






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			:		Optional[int]   				=    "\\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"

_UpperCAmelCase			:		Optional[int]   				=    "\\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"

_UpperCAmelCase			:		Dict   				=    "\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  lowerCAmelCase_       (				datasets.Metric					):




							def 						__snake_case					(   self     :   Dict	):
											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     :   Any ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=True ,							SCREAMING_SNAKE_CASE_     :   int=False	):
											if rouge_types is None:
															lowerCAmelCase__      			=						['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

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

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

											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  lowerCAmelCase_       (				snake_case__					):




							def __init__(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   pyspark.sql.DataFrame ,							SCREAMING_SNAKE_CASE_     :   Optional[NamedSplit] = None ,							SCREAMING_SNAKE_CASE_     :   Optional[Features] = None ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							SCREAMING_SNAKE_CASE_     :   str = None ,							SCREAMING_SNAKE_CASE_     :   bool = False ,							SCREAMING_SNAKE_CASE_     :   str = None ,							SCREAMING_SNAKE_CASE_     :   bool = True ,							SCREAMING_SNAKE_CASE_     :   str = "arrow" ,							**SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							):
											super().__init__(
											    split=SCREAMING_SNAKE_CASE_ ,							features=SCREAMING_SNAKE_CASE_ ,							cache_dir=SCREAMING_SNAKE_CASE_ ,							keep_in_memory=SCREAMING_SNAKE_CASE_ ,							streaming=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_ ,							)
											lowerCAmelCase__      			=						load_from_cache_file
											lowerCAmelCase__      			=						file_format
											lowerCAmelCase__      			=						Spark(
											    df=SCREAMING_SNAKE_CASE_ ,							features=SCREAMING_SNAKE_CASE_ ,							cache_dir=SCREAMING_SNAKE_CASE_ ,							working_dir=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_ ,							)




							def 						__snake_case					(   self     :   List[str]	):
											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=SCREAMING_SNAKE_CASE_ ,							file_format=self._file_format ,							)
											return self.builder.as_dataset(split=self.split	)





