




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						):
											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       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_=None       , UpperCAmelCase_=True       , UpperCAmelCase_=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









from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = "arrow"       , **UpperCAmelCase_       , ):
											super().__init__(
											    split=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_       , streaming=UpperCAmelCase_       , **UpperCAmelCase_       , )
											lowerCAmelCase   =		load_from_cache_file
											lowerCAmelCase   =		file_format
											lowerCAmelCase   =		Spark(
											    df=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , working_dir=UpperCAmelCase_       , **UpperCAmelCase_       , )






				def       __snake_case		( self						):
											if self.streaming:
																		return self.builder.as_streaming_dataset(split=self.split						)
											lowerCAmelCase   =		None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
											self.builder.download_and_prepare(
											    download_mode=UpperCAmelCase_       , file_format=self._file_format       , )
											return self.builder.as_dataset(split=self.split						)



