




from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowerCamelCase__			:       Optional[Any]         =							"""\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

lowerCamelCase__			:       int         =							"""\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

lowerCamelCase__			:       Any         =							"""
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     List[str]							) ->			str:
						return float((preds == labels).mean()							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Optional[Any]      ,      __UpperCAmelCase						:     Union[str, Any]							) ->			Optional[int]:
						SCREAMING_SNAKE_CASE_     						=      simple_accuracy(__UpperCAmelCase      ,      __UpperCAmelCase							)
						SCREAMING_SNAKE_CASE_     						=      float(fa_score(y_true=__UpperCAmelCase      ,      y_pred=__UpperCAmelCase							)							)
						return {
						    "accuracy": acc,
						    "f1": fa,
						}

def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     str							) ->			List[Any]:
						SCREAMING_SNAKE_CASE_     						=      float(pearsonr(__UpperCAmelCase      ,      __UpperCAmelCase							)[0]							)
						SCREAMING_SNAKE_CASE_     						=      float(spearmanr(__UpperCAmelCase      ,      __UpperCAmelCase							)[0]							)
						return {
						    "pearson": pearson_corr,
						    "spearmanr": spearman_corr,
						}





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


	'''simple docstring'''







	def 				lowerCAmelCase_  (							self  :		Tuple						):

							if self.config_name not in [
							    "sst2",
							    "mnli",
							    "mnli_mismatched",
							    "mnli_matched",
							    "cola",
							    "stsb",
							    "mrpc",
							    "qqp",
							    "qnli",
							    "rte",
							    "wnli",
							    "hans",
							]:
													raise KeyError(
													    'You should supply a configuration name selected in '
													    '[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", '
													    '\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]'						)
							return datasets.MetricInfo(
							    description=_DESCRIPTION    ,    citation=_CITATION    ,    inputs_description=_KWARGS_DESCRIPTION    ,    features=datasets.Features(
							        {
							            'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'						),
							            'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'						),
							        }						)    ,    codebase_urls=[]    ,    reference_urls=[]    ,    format='numpy'    ,    )




	def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		Tuple						):

							if self.config_name == "cola":
													return {"matthews_correlation": matthews_corrcoef(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE						)}
							elif self.config_name == "stsb":
													return pearson_and_spearman(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE						)
							elif self.config_name in ["mrpc", "qqp"]:
													return acc_and_fa(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE						)
							elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
													return {"accuracy": simple_accuracy(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE						)}
							else:
													raise KeyError(
													    'You should supply a configuration name selected in '
													    '[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", '
													    '\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]'						)





def  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     int							) ->			int:
      return int((input_a, input_a).count(0							) != 0							)

def  UpperCAmelCase_							(				) ->			None:
      assert nand_gate(0      ,      0							) == 1
      assert nand_gate(0      ,      1							) == 1
      assert nand_gate(1      ,      0							) == 1
      assert nand_gate(1      ,      1							) == 0


if __name__ == "__main__":
   print(nand_gate(0, 0))
   print(nand_gate(0, 1))
   print(nand_gate(1, 0))
   print(nand_gate(1, 1))