



'''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


UpperCamelCase__:   int								=			logging.getLogger(__name__)

UpperCamelCase__:   Tuple								=			"pytorch_model.bin"

@dataclasses.dataclass
class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






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

@dataclasses.dataclass
class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	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=UpperCamelCase__							,		metadata={"""help""": """A csv or a json file containing the validation data."""}			)
	lowerCamelCase__			 =       dataclasses.field(
	    default=UpperCamelCase__							,		metadata={"""help""": """The name of the task to train on."""}							,		)
	lowerCamelCase__			 =       dataclasses.field(
	    default=UpperCamelCase__							,		metadata={"""help""": """The list of labels for the task."""}			)

@dataclasses.dataclass
class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	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=UpperCamelCase__							,		metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}							,		)
	lowerCamelCase__			 =       dataclasses.field(
	    default=UpperCamelCase__							,		metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}							,		)
	lowerCamelCase__			 =       dataclasses.field(
	    default=UpperCamelCase__							,		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=UpperCamelCase__							,		metadata={"""help""": """Random seed for initialization."""}							,		)


def  snake_case_     (					_lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						str  )      ->   str:
							UpperCAmelCase :       List[str] 							=		datasets.concatenate_datasets([infer_input, infer_output]   ,      axis=1  )

							if args.do_filter_by_confidence:
														UpperCAmelCase :       List[Any] 							=		dataset.filter(lambda _lowerCAmelCase  : example["probability"] > args.confidence_threshold  )

							if args.do_filter_by_val_performance:
														assert eval_result >= 0.0 and eval_result <= 1.0
														UpperCAmelCase :       Union[str, Any] 							=		int(eval_result * len(_lowerCAmelCase  )  )
														print(_lowerCAmelCase  )
														UpperCAmelCase :       Dict 							=		dataset.sort('''probability'''   ,      reverse=_lowerCAmelCase  )
														UpperCAmelCase :       Tuple 							=		dataset.select(range(_lowerCAmelCase  )  )

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

							UpperCAmelCase :       List[str] 							=		os.path.join(_lowerCAmelCase   ,      f"""train_pseudo.{args.data_file_extension}"""  )
							if args.data_file_extension == "csv":
														dataset.to_csv(_lowerCAmelCase   ,      index=_lowerCAmelCase  )
							else:
														dataset.to_json(_lowerCAmelCase  )




def  snake_case_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						int   ,      **_lowerCAmelCase  :						Tuple  )      ->   Tuple:
							UpperCAmelCase :       Union[str, 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()

							UpperCAmelCase :       Union[str, Any] 							=		STModelArguments(model_name_or_path=_lowerCAmelCase  )
							UpperCAmelCase :       Dict 							=		STDataArguments(train_file=_lowerCAmelCase   ,      infer_file=_lowerCAmelCase  )
							UpperCAmelCase :       Optional[int] 							=		STTrainingArguments(output_dir=_lowerCAmelCase  )
							UpperCAmelCase :       Optional[int] 							=		argparse.Namespace()

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

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

    # Sanity checks
							UpperCAmelCase :       Optional[Any] 							=		{}
							UpperCAmelCase :       List[str] 							=		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
							UpperCAmelCase :       List[Any] 							=		args.train_file
							UpperCAmelCase :       Optional[Any] 							=		args.infer_file

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

							for key in data_files:
														UpperCAmelCase :       Optional[int] 							=		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:
																					UpperCAmelCase :       Optional[int] 							=		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...'''  )
							UpperCAmelCase :       Dict 							=		f"""{args.output_dir}/self-train_iter-{{}}""".format
							UpperCAmelCase :       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=_lowerCAmelCase  )
																					os.makedirs(_lowerCAmelCase   ,      exist_ok=_lowerCAmelCase  )
							accelerator.wait_for_everyone()

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

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

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

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

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

														UpperCAmelCase :       Union[str, Any] 							=		iteration
														UpperCAmelCase :       Any 							=		data_dir_format(iteration + 1  )

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

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

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

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

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					UpperCAmelCase :       Any 							=		eval_result

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

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









def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		if exponent == 1:
				return base
		if exponent % 2 == 0:
				lowercase__            =      _modexpt(SCREAMING_SNAKE_CASE						,				exponent // 2						,				SCREAMING_SNAKE_CASE     ) % modulo_value
				return (x * x) % modulo_value
		else:
				return (base * _modexpt(SCREAMING_SNAKE_CASE						,				exponent - 1						,				SCREAMING_SNAKE_CASE     )) % modulo_value
def 			_a (      SCREAMING_SNAKE_CASE = 17_77						,				SCREAMING_SNAKE_CASE = 18_55						,				SCREAMING_SNAKE_CASE = 8     ):


		"""simple docstring"""




		lowercase__            =      base
		for _ in range(1						,				SCREAMING_SNAKE_CASE     ):
				lowercase__            =      _modexpt(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				10**digits     )

		return result


if __name__ == "__main__":
			print(f"""{solution() = }""")


