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


UpperCAmelCase				 =   logging.getLogger(__name__)







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						str:
     random.seed(lowerCAmelCase_							)
     np.random.seed(lowerCAmelCase_							)
     torch.manual_seed(lowerCAmelCase_							)
     torch.cuda.manual_seed_all(lowerCAmelCase_							)







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







def 						UpperCAmelCase							(		lowerCAmelCase_="gpt2"							)						->						int:
     a	:     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_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						List[str]:
     a	:     Optional[Any]						      =    copy.deepcopy(lowerCAmelCase_							)
     model.to(lowerCAmelCase_							)

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







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Any:
     a	:     Any						      =    np.array(lowerCAmelCase_							).mean()
     a	:     Optional[Any]						      =    np.array(lowerCAmelCase_							).std()
     a	:     str						      =    (real_perps - avg) / std
     a	:     Tuple						      =    list(zip(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)							)
     joblib.dump(lowerCAmelCase_    ,   lowerCAmelCase_							)







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_="dev.jbl"    ,   lowerCAmelCase_=recopy_gpta    ,   )						->						Dict:
     a	:     Dict						      =    []
     a	:     Optional[int]						      =    []
     a	:     Union[str, Any]						      =    []

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

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

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

          # Periodically save the stored (X, IG(X)) pairs
          if step % 1_0_0_0 == 0 and step > 1:
               intermittent_save(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)

          # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
          a      ,				a      ,				a	:     int						      =    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_    ,   lowerCAmelCase_="data/tokenized_stories_train_wikitext103.jbl"    ,   lowerCAmelCase_=1_0_0    ,   lowerCAmelCase_=1_0_2_6    ,   lowerCAmelCase_=True							)						->						Union[str, Any]:
     a	:     Tuple						      =    joblib.load(lowerCAmelCase_							)
     print('''data loaded'''							)
     a	:     int						      =    []
     if trim:
          for i, example in enumerate(lowerCAmelCase_							):
               if len(example[0]							) > min_len:
                    a	:     Dict						      =    random.randint(0    ,   len(example[0]							) - context_len - 1							)
                    objective_set.append(example[0, start : start + context_len]							)
               if len(lowerCAmelCase_							) >= number:
                    break
          a	:     Tuple						      =    []
          for j in range(i + 1    ,   len(lowerCAmelCase_							)							):
               if len(data[j][0]							) > min_len:
                    train_data.append(data[j]							)
     else:
          a	:     str						      =    data[0:number]
          a	:     List[Any]						      =    data[number:]

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







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=5_0    ,   lowerCAmelCase_="secondary_learner.pt"							)						->						Optional[Any]:
     a	:     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
     a	:     str						      =    train_dataset[:5_1_2]
     a	:     Any						      =    train_dataset[5_1_2:]
     a	:     Optional[Any]						      =    DataLoader(lowerCAmelCase_    ,   shuffle=lowerCAmelCase_    ,   batch_size=lowerCAmelCase_							)
     a	:     Dict						      =    DataLoader(lowerCAmelCase_    ,   shuffle=lowerCAmelCase_    ,   batch_size=lowerCAmelCase_							)

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

                    secondary_learner.eval()
                    a	:     Optional[Any]						      =    0.0
                    a	:     int						      =    0.0
                    a	:     List[Any]						      =    []
                    a	:     Tuple						      =    []
                    # Compute performance of the secondary learner after this batch
                    for stepa, batcha in enumerate(lowerCAmelCase_							):
                         a	:     Any						      =    batcha[0].to(lowerCAmelCase_							)
                         a	:     Dict						      =    batcha[1].to(lowerCAmelCase_							)
                         a	:     Tuple						      =    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_							)
                         a	:     Dict						      =    q_lossa

               secondary_learner.train()
     return secondary_learner






class 				_SCREAMING_SNAKE_CASE( nn.Module						):
   def __init__(     self				,  lowerCAmelCase_     )						->str:
        super(lowerCAmelCase_				,  self     ).__init__()
        a	:     Optional[int]						      =    model.transformer.wte
        a	:     Union[str, Any]						      =    copy.deepcopy(model.transformer.wte.weight     )
        a	:     Union[str, Any]						      =    nn.Convad(self.embeddings.weight.size(1     )				,  2_56				,  3				,  padding=1     )
        a	:     List[str]						      =    nn.Sequential(nn.Linear(2_56				,  32     )				,  nn.Dropout(p=0.1     )				,  nn.Linear(32				,  32     )				,  nn.Linear(32				,  1     )     )
   def _lowercase(     self				,  lowerCAmelCase_     )						->Dict:
        a	:     Any						      =    torch.max(self.conv(self.embeddings(lowerCAmelCase_     ).squeeze(1     ).transpose(1				,  2     )     )				,  2     )[0]
        a	:     Optional[int]						      =    self.fc(lowerCAmelCase_     )
        return qs.squeeze(1     )


   @classmethod
   def _lowercase(     cls				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        a	:     Tuple						      =    cls(lowerCAmelCase_     )  # this calls __init__
        a	:     List[str]						      =    torch.load(lowerCAmelCase_     )
        secondary_learner.load_state_dict(lowerCAmelCase_     )
        a	:     List[Any]						      =    model.transformer.wte
        a	:     Dict						      =    copy.deepcopy(model.transformer.wte.weight     )
        return secondary_learner
import torch
from torch import autograd






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[str]:
        a	:     str						      =    inputs.numel()
        a	:     str						      =    int(0.0_05 * nb_elems     ) + 1
        if sigmoid:
             a	:     Optional[Any]						      =    (torch.sigmoid(lowerCAmelCase_     ) > threshold).type(inputs.type()     )
        else:
             a	:     Union[str, Any]						      =    (inputs > threshold).type(inputs.type()     )
        if mask.sum() < nb_min:
             # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
             a	:     str						      =    inputs.flatten().kthvalue(max(nb_elems - nb_min				,  1     )     ).values
             a	:     Tuple						      =    (inputs > k_threshold).type(inputs.type()     )
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        return gradOutput, None, None






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[Any]:
        a	:     List[Any]						      =    inputs.clone()
        a      ,				a	:     Optional[Any]						      =    inputs.flatten().sort(descending=lowerCAmelCase_     )
        a	:     Tuple						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Optional[Any]						      =    0
        a	:     List[Any]						      =    1
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        return gradOutput, None






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        a	:     Union[str, Any]						      =    inputs.clone()
        a      ,				a	:     List[Any]						      =    inputs.abs().flatten().sort(descending=lowerCAmelCase_     )
        a	:     Optional[int]						      =    int(threshold * inputs.numel()     )

        # flat_out and mask access the same memory.
        a	:     Dict						      =    mask.flatten()
        a	:     Tuple						      =    0
        a	:     List[str]						      =    1
        return mask