

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







@dataclasses.dataclass
class 	__UpperCamelCase    :
  _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=a__							,							metadata={"help": "A csv or a json file containing the validation data."}						)
  _UpperCAmelCase          =		dataclasses.field(
      default=a__							,							metadata={"help": "The name of the task to train on."}							,							)
  _UpperCAmelCase          =		dataclasses.field(
      default=a__							,							metadata={"help": "The list of labels for the task."}						)







@dataclasses.dataclass
class 	__UpperCamelCase    :
  _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=a__							,							metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}							,							)
  _UpperCAmelCase          =		dataclasses.field(
      default=a__							,							metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}							,							)
  _UpperCAmelCase          =		dataclasses.field(
      default=a__							,							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=a__							,							metadata={"help": "Random seed for initialization."}							,							)





def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



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

    if args.do_filter_by_confidence:
        _lowerCAmelCase       :   Optional[Any]  =    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       :   Optional[int]  =    int(eval_result * len(__lowerCAmelCase   )   )
        print(__lowerCAmelCase   )
        _lowerCAmelCase       :   Optional[Any]  =    dataset.sort('probability' ,   reverse=__lowerCAmelCase   )
        _lowerCAmelCase       :   Optional[int]  =    dataset.select(range(__lowerCAmelCase   )   )

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

    _lowerCAmelCase       :   Union[str, 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 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   **_lowerCamelCase   ):

    '''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       :   List[str]  =    STModelArguments(model_name_or_path=__lowerCAmelCase   )
    _lowerCAmelCase       :   int  =    STDataArguments(train_file=__lowerCAmelCase ,   infer_file=__lowerCAmelCase   )
    _lowerCAmelCase       :   Any  =    STTrainingArguments(output_dir=__lowerCAmelCase   )
    _lowerCAmelCase       :   Optional[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
    _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       :   List[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       :   Union[str, Any]  =    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       :   Tuple  =    f"""{args.output_dir}/self-train_iter-{{}}""".format
    _lowerCAmelCase       :   str  =    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       :   Tuple  =    None
    _lowerCAmelCase       :   List[Any]  =    None
    _lowerCAmelCase       :   List[str]  =    0
    _lowerCAmelCase       :   Any  =    False
    # Show the progress bar
    _lowerCAmelCase       :   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   )   ):
        _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       :   Optional[int]  =    os.path.join(__lowerCAmelCase ,   'stage-1'   )
        _lowerCAmelCase       :   List[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(__lowerCAmelCase ,   __lowerCAmelCase   ):
                arguments_dict.update({key: value}   )

        _lowerCAmelCase       :   Tuple  =    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       :   Tuple  =    os.path.join(__lowerCAmelCase ,   'best-checkpoint'   )
            _lowerCAmelCase       :   Tuple  =    os.path.join(__lowerCAmelCase ,   'stage-2'   )
            # Update arguments_dict
            _lowerCAmelCase       :   Dict  =    model_path
            _lowerCAmelCase       :   Optional[int]  =    data_files['train']
            _lowerCAmelCase       :   Union[str, Any]  =    current_output_dir

            _lowerCAmelCase       :   Optional[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: 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       :   int  =    iteration
        _lowerCAmelCase       :   Dict  =    data_dir_format(iteration + 1   )

        _lowerCAmelCase       :   int  =    AutoConfig.from_pretrained(os.path.join(__lowerCAmelCase ,   'best-checkpoint'   )   )
        _lowerCAmelCase       :   List[Any]  =    config.idalabel
        _lowerCAmelCase       :   Dict  =    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       :   List[str]  =    float(json.load(__lowerCAmelCase   )[args.eval_metric]   )
        _lowerCAmelCase       :   int  =    os.path.join(__lowerCAmelCase ,   'infer_output_best-checkpoint.csv'   )
        assert os.path.exists(__lowerCAmelCase   )
        # Loading the dataset from local csv or json files.
        _lowerCAmelCase       :   int  =    load_dataset(args.data_file_extension ,   data_files={'data': data_files['infer']}   )['data']
        _lowerCAmelCase       :   Optional[int]  =    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       :   Union[str, Any]  =    os.path.join(__lowerCAmelCase ,   f"""train_pseudo.{args.data_file_extension}"""   )

        if args.evaluation_strategy != IntervalStrategy.NO.value:
            _lowerCAmelCase       :   Tuple  =    eval_result

            if best_iteration is None:
                _lowerCAmelCase       :   Any  =    new_iteration
                _lowerCAmelCase       :   Any  =    new_eval_result
            else:
                if new_eval_result - best_eval_result > args.early_stopping_threshold:
                    _lowerCAmelCase       :   Tuple  =    new_iteration
                    _lowerCAmelCase       :   List[str]  =    new_eval_result
                    _lowerCAmelCase       :   List[Any]  =    0
                else:
                    if new_eval_result == best_eval_result:
                        _lowerCAmelCase       :   Optional[Any]  =    new_iteration
                        _lowerCAmelCase       :   Union[str, 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' ,   __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"""






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment







class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    inspect.getfile(accelerate.test_utils      )
      _lowerCAmelCase       :   Union[str, Any]  =    os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_script.py']      )
      _lowerCAmelCase       :   int  =    os.path.sep.join(
          mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_distributed_data_loop.py']      )
      _lowerCAmelCase       :   Optional[Any]  =    os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_ops.py']      )

  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      print(F"""Found {torch.cuda.device_count()} devices."""      )
      _lowerCAmelCase       :   int  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )

  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      print(F"""Found {torch.cuda.device_count()} devices."""      )
      _lowerCAmelCase       :   str  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
      print(F"""Command: {cmd}"""      )
      with patch_environment(omp_num_threads=1      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )

  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
      with patch_environment(omp_num_threads=1      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )







  @require_multi_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""      )
      _lowerCAmelCase       :   Tuple  =    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
      with patch_environment(omp_num_threads=1    ,cuda_visible_devices='0,1'      ):
          execute_subprocess_async(_A    ,env=os.environ.copy()      )


if __name__ == "__main__":
 _lowerCAmelCase						=		Accelerator()
 _lowerCAmelCase						=		(accelerator.state.process_index + 2, 1_0)
 _lowerCAmelCase						=		torch.randint(0, 1_0, shape).to(accelerator.device)

 _lowerCAmelCase						=		""""""

 _lowerCAmelCase						=		accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 _lowerCAmelCase						=		accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 _lowerCAmelCase						=		accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)





