

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    (  UpperCAmelCase__=32		,					UpperCAmelCase__=10		,					UpperCAmelCase__=100		,					UpperCAmelCase__=1_026		,					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
					__lowerCAmelCase,	__lowerCAmelCase					=							generate_datasets(
					    UpperCAmelCase__		,					UpperCAmelCase__		,					number=UpperCAmelCase__		,					min_len=1_026		,					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?
					__lowerCAmelCase					=							torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'      )

					# load pretrained model
					__lowerCAmelCase					=							load_gpta('gpt2'      ).to(UpperCAmelCase__      )
					print('computing perplexity on objective set'      )
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=15		,					UpperCAmelCase__=128		,					UpperCAmelCase__=100		,					UpperCAmelCase__="igf_model.pt"		,					):

					"""simple docstring"""






					set_seed(42      )

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

					# Initialize secondary learner to use embedding weights of model
					__lowerCAmelCase					=							SecondaryLearner(UpperCAmelCase__      )

					# Train secondary learner
					__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=32		,					UpperCAmelCase__=1_000		,					UpperCAmelCase__=16		,					UpperCAmelCase__=1.0		,					UpperCAmelCase__=recopy_gpta		,					UpperCAmelCase__=None		,					UpperCAmelCase__=10		,					UpperCAmelCase__="gpt2_finetuned.pt"		,					):

					"""simple docstring"""






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

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

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

					__lowerCAmelCase					=							[]
					__lowerCAmelCase					=							[]

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

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

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

					"""simple docstring"""






					__lowerCAmelCase					=							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=1_000		,					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=1_026		,					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=1_026		,					trim=UpperCAmelCase__		,					data_file='data/tokenized_stories_train_wikitext103.jbl'		,					igf_data_file='igf_context_pairs.jbl'		,					)

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

					# Train secondary learner
					__lowerCAmelCase					=							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
					__lowerCAmelCase					=							GPTaLMHeadModel.from_pretrained('gpt2'      )
					set_seed(42      )

					# Generate train and test data to train and evaluate gpt2 model
					__lowerCAmelCase,	__lowerCAmelCase					=							generate_datasets(
					    context_len=32		,					file='data/tokenized_stories_train_wikitext103.jbl'		,					number=100		,					min_len=1_026		,					trim=UpperCAmelCase__      )

					# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
					finetune(
					    UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					context_len=32		,					max_steps=1_000		,					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()









import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


lowerCamelCase      				=     logging.get_logger(__name__)





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						*_A					,						**_A							):
												warnings.warn(
												    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
												    ' Please use PoolFormerImageProcessor instead.'					,						_A					,						)
												super().__init__(*_A					,						**_A							)






