"""simple docstring"""







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


SCREAMING_SNAKE_CASE									=						logging.getLogger(__name__)

SCREAMING_SNAKE_CASE									=						"pytorch_model.bin"



@dataclasses.dataclass
class  _lowerCamelCase			:
				_snake_case     =  dataclasses.field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}     )
				_snake_case     =  dataclasses.field(
				    default=_SCREAMING_SNAKE_CASE						,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}						,							)



@dataclasses.dataclass
class  _lowerCamelCase			:
				_snake_case     =  dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}     )
				_snake_case     =  dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}     )
				_snake_case     =  dataclasses.field(
				    default=_SCREAMING_SNAKE_CASE						,							metadata={"help": "A csv or a json file containing the validation data."}     )
				_snake_case     =  dataclasses.field(
				    default=_SCREAMING_SNAKE_CASE						,							metadata={"help": "The name of the task to train on."}						,							)
				_snake_case     =  dataclasses.field(
				    default=_SCREAMING_SNAKE_CASE						,							metadata={"help": "The list of labels for the task."}     )



@dataclasses.dataclass
class  _lowerCamelCase			:
				_snake_case     =  dataclasses.field(
				    metadata={"help": "The output directory where the model predictions and checkpoints will be written."}     )
				_snake_case     =  dataclasses.field(
				    default="accuracy"						,							metadata={"help": "The evaluation metric used for the task."}     )
				_snake_case     =  dataclasses.field(
				    default="no"						,							metadata={
				        "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
				    }						,							)
				_snake_case     =  dataclasses.field(
				    default=10						,							metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}						,							)
				_snake_case     =  dataclasses.field(
				    default=0.0						,							metadata={
				        "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
				    }						,							)
				_snake_case     =  dataclasses.field(
				    default=_SCREAMING_SNAKE_CASE						,							metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}						,							)
				_snake_case     =  dataclasses.field(
				    default=_SCREAMING_SNAKE_CASE						,							metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}						,							)
				_snake_case     =  dataclasses.field(
				    default=_SCREAMING_SNAKE_CASE						,							metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}						,							)
				_snake_case     =  dataclasses.field(
				    default=0.0						,							metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}						,							)
				_snake_case     =  dataclasses.field(
				    default=100						,							metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}						,							)
				_snake_case     =  dataclasses.field(
				    default=_SCREAMING_SNAKE_CASE						,							metadata={"help": "Random seed for initialization."}						,							)



def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Optional[int]							=							datasets.concatenate_datasets([infer_input, infer_output]     ,axis=1			)

	if args.do_filter_by_confidence:
		_lowercase :    List[str]							=							dataset.filter(lambda __UpperCAmelCase			: example["probability"] > args.confidence_threshold			)

	if args.do_filter_by_val_performance:
		assert eval_result >= 0.0 and eval_result <= 1.0
		_lowercase :    Optional[Any]							=							int(eval_result * len(__UpperCAmelCase			)			)
		print(__UpperCAmelCase			)
		_lowercase :    Optional[int]							=							dataset.sort('probability'     ,reverse=__UpperCAmelCase			)
		_lowercase :    Optional[int]							=							dataset.select(range(__UpperCAmelCase			)			)

	_lowercase :    Union[str, Any]							=							dataset.remove_columns(['label', 'probability']			)
	_lowercase :    Tuple							=							dataset.rename_column('prediction'     ,'label'			)
	_lowercase :    List[str]							=							dataset.map(lambda __UpperCAmelCase			: {"label": idalabel[example["label"]]}			)
	_lowercase :    Optional[Any]							=							dataset.shuffle(seed=args.seed			)

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







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,**__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    int							=							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()

	_lowercase :    Dict							=							STModelArguments(model_name_or_path=__UpperCAmelCase			)
	_lowercase :    Optional[int]							=							STDataArguments(train_file=__UpperCAmelCase     ,infer_file=__UpperCAmelCase			)
	_lowercase :    Any							=							STTrainingArguments(output_dir=__UpperCAmelCase			)
	_lowercase :    Optional[int]							=							argparse.Namespace()

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

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

    # Sanity checks
	_lowercase :    List[str]							=							{}
	_lowercase :    Any							=							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
	_lowercase :    Union[str, Any]							=							args.train_file
	_lowercase :    List[str]							=							args.infer_file

	if args.evaluation_strategy != IntervalStrategy.NO.value:
		assert args.eval_file is not None
		_lowercase :    List[str]							=							args.eval_file

	for key in data_files:
		_lowercase :    Optional[Any]							=							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:
			_lowercase :    int							=							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...'			)
	_lowercase :    List[Any]							=							F'''{args.output_dir}/self-train_iter-{{}}'''.format
	_lowercase :    Dict							=							data_dir_format(0			)

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

	_lowercase :    Dict							=							None
	_lowercase :    int							=							None
	_lowercase :    Any							=							0
	_lowercase :    Union[str, Any]							=							False
	# Show the progress bar
	_lowercase :    List[Any]							=							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			)			):
		_lowercase :    Any							=							data_dir_format(__UpperCAmelCase			)
		assert os.path.exists(__UpperCAmelCase			)

		# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
		# iteration > 0
		_lowercase :    Any							=							os.path.join(__UpperCAmelCase     ,'stage-1'			)
		_lowercase :    List[Any]							=							{
		    '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(__UpperCAmelCase     ,__UpperCAmelCase			):
				arguments_dict.update({key: value}			)

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

		if iteration > 0 and args.finetune_on_labeled_data:
			# Stage 2 (optional): fine-tuning on the original labeled data
			_lowercase :    Any							=							os.path.join(__UpperCAmelCase     ,'best-checkpoint'			)
			_lowercase :    str							=							os.path.join(__UpperCAmelCase     ,'stage-2'			)
			# Update arguments_dict
			_lowercase :    List[str]							=							model_path
			_lowercase :    Union[str, Any]							=							data_files['train']
			_lowercase :    Union[str, Any]							=							current_output_dir

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

		_lowercase :    List[Any]							=							iteration
		_lowercase :    str							=							data_dir_format(iteration + 1			)

		_lowercase :    Tuple							=							AutoConfig.from_pretrained(os.path.join(__UpperCAmelCase     ,'best-checkpoint'			)			)
		_lowercase :    Any							=							config.idalabel
		_lowercase :    Dict							=							os.path.join(__UpperCAmelCase     ,'eval_results_best-checkpoint.json'			)
		_lowercase :    Any							=							os.path.join(__UpperCAmelCase     ,'test_results_best-checkpoint.json'			)
		assert os.path.exists(__UpperCAmelCase			)

		with open(__UpperCAmelCase     ,'r'			) as f:
			_lowercase :    Tuple							=							float(json.load(__UpperCAmelCase			)[args.eval_metric]			)
		_lowercase :    Optional[int]							=							os.path.join(__UpperCAmelCase     ,'infer_output_best-checkpoint.csv'			)
		assert os.path.exists(__UpperCAmelCase			)
		# Loading the dataset from local csv or json files.
		_lowercase :    Tuple							=							load_dataset(args.data_file_extension     ,data_files={'data': data_files['infer']}			)['data']
		_lowercase :    List[Any]							=							load_dataset('csv'     ,data_files={'data': infer_output_file}			)['data']

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

		_lowercase :    Dict							=							os.path.join(__UpperCAmelCase     ,F'''train_pseudo.{args.data_file_extension}'''			)

		if args.evaluation_strategy != IntervalStrategy.NO.value:
			_lowercase :    Tuple							=							eval_result

			if best_iteration is None:
				_lowercase :    Union[str, Any]							=							new_iteration
				_lowercase :    Dict							=							new_eval_result
			else:
				if new_eval_result - best_eval_result > args.early_stopping_threshold:
					_lowercase :    Dict							=							new_iteration
					_lowercase :    str							=							new_eval_result
					_lowercase :    Tuple							=							0
				else:
					if new_eval_result == best_eval_result:
						_lowercase :    List[str]							=							new_iteration
						_lowercase :    int							=							new_eval_result
					early_stopping_patience_counter += 1

				if early_stopping_patience_counter >= args.early_stopping_patience:
					_lowercase :    Optional[Any]							=							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'     ,__UpperCAmelCase			)
		logger.info('Best evaluation result: %s = %f'     ,args.eval_metric     ,__UpperCAmelCase			)
		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			shutil.copy(
			    os.path.join(__UpperCAmelCase     ,F'''eval_results_iter-{iteration}.json'''			)     ,os.path.join(__UpperCAmelCase     ,'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     ,__UpperCAmelCase			)
		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			shutil.copy(
			    os.path.join(__UpperCAmelCase     ,F'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''			)     ,os.path.join(__UpperCAmelCase     ,'eval_results_best-iteration.json'			)     ,)


"""simple docstring"""







from __future__ import annotations


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[True] * limit
	_lowercase :    Union[str, Any]							=							False
	_lowercase :    Any							=							False
	_lowercase :    List[Any]							=							True

	for i in range(3     ,int(limit**0.5 + 1			)     ,2			):
		_lowercase :    Optional[Any]							=							i * 2
		while index < limit:
			_lowercase :    int							=							False
			_lowercase :    Tuple							=							index + i

	_lowercase :    Optional[Any]							=							[2]

	for i in range(3     ,__UpperCAmelCase     ,2			):
		if is_prime[i]:
			primes.append(__UpperCAmelCase			)

	return primes


def      __lowerCAmelCase(		__UpperCAmelCase = 1_000_000			):






	"""simple docstring"""





	_lowercase :    Optional[Any]							=							prime_sieve(__UpperCAmelCase			)
	_lowercase :    int							=							0
	_lowercase :    List[Any]							=							0

	for i in range(len(__UpperCAmelCase			)			):
		for j in range(i + length     ,len(__UpperCAmelCase			)			):
			_lowercase :    List[Any]							=							sum(primes[i:j]			)
			if sol >= ceiling:
				break

			if sol in primes:
				_lowercase :    Optional[int]							=							j - i
				_lowercase :    Tuple							=							sol

	return largest


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



