






"""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 _lowerCAmelCase	(  __lowerCamelCase:Tuple=3_2 ,						__lowerCamelCase:Optional[int]=1_0 ,						__lowerCamelCase:Optional[int]=1_0_0 ,						__lowerCamelCase:List[str]=1_0_2_6 ,						__lowerCamelCase:List[str]=True ,						__lowerCamelCase:List[str]="data/tokenized_stories_train_wikitext103.jbl" ,						__lowerCamelCase:List[Any]="igf_context_pairs.jbl" ,						):

						'''simple docstring'''






						set_seed(3   )
						# generate train_data and objective_set
						__magic_name__            =     generate_datasets(
						    __lowerCamelCase ,						__lowerCamelCase ,						number=__lowerCamelCase ,						min_len=1_0_2_6 ,						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?
						__magic_name__            =     torch.device("cuda:0" if torch.cuda.is_available() else "cpu"   )

						# load pretrained model
						__magic_name__            =     load_gpta("gpt2"   ).to(__lowerCamelCase   )
						print("computing perplexity on objective set"   )
						__magic_name__            =     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 _lowerCAmelCase	(  __lowerCamelCase:Tuple ,						__lowerCamelCase:Optional[int]=1_5 ,						__lowerCamelCase:List[Any]=1_2_8 ,						__lowerCamelCase:Tuple=1_0_0 ,						__lowerCamelCase:Tuple="igf_model.pt" ,						):

						'''simple docstring'''






						set_seed(4_2   )

						# Load pre-trained model
						__magic_name__            =     GPTaLMHeadModel.from_pretrained("gpt2"   )

						# Initialize secondary learner to use embedding weights of model
						__magic_name__            =     SecondaryLearner(__lowerCamelCase   )

						# Train secondary learner
						__magic_name__            =     train_secondary_learner(
						    __lowerCamelCase ,						__lowerCamelCase ,						max_epochs=__lowerCamelCase ,						batch_size=__lowerCamelCase ,						eval_freq=1_0_0 ,						igf_model_path=__lowerCamelCase ,						)

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner






def _lowerCAmelCase	(  __lowerCamelCase:List[Any] ,						__lowerCamelCase:List[str] ,						__lowerCamelCase:Dict ,						__lowerCamelCase:Any=3_2 ,						__lowerCamelCase:Dict=1_0_0_0 ,						__lowerCamelCase:List[Any]=1_6 ,						__lowerCamelCase:Dict=1.0 ,						__lowerCamelCase:Union[str, Any]=recopy_gpta ,						__lowerCamelCase:int=None ,						__lowerCamelCase:Optional[int]=1_0 ,						__lowerCamelCase:Tuple="gpt2_finetuned.pt" ,						):

						'''simple docstring'''






						__magic_name__            =     torch.device("cuda:0" if torch.cuda.is_available() else "cpu"   )
						__magic_name__            =     RandomSampler(__lowerCamelCase   )
						__magic_name__            =     DataLoader(__lowerCamelCase ,						sampler=__lowerCamelCase   )

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

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

						__magic_name__            =     []
						__magic_name__            =     []

						# Compute the performance of the transformer model at the beginning
						__magic_name__            =     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()
																		__magic_name__            =     random.randint(0 ,						example.size(2   ) - context_len - 1   )
																		__magic_name__            =     example[0, 0, start : start + context_len]
																		lm_optimizer.zero_grad()
																		__magic_name__            =     model(__lowerCamelCase ,						labels=__lowerCamelCase   )
																		__magic_name__            =     True

																		if secondary_learner is not None:
																								__magic_name__            =     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 == 1_0:
																														__magic_name__            =     -1
																								if predicted_q < threshold:
																														__magic_name__            =     False

																								# If we passed the filter, add the context to the batch!
																		if do_backprop:
																								contexts.append(np.array(context.cpu()   )   )
																								__magic_name__            =     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()
																								__magic_name__            =     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:
																														__magic_name__            =     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 > 6_0:
																								break
												if max_steps > 0 and global_step > 6_0:
																		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 _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     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=3_2 ,						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=1_0_0 ,						type=__lowerCamelCase ,						help="number of articles that are long enough to be used as our objective set" ,						)
						parser.add_argument(
						    "--eval_freq" ,						default=1_0_0 ,						type=__lowerCamelCase ,						help="secondary model evaluation is triggered at eval_freq"   )

						parser.add_argument("--max_steps" ,						default=1_0_0_0 ,						type=__lowerCamelCase ,						help="To calculate training epochs"   )

						parser.add_argument(
						    "--secondary_learner_batch_size" ,						default=1_2_8 ,						type=__lowerCamelCase ,						help="batch size of training data for secondary learner" ,						)

						parser.add_argument(
						    "--batch_size" ,						default=1_6 ,						type=__lowerCamelCase ,						help="batch size of training data of language model(gpt2) "   )

						parser.add_argument(
						    "--eval_interval" ,						default=1_0 ,						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=1_0_0 ,						type=__lowerCamelCase ,						help="The number of examples split to be used as objective_set/test_data"   )

						parser.add_argument(
						    "--min_len" ,						default=1_0_2_6 ,						type=__lowerCamelCase ,						help="The minimum length of the article to be used as objective set"   )

						parser.add_argument(
						    "--secondary_learner_max_epochs" ,						default=1_5 ,						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=3_2 ,						max_steps=1_0 ,						size_objective_set=1_0_0 ,						min_len=1_0_2_6 ,						trim=__lowerCamelCase ,						data_file="data/tokenized_stories_train_wikitext103.jbl" ,						igf_data_file="igf_context_pairs.jbl" ,						)

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

						# Train secondary learner
						__magic_name__            =     training_secondary_learner(
						    __lowerCamelCase ,						secondary_learner_max_epochs=1_5 ,						secondary_learner_batch_size=1_2_8 ,						eval_freq=1_0_0 ,						igf_model_path="igf_model.pt" ,						)

						# load pretrained gpt2 model
						__magic_name__            =     GPTaLMHeadModel.from_pretrained("gpt2"   )
						set_seed(4_2   )

						# Generate train and test data to train and evaluate gpt2 model
						__magic_name__            =     generate_datasets(
						    context_len=3_2 ,						file="data/tokenized_stories_train_wikitext103.jbl" ,						number=1_0_0 ,						min_len=1_0_2_6 ,						trim=__lowerCamelCase   )

						# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
						finetune(
						    __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						context_len=3_2 ,						max_steps=1_0_0_0 ,						batch_size=1_6 ,						threshold=1.0 ,						recopy_model=__lowerCamelCase ,						secondary_learner=__lowerCamelCase ,						eval_interval=1_0 ,						finetuned_model_name="gpt2_finetuned.pt" ,						)


if __name__ == "__main__":
			main()











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:int = 2_0_0_0_0_0_0   ):

						'''simple docstring'''






						__magic_name__            =     [0 for i in range(n + 1   )]
						__magic_name__            =     1
						__magic_name__            =     1

						for i in range(2 ,						int(n**0.5   ) + 1   ):
												if primality_list[i] == 0:
																		for j in range(i * i ,						n + 1 ,						__lowerCamelCase   ):
																								__magic_name__            =     1
						__magic_name__            =     0
						for i in range(__lowerCamelCase   ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


if __name__ == "__main__":
			print(f'''{solution() = }''')



