




"""simple docstring"""



import datasets

from .evaluate import evaluate


SCREAMING_SNAKE_CASE    =	"\\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"

SCREAMING_SNAKE_CASE    =	"\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"

SCREAMING_SNAKE_CASE    =	"\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 							__magic_name__							(   self						:	Dict				)				->							Optional[int]:




    '''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 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	str				)				->							Dict:




    '''simple docstring'''

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











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

SCREAMING_SNAKE_CASE    =	[{"type": "code", "content": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE    =	{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





