




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


lowercase__							:	str											=		logging.getLogger(__name__)

lowercase__							:	Optional[int]											=		"pytorch_model.bin"
@dataclasses.dataclass
class 						UpperCAmelCase  :







						'''simple docstring'''





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







@dataclasses.dataclass
class 						UpperCAmelCase  :







						'''simple docstring'''





						lowerCAmelCase_							=  dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}     )
						lowerCAmelCase_							=  dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}     )
						lowerCAmelCase_							=  dataclasses.field(
						    default=UpperCAmelCase__			,  metadata={'''help''': '''A csv or a json file containing the validation data.'''}     )
						lowerCAmelCase_							=  dataclasses.field(
						    default=UpperCAmelCase__			,  metadata={'''help''': '''The name of the task to train on.'''}			,  )
						lowerCAmelCase_							=  dataclasses.field(
						    default=UpperCAmelCase__			,  metadata={'''help''': '''The list of labels for the task.'''}     )







@dataclasses.dataclass
class 						UpperCAmelCase  :







						'''simple docstring'''





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







def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A	,      _A	,      _A				):







	'''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 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A	,      **_A				):







	'''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"				)	,      )






def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	1
	for i in range(1	,      num + 1				):
		fact *= i
	return fact


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	0
	while number > 0:
		snake_case_						       =	number % 10
		sum_of_digits += last_digit
		snake_case_						       =	number // 10  # Removing the last_digit from the given number
	return sum_of_digits


def 		lowerCamelCase__       (						_A = 100				):







	'''simple docstring'''





	snake_case_						       =	factorial(_A				)
	snake_case_						       =	split_and_add(_A				)
	return result


if __name__ == "__main__":
		print(solution(int(input("Enter the Number: ").strip())))
