



"""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    lowercase__     :







       '''simple docstring'''


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



@dataclasses.dataclass
class    lowercase__     :







       '''simple docstring'''


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



@dataclasses.dataclass
class    lowercase__     :







       '''simple docstring'''


       UpperCamelCase		=  dataclasses.field(
           metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}   )
       UpperCamelCase		=  dataclasses.field(
           default='''accuracy'''       ,      metadata={'''help''': '''The evaluation metric used for the task.'''}   )
       UpperCamelCase		=  dataclasses.field(
           default='''no'''       ,      metadata={
               '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
           }       ,      )
       UpperCamelCase		=  dataclasses.field(
           default=10       ,      metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}       ,      )
       UpperCamelCase		=  dataclasses.field(
           default=0.0       ,      metadata={
               '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
           }       ,      )
       UpperCamelCase		=  dataclasses.field(
           default=SCREAMING_SNAKE_CASE       ,      metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}       ,      )
       UpperCamelCase		=  dataclasses.field(
           default=SCREAMING_SNAKE_CASE       ,      metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}       ,      )
       UpperCamelCase		=  dataclasses.field(
           default=SCREAMING_SNAKE_CASE       ,      metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}       ,      )
       UpperCamelCase		=  dataclasses.field(
           default=0.0       ,      metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}       ,      )
       UpperCamelCase		=  dataclasses.field(
           default=1_00       ,      metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}       ,      )
       UpperCamelCase		=  dataclasses.field(
           default=SCREAMING_SNAKE_CASE       ,      metadata={'''help''': '''Random seed for initialization.'''}       ,      )



def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							datasets.concatenate_datasets([infer_input, infer_output]			,    axis=1   )

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

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

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

      UpperCAmelCase_    				=							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__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    **lowerCAmelCase__   ):
      UpperCAmelCase_    				=							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()

      UpperCAmelCase_    				=							STModelArguments(model_name_or_path=lowerCAmelCase__   )
      UpperCAmelCase_    				=							STDataArguments(train_file=lowerCAmelCase__			,    infer_file=lowerCAmelCase__   )
      UpperCAmelCase_    				=							STTrainingArguments(output_dir=lowerCAmelCase__   )
      UpperCAmelCase_    				=							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
      UpperCAmelCase_    				=							{}
      UpperCAmelCase_    				=							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
      UpperCAmelCase_    				=							args.train_file
      UpperCAmelCase_    				=							args.infer_file

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

      for key in data_files:
            UpperCAmelCase_    				=							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:
                  UpperCAmelCase_    				=							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..."   )
      UpperCAmelCase_    				=							f"""{args.output_dir}/self-train_iter-{{}}""".format
      UpperCAmelCase_    				=							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()

      UpperCAmelCase_    				=							None
      UpperCAmelCase_    				=							None
      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							False
      # Show the progress bar
      UpperCAmelCase_    				=							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   )   ):
            UpperCAmelCase_    				=							data_dir_format(lowerCAmelCase__   )
            assert os.path.exists(lowerCAmelCase__   )

            # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
            # iteration > 0
            UpperCAmelCase_    				=							os.path.join(lowerCAmelCase__			,    "stage-1"   )
            UpperCAmelCase_    				=							{
                "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}   )

            UpperCAmelCase_    				=							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
                  UpperCAmelCase_    				=							os.path.join(lowerCAmelCase__			,    "best-checkpoint"   )
                  UpperCAmelCase_    				=							os.path.join(lowerCAmelCase__			,    "stage-2"   )
                  # Update arguments_dict
                  UpperCAmelCase_    				=							model_path
                  UpperCAmelCase_    				=							data_files["train"]
                  UpperCAmelCase_    				=							current_output_dir

                  UpperCAmelCase_    				=							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__   )

            UpperCAmelCase_    				=							iteration
            UpperCAmelCase_    				=							data_dir_format(iteration + 1   )

            UpperCAmelCase_    				=							AutoConfig.from_pretrained(os.path.join(lowerCAmelCase__			,    "best-checkpoint"   )   )
            UpperCAmelCase_    				=							config.idalabel
            UpperCAmelCase_    				=							os.path.join(lowerCAmelCase__			,    "eval_results_best-checkpoint.json"   )
            UpperCAmelCase_    				=							os.path.join(lowerCAmelCase__			,    "test_results_best-checkpoint.json"   )
            assert os.path.exists(lowerCAmelCase__   )

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

            UpperCAmelCase_    				=							os.path.join(lowerCAmelCase__			,    f"""train_pseudo.{args.data_file_extension}"""   )

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

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

                        if early_stopping_patience_counter >= args.early_stopping_patience:
                              UpperCAmelCase_    				=							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"   )			,    )









"""simple docstring"""





from __future__ import annotations
def      a__			(   lowerCAmelCase__   ):
      if len(lowerCAmelCase__   ) == 0:
            return []
      UpperCAmelCase_				,					UpperCAmelCase_    				=							min(lowerCAmelCase__   ), max(lowerCAmelCase__   )
      UpperCAmelCase_    				=							int(max_value - min_value   ) + 1
      UpperCAmelCase_    				=							[[] for _ in range(lowerCAmelCase__   )]

      for i in my_list:
            buckets[int(i - min_value   )].append(lowerCAmelCase__   )

      return [v for bucket in buckets for v in sorted(lowerCAmelCase__   )]


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]




