



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 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]=32     ,   _lowerCAmelCase						:		List[str]=10     ,   _lowerCAmelCase						:		Union[str, Any]=100     ,   _lowerCAmelCase						:		Dict=1026     ,   _lowerCAmelCase						:		List[Any]=True     ,   _lowerCAmelCase						:		List[str]="data/tokenized_stories_train_wikitext103.jbl"     ,   _lowerCAmelCase						:		List[Any]="igf_context_pairs.jbl"     ,   )   ->     int:
							set_seed(3						)
							# generate train_data and objective_set
							UpperCAmelCase				: str											=	generate_datasets(
							    _lowerCAmelCase     ,   _lowerCAmelCase     ,   number=_lowerCAmelCase     ,   min_len=1026     ,   trim=_lowerCAmelCase						)
							# 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?
							UpperCAmelCase				: List[str]											=	torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)

							# load pretrained model
							UpperCAmelCase				: Optional[Any]											=	load_gpta('''gpt2'''						).to(_lowerCAmelCase						)
							print('''computing perplexity on objective set'''						)
							UpperCAmelCase				: str											=	compute_perplexity(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						).item()
							print('''perplexity on objective set:'''     ,   _lowerCAmelCase						)

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

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


def 				snake_case_  (      _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Union[str, Any]=15     ,   _lowerCAmelCase						:		Any=128     ,   _lowerCAmelCase						:		Dict=100     ,   _lowerCAmelCase						:		Union[str, Any]="igf_model.pt"     ,   )   ->     Any:
							set_seed(42						)

							# Load pre-trained model
							UpperCAmelCase				: List[str]											=	GPTaLMHeadModel.from_pretrained('''gpt2'''						)

							# Initialize secondary learner to use embedding weights of model
							UpperCAmelCase				: str											=	SecondaryLearner(_lowerCAmelCase						)

							# Train secondary learner
							UpperCAmelCase				: List[Any]											=	train_secondary_learner(
							    _lowerCAmelCase     ,   _lowerCAmelCase     ,   max_epochs=_lowerCAmelCase     ,   batch_size=_lowerCAmelCase     ,   eval_freq=100     ,   igf_model_path=_lowerCAmelCase     ,   )

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Any     ,   _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		Tuple=32     ,   _lowerCAmelCase						:		Optional[Any]=1000     ,   _lowerCAmelCase						:		str=16     ,   _lowerCAmelCase						:		Dict=1.0     ,   _lowerCAmelCase						:		List[Any]=recopy_gpta     ,   _lowerCAmelCase						:		List[str]=None     ,   _lowerCAmelCase						:		List[str]=10     ,   _lowerCAmelCase						:		Dict="gpt2_finetuned.pt"     ,   )   ->     Optional[int]:
							UpperCAmelCase				: Tuple											=	torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)
							UpperCAmelCase				: int											=	RandomSampler(_lowerCAmelCase						)
							UpperCAmelCase				: Dict											=	DataLoader(_lowerCAmelCase     ,   sampler=_lowerCAmelCase						)

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

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(_lowerCAmelCase						)
														secondary_learner.eval()
							UpperCAmelCase				: Optional[Any]											=	[]
							UpperCAmelCase				: Tuple											=	0

							UpperCAmelCase				: List[str]											=	[]
							UpperCAmelCase				: Optional[Any]											=	[]

							# Compute the performance of the transformer model at the beginning
							UpperCAmelCase				: Any											=	compute_perplexity(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)
							test_perps.append(_lowerCAmelCase						)
							print('''Test perplexity, step'''     ,   _lowerCAmelCase     ,   ''':'''     ,   _lowerCAmelCase						)
							for epoch in range(int(_lowerCAmelCase						)						):
														for step, example in enumerate(_lowerCAmelCase						):
																					torch.cuda.empty_cache()
																					UpperCAmelCase				: Any											=	random.randint(0     ,   example.size(2						) - context_len - 1						)
																					UpperCAmelCase				: List[Any]											=	example[0, 0, start : start + context_len]
																					lm_optimizer.zero_grad()
																					UpperCAmelCase				: Optional[int]											=	model(_lowerCAmelCase     ,   labels=_lowerCAmelCase						)
																					UpperCAmelCase				: Tuple											=	True

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

																												# 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:
																																			UpperCAmelCase				: Optional[int]											=	-1
																												if predicted_q < threshold:
																																			UpperCAmelCase				: Union[str, Any]											=	False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()						)						)
																												UpperCAmelCase				: Union[str, Any]											=	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()
																												UpperCAmelCase				: Optional[int]											=	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:
																																			UpperCAmelCase				: List[Any]											=	compute_perplexity(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)
																																			test_perps.append(_lowerCAmelCase						)

																																			print('''Test perplexity, step'''     ,   _lowerCAmelCase     ,   ''':'''     ,   _lowerCAmelCase						)
            # 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()     ,   _lowerCAmelCase						)
							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 				snake_case_  (      )   ->     Optional[int]:
							UpperCAmelCase				: Union[str, Any]											=	argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''						)

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

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

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

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

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

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

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

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

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

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

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

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

							parser.add_argument(
							    '''--recopy_model'''     ,   default=_lowerCAmelCase     ,   type=_lowerCAmelCase     ,   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=_lowerCAmelCase     ,   data_file='''data/tokenized_stories_train_wikitext103.jbl'''     ,   igf_data_file='''igf_context_pairs.jbl'''     ,   )

							# Load train data for secondary learner
							UpperCAmelCase				: str											=	joblib.load('''data/IGF_values.jbl'''						)

							# Train secondary learner
							UpperCAmelCase				: Tuple											=	training_secondary_learner(
							    _lowerCAmelCase     ,   secondary_learner_max_epochs=15     ,   secondary_learner_batch_size=128     ,   eval_freq=100     ,   igf_model_path='''igf_model.pt'''     ,   )

							# load pretrained gpt2 model
							UpperCAmelCase				: str											=	GPTaLMHeadModel.from_pretrained('''gpt2'''						)
							set_seed(42						)

							# Generate train and test data to train and evaluate gpt2 model
							UpperCAmelCase				: int											=	generate_datasets(
							    context_len=32     ,   file='''data/tokenized_stories_train_wikitext103.jbl'''     ,   number=100     ,   min_len=1026     ,   trim=_lowerCAmelCase						)

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


if __name__ == "__main__":
						main()










'''simple docstring'''






from __future__ import annotations

import time

import numpy as np

UpperCamelCase__:     Tuple										=    [8, 5, 9, 7]
UpperCamelCase__:     int										=    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
UpperCamelCase__:     List[str]										=    [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  list[int]	,    __snake_case    :  list[list[int]]	,    __snake_case    :  list[list[int]]	,    )    ->	None:
								UpperCAmelCase				: Union[str, Any]											=	claim_vector
								UpperCAmelCase				: Tuple											=	allocated_resources_table
								UpperCAmelCase				: List[str]											=	maximum_claim_table







	def   A			(		self    :  Optional[Any]	)    ->	list[int]:
								return [
								    sum(p_item[i] for p_item in self.__allocated_resources_table	)
								    for i in range(len(self.__allocated_resources_table[0]	)	)
								]







	def   A			(		self    :  Union[str, Any]	)    ->	list[int]:
								return np.array(self.__claim_vector	) - np.array(
								    self.__processes_resource_summation()	)







	def   A			(		self    :  str	)    ->	list[list[int]]:
								return [
								    list(np.array(self.__maximum_claim_table[i]	) - np.array(__snake_case	)	)
								    for i, allocated_resource in enumerate(self.__allocated_resources_table	)
								]







	def   A			(		self    :  List[Any]	)    ->	dict[int, list[int]]:
								return {self.__need().index(__snake_case	): i for i in self.__need()}







	def   A			(		self    :  Tuple	,    **__snake_case    :  Union[str, Any]	)    ->	None:
								UpperCAmelCase				: Tuple											=	self.__need()
								UpperCAmelCase				: Union[str, Any]											=	self.__allocated_resources_table
								UpperCAmelCase				: int											=	self.__available_resources()
								UpperCAmelCase				: Tuple											=	self.__need_index_manager()
								for kw, val in kwargs.items():
															if kw and val is True:
																						self.__pretty_data()
								print('''_''' * 50 + '''\n'''	)
								while need_list:
															UpperCAmelCase				: Dict											=	False
															for each_need in need_list:
																						UpperCAmelCase				: Optional[int]											=	True
																						for index, need in enumerate(__snake_case	):
																													if need > available_resources[index]:
																																				UpperCAmelCase				: List[str]											=	False
																																				break
																						if execution:
																													UpperCAmelCase				: str											=	True
																													# get the original index of the process from ind_ctrl db
																													for original_need_index, need_clone in need_index_manager.items():
																																				if each_need == need_clone:
																																											UpperCAmelCase				: Union[str, Any]											=	original_need_index
																													print(F"""Process {process_number + 1} is executing."""	)
																													# remove the process run from stack
																													need_list.remove(__snake_case	)
																													# update available/freed resources stack
																													UpperCAmelCase				: Optional[int]											=	np.array(__snake_case	) + np.array(
																													    alloc_resources_table[process_number]	)
																													print(
																													    '''Updated available resource stack for processes: '''
																													    + ''' '''.join([str(__snake_case	) for x in available_resources]	)	)
																													break
															if safe:
																						print('''The process is in a safe state.\n'''	)
															else:
																						print('''System in unsafe state. Aborting...\n'''	)
																						break







	def   A			(		self    :  Dict	)    ->	Optional[Any]:
								print(''' ''' * 9 + '''Allocated Resource Table'''	)
								for item in self.__allocated_resources_table:
															print(
															    F"""P{self.__allocated_resources_table.index(__snake_case	) + 1}"""
															    + ''' '''.join(F"""{it:>8}""" for it in item	)
															    + '''\n'''	)
								print(''' ''' * 9 + '''System Resource Table'''	)
								for item in self.__maximum_claim_table:
															print(
															    F"""P{self.__maximum_claim_table.index(__snake_case	) + 1}"""
															    + ''' '''.join(F"""{it:>8}""" for it in item	)
															    + '''\n'''	)
								print(
								    '''Current Usage by Active Processes: '''
								    + ''' '''.join(str(__snake_case	) for x in self.__claim_vector	)	)
								print(
								    '''Initial Available Resources:       '''
								    + ''' '''.join(str(__snake_case	) for x in self.__available_resources()	)	)
								time.sleep(1	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





