

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
     import torch


@require_torch
@require_optimum
@slow
class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       'hf-internal-testing/tiny-random-t5'
        A__   				=       AutoTokenizer.from_pretrained(lowercase_		)
        A__   				=       AutoModelForSeqaSeqLM.from_pretrained(lowercase_		)

        A__   				=       tokenizer('This is me',return_tensors='pt'		)

        A__   				=       model.to_bettertransformer()

        self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()		)		)

        A__   				=       model.generate(**lowercase_		)

        A__   				=       model.reverse_bettertransformer()

        self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()		)		)

        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(lowercase_		)

            A__   				=       AutoModelForSeqaSeqLM.from_pretrained(lowercase_		)

            self.assertFalse(
                any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()		)		)

            A__   				=       model_reloaded.generate(**lowercase_		)
            self.assertTrue(torch.allclose(lowercase_,lowercase_		)		)


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





        '''simple docstring'''







        A__   				=       'hf-internal-testing/tiny-random-t5'
        A__   				=       AutoModelForSeqaSeqLM.from_pretrained(lowercase_		)

        A__   				=       model.to_bettertransformer()

        with tempfile.TemporaryDirectory() as tmpdirname:
            with self.assertRaises(lowercase_		):
                model.save_pretrained(lowercase_		)

            A__   				=       model.reverse_bettertransformer()
            model.save_pretrained(lowercase_		)




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 			A						(							datasets.Metric			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any]		)->   Tuple:





        '''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			:	str,lowercase_			:	List[Any],lowercase_			:	Dict		)->   List[str]:





        '''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=lowercase_,predictions=lowercase_		)
        return score

