'''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


_a   :	Any 							=   logging.getLogger(__name__)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


       return (preds == labels).mean()






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






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





def _lowercase	(      )					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Any       				=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
       __UpperCAmelCase  :				List[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:
              __UpperCAmelCase  :				Tuple       				=		processors[data_args.task_name]()
              __UpperCAmelCase  :				str       				=		processor.get_labels()
              __UpperCAmelCase  :				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.

       __UpperCAmelCase  :				Tuple       				=		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							,    )
       __UpperCAmelCase  :				Tuple       				=		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  :				List[Any]       				=		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
       __UpperCAmelCase  :				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.train							,    )
           if training_args.do_train
           else None
       )
       __UpperCAmelCase  :				List[str]       				=		(
           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:
              __UpperCAmelCase  :				int       				=		np.argmax(p.predictions							,    axis=1						)
              return {"acc": simple_accuracy(lowerCamelCase__							,    p.label_ids						)}

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

       # Initialize our Trainer
       __UpperCAmelCase  :				Dict       				=		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
       __UpperCAmelCase  :				Optional[int]       				=		{}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"						)

              __UpperCAmelCase  :				int       				=		trainer.evaluate()

              __UpperCAmelCase  :				List[str]       				=		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 _lowercase	(      lowerCamelCase__						)					->       int:





       """simple docstring"""


       main()


if __name__ == "__main__":
   main()







'''simple docstring'''




from typing import TYPE_CHECKING

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


_a   :	Dict 							=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   ["ReformerTokenizer"]

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ReformerAttention",
       "ReformerForMaskedLM",
       "ReformerForQuestionAnswering",
       "ReformerForSequenceClassification",
       "ReformerLayer",
       "ReformerModel",
       "ReformerModelWithLMHead",
       "ReformerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

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






