

'''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


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

lowerCamelCase			:				List[str] 			=					"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=A__					,       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=A__					,       metadata={'help': 'A csv or a json file containing the validation data.'}  )
  A__					=    dataclasses.field(
      default=A__					,       metadata={'help': 'The name of the task to train on.'}					,       )
  A__					=    dataclasses.field(
      default=A__					,       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=A__					,       metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}					,       )
  A__					=    dataclasses.field(
      default=A__					,       metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}					,       )
  A__					=    dataclasses.field(
      default=A__					,       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=A__					,       metadata={'help': 'Random seed for initialization.'}					,       )


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					str    )				->      Any:
     """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 _UpperCamelCase    : 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(_UpperCamelCase    )    )
          print(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =dataset.sort('probability'							,			reverse=_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =dataset.select(range(_UpperCamelCase    )    )

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

     _SCREAMING_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						:					List[Any]							,			_UpperCamelCase						:					Tuple							,			_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Optional[int]							,			**_UpperCamelCase						:					Optional[int]    )				->      Optional[int]:
     """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=_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =STDataArguments(train_file=_UpperCamelCase							,			infer_file=_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =STTrainingArguments(output_dir=_UpperCamelCase    )
     _SCREAMING_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
     _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=_UpperCamelCase    )
               os.makedirs(_UpperCamelCase							,			exist_ok=_UpperCamelCase    )
     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(_UpperCamelCase    )
          assert os.path.exists(_UpperCamelCase    )

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

          _SCREAMING_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
               _SCREAMING_SNAKE_CASE							   =os.path.join(_UpperCamelCase							,			'best-checkpoint'    )
               _SCREAMING_SNAKE_CASE							   =os.path.join(_UpperCamelCase							,			'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(_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    )

          _SCREAMING_SNAKE_CASE							   =iteration
          _SCREAMING_SNAKE_CASE							   =data_dir_format(iteration + 1    )

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

          with open(_UpperCamelCase							,			'r'    ) as f:
               _SCREAMING_SNAKE_CASE							   =float(json.load(_UpperCamelCase    )[args.eval_metric]    )
          _SCREAMING_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.
          _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(_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()

          _SCREAMING_SNAKE_CASE							   =os.path.join(_UpperCamelCase							,			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'							,			_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'    )							,			)



'''simple docstring'''
import os


def 	_lowerCAmelCase (						)				->      List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =os.path.dirname(os.path.realpath(_UpperCamelCase    )    )
     _SCREAMING_SNAKE_CASE							   =os.path.join(_UpperCamelCase							,			'triangle.txt'    )

     with open(_UpperCamelCase    ) as f:
          _SCREAMING_SNAKE_CASE							   =f.readlines()

     _SCREAMING_SNAKE_CASE							   =[]
     for line in triangle:
          _SCREAMING_SNAKE_CASE							   =[]
          for number in line.strip().split(' '    ):
               numbers_from_line.append(int(_UpperCamelCase    )    )
          a.append(_UpperCamelCase    )

     for i in range(1							,			len(_UpperCamelCase    )    ):
          for j in range(len(a[i]    )    ):
               _SCREAMING_SNAKE_CASE							   =a[i - 1][j] if j != len(a[i - 1]    ) else 0
               _SCREAMING_SNAKE_CASE							   =a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(_UpperCamelCase							,			_UpperCamelCase    )
     return max(a[-1]    )


if __name__ == "__main__":
     print(solution())
