

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()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      				=     {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LiltForQuestionAnswering''',
		    '''LiltForSequenceClassification''',
		    '''LiltForTokenClassification''',
		    '''LiltModel''',
		    '''LiltPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_lilt import (
				    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LiltForQuestionAnswering,
				    LiltForSequenceClassification,
				    LiltForTokenClassification,
				    LiltModel,
				    LiltPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






