


import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


snake_case__			 =						version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
	from nltk import word_tokenize


snake_case__			 =						"\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

snake_case__			 =						"\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

snake_case__			 =						"\nComputes METEOR score of translated segments against one or more 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    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"


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




						def  _snake_case					(	self						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"]   ,     reference_urls=[
									        "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
									        "https://en.wikipedia.org/wiki/METEOR",
									    ]   ,     )




						def  _snake_case					(	self						:   int   ,     __A						:   Optional[Any]						)      ->Any:






									"""simple docstring"""






									import nltk

									nltk.download("wordnet"						)
									if NLTK_VERSION >= version.Version("3.6.5"						):
												nltk.download("punkt"						)
									if NLTK_VERSION >= version.Version("3.6.6"						):
												nltk.download("omw-1.4"						)






						def  _snake_case					(	self						:   str   ,     __A						:   Any   ,     __A						:   Optional[int]   ,     __A						:   List[str]=0.9   ,     __A						:   Optional[Any]=3   ,     __A						:   str=0.5						)      ->int:






									"""simple docstring"""






									if NLTK_VERSION >= version.Version("3.6.5"						):
												a__ :List[Any]						  =    [
												    meteor_score.single_meteor_score(
												        word_tokenize(lowerCamelCase_						)   ,     word_tokenize(lowerCamelCase_						)   ,     alpha=lowerCamelCase_   ,     beta=lowerCamelCase_   ,     gamma=lowerCamelCase_						)
												    for ref, pred in zip(lowerCamelCase_   ,     lowerCamelCase_						)
												]
									else:
												a__ :Dict						  =    [
												    meteor_score.single_meteor_score(lowerCamelCase_   ,     lowerCamelCase_   ,     alpha=lowerCamelCase_   ,     beta=lowerCamelCase_   ,     gamma=lowerCamelCase_						)
												    for ref, pred in zip(lowerCamelCase_   ,     lowerCamelCase_						)
												]

									return {"meteor": np.mean(lowerCamelCase_						)}






import datasets

from .evaluate import evaluate


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

snake_case__			 =						'''
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
'''

snake_case__			 =						'''
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the SQuAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_metric = datasets.load_metric("squad")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0}
'''


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




						def  _snake_case					(	self						:   List[str]						)      ->Union[str, Any]:






									"""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  _snake_case					(	self						:   Dict   ,     __A						:   List[Any]   ,     __A						:   Optional[int]						)      ->str:






									"""simple docstring"""






									a__ :Optional[int]						  =    {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
									a__ :Optional[Any]						  =    [
									    {
									        "paragraphs": [
									            {
									                "qas": [
									                    {
									                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
									                        "id": ref["id"],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									a__ :Union[str, Any]						  =    evaluate(dataset=__A   ,     predictions=__A						)
									return score


