




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 					_a							(					lowerCAmelCase=32    ,						lowerCAmelCase=10    ,						lowerCAmelCase=100    ,						lowerCAmelCase=1026    ,						lowerCAmelCase=True    ,						lowerCAmelCase="data/tokenized_stories_train_wikitext103.jbl"    ,						lowerCAmelCase="igf_context_pairs.jbl"    ,						)->	str:
		set_seed(3 )
		# generate train_data and objective_set
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							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?
		SCREAMING_SNAKE_CASE_					      =							torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )

		# load pretrained model
		SCREAMING_SNAKE_CASE_					      =							load_gpta('gpt2' ).to(lowerCAmelCase )
		print('computing perplexity on objective set' )
		SCREAMING_SNAKE_CASE_					      =							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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=15    ,						lowerCAmelCase=128    ,						lowerCAmelCase=100    ,						lowerCAmelCase="igf_model.pt"    ,						)->	Optional[int]:
		set_seed(42 )

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

		# Initialize secondary learner to use embedding weights of model
		SCREAMING_SNAKE_CASE_					      =							SecondaryLearner(lowerCAmelCase )

		# Train secondary learner
		SCREAMING_SNAKE_CASE_					      =							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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=32    ,						lowerCAmelCase=1000    ,						lowerCAmelCase=16    ,						lowerCAmelCase=1.0    ,						lowerCAmelCase=recopy_gpta    ,						lowerCAmelCase=None    ,						lowerCAmelCase=10    ,						lowerCAmelCase="gpt2_finetuned.pt"    ,						)->	List[Any]:
		SCREAMING_SNAKE_CASE_					      =							torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )
		SCREAMING_SNAKE_CASE_					      =							RandomSampler(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							DataLoader(lowerCAmelCase    ,						sampler=lowerCAmelCase )

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

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

		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							[]

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

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

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu() ) )
								SCREAMING_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()
								SCREAMING_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:
										SCREAMING_SNAKE_CASE_					      =							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 					_a							(					)->	List[Any]:
		SCREAMING_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=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
		SCREAMING_SNAKE_CASE_					      =							joblib.load('data/IGF_values.jbl' )

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

		# Generate train and test data to train and evaluate gpt2 model
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							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()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE:     Any									= {
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     List[Any]									= [
						    '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''PegasusXForConditionalGeneration''',
						    '''PegasusXModel''',
						    '''PegasusXPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_pegasus_x import (
												    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PegasusXForConditionalGeneration,
												    PegasusXModel,
												    PegasusXPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     List[Any]									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)