


"""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


__SCREAMING_SNAKE_CASE      		=    """\\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"""

__SCREAMING_SNAKE_CASE      		=    """\\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"""

__SCREAMING_SNAKE_CASE      		=    """\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 __snake_case		(       datasets.Metric   ):







   """simple docstring"""




   def SCREAMING_SNAKE_CASE_   (		self				:List[str]							):
          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 SCREAMING_SNAKE_CASE_   (		self				:int					,	UpperCamelCase__				:Tuple					,	UpperCamelCase__				:Tuple					,	UpperCamelCase__				:Optional[int]=None					,	UpperCamelCase__				:Optional[int]=True					,	UpperCamelCase__				:List[str]=False							):







          if rouge_types is None:
                 _a							 =					["rouge1", "rouge2", "rougeL", "rougeLsum"]

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

          for ref, pred in zip(SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_							):
                 _a							 =					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:
                 _a							 =					aggregator.aggregate()
          else:
                 _a							 =					{}
                 for key in scores[0]:
                        _a							 =					[score[key] for score in scores]

          return result


"""simple docstring"""




import datasets

from .evaluate import evaluate


lowerCAmelCase__				=					'\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

lowerCAmelCase__				=					'\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'

lowerCAmelCase__				=					'\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the SQuAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_metric = datasets.load_metric("squad")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0}\n'





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

							def _lowercase       (self     ):

									"""simple docstring"""







									return datasets.MetricInfo(
									    description=_DESCRIPTION							,							citation=_CITATION							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
									        {
									            '''predictions''': {'''id''': datasets.Value('''string'''     ), '''prediction_text''': datasets.Value('''string'''     )},
									            '''references''': {
									                '''id''': datasets.Value('''string'''     ),
									                '''answers''': datasets.features.Sequence(
									                    {
									                        '''text''': datasets.Value('''string'''     ),
									                        '''answer_start''': datasets.Value('''int32'''     ),
									                    }     ),
									            },
									        }     )							,							codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,							reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,							)







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
									SCREAMING_SNAKE_CASE_    			=			[
									    {
									        '''paragraphs''': [
									            {
									                '''qas''': [
									                    {
									                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
									                        '''id''': ref['''id'''],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									SCREAMING_SNAKE_CASE_    			=			evaluate(dataset=SCREAMING_SNAKE_CASE_							,							predictions=SCREAMING_SNAKE_CASE_     )
									return score