



'''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_							: str						 =	"""\
@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\",
}
"""

A_							: Tuple						 =	"""\
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
"""

A_							: List[Any]						 =	"""
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 							lowercase       (  datasets.Metric				):


 """simple docstring"""


 def _snake_case (							self							)		->   List[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 _snake_case (							self  ,a_  ,a_  ,a_=None  ,a_=True  ,a_=False							)		->   Union[str, Any]:
        if rouge_types is None:
               _UpperCAmelCase  :						Tuple			     = ["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

        _UpperCAmelCase  :						Dict			     = rouge_scorer.RougeScorer(rouge_types=a_  ,use_stemmer=a_							)
        if use_aggregator:
               _UpperCAmelCase  :						str			     = scoring.BootstrapAggregator()
        else:
               _UpperCAmelCase  :						Union[str, Any]			     = []

        for ref, pred in zip(a_  ,a_							):
               _UpperCAmelCase  :						Optional[int]			     = scorer.score(a_  ,a_							)
               if use_aggregator:
                      aggregator.add_scores(a_							)
               else:
                      scores.append(a_							)

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

        return result








'''simple docstring'''






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class 							lowercase       (  unittest.TestCase					,					_lowerCamelCase				):


 """simple docstring"""


 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						int			     = load_tool("""text-classification"""							)
        self.tool.setup()
        _UpperCAmelCase  :						Tuple			     = load_tool("""text-classification"""  ,remote=a_							)







 def _snake_case (							self							)		->   Union[str, Any]:
        _UpperCAmelCase  :						Tuple			     = self.tool("""That's quite cool"""  ,["""positive""", """negative"""]							)
        self.assertEqual(a_  ,"""positive"""							)







 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = self.remote_tool("""That's quite cool"""  ,["""positive""", """negative"""]							)
        self.assertEqual(a_  ,"""positive"""							)







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						List[Any]			     = self.tool(text="""That's quite cool"""  ,labels=["""positive""", """negative"""]							)
        self.assertEqual(a_  ,"""positive"""							)







 def _snake_case (							self							)		->   Union[str, Any]:
        _UpperCAmelCase  :						Any			     = self.remote_tool(text="""That's quite cool"""  ,labels=["""positive""", """negative"""]							)
        self.assertEqual(a_  ,"""positive"""							)



