




"""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


A_						=				logging.getLogger(__name__)

A_						=				'''pytorch_model.bin'''






@dataclasses.dataclass
class 					lowercase:







 '''simple docstring'''





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







@dataclasses.dataclass
class 					lowercase:







 '''simple docstring'''





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







@dataclasses.dataclass
class 					lowercase:







 '''simple docstring'''





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







def 					UpperCAmelCase__       (snake_case__							:					Tuple		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					str		,   snake_case__							:					Dict		,   snake_case__							:					List[str]		,   snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		int								=  datasets.concatenate_datasets([infer_input, infer_output]		,   axis=1    )

  if args.do_filter_by_confidence:
    _snake_case							:		str								=  dataset.filter(lambda snake_case__    : example["probability"] > args.confidence_threshold    )

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

  _snake_case							:		Dict								=  dataset.remove_columns(["""label""", """probability"""]    )
  _snake_case							:		int								=  dataset.rename_column("""prediction"""		,   """label"""    )
  _snake_case							:		Dict								=  dataset.map(lambda snake_case__    : {"label": idalabel[example["label"]]}    )
  _snake_case							:		Optional[int]								=  dataset.shuffle(seed=args.seed    )

  _snake_case							:		List[Any]								=  os.path.join(snake_case__		,   F"train_pseudo.{args.data_file_extension}"    )
  if args.data_file_extension == "csv":
    dataset.to_csv(snake_case__		,   index=snake_case__    )
  else:
    dataset.to_json(snake_case__    )






def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Dict		,   snake_case__							:					int		,   snake_case__							:					List[Any]		,   **snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  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()

  _snake_case							:		List[str]								=  STModelArguments(model_name_or_path=snake_case__    )
  _snake_case							:		Union[str, Any]								=  STDataArguments(train_file=snake_case__		,   infer_file=snake_case__    )
  _snake_case							:		List[Any]								=  STTrainingArguments(output_dir=snake_case__    )
  _snake_case							:		int								=  argparse.Namespace()

  for arg_class in (model_args, data_args, training_args):
    for key, value in vars(snake_case__    ).items():
      setattr(snake_case__		,   snake_case__		,   snake_case__    )

  for key, value in kwargs.items():
    if hasattr(snake_case__		,   snake_case__    ):
      setattr(snake_case__		,   snake_case__		,   snake_case__    )

    # Sanity checks
  _snake_case							:		str								=  {}
  _snake_case							:		int								=  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
  _snake_case							:		Any								=  args.train_file
  _snake_case							:		List[str]								=  args.infer_file

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

  for key in data_files:
    _snake_case							:		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:
      _snake_case							:		Tuple								=  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..."""    )
  _snake_case							:		Any								=  F"{args.output_dir}/self-train_iter-{{}}".format
  _snake_case							:		Optional[int]								=  data_dir_format(0    )

  if accelerator.is_main_process:
    if args.output_dir is not None:
      os.makedirs(args.output_dir		,   exist_ok=snake_case__    )
      os.makedirs(snake_case__		,   exist_ok=snake_case__    )
  accelerator.wait_for_everyone()

  _snake_case							:		str								=  None
  _snake_case							:		Union[str, Any]								=  None
  _snake_case							:		Optional[int]								=  0
  _snake_case							:		Optional[Any]								=  False
  # Show the progress bar
  _snake_case							:		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    )    ):
    _snake_case							:		List[str]								=  data_dir_format(snake_case__    )
    assert os.path.exists(snake_case__    )

    # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
    # iteration > 0
    _snake_case							:		Optional[int]								=  os.path.join(snake_case__		,   """stage-1"""    )
    _snake_case							:		Tuple								=  {
        """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(snake_case__		,   snake_case__    ):
        arguments_dict.update({key: value}    )

    _snake_case							:		List[str]								=  os.path.join(snake_case__		,   """best-checkpoint"""		,   snake_case__    )
    if os.path.exists(snake_case__    ):
      logger.info(
          """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."""		,   snake_case__		,   snake_case__		,   )
    else:
      logger.info("""***** Running self-training: iteration: %d, stage: 1 *****"""		,   snake_case__    )
      finetune(**snake_case__    )
      accelerator.wait_for_everyone()
      assert os.path.exists(snake_case__    )
      logger.info("""Self-training job completed: iteration: %d, stage: 1."""		,   snake_case__    )

    if iteration > 0 and args.finetune_on_labeled_data:
      # Stage 2 (optional): fine-tuning on the original labeled data
      _snake_case							:		Any								=  os.path.join(snake_case__		,   """best-checkpoint"""    )
      _snake_case							:		List[str]								=  os.path.join(snake_case__		,   """stage-2"""    )
      # Update arguments_dict
      _snake_case							:		Union[str, Any]								=  model_path
      _snake_case							:		Union[str, Any]								=  data_files["""train"""]
      _snake_case							:		Union[str, Any]								=  current_output_dir

      _snake_case							:		Dict								=  os.path.join(snake_case__		,   """best-checkpoint"""		,   snake_case__    )
      if os.path.exists(snake_case__    ):
        logger.info(
            """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."""		,   snake_case__		,   snake_case__		,   )
      else:
        logger.info("""***** Running self-training: iteration: %d, stage: 2 *****"""		,   snake_case__    )
        finetune(**snake_case__    )
        accelerator.wait_for_everyone()
        assert os.path.exists(snake_case__    )
        logger.info("""Self-training job completed: iteration: %d, stage: 2."""		,   snake_case__    )

    _snake_case							:		Any								=  iteration
    _snake_case							:		Any								=  data_dir_format(iteration + 1    )

    _snake_case							:		Dict								=  AutoConfig.from_pretrained(os.path.join(snake_case__		,   """best-checkpoint"""    )    )
    _snake_case							:		List[Any]								=  config.idalabel
    _snake_case							:		Optional[Any]								=  os.path.join(snake_case__		,   """eval_results_best-checkpoint.json"""    )
    _snake_case							:		int								=  os.path.join(snake_case__		,   """test_results_best-checkpoint.json"""    )
    assert os.path.exists(snake_case__    )

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

    if accelerator.is_main_process:
      os.makedirs(snake_case__		,   exist_ok=snake_case__    )
      shutil.copy(snake_case__		,   os.path.join(snake_case__		,   F"eval_results_iter-{iteration}.json"    )    )
      if os.path.exists(snake_case__    ):
        shutil.copy(snake_case__		,   os.path.join(snake_case__		,   F"test_results_iter-{iteration}.json"    )    )
      create_pseudo_labeled_data(snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__    )
    accelerator.wait_for_everyone()

    _snake_case							:		Any								=  os.path.join(snake_case__		,   F"train_pseudo.{args.data_file_extension}"    )

    if args.evaluation_strategy != IntervalStrategy.NO.value:
      _snake_case							:		Union[str, Any]								=  eval_result

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

        if early_stopping_patience_counter >= args.early_stopping_patience:
          _snake_case							:		str								=  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"""		,   snake_case__    )
    logger.info("""Best evaluation result: %s = %f"""		,   args.eval_metric		,   snake_case__    )
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      shutil.copy(
          os.path.join(snake_case__		,   F"eval_results_iter-{iteration}.json"    )		,   os.path.join(snake_case__		,   """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		,   snake_case__    )
    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      shutil.copy(
          os.path.join(snake_case__		,   F"eval_results_iter-{args.max_selftrain_iterations - 1}.json"    )		,   os.path.join(snake_case__		,   """eval_results_best-iteration.json"""    )		,   )






import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__lowerCAmelCase	: Union[str, Any]  				={
    '<': operator.lt,
    '<=': operator.le,
    '==': operator.eq,
    '!=': operator.ne,
    '>=': operator.ge,
    '>': operator.gt,
}


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      ):
						if got_ver is None or want_ver is None:
												raise ValueError(
												    F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
												    F''' reinstalling {pkg}.'''      )
						if not ops[op](version.parse(lowercase__      )    ,       version.parse(lowercase__      )      ):
												raise ImportError(
												    F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}'''      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__ = None      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		F'''\n{hint}''' if hint is not None else ''''''

						# non-versioned check
						if re.match(R'''^[\w_\-\d]+$'''    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     str					=		requirement, None, None
						else:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		re.findall(R'''^([^!=<>\s]+)([\s!=<>]{1,2}.+)'''    ,       lowercase__      )
												if not match:
																		raise ValueError(
																		    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'''
																		    F''' got {requirement}'''      )
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		match[0]
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		want_full.split(''','''      )  # there could be multiple requirements
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		{}
												for w in want_range:
																		__SCREAMING_SNAKE_CASE						:     Any					=		re.findall(R'''^([\s!=<>]{1,2})(.+)'''    ,       lowercase__      )
																		if not match:
																								raise ValueError(
																								    '''requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'''
																								    F''' but got {requirement}'''      )
																		__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		match[0]
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		want_ver
																		if op not in ops:
																								raise ValueError(F'''{requirement}: need one of {list(ops.keys()      )}, but got {op}'''      )

    # special case
						if pkg == "python":
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		'''.'''.join([str(lowercase__      ) for x in sys.version_info[:3]]      )
												for op, want_ver in wanted.items():
																		_compare_versions(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )
												return

						# check if any version is installed
						try:
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		importlib.metadata.version(lowercase__      )
						except importlib.metadata.PackageNotFoundError:
												raise importlib.metadata.PackageNotFoundError(
												    F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}'''      )

						# check that the right version is installed if version number or a range was provided
						if want_ver is not None:
												for op, want_ver in wanted.items():
																		_compare_versions(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		'''Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'''
						return require_version(lowercase__    ,       lowercase__      )
