





'''simple docstring'''

import datasets

from .evaluate import evaluate


__lowerCamelCase : Optional[Any]											=			"\\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 : Any											=			"\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 : Union[str, Any]											=			"\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       UpperCAmelCase (						datasets.Metric  ):




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Union[str, Any]:
						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 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	List[Any]							,			A__      :	List[str]				)					-> str:
						lowercase         = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
						lowercase         = [
						    {
						        "paragraphs": [
						            {
						                "qas": [
						                    {
						                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
						                        "id": ref["id"],
						                    }
						                    for ref in references
						                ]
						            }
						        ]
						    }
						]
						lowercase         = evaluate(dataset=A__							,			predictions=A__				)
						return score











'''simple docstring'''

import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCamelCase : Any											=			"\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__lowerCamelCase : Dict											=			"\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

__lowerCamelCase : List[Any]											=			"\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"

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




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Union[str, Any]:
						if version.parse(scb.__version__				) < version.parse("1.4.12"				):
										raise ImportWarning(
										    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
										    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."				)
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,			citation=_CITATION							,			homepage="https://github.com/mjpost/sacreBLEU#chrf--chrf"							,			inputs_description=_KWARGS_DESCRIPTION							,			features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"							,			id="sequence"				),
						            "references": datasets.Sequence(datasets.Value("string"							,			id="sequence"				)							,			id="references"				),
						        }				)							,			codebase_urls=["https://github.com/mjpost/sacreBLEU#chrf--chrf"]							,			reference_urls=[
						        "https://github.com/m-popovic/chrF",
						    ]							,			)





		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	Dict							,			A__      :	Tuple							,			A__      :	int = CHRF.CHAR_ORDER							,			A__      :	int = CHRF.WORD_ORDER							,			A__      :	int = CHRF.BETA							,			A__      :	bool = False							,			A__      :	bool = False							,			A__      :	bool = False							,			)					-> List[str]:
						lowercase         = len(references[0]				)
						if any(len(A__				) != references_per_prediction for refs in references				):
										raise ValueError("Sacrebleu requires the same number of references for each prediction"				)
						lowercase         = [[refs[i] for refs in references] for i in range(A__				)]

						lowercase         = CHRF(A__							,			A__							,			A__							,			A__							,			A__							,			A__				)
						lowercase         = sb_chrf.corpus_score(A__							,			A__				)

						return {
						    "score": output.score,
						    "char_order": output.char_order,
						    "word_order": output.word_order,
						    "beta": output.beta,
						}




