
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


_lowerCamelCase					   =logging.getLogger(__name__)





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 return (preds == labels).mean()





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





@dataclass
class 							A__		:
      _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=128  ,   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=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""})





def     _a					(						):
 # 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.

 lowerCamelCase		:	str							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
 lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[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""",	lowerCamelCase							)

 # Set seed
 set_seed(training_args.seed							)

 try:
  lowerCamelCase		:	List[Any]							=      processors[data_args.task_name]()
  lowerCamelCase		:	List[Any]							=      processor.get_labels()
  lowerCamelCase		:	Any							=      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.

 lowerCamelCase		:	int							=      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,	)
 lowerCamelCase		:	int							=      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,	)
 lowerCamelCase		:	Dict							=      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
 lowerCamelCase		:	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
 )
 lowerCamelCase		:	int							=      (
     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							) -> Dict:
  lowerCamelCase		:	Optional[Any]							=      np.argmax(p.predictions,	axis=1							)
  return {"acc": simple_accuracy(lowerCamelCase,	p.label_ids							)}

 # Data collator
 lowerCamelCase		:	Tuple							=      DataCollatorWithPadding(lowerCamelCase,	pad_to_multiple_of=8							) if training_args.fpaa else None

 # Initialize our Trainer
 lowerCamelCase		:	str							=      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
 lowerCamelCase		:	List[Any]							=      {}
 if training_args.do_eval:
  logger.info("""*** Evaluate ***"""							)

  lowerCamelCase		:	Tuple							=      trainer.evaluate()

  lowerCamelCase		:	Optional[Any]							=      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     _a					(						lowerCamelCase							):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()






def     _a					(						lowerCamelCase							):
 return " ".join(
     """""".join(word[::-1]							) if len(lowerCamelCase							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(reverse_long_words("""Hey wollef sroirraw"""))




