




'''simple docstring'''

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


A_      =							logging.get_logger(__name__)


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset

    from transformers import Trainer





    class 							_snake_case	(    _lowerCAmelCase  ):
       def __init__(	self     : List[Any]       ,SCREAMING_SNAKE_CASE__     : int = 101							):
         SCREAMING_SNAKE_CASE:Dict   =   length
       def __len__(	self     : Optional[int]							):
         return self.length







       def __getitem__(	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]							):
         return i





    class 							_snake_case	:
       def __call__(	self     : Tuple       ,SCREAMING_SNAKE_CASE__     : Dict							):
         return {"input_ids": torch.tensor(_lowerCamelCase							), "labels": torch.tensor(_lowerCamelCase							)}





    class 							_snake_case	(    nn.Module  ):
       def __init__(	self     : List[str]							):
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         SCREAMING_SNAKE_CASE:str   =   nn.Linear(120       ,80							)







       def       __UpperCamelCase  (	self     : Optional[Any]       ,SCREAMING_SNAKE_CASE__     : Tuple       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=None							):
         if labels is not None:
           return torch.tensor(0.0       ,device=input_ids.device							), input_ids
         else:
           return input_ids





def 				A_					(	):
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   HfArgumentParser((TrainingArguments,)	)
  sys.argv += ["--output_dir", "./examples"]
  SCREAMING_SNAKE_CASE:str   =   parser.parse_args_into_dataclasses()[0]

  logger.warning(
      F'''Process rank: {training_args.local_rank}, device: {training_args.device}, '''
      F'''tpu_num_cores: {training_args.tpu_num_cores}''' ,    )

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [1001, 256, 15]:
    SCREAMING_SNAKE_CASE:int   =   DummyDataset(__UpperCamelCase	)

    def compute_metrics(snake_case	) -> Dict:
      SCREAMING_SNAKE_CASE:Tuple   =   list(range(len(__UpperCamelCase	)	)	)
      SCREAMING_SNAKE_CASE:Any   =   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    SCREAMING_SNAKE_CASE:Optional[int]   =   Trainer(
        model=DummyModel() ,    args=__UpperCamelCase ,    data_collator=DummyDataCollator() ,    eval_dataset=__UpperCamelCase ,    compute_metrics=__UpperCamelCase ,    )
    SCREAMING_SNAKE_CASE:Any   =   trainer.evaluate()
    logger.info(__UpperCamelCase	)
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase	)
      exit(1	)

    SCREAMING_SNAKE_CASE:Union[str, Any]   =   trainer.predict(__UpperCamelCase	)
    logger.info(p.metrics	)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics	)
      exit(1	)

    SCREAMING_SNAKE_CASE:List[str]   =   2

    SCREAMING_SNAKE_CASE:Optional[Any]   =   trainer.evaluate()
    logger.info(__UpperCamelCase	)
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase	)
      exit(1	)

    SCREAMING_SNAKE_CASE:Optional[Any]   =   trainer.predict(__UpperCamelCase	)
    logger.info(p.metrics	)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics	)
      exit(1	)

    SCREAMING_SNAKE_CASE:Optional[int]   =   None

  logger.info("🔥 All distributed tests successful"	)





def 				A_					(	snake_case	):
  main()


if __name__ == "__main__":
    main()









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_      =							{"configuration_yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A_      =							[
        "YOSO_PRETRAINED_MODEL_ARCHIVE_LIST",
        "YosoForMaskedLM",
        "YosoForMultipleChoice",
        "YosoForQuestionAnswering",
        "YosoForSequenceClassification",
        "YosoForTokenClassification",
        "YosoLayer",
        "YosoModel",
        "YosoPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_yoso import (
            YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
            YosoForMaskedLM,
            YosoForMultipleChoice,
            YosoForQuestionAnswering,
            YosoForSequenceClassification,
            YosoForTokenClassification,
            YosoLayer,
            YosoModel,
            YosoPreTrainedModel,
        )


else:
    import sys

    A_      =							_LazyModule(__name__, globals()["__file__"], _import_structure)






