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     UpperCAmelCase__       (    _A   :				Optional[int]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    random.seed(_A							)
    np.random.seed(_A							)
    torch.manual_seed(_A							)
    torch.cuda.manual_seed_all(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[int]			,   _A   :				List[Any]			,   _A   :				Union[str, Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    model.eval()
    a__						=next(model.parameters()							).device
    a__						=1
    a__						=torch.zeros((eval_batch_size, context_len)			,   dtype=torch.long			,   device=_A							)
    a__						=DataLoader(_A			,   shuffle=_A			,   batch_size=_A							)
    a__						=torch.zeros(1			,   device=_A							)
    a__						=0
    for batch in eval_dataloader:
      batch.to(_A							)
      # pad
      context.zero_()
      for i in range(_A							):
        a__						=batch[i]
      a__						=model(_A			,   labels=_A							)
      eval_loss += outputs[0].sum().item()
      nb_eval_examples += batch.size(0							)
    a__						=eval_loss / nb_eval_examples
    a__						=torch.exp(_A							)
    model.train()
    return perplexity
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Union[str, Any]="gpt2"							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=GPTaLMHeadModel.from_pretrained(_A			,   output_hidden_states=_A							)
    torch.save(model.state_dict()			,   model_name + '''local.pt'''							)
    return model
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[Any]			,   _A   :				Tuple			,   _A   :				Optional[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=copy.deepcopy(_A							)
    model.to(_A							)
  
    a__						=['''bias''', '''LayerNorm.weight''']
    a__						=[
        {
            '''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},
    ]
    a__						=AdamW(_A			,   lr=5E-5			,   eps=1E-8							)
    a__						=get_linear_schedule_with_warmup(_A			,   0			,   _A							)
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Tuple			,   _A   :				int			,   _A   :				Dict			,   _A   :				Union[str, Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=np.array(_A							).mean()
    a__						=np.array(_A							).std()
    a__						=(real_perps - avg) / std
    a__						=list(zip(_A			,   _A			,   _A							)							)
    joblib.dump(_A			,   _A							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Dict			,   _A   :				int			,   _A   :				str			,   _A   :				Optional[int]			,   _A   :				List[str]			,   _A   :				Optional[int]			,   _A   :				List[str]			,   _A   :				Any="dev.jbl"			,   _A   :				Any=recopy_gpta			,   ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[]
    a__						=[]
    a__						=[]
  
    # Initialize the transformer model
    a__						=copy.deepcopy(_A							)
    orig_model.to(device='''cpu'''							)
    torch.cuda.empty_cache()
  
    # Compute perplexity of initial transformer model for comparison
    model.train()
    a__,   a__,   a__						=recopy_model(_A			,   _A			,   _A							)
  
    for step in tqdm(range(_A							)							):
      a__						=torch.zeros((1, context_len)			,   dtype=torch.long			,   device=_A							)
      a__						=random.choice(_A							)
      a__						=random.randint(0			,   len(story[0]							) - context_len - 1							)
      a__						=story[0][start : start + context_len]
      lm_optimizer.zero_grad()
      a__						=model(_A			,   labels=_A							)
      a__						=outputs[0]
      a__						=compute_perplexity(_A			,   _A			,   _A							)
      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
      a__						=compute_perplexity(_A			,   _A			,   _A							)
  
      # Periodically save the stored (X, IG(X)) pairs
      if step % 10_00 == 0 and step > 1:
        intermittent_save(_A			,   _A			,   _A			,   _A							)
  
      # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
      a__,   a__,   a__						=recopy_model(_A			,   _A			,   _A							)
  
      past_perps.append(past_perp.item()							)
      real_perps.append(orig_perp - real_perp.item()							)
      contexts.append(np.array(context.cpu()							)							)
  
    intermittent_save(_A			,   _A			,   _A			,   _A							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Union[str, Any]			,   _A   :				Optional[Any]="data/tokenized_stories_train_wikitext103.jbl"			,   _A   :				int=1_00			,   _A   :				int=10_26			,   _A   :				Tuple=True							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=joblib.load(_A							)
    print('''data loaded'''							)
    a__						=[]
    if trim:
      for i, example in enumerate(_A							):
        if len(example[0]							) > min_len:
          a__						=random.randint(0			,   len(example[0]							) - context_len - 1							)
          objective_set.append(example[0, start : start + context_len]							)
        if len(_A							) >= number:
          break
      a__						=[]
      for j in range(i + 1			,   len(_A							)							):
        if len(data[j][0]							) > min_len:
          train_data.append(data[j]							)
    else:
      a__						=data[0:number]
      a__						=data[number:]
  
    joblib.dump(_A			,   '''objective_set.jbl'''							)
    print('''objective set saved'''							)
    return train_data, objective_set
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				List[str]			,   _A   :				Optional[Any]			,   _A   :				Optional[int]			,   _A   :				Optional[Any]			,   _A   :				Union[str, Any]=50			,   _A   :				Dict="secondary_learner.pt"							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=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
    a__						=train_dataset[:5_12]
    a__						=train_dataset[5_12:]
    a__						=DataLoader(_A			,   shuffle=_A			,   batch_size=_A							)
    a__						=DataLoader(_A			,   shuffle=_A			,   batch_size=_A							)
  
    # secondary learner model set up
    a__						=nn.MSELoss()
    a__						=nn.MSELoss(reduction='''sum'''							)
    secondary_learner.to(_A							)
    a__						=torch.optim.Adam(secondary_learner.parameters()			,   lr=0.00_001							)
    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
    a__						=float('''inf'''							)
    # Iterate through batches until we've used max_steps batches
    for epoch in range(int(_A							)							):
      a__						=0.0
      secondary_learner.train()
      for step, batch in enumerate(_A							):
        a__						=batch[0].to(_A							)
        a__						=batch[1].to(_A							)
        a__						=secondary_learner(_A							)
        q_optimizer.zero_grad()
        a__						=loss(_A			,   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(_A							)):
          a__						=tr_q_loss / (step + 1)
  
          secondary_learner.eval()
          a__						=0.0
          a__						=0.0
          a__						=[]
          a__						=[]
          # Compute performance of the secondary learner after this batch
          for stepa, batcha in enumerate(_A							):
            a__						=batcha[0].to(_A							)
            a__						=batcha[1].to(_A							)
            a__						=secondary_learner(_A							)
            q_lossa += test_loss(_A			,   _A							).item()
            sum_qa += torch.sum(_A							).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(_A							)
          print(
              '''Epoch: '''			,   _A			,   '''step: '''			,   _A			,   '''Avg. q:'''			,   sum_qa / len(_A							)			,   '''Train Loss: '''			,   _A			,   '''Test Loss: '''			,   _A			,   )
          if q_lossa < best_test_loss:
            joblib.dump((predicted, actual)			,   '''pred_vs_actual.jbl'''							)
            torch.save(secondary_learner.state_dict()			,   _A							)
            a__						=q_lossa
  
        secondary_learner.train()
    return secondary_learner
  
  
  
  
  
  
  
  class 		__magic_name__    (							nn.Module							):
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					lowercase_ )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       super(lowercase_,					self ).__init__()
       a__						=model.transformer.wte
       a__						=copy.deepcopy(model.transformer.wte.weight )
       a__						=nn.Convad(self.embeddings.weight.size(1 ),					256,					3,					padding=1 )
       a__						=nn.Sequential(nn.Linear(256,					32 ),					nn.Dropout(p=0.1 ),					nn.Linear(32,					32 ),					nn.Linear(32,					1 ) )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=torch.max(self.conv(self.embeddings(lowercase_ ).squeeze(1 ).transpose(1,					2 ) ),					2 )[0]
       a__						=self.fc(lowercase_ )
       return qs.squeeze(1 )
  
  
  
     @classmethod
     def 				_UpperCAmelCase				(       cls,					lowercase_,					lowercase_ )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=cls(lowercase_ )  # this calls __init__
       a__						=torch.load(lowercase_ )
       secondary_learner.load_state_dict(lowercase_ )
       a__						=model.transformer.wte
       a__						=copy.deepcopy(model.transformer.wte.weight )
       return secondary_learner
  
style_context: 
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
         from .tokenization_xglm import XGLMTokenizer
  else:
         lowerCamelCase     =      None
  
  
  lowerCamelCase     =      logging.get_logger(__name__)
  
  lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  lowerCamelCase     =      {
      '''vocab_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
      },
      '''tokenizer_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
      },
  }
  
  lowerCamelCase     =      {
      '''facebook/xglm-564M''': 2048,
  }
  
  
  
  
  
  
  
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
     lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
     lowerCamelCase__				:			Any												=     XGLMTokenizer
  
  
  
     def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=7
       a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
  
       a__						=kwargs.get('''additional_special_tokens''',					[] )
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]
  
       super().__init__(
           lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)
  
       a__						=vocab_file
       a__						=False if not self.vocab_file else True
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
       a__						=[self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=[self.sep_token_id]
  
       if token_ids_a is None:
         return len(sep + token_ids_a ) * [0]
       return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.''' )
  
       if not os.path.isdir(lowercase_ ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
         return
       a__						=os.path.join(
           lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
  
       if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
         copyfile(self.vocab_file,					lowercase_ )
  
       return (out_vocab_file,)
  