

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_             =     get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
  from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowercase_             =     2_5_0_0_0_4
lowercase_             =     2_5_0_0_2_0





@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						MBartTokenizer
  _a		    =						MBartTokenizerFast
  _a		    =						True
  _a		    =						True




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        super().setUp()

        # We have a SentencePiece fixture for testing
        _lowercase			 =MBartTokenizer(lowerCAmelCase							,     keep_accents=lowerCAmelCase      )
        tokenizer.save_pretrained(self.tmpdirname      )




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =MBartTokenizer(lowerCAmelCase							,     keep_accents=lowerCAmelCase      )

        _lowercase			 =tokenizer.tokenize('This is a test'      )
        self.assertListEqual(lowerCAmelCase							,     ['▁This', '▁is', '▁a', '▁t', 'est']      )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowerCAmelCase      )							,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]							,     )

        _lowercase			 =tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
        self.assertListEqual(
            lowerCAmelCase							,     [
                SPIECE_UNDERLINE + 'I',
                SPIECE_UNDERLINE + 'was',
                SPIECE_UNDERLINE + 'b',
                'or',
                'n',
                SPIECE_UNDERLINE + 'in',
                SPIECE_UNDERLINE + '',
                '9',
                '2',
                '0',
                '0',
                '0',
                ',',
                SPIECE_UNDERLINE + 'and',
                SPIECE_UNDERLINE + 'this',
                SPIECE_UNDERLINE + 'is',
                SPIECE_UNDERLINE + 'f',
                'al',
                's',
                'é',
                '.',
            ]							,     )
        _lowercase			 =tokenizer.convert_tokens_to_ids(lowerCAmelCase      )
        self.assertListEqual(
            lowerCAmelCase							,     [
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
            ]							,     )

        _lowercase			 =tokenizer.convert_ids_to_tokens(lowerCAmelCase      )
        self.assertListEqual(
            lowerCAmelCase							,     [
                SPIECE_UNDERLINE + 'I',
                SPIECE_UNDERLINE + 'was',
                SPIECE_UNDERLINE + 'b',
                'or',
                'n',
                SPIECE_UNDERLINE + 'in',
                SPIECE_UNDERLINE + '',
                '<unk>',
                '2',
                '0',
                '0',
                '0',
                ',',
                SPIECE_UNDERLINE + 'and',
                SPIECE_UNDERLINE + 'this',
                SPIECE_UNDERLINE + 'is',
                SPIECE_UNDERLINE + 'f',
                'al',
                's',
                '<unk>',
                '.',
            ]							,     )




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

        _lowercase			 =(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
                    _lowercase			 =self.rust_tokenizer_class.from_pretrained(lowerCAmelCase							,     **lowerCAmelCase      )
                    _lowercase			 =self.tokenizer_class.from_pretrained(lowerCAmelCase							,     **lowerCAmelCase      )

                    _lowercase			 =tempfile.mkdtemp()

                    _lowercase			 =tokenizer_r.save_pretrained(lowerCAmelCase      )
                    _lowercase			 =tokenizer_p.save_pretrained(lowerCAmelCase      )

                    # Checks it save with the same files + the tokenizer.json file for the fast one
                    self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )
                    _lowercase			 =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f      )
                    self.assertSequenceEqual(lowerCAmelCase							,     lowerCAmelCase      )

                    # Checks everything loads correctly in the same way
                    _lowercase			 =tokenizer_r.from_pretrained(lowerCAmelCase      )
                    _lowercase			 =tokenizer_p.from_pretrained(lowerCAmelCase      )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(lowerCAmelCase							,     lowerCAmelCase      )      )
                          # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                          # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                    shutil.rmtree(lowerCAmelCase      )

                    # Save tokenizer rust, legacy_format=True
                    _lowercase			 =tempfile.mkdtemp()

                    _lowercase			 =tokenizer_r.save_pretrained(lowerCAmelCase							,     legacy_format=lowerCAmelCase      )
                    _lowercase			 =tokenizer_p.save_pretrained(lowerCAmelCase      )

                    # Checks it save with the same files
                    self.assertSequenceEqual(lowerCAmelCase							,     lowerCAmelCase      )

                    # Checks everything loads correctly in the same way
                    _lowercase			 =tokenizer_r.from_pretrained(lowerCAmelCase      )
                    _lowercase			 =tokenizer_p.from_pretrained(lowerCAmelCase      )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(lowerCAmelCase							,     lowerCAmelCase      )      )

                    shutil.rmtree(lowerCAmelCase      )

                    # Save tokenizer rust, legacy_format=False
                    _lowercase			 =tempfile.mkdtemp()

                    _lowercase			 =tokenizer_r.save_pretrained(lowerCAmelCase							,     legacy_format=lowerCAmelCase      )
                    _lowercase			 =tokenizer_p.save_pretrained(lowerCAmelCase      )

                    # Checks it saved the tokenizer.json file
                    self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files      )      )

                    # Checks everything loads correctly in the same way
                    _lowercase			 =tokenizer_r.from_pretrained(lowerCAmelCase      )
                    _lowercase			 =tokenizer_p.from_pretrained(lowerCAmelCase      )

                    # Check special tokens are set accordingly on Rust and Python
                    for key in tokenizer_pp.special_tokens_map:
                          self.assertTrue(hasattr(lowerCAmelCase							,     lowerCAmelCase      )      )

                    shutil.rmtree(lowerCAmelCase      )





@require_torch
@require_sentencepiece
@require_tokenizers
class 				__lowerCAmelCase  (      unittest.TestCase							):
  _a		    =						"""facebook/mbart-large-en-ro"""
  _a		    =						[
      """ UN Chief Says There Is No Military Solution in Syria""",
      """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
  ]
  _a		    =						[
      """Şeful ONU declară că nu există o soluţie militară în Siria""",
      """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
      """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
      """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
  ]
  _a		    =						[8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]




  @classmethod
  def 						A__  (       cls      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =MBartTokenizer.from_pretrained(
            cls.checkpoint_name							,     src_lang='en_XX'							,     tgt_lang='ro_RO'      )
        _lowercase			 =1
        return cls




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']							,     250_001      )
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']							,     250_004      )
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']							,     250_020      )




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
        self.assertListEqual(self.expected_src_tokens							,     lowerCAmelCase      )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        self.assertIn(lowerCAmelCase							,     self.tokenizer.all_special_ids      )
        _lowercase			 =[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
        _lowercase			 =self.tokenizer.decode(lowerCAmelCase							,     skip_special_tokens=lowerCAmelCase      )
        _lowercase			 =self.tokenizer.decode(generated_ids[1:]							,     skip_special_tokens=lowerCAmelCase      )
        self.assertEqual(lowerCAmelCase							,     lowerCAmelCase      )
        self.assertNotIn(self.tokenizer.eos_token							,     lowerCAmelCase      )




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =['this is gunna be a long sentence ' * 20]
        assert isinstance(src_text[0]							,     lowerCAmelCase      )
        _lowercase			 =10
        _lowercase			 =self.tokenizer(lowerCAmelCase							,     max_length=lowerCAmelCase							,     truncation=lowerCAmelCase      ).input_ids[0]
        self.assertEqual(ids[-2]							,     2      )
        self.assertEqual(ids[-1]							,     lowerCAmelCase      )
        self.assertEqual(len(lowerCAmelCase      )							,     lowerCAmelCase      )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']      )							,     [250_026, 250_001]      )




  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        _lowercase			 =tempfile.mkdtemp()
        _lowercase			 =self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(lowerCAmelCase      )
        _lowercase			 =MBartTokenizer.from_pretrained(lowerCAmelCase      )
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,     lowerCAmelCase      )




  @require_torch
  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        _lowercase			 =self.tokenizer(self.src_text							,     text_target=self.tgt_text							,     padding=lowerCAmelCase							,     return_tensors='pt'      )
        _lowercase			 =shift_tokens_right(batch['labels']							,     self.tokenizer.pad_token_id      )

        # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
        assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
        assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
        assert batch.decoder_input_ids[1][-1] == 2
        assert batch.labels[1][-2:].tolist() == [2, RO_CODE]




  @require_torch
  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        _lowercase			 =self.tokenizer(
            self.src_text							,     text_target=self.tgt_text							,     padding=lowerCAmelCase							,     truncation=lowerCAmelCase							,     max_length=len(self.expected_src_tokens      )							,     return_tensors='pt'							,     )

        _lowercase			 =shift_tokens_right(batch['labels']							,     self.tokenizer.pad_token_id      )

        self.assertIsInstance(lowerCAmelCase							,     lowerCAmelCase      )

        self.assertEqual((2, 14)							,     batch.input_ids.shape      )
        self.assertEqual((2, 14)							,     batch.attention_mask.shape      )
        _lowercase			 =batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens							,     lowerCAmelCase      )
        self.assertEqual(2							,     batch.decoder_input_ids[0, -1]      )  # EOS
        # Test that special tokens are reset
        self.assertEqual(self.tokenizer.prefix_tokens							,     []      )
        self.assertEqual(self.tokenizer.suffix_tokens							,     [self.tokenizer.eos_token_id, EN_CODE]      )




  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =self.tokenizer(self.src_text							,     padding=lowerCAmelCase							,     truncation=lowerCAmelCase							,     max_length=3							,     return_tensors='pt'      )
        _lowercase			 =self.tokenizer(
            text_target=self.tgt_text							,     padding=lowerCAmelCase							,     truncation=lowerCAmelCase							,     max_length=10							,     return_tensors='pt'      )
        _lowercase			 =targets['input_ids']
        _lowercase			 =shift_tokens_right(lowerCAmelCase							,     self.tokenizer.pad_token_id      )

        self.assertEqual(batch.input_ids.shape[1]							,     3      )
        self.assertEqual(batch.decoder_input_ids.shape[1]							,     10      )




  @require_torch
  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        _lowercase			 =self.tokenizer._build_translation_inputs(
            'A test'							,     return_tensors='pt'							,     src_lang='en_XX'							,     tgt_lang='ar_AR'      )

        self.assertEqual(
            nested_simplify(lowerCAmelCase      )							,     {
                # A, test, EOS, en_XX
                'input_ids': [[62, 3_034, 2, 250_004]],
                'attention_mask': [[1, 1, 1, 1]],
                # ar_AR
                'forced_bos_token_id': 250_001,
            }							,     )





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__			:     Optional[int]=32   ,     A__			:     str=10   ,     A__			:     Tuple=100   ,     A__			:     Union[str, Any]=1026   ,     A__			:     Tuple=True   ,     A__			:     Dict="data/tokenized_stories_train_wikitext103.jbl"   ,     A__			:     Dict="igf_context_pairs.jbl"   ,     )					->   Optional[int]:





      """simple docstring"""



      set_seed(3  )
      # generate train_data and objective_set
      _lowercase , _lowercase			 =generate_datasets(
          A__   ,     A__   ,     number=A__   ,     min_len=1026   ,     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?
      _lowercase			 =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )

      # load pretrained model
      _lowercase			 =load_gpta('gpt2'  ).to(A__  )
      print('computing perplexity on objective set'  )
      _lowercase			 =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__			:     str   ,     A__			:     str=15   ,     A__			:     Optional[int]=128   ,     A__			:     Dict=100   ,     A__			:     Optional[Any]="igf_model.pt"   ,     )					->   int:





      """simple docstring"""



      set_seed(42  )

      # Load pre-trained model
      _lowercase			 =GPTaLMHeadModel.from_pretrained('gpt2'  )

      # Initialize secondary learner to use embedding weights of model
      _lowercase			 =SecondaryLearner(A__  )

      # Train secondary learner
      _lowercase			 =train_secondary_learner(
          A__   ,     A__   ,     max_epochs=A__   ,     batch_size=A__   ,     eval_freq=100   ,     igf_model_path=A__   ,     )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner
def   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     str   ,     A__			:     Union[str, Any]=32   ,     A__			:     List[str]=1000   ,     A__			:     int=16   ,     A__			:     List[str]=1.0   ,     A__			:     Tuple=recopy_gpta   ,     A__			:     Any=None   ,     A__			:     Tuple=10   ,     A__			:     Tuple="gpt2_finetuned.pt"   ,     )					->   str:





      """simple docstring"""



      _lowercase			 =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
      _lowercase			 =RandomSampler(A__  )
      _lowercase			 =DataLoader(A__   ,     sampler=A__  )

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

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

      _lowercase			 =[]
      _lowercase			 =[]

      # Compute the performance of the transformer model at the beginning
      _lowercase			 =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()
                  _lowercase			 =random.randint(0   ,     example.size(2  ) - context_len - 1  )
                  _lowercase			 =example[0, 0, start : start + context_len]
                  lm_optimizer.zero_grad()
                  _lowercase			 =model(A__   ,     labels=A__  )
                  _lowercase			 =True

                  if secondary_learner is not None:
                        _lowercase			 =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 == 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(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 > 60:
                        break
            if max_steps > 0 and global_step > 60:
                  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   (    )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =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=32   ,     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=100   ,     type=A__   ,     help='number of articles that are long enough to be used as our objective set'   ,     )
      parser.add_argument(
          '--eval_freq'   ,     default=100   ,     type=A__   ,     help='secondary model evaluation is triggered at eval_freq'  )

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

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

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

      parser.add_argument(
          '--eval_interval'   ,     default=10   ,     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=100   ,     type=A__   ,     help='The number of examples split to be used as objective_set/test_data'  )

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

      parser.add_argument(
          '--secondary_learner_max_epochs'   ,     default=15   ,     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=32   ,     max_steps=10   ,     size_objective_set=100   ,     min_len=1026   ,     trim=A__   ,     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(
          A__   ,     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=A__  )

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


if __name__ == "__main__":
  main()


