




'''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     =logging.getLogger(__name__)





def 						snake_case_							(_a	:							Dict    ,   _a	:							Union[str, Any]     ):
    return (preds == labels).mean()


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






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






def 						snake_case_							():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    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'''    ,   _a     )

    # Set seed
    set_seed(training_args.seed     )

    try:
        UpperCAmelCase			       =		processors[data_args.task_name]()
        UpperCAmelCase			       =		processor.get_labels()
        UpperCAmelCase			       =		len(_a     )
    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=_a    ,   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=_a    ,   cache_dir=model_args.cache_dir    ,   )

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

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

    # Initialize our Trainer
    UpperCAmelCase			       =		Trainer(
        model=_a    ,   args=_a    ,   train_dataset=_a    ,   eval_dataset=_a    ,   compute_metrics=_a    ,   data_collator=_a    ,   )

    # 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(_a    ,   '''w'''     ) as writer:
                logger.info('''***** Eval results *****'''     )
                for key, value in result.items():
                    logger.info('''  %s = %s'''    ,   _a    ,   _a     )
                    writer.write('''%s = %s\n''' % (key, value)     )

                results.update(_a     )

    return results





def 						snake_case_							(_a	:							Optional[int]     ):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
   main()











'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       int:


     '''simple docstring'''



     if not isinstance(__lowercase			,     __lowercase       ) or number < 0:
          raise ValueError("Input must be a non-negative integer"       )

     _UpperCAmelCase     							=   0
     while number:
          # This way we arrive at next set bit (next 1) instead of looping
          # through each bit and checking for 1s hence the
          # loop won't run 32 times it will only run the number of `1` times
          number &= number - 1
          count += 1
     return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()




