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

						# load pretrained model
						UpperCamelCase_		:							Tuple  =		load_gpta("""gpt2"""			).to(_SCREAMING_SNAKE_CASE			)
						print("""computing perplexity on objective set"""			)
						UpperCamelCase_		:							int  =		compute_perplexity(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			).item()
						print("""perplexity on objective set:"""		, _SCREAMING_SNAKE_CASE			)

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

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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]		, _SCREAMING_SNAKE_CASE     :				Optional[int]=15		, _SCREAMING_SNAKE_CASE     :				Dict=128		, _SCREAMING_SNAKE_CASE     :				Union[str, Any]=100		, _SCREAMING_SNAKE_CASE     :				List[Any]="igf_model.pt"		, ):
						set_seed(42			)

						# Load pre-trained model
						UpperCamelCase_		:							Any  =		GPTaLMHeadModel.from_pretrained("""gpt2"""			)

						# Initialize secondary learner to use embedding weights of model
						UpperCamelCase_		:							int  =		SecondaryLearner(_SCREAMING_SNAKE_CASE			)

						# Train secondary learner
						UpperCamelCase_		:							Union[str, Any]  =		train_secondary_learner(
						    _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, max_epochs=_SCREAMING_SNAKE_CASE		, batch_size=_SCREAMING_SNAKE_CASE		, eval_freq=100		, igf_model_path=_SCREAMING_SNAKE_CASE		, )

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Any		, _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				Optional[int]		, _SCREAMING_SNAKE_CASE     :				Dict=32		, _SCREAMING_SNAKE_CASE     :				Any=1000		, _SCREAMING_SNAKE_CASE     :				List[str]=16		, _SCREAMING_SNAKE_CASE     :				Optional[Any]=1.0		, _SCREAMING_SNAKE_CASE     :				Dict=recopy_gpta		, _SCREAMING_SNAKE_CASE     :				Tuple=None		, _SCREAMING_SNAKE_CASE     :				Optional[Any]=10		, _SCREAMING_SNAKE_CASE     :				int="gpt2_finetuned.pt"		, ):
						UpperCamelCase_		:							int  =		torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""			)
						UpperCamelCase_		:							List[str]  =		RandomSampler(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							int  =		DataLoader(_SCREAMING_SNAKE_CASE		, sampler=_SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							List[str]  =		max_steps // (len(_SCREAMING_SNAKE_CASE			)) + 1
						UpperCamelCase_		:							List[Any]  =		0
						UpperCamelCase_		:							Union[str, Any]  =		torch.zeros((1, context_len)		, dtype=torch.long		, device=_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							List[Any]  =		recopy_model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

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

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

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

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

																								# 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_		:							Dict  =		-1
																								if predicted_q < threshold:
																														UpperCamelCase_		:							Optional[Any]  =		False

            # If we passed the filter, add the context to the batch!
																		if do_backprop:
																								contexts.append(np.array(context.cpu()			)			)
																								UpperCamelCase_		:							Optional[int]  =		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_		:							Any  =		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_		:							Optional[Any]  =		compute_perplexity(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
																														test_perps.append(_SCREAMING_SNAKE_CASE			)

																														print("""Test perplexity, step"""		, _SCREAMING_SNAKE_CASE		, """:"""		, _SCREAMING_SNAKE_CASE			)
            # 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()		, _SCREAMING_SNAKE_CASE			)
						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_			(   ):
						UpperCamelCase_		:							Optional[int]  =		argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""			)

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

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

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

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

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

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

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

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

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

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

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

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

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

						# Load train data for secondary learner
						UpperCamelCase_		:							Any  =		joblib.load("""data/IGF_values.jbl"""			)

						# Train secondary learner
						UpperCamelCase_		:							Optional[Any]  =		training_secondary_learner(
						    _SCREAMING_SNAKE_CASE		, secondary_learner_max_epochs=15		, secondary_learner_batch_size=128		, eval_freq=100		, igf_model_path="""igf_model.pt"""		, )

						# load pretrained gpt2 model
						UpperCamelCase_		:							Union[str, Any]  =		GPTaLMHeadModel.from_pretrained("""gpt2"""			)
						set_seed(42			)

						# Generate train and test data to train and evaluate gpt2 model
						UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		generate_datasets(
						    context_len=32		, file="""data/tokenized_stories_train_wikitext103.jbl"""		, number=100		, min_len=1026		, trim=_SCREAMING_SNAKE_CASE			)

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


if __name__ == "__main__":
	main()







import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812






def     lowerCAmelCase_			(   ):
						print("""Making key files..."""			)
						make_key_files("""rsa"""		, 1024			)
						print("""Key files generation successful."""			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				int			):
						print("""Generating prime p..."""			)
						UpperCamelCase_		:							Tuple  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						print("""Generating prime q..."""			)
						UpperCamelCase_		:							List[Any]  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							List[str]  =		p * q

						print("""Generating e that is relatively prime to (p - 1) * (q - 1)..."""			)
						while True:
												UpperCamelCase_		:							Union[str, Any]  =		random.randrange(2 ** (key_size - 1)		, 2 ** (key_size)			)
												if cryptoMath.gcd(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			) == 1:
																		break

						print("""Calculating d that is mod inverse of e..."""			)
						UpperCamelCase_		:							Tuple  =		cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			)

						UpperCamelCase_		:							Tuple  =		(n, e)
						UpperCamelCase_		:							int  =		(n, d)
						return (public_key, private_key)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				int			):
						if os.path.exists(f'''{name}_pubkey.txt'''			) or os.path.exists(f'''{name}_privkey.txt'''			):
												print("""\nWARNING:"""			)
												print(
												    f'''"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'''
												    """Use a different name or delete these files and re-run this program."""			)
												sys.exit()

						UpperCamelCase_,UpperCamelCase_		:							str  =		generate_key(_SCREAMING_SNAKE_CASE			)
						print(f'''\nWriting public key to file {name}_pubkey.txt...'''			)
						with open(f'''{name}_pubkey.txt'''		, """w"""			) as out_file:
												out_file.write(f'''{key_size},{public_key[0]},{public_key[1]}'''			)

						print(f'''Writing private key to file {name}_privkey.txt...'''			)
						with open(f'''{name}_privkey.txt'''		, """w"""			) as out_file:
												out_file.write(f'''{key_size},{private_key[0]},{private_key[1]}'''			)


if __name__ == "__main__":
	main()






