
"""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__        =		logging.getLogger(__name__)

lowerCamelCase__        =		"""pytorch_model.bin"""




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




@dataclasses.dataclass
class 			A__	:
  A_						:	str           =     dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'})
  A_						:	str           =     dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'})
  A_						:	Optional[str]           =     dataclasses.field(
      default=__snake_case	,					metadata={'help': 'A csv or a json file containing the validation data.'})
  A_						:	Optional[str]           =     dataclasses.field(
      default=__snake_case	,					metadata={'help': 'The name of the task to train on.'}	,					)
  A_						:	Optional[List[str]]           =     dataclasses.field(
      default=__snake_case	,					metadata={'help': 'The list of labels for the task.'})




@dataclasses.dataclass
class 			A__	:
  A_						:	str           =     dataclasses.field(
      metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'})
  A_						:	Optional[str]           =     dataclasses.field(
      default='accuracy'	,					metadata={'help': 'The evaluation metric used for the task.'})
  A_						:	Optional[str]           =     dataclasses.field(
      default='no'	,					metadata={
          'help': 'The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]'
      }	,					)
  A_						:	Optional[int]           =     dataclasses.field(
      default=1_0	,					metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}	,					)
  A_						:	Optional[float]           =     dataclasses.field(
      default=0.0	,					metadata={
          'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
      }	,					)
  A_						:	Optional[bool]           =     dataclasses.field(
      default=__snake_case	,					metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}	,					)
  A_						:	Optional[bool]           =     dataclasses.field(
      default=__snake_case	,					metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}	,					)
  A_						:	Optional[bool]           =     dataclasses.field(
      default=__snake_case	,					metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'}	,					)
  A_						:	Optional[float]           =     dataclasses.field(
      default=0.0	,					metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'}	,					)
  A_						:	Optional[int]           =     dataclasses.field(
      default=1_0_0	,					metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}	,					)
  A_						:	Optional[int]           =     dataclasses.field(
      default=__snake_case	,					metadata={'help': 'Random seed for initialization.'}	,					)



def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
 __lowerCAmelCase      :						Tuple						      =				datasets.concatenate_datasets([infer_input, infer_output]	,  axis=1		)

 if args.do_filter_by_confidence:
  __lowerCAmelCase      :						List[str]						      =				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
  __lowerCAmelCase      :						List[str]						      =				int(eval_result * len(_a		)		)
  print(_a		)
  __lowerCAmelCase      :						int						      =				dataset.sort('probability'	,  reverse=_a		)
  __lowerCAmelCase      :						Optional[int]						      =				dataset.select(range(_a		)		)

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

 __lowerCAmelCase      :						int						      =				os.path.join(_a	,  F"train_pseudo.{args.data_file_extension}"		)
 if args.data_file_extension == "csv":
  dataset.to_csv(_a	,  index=_a		)
 else:
  dataset.to_json(_a		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  **_UpperCamelCase		):
 __lowerCAmelCase      :						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()

 __lowerCAmelCase      :						Tuple						      =				STModelArguments(model_name_or_path=_a		)
 __lowerCAmelCase      :						str						      =				STDataArguments(train_file=_a	,  infer_file=_a		)
 __lowerCAmelCase      :						Optional[Any]						      =				STTrainingArguments(output_dir=_a		)
 __lowerCAmelCase      :						Optional[Any]						      =				argparse.Namespace()

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

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

    # Sanity checks
 __lowerCAmelCase      :						List[str]						      =				{}
 __lowerCAmelCase      :						Any						      =				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
 __lowerCAmelCase      :						List[Any]						      =				args.train_file
 __lowerCAmelCase      :						Tuple						      =				args.infer_file

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

 for key in data_files:
  __lowerCAmelCase      :						List[str]						      =				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:
   __lowerCAmelCase      :						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...'		)
 __lowerCAmelCase      :						int						      =				F"{args.output_dir}/self-train_iter-{{}}".format
 __lowerCAmelCase      :						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=_a		)
   os.makedirs(_a	,  exist_ok=_a		)
 accelerator.wait_for_everyone()

 __lowerCAmelCase      :						Any						      =				None
 __lowerCAmelCase      :						Tuple						      =				None
 __lowerCAmelCase      :						List[str]						      =				0
 __lowerCAmelCase      :						List[Any]						      =				False
 # Show the progress bar
 __lowerCAmelCase      :						List[str]						      =				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		)		):
  __lowerCAmelCase      :						Any						      =				data_dir_format(_a		)
  assert os.path.exists(_a		)

  # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
  # iteration > 0
  __lowerCAmelCase      :						List[str]						      =				os.path.join(_a	,  'stage-1'		)
  __lowerCAmelCase      :						Optional[int]						      =				{
      """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(_a	,  _a		):
    arguments_dict.update({key: value}		)

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

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

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

  __lowerCAmelCase      :						Optional[Any]						      =				iteration
  __lowerCAmelCase      :						List[str]						      =				data_dir_format(iteration + 1		)

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

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

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

  __lowerCAmelCase      :						Tuple						      =				os.path.join(_a	,  F"train_pseudo.{args.data_file_extension}"		)

  if args.evaluation_strategy != IntervalStrategy.NO.value:
   __lowerCAmelCase      :						Optional[Any]						      =				eval_result

   if best_iteration is None:
    __lowerCAmelCase      :						Optional[int]						      =				new_iteration
    __lowerCAmelCase      :						Union[str, Any]						      =				new_eval_result
   else:
    if new_eval_result - best_eval_result > args.early_stopping_threshold:
     __lowerCAmelCase      :						List[str]						      =				new_iteration
     __lowerCAmelCase      :						Union[str, Any]						      =				new_eval_result
     __lowerCAmelCase      :						int						      =				0
    else:
     if new_eval_result == best_eval_result:
      __lowerCAmelCase      :						Dict						      =				new_iteration
      __lowerCAmelCase      :						Optional[Any]						      =				new_eval_result
     early_stopping_patience_counter += 1

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

"""simple docstring"""







def    __lowerCAmelCase    (_UpperCamelCase		):
	if len(_UpperCamelCase		) <= 1:
		return lst

	__lowerCAmelCase      :						str						      =				1

	while i < len(_UpperCamelCase		):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			__lowerCAmelCase      ,       __lowerCAmelCase      :						List[Any]						      =				lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				__lowerCAmelCase      :						int						      =				1

	return lst


if __name__ == "__main__":
							lowerCamelCase__        =		input("""Enter numbers separated by a comma:\n""").strip()
							lowerCamelCase__        =		[int(item) for item in user_input.split(""",""")]
							print(gnome_sort(unsorted))