

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,
)


__a						:Union[str, Any]            = '\\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'

__a						:List[str]            = '\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'

__a						:Optional[int]            = '\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      _a				( datasets.Metric							):




      """simple docstring"""

      def 		__A  (      self       :							List[str]  ):
            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 		__A  (      self       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Union[str, Any]=1.0  ):
            A_	     =							{p["id"]: p["no_answer_probability"] for p in predictions}
            A_	     =							[{"paragraphs": [{"qas": references}]}]
            A_	     =							{p["id"]: p["prediction_text"] for p in predictions}

            A_	     =							make_qid_to_has_ans(UpperCAmelCase  )  # maps qid to True/False
            A_	     =							[k for k, v in qid_to_has_ans.items() if v]
            A_	     =							[k for k, v in qid_to_has_ans.items() if not v]

            A_     ,    A_	     =							get_raw_scores(UpperCAmelCase	, UpperCAmelCase  )
            A_	     =							apply_no_ans_threshold(UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase  )
            A_	     =							apply_no_ans_threshold(UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase  )
            A_	     =							make_eval_dict(UpperCAmelCase	, UpperCAmelCase  )

            if has_ans_qids:
                  A_	     =							make_eval_dict(UpperCAmelCase	, UpperCAmelCase	, qid_list=UpperCAmelCase  )
                  merge_eval(UpperCAmelCase	, UpperCAmelCase	, "HasAns"  )
            if no_ans_qids:
                  A_	     =							make_eval_dict(UpperCAmelCase	, UpperCAmelCase	, qid_list=UpperCAmelCase  )
                  merge_eval(UpperCAmelCase	, UpperCAmelCase	, "NoAns"  )
            find_all_best_thresh(UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase	, UpperCAmelCase  )
            return dict(UpperCAmelCase  )


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      __a						:int            = None

__a						:List[str]            = logging.get_logger(__name__)

__a						:str            = {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

__a						:List[Any]            = {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

__a						:Dict            = {
    'microsoft/deberta-v2-xlarge': 512,
    'microsoft/deberta-v2-xxlarge': 512,
    'microsoft/deberta-v2-xlarge-mnli': 512,
    'microsoft/deberta-v2-xxlarge-mnli': 512,
}

__a						:List[Any]            = {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}

class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: Dict					=					VOCAB_FILES_NAMES
      _lowerCamelCase					: Optional[Any]					=					PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase					: Tuple					=					PRETRAINED_INIT_CONFIGURATION
      _lowerCamelCase					: Tuple					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase					: Optional[int]					=					DebertaVaTokenizer





      def __init__(      self       :							List[Any]	, UpperCAmelCase       :							str=None	, UpperCAmelCase       :							int=None	, UpperCAmelCase       :							List[str]=False	, UpperCAmelCase       :							Optional[Any]=False	, UpperCAmelCase       :							List[str]="[CLS]"	, UpperCAmelCase       :							str="[SEP]"	, UpperCAmelCase       :							List[str]="[UNK]"	, UpperCAmelCase       :							Tuple="[SEP]"	, UpperCAmelCase       :							Union[str, Any]="[PAD]"	, UpperCAmelCase       :							Union[str, Any]="[CLS]"	, UpperCAmelCase       :							Any="[MASK]"	, **UpperCAmelCase       :							Dict	, ):
            super().__init__(
                UpperCAmelCase	, tokenizer_file=UpperCAmelCase	, do_lower_case=UpperCAmelCase	, bos_token=UpperCAmelCase	, eos_token=UpperCAmelCase	, unk_token=UpperCAmelCase	, sep_token=UpperCAmelCase	, pad_token=UpperCAmelCase	, cls_token=UpperCAmelCase	, mask_token=UpperCAmelCase	, split_by_punct=UpperCAmelCase	, **UpperCAmelCase	, )

            A_	     =							do_lower_case
            A_	     =							split_by_punct
            A_	     =							vocab_file
            A_	     =							False if not self.vocab_file else True





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[str]=None  ):

            if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            A_	     =							[self.cls_token_id]
            A_	     =							[self.sep_token_id]
            return cls + token_ids_a + sep + token_ids_a + sep





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							int=False  ):

            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCAmelCase	, token_ids_a=UpperCAmelCase	, already_has_special_tokens=UpperCAmelCase  )

            if token_ids_a is not None:
                  return [1] + ([0] * len(UpperCAmelCase  )) + [1] + ([0] * len(UpperCAmelCase  )) + [1]
            return [1] + ([0] * len(UpperCAmelCase  )) + [1]





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]=None  ):
            A_	     =							[self.sep_token_id]
            A_	     =							[self.cls_token_id]
            if token_ids_a is None:
                  return len(cls + token_ids_a + sep  ) * [0]
            return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







      def 		__A  (      self       :							str	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[str] = None  ):
            if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                      "tokenizer."  )

            if not os.path.isdir(UpperCAmelCase  ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                  return
            A_	     =							os.path.join(
                UpperCAmelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

            if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCAmelCase  ):
                  copyfile(self.vocab_file	, UpperCAmelCase  )

            return (out_vocab_file,)