






import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout


def _A       (   __magic_name__	):
   lowercase__			       =  FileLock(str(tmpdir / "foo.lock"	)	)
   lowercase__			       =  FileLock(str(tmpdir / "foo.lock"	)	)
   lowercase__			       =  0.01
   with locka.acquire():
      with pytest.raises(__magic_name__	):
         lowercase__			       =  time.time()
         locka.acquire(__magic_name__	)
         assert time.time() - _start > timeout







def _A       (   __magic_name__	):
   lowercase__			       =  "a" * 1000 + ".lock"
   lowercase__			       =  FileLock(str(tmpdir / filename	)	)
   assert locka._lock_file.endswith(".lock"	)
   assert not locka._lock_file.endswith(__magic_name__	)
   assert len(os.path.basename(locka._lock_file	)	) <= 255
   lowercase__			       =  FileLock(tmpdir / filename	)
   with locka.acquire():
      with pytest.raises(__magic_name__	):
         locka.acquire(0	)













import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel


def _A       (   __magic_name__=32  , __magic_name__=10  , __magic_name__=100  , __magic_name__=1026  , __magic_name__=True  , __magic_name__="data/tokenized_stories_train_wikitext103.jbl"  , __magic_name__="igf_context_pairs.jbl"  , ):
   set_seed(3	)
   # generate train_data and objective_set
   lowercase__       ,							lowercase__			       =  generate_datasets(
       __magic_name__  , __magic_name__  , number=__magic_name__  , min_len=1026  , trim=__magic_name__	)
   # keeps model same across runs
   set_seed(4	)
   # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
   # can we train on GPU?
   lowercase__			       =  torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)

   # load pretrained model
   lowercase__			       =  load_gpta("gpt2"	).to(__magic_name__	)
   print("computing perplexity on objective set"	)
   lowercase__			       =  compute_perplexity(__magic_name__  , __magic_name__  , __magic_name__	).item()
   print("perplexity on objective set:"  , __magic_name__	)

   # collect igf pairs and save to file demo.jbl
   collect_objective_set(__magic_name__  , __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__	)

   # clean up, delete model and data we don't need anymore
   del model, train_data, objective_set
   torch.cuda.empty_cache()


def _A       (   __magic_name__  , __magic_name__=15  , __magic_name__=128  , __magic_name__=100  , __magic_name__="igf_model.pt"  , ):
   set_seed(42	)

   # Load pre-trained model
   lowercase__			       =  GPTaLMHeadModel.from_pretrained("gpt2"	)

   # Initialize secondary learner to use embedding weights of model
   lowercase__			       =  SecondaryLearner(__magic_name__	)

   # Train secondary learner
   lowercase__			       =  train_secondary_learner(
       __magic_name__  , __magic_name__  , max_epochs=__magic_name__  , batch_size=__magic_name__  , eval_freq=100  , igf_model_path=__magic_name__  , )

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner


def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__=32  , __magic_name__=1000  , __magic_name__=16  , __magic_name__=1.0  , __magic_name__=recopy_gpta  , __magic_name__=None  , __magic_name__=10  , __magic_name__="gpt2_finetuned.pt"  , ):
   lowercase__			       =  torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)
   lowercase__			       =  RandomSampler(__magic_name__	)
   lowercase__			       =  DataLoader(__magic_name__  , sampler=__magic_name__	)

   lowercase__			       =  max_steps // (len(__magic_name__	)) + 1
   lowercase__			       =  0
   lowercase__			       =  torch.zeros((1, context_len)  , dtype=torch.long  , device=__magic_name__	)
   lowercase__       ,							lowercase__       ,							lowercase__			       =  recopy_model(__magic_name__  , __magic_name__  , __magic_name__	)

   model.train()
   if secondary_learner is not None:
      secondary_learner.to(__magic_name__	)
      secondary_learner.eval()
   lowercase__			       =  []
   lowercase__			       =  0

   lowercase__			       =  []
   lowercase__			       =  []

   # Compute the performance of the transformer model at the beginning
   lowercase__			       =  compute_perplexity(__magic_name__  , __magic_name__  , __magic_name__	)
   test_perps.append(__magic_name__	)
   print("Test perplexity, step"  , __magic_name__  , ":"  , __magic_name__	)
   for epoch in range(int(__magic_name__	)	):
      for step, example in enumerate(__magic_name__	):
         torch.cuda.empty_cache()
         lowercase__			       =  random.randint(0  , example.size(2	) - context_len - 1	)
         lowercase__			       =  example[0, 0, start : start + context_len]
         lm_optimizer.zero_grad()
         lowercase__			       =  model(__magic_name__  , labels=__magic_name__	)
         lowercase__			       =  True

         if secondary_learner is not None:
            lowercase__			       =  secondary_learner.forward(
                torch.tensor(__magic_name__  , dtype=torch.long  , device=__magic_name__	).unsqueeze(0	)	)[0].item()
            observed_qs.append(float(__magic_name__	)	)

            # Here we implement the simple non-constant threshold for the predicted IG(X) value
            # We will decay the selectivity of our secondary learner filter from
            # 1 standard deviation above average to 1 below average after 10 batches.

            if global_step == 10:
               lowercase__			       =  -1
            if predicted_q < threshold:
               lowercase__			       =  False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()	)	)
            lowercase__			       =  outputs[0]
            lm_loss.backward()
            examples += 1

         del outputs

         # Once the batch is filled with enough contexts, backprop on the batch.
         if examples == batch_size:
            torch.cuda.empty_cache()
            lowercase__			       =  0
            # Do LM backprop
            torch.nn.utils.clip_grad_norm_(model.parameters()  , 3.0	)
            lm_optimizer.step()
            lm_scheduler.step()  # Update learning rate schedule
            global_step += 1
            # Compute the performance of the transformer model at this batch
            if global_step % eval_interval == 0:
               lowercase__			       =  compute_perplexity(__magic_name__  , __magic_name__  , __magic_name__	)
               test_perps.append(__magic_name__	)

               print("Test perplexity, step"  , __magic_name__  , ":"  , __magic_name__	)
            # Break out of the loop after 60 batches
         if max_steps > 0 and global_step > 60:
            break
      if max_steps > 0 and global_step > 60:
         break

    # save finetuned transformer model
   torch.save(model.state_dict()  , __magic_name__	)
   torch.cuda.empty_cache()
   # Do some cleaning up so we can reinitialize for the next run of this function
   del lm_optimizer
   del lm_scheduler
   return model


def _A       (   ):
   lowercase__			       =  argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"	)

   # Required parameters
   parser.add_argument(
       "--data_dir"  , default=__magic_name__  , type=__magic_name__  , required=__magic_name__  , help="The input data dir. Should contain data files for WikiText."  , )
   parser.add_argument(
       "--model_name_or_path"  , default=__magic_name__  , type=__magic_name__  , required=__magic_name__  , help="Path to pretrained model or model identifier from huggingface.co/models"  , )
   parser.add_argument(
       "--data_file"  , type=__magic_name__  , default=__magic_name__  , help=(
           "A jbl file containing tokenized data which can be split as objective dataset, "
           "train_dataset and test_dataset."
       )  , )

   parser.add_argument(
       "--igf_data_file"  , type=__magic_name__  , default=__magic_name__  , help="A jbl file containing the context and information gain pairs to train secondary learner."  , )

   parser.add_argument(
       "--output_dir"  , default=__magic_name__  , type=__magic_name__  , required=__magic_name__  , help="The output directory where the final fine-tuned model is stored."  , )

   parser.add_argument(
       "--tokenizer_name"  , default=__magic_name__  , type=__magic_name__  , help="Pretrained tokenizer name or path if not the same as model_name"  , )
   parser.add_argument("--seed"  , type=__magic_name__  , default=__magic_name__  , help="A seed for reproducible training."	)

   parser.add_argument(
       "--context_len"  , default=32  , type=__magic_name__  , help=(
           "The maximum total input sequence length after tokenization. Sequences longer "
           "than this will be truncated, sequences shorter will be padded."
       )  , )

   parser.add_argument(
       "--size_objective_set"  , default=100  , type=__magic_name__  , help="number of articles that are long enough to be used as our objective set"  , )
   parser.add_argument(
       "--eval_freq"  , default=100  , type=__magic_name__  , help="secondary model evaluation is triggered at eval_freq"	)

   parser.add_argument("--max_steps"  , default=1000  , type=__magic_name__  , help="To calculate training epochs"	)

   parser.add_argument(
       "--secondary_learner_batch_size"  , default=128  , type=__magic_name__  , help="batch size of training data for secondary learner"  , )

   parser.add_argument(
       "--batch_size"  , default=16  , type=__magic_name__  , help="batch size of training data of language model(gpt2) "	)

   parser.add_argument(
       "--eval_interval"  , default=10  , type=__magic_name__  , help=(
           "decay the selectivity of our secondary learner filter from"
           "1 standard deviation above average to 1 below average after 10 batches"
       )  , )

   parser.add_argument(
       "--number"  , default=100  , type=__magic_name__  , help="The number of examples split to be used as objective_set/test_data"	)

   parser.add_argument(
       "--min_len"  , default=1026  , type=__magic_name__  , help="The minimum length of the article to be used as objective set"	)

   parser.add_argument(
       "--secondary_learner_max_epochs"  , default=15  , type=__magic_name__  , help="number of epochs to train secondary learner"	)

   parser.add_argument("--trim"  , default=__magic_name__  , type=__magic_name__  , help="truncate the example if it exceeds context length"	)

   parser.add_argument(
       "--threshold"  , default=1.0  , type=__magic_name__  , help=(
           "The threshold value used by secondary learner to filter the train_data and allow only"
           " informative data as input to the model"
       )  , )

   parser.add_argument("--finetuned_model_name"  , default="gpt2_finetuned.pt"  , type=__magic_name__  , help="finetuned_model_name"	)

   parser.add_argument(
       "--recopy_model"  , default=__magic_name__  , type=__magic_name__  , help="Reset the model to the original pretrained GPT-2 weights after each iteration"  , )

   # function calls
   # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
   generate_n_pairs(
       context_len=32  , max_steps=10  , size_objective_set=100  , min_len=1026  , trim=__magic_name__  , data_file="data/tokenized_stories_train_wikitext103.jbl"  , igf_data_file="igf_context_pairs.jbl"  , )

   # Load train data for secondary learner
   lowercase__			       =  joblib.load("data/IGF_values.jbl"	)

   # Train secondary learner
   lowercase__			       =  training_secondary_learner(
       __magic_name__  , secondary_learner_max_epochs=15  , secondary_learner_batch_size=128  , eval_freq=100  , igf_model_path="igf_model.pt"  , )

   # load pretrained gpt2 model
   lowercase__			       =  GPTaLMHeadModel.from_pretrained("gpt2"	)
   set_seed(42	)

   # Generate train and test data to train and evaluate gpt2 model
   lowercase__       ,							lowercase__			       =  generate_datasets(
       context_len=32  , file="data/tokenized_stories_train_wikitext103.jbl"  , number=100  , min_len=1026  , trim=__magic_name__	)

   # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
   finetune(
       __magic_name__  , __magic_name__  , __magic_name__  , context_len=32  , max_steps=1000  , batch_size=16  , threshold=1.0  , recopy_model=__magic_name__  , secondary_learner=__magic_name__  , eval_interval=10  , finetuned_model_name="gpt2_finetuned.pt"  , )


if __name__ == "__main__":
  main()






