





"""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


_lowerCamelCase       =	logging.getLogger(__name__)




def 		lowerCAmelCase_	(  lowercase_   :							Union[str, Any]		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							Dict ,      lowercase_   :							Union[str, Any] ,      lowercase_   :							Tuple		):

 '''simple docstring'''






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




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int]="gpt2"		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       GPTaLMHeadModel.from_pretrained(__UpperCamelCase ,      output_hidden_states=__UpperCamelCase		)
 torch.save(model.state_dict() ,      model_name + '''local.pt'''		)
 return model




def 		lowerCAmelCase_	(  lowercase_   :							Optional[int] ,      lowercase_   :							List[str] ,      lowercase_   :							Optional[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Dict												=       copy.deepcopy(__UpperCamelCase		)
 model.to(__UpperCamelCase		)

 __SCREAMING_SNAKE_CASE :			Optional[int]												=       ['''bias''', '''LayerNorm.weight''']
 __SCREAMING_SNAKE_CASE :			str												=       [
     {
         '''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},
 ]
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       AdamW(__UpperCamelCase ,      lr=5E-5 ,      eps=1E-8		)
 __SCREAMING_SNAKE_CASE :			Any												=       get_linear_schedule_with_warmup(__UpperCamelCase ,      0 ,      __UpperCamelCase		)
 torch.cuda.empty_cache()
 return model, lm_optimizer, lm_scheduler




def 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							Optional[Any] ,      lowercase_   :							List[Any] ,      lowercase_   :							List[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       np.array(__UpperCamelCase		).mean()
 __SCREAMING_SNAKE_CASE :			List[Any]												=       np.array(__UpperCamelCase		).std()
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       (real_perps - avg) / std
 __SCREAMING_SNAKE_CASE :			List[Any]												=       list(zip(__UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase		)		)
 joblib.dump(__UpperCamelCase ,      __UpperCamelCase		)




def 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							str ,      lowercase_   :							List[Any] ,      lowercase_   :							str ,      lowercase_   :							List[Any] ,      lowercase_   :							str ,      lowercase_   :							int ,      lowercase_   :							Tuple="dev.jbl" ,      lowercase_   :							int=recopy_gpta ,      ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       []
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       []
 __SCREAMING_SNAKE_CASE :			int												=       []

 # Initialize the transformer model
 __SCREAMING_SNAKE_CASE :			Optional[int]												=       copy.deepcopy(__UpperCamelCase		)
 orig_model.to(device='''cpu'''		)
 torch.cuda.empty_cache()

 # Compute perplexity of initial transformer model for comparison
 model.train()
 __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			str												=       recopy_model(__UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase		)

 for step in tqdm(range(__UpperCamelCase		)		):
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       torch.zeros((1, context_len) ,      dtype=torch.long ,      device=__UpperCamelCase		)
  __SCREAMING_SNAKE_CASE :			Optional[int]												=       random.choice(__UpperCamelCase		)
  __SCREAMING_SNAKE_CASE :			Tuple												=       random.randint(0 ,      len(story[0]		) - context_len - 1		)
  __SCREAMING_SNAKE_CASE :			str												=       story[0][start : start + context_len]
  lm_optimizer.zero_grad()
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       model(__UpperCamelCase ,      labels=__UpperCamelCase		)
  __SCREAMING_SNAKE_CASE :			List[Any]												=       outputs[0]
  __SCREAMING_SNAKE_CASE :			str												=       compute_perplexity(__UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase		)
  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
  __SCREAMING_SNAKE_CASE :			str												=       compute_perplexity(__UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase		)

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

  # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
  __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       recopy_model(__UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase		)

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

 intermittent_save(__UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase ,      __UpperCamelCase		)




def 		lowerCAmelCase_	(  lowercase_   :							Union[str, Any] ,      lowercase_   :							int="data/tokenized_stories_train_wikitext103.jbl" ,      lowercase_   :							Union[str, Any]=100 ,      lowercase_   :							Optional[Any]=1026 ,      lowercase_   :							Union[str, Any]=True		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			str												=       joblib.load(__UpperCamelCase		)
 print('''data loaded'''		)
 __SCREAMING_SNAKE_CASE :			str												=       []
 if trim:
  for i, example in enumerate(__UpperCamelCase		):
   if len(example[0]		) > min_len:
    __SCREAMING_SNAKE_CASE :			Dict												=       random.randint(0 ,      len(example[0]		) - context_len - 1		)
    objective_set.append(example[0, start : start + context_len]		)
   if len(__UpperCamelCase		) >= number:
    break
  __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       []
  for j in range(i + 1 ,      len(__UpperCamelCase		)		):
   if len(data[j][0]		) > min_len:
    train_data.append(data[j]		)
 else:
  __SCREAMING_SNAKE_CASE :			Optional[Any]												=       data[0:number]
  __SCREAMING_SNAKE_CASE :			int												=       data[number:]

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




def 		lowerCAmelCase_	(  lowercase_   :							List[Any] ,      lowercase_   :							List[Any] ,      lowercase_   :							List[Any] ,      lowercase_   :							int ,      lowercase_   :							Any=50 ,      lowercase_   :							List[str]="secondary_learner.pt"		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			int												=       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
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       train_dataset[:512]
 __SCREAMING_SNAKE_CASE :			List[Any]												=       train_dataset[512:]
 __SCREAMING_SNAKE_CASE :			List[Any]												=       DataLoader(__UpperCamelCase ,      shuffle=__UpperCamelCase ,      batch_size=__UpperCamelCase		)
 __SCREAMING_SNAKE_CASE :			Tuple												=       DataLoader(__UpperCamelCase ,      shuffle=__UpperCamelCase ,      batch_size=__UpperCamelCase		)

 # secondary learner model set up
 __SCREAMING_SNAKE_CASE :			int												=       nn.MSELoss()
 __SCREAMING_SNAKE_CASE :			Dict												=       nn.MSELoss(reduction='''sum'''		)
 secondary_learner.to(__UpperCamelCase		)
 __SCREAMING_SNAKE_CASE :			List[Any]												=       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
 __SCREAMING_SNAKE_CASE :			List[str]												=       float('''inf'''		)
 # Iterate through batches until we've used max_steps batches
 for epoch in range(int(__UpperCamelCase		)		):
  __SCREAMING_SNAKE_CASE :			str												=       0.0
  secondary_learner.train()
  for step, batch in enumerate(__UpperCamelCase		):
   __SCREAMING_SNAKE_CASE :			Dict												=       batch[0].to(__UpperCamelCase		)
   __SCREAMING_SNAKE_CASE :			List[str]												=       batch[1].to(__UpperCamelCase		)
   __SCREAMING_SNAKE_CASE :			Any												=       secondary_learner(__UpperCamelCase		)
   q_optimizer.zero_grad()
   __SCREAMING_SNAKE_CASE :			List[Any]												=       loss(__UpperCamelCase ,      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(__UpperCamelCase		)):
    __SCREAMING_SNAKE_CASE :			List[str]												=       tr_q_loss / (step + 1)

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

   secondary_learner.train()
 return secondary_learner






class 			snake_case		(   nn.Module   ):

      def __init__(			self   :Union[str, Any]						,							_lowerCamelCase   :Dict						):
       super(__UpperCAmelCase						,							self						).__init__()
       __SCREAMING_SNAKE_CASE :			List[str]												=       model.transformer.wte
       __SCREAMING_SNAKE_CASE :			List[str]												=       copy.deepcopy(model.transformer.wte.weight						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       nn.Convad(self.embeddings.weight.size(1						)						,							2_5_6						,							3						,							padding=1						)
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						,							_lowerCamelCase   :Dict						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       torch.max(self.conv(self.embeddings(__UpperCAmelCase						).squeeze(1						).transpose(1						,							2						)						)						,							2						)[0]
       __SCREAMING_SNAKE_CASE :			Tuple												=       self.fc(__UpperCAmelCase						)
       return qs.squeeze(1						)



      @classmethod
      def 						SCREAMING_SNAKE_CASE_			(			cls   :Optional[Any]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			Dict												=       cls(__UpperCAmelCase						)  # this calls __init__
       __SCREAMING_SNAKE_CASE :			Any												=       torch.load(__UpperCAmelCase						)
       secondary_learner.load_state_dict(__UpperCAmelCase						)
       __SCREAMING_SNAKE_CASE :			Any												=       model.transformer.wte
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       copy.deepcopy(model.transformer.wte.weight						)
       return secondary_learner



"""simple docstring"""


def     _lowerCamelCase(__UpperCamelCase       )				-> list:
	_lowerCAmelCase								=[True for i in range(n + 1       )]
	_lowerCAmelCase								=[i - 1 for i in range(n + 1       )]
	_lowerCAmelCase								=[]
	for i in range(2    , n + 1       ):
		if is_prime[i]:
			primes.append(__UpperCamelCase       )
		for j in range(0    , len(__UpperCamelCase       )       ):
			if i * primes[j] >= n:
				break
			_lowerCAmelCase								=False

			if i % primes[j] == 0:
				_lowerCAmelCase								=totients[i] * primes[j]
				break

			_lowerCAmelCase								=totients[i] * (primes[j] - 1)

	return totients


if __name__ == "__main__":
			import doctest

			doctest.testmod()



