



"""simple docstring"""







from __future__ import annotations

from collections.abc import Iterator






class       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				A__											=					value
				A__											=					None
				A__											=					None






class       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				A__											=					tree


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				if node is None:
					return 0
				return node.value + (
				    self.depth_first_search(node.left		) + self.depth_first_search(node.right		)
				)







			def __iter__(      self		)      ->	Iterator[int]:
				yield self.depth_first_search(self.tree		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()











'''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__					:			str          =     logging.getLogger(__name__)

a__					:			Tuple          =     '''pytorch_model.bin'''






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






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






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





def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->int:
	snake_case__	       =   datasets.concatenate_datasets([infer_input, infer_output]					,				axis=1							)

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

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

	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_							)   ->Tuple:
	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=UpperCAmelCase_							)
	snake_case__	       =   STDataArguments(train_file=UpperCAmelCase_					,				infer_file=UpperCAmelCase_							)
	snake_case__	       =   STTrainingArguments(output_dir=UpperCAmelCase_							)
	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
	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=UpperCAmelCase_							)
			os.makedirs(UpperCAmelCase_					,				exist_ok=UpperCAmelCase_							)
	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(UpperCAmelCase_							)
		assert os.path.exists(UpperCAmelCase_							)

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

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

			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_							)

		snake_case__	       =   iteration
		snake_case__	       =   data_dir_format(iteration + 1							)

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

		with open(UpperCAmelCase_					,				'r'							) as f:
			snake_case__	       =   float(json.load(UpperCAmelCase_							)[args.eval_metric]							)
		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.
		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(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()

		snake_case__	       =   os.path.join(UpperCAmelCase_					,				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'					,				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'							)					,				)



