





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							:      List[str]						=	logging.getLogger(__name__)

def      __snake_case						(					_lowerCAmelCase : Tuple	) ->						Optional[Any]:
   random.seed(_lowerCAmelCase	)
   np.random.seed(_lowerCAmelCase	)
   torch.manual_seed(_lowerCAmelCase	)
   torch.cuda.manual_seed_all(_lowerCAmelCase	)

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

def      __snake_case						(					_lowerCAmelCase : List[Any]="gpt2"	) ->						Tuple:
   A_					:				Optional[Any]          =			GPTaLMHeadModel.from_pretrained(_lowerCAmelCase				,				output_hidden_states=_lowerCAmelCase	)
   torch.save(model.state_dict()				,				model_name + "local.pt"	)
   return model

def      __snake_case						(					_lowerCAmelCase : List[str]				,				_lowerCAmelCase : Optional[Any]				,				_lowerCAmelCase : str	) ->						Tuple:
   A_					:				List[str]          =			copy.deepcopy(_lowerCAmelCase	)
   model.to(_lowerCAmelCase	)

   A_					:				List[Any]          =			['bias', 'LayerNorm.weight']
   A_					:				List[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},
   ]
   A_					:				Tuple          =			AdamW(_lowerCAmelCase				,				lr=5e-5				,				eps=1e-8	)
   A_					:				int          =			get_linear_schedule_with_warmup(_lowerCAmelCase				,				0				,				_lowerCAmelCase	)
   torch.cuda.empty_cache()
   return model, lm_optimizer, lm_scheduler

def      __snake_case						(					_lowerCAmelCase : Union[str, Any]				,				_lowerCAmelCase : str				,				_lowerCAmelCase : Dict				,				_lowerCAmelCase : List[Any]	) ->						Optional[int]:
   A_					:				int          =			np.array(_lowerCAmelCase	).mean()
   A_					:				Tuple          =			np.array(_lowerCAmelCase	).std()
   A_					:				str          =			(real_perps - avg) / std
   A_					:				List[str]          =			list(zip(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase	)	)
   joblib.dump(_lowerCAmelCase				,				_lowerCAmelCase	)

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

   # Initialize the transformer model
   A_					:				List[Any]          =			copy.deepcopy(_lowerCAmelCase	)
   orig_model.to(device="cpu"	)
   torch.cuda.empty_cache()

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

   for step in tqdm(range(_lowerCAmelCase	)	):
      A_					:				Any          =			torch.zeros((1, context_len)				,				dtype=torch.long				,				device=_lowerCAmelCase	)
      A_					:				Optional[Any]          =			random.choice(_lowerCAmelCase	)
      A_					:				Tuple          =			random.randint(0				,				len(story[0]	) - context_len - 1	)
      A_					:				int          =			story[0][start : start + context_len]
      lm_optimizer.zero_grad()
      A_					:				Dict          =			model(_lowerCAmelCase				,				labels=_lowerCAmelCase	)
      A_					:				str          =			outputs[0]
      A_					:				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
      A_					:				Dict          =			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
      A_					:				Union[str, 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      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : Any="data/tokenized_stories_train_wikitext103.jbl"				,				_lowerCAmelCase : str=100				,				_lowerCAmelCase : Any=1026				,				_lowerCAmelCase : List[str]=True	) ->						Optional[Any]:
   A_					:				Dict          =			joblib.load(_lowerCAmelCase	)
   print("data loaded"	)
   A_					:				List[str]          =			[]
   if trim:
      for i, example in enumerate(_lowerCAmelCase	):
         if len(example[0]	) > min_len:
            A_					:				Optional[Any]          =			random.randint(0				,				len(example[0]	) - context_len - 1	)
            objective_set.append(example[0, start : start + context_len]	)
         if len(_lowerCAmelCase	) >= number:
            break
      A_					:				Optional[int]          =			[]
      for j in range(i + 1				,				len(_lowerCAmelCase	)	):
         if len(data[j][0]	) > min_len:
            train_data.append(data[j]	)
   else:
      A_					:				int          =			data[0:number]
      A_					:				Any          =			data[number:]

   joblib.dump(_lowerCAmelCase				,				"objective_set.jbl"	)
   print("objective set saved"	)
   return train_data, objective_set

def      __snake_case						(					_lowerCAmelCase : List[str]				,				_lowerCAmelCase : Union[str, Any]				,				_lowerCAmelCase : Tuple				,				_lowerCAmelCase : Union[str, Any]				,				_lowerCAmelCase : Optional[Any]=50				,				_lowerCAmelCase : Dict="secondary_learner.pt"	) ->						Union[str, Any]:
   A_					:				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
   A_					:				int          =			train_dataset[:512]
   A_					:				Optional[int]          =			train_dataset[512:]
   A_					:				Dict          =			DataLoader(_lowerCAmelCase				,				shuffle=_lowerCAmelCase				,				batch_size=_lowerCAmelCase	)
   A_					:				Optional[int]          =			DataLoader(_lowerCAmelCase				,				shuffle=_lowerCAmelCase				,				batch_size=_lowerCAmelCase	)

   # secondary learner model set up
   A_					:				List[Any]          =			nn.MSELoss()
   A_					:				Any          =			nn.MSELoss(reduction="sum"	)
   secondary_learner.to(_lowerCAmelCase	)
   A_					:				Union[str, Any]          =			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_					:				int          =			0.0
      secondary_learner.train()
      for step, batch in enumerate(_lowerCAmelCase	):
         A_					:				List[str]          =			batch[0].to(_lowerCAmelCase	)
         A_					:				Optional[Any]          =			batch[1].to(_lowerCAmelCase	)
         A_					:				Dict          =			secondary_learner(_lowerCAmelCase	)
         q_optimizer.zero_grad()
         A_					:				List[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_					:				Optional[int]          =			tr_q_loss / (step + 1)

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

         secondary_learner.train()
   return secondary_learner





class 					__magic_name__  (							nn.Module							):





     """simple docstring"""



     def __init__(							self       :List[Any]			,      snake_case       :Dict  ):





        '''simple docstring'''




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



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :Union[str, Any]  ):





        '''simple docstring'''




        A_					:				Optional[int]          =			torch.max(self.conv(self.embeddings(_A  ).squeeze(1  ).transpose(1			,      2  )  )			,      2  )[0]
        A_					:				Any          =			self.fc(_A  )
        return qs.squeeze(1  )





     @classmethod
     def 						SCREAMING_SNAKE_CASE		(							cls       :List[Any]			,      snake_case       :List[Any]			,      snake_case       :str  ):





        '''simple docstring'''




        A_					:				List[str]          =			cls(_A  )  # this calls __init__
        A_					:				List[Any]          =			torch.load(_A  )
        secondary_learner.load_state_dict(_A  )
        A_					:				List[Any]          =			model.transformer.wte
        A_					:				Any          =			copy.deepcopy(model.transformer.wte.weight  )
        return secondary_learner








from __future__ import annotations

def      __snake_case						(					_lowerCAmelCase : int	) ->						list[int]:
   assert (
       isinstance(_lowerCAmelCase				,				_lowerCAmelCase	) and mask > 0
   ), f"mask needs to be positive integer, your input {mask}"
   A_					:				Optional[Any]          =			[]
   A_					:				Optional[int]          =			mask

   while submask:
      all_submasks.append(_lowerCAmelCase	)
      A_					:				List[str]          =			(submask - 1) & mask

   return all_submasks


if __name__ == "__main__":
 import doctest

 doctest.testmod()
