

from argparse import ArgumentParser

from . import BaseTransformersCLICommand

def    A_  (							a  ):

      """simple docstring"""



      return DownloadCommand(args.model					,   args.cache_dir					,   args.force					,   args.trust_remote_code  )



class  _A		(  __magic_name__):

  @staticmethod
  def  UpperCAmelCase    (				_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     parser.add_parser('download'					)
        download_parser.add_argument(
            '--cache-dir' ,					type=_SCREAMING_SNAKE_CASE ,					default=_SCREAMING_SNAKE_CASE ,					help='Path to location to store the models'					)
        download_parser.add_argument(
            '--force' ,					action='store_true' ,					help='Force the model to be download even if already in cache-dir'					)
        download_parser.add_argument(
            '--trust-remote-code' ,					action='store_true' ,					help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine' ,					)
        download_parser.add_argument('model' ,					type=_SCREAMING_SNAKE_CASE ,					help='Name of the model to download'					)
        download_parser.set_defaults(func=_SCREAMING_SNAKE_CASE					)

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     model
        SCREAMING_SNAKE_CASE_				:  Any           =     cache
        SCREAMING_SNAKE_CASE_				:  Any           =     force
        SCREAMING_SNAKE_CASE_				:  List[str]           =     trust_remote_code



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        from ..models.auto import AutoModel, AutoTokenizer

        AutoModel.from_pretrained(
            self._model ,					cache_dir=self._cache ,					force_download=self._force ,					trust_remote_code=self._trust_remote_code					)
        AutoTokenizer.from_pretrained(
            self._model ,					cache_dir=self._cache ,					force_download=self._force ,					trust_remote_code=self._trust_remote_code					)









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_  (							a=3_2					,   a=1_0					,   a=1_0_0					,   a=1_0_2_6					,   a=True					,   a="data/tokenized_stories_train_wikitext103.jbl"					,   a="igf_context_pairs.jbl"					,   ):

      """simple docstring"""



      set_seed(3  )
      # generate train_data and objective_set
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     generate_datasets(
          a					,   a					,   number=a					,   min_len=1_0_2_6					,   trim=a  )
      # 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?
      SCREAMING_SNAKE_CASE_				:  str           =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )

      # load pretrained model
      SCREAMING_SNAKE_CASE_				:  List[str]           =     load_gpta('gpt2'  ).to(a  )
      print('computing perplexity on objective set'  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     compute_perplexity(a					,   a					,   a  ).item()
      print('perplexity on objective set:'					,   a  )

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

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

def    A_  (							a					,   a=1_5					,   a=1_2_8					,   a=1_0_0					,   a="igf_model.pt"					,   ):

      """simple docstring"""



      set_seed(4_2  )

      # Load pre-trained model
      SCREAMING_SNAKE_CASE_				:  int           =     GPTaLMHeadModel.from_pretrained('gpt2'  )

      # Initialize secondary learner to use embedding weights of model
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     SecondaryLearner(a  )

      # Train secondary learner
      SCREAMING_SNAKE_CASE_				:  Any           =     train_secondary_learner(
          a					,   a					,   max_epochs=a					,   batch_size=a					,   eval_freq=1_0_0					,   igf_model_path=a					,   )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner

def    A_  (							a					,   a					,   a					,   a=3_2					,   a=1_0_0_0					,   a=1_6					,   a=1.0					,   a=recopy_gpta					,   a=None					,   a=1_0					,   a="gpt2_finetuned.pt"					,   ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Dict           =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     RandomSampler(a  )
      SCREAMING_SNAKE_CASE_				:  Dict           =     DataLoader(a					,   sampler=a  )

      SCREAMING_SNAKE_CASE_				:  int           =     max_steps // (len(a  )) + 1
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     0
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.zeros((1, context_len)					,   dtype=torch.long					,   device=a  )
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     recopy_model(a					,   a					,   a  )

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(a  )
            secondary_learner.eval()
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     []
      SCREAMING_SNAKE_CASE_				:  List[str]           =     0

      SCREAMING_SNAKE_CASE_				:  List[Any]           =     []
      SCREAMING_SNAKE_CASE_				:  Dict           =     []

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

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

                        # 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 == 1_0:
                              SCREAMING_SNAKE_CASE_				:  Any           =     -1
                        if predicted_q < threshold:
                              SCREAMING_SNAKE_CASE_				:  Tuple           =     False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()  )  )
                        SCREAMING_SNAKE_CASE_				:  Tuple           =     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()
                        SCREAMING_SNAKE_CASE_				:  Tuple           =     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:
                              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     compute_perplexity(a					,   a					,   a  )
                              test_perps.append(a  )

                              print('Test perplexity, step'					,   a					,   ':'					,   a  )
            # Break out of the loop after 60 batches
                  if max_steps > 0 and global_step > 6_0:
                        break
            if max_steps > 0 and global_step > 6_0:
                  break

    # save finetuned transformer model
      torch.save(model.state_dict()					,   a  )
      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_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'  )

      # Required parameters
      parser.add_argument(
          '--data_dir'					,   default=a					,   type=a					,   required=a					,   help='The input data dir. Should contain data files for WikiText.'					,   )
      parser.add_argument(
          '--model_name_or_path'					,   default=a					,   type=a					,   required=a					,   help='Path to pretrained model or model identifier from huggingface.co/models'					,   )
      parser.add_argument(
          '--data_file'					,   type=a					,   default=a					,   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=a					,   default=a					,   help='A jbl file containing the context and information gain pairs to train secondary learner.'					,   )

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

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

      parser.add_argument(
          '--context_len'					,   default=3_2					,   type=a					,   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=1_0_0					,   type=a					,   help='number of articles that are long enough to be used as our objective set'					,   )
      parser.add_argument(
          '--eval_freq'					,   default=1_0_0					,   type=a					,   help='secondary model evaluation is triggered at eval_freq'  )

      parser.add_argument('--max_steps'					,   default=1_0_0_0					,   type=a					,   help='To calculate training epochs'  )

      parser.add_argument(
          '--secondary_learner_batch_size'					,   default=1_2_8					,   type=a					,   help='batch size of training data for secondary learner'					,   )

      parser.add_argument(
          '--batch_size'					,   default=1_6					,   type=a					,   help='batch size of training data of language model(gpt2) '  )

      parser.add_argument(
          '--eval_interval'					,   default=1_0					,   type=a					,   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=1_0_0					,   type=a					,   help='The number of examples split to be used as objective_set/test_data'  )

      parser.add_argument(
          '--min_len'					,   default=1_0_2_6					,   type=a					,   help='The minimum length of the article to be used as objective set'  )

      parser.add_argument(
          '--secondary_learner_max_epochs'					,   default=1_5					,   type=a					,   help='number of epochs to train secondary learner'  )

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

      parser.add_argument(
          '--threshold'					,   default=1.0					,   type=a					,   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=a					,   help='finetuned_model_name'  )

      parser.add_argument(
          '--recopy_model'					,   default=a					,   type=a					,   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=3_2					,   max_steps=1_0					,   size_objective_set=1_0_0					,   min_len=1_0_2_6					,   trim=a					,   data_file='data/tokenized_stories_train_wikitext103.jbl'					,   igf_data_file='igf_context_pairs.jbl'					,   )

      # Load train data for secondary learner
      SCREAMING_SNAKE_CASE_				:  Tuple           =     joblib.load('data/IGF_values.jbl'  )

      # Train secondary learner
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     training_secondary_learner(
          a					,   secondary_learner_max_epochs=1_5					,   secondary_learner_batch_size=1_2_8					,   eval_freq=1_0_0					,   igf_model_path='igf_model.pt'					,   )

      # load pretrained gpt2 model
      SCREAMING_SNAKE_CASE_				:  int           =     GPTaLMHeadModel.from_pretrained('gpt2'  )
      set_seed(4_2  )

      # Generate train and test data to train and evaluate gpt2 model
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  List[str]           =     generate_datasets(
          context_len=3_2					,   file='data/tokenized_stories_train_wikitext103.jbl'					,   number=1_0_0					,   min_len=1_0_2_6					,   trim=a  )

      # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
      finetune(
          a					,   a					,   a					,   context_len=3_2					,   max_steps=1_0_0_0					,   batch_size=1_6					,   threshold=1.0					,   recopy_model=a					,   secondary_learner=a					,   eval_interval=1_0					,   finetuned_model_name='gpt2_finetuned.pt'					,   )


if __name__ == "__main__":
    main()






