






'''simple docstring'''







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

import datasets


_A	:			Union[str, 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}
}
'''

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

_A	:			Optional[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  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       List[str]:
							return float((preds == labels).mean()      )





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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       str:
							lowerCamelCase__					:	int         =       float(pearsonr(UpperCamelCase				,			UpperCamelCase      )[0]      )
							lowerCamelCase__					:	Tuple         =       float(spearmanr(UpperCamelCase				,			UpperCamelCase      )[0]      )
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							}



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




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														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:				List[str]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Optional[Any]     ):
														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

UpperCAmelCase_      : Tuple  					=       []







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]  ,    __A :			int  ,    __A :			int				)	->		bool:

       """simple docstring"""

       for i in range(len(__A				)				):
              if board[row][i] == 1:
                     return False
       for i in range(len(__A				)				):
              if board[i][column] == 1:
                     return False
       for i, j in zip(range(__A  ,    -1  ,    -1				)  ,    range(__A  ,    -1  ,    -1				)				):
              if board[i][j] == 1:
                     return False
       for i, j in zip(range(__A  ,    -1  ,    -1				)  ,    range(__A  ,    len(__A				)				)				):
              if board[i][j] == 1:
                     return False
       return True







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]  ,    __A :			int				)	->		bool:

       """simple docstring"""

       if row >= len(__A				):
              solution.append(__A				)
              printboard(__A				)
              print()
              return True
       for i in range(len(__A				)				):
              if is_safe(__A  ,    __A  ,    __A				):
                     a_					:		Any							=  1
                     solve(__A  ,    row + 1				)
                     a_					:		Tuple							=  0
       return False







def        SCREAMING_SNAKE_CASE_						(			__A :			list[list[int]]				)	->		None:

       """simple docstring"""

       for i in range(len(__A				)				):
              for j in range(len(__A				)				):
                     if board[i][j] == 1:
                            print('Q'  ,    end=' '				)
                     else:
                            print('.'  ,    end=' '				)
              print()


# n=int(input("The no. of queens"))
UpperCAmelCase_      : List[str]  					=       8
UpperCAmelCase_      : str  					=       [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('The total no. of solutions are :', len(solution))


