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_      =      logging.getLogger(__name__)

A_      =      "pytorch_model.bin"






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






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






@dataclasses.dataclass
class      __lowercase					:
						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=_A       ,     metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}       ,     )
						lowercase					 =     dataclasses.field(
						    default=_A       ,     metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}       ,     )
						lowercase					 =     dataclasses.field(
						    default=_A       ,     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=100       ,     metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}       ,     )
						lowercase					 =     dataclasses.field(
						    default=_A       ,     metadata={'help': 'Random seed for initialization.'}       ,     )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		datasets.concatenate_datasets([infer_input, infer_output],		axis=1       )

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

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

			lowercase							=		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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		**UpperCAmelCase       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		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()

			lowercase							=		STModelArguments(model_name_or_path=UpperCAmelCase       )
			lowercase							=		STDataArguments(train_file=UpperCAmelCase,		infer_file=UpperCAmelCase       )
			lowercase							=		STTrainingArguments(output_dir=UpperCAmelCase       )
			lowercase							=		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
			lowercase							=		{}
			lowercase							=		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
			lowercase							=		args.train_file
			lowercase							=		args.infer_file

			if args.evaluation_strategy != IntervalStrategy.NO.value:
						assert args.eval_file is not None
						lowercase							=		args.eval_file

			for key in data_files:
						lowercase							=		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:
									lowercase							=		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...'''       )
			lowercase							=		f'{args.output_dir}/self-train_iter-{{}}'.format
			lowercase							=		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()

			lowercase							=		None
			lowercase							=		None
			lowercase							=		0
			lowercase							=		False
			# Show the progress bar
			lowercase							=		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       )       ):
						lowercase							=		data_dir_format(UpperCAmelCase       )
						assert os.path.exists(UpperCAmelCase       )

						# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
						# iteration > 0
						lowercase							=		os.path.join(UpperCAmelCase,		'''stage-1'''       )
						lowercase							=		{
						    '''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}       )

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

									lowercase							=		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       )

						lowercase							=		iteration
						lowercase							=		data_dir_format(iteration + 1       )

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

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

						lowercase							=		os.path.join(UpperCAmelCase,		f'train_pseudo.{args.data_file_extension}'       )

						if args.evaluation_strategy != IntervalStrategy.NO.value:
									lowercase							=		eval_result

									if best_iteration is None:
												lowercase							=		new_iteration
												lowercase							=		new_eval_result
									else:
												if new_eval_result - best_eval_result > args.early_stopping_threshold:
															lowercase							=		new_iteration
															lowercase							=		new_eval_result
															lowercase							=		0
												else:
															if new_eval_result == best_eval_result:
																		lowercase							=		new_iteration
																		lowercase							=		new_eval_result
															early_stopping_patience_counter += 1

												if early_stopping_patience_counter >= args.early_stopping_patience:
															lowercase							=		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'''       ),		)






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			while a != 0:
						lowercase		,lowercase							=		b % a, a
			return b






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			if gcd(UpperCAmelCase,		UpperCAmelCase       ) != 1:
						lowercase							=		f'mod inverse of {a!r} and {m!r} does not exist'
						raise ValueError(UpperCAmelCase       )
			lowercase		,lowercase		,lowercase							=		1, 0, a
			lowercase		,lowercase		,lowercase							=		0, 1, m
			while va != 0:
						lowercase							=		ua // va
						lowercase		,lowercase		,lowercase		,lowercase		,lowercase		,lowercase							=		(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
			return ua % m





