


"""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


__SCREAMING_SNAKE_CASE					=						logging.getLogger(__name__)



def 						__a      (  a					):





       """simple docstring"""







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



def 						__a      (  a,		a,		a					):





       """simple docstring"""







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



def 						__a      (  a="gpt2"					):





       """simple docstring"""







       _a          =				GPTaLMHeadModel.from_pretrained(a,		output_hidden_states=a					)
       torch.save(model.state_dict(),		model_name + "local.pt"					)
       return model



def 						__a      (  a,		a,		a					):





       """simple docstring"""







       _a          =				copy.deepcopy(a					)
       model.to(a					)

       _a          =				["bias", "LayerNorm.weight"]
       _a          =				[
           {
               "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          =				AdamW(a,		lr=5e-5,		eps=1e-8					)
       _a          =				get_linear_schedule_with_warmup(a,		0,		a					)
       torch.cuda.empty_cache()
       return model, lm_optimizer, lm_scheduler



def 						__a      (  a,		a,		a,		a					):





       """simple docstring"""







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



def 						__a      (  a,		a,		a,		a,		a,		a,		a,		a="dev.jbl",		a=recopy_gpta,		):





       """simple docstring"""







       _a          =				[]
       _a          =				[]
       _a          =				[]

       # Initialize the transformer model
       _a          =				copy.deepcopy(a					)
       orig_model.to(device="cpu"					)
       torch.cuda.empty_cache()

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

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

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

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

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

       intermittent_save(a,		a,		a,		a					)



def 						__a      (  a,		a="data/tokenized_stories_train_wikitext103.jbl",		a=1_0_0,		a=1_0_2_6,		a=True					):





       """simple docstring"""







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

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



def 						__a      (  a,		a,		a,		a,		a=5_0,		a="secondary_learner.pt"					):





       """simple docstring"""







       _a          =				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          =				train_dataset[:5_1_2]
       _a          =				train_dataset[5_1_2:]
       _a          =				DataLoader(a,		shuffle=a,		batch_size=a					)
       _a          =				DataLoader(a,		shuffle=a,		batch_size=a					)

       # secondary learner model set up
       _a          =				nn.MSELoss()
       _a          =				nn.MSELoss(reduction="sum"					)
       secondary_learner.to(a					)
       _a          =				torch.optim.Adam(secondary_learner.parameters(),		lr=0.0_0001					)
       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          =				float("inf"					)
       # Iterate through batches until we've used max_steps batches
       for epoch in range(int(a					)					):
              _a          =				0.0
              secondary_learner.train()
              for step, batch in enumerate(a					):
                     _a          =				batch[0].to(a					)
                     _a          =				batch[1].to(a					)
                     _a          =				secondary_learner(a					)
                     q_optimizer.zero_grad()
                     _a          =				loss(a,		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(a					)):
                            _a          =				tr_q_loss / (step + 1)

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

                     secondary_learner.train()
       return secondary_learner





class __snake_case		(					nn.Module      ):







   """simple docstring"""




   def __init__(		self							:List[Any]				,  UpperCamelCase__							:Optional[Any]       ):
          super(UpperCamelCase__				,  self       ).__init__()
          _a          =				model.transformer.wte
          _a          =				copy.deepcopy(model.transformer.wte.weight       )
          _a          =				nn.Convad(self.embeddings.weight.size(1       )				,  256				,  3				,  padding=1       )
          _a          =				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[Any]				,  UpperCamelCase__							:str       ):
          _a          =				torch.max(self.conv(self.embeddings(UpperCamelCase__       ).squeeze(1       ).transpose(1				,  2       )       )				,  2       )[0]
          _a          =				self.fc(UpperCamelCase__       )
          return qs.squeeze(1       )




   @classmethod
   def SCREAMING_SNAKE_CASE_      (		cls							:Optional[int]				,  UpperCamelCase__							:Optional[int]				,  UpperCamelCase__							:Dict       ):
          _a          =				cls(UpperCamelCase__       )  # this calls __init__
          _a          =				torch.load(UpperCamelCase__       )
          secondary_learner.load_state_dict(UpperCamelCase__       )
          _a          =				model.transformer.wte
          _a          =				copy.deepcopy(model.transformer.wte.weight       )
          return secondary_learner




"""simple docstring"""




from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class __snake_case		:







   """simple docstring"""




   def __init__(		self							:List[Any]				,  UpperCamelCase__							:Any       ):
          _a          =				data
          _a          =				None




   def __str__(		self							:Any       ):
          return f'{self.data}'






class __snake_case		:







   """simple docstring"""




   def __init__(		self							:Any       ):
          _a          =				None
          _a          =				None




   def __iter__(		self							:List[str]       ):
          _a          =				self.front
          while node:
                 yield node.data
                 _a          =				node.next




   def __len__(		self							:List[str]       ):
          return len(tuple(iter(self       )       )       )




   def __str__(		self							:Union[str, Any]       ):
          return " <- ".join(str(UpperCamelCase__       ) for item in self       )




   def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
          return len(self       ) == 0




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Any       ):
          _a          =				Node(UpperCamelCase__       )
          if self.is_empty():
                 _a          =				_a          =				node
          else:
                 assert isinstance(self.rear				,  UpperCamelCase__       )
                 _a          =				node
                 _a          =				node




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
          if self.is_empty():
                 raise IndexError("dequeue from empty queue"       )
          assert isinstance(self.front				,  UpperCamelCase__       )
          _a          =				self.front
          _a          =				self.front.next
          if self.front is None:
                 _a          =				None
          return node.data




   def SCREAMING_SNAKE_CASE_      (		self							:int       ):
          _a          =				_a          =				None


if __name__ == "__main__":
   from doctest import testmod

   testmod()
