



"""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						:		List[Any]			= logging.getLogger(__name__)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						str:







  '''simple docstring'''


  return (preds == labels).mean()


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







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







def 			_SCREAMING_SNAKE_CASE						() ->						Optional[Any]:







  '''simple docstring'''


  lowercase_								= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
  lowercase_					,    lowercase_					,    lowercase_								= 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:
    lowercase_								= processors[data_args.task_name]()
    lowercase_								= processor.get_labels()
    lowercase_								= 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.

  lowercase_								= 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 ,  )
  lowercase_								= 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 ,  )
  lowercase_								= 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
  lowercase_								= (
      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
  )
  lowercase_								= (
      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:
    lowercase_								= np.argmax(p.predictions ,  axis=1							)
    return {"acc": simple_accuracy(__lowerCAmelCase ,  p.label_ids							)}

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

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

    lowercase_								= trainer.evaluate()

    lowercase_								= 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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Dict:







  '''simple docstring'''


  main()


if __name__ == "__main__":
       main()






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase						:		Union[str, Any]			= {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= ["LongformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LongformerForMaskedLM",
           "LongformerForMultipleChoice",
           "LongformerForQuestionAnswering",
           "LongformerForSequenceClassification",
           "LongformerForTokenClassification",
           "LongformerModel",
           "LongformerPreTrainedModel",
           "LongformerSelfAttention",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= [
           "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFLongformerForMaskedLM",
           "TFLongformerForMultipleChoice",
           "TFLongformerForQuestionAnswering",
           "TFLongformerForSequenceClassification",
           "TFLongformerForTokenClassification",
           "TFLongformerModel",
           "TFLongformerPreTrainedModel",
           "TFLongformerSelfAttention",
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       UpperCAmelCase						:		Optional[Any]			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

