


'''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    lowerCamelCase__   ( A     :    Union[str, Any]=32   ,     A     :    Dict=10   ,     A     :    int=1_00   ,     A     :    Optional[int]=10_26   ,     A     :    Dict=True   ,     A     :    Dict="data/tokenized_stories_train_wikitext103.jbl"   ,     A     :    Optional[int]="igf_context_pairs.jbl"   ,     ):




						'''simple docstring'''






						set_seed(3  )
						# generate train_data and objective_set
						UpperCAmelCase       ,       UpperCAmelCase         =			generate_datasets(
						    A   ,     A   ,     number=A   ,     min_len=10_26   ,     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?
						UpperCAmelCase         =			torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''  )

						# load pretrained model
						UpperCAmelCase         =			load_gpta('''gpt2'''  ).to(A  )
						print('''computing perplexity on objective set'''  )
						UpperCAmelCase         =			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    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    List[Any]=15   ,     A     :    Any=1_28   ,     A     :    Dict=1_00   ,     A     :    Any="igf_model.pt"   ,     ):




						'''simple docstring'''






						set_seed(42  )

						# Load pre-trained model
						UpperCAmelCase         =			GPTaLMHeadModel.from_pretrained('''gpt2'''  )

						# Initialize secondary learner to use embedding weights of model
						UpperCAmelCase         =			SecondaryLearner(A  )

						# Train secondary learner
						UpperCAmelCase         =			train_secondary_learner(
						    A   ,     A   ,     max_epochs=A   ,     batch_size=A   ,     eval_freq=1_00   ,     igf_model_path=A   ,     )

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner


def    lowerCamelCase__   ( A     :    int   ,     A     :    List[Any]   ,     A     :    Any   ,     A     :    Optional[Any]=32   ,     A     :    Union[str, Any]=10_00   ,     A     :    List[Any]=16   ,     A     :    Any=1.0   ,     A     :    List[str]=recopy_gpta   ,     A     :    Any=None   ,     A     :    Union[str, Any]=10   ,     A     :    Any="gpt2_finetuned.pt"   ,     ):




						'''simple docstring'''






						UpperCAmelCase         =			torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''  )
						UpperCAmelCase         =			RandomSampler(A  )
						UpperCAmelCase         =			DataLoader(A   ,     sampler=A  )

						UpperCAmelCase         =			max_steps // (len(A  )) + 1
						UpperCAmelCase         =			0
						UpperCAmelCase         =			torch.zeros((1, context_len)   ,     dtype=torch.long   ,     device=A  )
						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			recopy_model(A   ,     A   ,     A  )

						model.train()
						if secondary_learner is not None:
												secondary_learner.to(A  )
												secondary_learner.eval()
						UpperCAmelCase         =			[]
						UpperCAmelCase         =			0

						UpperCAmelCase         =			[]
						UpperCAmelCase         =			[]

						# Compute the performance of the transformer model at the beginning
						UpperCAmelCase         =			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()
																		UpperCAmelCase         =			random.randint(0   ,     example.size(2  ) - context_len - 1  )
																		UpperCAmelCase         =			example[0, 0, start : start + context_len]
																		lm_optimizer.zero_grad()
																		UpperCAmelCase         =			model(A   ,     labels=A  )
																		UpperCAmelCase         =			True

																		if secondary_learner is not None:
																								UpperCAmelCase         =			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:
																														UpperCAmelCase         =			-1
																								if predicted_q < threshold:
																														UpperCAmelCase         =			False

            # If we passed the filter, add the context to the batch!
																		if do_backprop:
																								contexts.append(np.array(context.cpu()  )  )
																								UpperCAmelCase         =			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         =			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         =			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    lowerCamelCase__   ( ):




						'''simple docstring'''






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

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

						parser.add_argument(
						    '''--secondary_learner_batch_size'''   ,     default=1_28   ,     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=1_00   ,     type=A   ,     help='''The number of examples split to be used as objective_set/test_data'''  )

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

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

						# Train secondary learner
						UpperCAmelCase         =			training_secondary_learner(
						    A   ,     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
						UpperCAmelCase         =			GPTaLMHeadModel.from_pretrained('''gpt2'''  )
						set_seed(42  )

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

						# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
						finetune(
						    A   ,     A   ,     A   ,     context_len=32   ,     max_steps=10_00   ,     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()









'''simple docstring'''



import functools


def    lowerCamelCase__   ( A     :    list[int]   ,     A     :    list[int]  ):




						'''simple docstring'''






						if not isinstance(A   ,     A  ) or not all(isinstance(A   ,     A  ) for day in days  ):
												raise ValueError('''The parameter days should be a list of integers'''  )

						if len(A  ) != 3 or not all(isinstance(A   ,     A  ) for cost in costs  ):
												raise ValueError('''The parameter costs should be a list of three integers'''  )

						if len(A  ) == 0:
												return 0

						if min(A  ) <= 0:
												raise ValueError('''All days elements should be greater than 0'''  )

						if max(A  ) >= 3_66:
												raise ValueError('''All days elements should be less than 366'''  )

						UpperCAmelCase         =			set(A  )

						@functools.cache
						def dynamic_programming(A     :    int  ) -> int:
												if index > 3_65:
																		return 0

												if index not in days_set:
																		return dynamic_programming(index + 1  )

												return min(
												    costs[0] + dynamic_programming(index + 1  )   ,     costs[1] + dynamic_programming(index + 7  )   ,     costs[2] + dynamic_programming(index + 30  )   ,     )

						return dynamic_programming(1  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





