





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


UpperCAmelCase_   :				Any						      = logging.getLogger(__name__)

UpperCAmelCase_   :				List[Any]						      = 'pytorch_model.bin'
@dataclasses.dataclass
class 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  Dict       =		dataclasses.field(
          metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}    )
      snake_case__       :  Optional[Any]       =		dataclasses.field(
          default=_A		,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}		,					)
@dataclasses.dataclass
class 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  Union[str, Any]       =		dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}    )
      snake_case__       :  Dict       =		dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}    )
      snake_case__       :  List[str]       =		dataclasses.field(
          default=_A		,					metadata={'''help''': '''A csv or a json file containing the validation data.'''}    )
      snake_case__       :  Optional[Any]       =		dataclasses.field(
          default=_A		,					metadata={'''help''': '''The name of the task to train on.'''}		,					)
      snake_case__       :  List[Any]       =		dataclasses.field(
          default=_A		,					metadata={'''help''': '''The list of labels for the task.'''}    )
@dataclasses.dataclass
class 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  List[Any]       =		dataclasses.field(
          metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}    )
      snake_case__       :  int       =		dataclasses.field(
          default='''accuracy'''		,					metadata={'''help''': '''The evaluation metric used for the task.'''}    )
      snake_case__       :  Any       =		dataclasses.field(
          default='''no'''		,					metadata={
              '''help''': '''The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]'''
          }		,					)
      snake_case__       :  List[str]       =		dataclasses.field(
          default=10		,					metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}		,					)
      snake_case__       :  Union[str, Any]       =		dataclasses.field(
          default=0.0		,					metadata={
              '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
          }		,					)
      snake_case__       :  Any       =		dataclasses.field(
          default=_A		,					metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}		,					)
      snake_case__       :  Optional[int]       =		dataclasses.field(
          default=_A		,					metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}		,					)
      snake_case__       :  List[Any]       =		dataclasses.field(
          default=_A		,					metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}		,					)
      snake_case__       :  str       =		dataclasses.field(
          default=0.0		,					metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}		,					)
      snake_case__       :  List[Any]       =		dataclasses.field(
          default=100		,					metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}		,					)
      snake_case__       :  Union[str, Any]       =		dataclasses.field(
          default=_A		,					metadata={'''help''': '''Random seed for initialization.'''}		,					)







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    Tuple						,					__A				:    Dict						,					__A				:    Union[str, Any]						,					__A				:    Optional[int]						,					__A				:    List[str]       )			->	Any:

       """simple docstring"""

       a_      :	Tuple							=						datasets.concatenate_datasets([infer_input, infer_output]						,					axis=1       )

       if args.do_filter_by_confidence:
              a_      :	Optional[int]							=						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
              a_      :	List[str]							=						int(eval_result * len(A_       )       )
              print(A_       )
              a_      :	Optional[int]							=						dataset.sort('probability'						,					reverse=A_       )
              a_      :	int							=						dataset.select(range(A_       )       )

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

       a_      :	Tuple							=						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  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    Union[str, Any]						,					__A				:    List[str]						,					__A				:    List[Any]						,					**__A				:    Optional[int]       )			->	str:

       """simple docstring"""

       a_      :	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()

       a_      :	List[str]							=						STModelArguments(model_name_or_path=A_       )
       a_      :	Tuple							=						STDataArguments(train_file=A_						,					infer_file=A_       )
       a_      :	Optional[Any]							=						STTrainingArguments(output_dir=A_       )
       a_      :	Optional[Any]							=						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
       a_      :	Tuple							=						{}
       a_      :	Tuple							=						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
       a_      :	str							=						args.train_file
       a_      :	List[Any]							=						args.infer_file

       if args.evaluation_strategy != IntervalStrategy.NO.value:
              assert args.eval_file is not None
              a_      :	List[Any]							=						args.eval_file

       for key in data_files:
              a_      :	Dict							=						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:
                     a_      :	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...'       )
       a_      :	Union[str, Any]							=						F"""{args.output_dir}/self-train_iter-{{}}""".format
       a_      :	int							=						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()

       a_      :	List[Any]							=						None
       a_      :	Tuple							=						None
       a_      :	List[str]							=						0
       a_      :	Tuple							=						False
       # Show the progress bar
       a_      :	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       )       ):
              a_      :	Tuple							=						data_dir_format(A_       )
              assert os.path.exists(A_       )

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

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

                     a_      :	Tuple							=						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_       )

              a_      :	List[str]							=						iteration
              a_      :	int							=						data_dir_format(iteration + 1       )

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

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

              a_      :	Union[str, Any]							=						os.path.join(A_						,					F"""train_pseudo.{args.data_file_extension}"""       )

              if args.evaluation_strategy != IntervalStrategy.NO.value:
                     a_      :	List[str]							=						eval_result

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

                            if early_stopping_patience_counter >= args.early_stopping_patience:
                                   a_      :	Dict							=						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 .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCAmelCase_   :				Dict						      = [
    'python',
    'tqdm',
    'regex',
    'requests',
    'packaging',
    'filelock',
    'numpy',
    'tokenizers',
    'huggingface-hub',
    'safetensors',
    'accelerate',
    'pyyaml',
]

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed
      elif pkg == "accelerate":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_accelerate_available

         # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
         # Transformers with PyTorch
         if not is_accelerate_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(F'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int=None       )			->	int:

       """simple docstring"""

       require_version(deps[pkg]						,					__A       )


