


'''simple docstring'''


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      _UpperCamelCase          =     None

_UpperCamelCase          =     logging.get_logger(__name__)

_UpperCamelCase          =     {"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase          =     {
    """vocab_file""": {
        """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"""
        ),
        """microsoft/deberta-v2-xxlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"""
        ),
    }
}

_UpperCamelCase          =     {
    """microsoft/deberta-v2-xlarge""": 512,
    """microsoft/deberta-v2-xxlarge""": 512,
    """microsoft/deberta-v2-xlarge-mnli""": 512,
    """microsoft/deberta-v2-xxlarge-mnli""": 512,
}

_UpperCamelCase          =     {
    """microsoft/deberta-v2-xlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xlarge-mnli""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge-mnli""": {"""do_lower_case""": False},
}







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							VOCAB_FILES_NAMES
     lowerCamelCase__						=							PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__						=							PRETRAINED_INIT_CONFIGURATION
     lowerCamelCase__						=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__						=							DebertaVaTokenizer


     def __init__(      self ,    lowerCamelCase=None ,    lowerCamelCase=None ,    lowerCamelCase=False ,    lowerCamelCase=False ,    lowerCamelCase="[CLS]" ,    lowerCamelCase="[SEP]" ,    lowerCamelCase="[UNK]" ,    lowerCamelCase="[SEP]" ,    lowerCamelCase="[PAD]" ,    lowerCamelCase="[CLS]" ,    lowerCamelCase="[MASK]" ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__(
              lowerCamelCase ,    tokenizer_file=lowerCamelCase ,    do_lower_case=lowerCamelCase ,    bos_token=lowerCamelCase ,    eos_token=lowerCamelCase ,    unk_token=lowerCamelCase ,    sep_token=lowerCamelCase ,    pad_token=lowerCamelCase ,    cls_token=lowerCamelCase ,    mask_token=lowerCamelCase ,    split_by_punct=lowerCamelCase ,    **lowerCamelCase ,    )

          __A  :					Any						     =       do_lower_case
          __A  :					int						     =       split_by_punct
          __A  :					Optional[int]						     =       vocab_file
          __A  :					Union[str, Any]						     =       False if not self.vocab_file else True


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase=None		):


          '''simple docstring'''





          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __A  :					Dict						     =       [self.cls_token_id]
          __A  :					Optional[int]						     =       [self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase=None ,    lowerCamelCase=False		):


          '''simple docstring'''





          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase ,    token_ids_a=lowerCamelCase ,    already_has_special_tokens=lowerCamelCase		)

          if token_ids_a is not None:
               return [1] + ([0] * len(lowerCamelCase		)) + [1] + ([0] * len(lowerCamelCase		)) + [1]
          return [1] + ([0] * len(lowerCamelCase		)) + [1]


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase=None		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       [self.sep_token_id]
          __A  :					Tuple						     =       [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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

          if not os.path.isdir(lowerCamelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          __A  :					Tuple						     =       os.path.join(
              lowerCamelCase ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

          if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		):
               copyfile(self.vocab_file ,    lowerCamelCase		)

          return (out_vocab_file,)





'''simple docstring'''


from sklearn.metrics import accuracy_score

import datasets


_UpperCamelCase          =     """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


_UpperCamelCase          =     """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


_UpperCamelCase          =     """
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,		_KWARGS_DESCRIPTION   )
class        __magic_name__  (					datasets.Metric   ):

     """simple docstring"""






     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return datasets.MetricInfo(
              description=_DESCRIPTION ,    citation=_CITATION ,    inputs_description=_KWARGS_DESCRIPTION ,    features=datasets.Features(
                  {
                      "predictions": datasets.Sequence(datasets.Value("int32"		)		),
                      "references": datasets.Sequence(datasets.Value("int32"		)		),
                  }
                  if self.config_name == "multilabel"
                  else {
                      "predictions": datasets.Value("int32"		),
                      "references": datasets.Value("int32"		),
                  }		) ,    reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"] ,    )


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase=True ,    lowerCamelCase=None		):


          '''simple docstring'''





          return {
              "accuracy": float(
                  accuracy_score(lowerCamelCase ,    lowerCamelCase ,    normalize=lowerCamelCase ,    sample_weight=lowerCamelCase		)		)
          }

