
"""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    (_UpperCamelCase=32	,  _UpperCamelCase=10	,  _UpperCamelCase=100	,  _UpperCamelCase=1026	,  _UpperCamelCase=True	,  _UpperCamelCase="data/tokenized_stories_train_wikitext103.jbl"	,  _UpperCamelCase="igf_context_pairs.jbl"	,  ):
	set_seed(3		)
	# generate train_data and objective_set
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Union[str, Any]						      =				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?
	__lowerCAmelCase      :						Union[str, Any]						      =				torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'		)

	# load pretrained model
	__lowerCAmelCase      :						List[str]						      =				load_gpta('gpt2'		).to(_UpperCamelCase		)
	print('computing perplexity on objective set'		)
	__lowerCAmelCase      :						Union[str, Any]						      =				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"	,  ):
	set_seed(42		)

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

	# Initialize secondary learner to use embedding weights of model
	__lowerCAmelCase      :						List[str]						      =				SecondaryLearner(_UpperCamelCase		)

	# Train secondary learner
	__lowerCAmelCase      :						Dict						      =				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"	,  ):
	__lowerCAmelCase      :						Any						      =				torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'		)
	__lowerCAmelCase      :						int						      =				RandomSampler(_UpperCamelCase		)
	__lowerCAmelCase      :						Optional[int]						      =				DataLoader(_UpperCamelCase	,  sampler=_UpperCamelCase		)

	__lowerCAmelCase      :						Union[str, Any]						      =				max_steps // (len(_UpperCamelCase		)) + 1
	__lowerCAmelCase      :						int						      =				0
	__lowerCAmelCase      :						Optional[int]						      =				torch.zeros((1, context_len)	,  dtype=torch.long	,  device=_UpperCamelCase		)
	__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[int]						      =				recopy_model(_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		)

	model.train()
	if secondary_learner is not None:
		secondary_learner.to(_UpperCamelCase		)
		secondary_learner.eval()
	__lowerCAmelCase      :						Tuple						      =				[]
	__lowerCAmelCase      :						Union[str, Any]						      =				0

	__lowerCAmelCase      :						Tuple						      =				[]
	__lowerCAmelCase      :						List[Any]						      =				[]

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

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

            # If we passed the filter, add the context to the batch!
			if do_backprop:
				contexts.append(np.array(context.cpu()		)		)
				__lowerCAmelCase      :						List[str]						      =				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      :						Union[str, Any]						      =				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      :						Tuple						      =				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    ():
	__lowerCAmelCase      :						Tuple						      =				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
	__lowerCAmelCase      :						int						      =				joblib.load('data/IGF_values.jbl'		)

	# Train secondary learner
	__lowerCAmelCase      :						List[Any]						      =				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      :						List[str]						      =				GPTaLMHeadModel.from_pretrained('gpt2'		)
	set_seed(42		)

	# Generate train and test data to train and evaluate gpt2 model
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Union[str, Any]						      =				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()

"""simple docstring"""







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size'	,  [None, 400 * 2**20, 600 * 2**20]		)
@pytest.mark.parametrize('input_in_memory_max_size'	,  ['default', 0, 100 * 2**20, 900 * 2**20]		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	if input_in_memory_max_size != "default":
		monkeypatch.setattr(datasets.config	,  'IN_MEMORY_MAX_SIZE'	,  _UpperCamelCase		)
	__lowerCAmelCase      :						Union[str, Any]						      =				datasets.config.IN_MEMORY_MAX_SIZE
	if input_in_memory_max_size == "default":
		assert in_memory_max_size == 0
	else:
		assert in_memory_max_size == input_in_memory_max_size
	if dataset_size and in_memory_max_size:
		__lowerCAmelCase      :						Tuple						      =				dataset_size < in_memory_max_size
	else:
		__lowerCAmelCase      :						str						      =				False
	__lowerCAmelCase      :						Optional[int]						      =				is_small_dataset(_UpperCamelCase		)
	assert result == expected