


import datasets

from .evaluate import evaluate


A      = "\\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"

A      = "\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"

A      = "\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     lowercase__  (					datasets.Metric		):






  def 			_UpperCAmelCase  (					self      :					Optional[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 			_UpperCAmelCase  (					self      :					Optional[Any]		,						_lowercase      :					Optional[int]		,						_lowercase      :					Union[str, Any] ):




         """simple docstring"""







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









import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


A      = logging.get_logger(__name__)


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					Union[str, Any]		,						*_lowercase      :					Any		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         warnings.warn(
             "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use DPTImageProcessor instead."		,						_lowercase		,						)
         super().__init__(*_lowercase		,						**_lowercase )





