





'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      list[list]       )		->       list[list]:


     '''simple docstring'''



     _UpperCAmelCase     							=   current_set.copy()
     for row_index, row in enumerate(__lowercase       ):
          _UpperCAmelCase     							=   row[0]
          for column_index, column in enumerate(__lowercase       ):
               if magnitude == 0:
                    _UpperCAmelCase     							=   column
                    continue
               _UpperCAmelCase     							=   column / magnitude
    # Subtract to cancel term
     _UpperCAmelCase     							=   current_set[0]
     _UpperCAmelCase     							=   [first_row]
     _UpperCAmelCase     							=   current_set[1::]
     for row in current_set:
          _UpperCAmelCase     							=   []
          # If first term is 0, it is already in form we want, so we preserve it
          if row[0] == 0:
               final_set.append(__lowercase       )
               continue
          for column_index in range(len(__lowercase       )       ):
               temp_row.append(first_row[column_index] - row[column_index]       )
          final_set.append(__lowercase       )
     # Create next recursion iteration set
     if len(final_set[0]       ) != 3:
          _UpperCAmelCase     							=   final_set[0]
          _UpperCAmelCase     							=   []
          _UpperCAmelCase     							=   []
          for row in final_set[1::]:
               current_first_column.append(row[0]       )
               next_iteration.append(row[1::]       )
          _UpperCAmelCase     							=   simplify(__lowercase       )
          for i in range(len(__lowercase       )       ):
               resultant[i].insert(0			,     current_first_column[i]       )
          resultant.insert(0			,     __lowercase       )
          _UpperCAmelCase     							=   resultant
     return final_set
def 	UpperCAmelCase_							(					__lowercase							:      list[list]       )		->       list:


     '''simple docstring'''



     if len(__lowercase       ) == 0:
          raise IndexError("solve_simultaneous() requires n lists of length n+1"       )
     _UpperCAmelCase     							=   len(__lowercase       ) + 1
     if any(len(__lowercase       ) != _length for item in equations       ):
          raise IndexError("solve_simultaneous() requires n lists of length n+1"       )
     for row in equations:
          if any(not isinstance(__lowercase			,     (int, float)       ) for column in row       ):
               raise ValueError("solve_simultaneous() requires lists of integers"       )
     if len(__lowercase       ) == 1:
          return [equations[0][-1] / equations[0][0]]
     _UpperCAmelCase     							=   equations.copy()
     if any(0 in row for row in data_set       ):
          _UpperCAmelCase     							=   data_set.copy()
          _UpperCAmelCase     							=   []
          for row_index, row in enumerate(__lowercase       ):
               if 0 not in row:
                    _UpperCAmelCase     							=   data_set.pop(__lowercase       )
                    break
          if not full_row:
               raise ValueError("solve_simultaneous() requires at least 1 full equation"       )
          data_set.insert(0			,     __lowercase       )
     _UpperCAmelCase     							=   data_set.copy()
     _UpperCAmelCase     							=   simplify(__lowercase       )
     _UpperCAmelCase     							=   simplified[::-1]
     _UpperCAmelCase     							=   []
     for row in simplified:
          _UpperCAmelCase     							=   row[-1]
          if not solutions:
               if row[-2] == 0:
                    solutions.append(0       )
                    continue
               solutions.append(current_solution / row[-2]       )
               continue
          _UpperCAmelCase     							=   row.copy()[: len(__lowercase       ) - 1 :]
          while temp_row[0] == 0:
               temp_row.pop(0       )
          if len(__lowercase       ) == 0:
               solutions.append(0       )
               continue
          _UpperCAmelCase     							=   temp_row[1::]
          _UpperCAmelCase     							=   temp_row[::-1]
          for column_index, column in enumerate(__lowercase       ):
               current_solution -= column * solutions[column_index]
          solutions.append(__lowercase       )
     _UpperCAmelCase     							=   []
     for item in solutions:
          final.append(float(round(__lowercase			,     5       )       )       )
     return final[::-1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  __SCREAMING_SNAKE_CASE     :Tuple       =				[
      [2, 1, 1, 1, 1, 4],
      [1, 2, 1, 1, 1, 5],
      [1, 1, 2, 1, 1, 6],
      [1, 1, 1, 2, 1, 7],
      [1, 1, 1, 1, 2, 8],
  ]
  print(solve_simultaneous(eq))
  print(solve_simultaneous([[4, 2]]))






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

import datasets


lowercase  :					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}
}
"""

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

lowercase  :					int						    =							"""
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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       return float((preds == labels).mean()				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Any       	=      simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      float(fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Union[str, Any]       	=      float(pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       lowercase							:     Dict       	=      float(spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,	_KWARGS_DESCRIPTION     )
class __snake_case		(      datasets.Metric     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''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		,snake_case		,snake_case      ):



           '''simple docstring'''







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