

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      (					UpperCamelCase__=32  ,				UpperCamelCase__=10  ,				UpperCamelCase__=100  ,				UpperCamelCase__=1026  ,				UpperCamelCase__=True  ,				UpperCamelCase__="data/tokenized_stories_train_wikitext103.jbl"  ,				UpperCamelCase__="igf_context_pairs.jbl"  ,				):







							'''simple docstring'''






							set_seed(3 )
							# generate train_data and objective_set
							snake_case_							,       snake_case_     							=       generate_datasets(
							    UpperCamelCase__  ,				UpperCamelCase__  ,				number=UpperCamelCase__  ,				min_len=1026  ,				trim=UpperCamelCase__ )
							# 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?
							snake_case_     							=       torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )

							# load pretrained model
							snake_case_     							=       load_gpta('gpt2' ).to(UpperCamelCase__ )
							print('computing perplexity on objective set' )
							snake_case_     							=       compute_perplexity(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ).item()
							print('perplexity on objective set:'  ,				UpperCamelCase__ )

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

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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__=15  ,				UpperCamelCase__=128  ,				UpperCamelCase__=100  ,				UpperCamelCase__="igf_model.pt"  ,				):







							'''simple docstring'''






							set_seed(42 )

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

							# Initialize secondary learner to use embedding weights of model
							snake_case_     							=       SecondaryLearner(UpperCamelCase__ )

							# Train secondary learner
							snake_case_     							=       train_secondary_learner(
							    UpperCamelCase__  ,				UpperCamelCase__  ,				max_epochs=UpperCamelCase__  ,				batch_size=UpperCamelCase__  ,				eval_freq=100  ,				igf_model_path=UpperCamelCase__  ,				)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=32  ,				UpperCamelCase__=1000  ,				UpperCamelCase__=16  ,				UpperCamelCase__=1.0  ,				UpperCamelCase__=recopy_gpta  ,				UpperCamelCase__=None  ,				UpperCamelCase__=10  ,				UpperCamelCase__="gpt2_finetuned.pt"  ,				):







							'''simple docstring'''






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

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

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

							snake_case_     							=       []
							snake_case_     							=       []

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

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

																												# 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:
																																			snake_case_     							=       -1
																												if predicted_q < threshold:
																																			snake_case_     							=       False

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

																																			print('Test perplexity, step'  ,				UpperCamelCase__  ,				':'  ,				UpperCamelCase__ )
            # 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()  ,				UpperCamelCase__ )
							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'''






							snake_case_     							=       argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task' )

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

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

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

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

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

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

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

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

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

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

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

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

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

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

							# Train secondary learner
							snake_case_     							=       training_secondary_learner(
							    UpperCamelCase__  ,				secondary_learner_max_epochs=15  ,				secondary_learner_batch_size=128  ,				eval_freq=100  ,				igf_model_path='igf_model.pt'  ,				)

							# load pretrained gpt2 model
							snake_case_     							=       GPTaLMHeadModel.from_pretrained('gpt2' )
							set_seed(42 )

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

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


if __name__ == "__main__":
					main()







_UpperCAmelCase						:  Any									=			6_5521





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       1
							snake_case_     							=       0
							for plain_chr in plain_text:
														snake_case_     							=       (a + ord(UpperCamelCase__ )) % MOD_ADLER
														snake_case_     							=       (b + a) % MOD_ADLER
							return (b << 16) | a




