"""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


__SCREAMING_SNAKE_CASE								=logging.getLogger(__name__)

__SCREAMING_SNAKE_CASE								="pytorch_model.bin"







@dataclasses.dataclass
class  UpperCamelCase	:
   lowercase							=						dataclasses.field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'}						)
   lowercase							=						dataclasses.field(
       default=lowercase_		,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}		,   )







@dataclasses.dataclass
class  UpperCamelCase	:
   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=lowercase_		,   metadata={'help': 'A csv or a json file containing the validation data.'}						)
   lowercase							=						dataclasses.field(
       default=lowercase_		,   metadata={'help': 'The name of the task to train on.'}		,   )
   lowercase							=						dataclasses.field(
       default=lowercase_		,   metadata={'help': 'The list of labels for the task.'}						)







@dataclasses.dataclass
class  UpperCamelCase	:
   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=1_0		,   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=lowercase_		,   metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}		,   )
   lowercase							=						dataclasses.field(
       default=lowercase_		,   metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}		,   )
   lowercase							=						dataclasses.field(
       default=lowercase_		,   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_0_0		,   metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}		,   )
   lowercase							=						dataclasses.field(
       default=lowercase_		,   metadata={'help': 'Random seed for initialization.'}		,   )






def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Tuple  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    __SCREAMING_SNAKE_CASE							:  Any   ):
       lowercase_			:       Optional[int]									=			datasets.concatenate_datasets([infer_input, infer_output]  ,    axis=1   )

       if args.do_filter_by_confidence:
              lowercase_			:       Tuple									=			dataset.filter(lambda __SCREAMING_SNAKE_CASE   : example["probability"] > args.confidence_threshold   )

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

       lowercase_			:       str									=			dataset.remove_columns(['label', 'probability']   )
       lowercase_			:       str									=			dataset.rename_column('prediction'  ,    'label'   )
       lowercase_			:       Tuple									=			dataset.map(lambda __SCREAMING_SNAKE_CASE   : {"label": idalabel[example["label"]]}   )
       lowercase_			:       str									=			dataset.shuffle(seed=args.seed   )

       lowercase_			:       Union[str, Any]									=			os.path.join(__SCREAMING_SNAKE_CASE  ,    F'''train_pseudo.{args.data_file_extension}'''   )
       if args.data_file_extension == "csv":
              dataset.to_csv(__SCREAMING_SNAKE_CASE  ,    index=__SCREAMING_SNAKE_CASE   )
       else:
              dataset.to_json(__SCREAMING_SNAKE_CASE   )







def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Dict  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,    **__SCREAMING_SNAKE_CASE							:  List[str]   ):
       lowercase_			:       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()

       lowercase_			:       Optional[Any]									=			STModelArguments(model_name_or_path=__SCREAMING_SNAKE_CASE   )
       lowercase_			:       List[str]									=			STDataArguments(train_file=__SCREAMING_SNAKE_CASE  ,    infer_file=__SCREAMING_SNAKE_CASE   )
       lowercase_			:       Dict									=			STTrainingArguments(output_dir=__SCREAMING_SNAKE_CASE   )
       lowercase_			:       List[Any]									=			argparse.Namespace()

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

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

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

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

       for key in data_files:
              lowercase_			:       Union[str, 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:
                     lowercase_			:       Any									=			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...'   )
       lowercase_			:       Optional[Any]									=			F'''{args.output_dir}/self-train_iter-{{}}'''.format
       lowercase_			:       Any									=			data_dir_format(0   )

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

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

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

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

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

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

              lowercase_			:       str									=			iteration
              lowercase_			:       int									=			data_dir_format(iteration + 1   )

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

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

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

              lowercase_			:       Optional[int]									=			os.path.join(__SCREAMING_SNAKE_CASE  ,    F'''train_pseudo.{args.data_file_extension}'''   )

              if args.evaluation_strategy != IntervalStrategy.NO.value:
                     lowercase_			:       List[Any]									=			eval_result

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

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


"""simple docstring"""







# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__SCREAMING_SNAKE_CASE								="2.13.1"

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse("3.7"):
       raise ImportWarning(
           "To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
       )

if version.parse(pyarrow.__version__).major < 8:
       raise ImportWarning(
           "To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
           "If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
       )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__SCREAMING_SNAKE_CASE								=concatenate_datasets
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadManager
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadConfig
__SCREAMING_SNAKE_CASE								=DownloadMode
__SCREAMING_SNAKE_CASE								=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager

