





import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


UpperCAmelCase_							=				"""\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

UpperCAmelCase_							=				"""\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

UpperCAmelCase_							=				"""
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				, _KWARGS_DESCRIPTION				)
class     UpperCamelCase__					(     datasets.Metric				):






		'''simple docstring'''






		def 						snake_case__				(							self  )       -> Optional[int]:






									"""simple docstring"""
									return datasets.MetricInfo(
									    description=_DESCRIPTION,   citation=_CITATION,   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
									        {
									            """predictions""": datasets.Value("""string""",   id="""sequence"""  ),
									            """references""": datasets.Value("""string""",   id="""sequence"""  ),
									        }  ),   codebase_urls=["""https://github.com/google-research/google-research/tree/master/rouge"""],   reference_urls=[
									        """https://en.wikipedia.org/wiki/ROUGE_(metric)""",
									        """https://github.com/google-research/google-research/tree/master/rouge""",
									    ],   )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__=None,   snake_case__=True,   snake_case__=False  )       -> Any:






									"""simple docstring"""
									if rouge_types is None:
																lowercase_	:   str  				=     ["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

									lowercase_	:   Tuple  				=     rouge_scorer.RougeScorer(rouge_types=snake_case__,   use_stemmer=snake_case__  )
									if use_aggregator:
																lowercase_	:   int  				=     scoring.BootstrapAggregator()
									else:
																lowercase_	:   Union[str, Any]  				=     []

									for ref, pred in zip(snake_case__,   snake_case__  ):
																lowercase_	:   int  				=     scorer.score(snake_case__,   snake_case__  )
																if use_aggregator:
																							aggregator.add_scores(snake_case__  )
																else:
																							scores.append(snake_case__  )

									if use_aggregator:
																lowercase_	:   int  				=     aggregator.aggregate()
									else:
																lowercase_	:   Union[str, Any]  				=     {}
																for key in scores[0]:
																							lowercase_	:   Tuple  				=     [score[key] for score in scores]

									return result






from __future__ import annotations

import numpy as np





def  __magic_name__  (				lowercase				)				-> Tuple:

							"""simple docstring"""



							return np.maximum(0				,       lowercase				)


if __name__ == "__main__":
							print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]