
"""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__						(     __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",	)				->						str:


					"""simple docstring"""





					set_seed(3      )
					# generate train_data and objective_set
					_UpperCamelCase					,    _UpperCamelCase									=    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?
					_UpperCamelCase									=    torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''      )

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


					"""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(__snake_case      )

					# Train secondary learner
					_UpperCamelCase									=    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  lowerCamelCase__						(     __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",	)				->						str:


					"""simple docstring"""





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

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

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

					_UpperCamelCase									=    []
					_UpperCamelCase									=    []

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

															if secondary_learner is not None:
																				_UpperCamelCase									=    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:
																									_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(__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  lowerCamelCase__						(     )				->						List[Any]:


					"""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=__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
					_UpperCamelCase									=    joblib.load('''data/IGF_values.jbl'''      )

					# Train secondary learner
					_UpperCamelCase									=    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
					_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=__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()








"""simple docstring"""





import fire

from utils import calculate_rouge, save_json



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case=None,	**__snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()]
					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()][: len(__snake_case      )]
					_UpperCamelCase									=    calculate_rouge(__snake_case,	__snake_case,	**__snake_case      )
					if save_path is not None:
										save_json(__snake_case,	__snake_case,	indent=__snake_case      )
					return metrics  # these print nicely


if __name__ == "__main__":
				fire.Fire(calculate_rouge_path)






