



import sys
from typing import Dict

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


__lowerCamelCase								=   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			):





     """simple docstring"""



     def __init__(  self		,      a = 1_0_1   )		->							List[Any]:




       """simple docstring"""







       _A          =      length
     def __len__(  self   )		->							Any:




       """simple docstring"""







       return self.length



     def __getitem__(  self		,      a   )		->							Optional[Any]:




       """simple docstring"""







       return i







    class 						_snake_case :





     """simple docstring"""



     def __call__(  self		,      a   )		->							int:




       """simple docstring"""







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







    class 						_snake_case (     nn.Module			):





     """simple docstring"""



     def __init__(  self   )		->							List[str]:




       """simple docstring"""







       super().__init__()
       # Add some (unused) params otherwise DDP will complain.
       _A          =      nn.Linear(1_2_0		,      8_0   )



     def lowercase_						(  self		,      a		,      a=None   )		->							Dict:




       """simple docstring"""







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




def       UpperCAmelCase__	(							)				->	Union[str, Any]:
  _A          =      HfArgumentParser((TrainingArguments,)							)
  sys.argv += ["--output_dir", "./examples"]
  _A          =      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 [1_001, 256, 15]:
    _A          =      DummyDataset(__UpperCamelCase							)

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

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

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

    _A          =      2

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

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

    _A          =      None

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




def       UpperCAmelCase__	(							__snake_case							)				->	Optional[int]:
  main()


if __name__ == "__main__":
    main()




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__lowerCamelCase								=   logging.get_logger(__name__)

__lowerCamelCase								=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase								=   {
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

__lowerCamelCase								=   {
    """microsoft/deberta-base""": 5_1_2,
    """microsoft/deberta-large""": 5_1_2,
    """microsoft/deberta-xlarge""": 5_1_2,
    """microsoft/deberta-base-mnli""": 5_1_2,
    """microsoft/deberta-large-mnli""": 5_1_2,
    """microsoft/deberta-xlarge-mnli""": 5_1_2,
}

__lowerCamelCase								=   {
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     VOCAB_FILES_NAMES
 lowerCamelCase_       =     PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase_       =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase_       =     ['''input_ids''', '''attention_mask''', '''token_type_ids''']
 lowerCamelCase_       =     DebertaTokenizer
 def __init__(  self		,      a=None		,      a=None		,      a=None		,      a="replace"		,      a="[CLS]"		,      a="[SEP]"		,      a="[SEP]"		,      a="[CLS]"		,      a="[UNK]"		,      a="[PAD]"		,      a="[MASK]"		,      a=False		,      **a		,      )		->							Any:




   """simple docstring"""







   super().__init__(
       a		,      a		,      tokenizer_file=a		,      errors=a		,      bos_token=a		,      eos_token=a		,      unk_token=a		,      sep_token=a		,      cls_token=a		,      pad_token=a		,      mask_token=a		,      add_prefix_space=a		,      **a		,      )
   _A          =      kwargs.pop('''add_bos_token'''		,      a   )

   _A          =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
   if pre_tok_state.get('''add_prefix_space'''		,      a   ) != add_prefix_space:
     _A          =      getattr(a		,      pre_tok_state.pop('''type'''   )   )
     _A          =      add_prefix_space
     _A          =      pre_tok_class(**a   )

   _A          =      add_prefix_space
 @property
 def lowercase_						(  self   )		->							str:




   """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 lowercase_						(  self		,      a   )		->							Dict:




   """simple docstring"""







   _A          =      AddedToken(a		,      lstrip=a		,      rstrip=a   ) if isinstance(a		,      a   ) else value
   _A          =      value
 def lowercase_						(  self		,      a		,      a = None   )		->							List[int]:




   """simple docstring"""







   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   _A          =      [self.cls_token_id]
   _A          =      [self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep
 def lowercase_						(  self		,      a		,      a = None   )		->							List[int]:




   """simple docstring"""







   _A          =      [self.sep_token_id]
   _A          =      [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   ) * [0] + len(token_ids_a + sep   ) * [1]
 def lowercase_						(  self		,      *a		,      **a   )		->							BatchEncoding:




   """simple docstring"""







   _A          =      kwargs.get('''is_split_into_words'''		,      a   )
   assert self.add_prefix_space or not is_split_into_words, (
       f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*a		,      **a   )
 def lowercase_						(  self		,      *a		,      **a   )		->							BatchEncoding:




   """simple docstring"""







   _A          =      kwargs.get('''is_split_into_words'''		,      a   )

   assert self.add_prefix_space or not is_split_into_words, (
       f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*a		,      **a   )
 def lowercase_						(  self		,      a		,      a = None   )		->							Tuple[str]:




   """simple docstring"""







   _A          =      self._tokenizer.model.save(a		,      name=a   )
   return tuple(a   )



 def lowercase_						(  self		,      a   )		->							List[int]:




   """simple docstring"""







   _A          =      []
   for is_user, text in conversation.iter_texts():
     input_ids.extend(self.encode(a		,      add_special_tokens=a   ) + [self.eos_token_id]   )

   if len(a   ) > self.model_max_length:
     _A          =      input_ids[-self.model_max_length :]
   return input_ids