




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

import datasets


SCREAMING_SNAKE_CASE:     Optional[int]									= """\
@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:     Tuple									= """\
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:     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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	List[Any]:
		return float((preds == labels).mean() )


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


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	int:
		SCREAMING_SNAKE_CASE_					      =							float(pearsonr(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ )[0] )
		SCREAMING_SNAKE_CASE_					      =							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    lowercase_		(datasets.Metric ):



						def 	__a					(			self	:   Tuple  ):

								"""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 	__a					(			self	:   Tuple					,							snake_case__	:   Optional[int]					,							snake_case__	:   List[Any]  ):

								"""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"]'  )







'''simple docstring'''



import os



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str = "matrix.txt"     )	->      int:

   """simple docstring"""
   with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__     ),       SCREAMING_SNAKE_CASE__     )     ) as in_file:
      __a					  =     in_file.read()

   __a					  =     [[int(SCREAMING_SNAKE_CASE__     ) for cell in row.split(','     )] for row in data.strip().splitlines()]
   __a					  =     [[0 for cell in row] for row in grid]
   __a					  =     len(grid[0]     )

   __a					  =     [[0 for i in range(SCREAMING_SNAKE_CASE__     )] for j in range(SCREAMING_SNAKE_CASE__     )]
   __a					  =     grid[0][0]
   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     grid[0][i] + dp[0][i - 1]
   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     grid[i][0] + dp[i - 1][0]

   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      for j in range(1,       SCREAMING_SNAKE_CASE__     ):
         __a					  =     grid[i][j] + min(dp[i - 1][j],       dp[i][j - 1]     )

   return dp[-1][-1]


if __name__ == "__main__":
 print(f"""{solution() = }""")