


"""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


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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




     '''simple docstring'''


     return (preds == labels).mean()




@dataclass
class 				UpperCamelCase_			:

 """simple docstring"""





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




@dataclass
class 				UpperCamelCase_			:

 """simple docstring"""





 snake_case__       :						str					      =    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
 snake_case__       :						str					      =    field(metadata={"help": "Should contain the data files for the task."})
 snake_case__       :						int					      =    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."
         )
     }    ,						)
 snake_case__       :						bool					      =    field(
     default=UpperCamelCase    ,						metadata={"help": "Overwrite the cached training and evaluation sets"})




def      UpperCAmelCase__ ():




     '''simple docstring'''


     __SCREAMING_SNAKE_CASE		     =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
     __SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       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"						,		lowerCAmelCase_     )

     # Set seed
     set_seed(training_args.seed     )

     try:
          __SCREAMING_SNAKE_CASE		     =       processors[data_args.task_name]()
          __SCREAMING_SNAKE_CASE		     =       processor.get_labels()
          __SCREAMING_SNAKE_CASE		     =       len(lowerCAmelCase_     )
     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		     =       AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path						,		num_labels=lowerCAmelCase_						,		finetuning_task=data_args.task_name						,		cache_dir=model_args.cache_dir						,		)
     __SCREAMING_SNAKE_CASE		     =       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		     =       AutoModelForMultipleChoice.from_pretrained(
         model_args.model_name_or_path						,		from_tf=bool(".ckpt" in model_args.model_name_or_path     )						,		config=lowerCAmelCase_						,		cache_dir=model_args.cache_dir						,		)

     # Get datasets
     __SCREAMING_SNAKE_CASE		     =       (
         MultipleChoiceDataset(
             data_dir=data_args.data_dir						,		tokenizer=lowerCAmelCase_						,		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		     =       (
         MultipleChoiceDataset(
             data_dir=data_args.data_dir						,		tokenizer=lowerCAmelCase_						,		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(lowerCAmelCase_     ) -> Dict:
          __SCREAMING_SNAKE_CASE		     =       np.argmax(p.predictions						,		axis=1     )
          return {"acc": simple_accuracy(lowerCAmelCase_						,		p.label_ids     )}

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

     # Initialize our Trainer
     __SCREAMING_SNAKE_CASE		     =       Trainer(
         model=lowerCAmelCase_						,		args=lowerCAmelCase_						,		train_dataset=lowerCAmelCase_						,		eval_dataset=lowerCAmelCase_						,		compute_metrics=lowerCAmelCase_						,		data_collator=lowerCAmelCase_						,		)

     # 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		     =       {}
     if training_args.do_eval:
          logger.info("*** Evaluate ***"     )

          __SCREAMING_SNAKE_CASE		     =       trainer.evaluate()

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

                    results.update(lowerCAmelCase_     )

     return results




def      UpperCAmelCase__ (lowerCAmelCase_     ):




     '''simple docstring'''


     main()


if __name__ == "__main__":
  main()




"""simple docstring"""







from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	@abstractmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       ArgumentParser     ) ->			Optional[Any]:
						raise NotImplementedError()




	@abstractmethod
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[str]:
						raise NotImplementedError()
