
def 						lowercase_      (       A__		)							->							int:
    """simple docstring"""

    if not isinstance(A__			,		A__		):
        raise ValueError("Input must be an integer"		)
    if input_num <= 0:
        raise ValueError("Input must be positive"		)
    return sum(
        divisor for divisor in range(1			,		input_num // 2 + 1		) if input_num % divisor == 0		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_A   =			logging.getLogger(__name__)

_A   =			"pytorch_model.bin"







@dataclasses.dataclass
class 					lowerCamelCase		:
     UpperCAmelCase__    :							str									=		dataclasses.field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}  )
     UpperCAmelCase__    :							Optional[str]									=		dataclasses.field(
         default=A_							,			metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}							,			)







@dataclasses.dataclass
class 					lowerCamelCase		:
     UpperCAmelCase__    :							str									=		dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}  )
     UpperCAmelCase__    :							str									=		dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}  )
     UpperCAmelCase__    :							Optional[str]									=		dataclasses.field(
         default=A_							,			metadata={"help": "A csv or a json file containing the validation data."}  )
     UpperCAmelCase__    :							Optional[str]									=		dataclasses.field(
         default=A_							,			metadata={"help": "The name of the task to train on."}							,			)
     UpperCAmelCase__    :							Optional[List[str]]									=		dataclasses.field(
         default=A_							,			metadata={"help": "The list of labels for the task."}  )







@dataclasses.dataclass
class 					lowerCamelCase		:
     UpperCAmelCase__    :							str									=		dataclasses.field(
         metadata={"help": "The output directory where the model predictions and checkpoints will be written."}  )
     UpperCAmelCase__    :							Optional[str]									=		dataclasses.field(
         default="accuracy"							,			metadata={"help": "The evaluation metric used for the task."}  )
     UpperCAmelCase__    :							Optional[str]									=		dataclasses.field(
         default="no"							,			metadata={
             "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
         }							,			)
     UpperCAmelCase__    :							Optional[int]									=		dataclasses.field(
         default=10							,			metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}							,			)
     UpperCAmelCase__    :							Optional[float]									=		dataclasses.field(
         default=0.0							,			metadata={
             "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
         }							,			)
     UpperCAmelCase__    :							Optional[bool]									=		dataclasses.field(
         default=A_							,			metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}							,			)
     UpperCAmelCase__    :							Optional[bool]									=		dataclasses.field(
         default=A_							,			metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}							,			)
     UpperCAmelCase__    :							Optional[bool]									=		dataclasses.field(
         default=A_							,			metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}							,			)
     UpperCAmelCase__    :							Optional[float]									=		dataclasses.field(
         default=0.0							,			metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}							,			)
     UpperCAmelCase__    :							Optional[int]									=		dataclasses.field(
         default=1_00							,			metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}							,			)
     UpperCAmelCase__    :							Optional[int]									=		dataclasses.field(
         default=A_							,			metadata={"help": "Random seed for initialization."}							,			)



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__			,		A__		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case								= datasets.concatenate_datasets([infer_input, infer_output]			,		axis=1		)

    if args.do_filter_by_confidence:
        snake_case								= dataset.filter(lambda A__		: example["probability"] > args.confidence_threshold		)

    if args.do_filter_by_val_performance:
        assert eval_result >= 0.0 and eval_result <= 1.0
        snake_case								= int(eval_result * len(A__		)		)
        print(A__		)
        snake_case								= dataset.sort("probability"			,		reverse=A__		)
        snake_case								= dataset.select(range(A__		)		)

    snake_case								= dataset.remove_columns(["label", "probability"]		)
    snake_case								= dataset.rename_column("prediction"			,		"label"		)
    snake_case								= dataset.map(lambda A__		: {"label": idalabel[example["label"]]}		)
    snake_case								= dataset.shuffle(seed=args.seed		)

    snake_case								= os.path.join(A__			,		F'train_pseudo.{args.data_file_extension}'		)
    if args.data_file_extension == "csv":
        dataset.to_csv(A__			,		index=A__		)
    else:
        dataset.to_json(A__		)





def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		**A__		)							->							List[Any]:
    """simple docstring"""

    snake_case								= Accelerator()
    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,		datefmt="%m/%d/%Y %H:%M:%S"			,		level=logging.INFO			,		)
    logger.info(accelerator.state		)

    # Setup logging, we only want one process per machine to log things on the
    # screen. accelerator.is_local_main_process is only True for one process per
    # machine.
    logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR		)

    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    snake_case								= STModelArguments(model_name_or_path=A__		)
    snake_case								= STDataArguments(train_file=A__			,		infer_file=A__		)
    snake_case								= STTrainingArguments(output_dir=A__		)
    snake_case								= argparse.Namespace()

    for arg_class in (model_args, data_args, training_args):
        for key, value in vars(A__		).items():
            setattr(A__			,		A__			,		A__		)

    for key, value in kwargs.items():
        if hasattr(A__			,		A__		):
            setattr(A__			,		A__			,		A__		)

    # Sanity checks
    snake_case								= {}
    snake_case								= None

    # You need to provide the training data and the data to predict on
    assert args.train_file is not None
    assert args.infer_file is not None
    snake_case								= args.train_file
    snake_case								= args.infer_file

    if args.evaluation_strategy != IntervalStrategy.NO.value:
        assert args.eval_file is not None
        snake_case								= args.eval_file

    for key in data_files:
        snake_case								= data_files[key].split("."		)[-1]
        assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
        if args.data_file_extension is None:
            snake_case								= extension
        else:
            assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'

    assert (
        args.eval_metric in datasets.list_metrics()
    ), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'

    # If passed along, set the training seed now.
    if args.seed is not None:
        set_seed(args.seed		)

    logger.info("Creating the initial data directory for self-training..."		)
    snake_case								= F'{args.output_dir}/self-train_iter-{{}}'.format
    snake_case								= data_dir_format(0		)

    if accelerator.is_main_process:
        if args.output_dir is not None:
            os.makedirs(args.output_dir			,		exist_ok=A__		)
            os.makedirs(A__			,		exist_ok=A__		)
    accelerator.wait_for_everyone()

    snake_case								= None
    snake_case								= None
    snake_case								= 0
    snake_case								= False
    # Show the progress bar
    snake_case								= tqdm(range(args.max_selftrain_iterations		)			,		disable=not accelerator.is_local_main_process		)

    # Self-train
    for iteration in range(0			,		int(args.max_selftrain_iterations		)		):
        snake_case								= data_dir_format(A__		)
        assert os.path.exists(A__		)

        # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
        # iteration > 0
        snake_case								= os.path.join(A__			,		"stage-1"		)
        snake_case								= {
            "accelerator": accelerator,
            "model_name_or_path": args.model_name_or_path,
            "cache_dir": args.cache_dir,
            "do_train": True,
            "train_file": data_files["train"] if iteration == 0 else data_files["train_pseudo"],
            "do_eval": True if args.eval_file is not None else False,
            "eval_file": data_files["eval"],
            "do_predict": True,
            "infer_file": data_files["infer"],
            "task_name": args.task_name,
            "label_list": args.label_list,
            "output_dir": current_output_dir,
            "eval_metric": args.eval_metric,
            "evaluation_strategy": args.evaluation_strategy,
            "early_stopping_patience": args.early_stopping_patience,
            "early_stopping_threshold": args.early_stopping_threshold,
            "seed": args.seed,
        }
        # Add additional training arguments
        for key, value in kwargs.items():
            if key not in arguments_dict and not hasattr(A__			,		A__		):
                arguments_dict.update({key: value}		)

        snake_case								= os.path.join(A__			,		"best-checkpoint"			,		A__		)
        if os.path.exists(A__		):
            logger.info(
                "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."			,		A__			,		A__			,		)
        else:
            logger.info("***** Running self-training: iteration: %d, stage: 1 *****"			,		A__		)
            finetune(**A__		)
            accelerator.wait_for_everyone()
            assert os.path.exists(A__		)
            logger.info("Self-training job completed: iteration: %d, stage: 1."			,		A__		)

        if iteration > 0 and args.finetune_on_labeled_data:
            # Stage 2 (optional): fine-tuning on the original labeled data
            snake_case								= os.path.join(A__			,		"best-checkpoint"		)
            snake_case								= os.path.join(A__			,		"stage-2"		)
            # Update arguments_dict
            snake_case								= model_path
            snake_case								= data_files["train"]
            snake_case								= current_output_dir

            snake_case								= os.path.join(A__			,		"best-checkpoint"			,		A__		)
            if os.path.exists(A__		):
                logger.info(
                    "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."			,		A__			,		A__			,		)
            else:
                logger.info("***** Running self-training: iteration: %d, stage: 2 *****"			,		A__		)
                finetune(**A__		)
                accelerator.wait_for_everyone()
                assert os.path.exists(A__		)
                logger.info("Self-training job completed: iteration: %d, stage: 2."			,		A__		)

        snake_case								= iteration
        snake_case								= data_dir_format(iteration + 1		)

        snake_case								= AutoConfig.from_pretrained(os.path.join(A__			,		"best-checkpoint"		)		)
        snake_case								= config.idalabel
        snake_case								= os.path.join(A__			,		"eval_results_best-checkpoint.json"		)
        snake_case								= os.path.join(A__			,		"test_results_best-checkpoint.json"		)
        assert os.path.exists(A__		)

        with open(A__			,		"r"		) as f:
            snake_case								= float(json.load(A__		)[args.eval_metric]		)
        snake_case								= os.path.join(A__			,		"infer_output_best-checkpoint.csv"		)
        assert os.path.exists(A__		)
        # Loading the dataset from local csv or json files.
        snake_case								= load_dataset(args.data_file_extension			,		data_files={"data": data_files["infer"]}		)["data"]
        snake_case								= load_dataset("csv"			,		data_files={"data": infer_output_file}		)["data"]

        if accelerator.is_main_process:
            os.makedirs(A__			,		exist_ok=A__		)
            shutil.copy(A__			,		os.path.join(A__			,		F'eval_results_iter-{iteration}.json'		)		)
            if os.path.exists(A__		):
                shutil.copy(A__			,		os.path.join(A__			,		F'test_results_iter-{iteration}.json'		)		)
            create_pseudo_labeled_data(A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
        accelerator.wait_for_everyone()

        snake_case								= os.path.join(A__			,		F'train_pseudo.{args.data_file_extension}'		)

        if args.evaluation_strategy != IntervalStrategy.NO.value:
            snake_case								= eval_result

            if best_iteration is None:
                snake_case								= new_iteration
                snake_case								= new_eval_result
            else:
                if new_eval_result - best_eval_result > args.early_stopping_threshold:
                    snake_case								= new_iteration
                    snake_case								= new_eval_result
                    snake_case								= 0
                else:
                    if new_eval_result == best_eval_result:
                        snake_case								= new_iteration
                        snake_case								= new_eval_result
                    early_stopping_patience_counter += 1

                if early_stopping_patience_counter >= args.early_stopping_patience:
                    snake_case								= True

        progress_bar.update(1		)

        if should_training_stop:
            break

    if best_iteration is not None:
        # Save the best iteration
        logger.info("Best iteration: %d"			,		A__		)
        logger.info("Best evaluation result: %s = %f"			,		args.eval_metric			,		A__		)
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(A__			,		F'eval_results_iter-{iteration}.json'		)			,		os.path.join(A__			,		"eval_results_best-iteration.json"		)			,		)
    else:
        # Assume that the last iteration is the best
        logger.info("Best iteration: %d"			,		args.max_selftrain_iterations - 1		)
        logger.info("Best evaluation result: %s = %f"			,		args.eval_metric			,		A__		)
        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            shutil.copy(
                os.path.join(A__			,		F'eval_results_iter-{args.max_selftrain_iterations - 1}.json'		)			,		os.path.join(A__			,		"eval_results_best-iteration.json"		)			,		)

