

import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
      from scipy.stats import pearsonr, spearmanr
      from sklearn.metrics import fa_score, matthews_corrcoef


lowerCAmelCase__       = (
    "This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate "
    "library. You can have a look at this example script for pointers: "
    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)
def      _lowerCAmelCase(	__A     :   Optional[Any]   ,       __A     :   List[Any]			):
  warnings.warn(__A   ,       __A			)
  requires_backends(__A   ,       "sklearn"			)
  return (preds == labels).mean()
def      _lowerCAmelCase(	__A     :   str   ,       __A     :   List[Any]			):
  warnings.warn(__A   ,       __A			)
  requires_backends(__A   ,       "sklearn"			)
  UpperCAmelCase					      =  simple_accuracy(__A   ,       __A			)
  UpperCAmelCase					      =  fa_score(y_true=__A   ,       y_pred=__A			)
  return {
      "acc": acc,
      "f1": fa,
      "acc_and_f1": (acc + fa) / 2,
  }
def      _lowerCAmelCase(	__A     :   List[str]   ,       __A     :   Optional[int]			):
  warnings.warn(__A   ,       __A			)
  requires_backends(__A   ,       "sklearn"			)
  UpperCAmelCase					      =  pearsonr(__A   ,       __A			)[0]
  UpperCAmelCase					      =  spearmanr(__A   ,       __A			)[0]
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
      "corr": (pearson_corr + spearman_corr) / 2,
  }
def      _lowerCAmelCase(	__A     :   Tuple   ,       __A     :   Optional[int]   ,       __A     :   Optional[int]			):
  warnings.warn(__A   ,       __A			)
  requires_backends(__A   ,       "sklearn"			)
  assert len(__A			) == len(__A			), F"Predictions and labels have mismatched lengths {len(__A			)} and {len(__A			)}"
  if task_name == "cola":
    return {"mcc": matthews_corrcoef(__A   ,       __A			)}
  elif task_name == "sst-2":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "mrpc":
    return acc_and_fa(__A   ,       __A			)
  elif task_name == "sts-b":
    return pearson_and_spearman(__A   ,       __A			)
  elif task_name == "qqp":
    return acc_and_fa(__A   ,       __A			)
  elif task_name == "mnli":
    return {"mnli/acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "mnli-mm":
    return {"mnli-mm/acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "qnli":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "rte":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "wnli":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  elif task_name == "hans":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  else:
    raise KeyError(__A			)





def      _lowerCAmelCase(	__A     :   int   ,       __A     :   Tuple   ,       __A     :   Optional[int]			):
  warnings.warn(__A   ,       __A			)
  requires_backends(__A   ,       "sklearn"			)
  if len(__A			) != len(__A			):
    raise ValueError(F"Predictions and labels have mismatched lengths {len(__A			)} and {len(__A			)}"			)
  if task_name == "xnli":
    return {"acc": simple_accuracy(__A   ,       __A			)}
  else:
    raise KeyError(__A			)






lowerCAmelCase__       = {
    "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",
    " ": " ",
}


lowerCAmelCase__       = {value: key for key, value in encode_dict.items()}
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  ""
  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(	__A			):
  if set(__A			) - {"A", "B", " "} != set():
    raise Exception("decode() accepts only 'A', 'B' and spaces"			)
  UpperCAmelCase					      =  ""
  for word in coded.split():
    while len(__A			) != 0:
      decoded += decode_dict[word[:5]]
      UpperCAmelCase					      =  word[5:]
    decoded += " "
  return decoded.strip()


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

      testmod()



