






'''simple docstring'''
import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


UpperCamelCase__	=     '\\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'

UpperCamelCase__	=     '\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering 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\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

UpperCamelCase__	=     '\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no 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 list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,   _KWARGS_DESCRIPTION     )
class     _UpperCAmelCase  (						datasets.Metric     ):




    def 			lowerCAmelCase__			(     self					:    List[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"     ),
                       "no_answer_probability": datasets.Value("float32"     ),
                   },
                   "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 			lowerCAmelCase__			(     self					:    List[Any]					,       a					:    List[str]					,       a					:    Tuple					,       a					:    List[str]=1.0     ):






       '''simple docstring'''






       lowercase_		:      Tuple          =       {p["id"]: p["no_answer_probability"] for p in predictions}
       lowercase_		:      List[str]          =       [{"paragraphs": [{"qas": references}]}]
       lowercase_		:      Any          =       {p["id"]: p["prediction_text"] for p in predictions}

       lowercase_		:      Dict          =       make_qid_to_has_ans(a     )  # maps qid to True/False
       lowercase_		:      Dict          =       [k for k, v in qid_to_has_ans.items() if v]
       lowercase_		:      Dict          =       [k for k, v in qid_to_has_ans.items() if not v]

       lowercase_      ,						lowercase_		:      List[Any]          =       get_raw_scores(a					,       a     )
       lowercase_		:      int          =       apply_no_ans_threshold(a					,       a					,       a					,       a     )
       lowercase_		:      Dict          =       apply_no_ans_threshold(a					,       a					,       a					,       a     )
       lowercase_		:      int          =       make_eval_dict(a					,       a     )

       if has_ans_qids:
          lowercase_		:      List[Any]          =       make_eval_dict(a					,       a					,       qid_list=a     )
          merge_eval(a					,       a					,       "HasAns"     )
       if no_ans_qids:
          lowercase_		:      Union[str, Any]          =       make_eval_dict(a					,       a					,       qid_list=a     )
          merge_eval(a					,       a					,       "NoAns"     )
       find_all_best_thresh(a					,       a					,       a					,       a					,       a					,       a     )
       return dict(a     )












'''simple docstring'''
import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english


@require_tokenizers
class     _UpperCAmelCase  (						snake_case			,   unittest.TestCase     ):
    __lowerCamelCase:				Optional[int]						    =      RealmTokenizer
    __lowerCamelCase:				Optional[Any]						    =      RealmTokenizerFast
    __lowerCamelCase:				Optional[int]						    =      True
    __lowerCamelCase:				str						    =      True
    __lowerCamelCase:				List[str]						    =      filter_non_english




    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






       super().setUp()

       lowercase_		:      Optional[int]          =       [
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "[PAD]",
           "[MASK]",
           "want",
           "##want",
           "##ed",
           "wa",
           "un",
           "runn",
           "##ing",
           ",",
           "low",
           "lowest",
       ]
       lowercase_		:      List[str]          =       os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES["vocab_file"]     )
       with open(self.vocab_file					,       "w"					,       encoding="utf-8"     ) as vocab_writer:
          vocab_writer.write("".join([x + "\n" for x in vocab_tokens]     )     )




    def 			lowerCAmelCase__			(     self					:    int					,       a					:    str     ):






       '''simple docstring'''






       lowercase_		:      List[Any]          =       "UNwant\u00E9d,running"
       lowercase_		:      Dict          =       "unwanted, running"
       return input_text, output_text




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      str          =       self.tokenizer_class(self.vocab_file     )

       lowercase_		:      Any          =       tokenizer.tokenize("UNwant\u00E9d,running"     )
       self.assertListEqual(a					,       ["un", "##want", "##ed", ",", "runn", "##ing"]     )
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a     )					,       [9, 6, 7, 1_2, 1_0, 1_1]     )




    def 			lowerCAmelCase__			(     self					:    Union[str, Any]     ):






       '''simple docstring'''






       if not self.test_rust_tokenizer:
          return

       lowercase_		:      Optional[Any]          =       self.get_tokenizer()
       lowercase_		:      Any          =       self.get_rust_tokenizer()

       lowercase_		:      Tuple          =       "UNwant\u00E9d,running"

       lowercase_		:      List[str]          =       tokenizer.tokenize(a     )
       lowercase_		:      Union[str, Any]          =       rust_tokenizer.tokenize(a     )
       self.assertListEqual(a					,       a     )

       lowercase_		:      Dict          =       tokenizer.encode(a					,       add_special_tokens=a     )
       lowercase_		:      Dict          =       rust_tokenizer.encode(a					,       add_special_tokens=a     )
       self.assertListEqual(a					,       a     )

       lowercase_		:      List[Any]          =       self.get_rust_tokenizer()
       lowercase_		:      Any          =       tokenizer.encode(a     )
       lowercase_		:      Optional[Any]          =       rust_tokenizer.encode(a     )
       self.assertListEqual(a					,       a     )

       # With lower casing
       lowercase_		:      Union[str, Any]          =       self.get_tokenizer(do_lower_case=a     )
       lowercase_		:      Optional[Any]          =       self.get_rust_tokenizer(do_lower_case=a     )

       lowercase_		:      int          =       "UNwant\u00E9d,running"

       lowercase_		:      List[str]          =       tokenizer.tokenize(a     )
       lowercase_		:      Dict          =       rust_tokenizer.tokenize(a     )
       self.assertListEqual(a					,       a     )

       lowercase_		:      Optional[Any]          =       tokenizer.encode(a					,       add_special_tokens=a     )
       lowercase_		:      Tuple          =       rust_tokenizer.encode(a					,       add_special_tokens=a     )
       self.assertListEqual(a					,       a     )

       lowercase_		:      Dict          =       self.get_rust_tokenizer()
       lowercase_		:      Tuple          =       tokenizer.encode(a     )
       lowercase_		:      int          =       rust_tokenizer.encode(a     )
       self.assertListEqual(a					,       a     )




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      Any          =       BasicTokenizer()

       self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"     )					,       ["ah", "\u535A", "\u63A8", "zz"]     )




    def 			lowerCAmelCase__			(     self					:    Dict     ):






       '''simple docstring'''






       lowercase_		:      str          =       BasicTokenizer(do_lower_case=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "     )					,       ["hello", "!", "how", "are", "you", "?"]     )
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"     )					,       ["hello"]     )




    def 			lowerCAmelCase__			(     self					:    Any     ):






       '''simple docstring'''






       lowercase_		:      Optional[int]          =       BasicTokenizer(do_lower_case=a					,       strip_accents=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "     )					,       ["hällo", "!", "how", "are", "you", "?"]     )
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"     )					,       ["h\u00E9llo"]     )




    def 			lowerCAmelCase__			(     self					:    Any     ):






       '''simple docstring'''






       lowercase_		:      List[Any]          =       BasicTokenizer(do_lower_case=a					,       strip_accents=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "     )					,       ["hallo", "!", "how", "are", "you", "?"]     )
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"     )					,       ["hello"]     )




    def 			lowerCAmelCase__			(     self					:    Union[str, Any]     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       BasicTokenizer(do_lower_case=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "     )					,       ["hallo", "!", "how", "are", "you", "?"]     )
       self.assertListEqual(tokenizer.tokenize("H\u00E9llo"     )					,       ["hello"]     )




    def 			lowerCAmelCase__			(     self					:    Union[str, Any]     ):






       '''simple docstring'''






       lowercase_		:      Any          =       BasicTokenizer(do_lower_case=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "     )					,       ["HeLLo", "!", "how", "Are", "yoU", "?"]     )




    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






       lowercase_		:      Union[str, Any]          =       BasicTokenizer(do_lower_case=a					,       strip_accents=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "     )					,       ["HäLLo", "!", "how", "Are", "yoU", "?"]     )




    def 			lowerCAmelCase__			(     self					:    Tuple     ):






       '''simple docstring'''






       lowercase_		:      str          =       BasicTokenizer(do_lower_case=a					,       strip_accents=a     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "     )					,       ["HaLLo", "!", "how", "Are", "yoU", "?"]     )




    def 			lowerCAmelCase__			(     self					:    Optional[Any]     ):






       '''simple docstring'''






       lowercase_		:      List[Any]          =       BasicTokenizer(do_lower_case=a					,       never_split=["[UNK]"]     )

       self.assertListEqual(
           tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"     )					,       ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]     )




    def 			lowerCAmelCase__			(     self					:    Tuple     ):






       '''simple docstring'''






       lowercase_		:      int          =       ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

       lowercase_		:      Tuple          =       {}
       for i, token in enumerate(a     ):
          lowercase_		:      Union[str, Any]          =       i
       lowercase_		:      List[str]          =       WordpieceTokenizer(vocab=a					,       unk_token="[UNK]"     )

       self.assertListEqual(tokenizer.tokenize(""     )					,       []     )

       self.assertListEqual(tokenizer.tokenize("unwanted running"     )					,       ["un", "##want", "##ed", "runn", "##ing"]     )

       self.assertListEqual(tokenizer.tokenize("unwantedX running"     )					,       ["[UNK]", "runn", "##ing"]     )




    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






       self.assertTrue(_is_whitespace(" "     )     )
       self.assertTrue(_is_whitespace("\t"     )     )
       self.assertTrue(_is_whitespace("\r"     )     )
       self.assertTrue(_is_whitespace("\n"     )     )
       self.assertTrue(_is_whitespace("\u00A0"     )     )

       self.assertFalse(_is_whitespace("A"     )     )
       self.assertFalse(_is_whitespace("-"     )     )




    def 			lowerCAmelCase__			(     self					:    Tuple     ):






       '''simple docstring'''






       self.assertTrue(_is_control("\u0005"     )     )

       self.assertFalse(_is_control("A"     )     )
       self.assertFalse(_is_control(" "     )     )
       self.assertFalse(_is_control("\t"     )     )
       self.assertFalse(_is_control("\r"     )     )




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       self.assertTrue(_is_punctuation("-"     )     )
       self.assertTrue(_is_punctuation("$"     )     )
       self.assertTrue(_is_punctuation("`"     )     )
       self.assertTrue(_is_punctuation("."     )     )

       self.assertFalse(_is_punctuation("A"     )     )
       self.assertFalse(_is_punctuation(" "     )     )




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       self.get_tokenizer()

       # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
       self.assertListEqual([tokenizer.tokenize(a     ) for t in ["Test", "\xad", "test"]]					,       [["[UNK]"], [], ["[UNK]"]]     )

       if self.test_rust_tokenizer:
          lowercase_		:      Dict          =       self.get_rust_tokenizer()
          self.assertListEqual(
              [rust_tokenizer.tokenize(a     ) for t in ["Test", "\xad", "test"]]					,       [["[UNK]"], [], ["[UNK]"]]     )




    @slow
    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       self.tokenizer_class.from_pretrained("bert-base-uncased"     )

       lowercase_		:      List[str]          =       tokenizer.encode("sequence builders"					,       add_special_tokens=a     )
       lowercase_		:      Optional[Any]          =       tokenizer.encode("multi-sequence build"					,       add_special_tokens=a     )

       lowercase_		:      Optional[Any]          =       tokenizer.build_inputs_with_special_tokens(a     )
       lowercase_		:      List[str]          =       tokenizer.build_inputs_with_special_tokens(a					,       a     )

       assert encoded_sentence == [1_0_1] + text + [1_0_2]
       assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]




    def 			lowerCAmelCase__			(     self					:    List[str]     ):






       '''simple docstring'''






       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
             lowercase_		:      Any          =       self.rust_tokenizer_class.from_pretrained(a					,       **a     )

             lowercase_		:      Dict          =       f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
             lowercase_		:      Union[str, Any]          =       tokenizer_r.encode_plus(
                 a					,       return_attention_mask=a					,       return_token_type_ids=a					,       return_offsets_mapping=a					,       add_special_tokens=a					,       )

             lowercase_		:      Optional[Any]          =       tokenizer_r.do_lower_case if hasattr(a					,       "do_lower_case"     ) else False
             lowercase_		:      int          =       (
                 [
                     ((0, 0), tokenizer_r.cls_token),
                     ((0, 1), "A"),
                     ((1, 2), ","),
                     ((3, 5), "na"),
                     ((5, 6), "##ï"),
                     ((6, 8), "##ve"),
                     ((9, 1_5), tokenizer_r.mask_token),
                     ((1_6, 2_1), "Allen"),
                     ((2_1, 2_3), "##NL"),
                     ((2_3, 2_4), "##P"),
                     ((2_5, 3_3), "sentence"),
                     ((3_3, 3_4), "."),
                     ((0, 0), tokenizer_r.sep_token),
                 ]
                 if not do_lower_case
                 else [
                     ((0, 0), tokenizer_r.cls_token),
                     ((0, 1), "a"),
                     ((1, 2), ","),
                     ((3, 8), "naive"),
                     ((9, 1_5), tokenizer_r.mask_token),
                     ((1_6, 2_1), "allen"),
                     ((2_1, 2_3), "##nl"),
                     ((2_3, 2_4), "##p"),
                     ((2_5, 3_3), "sentence"),
                     ((3_3, 3_4), "."),
                     ((0, 0), tokenizer_r.sep_token),
                 ]
             )

             self.assertEqual(
                 [e[1] for e in expected_results]					,       tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]     )     )
             self.assertEqual([e[0] for e in expected_results]					,       tokens["offset_mapping"]     )





    @slow
    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
             lowercase_		:      Optional[Any]          =       self.rust_tokenizer_class.from_pretrained(a					,       **a     )
             lowercase_		:      List[str]          =       self.tokenizer_class.from_pretrained(a					,       **a     )
             lowercase_		:      List[str]          =       [["Hello world!", "Nice to meet you!"], ["The cute cat.", "The adorable dog."]]

             lowercase_		:      int          =       tokenizer_r.batch_encode_candidates(a					,       max_length=1_0					,       return_tensors="np"     )
             lowercase_		:      List[str]          =       tokenizer_p.batch_encode_candidates(a					,       max_length=1_0					,       return_tensors="np"     )

             lowercase_		:      Any          =       (2, 2, 1_0)

             self.assertEqual(encoded_sentence_r["input_ids"].shape					,       a     )
             self.assertEqual(encoded_sentence_r["attention_mask"].shape					,       a     )
             self.assertEqual(encoded_sentence_r["token_type_ids"].shape					,       a     )

             self.assertEqual(encoded_sentence_p["input_ids"].shape					,       a     )
             self.assertEqual(encoded_sentence_p["attention_mask"].shape					,       a     )
             self.assertEqual(encoded_sentence_p["token_type_ids"].shape					,       a     )




