
'''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


lowercase					:							Dict   =      logging.getLogger(__name__)

lowercase					:							Optional[int]   =      "pytorch_model.bin"


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


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


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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A			) ->			Optional[int]:
	_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        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       **__A			) ->			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=__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'			)    ,       )



'''simple docstring'''







lowercase					:							Tuple   =      {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


lowercase					:							Union[str, Any]   =      {value: key for key, value in encode_dict.items()}







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	_snake_case     		=	''
	for letter in word.lower():
		if letter.isalpha() or letter == " ":
			encoded += encode_dict[letter]
		else:
			raise Exception('encode() accepts only letters of the alphabet and spaces'			)
	return encoded







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			str:
	if set(__A			) - {"A", "B", " "} != set():
		raise Exception('decode() accepts only \'A\', \'B\' and spaces'			)
	_snake_case     		=	''
	for word in coded.split():
		while len(__A			) != 0:
			decoded += decode_dict[word[:5]]
			_snake_case     		=	word[5:]
		decoded += " "
	return decoded.strip()


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

					testmod()

