



'''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 	_A							( _lowerCAmelCase=32	, _lowerCAmelCase=10	, _lowerCAmelCase=100	, _lowerCAmelCase=1_026	, _lowerCAmelCase=True	, _lowerCAmelCase="data/tokenized_stories_train_wikitext103.jbl"	, _lowerCAmelCase="igf_context_pairs.jbl"	, ):



							"""simple docstring"""



							set_seed(3       )
							# generate train_data and objective_set
							__lowercase													=generate_datasets(
							    _lowercase	, _lowercase	, number=_lowercase	, min_len=1_026	, trim=_lowercase       )
							# 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(_lowercase       )
							print('computing perplexity on objective set'       )
							__lowercase													=compute_perplexity(_lowercase	, _lowercase	, _lowercase       ).item()
							print('perplexity on objective set:'	, _lowercase       )

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

							# clean up, delete model and data we don't need anymore
							del model, train_data, objective_set
							torch.cuda.empty_cache()



def 	_A							( _lowerCAmelCase	, _lowerCAmelCase=15	, _lowerCAmelCase=128	, _lowerCAmelCase=100	, _lowerCAmelCase="igf_model.pt"	, ):



							"""simple docstring"""



							set_seed(42       )

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

							# Initialize secondary learner to use embedding weights of model
							__lowercase													=SecondaryLearner(_lowercase       )

							# Train secondary learner
							__lowercase													=train_secondary_learner(
							    _lowercase	, _lowercase	, max_epochs=_lowercase	, batch_size=_lowercase	, eval_freq=100	, igf_model_path=_lowercase	, )

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner



def 	_A							( _lowerCAmelCase	, _lowerCAmelCase	, _lowerCAmelCase	, _lowerCAmelCase=32	, _lowerCAmelCase=1_000	, _lowerCAmelCase=16	, _lowerCAmelCase=1.0	, _lowerCAmelCase=recopy_gpta	, _lowerCAmelCase=None	, _lowerCAmelCase=10	, _lowerCAmelCase="gpt2_finetuned.pt"	, ):



							"""simple docstring"""



							__lowercase													=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'       )
							__lowercase													=RandomSampler(_lowercase       )
							__lowercase													=DataLoader(_lowercase	, sampler=_lowercase       )

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

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

							__lowercase													=[]
							__lowercase													=[]

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

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

																												# 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(_lowercase	, _lowercase	, _lowercase       )
																																			test_perps.append(_lowercase       )

																																			print('Test perplexity, step'	, _lowercase	, ':'	, _lowercase       )
            # 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()	, _lowercase       )
							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 	_A							( ):



							"""simple docstring"""



							__lowercase													=argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'       )

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

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

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

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

							parser.add_argument('--max_steps'	, default=1_000	, type=_lowercase	, help='To calculate training epochs'       )

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

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

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

							parser.add_argument(
							    '--min_len'	, default=1_026	, type=_lowercase	, help='The minimum length of the article to be used as objective set'       )

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

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

							parser.add_argument(
							    '--threshold'	, default=1.0	, type=_lowercase	, 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=_lowercase	, help='finetuned_model_name'       )

							parser.add_argument(
							    '--recopy_model'	, default=_lowercase	, type=_lowercase	, 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=_lowercase	, 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(
							    _lowercase	, 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													=generate_datasets(
							    context_len=32	, file='data/tokenized_stories_train_wikitext103.jbl'	, number=100	, min_len=1_026	, trim=_lowercase       )

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    _lowercase	, _lowercase	, _lowercase	, context_len=32	, max_steps=1_000	, batch_size=16	, threshold=1.0	, recopy_model=_lowercase	, secondary_learner=_lowercase	, eval_interval=10	, finetuned_model_name='gpt2_finetuned.pt'	, )


if __name__ == "__main__":
		main()










"""simple docstring"""




from __future__ import annotations

from statistics import mean






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]       ,			_lowercase		:       list[int]       ,			_lowercase		:       int   )					->list[int]:






	'''simple docstring'''






	a :						Union[str, Any]      =							[0] * no_of_processes
	a :						int      =							[0] * no_of_processes

	# Initialize remaining_time to waiting_time.

	for i in range(_lowercase   ):
		a :						List[Any]      =							burst_time[i]
	a :						list[int]      =							[]

	a :						List[Any]      =							0
	a :						Any      =							0

	# When processes are not completed,
	# A process whose arrival time has passed \
	# and has remaining execution time is put into the ready_process.
	# The shortest process in the ready_process, target_process is executed.

	while completed != no_of_processes:
		a :						str      =							[]
		a :						Tuple      =							-1

		for i in range(_lowercase   ):
			if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
				ready_process.append(_lowercase   )

		if len(_lowercase   ) > 0:
			a :						List[Any]      =							ready_process[0]
			for i in ready_process:
				if remaining_time[i] < remaining_time[target_process]:
					a :						Optional[Any]      =							i
			total_time += burst_time[target_process]
			completed += 1
			a :						str      =							0
			a :						Union[str, Any]      =							(
			    total_time - arrival_time[target_process] - burst_time[target_process]
			)
		else:
			total_time += 1

	return waiting_time






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[int]       ,			_lowercase		:       int       ,			_lowercase		:       list[int]   )					->list[int]:






	'''simple docstring'''






	a :						List[str]      =							[0] * no_of_processes
	for i in range(_lowercase   ):
		a :						Optional[int]      =							burst_time[i] + waiting_time[i]
	return turn_around_time


if __name__ == "__main__":
		print('''[TEST CASE 01]''')

		a		:					Dict	      =     4
		a		:					Tuple	      =     [2, 5, 3, 7]
		a		:					List[Any]	      =     [0, 0, 0, 0]
		a		:					int	      =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)
		a		:					List[Any]	      =     calculate_turnaroundtime(
		    burst_time, no_of_processes, waiting_time
		)

		# Printing the Result
		print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''')
		for i, process_id in enumerate(list(range(1, 5))):
				print(
				    F'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
				    F'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
				)
		print(F'''\nAverage waiting time = {mean(waiting_time):.5f}''')
		print(F'''Average turnaround time = {mean(turn_around_time):.5f}''')


