code: 
  
  
  
  
  
  
  """simple docstring"""
  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
  
  
  snake_case   :   Any    		=		logging.getLogger(__name__)
  
  
  def        A	(							__snake_case:   Any     )						->		Optional[Any]:
  
         """simple docstring"""
  
  
  
  
  
         random.seed(__snake_case     )
         np.random.seed(__snake_case     )
         torch.manual_seed(__snake_case     )
         torch.cuda.manual_seed_all(__snake_case     )
  
  
  def        A	(							__snake_case:   Dict   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   str     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         model.eval()
         __magic_name__										=						next(model.parameters()     ).device
         __magic_name__										=						1
         __magic_name__										=						torch.zeros((eval_batch_size, context_len)   ,    dtype=torch.long   ,    device=__snake_case     )
         __magic_name__										=						DataLoader(__snake_case   ,    shuffle=__snake_case   ,    batch_size=__snake_case     )
         __magic_name__										=						torch.zeros(1   ,    device=__snake_case     )
         __magic_name__										=						0
         for batch in eval_dataloader:
                batch.to(__snake_case     )
                # pad
                context.zero_()
                for i in range(__snake_case     ):
                       __magic_name__										=						batch[i]
                __magic_name__										=						model(__snake_case   ,    labels=__snake_case     )
                eval_loss += outputs[0].sum().item()
                nb_eval_examples += batch.size(0     )
         __magic_name__										=						eval_loss / nb_eval_examples
         __magic_name__										=						torch.exp(__snake_case     )
         model.train()
         return perplexity
  
  
  def        A	(							__snake_case:   Optional[int]="gpt2"     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						GPTaLMHeadModel.from_pretrained(__snake_case   ,    output_hidden_states=__snake_case     )
         torch.save(model.state_dict()   ,    model_name + 'local.pt'     )
         return model
  
  
  def        A	(							__snake_case:   List[str]   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   List[Any]     )						->		Optional[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						copy.deepcopy(__snake_case     )
         model.to(__snake_case     )
  
         __magic_name__										=						['bias', 'LayerNorm.weight']
         __magic_name__										=						[
             {
                 '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},
         ]
         __magic_name__										=						AdamW(__snake_case   ,    lr=5E-5   ,    eps=1E-8     )
         __magic_name__										=						get_linear_schedule_with_warmup(__snake_case   ,    0   ,    __snake_case     )
         torch.cuda.empty_cache()
         return model, lm_optimizer, lm_scheduler
  
  
  def        A	(							__snake_case:   Optional[Any]   ,    __snake_case:   Any   ,    __snake_case:   Optional[int]   ,    __snake_case:   Any     )						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						np.array(__snake_case     ).mean()
         __magic_name__										=						np.array(__snake_case     ).std()
         __magic_name__										=						(real_perps - avg) / std
         __magic_name__										=						list(zip(__snake_case   ,    __snake_case   ,    __snake_case     )     )
         joblib.dump(__snake_case   ,    __snake_case     )
  
  
  def        A	(							__snake_case:   List[str]   ,    __snake_case:   Optional[int]   ,    __snake_case:   int   ,    __snake_case:   str   ,    __snake_case:   List[str]   ,    __snake_case:   List[Any]   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Optional[int]="dev.jbl"   ,    __snake_case:   List[str]=recopy_gpta   ,    )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						[]
         __magic_name__										=						[]
         __magic_name__										=						[]
  
         # Initialize the transformer model
         __magic_name__										=						copy.deepcopy(__snake_case     )
         orig_model.to(device='cpu'     )
         torch.cuda.empty_cache()
  
         # Compute perplexity of initial transformer model for comparison
         model.train()
         __magic_name__							,     __magic_name__							,     __magic_name__										=						recopy_model(__snake_case   ,    __snake_case   ,    __snake_case     )
  
         for step in tqdm(range(__snake_case     )     ):
                __magic_name__										=						torch.zeros((1, context_len)   ,    dtype=torch.long   ,    device=__snake_case     )
                __magic_name__										=						random.choice(__snake_case     )
                __magic_name__										=						random.randint(0   ,    len(story[0]     ) - context_len - 1     )
                __magic_name__										=						story[0][start : start + context_len]
                lm_optimizer.zero_grad()
                __magic_name__										=						model(__snake_case   ,    labels=__snake_case     )
                __magic_name__										=						outputs[0]
                __magic_name__										=						compute_perplexity(__snake_case   ,    __snake_case   ,    __snake_case     )
                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
                __magic_name__										=						compute_perplexity(__snake_case   ,    __snake_case   ,    __snake_case     )
  
                # Periodically save the stored (X, IG(X)) pairs
                if step % 1_0_0_0 == 0 and step > 1:
                       intermittent_save(__snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case     )
  
                # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
                __magic_name__							,     __magic_name__							,     __magic_name__										=						recopy_model(__snake_case   ,    __snake_case   ,    __snake_case     )
  
                past_perps.append(past_perp.item()     )
                real_perps.append(orig_perp - real_perp.item()     )
                contexts.append(np.array(context.cpu()     )     )
  
         intermittent_save(__snake_case   ,    __snake_case   ,    __snake_case   ,    __snake_case     )
  
  
  def        A	(							__snake_case:   Dict   ,    __snake_case:   int="data/tokenized_stories_train_wikitext103.jbl"   ,    __snake_case:   int=1_0_0   ,    __snake_case:   Optional[int]=1_0_2_6   ,    __snake_case:   Dict=True     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						joblib.load(__snake_case     )
         print('data loaded'     )
         __magic_name__										=						[]
         if trim:
                for i, example in enumerate(__snake_case     ):
                       if len(example[0]     ) > min_len:
                              __magic_name__										=						random.randint(0   ,    len(example[0]     ) - context_len - 1     )
                              objective_set.append(example[0, start : start + context_len]     )
                       if len(__snake_case     ) >= number:
                              break
                __magic_name__										=						[]
                for j in range(i + 1   ,    len(__snake_case     )     ):
                       if len(data[j][0]     ) > min_len:
                              train_data.append(data[j]     )
         else:
                __magic_name__										=						data[0:number]
                __magic_name__										=						data[number:]
  
         joblib.dump(__snake_case   ,    'objective_set.jbl'     )
         print('objective set saved'     )
         return train_data, objective_set
  
  
  def        A	(							__snake_case:   Dict   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   Optional[int]   ,    __snake_case:   Dict   ,    __snake_case:   List[str]=5_0   ,    __snake_case:   str="secondary_learner.pt"     )						->		str:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						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
         __magic_name__										=						train_dataset[:5_1_2]
         __magic_name__										=						train_dataset[5_1_2:]
         __magic_name__										=						DataLoader(__snake_case   ,    shuffle=__snake_case   ,    batch_size=__snake_case     )
         __magic_name__										=						DataLoader(__snake_case   ,    shuffle=__snake_case   ,    batch_size=__snake_case     )
  
         # secondary learner model set up
         __magic_name__										=						nn.MSELoss()
         __magic_name__										=						nn.MSELoss(reduction='sum'     )
         secondary_learner.to(__snake_case     )
         __magic_name__										=						torch.optim.Adam(secondary_learner.parameters()   ,    lr=0.00001     )
         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
         __magic_name__										=						float('inf'     )
         # Iterate through batches until we've used max_steps batches
         for epoch in range(int(__snake_case     )     ):
                __magic_name__										=						0.0
                secondary_learner.train()
                for step, batch in enumerate(__snake_case     ):
                       __magic_name__										=						batch[0].to(__snake_case     )
                       __magic_name__										=						batch[1].to(__snake_case     )
                       __magic_name__										=						secondary_learner(__snake_case     )
                       q_optimizer.zero_grad()
                       __magic_name__										=						loss(__snake_case   ,    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(__snake_case     )):
                              __magic_name__										=						tr_q_loss / (step + 1)
  
                              secondary_learner.eval()
                              __magic_name__										=						0.0
                              __magic_name__										=						0.0
                              __magic_name__										=						[]
                              __magic_name__										=						[]
                              # Compute performance of the secondary learner after this batch
                              for stepa, batcha in enumerate(__snake_case     ):
                                     __magic_name__										=						batcha[0].to(__snake_case     )
                                     __magic_name__										=						batcha[1].to(__snake_case     )
                                     __magic_name__										=						secondary_learner(__snake_case     )
                                     q_lossa += test_loss(__snake_case   ,    __snake_case     ).item()
                                     sum_qa += torch.sum(__snake_case     ).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(__snake_case     )
                              print(
                                  'Epoch: '   ,    __snake_case   ,    'step: '   ,    __snake_case   ,    'Avg. q:'   ,    sum_qa / len(__snake_case     )   ,    'Train Loss: '   ,    __snake_case   ,    'Test Loss: '   ,    __snake_case   ,    )
                              if q_lossa < best_test_loss:
                                     joblib.dump((predicted, actual)   ,    'pred_vs_actual.jbl'     )
                                     torch.save(secondary_learner.state_dict()   ,    __snake_case     )
                                     __magic_name__										=						q_lossa
  
                       secondary_learner.train()
         return secondary_learner
  
  
  
  
  
  
  class 					UpperCamelCase__   (							nn.Module):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  List[Any]							,						UpperCamelCase_   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             super(UpperCamelCase_							,						self   ).__init__()
             __magic_name__										=						model.transformer.wte
             __magic_name__										=						copy.deepcopy(model.transformer.wte.weight   )
             __magic_name__										=						nn.Convad(self.embeddings.weight.size(1   )							,						2_5_6							,						3							,						padding=1   )
             __magic_name__										=						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   :  List[str]							,						UpperCamelCase_   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						torch.max(self.conv(self.embeddings(UpperCamelCase_   ).squeeze(1   ).transpose(1							,						2   )   )							,						2   )[0]
             __magic_name__										=						self.fc(UpperCamelCase_   )
             return qs.squeeze(1   )
  
  
  
  
  
  
  
      @classmethod
      def 							a__ (		cls   :  str							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						cls(UpperCamelCase_   )  # this calls __init__
             __magic_name__										=						torch.load(UpperCamelCase_   )
             secondary_learner.load_state_dict(UpperCamelCase_   )
             __magic_name__										=						model.transformer.wte
             __magic_name__										=						copy.deepcopy(model.transformer.wte.weight   )
             return secondary_learner
  
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  snake_case   :   Optional[int]    		=		{
      """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
      """tokenization_splinter""": ["""SplinterTokenizer"""],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Dict    		=		["""SplinterTokenizerFast"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   snake_case   :   Dict    		=		[
       """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SplinterForQuestionAnswering""",
       """SplinterForPreTraining""",
       """SplinterLayer""",
       """SplinterModel""",
       """SplinterPreTrainedModel""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_splinter_fast import SplinterTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )
  
  
  else:
   import sys
  
   snake_case   :   List[str]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)