"""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 					lowerCAmelCase_      (     datasets.Metric   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""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						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

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



"""simple docstring"""
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     torch.nn.Linear(10						,      10      )
										snake_case        =     torch.optim.SGD(model.parameters()						,      0.1      )
										snake_case        =     Accelerator()
										snake_case        =     accelerator.prepare(lowerCAmelCase      )
										try:
																pickle.loads(pickle.dumps(lowerCAmelCase      )      )
										except Exception as e:
																self.fail(F"""Accelerated optimizer pickling failed with {e}"""      )
										AcceleratorState._reset_state()


