'''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__     :	Dict				=			logging.getLogger(__name__)







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Any    ):
   '''simple docstring'''
   return (preds == labels).mean()


@dataclass
class 				_lowercase	:
      '''simple docstring'''


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




@dataclass
class 				_lowercase	:
      '''simple docstring'''


      UpperCAmelCase_      :      str      							=   field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()   )}   )
      UpperCAmelCase_      :      str      							=   field(metadata={'''help''': '''Should contain the data files for the task.'''}   )
      UpperCAmelCase_      :      int      							=   field(
          default=1_28		,metadata={
              '''help''': (
                  '''The maximum total input sequence length after tokenization. Sequences longer '''
                  '''than this will be truncated, sequences shorter will be padded.'''
              )
          }		,)
      UpperCAmelCase_      :      bool      							=   field(
          default=UpperCAmelCase_		,metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )







def __UpperCamelCase(   ):
   '''simple docstring'''
   # 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__							:				List[str]   				=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
   UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				Any   				=  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'''  ,			__UpperCamelCase    )

   # Set seed
   set_seed(training_args.seed    )

   try:
      UpperCAmelCase__							:				Union[str, Any]   				=  processors[data_args.task_name]()
      UpperCAmelCase__							:				int   				=  processor.get_labels()
      UpperCAmelCase__							:				int   				=  len(__UpperCamelCase    )
   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__							:				List[Any]   				=  AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,			num_labels=__UpperCamelCase  ,			finetuning_task=data_args.task_name  ,			cache_dir=model_args.cache_dir  ,			)
   UpperCAmelCase__							:				List[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  ,			)
   UpperCAmelCase__							:				Union[str, Any]   				=  AutoModelForMultipleChoice.from_pretrained(
       model_args.model_name_or_path  ,			from_tf=bool('''.ckpt''' in model_args.model_name_or_path    )  ,			config=__UpperCamelCase  ,			cache_dir=model_args.cache_dir  ,			)

   # Get datasets
   UpperCAmelCase__							:				str   				=  (
       MultipleChoiceDataset(
           data_dir=data_args.data_dir  ,			tokenizer=__UpperCamelCase  ,			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__							:				Union[str, Any]   				=  (
       MultipleChoiceDataset(
           data_dir=data_args.data_dir  ,			tokenizer=__UpperCamelCase  ,			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					:				str    ) -> Dict:
      UpperCAmelCase__							:				List[str]   				=  np.argmax(p.predictions  ,			axis=1    )
      return {"acc": simple_accuracy(__UpperCamelCase  ,			p.label_ids    )}

   # Data collator
   UpperCAmelCase__							:				Optional[Any]   				=  DataCollatorWithPadding(__UpperCamelCase  ,			pad_to_multiple_of=8    ) if training_args.fpaa else None

   # Initialize our Trainer
   UpperCAmelCase__							:				List[str]   				=  Trainer(
       model=__UpperCamelCase  ,			args=__UpperCamelCase  ,			train_dataset=__UpperCamelCase  ,			eval_dataset=__UpperCamelCase  ,			compute_metrics=__UpperCamelCase  ,			data_collator=__UpperCamelCase  ,			)

   # 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__							:				Optional[int]   				=  {}
   if training_args.do_eval:
      logger.info('''*** Evaluate ***'''    )

      UpperCAmelCase__							:				Any   				=  trainer.evaluate()

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

            results.update(__UpperCamelCase    )

   return results







def __UpperCamelCase(   _A					:				List[str]    ):
   '''simple docstring'''
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()





'''simple docstring'''

from __future__ import annotations

from collections import namedtuple







def __UpperCamelCase(   _A					:				float  ,			_A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  namedtuple('''result'''  ,			'''name value'''    )
   if (voltage, current, power).count(0    ) != 1:
      raise ValueError('''Only one argument must be 0'''    )
   elif power < 0:
      raise ValueError(
          '''Power cannot be negative in any electrical/electronics system'''    )
   elif voltage == 0:
      return result('''voltage'''  ,			power / current    )
   elif current == 0:
      return result('''current'''  ,			power / voltage    )
   elif power == 0:
      return result('''power'''  ,			float(round(abs(voltage * current    )  ,			2    )    )    )
   else:
      raise ValueError('''Exactly one argument must be 0'''    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()




