
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


lowercase   =	'''\
@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",
}
'''

lowercase   =	'''\
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
'''

lowercase   =	'''
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    __A(					datasets.Metric							):

					def  lowercase__   (						self       :  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  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Tuple=None ,       __UpperCamelCase       :  List[Any]=True ,       __UpperCamelCase       :  Tuple=False ):
												if rouge_types is None:
																			lowerCamelCase_					=       ["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

												lowerCamelCase_					=       rouge_scorer.RougeScorer(rouge_types=__UpperCamelCase ,       use_stemmer=__UpperCamelCase )
												if use_aggregator:
																			lowerCamelCase_					=       scoring.BootstrapAggregator()
												else:
																			lowerCamelCase_					=       []

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

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

												return result







lowercase   =	8.314_4598



def   __lowerCAmelCase    (				UpperCAmelCase__					:   float      ,					UpperCAmelCase__					:   float							)	->     float:
							if temperature < 0:
														raise Exception("""Temperature cannot be less than 0 K"""							)
							if molar_mass <= 0:
														raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""							)
							else:
														return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		lowercase   =	3_0_0
		lowercase   =	2_8
		lowercase   =	rms_speed_of_molecule(temperature, molar_mass)
		print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")





