

"""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


A									=						logging.getLogger(__name__)







def       UpperCamelCase_					(     lowerCamelCase							:					Tuple   )						->     Any:



       """simple docstring"""







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







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					List[str]     , lowerCamelCase							:					Tuple   )						->     Optional[Any]:



       """simple docstring"""







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







def       UpperCamelCase_					(     lowerCamelCase							:					Dict="gpt2"   )						->     str:



       """simple docstring"""







       __magic_name__			:		Union[str, Any]        =     GPTaLMHeadModel.from_pretrained(lowerCamelCase     , output_hidden_states=lowerCamelCase   )
       torch.save(model.state_dict()     , model_name + '''local.pt'''   )
       return model







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					int     , lowerCamelCase							:					Union[str, Any]   )						->     Optional[Any]:



       """simple docstring"""







       __magic_name__			:		Union[str, Any]        =     copy.deepcopy(lowerCamelCase   )
       model.to(lowerCamelCase   )

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







def       UpperCamelCase_					(     lowerCamelCase							:					List[str]     , lowerCamelCase							:					Optional[Any]     , lowerCamelCase							:					Union[str, Any]     , lowerCamelCase							:					Union[str, Any]   )						->     Optional[Any]:



       """simple docstring"""







       __magic_name__			:		Union[str, Any]        =     np.array(lowerCamelCase   ).mean()
       __magic_name__			:		Tuple        =     np.array(lowerCamelCase   ).std()
       __magic_name__			:		Optional[Any]        =     (real_perps - avg) / std
       __magic_name__			:		List[Any]        =     list(zip(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )   )
       joblib.dump(lowerCamelCase     , lowerCamelCase   )







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



       """simple docstring"""







       __magic_name__			:		Tuple        =     []
       __magic_name__			:		Optional[int]        =     []
       __magic_name__			:		Any        =     []

       # Initialize the transformer model
       __magic_name__			:		Optional[int]        =     copy.deepcopy(lowerCamelCase   )
       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__			:		List[Any]        =     recopy_model(lowerCamelCase     , lowerCamelCase     , lowerCamelCase   )

       for step in tqdm(range(lowerCamelCase   )   ):
              __magic_name__			:		List[str]        =     torch.zeros((1, context_len)     , dtype=torch.long     , device=lowerCamelCase   )
              __magic_name__			:		Dict        =     random.choice(lowerCamelCase   )
              __magic_name__			:		int        =     random.randint(0     , len(story[0]   ) - context_len - 1   )
              __magic_name__			:		List[str]        =     story[0][start : start + context_len]
              lm_optimizer.zero_grad()
              __magic_name__			:		List[str]        =     model(lowerCamelCase     , labels=lowerCamelCase   )
              __magic_name__			:		List[str]        =     outputs[0]
              __magic_name__			:		Tuple        =     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
              __magic_name__			:		List[Any]        =     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
              __magic_name__       ,							__magic_name__       ,							__magic_name__			:		List[Any]        =     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       UpperCamelCase_					(     lowerCamelCase							:					Any     , lowerCamelCase							:					Tuple="data/tokenized_stories_train_wikitext103.jbl"     , lowerCamelCase							:					List[Any]=100     , lowerCamelCase							:					List[Any]=1026     , lowerCamelCase							:					Optional[int]=True   )						->     int:



       """simple docstring"""







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

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







def       UpperCamelCase_					(     lowerCamelCase							:					Tuple     , lowerCamelCase							:					str     , lowerCamelCase							:					str     , lowerCamelCase							:					Union[str, Any]     , lowerCamelCase							:					Dict=50     , lowerCamelCase							:					List[str]="secondary_learner.pt"   )						->     Dict:



       """simple docstring"""







       __magic_name__			:		List[Any]        =     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__			:		str        =     train_dataset[:512]
       __magic_name__			:		Optional[int]        =     train_dataset[512:]
       __magic_name__			:		Dict        =     DataLoader(lowerCamelCase     , shuffle=lowerCamelCase     , batch_size=lowerCamelCase   )
       __magic_name__			:		Dict        =     DataLoader(lowerCamelCase     , shuffle=lowerCamelCase     , batch_size=lowerCamelCase   )

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

                            secondary_learner.eval()
                            __magic_name__			:		Dict        =     0.0
                            __magic_name__			:		Dict        =     0.0
                            __magic_name__			:		Optional[Any]        =     []
                            __magic_name__			:		Union[str, Any]        =     []
                            # Compute performance of the secondary learner after this batch
                            for stepa, batcha in enumerate(lowerCamelCase   ):
                                   __magic_name__			:		Dict        =     batcha[0].to(lowerCamelCase   )
                                   __magic_name__			:		int        =     batcha[1].to(lowerCamelCase   )
                                   __magic_name__			:		int        =     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   )
                                   __magic_name__			:		int        =     q_lossa

                     secondary_learner.train()
       return secondary_learner



class        _UpperCamelCase	(						nn.Module	):


  """simple docstring"""





  def __init__( self					: Dict       ,       snake_case					: int      )	->			List[str]:


         '''simple docstring'''





         super(snake_case       ,       self      ).__init__()
         __magic_name__			:		str        =     model.transformer.wte
         __magic_name__			:		str        =     copy.deepcopy(model.transformer.wte.weight      )
         __magic_name__			:		List[Any]        =     nn.Convad(self.embeddings.weight.size(1      )       ,       256       ,       3       ,       padding=1      )
         __magic_name__			:		Any        =     nn.Sequential(nn.Linear(256       ,       32      )       ,       nn.Dropout(p=0.1      )       ,       nn.Linear(32       ,       32      )       ,       nn.Linear(32       ,       1      )      )



  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: List[Any]      )	->			List[str]:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     torch.max(self.conv(self.embeddings(snake_case      ).squeeze(1      ).transpose(1       ,       2      )      )       ,       2      )[0]
         __magic_name__			:		str        =     self.fc(snake_case      )
         return qs.squeeze(1      )




  @classmethod
  def 							_UpperCAmelCase      ( cls					: str       ,       snake_case					: int       ,       snake_case					: int      )	->			str:


         '''simple docstring'''





         __magic_name__			:		Optional[int]        =     cls(snake_case      )  # this calls __init__
         __magic_name__			:		Any        =     torch.load(snake_case      )
         secondary_learner.load_state_dict(snake_case      )
         __magic_name__			:		str        =     model.transformer.wte
         __magic_name__			:		Any        =     copy.deepcopy(model.transformer.wte.weight      )
         return secondary_learner



"""simple docstring"""
def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     str:



       """simple docstring"""







       return " ".join([word[::-1] for word in input_str.split()]   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
