

'''simple docstring'''


import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


lowerCAmelCase__						:       Optional[int]										=						"""\
    @inproceedings{kakwani2020indicnlpsuite,
    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
    year={2020},
    booktitle={Findings of EMNLP},
}
"""

lowerCAmelCase__						:       Dict										=						"""\
    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
"""

lowerCAmelCase__						:       str										=						"""
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
    predictions: list of predictions to score (as int64),
        except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).
    references: list of ground truth labels corresponding to the predictions (as int64),
        except for 'cvit-mkb-clsr' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"precision\": Precision@10
Examples:

    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli')  # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')
    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'precision@10': 1.0}

"""




def 	_a       (				__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      str     ):

		"""simple docstring"""

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




def 	_a       (				__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      Dict     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  simple_accuracy(__lowerCAmelCase   ,							__lowerCAmelCase     )
		snake_case__						:       Tuple       						=  float(fa_score(y_true=__lowerCAmelCase   ,							y_pred=__lowerCAmelCase     )     )
		return {
		    "accuracy": acc,
		    "f1": fa,
		}




def 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		snake_case__						:       int       						=  np.array(__lowerCAmelCase     )
		snake_case__						:       Union[str, Any]       						=  np.array(__lowerCAmelCase     )
		snake_case__						:       Dict       						=  en_sentvecs.shape[0]

		# mean centering
		snake_case__						:       Tuple       						=  en_sentvecs - np.mean(__lowerCAmelCase   ,							axis=0     )
		snake_case__						:       List[str]       						=  in_sentvecs - np.mean(__lowerCAmelCase   ,							axis=0     )

		snake_case__						:       Optional[int]       						=  cdist(__lowerCAmelCase   ,							__lowerCAmelCase   ,							'''cosine'''     )
		snake_case__						:       List[str]       						=  np.array(range(__lowerCAmelCase     )     )
		snake_case__						:       int       						=  sim.argsort(axis=1     )[:, :10]
		snake_case__						:       Union[str, Any]       						=  np.any(preds == actual[:, None]   ,							axis=1     )
		return float(matches.mean()     )





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






						"""simple docstring"""




						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								if self.config_name not in [
								    "wnli",
								    "copa",
								    "sna",
								    "csqa",
								    "wstp",
								    "inltkh",
								    "bbca",
								    "cvit-mkb-clsr",
								    "iitp-mr",
								    "iitp-pr",
								    "actsa-sc",
								    "md",
								    "wiki-ner",
								]:
										raise KeyError(
										    '''You should supply a configuration name selected in '''
										    '''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
										    '''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
										    '''"wiki-ner"]'''  )
								return datasets.MetricInfo(
								    description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''int64'''  )
								            if self.config_name != '''cvit-mkb-clsr'''
								            else datasets.Sequence(datasets.Value('''float32'''  )  ),
								            '''references''': datasets.Value('''int64'''  )
								            if self.config_name != '''cvit-mkb-clsr'''
								            else datasets.Sequence(datasets.Value('''float32'''  )  ),
								        }  )			,			codebase_urls=[]			,			reference_urls=[]			,			format='''numpy''' if self.config_name != '''cvit-mkb-clsr''' else None			,			)







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      List[str]			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								if self.config_name == "cvit-mkb-clsr":
										return {"precision@10": precision_at_aa(snake_case_			,			snake_case_  )}
								elif self.config_name in ["wiki-ner"]:
										return acc_and_fa(snake_case_			,			snake_case_  )
								elif self.config_name in [
								    "wnli",
								    "copa",
								    "sna",
								    "csqa",
								    "wstp",
								    "inltkh",
								    "bbca",
								    "iitp-mr",
								    "iitp-pr",
								    "actsa-sc",
								    "md",
								]:
										return {"accuracy": simple_accuracy(snake_case_			,			snake_case_  )}
								else:
										raise KeyError(
										    '''You should supply a configuration name selected in '''
										    '''["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '''
										    '''"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '''
										    '''"wiki-ner"]'''  )






'''simple docstring'''


from collections.abc import Sequence




def 	_a       (				__lowerCAmelCase		:      Sequence[int] | None = None     ):

		"""simple docstring"""

		if nums is None or not nums:
				raise ValueError('''Input sequence should not be empty'''     )

		snake_case__						:       Optional[Any]       						=  nums[0]
		for i in range(1   ,							len(__lowerCAmelCase     )     ):
				snake_case__						:       Tuple       						=  nums[i]
				snake_case__						:       Optional[int]       						=  max(__lowerCAmelCase   ,							ans + num   ,							__lowerCAmelCase     )

		return ans


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# Try on a sample input from the user
					lowerCAmelCase__						:       str										=						int(input("""Enter number of elements : """).strip())
					lowerCAmelCase__						:       Dict										=						list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
					print(max_subsequence_sum(array))



