"""simple docstring"""





import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/text-classification/requirements.txt''')

lowerCAmelCase_     =	logging.getLogger(__name__)




@dataclass
class  _snake_case		:







				"""simple docstring"""






				a			=						field(
				    default=1_28   ,       metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
				a			=						field(
				    default=__snake_case   ,       metadata={
				        "help": (
				            "Whether to pad all samples to `max_seq_length`. "
				            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
				        )
				    }   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of training examples to this "
				            "value if set."
				        )
				    }   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
				            "value if set."
				        )
				    }   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
				            "value if set."
				        )
				    }   ,       )




@dataclass
class  _snake_case		:







				"""simple docstring"""






				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Train language if it is different from the evaluation language."} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Pretrained config name or path if not the same as model_name"} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}   ,       )
				a			=						field(
				    default="main"   ,       metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={
				        "help": (
				            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
				            "with private models)."
				        )
				    }   ,       )
				a			=						field(
				    default=__snake_case   ,       metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}   ,       )



def      lowerCamelCase_()->    str:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry("""run_xnli"""     ,	__SCREAMING_SNAKE_CASE  )

							# Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""     ,	datefmt="""%m/%d/%Y %H:%M:%S"""     ,	handlers=[logging.StreamHandler(sys.stdout  )]     ,	)

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    training_args.get_process_log_level()
							logger.setLevel(__SCREAMING_SNAKE_CASE  )
							datasets.utils.logging.set_verbosity(__SCREAMING_SNAKE_CASE  )
							transformers.utils.logging.set_verbosity(__SCREAMING_SNAKE_CASE  )
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()

							# Log on each process the small summary:
							logger.warning(
							    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
							    + F"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
							logger.info(F"""Training/evaluation parameters {training_args}"""  )

							# Detecting last checkpoint.
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    None
							if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
														_SCREAMING_SNAKE_CASE		:						str			  =    get_last_checkpoint(training_args.output_dir  )
														if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
																					raise ValueError(
																					    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
																					    """Use --overwrite_output_dir to overcome."""  )
														elif last_checkpoint is not None:
																					logger.info(
																					    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
																					    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""  )

    # Set seed before initializing model.
							set_seed(training_args.seed  )

							# In distributed training, the load_dataset function guarantees that only one local process can concurrently
							# download the dataset.
							# Downloading and loading xnli dataset from the hub.
							if training_args.do_train:
														if model_args.train_language is None:
																					_SCREAMING_SNAKE_CASE		:						str			  =    load_dataset(
																					    """xnli"""     ,	model_args.language     ,	split="""train"""     ,	cache_dir=model_args.cache_dir     ,	use_auth_token=True if model_args.use_auth_token else None     ,	)
														else:
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    load_dataset(
																					    """xnli"""     ,	model_args.train_language     ,	split="""train"""     ,	cache_dir=model_args.cache_dir     ,	use_auth_token=True if model_args.use_auth_token else None     ,	)
														_SCREAMING_SNAKE_CASE		:						Any			  =    train_dataset.features["""label"""].names

							if training_args.do_eval:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    load_dataset(
														    """xnli"""     ,	model_args.language     ,	split="""validation"""     ,	cache_dir=model_args.cache_dir     ,	use_auth_token=True if model_args.use_auth_token else None     ,	)
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    eval_dataset.features["""label"""].names

							if training_args.do_predict:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    load_dataset(
														    """xnli"""     ,	model_args.language     ,	split="""test"""     ,	cache_dir=model_args.cache_dir     ,	use_auth_token=True if model_args.use_auth_token else None     ,	)
														_SCREAMING_SNAKE_CASE		:						str			  =    predict_dataset.features["""label"""].names

							# Labels
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    len(__SCREAMING_SNAKE_CASE  )

							# Load pretrained model and tokenizer
							# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,	num_labels=__SCREAMING_SNAKE_CASE     ,	idalabel={str(__SCREAMING_SNAKE_CASE  ): label for i, label in enumerate(__SCREAMING_SNAKE_CASE  )}     ,	labelaid={label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE  )}     ,	finetuning_task="""xnli"""     ,	cache_dir=model_args.cache_dir     ,	revision=model_args.model_revision     ,	use_auth_token=True if model_args.use_auth_token else None     ,	)
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,	do_lower_case=model_args.do_lower_case     ,	cache_dir=model_args.cache_dir     ,	use_fast=model_args.use_fast_tokenizer     ,	revision=model_args.model_revision     ,	use_auth_token=True if model_args.use_auth_token else None     ,	)
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    AutoModelForSequenceClassification.from_pretrained(
							    model_args.model_name_or_path     ,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )     ,	config=__SCREAMING_SNAKE_CASE     ,	cache_dir=model_args.cache_dir     ,	revision=model_args.model_revision     ,	use_auth_token=True if model_args.use_auth_token else None     ,	ignore_mismatched_sizes=model_args.ignore_mismatched_sizes     ,	)

							# Preprocessing the datasets
							# Padding strategy
							if data_args.pad_to_max_length:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    """max_length"""
							else:
														# We will pad later, dynamically at batch creation, to the max sequence length in each batch
														_SCREAMING_SNAKE_CASE		:						str			  =    False

							def preprocess_function(__SCREAMING_SNAKE_CASE  ):
														# Tokenize the texts
														return tokenizer(
														    examples["""premise"""]     ,	examples["""hypothesis"""]     ,	padding=__SCREAMING_SNAKE_CASE     ,	max_length=data_args.max_seq_length     ,	truncation=__SCREAMING_SNAKE_CASE     ,	)

							if training_args.do_train:
														if data_args.max_train_samples is not None:
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    min(len(__SCREAMING_SNAKE_CASE  )     ,	data_args.max_train_samples  )
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    train_dataset.select(range(__SCREAMING_SNAKE_CASE  )  )
														with training_args.main_process_first(desc="""train dataset map pre-processing"""  ):
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    train_dataset.map(
																					    __SCREAMING_SNAKE_CASE     ,	batched=__SCREAMING_SNAKE_CASE     ,	load_from_cache_file=not data_args.overwrite_cache     ,	desc="""Running tokenizer on train dataset"""     ,	)
														# Log a few random samples from the training set:
														for index in random.sample(range(len(__SCREAMING_SNAKE_CASE  )  )     ,	3  ):
																					logger.info(F"""Sample {index} of the training set: {train_dataset[index]}."""  )

							if training_args.do_eval:
														if data_args.max_eval_samples is not None:
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    min(len(__SCREAMING_SNAKE_CASE  )     ,	data_args.max_eval_samples  )
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    eval_dataset.select(range(__SCREAMING_SNAKE_CASE  )  )
														with training_args.main_process_first(desc="""validation dataset map pre-processing"""  ):
																					_SCREAMING_SNAKE_CASE		:						Any			  =    eval_dataset.map(
																					    __SCREAMING_SNAKE_CASE     ,	batched=__SCREAMING_SNAKE_CASE     ,	load_from_cache_file=not data_args.overwrite_cache     ,	desc="""Running tokenizer on validation dataset"""     ,	)

							if training_args.do_predict:
														if data_args.max_predict_samples is not None:
																					_SCREAMING_SNAKE_CASE		:						str			  =    min(len(__SCREAMING_SNAKE_CASE  )     ,	data_args.max_predict_samples  )
																					_SCREAMING_SNAKE_CASE		:						int			  =    predict_dataset.select(range(__SCREAMING_SNAKE_CASE  )  )
														with training_args.main_process_first(desc="""prediction dataset map pre-processing"""  ):
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    predict_dataset.map(
																					    __SCREAMING_SNAKE_CASE     ,	batched=__SCREAMING_SNAKE_CASE     ,	load_from_cache_file=not data_args.overwrite_cache     ,	desc="""Running tokenizer on prediction dataset"""     ,	)

    # Get the metric function
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    evaluate.load("""xnli"""  )

							# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
							# predictions and label_ids field) and has to return a dictionary string to float.
							def compute_metrics(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						str			  =    p.predictions[0] if isinstance(p.predictions     ,	__SCREAMING_SNAKE_CASE  ) else p.predictions
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.argmax(__SCREAMING_SNAKE_CASE     ,	axis=1  )
														return metric.compute(predictions=__SCREAMING_SNAKE_CASE     ,	references=p.label_ids  )

							# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
							if data_args.pad_to_max_length:
														_SCREAMING_SNAKE_CASE		:						str			  =    default_data_collator
							elif training_args.fpaa:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    DataCollatorWithPadding(__SCREAMING_SNAKE_CASE     ,	pad_to_multiple_of=8  )
							else:
														_SCREAMING_SNAKE_CASE		:						int			  =    None

							# Initialize our Trainer
							_SCREAMING_SNAKE_CASE		:						str			  =    Trainer(
							    model=__SCREAMING_SNAKE_CASE     ,	args=__SCREAMING_SNAKE_CASE     ,	train_dataset=train_dataset if training_args.do_train else None     ,	eval_dataset=eval_dataset if training_args.do_eval else None     ,	compute_metrics=__SCREAMING_SNAKE_CASE     ,	tokenizer=__SCREAMING_SNAKE_CASE     ,	data_collator=__SCREAMING_SNAKE_CASE     ,	)

							# Training
							if training_args.do_train:
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    None
														if training_args.resume_from_checkpoint is not None:
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    last_checkpoint
														_SCREAMING_SNAKE_CASE		:						int			  =    trainer.train(resume_from_checkpoint=__SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE		:						Any			  =    train_result.metrics
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    (
														    data_args.max_train_samples if data_args.max_train_samples is not None else len(__SCREAMING_SNAKE_CASE  )
														)
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    min(__SCREAMING_SNAKE_CASE     ,	len(__SCREAMING_SNAKE_CASE  )  )

														trainer.save_model()  # Saves the tokenizer too for easy upload

														trainer.log_metrics("""train"""     ,	__SCREAMING_SNAKE_CASE  )
														trainer.save_metrics("""train"""     ,	__SCREAMING_SNAKE_CASE  )
														trainer.save_state()

							# Evaluation
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""  )
														_SCREAMING_SNAKE_CASE		:						int			  =    trainer.evaluate(eval_dataset=__SCREAMING_SNAKE_CASE  )

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    min(__SCREAMING_SNAKE_CASE     ,	len(__SCREAMING_SNAKE_CASE  )  )

														trainer.log_metrics("""eval"""     ,	__SCREAMING_SNAKE_CASE  )
														trainer.save_metrics("""eval"""     ,	__SCREAMING_SNAKE_CASE  )

							# Prediction
							if training_args.do_predict:
														logger.info("""*** Predict ***"""  )
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    trainer.predict(__SCREAMING_SNAKE_CASE     ,	metric_key_prefix="""predict"""  )

														_SCREAMING_SNAKE_CASE		:						Tuple			  =    (
														    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__SCREAMING_SNAKE_CASE  )
														)
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    min(__SCREAMING_SNAKE_CASE     ,	len(__SCREAMING_SNAKE_CASE  )  )

														trainer.log_metrics("""predict"""     ,	__SCREAMING_SNAKE_CASE  )
														trainer.save_metrics("""predict"""     ,	__SCREAMING_SNAKE_CASE  )

														_SCREAMING_SNAKE_CASE		:						int			  =    np.argmax(__SCREAMING_SNAKE_CASE     ,	axis=1  )
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    os.path.join(training_args.output_dir     ,	"""predictions.txt"""  )
														if trainer.is_world_process_zero():
																					with open(__SCREAMING_SNAKE_CASE     ,	"""w"""  ) as writer:
																												writer.write("""index\tprediction\n"""  )
																												for index, item in enumerate(__SCREAMING_SNAKE_CASE  ):
																																			_SCREAMING_SNAKE_CASE		:						str			  =    label_list[item]
																																			writer.write(F"""{index}\t{item}\n"""  )


if __name__ == "__main__":
			main()



"""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_(__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE=100     ,	__SCREAMING_SNAKE_CASE=1_026     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"     ,	__SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"     ,	)->    Union[str, Any]:
							set_seed(3  )
							# generate train_data and objective_set
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    generate_datasets(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	number=__SCREAMING_SNAKE_CASE     ,	min_len=1_026     ,	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		:						Dict			  =    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""  )

							# load pretrained model
							_SCREAMING_SNAKE_CASE		:						Any			  =    load_gpta("""gpt2"""  ).to(__SCREAMING_SNAKE_CASE  )
							print("""computing perplexity on objective set"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=15     ,	__SCREAMING_SNAKE_CASE=128     ,	__SCREAMING_SNAKE_CASE=100     ,	__SCREAMING_SNAKE_CASE="igf_model.pt"     ,	)->    Optional[int]:
							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		:						Union[str, Any]			  =    SecondaryLearner(__SCREAMING_SNAKE_CASE  )

							# Train secondary learner
							_SCREAMING_SNAKE_CASE		:						Any			  =    train_secondary_learner(
							    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	max_epochs=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE     ,	eval_freq=100     ,	igf_model_path=__SCREAMING_SNAKE_CASE     ,	)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=1_000     ,	__SCREAMING_SNAKE_CASE=16     ,	__SCREAMING_SNAKE_CASE=1.0     ,	__SCREAMING_SNAKE_CASE=recopy_gpta     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"     ,	)->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    RandomSampler(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    DataLoader(__SCREAMING_SNAKE_CASE     ,	sampler=__SCREAMING_SNAKE_CASE  )

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

							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    []
							_SCREAMING_SNAKE_CASE		:						int			  =    []

							# Compute the performance of the transformer model at the beginning
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    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		:						int			  =    example[0, 0, start : start + context_len]
																					lm_optimizer.zero_grad()
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model(__SCREAMING_SNAKE_CASE     ,	labels=__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    True

																					if secondary_learner is not None:
																												_SCREAMING_SNAKE_CASE		:						List[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		:						Dict			  =    -1
																												if predicted_q < threshold:
																																			_SCREAMING_SNAKE_CASE		:						List[str]			  =    False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()  )  )
																												_SCREAMING_SNAKE_CASE		:						Union[str, 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()
																												_SCREAMING_SNAKE_CASE		:						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		:						Tuple			  =    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      lowerCamelCase_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    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=100     ,	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=100     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""secondary model evaluation is triggered at eval_freq"""  )

							parser.add_argument("""--max_steps"""     ,	default=1_000     ,	type=__SCREAMING_SNAKE_CASE     ,	help="""To calculate training epochs"""  )

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

							parser.add_argument(
							    """--min_len"""     ,	default=1_026     ,	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=100     ,	min_len=1_026     ,	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		:						Optional[int]			  =    joblib.load("""data/IGF_values.jbl"""  )

							# Train secondary learner
							_SCREAMING_SNAKE_CASE		:						int			  =    training_secondary_learner(
							    __SCREAMING_SNAKE_CASE     ,	secondary_learner_max_epochs=15     ,	secondary_learner_batch_size=128     ,	eval_freq=100     ,	igf_model_path="""igf_model.pt"""     ,	)

							# load pretrained gpt2 model
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    GPTaLMHeadModel.from_pretrained("""gpt2"""  )
							set_seed(42  )

							# Generate train and test data to train and evaluate gpt2 model
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    generate_datasets(
							    context_len=32     ,	file="""data/tokenized_stories_train_wikitext103.jbl"""     ,	number=100     ,	min_len=1_026     ,	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=1_000     ,	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()


