



'''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 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]=32     ,   _lowerCAmelCase						:		List[str]=10     ,   _lowerCAmelCase						:		Union[str, Any]=100     ,   _lowerCAmelCase						:		Dict=1026     ,   _lowerCAmelCase						:		List[Any]=True     ,   _lowerCAmelCase						:		List[str]="data/tokenized_stories_train_wikitext103.jbl"     ,   _lowerCAmelCase						:		List[Any]="igf_context_pairs.jbl"     ,   )   ->     int:
							set_seed(3						)
							# generate train_data and objective_set
							UpperCAmelCase							,	UpperCAmelCase				: str											=	generate_datasets(
							    _lowerCAmelCase     ,   _lowerCAmelCase     ,   number=_lowerCAmelCase     ,   min_len=1026     ,   trim=_lowerCAmelCase						)
							# 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?
							UpperCAmelCase				: List[str]											=	torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)

							# load pretrained model
							UpperCAmelCase				: Optional[Any]											=	load_gpta('''gpt2'''						).to(_lowerCAmelCase						)
							print('''computing perplexity on objective set'''						)
							UpperCAmelCase				: str											=	compute_perplexity(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						).item()
							print('''perplexity on objective set:'''     ,   _lowerCAmelCase						)

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

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


def 				snake_case_  (      _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Union[str, Any]=15     ,   _lowerCAmelCase						:		Any=128     ,   _lowerCAmelCase						:		Dict=100     ,   _lowerCAmelCase						:		Union[str, Any]="igf_model.pt"     ,   )   ->     Any:
							set_seed(42						)

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

							# Initialize secondary learner to use embedding weights of model
							UpperCAmelCase				: str											=	SecondaryLearner(_lowerCAmelCase						)

							# Train secondary learner
							UpperCAmelCase				: List[Any]											=	train_secondary_learner(
							    _lowerCAmelCase     ,   _lowerCAmelCase     ,   max_epochs=_lowerCAmelCase     ,   batch_size=_lowerCAmelCase     ,   eval_freq=100     ,   igf_model_path=_lowerCAmelCase     ,   )

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		Tuple=32     ,   _lowerCAmelCase						:		Optional[Any]=1000     ,   _lowerCAmelCase						:		str=16     ,   _lowerCAmelCase						:		Dict=1.0     ,   _lowerCAmelCase						:		List[Any]=recopy_gpta     ,   _lowerCAmelCase						:		List[str]=None     ,   _lowerCAmelCase						:		List[str]=10     ,   _lowerCAmelCase						:		Dict="gpt2_finetuned.pt"     ,   )   ->     Optional[int]:
							UpperCAmelCase				: Tuple											=	torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)
							UpperCAmelCase				: int											=	RandomSampler(_lowerCAmelCase						)
							UpperCAmelCase				: Dict											=	DataLoader(_lowerCAmelCase     ,   sampler=_lowerCAmelCase						)

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

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(_lowerCAmelCase						)
														secondary_learner.eval()
							UpperCAmelCase				: Optional[Any]											=	[]
							UpperCAmelCase				: Tuple											=	0

							UpperCAmelCase				: List[str]											=	[]
							UpperCAmelCase				: Optional[Any]											=	[]

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

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

																												# 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:
																																			UpperCAmelCase				: Optional[int]											=	-1
																												if predicted_q < threshold:
																																			UpperCAmelCase				: Union[str, Any]											=	False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()						)						)
																												UpperCAmelCase				: Union[str, 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()
																												UpperCAmelCase				: Optional[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:
																																			UpperCAmelCase				: List[Any]											=	compute_perplexity(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)
																																			test_perps.append(_lowerCAmelCase						)

																																			print('''Test perplexity, step'''     ,   _lowerCAmelCase     ,   ''':'''     ,   _lowerCAmelCase						)
            # 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()     ,   _lowerCAmelCase						)
							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_  (      )   ->     Optional[int]:
							UpperCAmelCase				: Union[str, Any]											=	argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''						)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

							# Train secondary learner
							UpperCAmelCase				: Tuple											=	training_secondary_learner(
							    _lowerCAmelCase     ,   secondary_learner_max_epochs=15     ,   secondary_learner_batch_size=128     ,   eval_freq=100     ,   igf_model_path='''igf_model.pt'''     ,   )

							# load pretrained gpt2 model
							UpperCAmelCase				: str											=	GPTaLMHeadModel.from_pretrained('''gpt2'''						)
							set_seed(42						)

							# Generate train and test data to train and evaluate gpt2 model
							UpperCAmelCase							,	UpperCAmelCase				: int											=	generate_datasets(
							    context_len=32     ,   file='''data/tokenized_stories_train_wikitext103.jbl'''     ,   number=100     ,   min_len=1026     ,   trim=_lowerCAmelCase						)

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


if __name__ == "__main__":
						main()










'''simple docstring'''






# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


UpperCamelCase__:     List[str]										=    re.compile(r"^(?P<major>\d+)" r"\.(?P<minor>\d+)" r"\.(?P<patch>\d+)$")

@total_ordering
@dataclass
class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__    				= 42
	lowerCamelCase__    				= None
	lowerCamelCase__    				= None
	lowerCamelCase__    				= None
	lowerCamelCase__    				= None







	def   A			(		self    :  Optional[Any]	)    ->	Dict:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Any											=	_str_to_version_tuple(self.version_str	)







	def __repr__(		self    :  str	)    ->	Optional[Any]:
								return F"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""







	@property
	def   A			(		self    :  Tuple	)    ->	Dict:
								return self.major, self.minor, self.patch







	def   A			(		self    :  str	,    __snake_case    :  Any	)    ->	Tuple:
								if isinstance(__snake_case	,    __snake_case	):
															return Version(__snake_case	)
								elif isinstance(__snake_case	,    __snake_case	):
															return other
								raise TypeError(F"""{other} (type {type(__snake_case	)}) cannot be compared to version."""	)







	def __eq__(		self    :  str	,    __snake_case    :  Union[str, Any]	)    ->	List[Any]:
								try:
															UpperCAmelCase				: Dict											=	self._validate_operand(__snake_case	)
								except (TypeError, ValueError):
															return False
								else:
															return self.tuple == other.tuple







	def __lt__(		self    :  Any	,    __snake_case    :  int	)    ->	Optional[int]:
								UpperCAmelCase				: int											=	self._validate_operand(__snake_case	)
								return self.tuple < other.tuple







	def __hash__(		self    :  List[str]	)    ->	str:
								return hash(_version_tuple_to_str(self.tuple	)	)







	@classmethod
	def   A			(		cls    :  Any	,    __snake_case    :  Tuple	)    ->	Union[str, Any]:
								UpperCAmelCase				: Optional[Any]											=	{f.name for f in dataclasses.fields(cls	)}
								return cls(**{k: v for k, v in dic.items() if k in field_names}	)







	def   A			(		self    :  Dict	)    ->	str:
								return self.version_str


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     List[Any]:
							UpperCAmelCase				: Optional[Any]											=	_VERSION_REG.match(_lowerCAmelCase						)
							if not res:
														raise ValueError(f"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."""						)
							return tuple(int(_lowerCAmelCase						) for v in [res.group('''major'''						), res.group('''minor'''						), res.group('''patch'''						)]						)




def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     List[Any]:
							return ".".join(str(_lowerCAmelCase						) for v in version_tuple						)





