






import sys
from typing import Dict

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


_snake_case					=					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      UpperCAmelCase_ ( _lowerCAmelCase   ):

      '''simple docstring'''




      def __init__(		self						,    __A = 101			):






        """simple docstring"""





        lowerCamelCase			: Optional[int]   =    length






      def __len__(		self			):






        """simple docstring"""





        return self.length






      def __getitem__(		self						,    __A			):






        """simple docstring"""





        return i



   class      UpperCAmelCase_ :

      '''simple docstring'''




      def __call__(		self						,    __A			):






        """simple docstring"""





        return {"input_ids": torch.tensor(_lowerCamelCase			), "labels": torch.tensor(_lowerCamelCase			)}



   class      UpperCAmelCase_ ( nn.Module   ):

      '''simple docstring'''




      def __init__(		self			):






        """simple docstring"""





        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        lowerCamelCase			: Any   =    nn.Linear(120						,    80			)






      def _snake_case   (		self						,    __A						,    __A=None			):






        """simple docstring"""





        if labels is not None:
          return torch.tensor(0.0						,    device=input_ids.device			), input_ids
        else:
          return input_ids






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase			: Optional[int]   =    HfArgumentParser((TrainingArguments,)     )
  sys.argv += ["--output_dir", "./examples"]
  lowerCamelCase			: List[Any]   =    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]:
    lowerCamelCase			: int   =    DummyDataset(__UpperCamelCase     )

    def compute_metrics(SCREAMING_SNAKE_CASE_     ) -> Dict:
      lowerCamelCase			: Optional[int]   =    list(range(len(__UpperCamelCase     )     )     )
      lowerCamelCase			: Union[str, Any]   =    p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

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

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

    lowerCamelCase			: Dict   =    2

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

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

    lowerCamelCase			: int   =    None

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  main()


if __name__ == "__main__":
   main()







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


_snake_case					=					logging.get_logger(__name__)

_snake_case					=					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_snake_case					=					{
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

_snake_case					=					{
    '''microsoft/mpnet-base''': 5_12,
}

_snake_case					=					{
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}
class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				List[str]							=		VOCAB_FILES_NAMES
   __A			:				int							=		PRETRAINED_VOCAB_FILES_MAP
   __A			:				str							=		PRETRAINED_INIT_CONFIGURATION
   __A			:				int							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A			:				Any							=		MPNetTokenizer
   __A			:				Dict							=		["input_ids", "attention_mask"]






   def __init__(		self						,    __A=None						,    __A=None						,    __A=True						,    __A="<s>"						,    __A="</s>"						,    __A="</s>"						,    __A="<s>"						,    __A="[UNK]"						,    __A="<pad>"						,    __A="<mask>"						,    __A=True						,    __A=None						,    **__A						,    ):






     """simple docstring"""





     super().__init__(
         __A						,    tokenizer_file=__A						,    do_lower_case=__A						,    bos_token=__A						,    eos_token=__A						,    sep_token=__A						,    cls_token=__A						,    unk_token=__A						,    pad_token=__A						,    mask_token=__A						,    tokenize_chinese_chars=__A						,    strip_accents=__A						,    **__A						,    )

     lowerCamelCase			: List[Any]   =    json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
     if (
         pre_tok_state.get("lowercase"						,    __A			) != do_lower_case
         or pre_tok_state.get("strip_accents"						,    __A			) != strip_accents
     ):
       lowerCamelCase			: Dict   =    getattr(__A						,    pre_tok_state.pop("type"			)			)
       lowerCamelCase			: List[str]   =    do_lower_case
       lowerCamelCase			: Tuple   =    strip_accents
       lowerCamelCase			: int   =    pre_tok_class(**__A			)

     lowerCamelCase			: List[Any]   =    do_lower_case






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     if self._mask_token is None:
       if self.verbose:
         logger.error("Using mask_token, but it is not set yet."			)
       return None
     return str(self._mask_token			)






   @mask_token.setter
   def _snake_case   (		self						,    __A			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    AddedToken(__A						,    lstrip=__A						,    rstrip=__A			) if isinstance(__A						,    __A			) else value
     lowerCamelCase			: Dict   =    value






   def _snake_case   (		self						,    __A						,    __A=None			):






     """simple docstring"""





     lowerCamelCase			: Optional[Any]   =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






   def _snake_case   (		self						,    __A						,    __A = None			):






     """simple docstring"""





     lowerCamelCase			: Any   =    [self.sep_token_id]
     lowerCamelCase			: Union[str, Any]   =    [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep			) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]






   def _snake_case   (		self						,    __A						,    __A = None			):






     """simple docstring"""





     lowerCamelCase			: int   =    self._tokenizer.model.save(__A						,    name=__A			)
     return tuple(__A			)



