




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 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Any:
 random.seed(lowercase_		)
 np.random.seed(lowercase_		)
 torch.manual_seed(lowercase_		)
 torch.cuda.manual_seed_all(lowercase_		)







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







def 						_SCREAMING_SNAKE_CASE     (							lowercase_="gpt2"		)		->	str:
 A__ =       GPTaLMHeadModel.from_pretrained(lowercase_    ,				output_hidden_states=lowercase_		)
 torch.save(model.state_dict()    ,				model_name + "local.pt"		)
 return model







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Tuple:
 A__ =       copy.deepcopy(lowercase_		)
 model.to(lowercase_		)

 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(lowercase_    ,				lr=5E-5    ,				eps=1E-8		)
 A__ =       get_linear_schedule_with_warmup(lowercase_    ,				0    ,				lowercase_		)
 torch.cuda.empty_cache()
 return model, lm_optimizer, lm_scheduler







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Any:
 A__ =       np.array(lowercase_		).mean()
 A__ =       np.array(lowercase_		).std()
 A__ =       (real_perps - avg) / std
 A__ =       list(zip(lowercase_    ,				lowercase_    ,				lowercase_		)		)
 joblib.dump(lowercase_    ,				lowercase_		)







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_="dev.jbl"    ,				lowercase_=recopy_gpta    ,				)		->	List[Any]:
 A__ =       []
 A__ =       []
 A__ =       []

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

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

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

  # Periodically save the stored (X, IG(X)) pairs
  if step % 10_00 == 0 and step > 1:
   intermittent_save(lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)

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

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

 intermittent_save(lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_="data/tokenized_stories_train_wikitext103.jbl"    ,				lowercase_=1_00    ,				lowercase_=10_26    ,				lowercase_=True		)		->	Any:
 A__ =       joblib.load(lowercase_		)
 print("data loaded"		)
 A__ =       []
 if trim:
  for i, example in enumerate(lowercase_		):
   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(lowercase_		) >= number:
    break
  A__ =       []
  for j in range(i + 1    ,				len(lowercase_		)		):
   if len(data[j][0]		) > min_len:
    train_data.append(data[j]		)
 else:
  A__ =       data[0:number]
  A__ =       data[number:]

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







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_=50    ,				lowercase_="secondary_learner.pt"		)		->	Tuple:
 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_12]
 A__ =       train_dataset[5_12:]
 A__ =       DataLoader(lowercase_    ,				shuffle=lowercase_    ,				batch_size=lowercase_		)
 A__ =       DataLoader(lowercase_    ,				shuffle=lowercase_    ,				batch_size=lowercase_		)

 # secondary learner model set up
 A__ =       nn.MSELoss()
 A__ =       nn.MSELoss(reduction="sum"		)
 secondary_learner.to(lowercase_		)
 A__ =       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
 A__ =       float("inf"		)
 # Iterate through batches until we've used max_steps batches
 for epoch in range(int(lowercase_		)		):
  A__ =       0.0
  secondary_learner.train()
  for step, batch in enumerate(lowercase_		):
   A__ =       batch[0].to(lowercase_		)
   A__ =       batch[1].to(lowercase_		)
   A__ =       secondary_learner(lowercase_		)
   q_optimizer.zero_grad()
   A__ =       loss(lowercase_    ,				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(lowercase_		)):
    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(lowercase_		):
     A__ =       batcha[0].to(lowercase_		)
     A__ =       batcha[1].to(lowercase_		)
     A__ =       secondary_learner(lowercase_		)
     q_lossa += test_loss(lowercase_    ,				lowercase_		).item()
     sum_qa += torch.sum(lowercase_		).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(lowercase_		)
    print(
        "Epoch: "    ,				lowercase_    ,				"step: "    ,				lowercase_    ,				"Avg. q:"    ,				sum_qa / len(lowercase_		)    ,				"Train Loss: "    ,				lowercase_    ,				"Test Loss: "    ,				lowercase_    ,				)
    if q_lossa < best_test_loss:
     joblib.dump((predicted, actual)    ,				"pred_vs_actual.jbl"		)
     torch.save(secondary_learner.state_dict()    ,				lowercase_		)
     A__ =       q_lossa

   secondary_learner.train()
 return secondary_learner






class 			UpperCAmelCase_						(		nn.Module							):




    def __init__(					self		: List[Any]			,				snake_case_		: Optional[Any]   )   ->							Tuple:




     '''simple docstring'''

     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 							__magic_name__							(					self		: Optional[Any]			,				snake_case_		: str   )   ->							Union[str, Any]:




     '''simple docstring'''

     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 							__magic_name__							(					cls		: Optional[int]			,				snake_case_		: Optional[int]			,				snake_case_		: Dict   )   ->							Optional[int]:




     '''simple docstring'''

     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"""



import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json







def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	List[Any]:
 A__ =       filter(lambda lowercase_		: p.requires_grad    ,				model.parameters()		)
 A__ =       sum([np.prod(p.size()		) for p in model_parameters]		)
 return params


SCREAMING_SNAKE_CASE       = logging.getLogger(__name__)




class 			UpperCAmelCase_						(		pl.Callback							):




    def 							__magic_name__							(					self		: List[str]			,				snake_case_		: Optional[Any]			,				snake_case_		: Tuple   )   ->							Optional[int]:




     '''simple docstring'''

     A__ =       {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
     pl_module.logger.log_metrics(snake_case_   )




    @rank_zero_only
    def 							__magic_name__							(					self		: Dict			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule			,				snake_case_		: str			,				snake_case_		: List[str]=True   )   ->							None:




     '''simple docstring'''

     logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""   )
     A__ =       trainer.callback_metrics
     trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}   )
     # Log results
     A__ =       Path(pl_module.hparams.output_dir   )
     if type_path == "test":
      A__ =       od / "test_results.txt"
      A__ =       od / "test_generations.txt"
     else:
      # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
      # If people want this it will be easy enough to add back.
      A__ =       od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
      A__ =       od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
      results_file.parent.mkdir(exist_ok=snake_case_   )
      generations_file.parent.mkdir(exist_ok=snake_case_   )
     with open(snake_case_			,				"a+"   ) as writer:
      for key in sorted(snake_case_   ):
       if key in ["log", "progress_bar", "preds"]:
        continue
       A__ =       metrics[key]
       if isinstance(snake_case_			,				torch.Tensor   ):
        A__ =       val.item()
       A__ =       F"""{key}: {val:.6f}\n"""
       writer.write(snake_case_   )

     if not save_generations:
      return

     if "preds" in metrics:
      A__ =       "\n".join(metrics["preds"]   )
      generations_file.open("w+"   ).write(snake_case_   )




    @rank_zero_only
    def 							__magic_name__							(					self		: str			,				snake_case_		: Tuple			,				snake_case_		: Dict   )   ->							Any:




     '''simple docstring'''

     try:
      A__ =       pl_module.model.model.num_parameters()
     except AttributeError:
      A__ =       pl_module.model.num_parameters()

     A__ =       count_trainable_parameters(snake_case_   )
     # mp stands for million parameters
     trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}   )




    @rank_zero_only
    def 							__magic_name__							(					self		: List[Any]			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule   )   ->							Union[str, Any]:




     '''simple docstring'''

     save_json(pl_module.metrics			,				pl_module.metrics_save_path   )
     return self._write_logs(snake_case_			,				snake_case_			,				"test"   )




    @rank_zero_only
    def 							__magic_name__							(					self		: Optional[int]			,				snake_case_		: pl.Trainer			,				snake_case_		: int   )   ->							Optional[Any]:




     '''simple docstring'''

     save_json(pl_module.metrics			,				pl_module.metrics_save_path   )
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_=1    ,				lowercase_=False		)		->	Dict:
 if metric == "rouge2":
  A__ =       "{val_avg_rouge2:.4f}-{step_count}"
 elif metric == "bleu":
  A__ =       "{val_avg_bleu:.4f}-{step_count}"
 elif metric == "loss":
  A__ =       "{val_avg_loss:.4f}-{step_count}"
 else:
  raise NotImplementedError(
      f"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
      " this function."		)

 A__ =       ModelCheckpoint(
     dirpath=lowercase_    ,				filename=lowercase_    ,				monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				save_top_k=lowercase_    ,				)
 return checkpoint_callback







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Union[str, Any]:
 return EarlyStopping(
     monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				patience=lowercase_    ,				verbose=lowercase_    ,				)





