


"""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 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       a  :    Optional[Any]       =dataclasses.field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}    )
       a  :    str       =dataclasses.field(
           default=__lowercase       ,   metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}       ,   )


@dataclasses.dataclass
class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       a  :    Tuple       =dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}    )
       a  :    List[str]       =dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}    )
       a  :    Union[str, Any]       =dataclasses.field(
           default=__lowercase       ,   metadata={"help": "A csv or a json file containing the validation data."}    )
       a  :    List[str]       =dataclasses.field(
           default=__lowercase       ,   metadata={"help": "The name of the task to train on."}       ,   )
       a  :    List[Any]       =dataclasses.field(
           default=__lowercase       ,   metadata={"help": "The list of labels for the task."}    )


@dataclasses.dataclass
class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


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

def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ,				SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   Tuple    ,				SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   Any    ):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							datasets.concatenate_datasets([infer_input, infer_output]    ,				axis=1    )

  if args.do_filter_by_confidence:
    lowerCAmelCase					:					Optional[int]								=							dataset.filter(lambda SCREAMING_SNAKE_CASE    : example["probability"] > args.confidence_threshold    )

  if args.do_filter_by_val_performance:
    assert eval_result >= 0.0 and eval_result <= 1.0
    lowerCAmelCase					:					Tuple								=							int(eval_result * len(__a    )    )
    print(__a    )
    lowerCAmelCase					:					Optional[Any]								=							dataset.sort("probability"    ,				reverse=__a    )
    lowerCAmelCase					:					List[str]								=							dataset.select(range(__a    )    )

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

  lowerCAmelCase					:					Tuple								=							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  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   str    ,				**SCREAMING_SNAKE_CASE   :   Union[str, Any]    ):


  '''simple docstring'''







  lowerCAmelCase					:					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					:					Dict								=							STModelArguments(model_name_or_path=__a    )
  lowerCAmelCase					:					Union[str, Any]								=							STDataArguments(train_file=__a    ,				infer_file=__a    )
  lowerCAmelCase					:					Union[str, Any]								=							STTrainingArguments(output_dir=__a    )
  lowerCAmelCase					:					Tuple								=							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					:					Union[str, Any]								=							{}
  lowerCAmelCase					:					Dict								=							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					:					Union[str, Any]								=							args.train_file
  lowerCAmelCase					:					List[str]								=							args.infer_file

  if args.evaluation_strategy != IntervalStrategy.NO.value:
    assert args.eval_file is not None
    lowerCAmelCase					:					List[str]								=							args.eval_file

  for key in data_files:
    lowerCAmelCase					:					Tuple								=							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					:					List[str]								=							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					:					Any								=							f"""{args.output_dir}/self-train_iter-{{}}""".format
  lowerCAmelCase					:					Dict								=							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					:					Union[str, Any]								=							None
  lowerCAmelCase					:					Dict								=							None
  lowerCAmelCase					:					str								=							0
  lowerCAmelCase					:					Optional[Any]								=							False
  # Show the progress bar
  lowerCAmelCase					:					Optional[Any]								=							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					:					Optional[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					:					str								=							os.path.join(__a    ,				"stage-1"    )
    lowerCAmelCase					:					Optional[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(__a    ,				__a    ):
        arguments_dict.update({key: value}    )

    lowerCAmelCase					:					Optional[int]								=							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					:					Tuple								=							os.path.join(__a    ,				"stage-2"    )
      # Update arguments_dict
      lowerCAmelCase					:					Any								=							model_path
      lowerCAmelCase					:					Tuple								=							data_files["train"]
      lowerCAmelCase					:					List[str]								=							current_output_dir

      lowerCAmelCase					:					Dict								=							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					:					List[str]								=							iteration
    lowerCAmelCase					:					str								=							data_dir_format(iteration + 1    )

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

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

    if args.evaluation_strategy != IntervalStrategy.NO.value:
      lowerCAmelCase					:					Optional[int]								=							eval_result

      if best_iteration is None:
        lowerCAmelCase					:					Tuple								=							new_iteration
        lowerCAmelCase					:					Optional[Any]								=							new_eval_result
      else:
        if new_eval_result - best_eval_result > args.early_stopping_threshold:
          lowerCAmelCase					:					Tuple								=							new_iteration
          lowerCAmelCase					:					Tuple								=							new_eval_result
          lowerCAmelCase					:					Optional[int]								=							0
        else:
          if new_eval_result == best_eval_result:
            lowerCAmelCase					:					Any								=							new_iteration
            lowerCAmelCase					:					List[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"""







import re

from filelock import FileLock


try:
     import nltk

     lowerCAmelCase__				    =       True
except (ImportError, ModuleNotFoundError):
     lowerCAmelCase__				    =       False

if NLTK_AVAILABLE:
     with FileLock('''.lock''') as lock:
          nltk.download('''punkt''', quiet=True)
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  re.sub("<n>"    ,				""    ,				SCREAMING_SNAKE_CASE    )  # remove pegasus newline char
  assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
  return "\n".join(nltk.sent_tokenize(SCREAMING_SNAKE_CASE    )    )





