
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			:     int           =   logging.getLogger(__name__)

__lowerCAmelCase			:     List[Any]           =   'pytorch_model.bin'




@dataclasses.dataclass
class        UpperCAmelCase_			:


  '''simple docstring'''







  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        UpperCAmelCase_			:


  '''simple docstring'''







  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        UpperCAmelCase_			:


  '''simple docstring'''







  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=1_00  ,       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 a__    (       A_,     A_,     A_,     A_,     A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       datasets.concatenate_datasets([infer_input, infer_output],     axis=1					)

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

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

  __magic_name__				     =       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 a__    (       A_,     A_,     A_,     A_,     **A_					):






  '''simple docstring'''





  __magic_name__				     =       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()

  __magic_name__				     =       STModelArguments(model_name_or_path=A_					)
  __magic_name__				     =       STDataArguments(train_file=A_,     infer_file=A_					)
  __magic_name__				     =       STTrainingArguments(output_dir=A_					)
  __magic_name__				     =       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
  __magic_name__				     =       {}
  __magic_name__				     =       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
  __magic_name__				     =       args.train_file
  __magic_name__				     =       args.infer_file

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

  for key in data_files:
    __magic_name__				     =       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:
      __magic_name__				     =       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..."""					)
  __magic_name__				     =       f'''{args.output_dir}/self-train_iter-{{}}'''.format
  __magic_name__				     =       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()

  __magic_name__				     =       None
  __magic_name__				     =       None
  __magic_name__				     =       0
  __magic_name__				     =       False
  # Show the progress bar
  __magic_name__				     =       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					)					):
    __magic_name__				     =       data_dir_format(A_					)
    assert os.path.exists(A_					)

    # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
    # iteration > 0
    __magic_name__				     =       os.path.join(A_,     """stage-1"""					)
    __magic_name__				     =       {
        """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}					)

    __magic_name__				     =       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
      __magic_name__				     =       os.path.join(A_,     """best-checkpoint"""					)
      __magic_name__				     =       os.path.join(A_,     """stage-2"""					)
      # Update arguments_dict
      __magic_name__				     =       model_path
      __magic_name__				     =       data_files["""train"""]
      __magic_name__				     =       current_output_dir

      __magic_name__				     =       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_					)

    __magic_name__				     =       iteration
    __magic_name__				     =       data_dir_format(iteration + 1					)

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

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

    __magic_name__				     =       os.path.join(A_,     f'''train_pseudo.{args.data_file_extension}'''					)

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

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

        if early_stopping_patience_counter >= args.early_stopping_patience:
          __magic_name__				     =       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"""					),     )



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			:     List[Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     int           =   {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""biogpt"""
  def __init__( self			: List[str]			,							UpperCamelCase__			: Optional[Any]=4_2384			,							UpperCamelCase__			: Union[str, Any]=1024			,							UpperCamelCase__			: Any=24			,							UpperCamelCase__			: Union[str, Any]=16			,							UpperCamelCase__			: Tuple=4096			,							UpperCamelCase__			: Tuple="gelu"			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: str=1024			,							UpperCamelCase__			: Any=0.02			,							UpperCamelCase__			: List[str]=1E-12			,							UpperCamelCase__			: Dict=True			,							UpperCamelCase__			: Optional[int]=True			,							UpperCamelCase__			: Union[str, Any]=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Dict=0			,							UpperCamelCase__			: List[str]=2			,							**UpperCamelCase__			: Optional[int]			,							)			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       initializer_range
    __magic_name__				     =       layer_norm_eps
    __magic_name__				     =       scale_embedding
    __magic_name__				     =       use_cache
    __magic_name__				     =       layerdrop
    __magic_name__				     =       activation_dropout
    super().__init__(pad_token_id=UpperCamelCase__			,							bos_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							**UpperCamelCase__    )

