






"""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=1_00    ,				__a=10_26    ,				__a=True    ,				__a="data/tokenized_stories_train_wikitext103.jbl"    ,				__a="igf_context_pairs.jbl"    ,				) ->       List[str]:
    """simple docstring"""






    set_seed(3							)
    # generate train_data and objective_set
    SCREAMING_SNAKE_CASE_      :  str    						=		generate_datasets(
        __SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE    ,				number=__SCREAMING_SNAKE_CASE    ,				min_len=10_26    ,				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?
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''							)

    # load pretrained model
    SCREAMING_SNAKE_CASE_      :  str    						=		load_gpta('''gpt2'''							).to(__SCREAMING_SNAKE_CASE							)
    print('''computing perplexity on objective set'''							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		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 					_A			(__a    ,				__a=15    ,				__a=1_28    ,				__a=1_00    ,				__a="igf_model.pt"    ,				) ->       Union[str, Any]:
    """simple docstring"""






    set_seed(42							)

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

    # Initialize secondary learner to use embedding weights of model
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		SecondaryLearner(__SCREAMING_SNAKE_CASE							)

    # Train secondary learner
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		train_secondary_learner(
        __SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE    ,				max_epochs=__SCREAMING_SNAKE_CASE    ,				batch_size=__SCREAMING_SNAKE_CASE    ,				eval_freq=1_00    ,				igf_model_path=__SCREAMING_SNAKE_CASE    ,				)

    del model, secondary_learner_train_data
    torch.cuda.empty_cache()

    return secondary_learner



def 					_A			(__a    ,				__a    ,				__a    ,				__a=32    ,				__a=10_00    ,				__a=16    ,				__a=1.0    ,				__a=recopy_gpta    ,				__a=None    ,				__a=10    ,				__a="gpt2_finetuned.pt"    ,				) ->       Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  str    						=		torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		RandomSampler(__SCREAMING_SNAKE_CASE							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		DataLoader(__SCREAMING_SNAKE_CASE    ,				sampler=__SCREAMING_SNAKE_CASE							)

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		max_steps // (len(__SCREAMING_SNAKE_CASE							)) + 1
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		0
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.zeros((1, context_len)    ,				dtype=torch.long    ,				device=__SCREAMING_SNAKE_CASE							)
    SCREAMING_SNAKE_CASE_      :  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()
    SCREAMING_SNAKE_CASE_      :  str    						=		[]
    SCREAMING_SNAKE_CASE_      :  Dict    						=		0

    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[]
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		[]

    # Compute the performance of the transformer model at the beginning
    SCREAMING_SNAKE_CASE_      :  List[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							)
    for epoch in range(int(__SCREAMING_SNAKE_CASE							)							):
        for step, example in enumerate(__SCREAMING_SNAKE_CASE							):
            torch.cuda.empty_cache()
            SCREAMING_SNAKE_CASE_      :  Any    						=		random.randint(0    ,				example.size(2							) - context_len - 1							)
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		example[0, 0, start : start + context_len]
            lm_optimizer.zero_grad()
            SCREAMING_SNAKE_CASE_      :  Dict    						=		model(__SCREAMING_SNAKE_CASE    ,				labels=__SCREAMING_SNAKE_CASE							)
            SCREAMING_SNAKE_CASE_      :  Any    						=		True

            if secondary_learner is not None:
                SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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:
                    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		-1
                if predicted_q < threshold:
                    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()							)							)
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		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()
                SCREAMING_SNAKE_CASE_      :  Optional[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:
                    SCREAMING_SNAKE_CASE_      :  str    						=		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 					_A			() ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  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=__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=1_00    ,				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=1_00    ,				type=__SCREAMING_SNAKE_CASE    ,				help='''secondary model evaluation is triggered at eval_freq'''							)

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

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

    parser.add_argument(
        '''--min_len'''    ,				default=10_26    ,				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=1_00    ,				min_len=10_26    ,				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
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		joblib.load('''data/IGF_values.jbl'''							)

    # Train secondary learner
    SCREAMING_SNAKE_CASE_      :  str    						=		training_secondary_learner(
        __SCREAMING_SNAKE_CASE    ,				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
    SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		GPTaLMHeadModel.from_pretrained('''gpt2'''							)
    set_seed(42							)

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





"""simple docstring"""


import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union





@dataclass
class     snake_case	:
				SCREAMING_SNAKE_CASE_   :      Optional[Union[str, Path]]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      Optional[Dict]         =  None
				SCREAMING_SNAKE_CASE_   :      Optional[str]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  True
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  None
				SCREAMING_SNAKE_CASE_   :      int         =  1
				SCREAMING_SNAKE_CASE_   :      Optional[Union[str, bool]]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      Optional[Dict]         =  None
				SCREAMING_SNAKE_CASE_   :      Optional[str]         =  None

				def 							lowercase_     (	self							:				str)->   "DownloadConfig":



						'''simple docstring'''


						return self.__class__(**{k: copy.deepcopy(UpperCamelCase__) for k, v in self.__dict__.items()})


