

import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor





class 				__lowerCAmelCase  (      unittest.TestCase							):




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase=7							,     lowerCAmelCase=3							,     lowerCAmelCase=18							,     lowerCAmelCase=30							,     lowerCAmelCase=400							,     lowerCAmelCase=True							,     lowerCAmelCase=None							,     lowerCAmelCase=True							,     lowerCAmelCase=None							,     lowerCAmelCase=True							,     lowerCAmelCase=[0.5, 0.5, 0.5]							,     lowerCAmelCase=[0.5, 0.5, 0.5]							,     lowerCAmelCase=False							,     )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =size if size is not None else {'height': 20, 'width': 20}
        _lowercase			 =crop_size if crop_size is not None else {'height': 18, 'width': 18}
        _lowercase			 =parent
        _lowercase			 =batch_size
        _lowercase			 =num_channels
        _lowercase			 =image_size
        _lowercase			 =min_resolution
        _lowercase			 =max_resolution
        _lowercase			 =do_resize
        _lowercase			 =size
        _lowercase			 =do_center_crop
        _lowercase			 =crop_size
        _lowercase			 =do_normalize
        _lowercase			 =image_mean
        _lowercase			 =image_std
        _lowercase			 =do_reduce_labels




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






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_reduce_labels": self.do_reduce_labels,
        }
def   a   (    )					->   Tuple:





      """simple docstring"""



      _lowercase			 =load_dataset('hf-internal-testing/fixtures_ade20k'   ,     split='test'  )

      _lowercase			 =Image.open(dataset[0]['file']  )
      _lowercase			 =Image.open(dataset[1]['file']  )

      return image, map
def   a   (    )					->   Union[str, Any]:





      """simple docstring"""



      _lowercase			 =load_dataset('hf-internal-testing/fixtures_ade20k'   ,     split='test'  )

      _lowercase			 =Image.open(ds[0]['file']  )
      _lowercase			 =Image.open(ds[1]['file']  )
      _lowercase			 =Image.open(ds[2]['file']  )
      _lowercase			 =Image.open(ds[3]['file']  )

      return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						BeitImageProcessor if is_vision_available() else None




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






        _lowercase			 =BeitImageProcessingTester(self      )




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






        return self.image_processor_tester.prepare_image_processor_dict()




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






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_resize'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'size'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_center_crop'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'center_crop'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_normalize'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'image_mean'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'image_std'      )      )




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






        _lowercase			 =self.image_processing_class.from_dict(self.image_processor_dict      )
        self.assertEqual(image_processor.size							,     {'height': 20, 'width': 20}      )
        self.assertEqual(image_processor.crop_size							,     {'height': 18, 'width': 18}      )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase      )

        _lowercase			 =self.image_processing_class.from_dict(
            self.image_processor_dict							,     size=42							,     crop_size=84							,     reduce_labels=lowerCAmelCase      )
        self.assertEqual(image_processor.size							,     {'height': 42, 'width': 42}      )
        self.assertEqual(image_processor.crop_size							,     {'height': 84, 'width': 84}      )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase      )




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






        pass




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






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random PIL images
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase      )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     Image.Image      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




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






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random numpy tensors
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase							,     numpify=lowerCAmelCase      )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     np.ndarray      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




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






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase							,     torchify=lowerCAmelCase      )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     torch.Tensor      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




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






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase							,     torchify=lowerCAmelCase      )
        _lowercase			 =[]
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     torch.Tensor      )
              maps.append(torch.zeros(image.shape[-2:]      ).long()      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     maps[0]							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                1,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test not batched input (PIL images)
        _lowercase , _lowercase			 =prepare_semantic_single_inputs()

        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                1,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test batched input (PIL images)
        _lowercase , _lowercase			 =prepare_semantic_batch_inputs()

        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                2,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )




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






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        _lowercase , _lowercase			 =prepare_semantic_single_inputs()
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 150      )

        _lowercase			 =True
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )





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()


