


'''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      a (     __a=32					,   __a=10					,   __a=100					,   __a=1026					,   __a=True					,   __a="data/tokenized_stories_train_wikitext103.jbl"					,   __a="igf_context_pairs.jbl"					,   )    ->      int:



					'''simple docstring'''






					set_seed(3  )
					# generate train_data and objective_set
					UpperCamelCase__   ,							UpperCamelCase__  :Optional[Any]				   =					generate_datasets(
					    __a					,   __a					,   number=__a					,   min_len=1026					,   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?
					UpperCamelCase__  :Tuple				   =					torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''  )

					# load pretrained model
					UpperCamelCase__  :Optional[Any]				   =					load_gpta('''gpt2'''  ).to(__a  )
					print('''computing perplexity on objective set'''  )
					UpperCamelCase__  :Any				   =					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      a (     __a					,   __a=15					,   __a=128					,   __a=100					,   __a="igf_model.pt"					,   )    ->      int:



					'''simple docstring'''






					set_seed(42  )

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

					# Initialize secondary learner to use embedding weights of model
					UpperCamelCase__  :List[str]				   =					SecondaryLearner(__a  )

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

					del model, secondary_learner_train_data
					torch.cuda.empty_cache()

					return secondary_learner


def      a (     __a					,   __a					,   __a					,   __a=32					,   __a=1000					,   __a=16					,   __a=1.0					,   __a=recopy_gpta					,   __a=None					,   __a=10					,   __a="gpt2_finetuned.pt"					,   )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :Tuple				   =					torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''  )
					UpperCamelCase__  :Optional[Any]				   =					RandomSampler(__a  )
					UpperCamelCase__  :Union[str, Any]				   =					DataLoader(__a					,   sampler=__a  )

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

					model.train()
					if secondary_learner is not None:
										secondary_learner.to(__a  )
										secondary_learner.eval()
					UpperCamelCase__  :List[str]				   =					[]
					UpperCamelCase__  :str				   =					0

					UpperCamelCase__  :Any				   =					[]
					UpperCamelCase__  :Optional[int]				   =					[]

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

															if secondary_learner is not None:
																				UpperCamelCase__  :Optional[int]				   =					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:
																									UpperCamelCase__  :int				   =					-1
																				if predicted_q < threshold:
																									UpperCamelCase__  :Tuple				   =					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__  :List[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__  :str				   =					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      a (     )    ->      Optional[Any]:



					'''simple docstring'''






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

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

					parser.add_argument(
					    '''--secondary_learner_batch_size'''					,   default=128					,   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=100					,   type=__a					,   help='''The number of examples split to be used as objective_set/test_data'''  )

					parser.add_argument(
					    '''--min_len'''					,   default=1026					,   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=100					,   min_len=1026					,   trim=__a					,   data_file='''data/tokenized_stories_train_wikitext103.jbl'''					,   igf_data_file='''igf_context_pairs.jbl'''					,   )

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

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

					# load pretrained gpt2 model
					UpperCamelCase__  :Dict				   =					GPTaLMHeadModel.from_pretrained('''gpt2'''  )
					set_seed(42  )

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

					# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
					finetune(
					    __a					,   __a					,   __a					,   context_len=32					,   max_steps=1000					,   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()



'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				 =	logging.getLogger(__name__)
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							field(
		    default=0.0      ,						metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Whether to SortishSamler or not.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'whether to use adafactor'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Attention dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default='linear'      ,						metadata={'help': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}'''}      ,						)