






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__      :			Optional[int]							=		logging.getLogger(__name__)

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		"""pytorch_model.bin"""
@dataclasses.dataclass
class 						lowerCamelCase_				:
				a__					       =  dataclasses.field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''} )
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}			,				)







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







@dataclasses.dataclass
class 						lowerCamelCase_				:
				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=lowerCamelCase			,				metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}			,				)
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}			,				)
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				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=1_00			,				metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}			,				)
				a__					       =  dataclasses.field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Random seed for initialization.'''}			,				)







def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]        =      datasets.concatenate_datasets([infer_input, infer_output],  axis=1 )

				if args.do_filter_by_confidence:
								__magic_name__		:Tuple        =      dataset.filter(lambda snake_case : example["probability"] > args.confidence_threshold )

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

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

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

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  **snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      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()

				__magic_name__		:str        =      STModelArguments(model_name_or_path=snake_case )
				__magic_name__		:Union[str, Any]        =      STDataArguments(train_file=snake_case,  infer_file=snake_case )
				__magic_name__		:str        =      STTrainingArguments(output_dir=snake_case )
				__magic_name__		:Union[str, Any]        =      argparse.Namespace()

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

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

    # Sanity checks
				__magic_name__		:Optional[int]        =      {}
				__magic_name__		: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
				__magic_name__		:Tuple        =      args.train_file
				__magic_name__		:List[str]        =      args.infer_file

				if args.evaluation_strategy != IntervalStrategy.NO.value:
								assert args.eval_file is not None
								__magic_name__		:Optional[Any]        =      args.eval_file

				for key in data_files:
								__magic_name__		:Dict        =      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:
												__magic_name__		: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...''' )
				__magic_name__		:List[str]        =      f'''{args.output_dir}/self-train_iter-{{}}'''.format
				__magic_name__		:List[Any]        =      data_dir_format(0 )

				if accelerator.is_main_process:
								if args.output_dir is not None:
												os.makedirs(args.output_dir,  exist_ok=snake_case )
												os.makedirs(snake_case,  exist_ok=snake_case )
				accelerator.wait_for_everyone()

				__magic_name__		:Optional[Any]        =      None
				__magic_name__		:Any        =      None
				__magic_name__		:Dict        =      0
				__magic_name__		:Tuple        =      False
				# Show the progress bar
				__magic_name__		: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 ) ):
								__magic_name__		:str        =      data_dir_format(snake_case )
								assert os.path.exists(snake_case )

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

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

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

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

								__magic_name__		:List[Any]        =      iteration
								__magic_name__		:Tuple        =      data_dir_format(iteration + 1 )

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

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

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

								__magic_name__		:Tuple        =      os.path.join(snake_case,  f'''train_pseudo.{args.data_file_extension}''' )

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

												if best_iteration is None:
																__magic_name__		:List[str]        =      new_iteration
																__magic_name__		:List[str]        =      new_eval_result
												else:
																if new_eval_result - best_eval_result > args.early_stopping_threshold:
																				__magic_name__		:Any        =      new_iteration
																				__magic_name__		:Dict        =      new_eval_result
																				__magic_name__		:str        =      0
																else:
																				if new_eval_result == best_eval_result:
																								__magic_name__		:Tuple        =      new_iteration
																								__magic_name__		:List[str]        =      new_eval_result
																				early_stopping_patience_counter += 1

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














import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      1_0







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      [1, 2, 3, 4]
								__magic_name__		:Union[str, Any]        =      [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								__magic_name__		:Optional[Any]        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
								__magic_name__		:str        =      [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
								self.assertEqual(truncate_or_pad(__lowerCAmelCase       ,					self.block_size       ,					0     )       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      '''It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this.'''
								__magic_name__     ,				__magic_name__		:Optional[int]        =      process_story(__lowerCAmelCase     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      ''''''
								__magic_name__     ,				__magic_name__		:Optional[int]        =      process_story(__lowerCAmelCase     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )
								self.assertEqual(__lowerCAmelCase       ,					[]     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      (
								    '''It was the year of Our Lord one thousand seven hundred and '''
								    '''seventy-five\n\nSpiritual revelations were conceded to England '''
								    '''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
								)
								__magic_name__     ,				__magic_name__		:str        =      process_story(__lowerCAmelCase     )

								__magic_name__		:Optional[int]        =      [
								    '''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
								    '''Spiritual revelations were conceded to England at that favoured period, as at this.''',
								]
								self.assertEqual(__lowerCAmelCase       ,					__lowerCAmelCase     )

								__magic_name__		:Union[str, Any]        =      ['''It was the best of times.''']
								self.assertEqual(__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      torch.tensor([1, 2, 3, 4]     )
								__magic_name__		:List[str]        =      torch.tensor([1, 1, 1, 1]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					0     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]     )
								__magic_name__		:Union[str, Any]        =      torch.tensor([1, 1, 1, 1, 0, 0, 0]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					2_3     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      torch.tensor([8, 2, 3, 4, 1, 1, 1]     )
								__magic_name__		:Dict        =      torch.tensor([1, 1, 1, 1, 0, 0, 0]     )
								np.testing.assert_array_equal(build_mask(__lowerCAmelCase       ,					1     ).numpy()       ,					expected.numpy()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      1_0_1
								__magic_name__		:List[Any]        =      torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]     )
								__magic_name__		:str        =      torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]     )

								__magic_name__		:Tuple        =      compute_token_type_ids(__lowerCAmelCase       ,					__lowerCAmelCase     )
								np.testing.assert_array_equal(__lowerCAmelCase       ,					__lowerCAmelCase     )






