






"""simple docstring"""
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


lowerCamelCase_       			=   '''\
@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",
}
'''

lowerCamelCase_       			=   '''\
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
'''

lowerCamelCase_       			=   '''
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  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Tuple:
				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   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Any=None			,			lowerCAmelCase_      :				Any=True			,			lowerCAmelCase_      :				Optional[int]=False		)		->    Dict:
				if rouge_types is None:
							UpperCAmelCase_  :				Optional[int]      =      ["rouge1", "rouge2", "rougeL", "rougeLsum"]

				UpperCAmelCase_  :				Dict      =      rouge_scorer.RougeScorer(rouge_types=lowerCAmelCase_			,			use_stemmer=lowerCAmelCase_		)
				if use_aggregator:
							UpperCAmelCase_  :				List[str]      =      scoring.BootstrapAggregator()
				else:
							UpperCAmelCase_  :				Any      =      []

				for ref, pred in zip(lowerCAmelCase_			,			lowerCAmelCase_		):
							UpperCAmelCase_  :				List[str]      =      scorer.score(lowerCAmelCase_			,			lowerCAmelCase_		)
							if use_aggregator:
										aggregator.add_scores(lowerCAmelCase_		)
							else:
										scores.append(lowerCAmelCase_		)

				if use_aggregator:
							UpperCAmelCase_  :				Tuple      =      aggregator.aggregate()
				else:
							UpperCAmelCase_  :				Optional[Any]      =      {}
							for key in scores[0]:
										UpperCAmelCase_  :				Any      =      [score[key] for score in scores]

				return result











"""simple docstring"""
from ..utils import DummyObject, requires_backends







class     UpperCamelCase_ (metaclass=__A  ):
	__magic_name__			   =       ['''onnx''']





	def __init__(   self      :				List[Any]			,			*lowerCAmelCase_      :				Dict			,			**lowerCAmelCase_      :				Dict		)		->    Dict:
				requires_backends(self			,			["onnx"]		)





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				Optional[Any]			,			*lowerCAmelCase_      :				Dict			,			**lowerCAmelCase_      :				Union[str, Any]		)		->    int:
				requires_backends(cls			,			["onnx"]		)





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				str			,			*lowerCAmelCase_      :				Optional[Any]			,			**lowerCAmelCase_      :				str		)		->    Optional[Any]:
				requires_backends(cls			,			["onnx"]		)



