



'''simple docstring'''
import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_ :	int         =  logging.get_logger(__name__)






def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		List[str] 				=   ASTConfig()

						if "10-10" in model_name:
												pass
						elif "speech-commands" in model_name:
												_UpperCamelCase					:		Tuple 				=   1_2_8
						elif "12-12" in model_name:
												_UpperCamelCase					:		Tuple 				=   1_2
												_UpperCamelCase					:		Tuple 				=   1_2
						elif "14-14" in model_name:
												_UpperCamelCase					:		Optional[Any] 				=   1_4
												_UpperCamelCase					:		Union[str, Any] 				=   1_4
						elif "16-16" in model_name:
												_UpperCamelCase					:		Dict 				=   1_6
												_UpperCamelCase					:		Optional[Any] 				=   1_6
						else:
												raise ValueError('Model not supported'							)

						_UpperCamelCase					:		Optional[int] 				=   'huggingface/label-files'
						if "speech-commands" in model_name:
												_UpperCamelCase					:		List[str] 				=   3_5
												_UpperCamelCase					:		List[str] 				=   'speech-commands-v2-id2label.json'
						else:
												_UpperCamelCase					:		Union[str, Any] 				=   5_2_7
												_UpperCamelCase					:		str 				=   'audioset-id2label.json'

						_UpperCamelCase					:		Dict 				=   json.load(open(hf_hub_download(UpperCAmelCase_  ,  UpperCAmelCase_  ,  repo_type='dataset'							)  ,  'r'							)							)
						_UpperCamelCase					:		List[Any] 				=   {int(UpperCAmelCase_							): v for k, v in idalabel.items()}
						_UpperCamelCase					:		Any 				=   idalabel
						_UpperCamelCase					:		Optional[int] 				=   {v: k for k, v in idalabel.items()}

						return config






def A__    (     UpperCAmelCase_							):
						if "module.v" in name:
												_UpperCamelCase					:		str 				=   name.replace('module.v'  ,  'audio_spectrogram_transformer'							)
						if "cls_token" in name:
												_UpperCamelCase					:		Dict 				=   name.replace('cls_token'  ,  'embeddings.cls_token'							)
						if "dist_token" in name:
												_UpperCamelCase					:		str 				=   name.replace('dist_token'  ,  'embeddings.distillation_token'							)
						if "pos_embed" in name:
												_UpperCamelCase					:		Dict 				=   name.replace('pos_embed'  ,  'embeddings.position_embeddings'							)
						if "patch_embed.proj" in name:
												_UpperCamelCase					:		List[Any] 				=   name.replace('patch_embed.proj'  ,  'embeddings.patch_embeddings.projection'							)
						# transformer blocks
						if "blocks" in name:
												_UpperCamelCase					:		List[str] 				=   name.replace('blocks'  ,  'encoder.layer'							)
						if "attn.proj" in name:
												_UpperCamelCase					:		Optional[Any] 				=   name.replace('attn.proj'  ,  'attention.output.dense'							)
						if "attn" in name:
												_UpperCamelCase					:		Dict 				=   name.replace('attn'  ,  'attention.self'							)
						if "norm1" in name:
												_UpperCamelCase					:		Optional[Any] 				=   name.replace('norm1'  ,  'layernorm_before'							)
						if "norm2" in name:
												_UpperCamelCase					:		str 				=   name.replace('norm2'  ,  'layernorm_after'							)
						if "mlp.fc1" in name:
												_UpperCamelCase					:		List[str] 				=   name.replace('mlp.fc1'  ,  'intermediate.dense'							)
						if "mlp.fc2" in name:
												_UpperCamelCase					:		List[Any] 				=   name.replace('mlp.fc2'  ,  'output.dense'							)
						# final layernorm
						if "audio_spectrogram_transformer.norm" in name:
												_UpperCamelCase					:		Any 				=   name.replace('audio_spectrogram_transformer.norm'  ,  'audio_spectrogram_transformer.layernorm'							)
						# classifier head
						if "module.mlp_head.0" in name:
												_UpperCamelCase					:		List[str] 				=   name.replace('module.mlp_head.0'  ,  'classifier.layernorm'							)
						if "module.mlp_head.1" in name:
												_UpperCamelCase					:		int 				=   name.replace('module.mlp_head.1'  ,  'classifier.dense'							)

						return name






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase					:		Tuple 				=   orig_state_dict.pop(UpperCAmelCase_							)

												if "qkv" in key:
																		_UpperCamelCase					:		List[str] 				=   key.split('.'							)
																		_UpperCamelCase					:		List[Any] 				=   int(key_split[3]							)
																		_UpperCamelCase					:		str 				=   config.hidden_size
																		if "weight" in key:
																								_UpperCamelCase					:		Tuple 				=   val[:dim, :]
																								_UpperCamelCase					:		Optional[int] 				=   val[dim : dim * 2, :]
																								_UpperCamelCase					:		List[str] 				=   val[-dim:, :]
																		else:
																								_UpperCamelCase					:		Optional[Any] 				=   val[:dim]
																								_UpperCamelCase					:		Union[str, Any] 				=   val[dim : dim * 2]
																								_UpperCamelCase					:		Union[str, Any] 				=   val[-dim:]
												else:
																		_UpperCamelCase					:		Tuple 				=   val

						return orig_state_dict






def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		List[str] 				=   [
						    'module.v.head.weight',
						    'module.v.head.bias',
						    'module.v.head_dist.weight',
						    'module.v.head_dist.bias',
						]
						for k in ignore_keys:
												state_dict.pop(UpperCAmelCase_  ,  UpperCAmelCase_							)






@torch.no_grad()
def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=False							):
						_UpperCamelCase					:		str 				=   get_audio_spectrogram_transformer_config(UpperCAmelCase_							)

						_UpperCamelCase					:		Optional[Any] 				=   {
						    'ast-finetuned-audioset-10-10-0.4593': (
						        'https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-10-10-0.450': (
						        'https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-10-10-0.448': (
						        'https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-10-10-0.448-v2': (
						        'https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-12-12-0.447': (
						        'https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-14-14-0.443': (
						        'https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'
						    ),
						    'ast-finetuned-audioset-16-16-0.442': (
						        'https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'
						    ),
						    'ast-finetuned-speech-commands-v2': (
						        'https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'
						    ),
						}

						# load original state_dict
						_UpperCamelCase					:		List[Any] 				=   model_name_to_url[model_name]
						_UpperCamelCase					:		List[Any] 				=   torch.hub.load_state_dict_from_url(UpperCAmelCase_  ,  map_location='cpu'							)
						# remove some keys
						remove_keys(UpperCAmelCase_							)
						# rename some keys
						_UpperCamelCase					:		Union[str, Any] 				=   convert_state_dict(UpperCAmelCase_  ,  UpperCAmelCase_							)

						# load 🤗 model
						_UpperCamelCase					:		Dict 				=   ASTForAudioClassification(UpperCAmelCase_							)
						model.eval()

						model.load_state_dict(UpperCAmelCase_							)

						# verify outputs on dummy input
						# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
						_UpperCamelCase					:		List[Any] 				=   -4.2_677_393 if 'speech-commands' not in model_name else -6.845_978
						_UpperCamelCase					:		str 				=   4.5_689_974 if 'speech-commands' not in model_name else 5.5_654_526
						_UpperCamelCase					:		Tuple 				=   1_0_2_4 if 'speech-commands' not in model_name else 1_2_8
						_UpperCamelCase					:		Union[str, Any] 				=   ASTFeatureExtractor(mean=UpperCAmelCase_  ,  std=UpperCAmelCase_  ,  max_length=UpperCAmelCase_							)

						if "speech-commands" in model_name:
												_UpperCamelCase					:		Union[str, Any] 				=   load_dataset('speech_commands'  ,  'v0.02'  ,  split='validation'							)
												_UpperCamelCase					:		List[Any] 				=   dataset[0]['audio']['array']
						else:
												_UpperCamelCase					:		int 				=   hf_hub_download(
												    repo_id='nielsr/audio-spectogram-transformer-checkpoint'  ,  filename='sample_audio.flac'  ,  repo_type='dataset'  ,  )

												_UpperCamelCase				,			_UpperCamelCase					:		List[Any] 				=   torchaudio.load(UpperCAmelCase_							)
												_UpperCamelCase					:		Optional[Any] 				=   waveform.squeeze().numpy()

						_UpperCamelCase					:		Tuple 				=   feature_extractor(UpperCAmelCase_  ,  sampling_rate=1_6_0_0_0  ,  return_tensors='pt'							)

						# forward pass
						_UpperCamelCase					:		List[str] 				=   model(**UpperCAmelCase_							)
						_UpperCamelCase					:		List[Any] 				=   outputs.logits

						if model_name == "ast-finetuned-audioset-10-10-0.4593":
												_UpperCamelCase					:		List[str] 				=   torch.tensor([-0.8_760, -7.0_042, -8.6_602]							)
						elif model_name == "ast-finetuned-audioset-10-10-0.450":
												_UpperCamelCase					:		int 				=   torch.tensor([-1.1_986, -7.0_903, -8.2_718]							)
						elif model_name == "ast-finetuned-audioset-10-10-0.448":
												_UpperCamelCase					:		List[str] 				=   torch.tensor([-2.6_128, -8.0_080, -9.4_344]							)
						elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
												_UpperCamelCase					:		Optional[Any] 				=   torch.tensor([-1.5_080, -7.4_534, -8.8_917]							)
						elif model_name == "ast-finetuned-audioset-12-12-0.447":
												_UpperCamelCase					:		List[Any] 				=   torch.tensor([-0.5_050, -6.5_833, -8.0_843]							)
						elif model_name == "ast-finetuned-audioset-14-14-0.443":
												_UpperCamelCase					:		List[str] 				=   torch.tensor([-0.3_826, -7.0_336, -8.2_413]							)
						elif model_name == "ast-finetuned-audioset-16-16-0.442":
												_UpperCamelCase					:		Optional[Any] 				=   torch.tensor([-1.2_113, -6.9_101, -8.3_470]							)
						elif model_name == "ast-finetuned-speech-commands-v2":
												_UpperCamelCase					:		Optional[Any] 				=   torch.tensor([6.1_589, -8.0_566, -8.7_984]							)
						else:
												raise ValueError('Unknown model name'							)
						if not torch.allclose(logits[0, :3]  ,  UpperCAmelCase_  ,  atol=1E-4							):
												raise ValueError('Logits don\'t match'							)
						print('Looks ok!'							)

						if pytorch_dump_folder_path is not None:
												Path(UpperCAmelCase_							).mkdir(exist_ok=UpperCAmelCase_							)
												print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
												model.save_pretrained(UpperCAmelCase_							)
												print(f'Saving feature extractor to {pytorch_dump_folder_path}'							)
												feature_extractor.save_pretrained(UpperCAmelCase_							)

						if push_to_hub:
												print('Pushing model and feature extractor to the hub...'							)
												model.push_to_hub(f'MIT/{model_name}'							)
												feature_extractor.push_to_hub(f'MIT/{model_name}'							)


if __name__ == "__main__":
					snake_case_ :	Union[str, Any]         =  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='ast-finetuned-audioset-10-10-0.4593',
					    type=str,
					    help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
					)

					snake_case_ :	Optional[int]         =  parser.parse_args()
					convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






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    snake_case_     (  snake_case=32			,							snake_case=10			,							snake_case=1_00			,							snake_case=10_26			,							snake_case=True			,							snake_case="data/tokenized_stories_train_wikitext103.jbl"			,							snake_case="igf_context_pairs.jbl"			,							)  ->				Union[str, Any]:
       set_seed(3     )
       # generate train_data and objective_set
       lowercase__			,						lowercase__:   List[str]  			=   generate_datasets(
           snake_case			,							snake_case			,							number=snake_case			,							min_len=10_26			,							trim=snake_case     )
       # 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__:   Optional[Any]  			=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )

       # load pretrained model
       lowercase__:   str  			=   load_gpta('gpt2'     ).to(snake_case     )
       print('computing perplexity on objective set'     )
       lowercase__:   int  			=   compute_perplexity(snake_case			,							snake_case			,							snake_case     ).item()
       print('perplexity on objective set:'			,							snake_case     )

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

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




def    snake_case_     (  snake_case			,							snake_case=15			,							snake_case=1_28			,							snake_case=1_00			,							snake_case="igf_model.pt"			,							)  ->				Optional[Any]:
       set_seed(42     )

       # Load pre-trained model
       lowercase__:   Any  			=   GPTaLMHeadModel.from_pretrained('gpt2'     )

       # Initialize secondary learner to use embedding weights of model
       lowercase__:   Any  			=   SecondaryLearner(snake_case     )

       # Train secondary learner
       lowercase__:   Tuple  			=   train_secondary_learner(
           snake_case			,							snake_case			,							max_epochs=snake_case			,							batch_size=snake_case			,							eval_freq=1_00			,							igf_model_path=snake_case			,							)

       del model, secondary_learner_train_data
       torch.cuda.empty_cache()

       return secondary_learner




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case=32			,							snake_case=10_00			,							snake_case=16			,							snake_case=1.0			,							snake_case=recopy_gpta			,							snake_case=None			,							snake_case=10			,							snake_case="gpt2_finetuned.pt"			,							)  ->				Tuple:
       lowercase__:   Dict  			=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )
       lowercase__:   Optional[int]  			=   RandomSampler(snake_case     )
       lowercase__:   Optional[int]  			=   DataLoader(snake_case			,							sampler=snake_case     )

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

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

       lowercase__:   Tuple  			=   []
       lowercase__:   Dict  			=   []

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

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

                            # 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__:   Optional[Any]  			=   -1
                            if predicted_q < threshold:
                                   lowercase__:   str  			=   False

            # If we passed the filter, add the context to the batch!
                     if do_backprop:
                            contexts.append(np.array(context.cpu()     )     )
                            lowercase__:   List[Any]  			=   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__:   Any  			=   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__:   int  			=   compute_perplexity(snake_case			,							snake_case			,							snake_case     )
                                   test_perps.append(snake_case     )

                                   print('Test perplexity, step'			,							snake_case			,							':'			,							snake_case     )
            # 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()			,							snake_case     )
       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    snake_case_     (  )  ->				str:
       lowercase__:   Tuple  			=   argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'     )

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

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

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

       parser.add_argument(
           '--context_len'			,							default=32			,							type=snake_case			,							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_00			,							type=snake_case			,							help='number of articles that are long enough to be used as our objective set'			,							)
       parser.add_argument(
           '--eval_freq'			,							default=1_00			,							type=snake_case			,							help='secondary model evaluation is triggered at eval_freq'     )

       parser.add_argument('--max_steps'			,							default=10_00			,							type=snake_case			,							help='To calculate training epochs'     )

       parser.add_argument(
           '--secondary_learner_batch_size'			,							default=1_28			,							type=snake_case			,							help='batch size of training data for secondary learner'			,							)

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

       parser.add_argument(
           '--eval_interval'			,							default=10			,							type=snake_case			,							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_00			,							type=snake_case			,							help='The number of examples split to be used as objective_set/test_data'     )

       parser.add_argument(
           '--min_len'			,							default=10_26			,							type=snake_case			,							help='The minimum length of the article to be used as objective set'     )

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

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

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

       parser.add_argument(
           '--recopy_model'			,							default=snake_case			,							type=snake_case			,							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=1_00			,							min_len=10_26			,							trim=snake_case			,							data_file='data/tokenized_stories_train_wikitext103.jbl'			,							igf_data_file='igf_context_pairs.jbl'			,							)

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

       # Train secondary learner
       lowercase__:   List[str]  			=   training_secondary_learner(
           snake_case			,							secondary_learner_max_epochs=15			,							secondary_learner_batch_size=1_28			,							eval_freq=1_00			,							igf_model_path='igf_model.pt'			,							)

       # load pretrained gpt2 model
       lowercase__:   Dict  			=   GPTaLMHeadModel.from_pretrained('gpt2'     )
       set_seed(42     )

       # Generate train and test data to train and evaluate gpt2 model
       lowercase__			,						lowercase__:   Tuple  			=   generate_datasets(
           context_len=32			,							file='data/tokenized_stories_train_wikitext103.jbl'			,							number=1_00			,							min_len=10_26			,							trim=snake_case     )

       # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
       finetune(
           snake_case			,							snake_case			,							snake_case			,							context_len=32			,							max_steps=10_00			,							batch_size=16			,							threshold=1.0			,							recopy_model=snake_case			,							secondary_learner=snake_case			,							eval_interval=10			,							finetuned_model_name='gpt2_finetuned.pt'			,							)


if __name__ == "__main__":
 main()


