

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

import datasets


a_     =			'''\
@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_     =			'''\
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_     =			'''
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__       (    _a							,	_a):
	return float((preds == labels).mean())

def        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				simple_accuracy(_a							,	_a)
	SCREAMING_SNAKE_CASE      :					Tuple    =				float(fa_score(y_true=_a							,	y_pred=_a))
	return {
	    "accuracy": acc,
	    "f1": fa,
	}

def        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				float(pearsonr(_a							,	_a)[0])
	SCREAMING_SNAKE_CASE      :					Tuple    =				float(spearmanr(_a							,	_a)[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 							__UpperCamelCase       (	self							:       Dict		)							->				Union[str, 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 							__UpperCamelCase       (	self							:       Tuple					,					a							:       List[Any]					,					a							:       int		)							->				int:

				"""simple docstring"""

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






def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      list[int]:
					__A	:						Optional[int]       =				int(a  )

					# Initialize Result
					__A	:						Optional[int]       =				[]

					# Traverse through all denomination
					for denomination in reversed(a  ):
										# Find denominations
										while int(a  ) >= int(a  ):
															total_value -= int(a  )
															answer.append(a  )  # Append the "answers" array

					return answer


# Driver Code
if __name__ == "__main__":
				UpperCAmelCase						:   List[str]			  =     []
				UpperCAmelCase						:   Optional[int]			  =     '''0'''

				if (
				    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
				    == "y"
				):
								UpperCAmelCase						:   List[Any]			  =     int(input('''Enter the number of denominations you want to add: ''').strip())

								for i in range(0, n):
												denominations.append(int(input(F"""Denomination {i}: """).strip()))
								UpperCAmelCase						:   int			  =     input('''Enter the change you want to make in Indian Currency: ''').strip()
				else:
								# All denominations of Indian Currency if user does not enter
								UpperCAmelCase						:   Optional[int]			  =     [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
								UpperCAmelCase						:   Tuple			  =     input('''Enter the change you want to make: ''').strip()

				if int(value) == 0 or int(value) < 0:
								print('''The total value cannot be zero or negative.''')

				else:
								print(F"""Following is minimal change for {value}: """)
								UpperCAmelCase						:   Optional[int]			  =     find_minimum_change(denominations, value)
								# Print result
								for i in range(len(answer)):
												print(answer[i], end=''' ''')




