






"""simple docstring"""


import datasets

from .evaluate import evaluate


lowercase_				     =     '\\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'

lowercase_				     =     '\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'

lowercase_				     =     '\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						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''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 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     Any,       _lowerCamelCase :     Optional[Any]  ):

     '''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=_lowerCamelCase,       predictions=_lowerCamelCase  )
     return score












"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


lowercase_				     =     logging.get_logger(__name__)


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

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

     '''simple docstring'''
     warnings.warn(
         '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
         ''' Please use SegformerImageProcessor instead.''',       _lowerCamelCase,       )
     super().__init__(*_lowerCamelCase,       **_lowerCamelCase  )




