






"""simple docstring"""






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


UpperCAmelCase_   : List[str]   			= logging.getLogger(__name__)



def 					_A			(__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    return (preds == labels).mean()




@dataclass
class 							lowerCAmelCase__				:






      '''simple docstring'''

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




@dataclass
class 							lowerCAmelCase__				:






      '''simple docstring'''

      __UpperCamelCase	 =  field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
      __UpperCamelCase	 =  field(metadata={"help": "Should contain the data files for the task."} )
      __UpperCamelCase	 =  field(
          default=1_2_8				,		metadata={
              "help": (
                  "The maximum total input sequence length after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              )
          }				,		)
      __UpperCamelCase	 =  field(
          default=__snake_case				,		metadata={"help": "Overwrite the cached training and evaluation sets"} )



def 					_A			() ->       List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  Any    						=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		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:
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		processors[data_args.task_name]()
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		processor.get_labels()
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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.

    SCREAMING_SNAKE_CASE_      :  Dict    						=		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    ,				)
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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    ,				)
    SCREAMING_SNAKE_CASE_      :  str    						=		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
    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		(
        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
    )
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		(
        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(__a							) -> Dict:
        SCREAMING_SNAKE_CASE_      :  List[str]    						=		np.argmax(p.predictions    ,				axis=1							)
        return {"acc": simple_accuracy(__SCREAMING_SNAKE_CASE    ,				p.label_ids							)}

    # Data collator
    SCREAMING_SNAKE_CASE_      :  int    						=		DataCollatorWithPadding(__SCREAMING_SNAKE_CASE    ,				pad_to_multiple_of=8							) if training_args.fpaa else None

    # Initialize our Trainer
    SCREAMING_SNAKE_CASE_      :  Tuple    						=		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
    SCREAMING_SNAKE_CASE_      :  str    						=		{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''							)

        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		trainer.evaluate()

        SCREAMING_SNAKE_CASE_      :  Any    						=		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 					_A			(__a							) ->       str:
    """simple docstring"""






    main()


if __name__ == "__main__":
  main()





"""simple docstring"""


import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union





@dataclass
class     snake_case	:
				SCREAMING_SNAKE_CASE_   :      Optional[Union[str, Path]]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      Optional[Dict]         =  None
				SCREAMING_SNAKE_CASE_   :      Optional[str]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      bool         =  True
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  None
				SCREAMING_SNAKE_CASE_   :      int         =  1
				SCREAMING_SNAKE_CASE_   :      Optional[Union[str, bool]]         =  None
				SCREAMING_SNAKE_CASE_   :      bool         =  False
				SCREAMING_SNAKE_CASE_   :      Optional[Dict]         =  None
				SCREAMING_SNAKE_CASE_   :      Optional[str]         =  None

				def 							lowercase_     (	self							:				str)->   "DownloadConfig":



						'''simple docstring'''


						return self.__class__(**{k: copy.deepcopy(UpperCamelCase__) for k, v in self.__dict__.items()})


