


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_ :List[str]  							=			logging.getLogger(__name__)

a_ :List[Any]  							=			'pytorch_model.bin'

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




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




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




def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__      ,    A__							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  str						     =		datasets.concatenate_datasets([infer_input, infer_output]      ,    axis=1							)

      if args.do_filter_by_confidence:
            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		dataset.filter(lambda A__							: 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						     =		int(eval_result * len(__lowerCamelCase							)							)
            print(__lowerCamelCase							)
            SCREAMING_SNAKE_CASE__       :  Any						     =		dataset.sort('''probability'''      ,    reverse=__lowerCamelCase							)
            SCREAMING_SNAKE_CASE__       :  int						     =		dataset.select(range(__lowerCamelCase							)							)

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

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		os.path.join(__lowerCamelCase      ,    f"""train_pseudo.{args.data_file_extension}"""							)
      if args.data_file_extension == "csv":
            dataset.to_csv(__lowerCamelCase      ,    index=__lowerCamelCase							)
      else:
            dataset.to_json(__lowerCamelCase							)





def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    **A__							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		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__       :  int						     =		STModelArguments(model_name_or_path=__lowerCamelCase							)
      SCREAMING_SNAKE_CASE__       :  Dict						     =		STDataArguments(train_file=__lowerCamelCase      ,    infer_file=__lowerCamelCase							)
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		STTrainingArguments(output_dir=__lowerCamelCase							)
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		argparse.Namespace()

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

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

    # Sanity checks
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		{}
      SCREAMING_SNAKE_CASE__       :  int						     =		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__       :  Union[str, Any]						     =		args.train_file
      SCREAMING_SNAKE_CASE__       :  int						     =		args.infer_file

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

      for key in data_files:
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		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__       :  Optional[Any]						     =		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__       :  List[Any]						     =		f"""{args.output_dir}/self-train_iter-{{}}""".format
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		data_dir_format(0							)

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

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

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

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

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

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

            SCREAMING_SNAKE_CASE__       :  Tuple						     =		iteration
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		data_dir_format(iteration + 1							)

            SCREAMING_SNAKE_CASE__       :  int						     =		AutoConfig.from_pretrained(os.path.join(__lowerCamelCase      ,    '''best-checkpoint'''							)							)
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		config.idalabel
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		os.path.join(__lowerCamelCase      ,    '''eval_results_best-checkpoint.json'''							)
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		os.path.join(__lowerCamelCase      ,    '''test_results_best-checkpoint.json'''							)
            assert os.path.exists(__lowerCamelCase							)

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

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

            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		os.path.join(__lowerCamelCase      ,    f"""train_pseudo.{args.data_file_extension}"""							)

            if args.evaluation_strategy != IntervalStrategy.NO.value:
                  SCREAMING_SNAKE_CASE__       :  Dict						     =		eval_result

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

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








def      UpperCamelCase   (       __lowerCamelCase	:					int = 1							,   __lowerCamelCase	:					int = 1000       ):
       snake_case			:  int     							= 1
       snake_case			:  int     							= 0

       for divide_by_number in range(__lowerCamelCase							,   digit + 1       ):
              snake_case			:  list[int]     							= []
              snake_case			:  Optional[int]     							= numerator
              for _ in range(1							,   digit + 1       ):
                     if now_divide in has_been_divided:
                            if longest_list_length < len(__lowerCamelCase       ):
                                   snake_case			:  List[Any]     							= len(__lowerCamelCase       )
                                   snake_case			:  List[str]     							= divide_by_number
                     else:
                            has_been_divided.append(__lowerCamelCase       )
                            snake_case			:  Any     							= now_divide * 10 % divide_by_number

       return the_digit


# Tests
if __name__ == "__main__":
   import doctest

   doctest.testmod()





