

# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    SCREAMING_SNAKE_CASE    ,    SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						StableDiffusionControlNetImgaImgPipeline
  _a		    =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
  _a		    =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  _a		    =						IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}							)
  _a		    =						IMAGE_TO_IMAGE_IMAGE_PARAMS




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






        torch.manual_seed(0      )
        _lowercase			 =UNetaDConditionModel(
            block_out_channels=(32, 64)							,     layers_per_block=2							,     sample_size=32							,     in_channels=4							,     out_channels=4							,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,     cross_attention_dim=32							,     )
        torch.manual_seed(0      )
        _lowercase			 =ControlNetModel(
            block_out_channels=(32, 64)							,     layers_per_block=2							,     in_channels=4							,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,     cross_attention_dim=32							,     conditioning_embedding_out_channels=(16, 32)							,     )
        torch.manual_seed(0      )
        _lowercase			 =DDIMScheduler(
            beta_start=0.00085							,     beta_end=0.012							,     beta_schedule='scaled_linear'							,     clip_sample=lowerCAmelCase							,     set_alpha_to_one=lowerCAmelCase							,     )
        torch.manual_seed(0      )
        _lowercase			 =AutoencoderKL(
            block_out_channels=[32, 64]							,     in_channels=3							,     out_channels=3							,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,     latent_channels=4							,     )
        torch.manual_seed(0      )
        _lowercase			 =CLIPTextConfig(
            bos_token_id=0							,     eos_token_id=2							,     hidden_size=32							,     intermediate_size=37							,     layer_norm_eps=1e-05							,     num_attention_heads=4							,     num_hidden_layers=5							,     pad_token_id=1							,     vocab_size=1_000							,     )
        _lowercase			 =CLIPTextModel(lowerCAmelCase      )
        _lowercase			 =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

        _lowercase			 ={
            'unet': unet,
            'controlnet': controlnet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase=0      )						->				List[str]:
        '''simple docstring'''






        if str(lowerCAmelCase      ).startswith('mps'      ):
              _lowercase			 =torch.manual_seed(lowerCAmelCase      )
        else:
              _lowercase			 =torch.Generator(device=lowerCAmelCase      ).manual_seed(lowerCAmelCase      )

        _lowercase			 =2
        _lowercase			 =randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,     generator=lowerCAmelCase							,     device=torch.device(lowerCAmelCase      )							,     )
        _lowercase			 =floats_tensor(control_image.shape							,     rng=random.Random(lowerCAmelCase      )      ).to(lowerCAmelCase      )
        _lowercase			 =image.cpu().permute(0							,     2							,     3							,     1      )[0]
        _lowercase			 =Image.fromarray(np.uinta(lowerCAmelCase      )      ).convert('RGB'      ).resize((64, 64)      )
        _lowercase			 ={
            'prompt': 'A painting of a squirrel eating a burger',
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
            'image': image,
            'control_image': control_image,
        }

        return inputs




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






        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3      )




  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()							,     reason='XFormers attention is only available with CUDA and `xformers` installed'							,     )
  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3      )




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






        self._test_inference_batch_single_identical(expected_max_diff=2e-3      )





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						StableDiffusionControlNetImgaImgPipeline
  _a		    =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
  _a		    =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  _a		    =						frozenset([]							)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




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






        torch.manual_seed(0      )
        _lowercase			 =UNetaDConditionModel(
            block_out_channels=(32, 64)							,     layers_per_block=2							,     sample_size=32							,     in_channels=4							,     out_channels=4							,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,     cross_attention_dim=32							,     )
        torch.manual_seed(0      )

        def init_weights(lowerCAmelCase      ):
              if isinstance(lowerCAmelCase							,     torch.nn.Convad      ):
                    torch.nn.init.normal(m.weight      )
                    m.bias.data.fill_(1.0      )

        _lowercase			 =ControlNetModel(
            block_out_channels=(32, 64)							,     layers_per_block=2							,     in_channels=4							,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,     cross_attention_dim=32							,     conditioning_embedding_out_channels=(16, 32)							,     )
        controlneta.controlnet_down_blocks.apply(lowerCAmelCase      )

        torch.manual_seed(0      )
        _lowercase			 =ControlNetModel(
            block_out_channels=(32, 64)							,     layers_per_block=2							,     in_channels=4							,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,     cross_attention_dim=32							,     conditioning_embedding_out_channels=(16, 32)							,     )
        controlneta.controlnet_down_blocks.apply(lowerCAmelCase      )

        torch.manual_seed(0      )
        _lowercase			 =DDIMScheduler(
            beta_start=0.00085							,     beta_end=0.012							,     beta_schedule='scaled_linear'							,     clip_sample=lowerCAmelCase							,     set_alpha_to_one=lowerCAmelCase							,     )
        torch.manual_seed(0      )
        _lowercase			 =AutoencoderKL(
            block_out_channels=[32, 64]							,     in_channels=3							,     out_channels=3							,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,     latent_channels=4							,     )
        torch.manual_seed(0      )
        _lowercase			 =CLIPTextConfig(
            bos_token_id=0							,     eos_token_id=2							,     hidden_size=32							,     intermediate_size=37							,     layer_norm_eps=1e-05							,     num_attention_heads=4							,     num_hidden_layers=5							,     pad_token_id=1							,     vocab_size=1_000							,     )
        _lowercase			 =CLIPTextModel(lowerCAmelCase      )
        _lowercase			 =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

        _lowercase			 =MultiControlNetModel([controlneta, controlneta]      )

        _lowercase			 ={
            'unet': unet,
            'controlnet': controlnet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase=0      )						->				Optional[Any]:
        '''simple docstring'''






        if str(lowerCAmelCase      ).startswith('mps'      ):
              _lowercase			 =torch.manual_seed(lowerCAmelCase      )
        else:
              _lowercase			 =torch.Generator(device=lowerCAmelCase      ).manual_seed(lowerCAmelCase      )

        _lowercase			 =2

        _lowercase			 =[
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,     generator=lowerCAmelCase							,     device=torch.device(lowerCAmelCase      )							,     ),
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,     generator=lowerCAmelCase							,     device=torch.device(lowerCAmelCase      )							,     ),
        ]

        _lowercase			 =floats_tensor(control_image[0].shape							,     rng=random.Random(lowerCAmelCase      )      ).to(lowerCAmelCase      )
        _lowercase			 =image.cpu().permute(0							,     2							,     3							,     1      )[0]
        _lowercase			 =Image.fromarray(np.uinta(lowerCAmelCase      )      ).convert('RGB'      ).resize((64, 64)      )
        _lowercase			 ={
            'prompt': 'A painting of a squirrel eating a burger',
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
            'image': image,
            'control_image': control_image,
        }

        return inputs




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






        _lowercase			 =self.get_dummy_components()
        _lowercase			 =self.pipeline_class(**lowerCAmelCase      )
        pipe.to(lowerCAmelCase      )

        _lowercase			 =10.0
        _lowercase			 =4

        _lowercase			 =self.get_dummy_inputs(lowerCAmelCase      )
        _lowercase			 =steps
        _lowercase			 =scale
        _lowercase			 =pipe(**lowerCAmelCase      )[0]

        _lowercase			 =self.get_dummy_inputs(lowerCAmelCase      )
        _lowercase			 =steps
        _lowercase			 =scale
        _lowercase			 =pipe(**lowerCAmelCase							,     control_guidance_start=0.1							,     control_guidance_end=0.2      )[0]

        _lowercase			 =self.get_dummy_inputs(lowerCAmelCase      )
        _lowercase			 =steps
        _lowercase			 =scale
        _lowercase			 =pipe(**lowerCAmelCase							,     control_guidance_start=[0.1, 0.3]							,     control_guidance_end=[0.2, 0.7]      )[0]

        _lowercase			 =self.get_dummy_inputs(lowerCAmelCase      )
        _lowercase			 =steps
        _lowercase			 =scale
        _lowercase			 =pipe(**lowerCAmelCase							,     control_guidance_start=0.4							,     control_guidance_end=[0.5, 0.8]      )[0]

        # make sure that all outputs are different
        assert np.sum(np.abs(output_a - output_a      )      ) > 1e-3
        assert np.sum(np.abs(output_a - output_a      )      ) > 1e-3
        assert np.sum(np.abs(output_a - output_a      )      ) > 1e-3




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






        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3      )




  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()							,     reason='XFormers attention is only available with CUDA and `xformers` installed'							,     )
  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3      )




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






        self._test_inference_batch_single_identical(expected_max_diff=2e-3      )




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






        _lowercase			 =self.get_dummy_components()
        _lowercase			 =self.pipeline_class(**lowerCAmelCase      )
        pipe.to(lowerCAmelCase      )
        pipe.set_progress_bar_config(disable=lowerCAmelCase      )
        with tempfile.TemporaryDirectory() as tmpdir:
              try:
                    # save_pretrained is not implemented for Multi-ControlNet
                    pipe.save_pretrained(lowerCAmelCase      )
              except NotImplementedError:
                    pass





@slow
@require_torch_gpu
class 				__lowerCAmelCase  (      unittest.TestCase							):




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






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




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






        _lowercase			 =ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'      )

        _lowercase			 =StableDiffusionControlNetImgaImgPipeline.from_pretrained(
            'runwayml/stable-diffusion-v1-5'							,     safety_checker=lowerCAmelCase							,     controlnet=lowerCAmelCase      )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=lowerCAmelCase      )

        _lowercase			 =torch.Generator(device='cpu'      ).manual_seed(0      )
        _lowercase			 ='evil space-punk bird'
        _lowercase			 =load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'      ).resize((512, 512)      )
        _lowercase			 =load_image(
            'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'      ).resize((512, 512)      )

        _lowercase			 =pipe(
            lowerCAmelCase							,     lowerCAmelCase							,     control_image=lowerCAmelCase							,     generator=lowerCAmelCase							,     output_type='np'							,     num_inference_steps=50							,     strength=0.6							,     )

        _lowercase			 =output.images[0]

        assert image.shape == (512, 512, 3)

        _lowercase			 =load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'      )

        assert np.abs(expected_image - image      ).max() < 9e-2





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()


