
'''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


UpperCAmelCase    					=   logging.getLogger(__name__)

UpperCAmelCase    					=   "pytorch_model.bin"

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




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




@dataclasses.dataclass
class 						a   :
				_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=__magic_name__				,metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}				,)
				_snake_case		    = dataclasses.field(
				    default=__magic_name__				,metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}				,)
				_snake_case		    = dataclasses.field(
				    default=__magic_name__				,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=__magic_name__				,metadata={'''help''': '''Random seed for initialization.'''}				,)




def      A (       A_							:					List[Any]    ,							A_							:					Optional[Any]    ,							A_							:					Optional[Any]    ,							A_							:					List[str]    ,							A_							:					Dict    ,							A_							:					Union[str, Any]       ):
						snake_case       :     List[str] =    datasets.concatenate_datasets([infer_input, infer_output]    ,							axis=1       )

						if args.do_filter_by_confidence:
												snake_case       :     List[Any] =    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 =    int(eval_result * len(A_       )       )
												print(A_       )
												snake_case       :     Tuple =    dataset.sort('''probability'''    ,							reverse=A_       )
												snake_case       :     Optional[Any] =    dataset.select(range(A_       )       )

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

						snake_case       :     Dict =    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      A (       A_							:					int    ,							A_							:					Optional[Any]    ,							A_							:					List[Any]    ,							A_							:					Optional[Any]    ,							**A_							:					List[str]       ):
						snake_case       :     Tuple =    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       :     List[Any] =    STModelArguments(model_name_or_path=A_       )
						snake_case       :     Optional[int] =    STDataArguments(train_file=A_    ,							infer_file=A_       )
						snake_case       :     Tuple =    STTrainingArguments(output_dir=A_       )
						snake_case       :     Tuple =    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       :     Optional[Any] =    {}
						snake_case       :     Optional[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
						snake_case       :     Dict =    args.train_file
						snake_case       :     Optional[Any] =    args.infer_file

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

						for key in data_files:
												snake_case       :     Dict =    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       :     List[str] =    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       :     List[Any] =    F"""{args.output_dir}/self-train_iter-{{}}""".format
						snake_case       :     Dict =    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       :     List[Any] =    None
						snake_case       :     List[str] =    None
						snake_case       :     Union[str, Any] =    0
						snake_case       :     Tuple =    False
						# Show the progress bar
						snake_case       :     str =    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       :     Any =    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       :     int =    os.path.join(A_    ,							'''stage-1'''       )
												snake_case       :     List[str] =    {
												    '''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       :     int =    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       :     Optional[int] =    os.path.join(A_    ,							'''best-checkpoint'''       )
																		snake_case       :     List[Any] =    os.path.join(A_    ,							'''stage-2'''       )
																		# Update arguments_dict
																		snake_case       :     Optional[int] =    model_path
																		snake_case       :     Dict =    data_files['''train''']
																		snake_case       :     Optional[int] =    current_output_dir

																		snake_case       :     Tuple =    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       :     Dict =    iteration
												snake_case       :     Union[str, Any] =    data_dir_format(iteration + 1       )

												snake_case       :     Dict =    AutoConfig.from_pretrained(os.path.join(A_    ,							'''best-checkpoint'''       )       )
												snake_case       :     Optional[Any] =    config.idalabel
												snake_case       :     int =    os.path.join(A_    ,							'''eval_results_best-checkpoint.json'''       )
												snake_case       :     Dict =    os.path.join(A_    ,							'''test_results_best-checkpoint.json'''       )
												assert os.path.exists(A_       )

												with open(A_    ,							'''r'''       ) as f:
																		snake_case       :     str =    float(json.load(A_       )[args.eval_metric]       )
												snake_case       :     Optional[Any] =    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       :     Tuple =    load_dataset(args.data_file_extension    ,							data_files={'''data''': data_files['''infer''']}       )['''data''']
												snake_case       :     int =    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       :     Optional[Any] =    os.path.join(A_    ,							F"""train_pseudo.{args.data_file_extension}"""       )

												if args.evaluation_strategy != IntervalStrategy.NO.value:
																		snake_case       :     List[str] =    eval_result

																		if best_iteration is None:
																								snake_case       :     Any =    new_iteration
																								snake_case       :     Optional[int] =    new_eval_result
																		else:
																								if new_eval_result - best_eval_result > args.early_stopping_threshold:
																														snake_case       :     int =    new_iteration
																														snake_case       :     List[str] =    new_eval_result
																														snake_case       :     str =    0
																								else:
																														if new_eval_result == best_eval_result:
																																				snake_case       :     int =    new_iteration
																																				snake_case       :     str =    new_eval_result
																														early_stopping_patience_counter += 1

																								if early_stopping_patience_counter >= args.early_stopping_patience:
																														snake_case       :     List[str] =    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'''       )    ,							)



'''simple docstring'''





from __future__ import annotations

import math

def      A (       A_							:					int       ):

						if num <= 0:
												snake_case       :     List[Any] =    F"""{num}: Invalid input, please enter a positive integer."""
												raise ValueError(A_       )

						snake_case       :     Optional[Any] =    [True] * (num + 1)
						snake_case       :     List[str] =    []
						snake_case       :     List[Any] =    2
						snake_case       :     str =    int(math.sqrt(A_       )       )

						while start <= end:
												# If start is a prime
												if sieve[start] is True:
																		prime.append(A_       )

																		# Set multiples of start be False
																		for i in range(start * start    ,							num + 1    ,							A_       ):
																								if sieve[i] is True:
																														snake_case       :     int =    False

												start += 1

						for j in range(end + 1    ,							num + 1       ):
												if sieve[j] is True:
																		prime.append(A_       )

						return prime


if __name__ == "__main__":
						print(prime_sieve(int(input("Enter a positive integer: ").strip())))

