





"""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     (							SCREAMING_SNAKE_CASE				)		->							List[Any]:






       """simple docstring"""




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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							List[str]:






       """simple docstring"""




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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE="gpt2"				)		->							Dict:






       """simple docstring"""




       _UpperCAmelCase			     =  GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE,output_hidden_states=SCREAMING_SNAKE_CASE				)
       torch.save(model.state_dict(),model_name + 'local.pt'				)
       return model

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase			     =  copy.deepcopy(SCREAMING_SNAKE_CASE				)
       model.to(SCREAMING_SNAKE_CASE				)

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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							Optional[Any]:






       """simple docstring"""




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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE="dev.jbl",SCREAMING_SNAKE_CASE=recopy_gpta,)		->							Dict:






       """simple docstring"""




       _UpperCAmelCase			     =  []
       _UpperCAmelCase			     =  []
       _UpperCAmelCase			     =  []

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

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

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

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

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

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

       intermittent_save(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl",SCREAMING_SNAKE_CASE=100,SCREAMING_SNAKE_CASE=1026,SCREAMING_SNAKE_CASE=True				)		->							Any:






       """simple docstring"""




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

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

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=50,SCREAMING_SNAKE_CASE="secondary_learner.pt"				)		->							Dict:






       """simple docstring"""




       _UpperCAmelCase			     =  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
       _UpperCAmelCase			     =  train_dataset[:512]
       _UpperCAmelCase			     =  train_dataset[512:]
       _UpperCAmelCase			     =  DataLoader(SCREAMING_SNAKE_CASE,shuffle=SCREAMING_SNAKE_CASE,batch_size=SCREAMING_SNAKE_CASE				)
       _UpperCAmelCase			     =  DataLoader(SCREAMING_SNAKE_CASE,shuffle=SCREAMING_SNAKE_CASE,batch_size=SCREAMING_SNAKE_CASE				)

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

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

                     secondary_learner.train()
       return secondary_learner



class 						lowerCAmelCase       (   nn.Module				):






       def __init__( self     ,	a__  ):
              super(a__     ,	self  ).__init__()
              _UpperCAmelCase			     =  model.transformer.wte
              _UpperCAmelCase			     =  copy.deepcopy(model.transformer.wte.weight  )
              _UpperCAmelCase			     =  nn.Convad(self.embeddings.weight.size(1  )     ,	2_56     ,	3     ,	padding=1  )
              _UpperCAmelCase			     =  nn.Sequential(nn.Linear(2_56     ,	32  )     ,	nn.Dropout(p=0.1  )     ,	nn.Linear(32     ,	32  )     ,	nn.Linear(32     ,	1  )  )






       def 				__A			( self     ,	a__  ):
              _UpperCAmelCase			     =  torch.max(self.conv(self.embeddings(a__  ).squeeze(1  ).transpose(1     ,	2  )  )     ,	2  )[0]
              _UpperCAmelCase			     =  self.fc(a__  )
              return qs.squeeze(1  )






       @classmethod
       def 				__A			( cls     ,	a__     ,	a__  ):
              _UpperCAmelCase			     =  cls(a__  )  # this calls __init__
              _UpperCAmelCase			     =  torch.load(a__  )
              secondary_learner.load_state_dict(a__  )
              _UpperCAmelCase			     =  model.transformer.wte
              _UpperCAmelCase			     =  copy.deepcopy(model.transformer.wte.weight  )
              return secondary_learner













"""simple docstring"""






def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							bool:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       if len(SCREAMING_SNAKE_CASE				) == 1:
              return True
       try:
              _UpperCAmelCase			     =  series[1] / series[0]
              for index in range(len(SCREAMING_SNAKE_CASE				) - 1				):
                     if series[index + 1] / series[index] != common_ratio:
                            return False
       except ZeroDivisionError:
              return False
       return True

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							float:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       _UpperCAmelCase			     =  1
       for value in series:
              answer *= value
       return pow(SCREAMING_SNAKE_CASE,1 / len(SCREAMING_SNAKE_CASE				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






