
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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE=32					,				__SCREAMING_SNAKE_CASE=10					,				__SCREAMING_SNAKE_CASE=100					,				__SCREAMING_SNAKE_CASE=1026					,				__SCREAMING_SNAKE_CASE=True					,				__SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"					,				__SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"					,				):
	set_seed(3				)
	# generate train_data and objective_set
	lowercase    ,   lowercase	    =       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?
	lowercase	    =       torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'				)

	# load pretrained model
	lowercase	    =       load_gpta('gpt2'				).to(UpperCamelCase__				)
	print('computing perplexity on objective set'				)
	lowercase	    =       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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=15					,				__SCREAMING_SNAKE_CASE=128					,				__SCREAMING_SNAKE_CASE=100					,				__SCREAMING_SNAKE_CASE="igf_model.pt"					,				):
	set_seed(42				)

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

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

	# Train secondary learner
	lowercase	    =       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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=32					,				__SCREAMING_SNAKE_CASE=1000					,				__SCREAMING_SNAKE_CASE=16					,				__SCREAMING_SNAKE_CASE=1.0					,				__SCREAMING_SNAKE_CASE=recopy_gpta					,				__SCREAMING_SNAKE_CASE=None					,				__SCREAMING_SNAKE_CASE=10					,				__SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"					,				):
	lowercase	    =       torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'				)
	lowercase	    =       RandomSampler(UpperCamelCase__				)
	lowercase	    =       DataLoader(UpperCamelCase__					,				sampler=UpperCamelCase__				)

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

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

	lowercase	    =       []
	lowercase	    =       []

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

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

            # If we passed the filter, add the context to the batch!
			if do_backprop:
				contexts.append(np.array(context.cpu()				)				)
				lowercase	    =       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()
				lowercase	    =       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:
					lowercase	    =       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 				UpperCAmelCase_						(				):
	lowercase	    =       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
	lowercase	    =       joblib.load('data/IGF_values.jbl'				)

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

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:  int									=			{
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  str									=			[
					    """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

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




