerNorm.weight']
    __lowercase=    [
        {
            'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)],
            'weight_decay': 0.0,
        },
        {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay			)], 'weight_decay': 0.0},
    ]
    __lowercase=    AdamW(lowercase__     ,				lr=5E-5     ,				eps=1E-8			)
    __lowercase=    get_linear_schedule_with_warmup(lowercase__     ,				0     ,				lowercase__			)
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)		->			Optional[Any]:
  
    '''simple docstring'''
  
  
  
  
  
  
  
    __lowercase=    np.array(lowercase__			).mean()
    __lowercase=    np.array(lowercase__			).std()
    __lowercase=    (real_perps - avg) / std
    __lowercase=    list(zip(lowercase__     ,				lowercase__     ,				lowercase__			)			)
    joblib.dump(lowercase__     ,				lowercase__			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__="dev.jbl"     ,				lowercase__=recopy_gpta     ,				)		->			int:
  
    '''simple docstring'''
  
  
  
  
  
  
  
    __lowercase=    []
    __lowercase=    []
    __lowercase=    []
  
    # Initialize the transformer model
    __lowercase=    copy.deepcopy(lowercase__			)
    orig_model.to(device='cpu'			)
    torch.cuda.empty_cache()
  
    # Compute perplexity of initial transformer model for comparison
    model.train()
    __lowercase,							__lowercase,							__lowercase=    recopy_model(lowercase__     ,				lowercase__     ,				lowercase__			)
  
    for step in tqdm(range(lowercase__			)			):
      __lowercase=    torch.zeros((1, context_len)     ,				dtype=torch.long     ,				device=lowercase__			)
      __lowercase=    random.choice(lowercase__			)
      __lowercase=    random.randint(0     ,				len(story[0]			) - context_len - 1			)
      __lowercase=    story[0][start : start + context_len]
      lm_optimizer.zero_grad()
      __lowercase=    model(lowercase__     ,				labels=lowercase__			)
      __lowercase=    outputs[0]
      __lowercase=    compute_perplexity(lowercase__     ,				lowercase__     ,				lowercase__			)
      model.train()
      lm_loss.backward()
      # Do LM backprop
      torch.nn.utils.clip_grad_norm_(model.parameters()     ,				3.0			)
      lm_optimizer.step()
      lm_scheduler.step()  # Update learning rate schedule
  
      # Compute perplexity after back-propagating on the selected context
      __lowercase=    compute_perplexity(lowercase__     ,				lowercase__     ,				lowercase__			)
  
      # Periodically save the stored (X, IG(X)) pairs
      if step % 1_0_0_0 == 0 and step > 1:
        intermittent_save(lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)
  
      # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
      __lowercase,							__lowercase,							__lowercase=    recopy_model(lowercase__     ,				lowercase__     ,				lowercase__			)
  
      past_perps.append(past_perp.item()			)
      real_perps.append(orig_perp - real_perp.item()			)
      contexts.append(np.array(context.cpu()			)			)
  
    intermittent_save(lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__="data/tokenized_stories_train_wikitext103.jbl"     ,				lowercase__=1_0_0     ,				lowercase__=1_0_2_6     ,				lowercase__=True			)		->			str:
  
    '''simple docstring'''
  
  
  
  
  
  
  
    __lowercase=    joblib.load(lowercase__			)
    print('data loaded'			)
    __lowercase=    []
    if trim:
      for i, example in enumerate(lowercase__			):
        if len(example[0]			) > min_len:
          __lowercase=    random.randint(0     ,				len(example[0]			) - context_len - 1			)
          objective_set.append(example[0, start : start + context_len]			)
        if len(lowercase__			) >= number:
          break
      __lowercase=    []
      for j in range(i + 1     ,				len(lowercase__			)			):
        if len(data[j][0]			) > min_len:
          train_data.append(data[j]			)
    else:
      __lowercase=    data[0:number]
      __lowercase=    data[number:]
  
    joblib.dump(lowercase__     ,				'objective_set.jbl'			)
    print('objective set saved'			)
    return train_data, objective_set
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=5_0     ,				lowercase__="secondary_learner.pt"			)		->			Tuple:
  
    '''simple docstring'''
  
  
  
  
  
  
  
    __lowercase=    torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'			)
    # We will use the first 512 pairs from our dataset as a test set for
    # our secondary learner and the rest to train
    __lowercase=    train_dataset[:5_1_2]
    __lowercase=    train_dataset[5_1_2:]
    __lowercase=    DataLoader(lowercase__     ,				shuffle=lowercase__     ,				batch_size=lowercase__			)
    __lowercase=    DataLoader(lowercase__     ,				shuffle=lowercase__     ,				batch_size=lowercase__			)
  
    # secondary learner model set up
    __lowercase=    nn.MSELoss()
    __lowercase=    nn.MSELoss(reduction='sum'			)
    secondary_learner.to(lowercase__			)
    __lowercase=    torch.optim.Adam(secondary_learner.parameters()     ,				lr=0.0_0001			)
    secondary_learner.train()
  
    # TODO in original code this is written as number of actual batches seen
    # not number of items seen but other places it is number of items instead.
    # improve consistency! changed this to epochs for clarity
    __lowercase=    float('inf'			)
    # Iterate through batches until we've used max_steps batches
    for epoch in range(int(lowercase__			)			):
      __lowercase=    0.0
      secondary_learner.train()
      for step, batch in enumerate(lowercase__			):
        __lowercase=    batch[0].to(lowercase__			)
        __lowercase=    batch[1].to(lowercase__			)
        __lowercase=    secondary_learner(lowercase__			)
        q_optimizer.zero_grad()
        __lowercase=    loss(lowercase__     ,				real_q.float()			)
        q_loss.backward()
        q_optimizer.step()
        tr_q_loss += q_loss.item()
  
        # model trains fairly quickly so we won't wait for a full epoch
        # eval is triggered at eval_freq and end of epochs
        if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowercase__			)):
          __lowercase=    tr_q_loss / (step + 1)
  
          secondary_learner.eval()
          __lowercase=    0.0
          __lowercase=    0.0
          __lowercase=    []
          __lowercase=    []
          # Compute performance of the secondary learner after this batch
          for stepa, batcha in enumerate(lowercase__			):
            __lowercase=    batcha[0].to(lowercase__			)
            __lowercase=    batcha[1].to(lowercase__			)
            __lowercase=    secondary_learner(lowercase__			)
            q_lossa += test_loss(lowercase__     ,				lowercase__			).item()
            sum_qa += torch.sum(lowercase__			).item()
            for ei, i in enumerate(predicted_qa.cpu().detach().numpy()			):
              predicted.append(i.item()			)
            for ei, i in enumerate(real_qa.cpu().detach().numpy()			):
              actual.append(i.item()			)
  
          q_lossa /= len(lowercase__			)
          print(
              'Epoch: '     ,				lowercase__     ,				'step: '     ,				lowercase__     ,				'Avg. q:'     ,				sum_qa / len(lowercase__			)     ,				'Train Loss: '     ,				lowercase__     ,				'Test Loss: '     ,				lowercase__     ,				)
          if q_lossa < best_test_loss:
            joblib.dump((predicted, actual)     ,				'pred_vs_actual.jbl'			)
            torch.save(secondary_learner.state_dict()     ,				lowercase__			)
            __lowercase=    q_lossa
  
        secondary_learner.train()
    return secondary_learner
  
  
  
  
  
  class    A					(     nn.Module				):
  
  
         def __init__(self   ,       lowerCAmelCase      ):
           super(lowerCAmelCase   ,       self      ).__init__()
           __lowercase=    model.transformer.wte
           __lowercase=    copy.deepcopy(model.transformer.wte.weight      )
           __lowercase=    nn.Convad(self.embeddings.weight.size(1      )   ,       2_5_6   ,       3   ,       padding=1      )
           __lowercase=    nn.Sequential(nn.Linear(2_5_6   ,       3_2      )   ,       nn.Dropout(p=0.1      )   ,       nn.Linear(3_2   ,       3_2      )   ,       nn.Linear(3_2   ,       1      )      )
  
  
         def     _A (self   ,       lowerCAmelCase      ):
           __lowercase=    torch.max(self.conv(self.embeddings(lowerCAmelCase      ).squeeze(1      ).transpose(1   ,       2      )      )   ,       2      )[0]
           __lowercase=    self.fc(lowerCAmelCase      )
           return qs.squeeze(1      )
  
  
  
  
  
  
  
         @classmethod
         def     _A (cls   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
           __lowercase=    cls(lowerCAmelCase      )  # this calls __init__
           __lowercase=    torch.load(lowerCAmelCase      )
           secondary_learner.load_state_dict(lowerCAmelCase      )
           __lowercase=    model.transformer.wte
           __lowercase=    copy.deepcopy(model.transformer.wte.weight      )
           return secondary_learner
  
  
  
  
  
  
  
style_context: 
  
  
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  lowerCAmelCase					 =	logging.get_logger(__name__)
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							Any =['''input_ids''', '''attention_mask''']
  
  
  							def __init__(self   ,       lowerCAmelCase="[PAD]"   ,       lowerCAmelCase="[BOS]"   ,       lowerCAmelCase="[EOS]"   ,       lowerCAmelCase="[MASK]"   ,       lowerCAmelCase="[CLS]"   ,       lowerCAmelCase="[SEP]"   ,       lowerCAmelCase=2_0_4_8   ,       **lowerCAmelCase   ,       ):
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else pad_token
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else bos_token
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else eos_token
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else mask_token
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else cls_token
  									__lowercase=    AddedToken(lowerCAmelCase   ,       lstrip=lowerCAmelCase   ,       rstrip=lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else sep_token
  
  									super().__init__(
  									    pad_token=lowerCAmelCase   ,       bos_token=lowerCAmelCase   ,       eos_token=lowerCAmelCase   ,       mask_token=lowerCAmelCase   ,       cls_token=lowerCAmelCase   ,       sep_token=lowerCAmelCase   ,       model_max_length=lowerCAmelCase   ,       **lowerCAmelCase   ,       )
  
  									__lowercase=    2**8  # utf is 8 bits
  
  									# define special tokens dict
  									__lowercase=    {
  									    self.pad_token: 0,
  									    self.bos_token: 1,
  									    self.eos_token: 2,
  									    self.mask_token: 3,
  									    self.cls_token: 4,
  									    self.sep_token: 5,
  									}
  									__lowercase=    len(self.special_tokens_encoder      )
  									__lowercase=    {v: k for k, v in self.special_tokens_encoder.items()}
  
  
  							def     _A (self      ):
  									__lowercase=    self.special_tokens_encoder.copy()
  									vocab.update(self.added_tokens_encoder      )
  									for i in range(self._utf_vocab_size      ):
  											__lowercase=    chr(lowerCAmelCase      )
  											__lowercase=    i + len(self.special_tokens_encoder      )
  									return vocab
  
  
  							@property
  							def     _A (self      ):
  									return self._utf_vocab_size + self._num_special_tokens
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None   ,       lowerCAmelCase = False      ):
  									if already_has_special_tokens:
  											return super().get_special_tokens_mask(
  											    token_ids_a=lowerCAmelCase   ,       token_ids_a=lowerCAmelCase   ,       already_has_special_tokens=lowerCAmelCase      )
  
  									# normal case: some special tokens
  									if token_ids_a is None:
  											return [1] + [0] * len(lowerCAmelCase      ) + [1]
  									return [1] + ([0] * len(lowerCAmelCase      )) + [1] + ([0] * len(lowerCAmelCase      )) + [1]
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None      ):
  									if token_ids_a is None:
  											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  									else:
  											return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									__lowercase=    [chr(lowerCAmelCase      ) for i in text.encode('utf-8'      )]
  									return tokens
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									if token in self.special_tokens_encoder:
  											__lowercase=    self.special_tokens_encoder[token]
  									elif token in self.added_tokens_encoder:
  											__lowercase=    self.added_tokens_encoder[token]
  									elif len(lowerCAmelCase      ) != 1:
  											__lowercase=    self.unk_token_id
  									else:
  											__lowercase=    ord(lowerCAmelCase      ) + self._num_special_tokens
  									return token_id
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									if index in self.special_tokens_decoder:
  											__lowercase=    self.special_tokens_decoder[index]
  									elif index in self.added_tokens_decoder:
  											__lowercase=    self.added_tokens_decoder[index]
  									else:
  											__lowercase=    chr(index - self._num_special_tokens      )
  									return token
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									__lowercase=    B''
  									for token in tokens:
  											if token in self.special_tokens_decoder:
  													__lowercase=    self.special_tokens_decoder[token].encode('utf-8'      )
  											elif token in self.added_tokens_decoder:
  													__lowercase=    self.special_tokens_decoder[token].encode('utf-8'      )
  											elif token in self.special_tokens_encoder:
  													__lowercase=    token.encode('utf-8'      )
  											elif token in self.added_tokens_encoder:
  													__lowercase=    token.encode('utf-8'      )
  											else:
  													__lowercase=    bytes([ord(lowerCAmelCase      )]      )
  											bstring += tok_string
  									__lowercase=    bstring.decode('utf-8'   ,       errors='replace'      )
  									return string
  
  
  
  
  
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase = None      ):
  									return ()
  
  
  
  
  
  
  