


"""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"    , ):

			'''simple docstring'''




			set_seed(3      )
			# generate train_data and objective_set
			__lowerCAmelCase				    =			generate_datasets(
			    A_    , A_    , number=A_    , min_len=1026    , trim=A_      )
			# 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(A_      )
			print("computing perplexity on objective set"      )
			__lowerCAmelCase				    =			compute_perplexity(A_    , A_    , A_      ).item()
			print("perplexity on objective set:"    , A_      )

			# collect igf pairs and save to file demo.jbl
			collect_objective_set(A_    , A_    , A_    , A_    , A_    , A_    , A_    , A_      )

			# 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"    , ):

			'''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(A_      )

			# Train secondary learner
			__lowerCAmelCase				    =			train_secondary_learner(
			    A_    , A_    , max_epochs=A_    , batch_size=A_    , eval_freq=100    , igf_model_path=A_    , )

			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"    , ):

			'''simple docstring'''




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

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

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

			__lowerCAmelCase				    =			[]
			__lowerCAmelCase				    =			[]

			# Compute the performance of the transformer model at the beginning
			__lowerCAmelCase				    =			compute_perplexity(A_    , A_    , A_      )
			test_perps.append(A_      )
			print("Test perplexity, step"    , A_    , ":"    , A_      )
			for epoch in range(int(A_      )      ):
						for step, example in enumerate(A_      ):
									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(A_    , labels=A_      )
									__lowerCAmelCase				    =			True

									if secondary_learner is not None:
												__lowerCAmelCase				    =			secondary_learner.forward(
												    torch.tensor(A_    , dtype=torch.long    , device=A_      ).unsqueeze(0      )      )[0].item()
												observed_qs.append(float(A_      )      )

												# 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(A_    , A_    , A_      )
															test_perps.append(A_      )

															print("Test perplexity, step"    , A_    , ":"    , A_      )
															  # 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()    , A_      )
			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	(		):

			'''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=A_    , type=A_    , required=A_    , help="The input data dir. Should contain data files for WikiText."    , )
			parser.add_argument(
			    "--model_name_or_path"    , default=A_    , type=A_    , required=A_    , help="Path to pretrained model or model identifier from huggingface.co/models"    , )
			parser.add_argument(
			    "--data_file"    , type=A_    , default=A_    , 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=A_    , default=A_    , help="A jbl file containing the context and information gain pairs to train secondary learner."    , )

			parser.add_argument(
			    "--output_dir"    , default=A_    , type=A_    , required=A_    , help="The output directory where the final fine-tuned model is stored."    , )

			parser.add_argument(
			    "--tokenizer_name"    , default=A_    , type=A_    , help="Pretrained tokenizer name or path if not the same as model_name"    , )
			parser.add_argument("--seed"    , type=A_    , default=A_    , help="A seed for reproducible training."      )

			parser.add_argument(
			    "--context_len"    , default=32    , type=A_    , 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=A_    , help="number of articles that are long enough to be used as our objective set"    , )
			parser.add_argument(
			    "--eval_freq"    , default=100    , type=A_    , help="secondary model evaluation is triggered at eval_freq"      )

			parser.add_argument("--max_steps"    , default=1000    , type=A_    , help="To calculate training epochs"      )

			parser.add_argument(
			    "--secondary_learner_batch_size"    , default=128    , type=A_    , help="batch size of training data for secondary learner"    , )

			parser.add_argument(
			    "--batch_size"    , default=16    , type=A_    , help="batch size of training data of language model(gpt2) "      )

			parser.add_argument(
			    "--eval_interval"    , default=10    , type=A_    , 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=A_    , help="The number of examples split to be used as objective_set/test_data"      )

			parser.add_argument(
			    "--min_len"    , default=1026    , type=A_    , help="The minimum length of the article to be used as objective set"      )

			parser.add_argument(
			    "--secondary_learner_max_epochs"    , default=15    , type=A_    , help="number of epochs to train secondary learner"      )

			parser.add_argument("--trim"    , default=A_    , type=A_    , help="truncate the example if it exceeds context length"      )

			parser.add_argument(
			    "--threshold"    , default=1.0    , type=A_    , 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=A_    , help="finetuned_model_name"      )

			parser.add_argument(
			    "--recopy_model"    , default=A_    , type=A_    , 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=A_    , 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(
			    A_    , 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				    =			generate_datasets(
			    context_len=32    , file="data/tokenized_stories_train_wikitext103.jbl"    , number=100    , min_len=1026    , trim=A_      )

			# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
			finetune(
			    A_    , A_    , A_    , context_len=32    , max_steps=1000    , batch_size=16    , threshold=1.0    , recopy_model=A_    , secondary_learner=A_    , eval_interval=10    , finetuned_model_name="gpt2_finetuned.pt"    , )


if __name__ == "__main__":
	main()








"""simple docstring"""







def      lowercase			(				A_  ,   A_       )->      float:
  '''simple docstring'''







  if mass < 0:
    raise ValueError("The mass of a body cannot be negative"       )
  return 0.5 * mass * abs(A_       ) * abs(A_       )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)






