
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


lowercase__       =logging.getLogger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ,   lowerCAmelCase__							:				List[Any]  ):
 return (preds == labels).mean()



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





@dataclass
class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE      :							str	       =      field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()    )}    )
   _SCREAMING_SNAKE_CASE      :							List[Any]	       =      field(metadata={"help": "Should contain the data files for the task."}    )
   _SCREAMING_SNAKE_CASE      :							Tuple	       =      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."
           )
       }    ,)
   _SCREAMING_SNAKE_CASE      :							str	       =      field(
       default=__lowercase    ,metadata={"help": "Overwrite the cached training and evaluation sets"}    )





def    __UpperCamelCase     (				):
 __a :  int						=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
 __a				,       __a				,       __a :  str						=  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:
  __a :  Union[str, Any]						=  processors[data_args.task_name]()
  __a :  int						=  processor.get_labels()
  __a :  str						=  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.

 __a :  Union[str, Any]						=  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  ,   )
 __a :  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  ,   )
 __a :  Optional[int]						=  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
 __a :  Tuple						=  (
     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
 )
 __a :  Union[str, Any]						=  (
     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__							:				EvalPrediction  ) -> Dict:
  __a :  str						=  np.argmax(p.predictions  ,   axis=1  )
  return {"acc": simple_accuracy(lowerCAmelCase__  ,   p.label_ids  )}

 # Data collator
 __a :  str						=  DataCollatorWithPadding(lowerCAmelCase__  ,   pad_to_multiple_of=8  ) if training_args.fpaa else None

 # Initialize our Trainer
 __a :  Any						=  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
 __a :  Optional[int]						=  {}
 if training_args.do_eval:
  logger.info('''*** Evaluate ***'''  )

  __a :  Any						=  trainer.evaluate()

  __a :  Optional[int]						=  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__							:				str  ):
 main()


if __name__ == "__main__":
      main()






import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


lowerCamelCase				:		Union[str, Any]     =   logging.get_logger(__name__)







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  int						,							*A_					:  str						,							**A_					:  Optional[int]  )   ->	None:

       """simple docstring"""






       warnings.warn(
           'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
           ' use GLPNImageProcessor instead.'						,							A_						,							)
       super().__init__(*A_						,							**A_  )



