




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


SCREAMING_SNAKE_CASE__     :     str		    =       logging.getLogger(__name__)

SCREAMING_SNAKE_CASE__     :     List[Any]		    =       "pytorch_model.bin"



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




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




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




def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Tuple:


			'''simple docstring'''







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

			if args.do_filter_by_confidence:
						SCREAMING_SNAKE_CASE_								=						dataset.filter(lambda SCREAMING_SNAKE_CASE : example["probability"] > args.confidence_threshold )

			if args.do_filter_by_val_performance:
						assert eval_result >= 0.0 and eval_result <= 1.0
						SCREAMING_SNAKE_CASE_								=						int(eval_result * len(SCREAMING_SNAKE_CASE ) )
						print(SCREAMING_SNAKE_CASE )
						SCREAMING_SNAKE_CASE_								=						dataset.sort('probability'       ,    reverse=SCREAMING_SNAKE_CASE )
						SCREAMING_SNAKE_CASE_								=						dataset.select(range(SCREAMING_SNAKE_CASE ) )

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

			SCREAMING_SNAKE_CASE_								=						os.path.join(SCREAMING_SNAKE_CASE       ,    F'train_pseudo.{args.data_file_extension}' )
			if args.data_file_extension == "csv":
						dataset.to_csv(SCREAMING_SNAKE_CASE       ,    index=SCREAMING_SNAKE_CASE )
			else:
						dataset.to_json(SCREAMING_SNAKE_CASE )

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    **SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_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()

			SCREAMING_SNAKE_CASE_								=						STModelArguments(model_name_or_path=SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						STDataArguments(train_file=SCREAMING_SNAKE_CASE       ,    infer_file=SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						STTrainingArguments(output_dir=SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						argparse.Namespace()

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

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

    # Sanity checks
			SCREAMING_SNAKE_CASE_								=						{}
			SCREAMING_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
			SCREAMING_SNAKE_CASE_								=						args.train_file
			SCREAMING_SNAKE_CASE_								=						args.infer_file

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

			for key in data_files:
						SCREAMING_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:
									SCREAMING_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...' )
			SCREAMING_SNAKE_CASE_								=						F'{args.output_dir}/self-train_iter-{{}}'.format
			SCREAMING_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=SCREAMING_SNAKE_CASE )
									os.makedirs(SCREAMING_SNAKE_CASE       ,    exist_ok=SCREAMING_SNAKE_CASE )
			accelerator.wait_for_everyone()

			SCREAMING_SNAKE_CASE_								=						None
			SCREAMING_SNAKE_CASE_								=						None
			SCREAMING_SNAKE_CASE_								=						0
			SCREAMING_SNAKE_CASE_								=						False
			# Show the progress bar
			SCREAMING_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 ) ):
						SCREAMING_SNAKE_CASE_								=						data_dir_format(SCREAMING_SNAKE_CASE )
						assert os.path.exists(SCREAMING_SNAKE_CASE )

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

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

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

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

						SCREAMING_SNAKE_CASE_								=						iteration
						SCREAMING_SNAKE_CASE_								=						data_dir_format(iteration + 1 )

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

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

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

						SCREAMING_SNAKE_CASE_								=						os.path.join(SCREAMING_SNAKE_CASE       ,    F'train_pseudo.{args.data_file_extension}' )

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

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

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






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
				import tensorflow as tf

				from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class       a_       (     unittest.TestCase     ):





	@slow
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('google/mt5-small'      )

				SCREAMING_SNAKE_CASE_								=						tokenizer('Hello there'      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						tokenizer('Hi I am'      ,     return_tensors='tf'      ).input_ids

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      ).loss
				SCREAMING_SNAKE_CASE_								=						-tf.math.reduce_mean(SCREAMING_SNAKE_CASE      ).numpy()

				SCREAMING_SNAKE_CASE_								=						-2_1.2_2_8_1_6_8
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )
