

from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       return DownloadCommand(args.model	,args.cache_dir	,args.force	,args.trust_remote_code		)




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       @staticmethod
       def 			a		(		_UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							parser.add_parser("""download"""   )
              download_parser.add_argument(
                  """--cache-dir"""      , type=_UpperCamelCase      , default=_UpperCamelCase      , help="""Path to location to store the models"""   )
              download_parser.add_argument(
                  """--force"""      , action="""store_true"""      , help="""Force the model to be download even if already in cache-dir"""   )
              download_parser.add_argument(
                  """--trust-remote-code"""      , action="""store_true"""      , help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""      , )
              download_parser.add_argument("""model"""      , type=_UpperCamelCase      , help="""Name of the model to download"""   )
              download_parser.set_defaults(func=_UpperCamelCase   )


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							model
              __snake_case       	=							cache
              __snake_case       	=							force
              __snake_case       	=							trust_remote_code






       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              from ..models.auto import AutoModel, AutoTokenizer

              AutoModel.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )
              AutoTokenizer.from_pretrained(
                  self._model      , cache_dir=self._cache      , force_download=self._force      , trust_remote_code=self._trust_remote_code   )




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__		 =     logging.getLogger(__name__)







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :str		):


       """simple docstring"""




       return (preds == labels).mean()

@dataclass
class     __snake_case    :
       """simple docstring"""







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






@dataclass
class     __snake_case    :
       """simple docstring"""







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







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
       __snake_case	,    __snake_case	,    __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"""	,__A		)

       # Set seed
       set_seed(training_args.seed		)

       try:
              __snake_case       	=							processors[data_args.task_name]()
              __snake_case       	=							processor.get_labels()
              __snake_case       	=							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.

       __snake_case       	=							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	,)
       __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	,)
       __snake_case       	=							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
       __snake_case       	=							(
           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
       )
       __snake_case       	=							(
           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:
              __snake_case       	=							np.argmax(p.predictions	,axis=1		)
              return {"acc": simple_accuracy(__A	,p.label_ids		)}

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

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

              __snake_case       	=							trainer.evaluate()

              __snake_case       	=							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 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       main()


if __name__ == "__main__":
    main()

