




class        lowercase      :







				def __init__(							self   :					Optional[Any]       ):



					"""simple docstring"""

					A_  :				Any	   =		""""""
					A_  :				List[str]	   =		""""""
					A_  :				List[Any]	   =		[]







				def 					a_  (							self   :					int      ,     _lowerCamelCase   :					List[Any]      ,     _lowerCamelCase   :					List[Any]       ):



					"""simple docstring"""

					if m == -1:
						return n + 1
					elif n == -1:
						return m + 1
					elif self.dp[m][n] > -1:
						return self.dp[m][n]
					else:
						if self.worda[m] == self.worda[n]:
							A_  :				int	   =		self.__min_dist_top_down_dp(m - 1      ,     n - 1       )
						else:
							A_  :				Tuple	   =		self.__min_dist_top_down_dp(_lowerCamelCase      ,     n - 1       )
							A_  :				str	   =		self.__min_dist_top_down_dp(m - 1      ,     _lowerCamelCase       )
							A_  :				List[Any]	   =		self.__min_dist_top_down_dp(m - 1      ,     n - 1       )
							A_  :				Optional[int]	   =		1 + min(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       )

						return self.dp[m][n]







				def 					a_  (							self   :					str      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		worda
					A_  :				int	   =		worda
					A_  :				str	   =		[[-1 for _ in range(len(_lowerCamelCase       )       )] for _ in range(len(_lowerCamelCase       )       )]

					return self.__min_dist_top_down_dp(len(_lowerCamelCase       ) - 1      ,     len(_lowerCamelCase       ) - 1       )







				def 					a_  (							self   :					int      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				str	   =		worda
					A_  :				Any	   =		worda
					A_  :				str	   =		len(_lowerCamelCase       )
					A_  :				Tuple	   =		len(_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		[[0 for _ in range(n + 1       )] for _ in range(m + 1       )]

					for i in range(m + 1       ):
						for j in range(n + 1       ):
							if i == 0:  # first string is empty
								A_  :				List[Any]	   =		j
							elif j == 0:  # second string is empty
								A_  :				List[Any]	   =		i
							elif worda[i - 1] == worda[j - 1]:  # last characters are equal
								A_  :				List[Any]	   =		self.dp[i - 1][j - 1]
							else:
								A_  :				int	   =		self.dp[i][j - 1]
								A_  :				List[Any]	   =		self.dp[i - 1][j]
								A_  :				Union[str, Any]	   =		self.dp[i - 1][j - 1]
								A_  :				List[Any]	   =		1 + min(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       )
					return self.dp[m][n]


if __name__ == "__main__":
		_lowerCamelCase  :			Dict        =       EditDistance()

		print('****************** Testing Edit Distance DP Algorithm ******************')
		print()

		_lowerCamelCase  :			int        =       input('Enter the first string: ').strip()
		_lowerCamelCase  :			Optional[int]        =       input('Enter the second string: ').strip()

		print()
		print(f'The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}')
		print(f'The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}')
		print()
		print('*************** End of Testing Edit Distance DP Algorithm ***************')











"""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


_lowerCamelCase  :			int        =       logging.getLogger(__name__)

_lowerCamelCase  :			List[Any]        =       'pytorch_model.bin'

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

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

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

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		datasets.concatenate_datasets([infer_input, infer_output]  ,      axis=1	)

	if args.do_filter_by_confidence:
		A_  :				Optional[int]	   =		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
		A_  :				Optional[Any]	   =		int(eval_result * len(_UpperCAmelCase	)	)
		print(_UpperCAmelCase	)
		A_  :				str	   =		dataset.sort('''probability'''  ,      reverse=_UpperCAmelCase	)
		A_  :				str	   =		dataset.select(range(_UpperCAmelCase	)	)

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

	A_  :				str	   =		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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      **_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Any	   =		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_  :				str	   =		STModelArguments(model_name_or_path=_UpperCAmelCase	)
	A_  :				List[str]	   =		STDataArguments(train_file=_UpperCAmelCase  ,      infer_file=_UpperCAmelCase	)
	A_  :				Any	   =		STTrainingArguments(output_dir=_UpperCAmelCase	)
	A_  :				int	   =		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
	A_  :				Any	   =		{}
	A_  :				Dict	   =		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_  :				str	   =		args.train_file
	A_  :				str	   =		args.infer_file

	if args.evaluation_strategy != IntervalStrategy.NO.value:
		assert args.eval_file is not None
		A_  :				List[str]	   =		args.eval_file

	for key in data_files:
		A_  :				Dict	   =		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_  :				Optional[Any]	   =		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_  :				List[Any]	   =		f"""{args.output_dir}/self-train_iter-{{}}""".format
	A_  :				Union[str, Any]	   =		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()

	A_  :				Optional[int]	   =		None
	A_  :				Union[str, Any]	   =		None
	A_  :				Optional[int]	   =		0
	A_  :				List[Any]	   =		False
	# Show the progress bar
	A_  :				Union[str, Any]	   =		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_  :				Dict	   =		data_dir_format(_UpperCAmelCase	)
		assert os.path.exists(_UpperCAmelCase	)

		# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
		# iteration > 0
		A_  :				int	   =		os.path.join(_UpperCAmelCase  ,      '''stage-1'''	)
		A_  :				List[Any]	   =		{
		    '''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}	)

		A_  :				Any	   =		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
			A_  :				List[Any]	   =		os.path.join(_UpperCAmelCase  ,      '''best-checkpoint'''	)
			A_  :				Optional[int]	   =		os.path.join(_UpperCAmelCase  ,      '''stage-2'''	)
			# Update arguments_dict
			A_  :				int	   =		model_path
			A_  :				Optional[Any]	   =		data_files['''train''']
			A_  :				Optional[int]	   =		current_output_dir

			A_  :				str	   =		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	)

		A_  :				Dict	   =		iteration
		A_  :				List[Any]	   =		data_dir_format(iteration + 1	)

		A_  :				str	   =		AutoConfig.from_pretrained(os.path.join(_UpperCAmelCase  ,      '''best-checkpoint'''	)	)
		A_  :				List[Any]	   =		config.idalabel
		A_  :				Any	   =		os.path.join(_UpperCAmelCase  ,      '''eval_results_best-checkpoint.json'''	)
		A_  :				Union[str, Any]	   =		os.path.join(_UpperCAmelCase  ,      '''test_results_best-checkpoint.json'''	)
		assert os.path.exists(_UpperCAmelCase	)

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

		A_  :				int	   =		os.path.join(_UpperCAmelCase  ,      f"""train_pseudo.{args.data_file_extension}"""	)

		if args.evaluation_strategy != IntervalStrategy.NO.value:
			A_  :				Union[str, Any]	   =		eval_result

			if best_iteration is None:
				A_  :				str	   =		new_iteration
				A_  :				Dict	   =		new_eval_result
			else:
				if new_eval_result - best_eval_result > args.early_stopping_threshold:
					A_  :				int	   =		new_iteration
					A_  :				Tuple	   =		new_eval_result
					A_  :				Union[str, Any]	   =		0
				else:
					if new_eval_result == best_eval_result:
						A_  :				Union[str, Any]	   =		new_iteration
						A_  :				List[str]	   =		new_eval_result
					early_stopping_patience_counter += 1

				if early_stopping_patience_counter >= args.early_stopping_patience:
					A_  :				int	   =		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'''	)  ,      )





