
"""simple docstring"""




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 			snake_case  (				__lowercase							,      unittest.TestCase ):
							UpperCAmelCase__      =   MgpstrTokenizer
							UpperCAmelCase__      =   False
							UpperCAmelCase__      =   {}
							UpperCAmelCase__      =   False

							def _lowercase       (self     ):

									"""simple docstring"""







									super().setUp()

									# fmt: off
									SCREAMING_SNAKE_CASE_    			=			['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
									# fmt: on
									SCREAMING_SNAKE_CASE_    			=			dict(zip(SCREAMING_SNAKE_CASE_							,							range(len(SCREAMING_SNAKE_CASE_     )     )     )     )

									SCREAMING_SNAKE_CASE_    			=			os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES['''vocab_file''']     )
									with open(self.vocab_file							,							'''w'''							,							encoding='''utf-8'''     ) as fp:
											fp.write(json.dumps(SCREAMING_SNAKE_CASE_     ) + '''\n'''     )

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									return MgpstrTokenizer.from_pretrained(self.tmpdirname							,							**SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			'''tester'''
									SCREAMING_SNAKE_CASE_    			=			'''tester'''
									return input_text, output_text

							@unittest.skip('''MGP-STR always lower cases letters.'''     )
							def _lowercase       (self     ):

									"""simple docstring"""







									pass

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE_     )
									for tokenizer in tokenizers:
											with self.subTest(f'{tokenizer.__class__.__name__}'     ):
													SCREAMING_SNAKE_CASE_    			=			'''[SPECIAL_TOKEN]'''

													tokenizer.add_special_tokens({'''cls_token''': special_token}     )
													SCREAMING_SNAKE_CASE_    			=			tokenizer.encode([special_token]							,							add_special_tokens=SCREAMING_SNAKE_CASE_     )
													self.assertEqual(len(SCREAMING_SNAKE_CASE_     )							,							1     )

													SCREAMING_SNAKE_CASE_    			=			tokenizer.decode(SCREAMING_SNAKE_CASE_							,							skip_special_tokens=SCREAMING_SNAKE_CASE_     )
													self.assertTrue(special_token not in decoded     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.get_tokenizers()
									for tokenizer in tokenizers:
											with self.subTest(f'{tokenizer.__class__.__name__}'     ):
													SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			self.get_input_output_texts(SCREAMING_SNAKE_CASE_     )

													SCREAMING_SNAKE_CASE_    			=			tokenizer.tokenize(SCREAMING_SNAKE_CASE_     )
													SCREAMING_SNAKE_CASE_    			=			tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_     )
													SCREAMING_SNAKE_CASE_    			=			tokenizer.encode(SCREAMING_SNAKE_CASE_							,							add_special_tokens=SCREAMING_SNAKE_CASE_     )
													self.assertListEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

													SCREAMING_SNAKE_CASE_    			=			tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_     )
													self.assertNotEqual(len(SCREAMING_SNAKE_CASE_     )							,							0     )
													SCREAMING_SNAKE_CASE_    			=			tokenizer.decode(SCREAMING_SNAKE_CASE_     )
													self.assertIsInstance(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

													self.assertEqual(text_a.replace(''' '''							,							''''''     )							,							SCREAMING_SNAKE_CASE_     )

							@unittest.skip('''MGP-STR tokenizer only handles one sequence.'''     )
							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''     )
							def _lowercase       (self     ):

									"""simple docstring"""







									pass

"""simple docstring"""




import datasets

from .evaluate import evaluate


lowerCAmelCase__				=					'\\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'

lowerCAmelCase__				=					'\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'

lowerCAmelCase__				=					'\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 ):

							def _lowercase       (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 _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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