





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		:  Dict         =	'''pytorch_model.bin'''





@dataclasses.dataclass
class 		_A:


				"""simple docstring"""


				UpperCamelCase       :    List[str]          =      dataclasses.field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}  )
				UpperCamelCase       :    Any          =      dataclasses.field(
				    default=snake_case__							,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}							,					)





@dataclasses.dataclass
class 		_A:


				"""simple docstring"""


				UpperCamelCase       :    Union[str, Any]          =      dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}  )
				UpperCamelCase       :    List[Any]          =      dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}  )
				UpperCamelCase       :    int          =      dataclasses.field(
				    default=snake_case__							,					metadata={'''help''': '''A csv or a json file containing the validation data.'''}  )
				UpperCamelCase       :    Dict          =      dataclasses.field(
				    default=snake_case__							,					metadata={'''help''': '''The name of the task to train on.'''}							,					)
				UpperCamelCase       :    str          =      dataclasses.field(
				    default=snake_case__							,					metadata={'''help''': '''The list of labels for the task.'''}  )





@dataclasses.dataclass
class 		_A:


				"""simple docstring"""


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







def 					_SCREAMING_SNAKE_CASE							(  a   ,		a   ,		a   ,		a   ,		a   ,		a    ) -> List[str]:
					__A      :		Optional[Any]         =							datasets.concatenate_datasets([infer_input, infer_output]   ,		axis=1    )

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

					__A      :		int         =							dataset.remove_columns(['label', 'probability']    )
					__A      :		Union[str, Any]         =							dataset.rename_column('prediction'   ,		'label'    )
					__A      :		Optional[int]         =							dataset.map(lambda a    : {"label": idalabel[example["label"]]}    )
					__A      :		Dict         =							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 					_SCREAMING_SNAKE_CASE							(  a   ,		a   ,		a   ,		a   ,		**a    ) -> Optional[int]:
					__A      :		List[str]         =							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      :		Any         =							STModelArguments(model_name_or_path=UpperCAmelCase__    )
					__A      :		Tuple         =							STDataArguments(train_file=UpperCAmelCase__   ,		infer_file=UpperCAmelCase__    )
					__A      :		int         =							STTrainingArguments(output_dir=UpperCAmelCase__    )
					__A      :		Union[str, Any]         =							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      :		List[str]         =							{}
					__A      :		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
					__A      :		List[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[Any]         =							args.eval_file

					for key in data_files:
										__A      :		List[str]         =							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[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...'    )
					__A      :		List[Any]         =							F"""{args.output_dir}/self-train_iter-{{}}""".format
					__A      :		List[str]         =							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      :		Any         =							None
					__A      :		Dict         =							None
					__A      :		Optional[int]         =							0
					__A      :		List[str]         =							False
					# Show the progress bar
					__A      :		Tuple         =							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      :		str         =							data_dir_format(UpperCAmelCase__    )
										assert os.path.exists(UpperCAmelCase__    )

										# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
										# iteration > 0
										__A      :		str         =							os.path.join(UpperCAmelCase__   ,		'stage-1'    )
										__A      :		int         =							{
										    '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      :		Optional[int]         =							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      :		Optional[Any]         =							os.path.join(UpperCAmelCase__   ,		'best-checkpoint'    )
															__A      :		int         =							os.path.join(UpperCAmelCase__   ,		'stage-2'    )
															# Update arguments_dict
															__A      :		Tuple         =							model_path
															__A      :		List[str]         =							data_files['train']
															__A      :		str         =							current_output_dir

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

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

										with open(UpperCAmelCase__   ,		'r'    ) as f:
															__A      :		Tuple         =							float(json.load(UpperCAmelCase__    )[args.eval_metric]    )
										__A      :		int         =							os.path.join(UpperCAmelCase__   ,		'infer_output_best-checkpoint.csv'    )
										assert os.path.exists(UpperCAmelCase__    )
										# Loading the dataset from local csv or json files.
										__A      :		Optional[int]         =							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      :		Dict         =							os.path.join(UpperCAmelCase__   ,		F"""train_pseudo.{args.data_file_extension}"""    )

										if args.evaluation_strategy != IntervalStrategy.NO.value:
															__A      :		int         =							eval_result

															if best_iteration is None:
																				__A      :		Dict         =							new_iteration
																				__A      :		Any         =							new_eval_result
															else:
																				if new_eval_result - best_eval_result > args.early_stopping_threshold:
																									__A      :		List[str]         =							new_iteration
																									__A      :		str         =							new_eval_result
																									__A      :		str         =							0
																				else:
																									if new_eval_result == best_eval_result:
																														__A      :		Dict         =							new_iteration
																														__A      :		Dict         =							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'    )   ,		)






import os
import sys

lowercase   =	os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase   =	[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]



@add_start_docstrings(AutoConfig.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   List[str]							)	->     List[str]:
							return AutoConfig.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoTokenizer.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Any:
							return AutoTokenizer.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModel.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Union[str, Any]:
							return AutoModel.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Optional[Any]:
							return AutoModelForCausalLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     int:
							return AutoModelForMaskedLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Dict:
							return AutoModelForSequenceClassification.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Any      ,					**UpperCAmelCase__					:   List[str]							)	->     Union[str, Any]:
							return AutoModelForQuestionAnswering.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)





