






'''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


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

A:      Tuple  				=				"\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

A:      Optional[int]  				=				"\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    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))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"






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







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							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        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase=None  ,      _lowercase=True  ,      _lowercase=False       )						->  str:
							if rouge_types is None:
													lowercase_      :   Union[str, Any]        =			['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

							lowercase_      :   Optional[Any]        =			rouge_scorer.RougeScorer(rouge_types=_lowercase  ,      use_stemmer=_lowercase       )
							if use_aggregator:
													lowercase_      :   List[str]        =			scoring.BootstrapAggregator()
							else:
													lowercase_      :   str        =			[]

							for ref, pred in zip(_lowercase  ,      _lowercase       ):
													lowercase_      :   Optional[Any]        =			scorer.score(_lowercase  ,      _lowercase       )
													if use_aggregator:
																			aggregator.add_scores(_lowercase       )
													else:
																			scores.append(_lowercase       )

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

							return result








'''simple docstring'''

from typing import TYPE_CHECKING

from ..utils import _LazyModule


A:      int  				=				{
    "config": [
        "EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
        "OnnxConfig",
        "OnnxConfigWithPast",
        "OnnxSeq2SeqConfigWithPast",
        "PatchingSpec",
    ],
    "convert": ["export", "validate_model_outputs"],
    "features": ["FeaturesManager"],
    "utils": ["ParameterFormat", "compute_serialized_parameters_size"],
}


if TYPE_CHECKING:
						from .config import (
						    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
						    OnnxConfig,
						    OnnxConfigWithPast,
						    OnnxSeqaSeqConfigWithPast,
						    PatchingSpec,
						)
						from .convert import export, validate_model_outputs
						from .features import FeaturesManager
						from .utils import ParameterFormat, compute_serialized_parameters_size

else:
						import sys

						A:      Dict  				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
