


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      = logging.getLogger(__name__)





def __UpperCAmelCase       (		__A ,						__A			)						->					str:



       '''simple docstring'''





       return (preds == labels).mean()


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




@dataclass
class     lowercase__  :
  A__=  field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()		)}		)
  A__=  field(metadata={'help': 'Should contain the data files for the task.'}		)
  A__=  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.'
          )
      }					,							)
  A__=  field(
      default=__UpperCAmelCase					,							metadata={'help': 'Overwrite the cached training and evaluation sets'}		)





def __UpperCAmelCase       (		)						->					Optional[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
       UpperCAmelCase__						,			UpperCAmelCase__						,			UpperCAmelCase__										=     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:
              UpperCAmelCase__										=     processors[data_args.task_name]()
              UpperCAmelCase__										=     processor.get_labels()
              UpperCAmelCase__										=     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.

       UpperCAmelCase__										=     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 ,						)
       UpperCAmelCase__										=     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 ,						)
       UpperCAmelCase__										=     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
       UpperCAmelCase__										=     (
           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
       )
       UpperCAmelCase__										=     (
           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(__A			) -> Dict:
              UpperCAmelCase__										=     np.argmax(p.predictions ,						axis=1			)
              return {"acc": simple_accuracy(__lowerCAmelCase ,						p.label_ids			)}

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

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

              UpperCAmelCase__										=     trainer.evaluate()

              UpperCAmelCase__										=     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       (		__A			)						->					Dict:



       '''simple docstring'''





       main()


if __name__ == "__main__":
    main()









from .imports import is_tqdm_available


if is_tqdm_available():
    from tqdm.auto import tqdm as _tqdm

from ..state import PartialState





def __UpperCAmelCase       (		__A = True ,						*__A ,						**__A			)						->					Any:



       '''simple docstring'''





       if not is_tqdm_available():
              raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."			)
       UpperCAmelCase__										=     False
       if main_process_only:
              UpperCAmelCase__										=     PartialState().local_process_index == 0
       return _tqdm(*__A ,						**__A ,						disable=__A			)





