



'''simple docstring'''

import unittest

import numpy as np

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

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 DPTImageProcessor


class 	a__     (				unittest.TestCase	):


	def __init__(self : str,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Optional[Any]=7,					__UpperCAmelCase : str=3,					__UpperCAmelCase : List[str]=18,					__UpperCAmelCase : Optional[int]=30,					__UpperCAmelCase : Any=400,					__UpperCAmelCase : List[Any]=True,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : List[str]=True,					__UpperCAmelCase : int=[0.5, 0.5, 0.5],					__UpperCAmelCase : Any=[0.5, 0.5, 0.5],					)     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= size if size is not None else {'''height''': 18, '''width''': 18}
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= parent
					SCREAMING_SNAKE_CASE  :						List[Any]      					= batch_size
					SCREAMING_SNAKE_CASE  :						int      					= num_channels
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= image_size
					SCREAMING_SNAKE_CASE  :						Any      					= min_resolution
					SCREAMING_SNAKE_CASE  :						List[Any]      					= max_resolution
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= do_resize
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= size
					SCREAMING_SNAKE_CASE  :						Tuple      					= do_normalize
					SCREAMING_SNAKE_CASE  :						Dict      					= image_mean
					SCREAMING_SNAKE_CASE  :						int      					= image_std






	def 				lowercase__  (self : Union[str, Any]      )     ->			Tuple:

					"""simple docstring"""

					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					}



@require_torch
@require_vision
class 	a__     (				_lowercase,			unittest.TestCase	):
	__magic_name__		:    List[str]	    =     DPTImageProcessor if is_vision_available() else None


	def 				lowercase__  (self : Tuple      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= DPTImageProcessingTester(self      )


	@property
	def 				lowercase__  (self : List[str]      )     ->			Union[str, Any]:

					"""simple docstring"""

					return self.image_processor_tester.prepare_image_processor_dict()


	def 				lowercase__  (self : str      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.image_processing_class(**self.image_processor_dict      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''image_mean'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''image_std'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''do_normalize'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''do_resize'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''size'''      )      )


	def 				lowercase__  (self : List[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.image_processing_class.from_dict(self.image_processor_dict      )
					self.assertEqual(image_processor.size,					{'''height''': 18, '''width''': 18}      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.image_processing_class.from_dict(self.image_processor_dict,					size=42      )
					self.assertEqual(image_processor.size,					{'''height''': 42, '''width''': 42}      )


	def 				lowercase__  (self : Optional[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.image_processing_class(**self.image_processor_dict      )
					# create random PIL images
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= prepare_image_inputs(self.image_processor_tester,					equal_resolution=__UpperCAmelCase      )
					for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase,					Image.Image      )

					# Test not batched input
					SCREAMING_SNAKE_CASE  :						List[str]      					= 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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    ),					)

					# Test batched
					SCREAMING_SNAKE_CASE  :						Tuple      					= image_processing(__UpperCAmelCase,					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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    ),					)


	def 				lowercase__  (self : Tuple      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.image_processing_class(**self.image_processor_dict      )
					# create random numpy tensors
					SCREAMING_SNAKE_CASE  :						List[str]      					= prepare_image_inputs(self.image_processor_tester,					equal_resolution=__UpperCAmelCase,					numpify=__UpperCAmelCase      )
					for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase,					np.ndarray      )

					# Test not batched input
					SCREAMING_SNAKE_CASE  :						Dict      					= 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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    ),					)

					# Test batched
					SCREAMING_SNAKE_CASE  :						int      					= image_processing(__UpperCAmelCase,					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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    ),					)






	def 				lowercase__  (self : List[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.image_processing_class(**self.image_processor_dict      )
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE  :						Any      					= prepare_image_inputs(self.image_processor_tester,					equal_resolution=__UpperCAmelCase,					torchify=__UpperCAmelCase      )
					for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase,					torch.Tensor      )

					# Test not batched input
					SCREAMING_SNAKE_CASE  :						str      					= 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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    ),					)

					# Test batched
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= image_processing(__UpperCAmelCase,					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.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    ),					)









'''simple docstring'''

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       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]=32    ,	_SCREAMING_SNAKE_CASE      :List[Any]=10    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]=1_00    ,	_SCREAMING_SNAKE_CASE      :int=10_26    ,	_SCREAMING_SNAKE_CASE      :List[Any]=True    ,	_SCREAMING_SNAKE_CASE      :str="data/tokenized_stories_train_wikitext103.jbl"    ,	_SCREAMING_SNAKE_CASE      :List[Any]="igf_context_pairs.jbl"    ,	):
				set_seed(3       )
				# generate train_data and objective_set
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= generate_datasets(
				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	number=_SCREAMING_SNAKE_CASE    ,	min_len=10_26    ,	trim=_SCREAMING_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?
				SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''       )

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

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

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]=15    ,	_SCREAMING_SNAKE_CASE      :Any=1_28    ,	_SCREAMING_SNAKE_CASE      :Any=1_00    ,	_SCREAMING_SNAKE_CASE      :List[str]="igf_model.pt"    ,	):
				set_seed(42       )

				# Load pre-trained model
				SCREAMING_SNAKE_CASE  :						List[Any]      					= GPTaLMHeadModel.from_pretrained('''gpt2'''       )

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

				# Train secondary learner
				SCREAMING_SNAKE_CASE  :						Any      					= train_secondary_learner(
				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	max_epochs=_SCREAMING_SNAKE_CASE    ,	batch_size=_SCREAMING_SNAKE_CASE    ,	eval_freq=1_00    ,	igf_model_path=_SCREAMING_SNAKE_CASE    ,	)

				del model, secondary_learner_train_data
				torch.cuda.empty_cache()

				return secondary_learner






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=32    ,	_SCREAMING_SNAKE_CASE      :Tuple=10_00    ,	_SCREAMING_SNAKE_CASE      :int=16    ,	_SCREAMING_SNAKE_CASE      :List[str]=1.0    ,	_SCREAMING_SNAKE_CASE      :Any=recopy_gpta    ,	_SCREAMING_SNAKE_CASE      :Tuple=None    ,	_SCREAMING_SNAKE_CASE      :int=10    ,	_SCREAMING_SNAKE_CASE      :Optional[int]="gpt2_finetuned.pt"    ,	):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''       )
				SCREAMING_SNAKE_CASE  :						Any      					= RandomSampler(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= DataLoader(_SCREAMING_SNAKE_CASE    ,	sampler=_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						Dict      					= max_steps // (len(_SCREAMING_SNAKE_CASE       )) + 1
				SCREAMING_SNAKE_CASE  :						str      					= 0
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.zeros((1, context_len)    ,	dtype=torch.long    ,	device=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[Any]      					= recopy_model(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				model.train()
				if secondary_learner is not None:
								secondary_learner.to(_SCREAMING_SNAKE_CASE       )
								secondary_learner.eval()
				SCREAMING_SNAKE_CASE  :						str      					= []
				SCREAMING_SNAKE_CASE  :						List[str]      					= 0

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []

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

												if secondary_learner is not None:
																SCREAMING_SNAKE_CASE  :						str      					= secondary_learner.forward(
																    torch.tensor(_SCREAMING_SNAKE_CASE    ,	dtype=torch.long    ,	device=_SCREAMING_SNAKE_CASE       ).unsqueeze(0       )       )[0].item()
																observed_qs.append(float(_SCREAMING_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:
																				SCREAMING_SNAKE_CASE  :						Dict      					= -1
																if predicted_q < threshold:
																				SCREAMING_SNAKE_CASE  :						Dict      					= False

            # If we passed the filter, add the context to the batch!
												if do_backprop:
																contexts.append(np.array(context.cpu()       )       )
																SCREAMING_SNAKE_CASE  :						Tuple      					= outputs[0]
																lm_loss.backward()
																examples += 1

												del outputs

												# Once the batch is filled with enough contexts, backprop on the batch.
												if examples == batch_size:
																torch.cuda.empty_cache()
																SCREAMING_SNAKE_CASE  :						int      					= 0
																# Do LM backprop
																torch.nn.utils.clip_grad_norm_(model.parameters()    ,	3.0       )
																lm_optimizer.step()
																lm_scheduler.step()  # Update learning rate schedule
																global_step += 1
																# Compute the performance of the transformer model at this batch
																if global_step % eval_interval == 0:
																				SCREAMING_SNAKE_CASE  :						Any      					= compute_perplexity(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
																				test_perps.append(_SCREAMING_SNAKE_CASE       )

																				print('''Test perplexity, step'''    ,	_SCREAMING_SNAKE_CASE    ,	''':'''    ,	_SCREAMING_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()    ,	_SCREAMING_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       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						Dict      					= argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''       )

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

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

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

				parser.add_argument(
				    '''--context_len'''    ,	default=32    ,	type=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE    ,	help='''secondary model evaluation is triggered at eval_freq'''       )

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

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

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

				parser.add_argument(
				    '''--eval_interval'''    ,	default=10    ,	type=_SCREAMING_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=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE    ,	help='''number of epochs to train secondary learner'''       )

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

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

				parser.add_argument(
				    '''--recopy_model'''    ,	default=_SCREAMING_SNAKE_CASE    ,	type=_SCREAMING_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=_SCREAMING_SNAKE_CASE    ,	data_file='''data/tokenized_stories_train_wikitext103.jbl'''    ,	igf_data_file='''igf_context_pairs.jbl'''    ,	)

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

				# Train secondary learner
				SCREAMING_SNAKE_CASE  :						List[Any]      					= training_secondary_learner(
				    _SCREAMING_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
				SCREAMING_SNAKE_CASE  :						List[Any]      					= GPTaLMHeadModel.from_pretrained('''gpt2'''       )
				set_seed(42       )

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

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


if __name__ == "__main__":
	main()




