


import math
from decimal import Decimal, getcontext



def      _lowerCAmelCase  (							lowerCAmelCase_							:int = 4_000_000							)->int:
   '''simple docstring'''






   try:
      snake_case_         =   int(lowerCAmelCase_							)
   except (TypeError, ValueError):
      raise TypeError("Parameter n must be int or castable to int."							)
   if n <= 0:
      raise ValueError("Parameter n must be greater than or equal to one."							)
   snake_case_         =   100
   snake_case_         =   (Decimal(5							) ** Decimal(0.5							) + 1) / Decimal(2							)

   snake_case_         =   (math.floor(math.log(n * (phi + 2)						,  lowerCAmelCase_							) - 1							) // 3) * 3 + 2
   snake_case_         =   Decimal(round(phi ** Decimal(index + 1							)							)							) / (phi + 2)
   snake_case_         =   num // 2
   return int(lowerCAmelCase_							)


if __name__ == "__main__":
   print(F'''{solution() = }''')








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


SCREAMING_SNAKE_CASE		:Dict											=       logging.getLogger(__name__)



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[str]							)->str:
   '''simple docstring'''






   random.seed(lowerCAmelCase_							)
   np.random.seed(lowerCAmelCase_							)
   torch.manual_seed(lowerCAmelCase_							)
   torch.cuda.manual_seed_all(lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Optional[Any]							)->Dict:
   '''simple docstring'''






   model.eval()
   snake_case_         =   next(model.parameters()							).device
   snake_case_         =   1
   snake_case_         =   torch.zeros((eval_batch_size, context_len)						,  dtype=torch.long						,  device=lowerCAmelCase_							)
   snake_case_         =   DataLoader(lowerCAmelCase_						,  shuffle=lowerCAmelCase_						,  batch_size=lowerCAmelCase_							)
   snake_case_         =   torch.zeros(1						,  device=lowerCAmelCase_							)
   snake_case_         =   0
   for batch in eval_dataloader:
      batch.to(lowerCAmelCase_							)
      # pad
      context.zero_()
      for i in range(lowerCAmelCase_							):
         snake_case_         =   batch[i]
      snake_case_         =   model(lowerCAmelCase_						,  labels=lowerCAmelCase_							)
      eval_loss += outputs[0].sum().item()
      nb_eval_examples += batch.size(0							)
   snake_case_         =   eval_loss / nb_eval_examples
   snake_case_         =   torch.exp(lowerCAmelCase_							)
   model.train()
   return perplexity



def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]="gpt2"							)->Union[str, Any]:
   '''simple docstring'''






   snake_case_         =   GPTaLMHeadModel.from_pretrained(lowerCAmelCase_						,  output_hidden_states=lowerCAmelCase_							)
   torch.save(model.state_dict()						,  model_name + "local.pt"							)
   return model



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:List[Any]							)->int:
   '''simple docstring'''






   snake_case_         =   copy.deepcopy(lowerCAmelCase_							)
   model.to(lowerCAmelCase_							)

   snake_case_         =   ["bias", "LayerNorm.weight"]
   snake_case_         =   [
       {
           "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},
   ]
   snake_case_         =   AdamW(lowerCAmelCase_						,  lr=5e-5						,  eps=1e-8							)
   snake_case_         =   get_linear_schedule_with_warmup(lowerCAmelCase_						,  0						,  lowerCAmelCase_							)
   torch.cuda.empty_cache()
   return model, lm_optimizer, lm_scheduler



def      _lowerCAmelCase  (							lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:str							)->Tuple:
   '''simple docstring'''






   snake_case_         =   np.array(lowerCAmelCase_							).mean()
   snake_case_         =   np.array(lowerCAmelCase_							).std()
   snake_case_         =   (real_perps - avg) / std
   snake_case_         =   list(zip(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)							)
   joblib.dump(lowerCAmelCase_						,  lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:str						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:str="dev.jbl"						,  lowerCAmelCase_							:int=recopy_gpta						,  )->Tuple:
   '''simple docstring'''






   snake_case_         =   []
   snake_case_         =   []
   snake_case_         =   []

   # Initialize the transformer model
   snake_case_         =   copy.deepcopy(lowerCAmelCase_							)
   orig_model.to(device="cpu"							)
   torch.cuda.empty_cache()

   # Compute perplexity of initial transformer model for comparison
   model.train()
   snake_case_      ,      snake_case_      ,      snake_case_         =   recopy_model(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)

   for step in tqdm(range(lowerCAmelCase_							)							):
      snake_case_         =   torch.zeros((1, context_len)						,  dtype=torch.long						,  device=lowerCAmelCase_							)
      snake_case_         =   random.choice(lowerCAmelCase_							)
      snake_case_         =   random.randint(0						,  len(story[0]							) - context_len - 1							)
      snake_case_         =   story[0][start : start + context_len]
      lm_optimizer.zero_grad()
      snake_case_         =   model(lowerCAmelCase_						,  labels=lowerCAmelCase_							)
      snake_case_         =   outputs[0]
      snake_case_         =   compute_perplexity(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)
      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
      snake_case_         =   compute_perplexity(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)

      # Periodically save the stored (X, IG(X)) pairs
      if step % 1_000 == 0 and step > 1:
         intermittent_save(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)

      # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
      snake_case_      ,      snake_case_      ,      snake_case_         =   recopy_model(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)

      past_perps.append(past_perp.item()							)
      real_perps.append(orig_perp - real_perp.item()							)
      contexts.append(np.array(context.cpu()							)							)

   intermittent_save(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Union[str, Any]="data/tokenized_stories_train_wikitext103.jbl"						,  lowerCAmelCase_							:Tuple=100						,  lowerCAmelCase_							:int=1_026						,  lowerCAmelCase_							:int=True							)->List[Any]:
   '''simple docstring'''






   snake_case_         =   joblib.load(lowerCAmelCase_							)
   print("data loaded"							)
   snake_case_         =   []
   if trim:
      for i, example in enumerate(lowerCAmelCase_							):
         if len(example[0]							) > min_len:
            snake_case_         =   random.randint(0						,  len(example[0]							) - context_len - 1							)
            objective_set.append(example[0, start : start + context_len]							)
         if len(lowerCAmelCase_							) >= number:
            break
      snake_case_         =   []
      for j in range(i + 1						,  len(lowerCAmelCase_							)							):
         if len(data[j][0]							) > min_len:
            train_data.append(data[j]							)
   else:
      snake_case_         =   data[0:number]
      snake_case_         =   data[number:]

   joblib.dump(lowerCAmelCase_						,  "objective_set.jbl"							)
   print("objective set saved"							)
   return train_data, objective_set



def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:Dict=50						,  lowerCAmelCase_							:Dict="secondary_learner.pt"							)->Union[str, Any]:
   '''simple docstring'''






   snake_case_         =   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
   snake_case_         =   train_dataset[:512]
   snake_case_         =   train_dataset[512:]
   snake_case_         =   DataLoader(lowerCAmelCase_						,  shuffle=lowerCAmelCase_						,  batch_size=lowerCAmelCase_							)
   snake_case_         =   DataLoader(lowerCAmelCase_						,  shuffle=lowerCAmelCase_						,  batch_size=lowerCAmelCase_							)

   # secondary learner model set up
   snake_case_         =   nn.MSELoss()
   snake_case_         =   nn.MSELoss(reduction="sum"							)
   secondary_learner.to(lowerCAmelCase_							)
   snake_case_         =   torch.optim.Adam(secondary_learner.parameters()						,  lr=0.0_0_0_0_1							)
   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
   snake_case_         =   float("inf"							)
   # Iterate through batches until we've used max_steps batches
   for epoch in range(int(lowerCAmelCase_							)							):
      snake_case_         =   0.0
      secondary_learner.train()
      for step, batch in enumerate(lowerCAmelCase_							):
         snake_case_         =   batch[0].to(lowerCAmelCase_							)
         snake_case_         =   batch[1].to(lowerCAmelCase_							)
         snake_case_         =   secondary_learner(lowerCAmelCase_							)
         q_optimizer.zero_grad()
         snake_case_         =   loss(lowerCAmelCase_						,  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(lowerCAmelCase_							)):
            snake_case_         =   tr_q_loss / (step + 1)

            secondary_learner.eval()
            snake_case_         =   0.0
            snake_case_         =   0.0
            snake_case_         =   []
            snake_case_         =   []
            # Compute performance of the secondary learner after this batch
            for stepa, batcha in enumerate(lowerCAmelCase_							):
               snake_case_         =   batcha[0].to(lowerCAmelCase_							)
               snake_case_         =   batcha[1].to(lowerCAmelCase_							)
               snake_case_         =   secondary_learner(lowerCAmelCase_							)
               q_lossa += test_loss(lowerCAmelCase_						,  lowerCAmelCase_							).item()
               sum_qa += torch.sum(lowerCAmelCase_							).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(lowerCAmelCase_							)
            print(
                "Epoch: "						,  lowerCAmelCase_						,  "step: "						,  lowerCAmelCase_						,  "Avg. q:"						,  sum_qa / len(lowerCAmelCase_							)						,  "Train Loss: "						,  lowerCAmelCase_						,  "Test Loss: "						,  lowerCAmelCase_						,  )
            if q_lossa < best_test_loss:
               joblib.dump((predicted, actual)						,  "pred_vs_actual.jbl"							)
               torch.save(secondary_learner.state_dict()						,  lowerCAmelCase_							)
               snake_case_         =   q_lossa

         secondary_learner.train()
   return secondary_learner



class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def __init__(       self    :    Optional[int]      ,			_lowerCAmelCase    :    Dict					)				->	List[Any]:




     """simple docstring"""
     super(_lowerCAmelCase      ,			self					).__init__()
     snake_case_         =   model.transformer.wte
     snake_case_         =   copy.deepcopy(model.transformer.wte.weight					)
     snake_case_         =   nn.Convad(self.embeddings.weight.size(1					)      ,			2_5_6      ,			3      ,			padding=1					)
     snake_case_         =   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    lowerCAmelCase__					(       self    :    Union[str, Any]      ,			_lowerCAmelCase    :    Optional[int]					)				->	List[Any]:




     """simple docstring"""
     snake_case_         =   torch.max(self.conv(self.embeddings(_lowerCAmelCase					).squeeze(1					).transpose(1      ,			2					)					)      ,			2					)[0]
     snake_case_         =   self.fc(_lowerCAmelCase					)
     return qs.squeeze(1					)



  @classmethod
  def    lowerCAmelCase__					(       cls    :    List[Any]      ,			_lowerCAmelCase    :    List[Any]      ,			_lowerCAmelCase    :    Dict					)				->	Dict:




     """simple docstring"""
     snake_case_         =   cls(_lowerCAmelCase					)  # this calls __init__
     snake_case_         =   torch.load(_lowerCAmelCase					)
     secondary_learner.load_state_dict(_lowerCAmelCase					)
     snake_case_         =   model.transformer.wte
     snake_case_         =   copy.deepcopy(model.transformer.wte.weight					)
     return secondary_learner




