





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__A							:			Any         =			logging.getLogger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    return (preds == labels).mean()







@dataclass
class      lowerCamelCase:
     '''simple docstring'''


     __magic_name__   =			field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}	)
     __magic_name__   =			field(
         default=__snake_case			, metadata={'help': 'Pretrained config name or path if not the same as model_name'}	)
     __magic_name__   =			field(
         default=__snake_case			, metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}	)
     __magic_name__   =			field(
         default=__snake_case			, metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}			, )







@dataclass
class      lowerCamelCase:
     '''simple docstring'''


     __magic_name__   =			field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()	)}	)
     __magic_name__   =			field(metadata={'help': 'Should contain the data files for the task.'}	)
     __magic_name__   =			field(
         default=128			, metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }			, )
     __magic_name__   =			field(
         default=__snake_case			, metadata={'help': 'Overwrite the cached training and evaluation sets'}	)





def 				__lowerCAmelCase(       )							-> Union[str, Any]:




    """simple docstring"""




    _A     =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
    _A,			_A,			_A     =							parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir				)
        and os.listdir(training_args.output_dir				)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
            ' --overwrite_output_dir to overcome.'				)

    # Setup logging
    logging.basicConfig(
        format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,     datefmt='%m/%d/%Y %H:%M:%S'							,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,     )
    logger.warning(
        'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'							,     training_args.local_rank							,     training_args.device							,     training_args.n_gpu							,     bool(training_args.local_rank != -1				)							,     training_args.fpaa							,     )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank				):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info('Training/evaluation parameters %s'							,     _SCREAMING_SNAKE_CASE				)

    # Set seed
    set_seed(training_args.seed				)

    try:
        _A     =							processors[data_args.task_name]()
        _A     =							processor.get_labels()
        _A     =							len(_SCREAMING_SNAKE_CASE				)
    except KeyError:
        raise ValueError('Task not found: %s' % (data_args.task_name)				)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    _A     =							AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path							,     num_labels=_SCREAMING_SNAKE_CASE							,     finetuning_task=data_args.task_name							,     cache_dir=model_args.cache_dir							,     )
    _A     =							AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,     cache_dir=model_args.cache_dir							,     )
    _A     =							AutoModelForMultipleChoice.from_pretrained(
        model_args.model_name_or_path							,     from_tf=bool('.ckpt' in model_args.model_name_or_path				)							,     config=_SCREAMING_SNAKE_CASE							,     cache_dir=model_args.cache_dir							,     )

    # Get datasets
    _A     =							(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir							,     tokenizer=_SCREAMING_SNAKE_CASE							,     task=data_args.task_name							,     max_seq_length=data_args.max_seq_length							,     overwrite_cache=data_args.overwrite_cache							,     mode=Split.train							,     )
        if training_args.do_train
        else None
    )
    _A     =							(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir							,     tokenizer=_SCREAMING_SNAKE_CASE							,     task=data_args.task_name							,     max_seq_length=data_args.max_seq_length							,     overwrite_cache=data_args.overwrite_cache							,     mode=Split.dev							,     )
        if training_args.do_eval
        else None
    )

    def compute_metrics(_SCREAMING_SNAKE_CASE				) -> Dict:
        _A     =							np.argmax(p.predictions							,     axis=1				)
        return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE							,     p.label_ids				)}

    # Data collator
    _A     =							DataCollatorWithPadding(_SCREAMING_SNAKE_CASE							,     pad_to_multiple_of=8				) if training_args.fpaa else None

    # Initialize our Trainer
    _A     =							Trainer(
        model=_SCREAMING_SNAKE_CASE							,     args=_SCREAMING_SNAKE_CASE							,     train_dataset=_SCREAMING_SNAKE_CASE							,     eval_dataset=_SCREAMING_SNAKE_CASE							,     compute_metrics=_SCREAMING_SNAKE_CASE							,     data_collator=_SCREAMING_SNAKE_CASE							,     )

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
    _A     =							{}
    if training_args.do_eval:
        logger.info('*** Evaluate ***'				)

        _A     =							trainer.evaluate()

        _A     =							os.path.join(training_args.output_dir							,     'eval_results.txt'				)
        if trainer.is_world_master():
            with open(_SCREAMING_SNAKE_CASE							,     'w'				) as writer:
                logger.info('***** Eval results *****'				)
                for key, value in result.items():
                    logger.info('  %s = %s'							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
                    writer.write('%s = %s\n' % (key, value)				)

                results.update(_SCREAMING_SNAKE_CASE				)

    return results





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[str]:




    """simple docstring"""




    main()


if __name__ == "__main__":
 main()












def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> float:




    """simple docstring"""




    return price * (1 + tax_rate)


if __name__ == "__main__":
 print(f"{price_plus_tax(100, 0.2_5) = }")
 print(f"{price_plus_tax(1_2_5.5_0, 0.0_5) = }")





