






"""simple docstring"""





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

import datasets


_SCREAMING_SNAKE_CASE					:				List[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}
}
'''

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

_SCREAMING_SNAKE_CASE					:				Dict					    =						'''
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      lowerCamelCase__       (							_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		str )      ->				Any:
  return float((preds == labels).mean() )







def      lowerCamelCase__       (							_lowerCamelCase       :		Dict		,				_lowerCamelCase       :		Any )      ->				List[str]:
  lowerCamelCase_         =						simple_accuracy(_lowerCamelCase		,				_lowerCamelCase )
  lowerCamelCase_         =						float(fa_score(y_true=_lowerCamelCase		,				y_pred=_lowerCamelCase ) )
  return {
      "accuracy": acc,
      "f1": fa,
  }







def      lowerCamelCase__       (							_lowerCamelCase       :		Tuple		,				_lowerCamelCase       :		Optional[int] )      ->				List[Any]:
  lowerCamelCase_         =						float(pearsonr(_lowerCamelCase		,				_lowerCamelCase )[0] )
  lowerCamelCase_         =						float(spearmanr(_lowerCamelCase		,				_lowerCamelCase )[0] )
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
  }






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






    def    UpperCamelCase       (       self     :							Any					)     ->		List[str]:
      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     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		str:
      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"]'					)












"""simple docstring"""





from typing import Any






class      a						:






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						None






class      a						:






    def __init__(       self     :							Union[str, Any]					)     ->		List[Any]:
      lowerCamelCase_         =						None






    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[int]:
      lowerCamelCase_         =						self.head
      while temp is not None:
        print(temp.data       ,						end=' '					)
        lowerCamelCase_         =						temp.next
      print()






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Union[str, Any]:
      lowerCamelCase_         =						Node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.head
      lowerCamelCase_         =						new_node






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		Union[str, Any]:
      if node_data_a == node_data_a:
        return
      else:
        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        if node_a is None or node_a is None:
          return

        lowerCamelCase_     ,	lowerCamelCase_         =						node_a.data, node_a.data


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						LinkedList()
  for i in range(5, 0, -1):
    ll.push(i)

  ll.print_list()

  ll.swap_nodes(1, 4)
  print('''After swapping''')
  ll.print_list()




