


"""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


a_       							=    logging.getLogger(__name__)

a_       							=    "pytorch_model.bin"
@dataclasses.dataclass
class snake_case      :
		__UpperCamelCase           =			dataclasses.field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'})
		__UpperCamelCase           =			dataclasses.field(
		    default=_UpperCamelCase     ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}     ,				)





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





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






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )	->	Union[str, Any]:
				_A					  =			datasets.concatenate_datasets([infer_input, infer_output]     ,			axis=1   )

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

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

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

				_A					  =			os.path.join(__lowercase     ,			f"""train_pseudo.{args.data_file_extension}"""   )
				if args.data_file_extension == "csv":
								dataset.to_csv(__lowercase     ,			index=__lowercase   )
				else:
								dataset.to_json(__lowercase   )






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			**__lowercase   )	->	List[Any]:
				_A					  =			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()

				_A					  =			STModelArguments(model_name_or_path=__lowercase   )
				_A					  =			STDataArguments(train_file=__lowercase     ,			infer_file=__lowercase   )
				_A					  =			STTrainingArguments(output_dir=__lowercase   )
				_A					  =			argparse.Namespace()

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

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

    # Sanity checks
				_A					  =			{}
				_A					  =			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
				_A					  =			args.train_file
				_A					  =			args.infer_file

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

				for key in data_files:
								_A					  =			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:
												_A					  =			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..."   )
				_A					  =			f"""{args.output_dir}/self-train_iter-{{}}""".format
				_A					  =			data_dir_format(0   )

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

				_A					  =			None
				_A					  =			None
				_A					  =			0
				_A					  =			False
				# Show the progress bar
				_A					  =			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   )   ):
								_A					  =			data_dir_format(__lowercase   )
								assert os.path.exists(__lowercase   )

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

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

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

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

								_A					  =			iteration
								_A					  =			data_dir_format(iteration + 1   )

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

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

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

								_A					  =			os.path.join(__lowercase     ,			f"""train_pseudo.{args.data_file_extension}"""   )

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

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

																if early_stopping_patience_counter >= args.early_stopping_patience:
																				_A					  =			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"     ,			__lowercase   )
								logger.info("Best evaluation result: %s = %f"     ,			args.eval_metric     ,			__lowercase   )
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(__lowercase     ,			f"""eval_results_iter-{iteration}.json"""   )     ,			os.path.join(__lowercase     ,			"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     ,			__lowercase   )
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(__lowercase     ,			f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""   )     ,			os.path.join(__lowercase     ,			"eval_results_best-iteration.json"   )     ,			)










"""simple docstring"""


import random






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	Optional[Any]:
				_A					  =			a[left_index]
				_A					  =			left_index + 1
				for j in range(left_index + 1     ,			__lowercase   ):
								if a[j] < pivot:
												_A  ,		_A					  =			a[i], a[j]
												i += 1
				_A  ,		_A					  =			a[i - 1], a[left_index]
				return i - 1






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	int:
				if left < right:
								_A					  =			random.randint(__lowercase     ,			right - 1   )
								_A  ,		_A					  =			(
								    a[left],
								    a[pivot],
								)  # switches the pivot with the left most bound
								_A					  =			partition(__lowercase     ,			__lowercase     ,			__lowercase   )
								quick_sort_random(
								    __lowercase     ,			__lowercase     ,			__lowercase   )  # recursive quicksort to the left of the pivot point
								quick_sort_random(
								    __lowercase     ,			pivot_index + 1     ,			__lowercase   )  # recursive quicksort to the right of the pivot point






def      a__ (				)	->	Dict:
				_A					  =			input("Enter numbers separated by a comma:\n"   ).strip()
				_A					  =			[int(__lowercase   ) for item in user_input.split(","   )]

				quick_sort_random(__lowercase     ,			0     ,			len(__lowercase   )   )

				print(__lowercase   )


if __name__ == "__main__":
		main()