






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_		:    Dict   							=     logging.getLogger(__name__)

a_		:    Tuple   							=     'pytorch_model.bin'

@dataclasses.dataclass
class 		_snake_case	:
			_lowercase		:     str        =				dataclasses.field(
			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}       )
			_lowercase		:     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	:
			_lowercase		:     str        =				dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}       )
			_lowercase		:     str        =				dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}       )
			_lowercase		:     Optional[str]        =				dataclasses.field(
			    default=A__ ,     metadata={'''help''': '''A csv or a json file containing the validation data.'''}       )
			_lowercase		:     Optional[str]        =				dataclasses.field(
			    default=A__ ,     metadata={'''help''': '''The name of the task to train on.'''} ,     )
			_lowercase		:     Optional[List[str]]        =				dataclasses.field(
			    default=A__ ,     metadata={'''help''': '''The list of labels for the task.'''}       )



@dataclasses.dataclass
class 		_snake_case	:
			_lowercase		:     str        =				dataclasses.field(
			    metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}       )
			_lowercase		:     Optional[str]        =				dataclasses.field(
			    default='''accuracy''' ,     metadata={'''help''': '''The evaluation metric used for the task.'''}       )
			_lowercase		:     Optional[str]        =				dataclasses.field(
			    default='''no''' ,     metadata={
			        '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
			    } ,     )
			_lowercase		:     Optional[int]        =				dataclasses.field(
			    default=10 ,     metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} ,     )
			_lowercase		:     Optional[float]        =				dataclasses.field(
			    default=0.0 ,     metadata={
			        '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
			    } ,     )
			_lowercase		:     Optional[bool]        =				dataclasses.field(
			    default=A__ ,     metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''} ,     )
			_lowercase		:     Optional[bool]        =				dataclasses.field(
			    default=A__ ,     metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''} ,     )
			_lowercase		:     Optional[bool]        =				dataclasses.field(
			    default=A__ ,     metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''} ,     )
			_lowercase		:     Optional[float]        =				dataclasses.field(
			    default=0.0 ,     metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''} ,     )
			_lowercase		:     Optional[int]        =				dataclasses.field(
			    default=1_00 ,     metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''} ,     )
			_lowercase		:     Optional[int]        =				dataclasses.field(
			    default=A__ ,     metadata={'''help''': '''Random seed for initialization.'''} ,     )






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase):
			SCREAMING_SNAKE_CASE							=			datasets.concatenate_datasets([infer_input, infer_output]				,							axis=1)

			if args.do_filter_by_confidence:
						SCREAMING_SNAKE_CASE							=			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
						SCREAMING_SNAKE_CASE							=			int(eval_result * len(_UpperCAmelCase))
						print(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE							=			dataset.sort('probability'				,							reverse=_UpperCAmelCase)
						SCREAMING_SNAKE_CASE							=			dataset.select(range(_UpperCAmelCase))

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

			SCREAMING_SNAKE_CASE							=			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):
			SCREAMING_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()

			SCREAMING_SNAKE_CASE							=			STModelArguments(model_name_or_path=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			STDataArguments(train_file=_UpperCAmelCase				,							infer_file=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			STTrainingArguments(output_dir=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			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
			SCREAMING_SNAKE_CASE							=			{}
			SCREAMING_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
			SCREAMING_SNAKE_CASE							=			args.train_file
			SCREAMING_SNAKE_CASE							=			args.infer_file

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

			for key in data_files:
						SCREAMING_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:
									SCREAMING_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...')
			SCREAMING_SNAKE_CASE							=			F'''{args.output_dir}/self-train_iter-{{}}'''.format
			SCREAMING_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=_UpperCAmelCase)
									os.makedirs(_UpperCAmelCase				,							exist_ok=_UpperCAmelCase)
			accelerator.wait_for_everyone()

			SCREAMING_SNAKE_CASE							=			None
			SCREAMING_SNAKE_CASE							=			None
			SCREAMING_SNAKE_CASE							=			0
			SCREAMING_SNAKE_CASE							=			False
			# Show the progress bar
			SCREAMING_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)):
						SCREAMING_SNAKE_CASE							=			data_dir_format(_UpperCAmelCase)
						assert os.path.exists(_UpperCAmelCase)

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

						SCREAMING_SNAKE_CASE							=			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
									SCREAMING_SNAKE_CASE							=			os.path.join(_UpperCAmelCase				,							'best-checkpoint')
									SCREAMING_SNAKE_CASE							=			os.path.join(_UpperCAmelCase				,							'stage-2')
									# Update arguments_dict
									SCREAMING_SNAKE_CASE							=			model_path
									SCREAMING_SNAKE_CASE							=			data_files['train']
									SCREAMING_SNAKE_CASE							=			current_output_dir

									SCREAMING_SNAKE_CASE							=			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)

						SCREAMING_SNAKE_CASE							=			iteration
						SCREAMING_SNAKE_CASE							=			data_dir_format(iteration + 1)

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

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

						SCREAMING_SNAKE_CASE							=			os.path.join(_UpperCAmelCase				,							F'''train_pseudo.{args.data_file_extension}''')

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

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

												if early_stopping_patience_counter >= args.early_stopping_patience:
															SCREAMING_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'				,							_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')				,							)









from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_lowerCAmelCase	:   List[str]    =   TypeVar("T")


class 	__snake_case   ( Generic[T] ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  data
						lowerCAmelCase__		  =  None



		def __str__(							self						):







						"""simple docstring"""
						return f'{self.data}'



class 	__snake_case   ( Generic[T] ):
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None
		def __iter__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.top
						while node:
										yield node.data
										lowerCAmelCase__		  =  node.next
		def __str__(							self						):







						"""simple docstring"""
						return "->".join([str(a_						) for item in self]						)
		def __len__(							self						):







						"""simple docstring"""
						return len(tuple(iter(self						)						)						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.top is None
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  Node(a_						)
						if not self.is_empty():
										lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  node
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('pop from empty stack'						)
						assert isinstance(self.top    ,a_						)
						lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  self.top.next
						return pop_node.data
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('peek from empty stack'						)

						assert self.top is not None
						return self.top.data



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None


if __name__ == "__main__":
	from doctest import testmod

	testmod()
