






"""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


_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						logging.getLogger(__name__)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Tuple			)				->    Tuple:
  random.seed(_lowerCamelCase			)
  np.random.seed(_lowerCamelCase			)
  torch.manual_seed(_lowerCamelCase			)
  torch.cuda.manual_seed_all(_lowerCamelCase			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		Optional[int]			)				->    Dict:
  model.eval()
  lowerCamelCase_   					=						next(model.parameters()			).device
  lowerCamelCase_   					=						1
  lowerCamelCase_   					=						torch.zeros((eval_batch_size, context_len)		,    dtype=torch.long		,    device=_lowerCamelCase			)
  lowerCamelCase_   					=						DataLoader(_lowerCamelCase		,    shuffle=_lowerCamelCase		,    batch_size=_lowerCamelCase			)
  lowerCamelCase_   					=						torch.zeros(1		,    device=_lowerCamelCase			)
  lowerCamelCase_   					=						0
  for batch in eval_dataloader:
    batch.to(_lowerCamelCase			)
    # pad
    context.zero_()
    for i in range(_lowerCamelCase			):
      lowerCamelCase_   					=						batch[i]
    lowerCamelCase_   					=						model(_lowerCamelCase		,    labels=_lowerCamelCase			)
    eval_loss += outputs[0].sum().item()
    nb_eval_examples += batch.size(0			)
  lowerCamelCase_   					=						eval_loss / nb_eval_examples
  lowerCamelCase_   					=						torch.exp(_lowerCamelCase			)
  model.train()
  return perplexity







def 					lowerCamelCase__	(							_lowerCamelCase	:		Union[str, Any]="gpt2"			)				->    int:
  lowerCamelCase_   					=						GPTaLMHeadModel.from_pretrained(_lowerCamelCase		,    output_hidden_states=_lowerCamelCase			)
  torch.save(model.state_dict()		,    model_name + 'local.pt'			)
  return model







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		int			)				->    Dict:
  lowerCamelCase_   					=						copy.deepcopy(_lowerCamelCase			)
  model.to(_lowerCamelCase			)

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







def 					lowerCamelCase__	(							_lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		str		,    _lowerCamelCase	:		List[str]			)				->    int:
  lowerCamelCase_   					=						np.array(_lowerCamelCase			).mean()
  lowerCamelCase_   					=						np.array(_lowerCamelCase			).std()
  lowerCamelCase_   					=						(real_perps - avg) / std
  lowerCamelCase_   					=						list(zip(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)			)
  joblib.dump(_lowerCamelCase		,    _lowerCamelCase			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Union[str, Any]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		List[Any]		,    _lowerCamelCase	:		Optional[int]		,    _lowerCamelCase	:		Tuple="dev.jbl"		,    _lowerCamelCase	:		Dict=recopy_gpta		,    )				->    Any:
  lowerCamelCase_   					=						[]
  lowerCamelCase_   					=						[]
  lowerCamelCase_   					=						[]

  # Initialize the transformer model
  lowerCamelCase_   					=						copy.deepcopy(_lowerCamelCase			)
  orig_model.to(device='cpu'			)
  torch.cuda.empty_cache()

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

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

    # Periodically save the stored (X, IG(X)) pairs
    if step % 1000 == 0 and step > 1:
      intermittent_save(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    lowerCamelCase_					,     lowerCamelCase_					,     lowerCamelCase_   					=						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	:		Optional[Any]		,    _lowerCamelCase	:		Dict="data/tokenized_stories_train_wikitext103.jbl"		,    _lowerCamelCase	:		Tuple=100		,    _lowerCamelCase	:		int=1026		,    _lowerCamelCase	:		Optional[int]=True			)				->    List[str]:
  lowerCamelCase_   					=						joblib.load(_lowerCamelCase			)
  print('data loaded'			)
  lowerCamelCase_   					=						[]
  if trim:
    for i, example in enumerate(_lowerCamelCase			):
      if len(example[0]			) > min_len:
        lowerCamelCase_   					=						random.randint(0		,    len(example[0]			) - context_len - 1			)
        objective_set.append(example[0, start : start + context_len]			)
      if len(_lowerCamelCase			) >= number:
        break
    lowerCamelCase_   					=						[]
    for j in range(i + 1		,    len(_lowerCamelCase			)			):
      if len(data[j][0]			) > min_len:
        train_data.append(data[j]			)
  else:
    lowerCamelCase_   					=						data[0:number]
    lowerCamelCase_   					=						data[number:]

  joblib.dump(_lowerCamelCase		,    'objective_set.jbl'			)
  print('objective set saved'			)
  return train_data, objective_set







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		List[Any]=50		,    _lowerCamelCase	:		Dict="secondary_learner.pt"			)				->    Optional[Any]:
  lowerCamelCase_   					=						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
  lowerCamelCase_   					=						train_dataset[:512]
  lowerCamelCase_   					=						train_dataset[512:]
  lowerCamelCase_   					=						DataLoader(_lowerCamelCase		,    shuffle=_lowerCamelCase		,    batch_size=_lowerCamelCase			)
  lowerCamelCase_   					=						DataLoader(_lowerCamelCase		,    shuffle=_lowerCamelCase		,    batch_size=_lowerCamelCase			)

  # secondary learner model set up
  lowerCamelCase_   					=						nn.MSELoss()
  lowerCamelCase_   					=						nn.MSELoss(reduction='sum'			)
  secondary_learner.to(_lowerCamelCase			)
  lowerCamelCase_   					=						torch.optim.Adam(secondary_learner.parameters()		,    lr=0.0_00_01			)
  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
  lowerCamelCase_   					=						float('inf'			)
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(_lowerCamelCase			)			):
    lowerCamelCase_   					=						0.0
    secondary_learner.train()
    for step, batch in enumerate(_lowerCamelCase			):
      lowerCamelCase_   					=						batch[0].to(_lowerCamelCase			)
      lowerCamelCase_   					=						batch[1].to(_lowerCamelCase			)
      lowerCamelCase_   					=						secondary_learner(_lowerCamelCase			)
      q_optimizer.zero_grad()
      lowerCamelCase_   					=						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			)):
        lowerCamelCase_   					=						tr_q_loss / (step + 1)

        secondary_learner.eval()
        lowerCamelCase_   					=						0.0
        lowerCamelCase_   					=						0.0
        lowerCamelCase_   					=						[]
        lowerCamelCase_   					=						[]
        # Compute performance of the secondary learner after this batch
        for stepa, batcha in enumerate(_lowerCamelCase			):
          lowerCamelCase_   					=						batcha[0].to(_lowerCamelCase			)
          lowerCamelCase_   					=						batcha[1].to(_lowerCamelCase			)
          lowerCamelCase_   					=						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			)
          lowerCamelCase_   					=						q_lossa

      secondary_learner.train()
  return secondary_learner






class 					a						( nn.Module		):






    def __init__(	self					:							List[str]	,						__SCREAMING_SNAKE_CASE					:							Tuple )					->		List[str]:
      super(__SCREAMING_SNAKE_CASE	,						self ).__init__()
      lowerCamelCase_   					=						model.transformer.wte
      lowerCamelCase_   					=						copy.deepcopy(model.transformer.wte.weight )
      lowerCamelCase_   					=						nn.Convad(self.embeddings.weight.size(1 )	,						256	,						3	,						padding=1 )
      lowerCamelCase_   					=						nn.Sequential(nn.Linear(256	,						32 )	,						nn.Dropout(p=0.1 )	,						nn.Linear(32	,						32 )	,						nn.Linear(32	,						1 ) )






    def        UpperCamelCase	(	self					:							Dict	,						__SCREAMING_SNAKE_CASE					:							int )					->		str:
      lowerCamelCase_   					=						torch.max(self.conv(self.embeddings(__SCREAMING_SNAKE_CASE ).squeeze(1 ).transpose(1	,						2 ) )	,						2 )[0]
      lowerCamelCase_   					=						self.fc(__SCREAMING_SNAKE_CASE )
      return qs.squeeze(1 )






    @classmethod
    def        UpperCamelCase	(	cls					:							int	,						__SCREAMING_SNAKE_CASE					:							Optional[Any]	,						__SCREAMING_SNAKE_CASE					:							Tuple )					->		Union[str, Any]:
      lowerCamelCase_   					=						cls(__SCREAMING_SNAKE_CASE )  # this calls __init__
      lowerCamelCase_   					=						torch.load(__SCREAMING_SNAKE_CASE )
      secondary_learner.load_state_dict(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						model.transformer.wte
      lowerCamelCase_   					=						copy.deepcopy(model.transformer.wte.weight )
      return secondary_learner












"""simple docstring"""





def 					lowerCamelCase__	(							_lowerCamelCase	:		int = 1000			)				->    int:
  lowerCamelCase_   					=						0
  lowerCamelCase_   					=						(n - 1) // 3
  total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
  lowerCamelCase_   					=						(n - 1) // 5
  total += ((terms) * (10 + (terms - 1) * 5)) // 2
  lowerCamelCase_   					=						(n - 1) // 15
  total -= ((terms) * (30 + (terms - 1) * 15)) // 2
  return total


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




