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







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







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




def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				List[Any]:

  """simple docstring"""






  _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 )		->				Union[str, Any]:

  """simple docstring"""






  _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""" )						,  )





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							*a       ,							**a				)					->      None:


								'''simple docstring'''

								warnings.warn(
								    """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
								    """ use BeitImageProcessor instead."""       ,							a       ,							)
								super().__init__(*a       ,							**a				)



