






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






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






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





def 	_a(					UpperCamelCase__			:  Dict,		UpperCamelCase__			:  List[str],		UpperCamelCase__			:  int,		UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  Optional[int]							):




      '''simple docstring'''






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

      if args.do_filter_by_confidence:
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=dataset.filter(lambda UpperCamelCase__							: example["probability"] > args.confidence_threshold							)

      if args.do_filter_by_val_performance:
            assert eval_result >= 0.0 and eval_result <= 1.0
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=int(eval_result * len(UpperCamelCase__							)							)
            print(UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__       :			Tuple													=dataset.sort('''probability''',		reverse=UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__       :			List[str]													=dataset.select(range(UpperCamelCase__							)							)

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

      SCREAMING_SNAKE_CASE__       :			Dict													=os.path.join(UpperCamelCase__,		f"train_pseudo.{args.data_file_extension}"							)
      if args.data_file_extension == "csv":
            dataset.to_csv(UpperCamelCase__,		index=UpperCamelCase__							)
      else:
            dataset.to_json(UpperCamelCase__							)







def 	_a(					UpperCamelCase__			:  int,		UpperCamelCase__			:  Any,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  Dict,		**UpperCamelCase__			:  Tuple							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			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()

      SCREAMING_SNAKE_CASE__       :			Optional[int]													=STModelArguments(model_name_or_path=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Tuple													=STDataArguments(train_file=UpperCamelCase__,		infer_file=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			str													=STTrainingArguments(output_dir=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			int													=argparse.Namespace()

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

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

    # Sanity checks
      SCREAMING_SNAKE_CASE__       :			int													={}
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=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
      SCREAMING_SNAKE_CASE__       :			Tuple													=args.train_file
      SCREAMING_SNAKE_CASE__       :			List[Any]													=args.infer_file

      if args.evaluation_strategy != IntervalStrategy.NO.value:
            assert args.eval_file is not None
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=args.eval_file

      for key in data_files:
            SCREAMING_SNAKE_CASE__       :			List[Any]													=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:
                  SCREAMING_SNAKE_CASE__       :			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...'''							)
      SCREAMING_SNAKE_CASE__       :			List[Any]													=f"{args.output_dir}/self-train_iter-{{}}".format
      SCREAMING_SNAKE_CASE__       :			List[str]													=data_dir_format(0							)

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

      SCREAMING_SNAKE_CASE__       :			int													=None
      SCREAMING_SNAKE_CASE__       :			Tuple													=None
      SCREAMING_SNAKE_CASE__       :			str													=0
      SCREAMING_SNAKE_CASE__       :			Any													=False
      # Show the progress bar
      SCREAMING_SNAKE_CASE__       :			int													=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							)							):
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=data_dir_format(UpperCamelCase__							)
            assert os.path.exists(UpperCamelCase__							)

            # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
            # iteration > 0
            SCREAMING_SNAKE_CASE__       :			str													=os.path.join(UpperCamelCase__,		'''stage-1'''							)
            SCREAMING_SNAKE_CASE__       :			Dict													={
                '''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(UpperCamelCase__,		UpperCamelCase__							):
                        arguments_dict.update({key: value}							)

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

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

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

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

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

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

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

            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=os.path.join(UpperCamelCase__,		f"train_pseudo.{args.data_file_extension}"							)

            if args.evaluation_strategy != IntervalStrategy.NO.value:
                  SCREAMING_SNAKE_CASE__       :			Dict													=eval_result

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

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







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					=	logging.get_logger(__name__)

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






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							"""biogpt"""




 def __init__( self		:							str   , __lowercase		:							Union[str, Any]=4_23_84   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							Any=24   , __lowercase		:							Any=16   , __lowercase		:							Optional[Any]=40_96   , __lowercase		:							Any="gelu"   , __lowercase		:							Optional[Any]=0.1   , __lowercase		:							List[Any]=0.1   , __lowercase		:							Union[str, Any]=10_24   , __lowercase		:							List[Any]=0.02   , __lowercase		:							Tuple=1e-12   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Optional[Any]=True   , __lowercase		:							Any=0.0   , __lowercase		:							int=0.0   , __lowercase		:							str=1   , __lowercase		:							int=0   , __lowercase		:							Optional[int]=2   , **__lowercase		:							Dict   , )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=vocab_size
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=max_position_embeddings
       SCREAMING_SNAKE_CASE__       :			str													=hidden_size
       SCREAMING_SNAKE_CASE__       :			int													=num_hidden_layers
       SCREAMING_SNAKE_CASE__       :			Tuple													=num_attention_heads
       SCREAMING_SNAKE_CASE__       :			Any													=intermediate_size
       SCREAMING_SNAKE_CASE__       :			int													=hidden_act
       SCREAMING_SNAKE_CASE__       :			List[Any]													=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Dict													=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=initializer_range
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=layer_norm_eps
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=scale_embedding
       SCREAMING_SNAKE_CASE__       :			str													=use_cache
       SCREAMING_SNAKE_CASE__       :			str													=layerdrop
       SCREAMING_SNAKE_CASE__       :			Dict													=activation_dropout
       super().__init__(pad_token_id=__lowercase   , bos_token_id=__lowercase   , eos_token_id=__lowercase   , **__lowercase )