
'''simple docstring'''



import datasets


UpperCamelCase__      :		int       							=  "\\n@InProceedings{conneau2018xnli,\n  author = \"Conneau, Alexis\n                 and Rinott, Ruty\n                 and Lample, Guillaume\n                 and Williams, Adina\n                 and Bowman, Samuel R.\n                 and Schwenk, Holger\n                 and Stoyanov, Veselin\",\n  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\n  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\n               in Natural Language Processing\",\n  year = \"2018\",\n  publisher = \"Association for Computational Linguistics\",\n  location = \"Brussels, Belgium\",\n}\n"

UpperCamelCase__      :		Tuple       							=  "\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n"

UpperCamelCase__      :		str       							=  "\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    'accuracy': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric(\"xnli\")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n"
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   return (preds == labels).mean()







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





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			int:
          return datasets.MetricInfo(
              description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
                  {
                      """predictions""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""      ),
                      """references""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""      ),
                  }      )       ,							codebase_urls=[]       ,							reference_urls=[]       ,							format="""numpy"""       ,							)






       def        UpperCamelCase				(							self       ,							A__       ,							A__      )    ->			Tuple:
          return {"accuracy": simple_accuracy(A__       ,							A__      )}






'''simple docstring'''



UpperCamelCase__      :		Dict       							=  {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


UpperCamelCase__      :		str       							=  {value: key for key, value in encode_dict.items()}
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				""""""
   for letter in word.lower():
      if letter.isalpha() or letter == " ":
         encoded += encode_dict[letter]
      else:
         raise Exception("""encode() accepts only letters of the alphabet and spaces"""						)
   return encoded
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   if set(SCREAMING_SNAKE_CASE_						) - {"A", "B", " "} != set():
      raise Exception("""decode() accepts only 'A', 'B' and spaces"""						)
   _SCREAMING_SNAKE_CASE      =				""""""
   for word in coded.split():
      while len(SCREAMING_SNAKE_CASE_						) != 0:
         decoded += decode_dict[word[:5]]
         _SCREAMING_SNAKE_CASE      =				word[5:]
      decoded += " "
   return decoded.strip()


if __name__ == "__main__":
     from doctest import testmod

     testmod()






