






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

import datasets


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

a		:       Dict			     = "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

a		:       Any			     = "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"






def 				lowerCamelCase__      (    __lowerCamelCase			:     Union[str, Any]  ,		__lowerCamelCase			:     List[str]		):
  return float((preds == labels).mean()		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Any  ,		__lowerCamelCase			:     List[str]		):
  __UpperCAmelCase							:	Union[str, Any]           =  simple_accuracy(snake_case_  ,		snake_case_		)
  __UpperCAmelCase							:	Optional[int]           =  float(fa_score(y_true=snake_case_  ,		y_pred=snake_case_		)		)
  return {
      "accuracy": acc,
      "f1": fa,
  }






def 				lowerCamelCase__      (    __lowerCamelCase			:     List[str]  ,		__lowerCamelCase			:     str		):
  __UpperCAmelCase							:	int           =  float(pearsonr(snake_case_  ,		snake_case_		)[0]		)
  __UpperCAmelCase							:	Dict           =  float(spearmanr(snake_case_  ,		snake_case_		)[0]		)
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
  }







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



      """simple docstring"""


      def 	UpperCAmelCase      (      self					:			Dict					)							->      int:




        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 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			Optional[Any]		,      __lowercase					:			Any					)							->      str:




        if self.config_name == "cola":
          return {"matthews_correlation": matthews_corrcoef(__lowerCamelCase		,      __lowerCamelCase					)}
        elif self.config_name == "stsb":
          return pearson_and_spearman(__lowerCamelCase		,      __lowerCamelCase					)
        elif self.config_name in ["mrpc", "qqp"]:
          return acc_and_fa(__lowerCamelCase		,      __lowerCamelCase					)
        elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
          return {"accuracy": simple_accuracy(__lowerCamelCase		,      __lowerCamelCase					)}
        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\"]"""					)









"""simple docstring"""



def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						List[str]						 =     set()
    # edges = list of graph's edges
    __A    :						Optional[int]						 =     get_edges(snake_case_						)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        __A ,       __A    :						str						 =     edges.pop()
        chosen_vertices.add(snake_case_						)
        chosen_vertices.add(snake_case_						)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case_						)
    return chosen_vertices







def 		__lowercase     (   snake_case_  :  dict						)   ->set:



    '''simple docstring'''







    __A    :						Tuple						 =     set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)						)
    return edges


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
       # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")




