





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a__					:			str          =     {
    '''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
    '''tokenization_transfo_xl''': ['''TransfoXLCorpus''', '''TransfoXLTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			Optional[int]          =     [
			    '''TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''AdaptiveEmbedding''',
			    '''TransfoXLForSequenceClassification''',
			    '''TransfoXLLMHeadModel''',
			    '''TransfoXLModel''',
			    '''TransfoXLPreTrainedModel''',
			    '''load_tf_weights_in_transfo_xl''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			str          =     [
			    '''TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFAdaptiveEmbedding''',
			    '''TFTransfoXLForSequenceClassification''',
			    '''TFTransfoXLLMHeadModel''',
			    '''TFTransfoXLMainLayer''',
			    '''TFTransfoXLModel''',
			    '''TFTransfoXLPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
			from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_transfo_xl import (
						    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    AdaptiveEmbedding,
						    TransfoXLForSequenceClassification,
						    TransfoXLLMHeadModel,
						    TransfoXLModel,
						    TransfoXLPreTrainedModel,
						    load_tf_weights_in_transfo_xl,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_transfo_xl import (
						    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFAdaptiveEmbedding,
						    TFTransfoXLForSequenceClassification,
						    TFTransfoXLLMHeadModel,
						    TFTransfoXLMainLayer,
						    TFTransfoXLModel,
						    TFTransfoXLPreTrainedModel,
						)

else:
			import sys

			a__					:			str          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










'''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


a__					:			str          =     logging.getLogger(__name__)

a__					:			Tuple          =     '''pytorch_model.bin'''






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






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






@dataclasses.dataclass
class 				__snake_case     :
	__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=__magic_name__       ,       metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}       ,       )
	__lowerCAmelCase       						=  dataclasses.field(
	    default=__magic_name__       ,       metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}       ,       )
	__lowerCAmelCase       						=  dataclasses.field(
	    default=__magic_name__       ,       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=__magic_name__       ,       metadata={'''help''': '''Random seed for initialization.'''}       ,       )





def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->int:
	snake_case__	       =   datasets.concatenate_datasets([infer_input, infer_output]					,				axis=1							)

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

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

	snake_case__	       =   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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				**UpperCAmelCase_							)   ->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=UpperCAmelCase_							)
	snake_case__	       =   STDataArguments(train_file=UpperCAmelCase_					,				infer_file=UpperCAmelCase_							)
	snake_case__	       =   STTrainingArguments(output_dir=UpperCAmelCase_							)
	snake_case__	       =   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
	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=UpperCAmelCase_							)
			os.makedirs(UpperCAmelCase_					,				exist_ok=UpperCAmelCase_							)
	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(UpperCAmelCase_							)
		assert os.path.exists(UpperCAmelCase_							)

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

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

			snake_case__	       =   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_							)

		snake_case__	       =   iteration
		snake_case__	       =   data_dir_format(iteration + 1							)

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

		with open(UpperCAmelCase_					,				'r'							) as f:
			snake_case__	       =   float(json.load(UpperCAmelCase_							)[args.eval_metric]							)
		snake_case__	       =   os.path.join(UpperCAmelCase_					,				'infer_output_best-checkpoint.csv'							)
		assert os.path.exists(UpperCAmelCase_							)
		# 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(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()

		snake_case__	       =   os.path.join(UpperCAmelCase_					,				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'					,				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'							)					,				)



