



"""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


lowercase__		:				Union[str, Any]          =	logging.getLogger(__name__)

lowercase__		:				str          =	"""pytorch_model.bin"""







@dataclasses.dataclass
class       UpperCamelCase__   :




      """simple docstring"""




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







@dataclasses.dataclass
class       UpperCamelCase__   :




      """simple docstring"""




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







@dataclasses.dataclass
class       UpperCamelCase__   :




      """simple docstring"""




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




def 				UpperCamelCase_    (    lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				Tuple						)						->   Optional[int]:

  """simple docstring"""




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

  if args.do_filter_by_confidence:
    lowerCAmelCase_   :				str								= 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
    lowerCAmelCase_   :				str								= int(eval_result * len(lowerCAmelCase__						)						)
    print(lowerCAmelCase__						)
    lowerCAmelCase_   :				str								= dataset.sort('probability'    ,   reverse=lowerCAmelCase__						)
    lowerCAmelCase_   :				Union[str, Any]								= dataset.select(range(lowerCAmelCase__						)						)

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

  lowerCAmelCase_   :				List[Any]								= 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 				UpperCamelCase_    (    lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				List[str]    ,   **lowerCAmelCase__      :				List[str]						)						->   Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Union[str, Any]								= 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_   :				List[str]								= STModelArguments(model_name_or_path=lowerCAmelCase__						)
  lowerCAmelCase_   :				Tuple								= STDataArguments(train_file=lowerCAmelCase__    ,   infer_file=lowerCAmelCase__						)
  lowerCAmelCase_   :				Optional[Any]								= STTrainingArguments(output_dir=lowerCAmelCase__						)
  lowerCAmelCase_   :				List[str]								= 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
  lowerCAmelCase_   :				Union[str, Any]								= {}
  lowerCAmelCase_   :				str								= 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_   :				Any								= args.train_file
  lowerCAmelCase_   :				Optional[int]								= 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_   :				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:
      lowerCAmelCase_   :				Optional[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_   :				Tuple								= f"{args.output_dir}/self-train_iter-{{}}".format
  lowerCAmelCase_   :				int								= 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()

  lowerCAmelCase_   :				Optional[int]								= None
  lowerCAmelCase_   :				List[str]								= None
  lowerCAmelCase_   :				str								= 0
  lowerCAmelCase_   :				int								= 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_   :				Tuple								= data_dir_format(lowerCAmelCase__						)
    assert os.path.exists(lowerCAmelCase__						)

    # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
    # iteration > 0
    lowerCAmelCase_   :				List[Any]								= os.path.join(lowerCAmelCase__    ,   'stage-1'						)
    lowerCAmelCase_   :				List[str]								= {
        '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}						)

    lowerCAmelCase_   :				int								= 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
      lowerCAmelCase_   :				int								= os.path.join(lowerCAmelCase__    ,   'best-checkpoint'						)
      lowerCAmelCase_   :				Union[str, Any]								= os.path.join(lowerCAmelCase__    ,   'stage-2'						)
      # Update arguments_dict
      lowerCAmelCase_   :				str								= model_path
      lowerCAmelCase_   :				List[Any]								= data_files['train']
      lowerCAmelCase_   :				Union[str, Any]								= current_output_dir

      lowerCAmelCase_   :				Union[str, 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: 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__						)

    lowerCAmelCase_   :				Any								= iteration
    lowerCAmelCase_   :				List[Any]								= data_dir_format(iteration + 1						)

    lowerCAmelCase_   :				Optional[Any]								= AutoConfig.from_pretrained(os.path.join(lowerCAmelCase__    ,   'best-checkpoint'						)						)
    lowerCAmelCase_   :				List[Any]								= config.idalabel
    lowerCAmelCase_   :				Optional[Any]								= os.path.join(lowerCAmelCase__    ,   'eval_results_best-checkpoint.json'						)
    lowerCAmelCase_   :				str								= os.path.join(lowerCAmelCase__    ,   'test_results_best-checkpoint.json'						)
    assert os.path.exists(lowerCAmelCase__						)

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

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

    if args.evaluation_strategy != IntervalStrategy.NO.value:
      lowerCAmelCase_   :				List[Any]								= eval_result

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

        if early_stopping_patience_counter >= args.early_stopping_patience:
          lowerCAmelCase_   :				Optional[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'						)    ,   )










"""simple docstring"""







from typing import Union

import fire
import torch
from tqdm import tqdm

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str = "cpu"    ,   lowerCAmelCase__      :				Union[str, None] = None						)						->   None:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= torch.load(lowerCAmelCase__    ,   map_location=lowerCAmelCase__						)
  for k, v in tqdm(state_dict.items()						):
    if not isinstance(lowerCAmelCase__    ,   torch.Tensor						):
      raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'						)
    lowerCAmelCase_   :				str								= v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_   :				Dict								= src_path
  torch.save(lowerCAmelCase__    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     fire.Fire(convert)





