




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

import datasets


_lowerCamelCase     :		List[str]								=       """\
@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     :		Optional[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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Dict:



       """simple docstring"""

       return float((preds == labels).mean()						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	List[str]:



       """simple docstring"""

       A__							=   simple_accuracy(lowercase_			,    lowercase_						)
       A__							=   float(fa_score(y_true=lowercase_			,    y_pred=lowercase_						)						)
       return {
           "accuracy": acc,
           "f1": fa,
       }







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   float(pearsonr(lowercase_			,    lowercase_						)[0]						)
       A__							=   float(spearmanr(lowercase_			,    lowercase_						)[0]						)
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }


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

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

           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 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Dict)						->Optional[Any]:





           '''simple docstring'''

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








from __future__ import annotations

from typing import Any







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       create_state_space_tree(lowercase_			,    []			,    0						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       if index == len(lowercase_						):
              print(lowercase_						)
              return

       create_state_space_tree(lowercase_			,    lowercase_			,    index + 1						)
       current_subsequence.append(sequence[index]						)
       create_state_space_tree(lowercase_			,    lowercase_			,    index + 1						)
       current_subsequence.pop()


if __name__ == "__main__":
  _lowerCamelCase     :		list[Any]								=       [3, 1, 2, 4]
  generate_all_subsequences(seq)

  seq.clear()
  seq.extend(["""A""", """B""", """C"""])
  generate_all_subsequences(seq)


