

"""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


SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			logging.getLogger(__name__)

SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			'''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=100				,       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 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__	,    A__	,    A__   )      ->      List[Any]:




   """simple docstring"""
   lowerCamelCase__						=			datasets.concatenate_datasets([infer_input, infer_output]	,    axis=1   )

   if args.do_filter_by_confidence:
      lowerCamelCase__						=			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
      lowerCamelCase__						=			int(eval_result * len(A__   )   )
      print(A__   )
      lowerCamelCase__						=			dataset.sort("probability"	,    reverse=A__   )
      lowerCamelCase__						=			dataset.select(range(A__   )   )

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

   lowerCamelCase__						=			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 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__	,    **A__   )      ->      Optional[int]:




   """simple docstring"""
   lowerCamelCase__						=			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__						=			STModelArguments(model_name_or_path=A__   )
   lowerCamelCase__						=			STDataArguments(train_file=A__	,    infer_file=A__   )
   lowerCamelCase__						=			STTrainingArguments(output_dir=A__   )
   lowerCamelCase__						=			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__						=			{}
   lowerCamelCase__						=			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__						=			args.train_file
   lowerCamelCase__						=			args.infer_file

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

   for key in data_files:
      lowerCamelCase__						=			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__						=			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__						=			f'{args.output_dir}/self-train_iter-{{}}'.format
   lowerCamelCase__						=			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__						=			None
   lowerCamelCase__						=			None
   lowerCamelCase__						=			0
   lowerCamelCase__						=			False
   # Show the progress bar
   lowerCamelCase__						=			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__						=			data_dir_format(A__   )
      assert os.path.exists(A__   )

      # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
      # iteration > 0
      lowerCamelCase__						=			os.path.join(A__	,    "stage-1"   )
      lowerCamelCase__						=			{
          "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__						=			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__						=			os.path.join(A__	,    "best-checkpoint"   )
         lowerCamelCase__						=			os.path.join(A__	,    "stage-2"   )
         # Update arguments_dict
         lowerCamelCase__						=			model_path
         lowerCamelCase__						=			data_files["train"]
         lowerCamelCase__						=			current_output_dir

         lowerCamelCase__						=			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__						=			iteration
      lowerCamelCase__						=			data_dir_format(iteration + 1   )

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

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

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

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

            if early_stopping_patience_counter >= args.early_stopping_patience:
               lowerCamelCase__						=			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 numpy as np



def 	UpperCAmelCase__   (				A__   )      ->      np.array:




   """simple docstring"""
   return 1 / (1 + np.exp(-vector   ))


if __name__ == "__main__":
     import doctest

     doctest.testmod()




