



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

		# load pretrained model
		lowerCAmelCase_     :	int							   =					load_gpta('''gpt2'''					).to(UpperCamelCase__					)
		print('''computing perplexity on objective set'''					)
		lowerCAmelCase_     :	Any							   =					compute_perplexity(UpperCamelCase__	,UpperCamelCase__	,UpperCamelCase__					).item()
		print('''perplexity on objective set:'''	,UpperCamelCase__					)

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

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


def      UpperCamelCase(    __UpperCamelCase				:						Optional[Any]	,__UpperCamelCase				:						Optional[int]=15	,__UpperCamelCase				:						Tuple=128	,__UpperCamelCase				:						Tuple=100	,__UpperCamelCase				:						Dict="igf_model.pt"	,):
		set_seed(42					)

		# Load pre-trained model
		lowerCAmelCase_     :	Any							   =					GPTaLMHeadModel.from_pretrained('''gpt2'''					)

		# Initialize secondary learner to use embedding weights of model
		lowerCAmelCase_     :	Tuple							   =					SecondaryLearner(UpperCamelCase__					)

		# Train secondary learner
		lowerCAmelCase_     :	Optional[int]							   =					train_secondary_learner(
		    UpperCamelCase__	,UpperCamelCase__	,max_epochs=UpperCamelCase__	,batch_size=UpperCamelCase__	,eval_freq=100	,igf_model_path=UpperCamelCase__	,)

		del model, secondary_learner_train_data
		torch.cuda.empty_cache()

		return secondary_learner


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

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

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

		lowerCAmelCase_     :	Optional[int]							   =					[]
		lowerCAmelCase_     :	Optional[Any]							   =					[]

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

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

								# 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_     :	Tuple							   =					-1
								if predicted_q < threshold:
										lowerCAmelCase_     :	Optional[int]							   =					False

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu()					)					)
								lowerCAmelCase_     :	List[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()
								lowerCAmelCase_     :	List[str]							   =					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_     :	str							   =					compute_perplexity(UpperCamelCase__	,UpperCamelCase__	,UpperCamelCase__					)
										test_perps.append(UpperCamelCase__					)

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

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

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

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

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

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

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

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

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

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

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

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

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

		# Load train data for secondary learner
		lowerCAmelCase_     :	List[str]							   =					joblib.load('''data/IGF_values.jbl'''					)

		# Train secondary learner
		lowerCAmelCase_     :	Dict							   =					training_secondary_learner(
		    UpperCamelCase__	,secondary_learner_max_epochs=15	,secondary_learner_batch_size=128	,eval_freq=100	,igf_model_path='''igf_model.pt'''	,)

		# load pretrained gpt2 model
		lowerCAmelCase_     :	Tuple							   =					GPTaLMHeadModel.from_pretrained('''gpt2'''					)
		set_seed(42					)

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

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


if __name__ == "__main__":
						main()






'''simple docstring'''


from collections.abc import Sequence





def    lowerCAmelCase       (		UpperCamelCase__					: Sequence[float]     ,       UpperCamelCase__					: bool = False							):


	"""simple docstring"""
	if not arr:
		return 0

	__UpperCAmelCase      					=      0 if allow_empty_subarrays else float('''-inf'''							)
	__UpperCAmelCase      					=      0.0
	for num in arr:
		__UpperCAmelCase      					=      max(0 if allow_empty_subarrays else num     ,       curr_sum + num							)
		__UpperCAmelCase      					=      max(UpperCamelCase__     ,       UpperCamelCase__							)

	return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					__lowerCAmelCase						:							int    							=						[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")
