






"""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_:							List[str]=32     , UpperCamelCase_:							Any=10     , UpperCamelCase_:							str=100     , UpperCamelCase_:							Optional[Any]=1026     , UpperCamelCase_:							str=True     , UpperCamelCase_:							int="data/tokenized_stories_train_wikitext103.jbl"     , UpperCamelCase_:							Any="igf_context_pairs.jbl"     , )       ->							str:







		'''simple docstring'''







		set_seed(3					)
		# generate train_data and objective_set
		_a			,	_a							=	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?
		_a							=	torch.device("cuda:0" if torch.cuda.is_available() else "cpu"					)

		# load pretrained model
		_a							=	load_gpta("gpt2"					).to(UpperCamelCase_					)
		print("computing perplexity on objective set"					)
		_a							=	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_:							Union[str, Any]     , UpperCamelCase_:							Any=15     , UpperCamelCase_:							Optional[Any]=128     , UpperCamelCase_:							Tuple=100     , UpperCamelCase_:							str="igf_model.pt"     , )       ->							Optional[Any]:







		'''simple docstring'''







		set_seed(42					)

		# Load pre-trained model
		_a							=	GPTaLMHeadModel.from_pretrained("gpt2"					)

		# Initialize secondary learner to use embedding weights of model
		_a							=	SecondaryLearner(UpperCamelCase_					)

		# Train secondary learner
		_a							=	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_:							Any     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							List[Any]     , UpperCamelCase_:							List[Any]=32     , UpperCamelCase_:							List[Any]=1000     , UpperCamelCase_:							int=16     , UpperCamelCase_:							Dict=1.0     , UpperCamelCase_:							Union[str, Any]=recopy_gpta     , UpperCamelCase_:							Tuple=None     , UpperCamelCase_:							Optional[int]=10     , UpperCamelCase_:							Optional[int]="gpt2_finetuned.pt"     , )       ->							List[str]:







		'''simple docstring'''







		_a							=	torch.device("cuda:0" if torch.cuda.is_available() else "cpu"					)
		_a							=	RandomSampler(UpperCamelCase_					)
		_a							=	DataLoader(UpperCamelCase_     , sampler=UpperCamelCase_					)

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

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

		_a							=	[]
		_a							=	[]

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

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

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







		'''simple docstring'''







		_a							=	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
		_a							=	joblib.load("data/IGF_values.jbl"					)

		# Train secondary learner
		_a							=	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
		_a							=	GPTaLMHeadModel.from_pretrained("gpt2"					)
		set_seed(42					)

		# Generate train and test data to train and evaluate gpt2 model
		_a			,	_a							=	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 warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


UpperCamelCase							       =    logging.get_logger(__name__)




class    lowercase_    (_UpperCAmelCase     ):






      def __init__(    self     ,     *a_     ,     **a_	)	->None:







        '''simple docstring'''


        warnings.warn(
            "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use YolosImageProcessor instead."     ,     a_     ,     )
        super().__init__(*a_     ,     **a_	)


