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					(   a_				: Optional[int]=32						,			a_				: Any=10						,			a_				: Dict=1_00						,			a_				: int=10_26						,			a_				: List[str]=True						,			a_				: Dict="data/tokenized_stories_train_wikitext103.jbl"						,			a_				: int="igf_context_pairs.jbl"						,			):
			set_seed(3)
			# generate train_data and objective_set
			lowerCamelCase						:Any						  =     generate_datasets(
			    a_						,			a_						,			number=a_						,			min_len=10_26						,			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						:Tuple						  =     torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''')

			# load pretrained model
			lowerCamelCase						:List[str]						  =     load_gpta('''gpt2''').to(a_)
			print('''computing perplexity on objective set''')
			lowerCamelCase						:Tuple						  =     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					(   a_				: Optional[Any]						,			a_				: List[Any]=15						,			a_				: List[Any]=1_28						,			a_				: Optional[Any]=1_00						,			a_				: Tuple="igf_model.pt"						,			):
			set_seed(42)

			# Load pre-trained model
			lowerCamelCase						:Tuple						  =     GPTaLMHeadModel.from_pretrained('''gpt2''')

			# Initialize secondary learner to use embedding weights of model
			lowerCamelCase						:List[Any]						  =     SecondaryLearner(a_)

			# Train secondary learner
			lowerCamelCase						:Tuple						  =     train_secondary_learner(
			    a_						,			a_						,			max_epochs=a_						,			batch_size=a_						,			eval_freq=1_00						,			igf_model_path=a_						,			)

			del model, secondary_learner_train_data
			torch.cuda.empty_cache()

			return secondary_learner





def  _lowerCamelCase					(   a_				: Union[str, Any]						,			a_				: int						,			a_				: Optional[Any]						,			a_				: List[Any]=32						,			a_				: Tuple=10_00						,			a_				: List[Any]=16						,			a_				: List[str]=1.0						,			a_				: Tuple=recopy_gpta						,			a_				: Tuple=None						,			a_				: List[Any]=10						,			a_				: str="gpt2_finetuned.pt"						,			):
			lowerCamelCase						:int						  =     torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''')
			lowerCamelCase						:Any						  =     RandomSampler(a_)
			lowerCamelCase						:Any						  =     DataLoader(a_						,			sampler=a_)

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

			model.train()
			if secondary_learner is not None:
						secondary_learner.to(a_)
						secondary_learner.eval()
			lowerCamelCase						:Optional[int]						  =     []
			lowerCamelCase						:Any						  =     0

			lowerCamelCase						:Optional[Any]						  =     []
			lowerCamelCase						:Union[str, Any]						  =     []

			# Compute the performance of the transformer model at the beginning
			lowerCamelCase						:Optional[Any]						  =     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						:List[str]						  =     random.randint(0						,			example.size(2) - context_len - 1)
									lowerCamelCase						:Dict						  =     example[0, 0, start : start + context_len]
									lm_optimizer.zero_grad()
									lowerCamelCase						:Optional[Any]						  =     model(a_						,			labels=a_)
									lowerCamelCase						:Optional[Any]						  =     True

									if secondary_learner is not None:
												lowerCamelCase						:Optional[Any]						  =     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						:str						  =     -1
												if predicted_q < threshold:
															lowerCamelCase						:Tuple						  =     False

            # If we passed the filter, add the context to the batch!
									if do_backprop:
												contexts.append(np.array(context.cpu()))
												lowerCamelCase						: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()
												lowerCamelCase						: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:
															lowerCamelCase						:int						  =     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					(   ):
			lowerCamelCase						:Optional[Any]						  =     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=1_00						,			type=a_						,			help='''number of articles that are long enough to be used as our objective set'''						,			)
			parser.add_argument(
			    '''--eval_freq'''						,			default=1_00						,			type=a_						,			help='''secondary model evaluation is triggered at eval_freq''')

			parser.add_argument('''--max_steps'''						,			default=10_00						,			type=a_						,			help='''To calculate training epochs''')

			parser.add_argument(
			    '''--secondary_learner_batch_size'''						,			default=1_28						,			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=1_00						,			type=a_						,			help='''The number of examples split to be used as objective_set/test_data''')

			parser.add_argument(
			    '''--min_len'''						,			default=10_26						,			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=1_00						,			min_len=10_26						,			trim=a_						,			data_file='''data/tokenized_stories_train_wikitext103.jbl'''						,			igf_data_file='''igf_context_pairs.jbl'''						,			)

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

			# Train secondary learner
			lowerCamelCase						:Tuple						  =     training_secondary_learner(
			    a_						,			secondary_learner_max_epochs=15						,			secondary_learner_batch_size=1_28						,			eval_freq=1_00						,			igf_model_path='''igf_model.pt'''						,			)

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

			# Generate train and test data to train and evaluate gpt2 model
			lowerCamelCase						:int						  =     generate_datasets(
			    context_len=32						,			file='''data/tokenized_stories_train_wikitext103.jbl'''						,			number=1_00						,			min_len=10_26						,			trim=a_)

			# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
			finetune(
			    a_						,			a_						,			a_						,			context_len=32						,			max_steps=10_00						,			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()




from maths.prime_factors import prime_factors





def  _lowerCamelCase					(   a_				: int):
			if not isinstance(a_						,			a_):
						lowerCamelCase						:Tuple						  =     F"Input value of [number={number}] must be an integer"
						raise TypeError(a_)
			if number < 1:
						raise ValueError('''Input must be a positive integer''')
			return -1 if len(prime_factors(a_)) % 2 else 1


if __name__ == "__main__":
			import doctest

			doctest.testmod()



