

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


lowerCAmelCase_			      =      logging.getLogger(__name__)

lowerCAmelCase_			      =      '''pytorch_model.bin'''





@dataclasses.dataclass
class       snake_case_  :







			'''simple docstring'''

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






@dataclasses.dataclass
class       snake_case_  :







			'''simple docstring'''

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






@dataclasses.dataclass
class       snake_case_  :







			'''simple docstring'''

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






def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      datasets.concatenate_datasets([infer_input, infer_output]    ,						axis=1			)

						if args.do_filter_by_confidence:
												snake_case_  =      dataset.filter(lambda SCREAMING_SNAKE_CASE__			: 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(SCREAMING_SNAKE_CASE__			)			)
												print(SCREAMING_SNAKE_CASE__			)
												snake_case_  =      dataset.sort('''probability'''    ,						reverse=SCREAMING_SNAKE_CASE__			)
												snake_case_  =      dataset.select(range(SCREAMING_SNAKE_CASE__			)			)

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

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


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__			):
						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=SCREAMING_SNAKE_CASE__			)
						snake_case_  =      STDataArguments(train_file=SCREAMING_SNAKE_CASE__    ,						infer_file=SCREAMING_SNAKE_CASE__			)
						snake_case_  =      STTrainingArguments(output_dir=SCREAMING_SNAKE_CASE__			)
						snake_case_  =      argparse.Namespace()

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

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

    # 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=SCREAMING_SNAKE_CASE__			)
																		os.makedirs(SCREAMING_SNAKE_CASE__    ,						exist_ok=SCREAMING_SNAKE_CASE__			)
						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(SCREAMING_SNAKE_CASE__			)
												assert os.path.exists(SCREAMING_SNAKE_CASE__			)

												# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
												# iteration > 0
												snake_case_  =      os.path.join(SCREAMING_SNAKE_CASE__    ,						'''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(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
																								arguments_dict.update({key: value}			)

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

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

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

												snake_case_  =      iteration
												snake_case_  =      data_dir_format(iteration + 1			)

												snake_case_  =      AutoConfig.from_pretrained(os.path.join(SCREAMING_SNAKE_CASE__    ,						'''best-checkpoint'''			)			)
												snake_case_  =      config.idalabel
												snake_case_  =      os.path.join(SCREAMING_SNAKE_CASE__    ,						'''eval_results_best-checkpoint.json'''			)
												snake_case_  =      os.path.join(SCREAMING_SNAKE_CASE__    ,						'''test_results_best-checkpoint.json'''			)
												assert os.path.exists(SCREAMING_SNAKE_CASE__			)

												with open(SCREAMING_SNAKE_CASE__    ,						'''r'''			) as f:
																		snake_case_  =      float(json.load(SCREAMING_SNAKE_CASE__			)[args.eval_metric]			)
												snake_case_  =      os.path.join(SCREAMING_SNAKE_CASE__    ,						'''infer_output_best-checkpoint.csv'''			)
												assert os.path.exists(SCREAMING_SNAKE_CASE__			)
												# 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(SCREAMING_SNAKE_CASE__    ,						exist_ok=SCREAMING_SNAKE_CASE__			)
																		shutil.copy(SCREAMING_SNAKE_CASE__    ,						os.path.join(SCREAMING_SNAKE_CASE__    ,						F'''eval_results_iter-{iteration}.json'''			)			)
																		if os.path.exists(SCREAMING_SNAKE_CASE__			):
																								shutil.copy(SCREAMING_SNAKE_CASE__    ,						os.path.join(SCREAMING_SNAKE_CASE__    ,						F'''test_results_iter-{iteration}.json'''			)			)
																		create_pseudo_labeled_data(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)
												accelerator.wait_for_everyone()

												snake_case_  =      os.path.join(SCREAMING_SNAKE_CASE__    ,						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'''    ,						SCREAMING_SNAKE_CASE__			)
												logger.info('''Best evaluation result: %s = %f'''    ,						args.eval_metric    ,						SCREAMING_SNAKE_CASE__			)
												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		shutil.copy(
																		    os.path.join(SCREAMING_SNAKE_CASE__    ,						F'''eval_results_iter-{iteration}.json'''			)    ,						os.path.join(SCREAMING_SNAKE_CASE__    ,						'''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    ,						SCREAMING_SNAKE_CASE__			)
												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		shutil.copy(
																		    os.path.join(SCREAMING_SNAKE_CASE__    ,						F'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''			)    ,						os.path.join(SCREAMING_SNAKE_CASE__    ,						'''eval_results_best-iteration.json'''			)    ,						)

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase     ,	lowercase         =      [], []
				while len(__magic_name__						) > 1:
								lowercase     ,	lowercase         =      min(__magic_name__						), max(__magic_name__						)
								start.append(__magic_name__						)
								end.append(__magic_name__						)
								collection.remove(__magic_name__						)
								collection.remove(__magic_name__						)
				end.reverse()
				return start + collection + end


if __name__ == "__main__":
						_snake_case      :							int     	=    input("Enter numbers separated by a comma:\n").strip()
						_snake_case      :							Dict     	=    [int(item) for item in user_input.split(",")]
						print(*merge_sort(unsorted), sep=",")
