


'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase							=logging.get_logger(__name__)

lowercase							={
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}






class  __magic_name__							(		__SCREAMING_SNAKE_CASE    ):
       UpperCAmelCase										="roberta-prelayernorm"






       def __init__(      self  ,							snake_case=5_0_2_6_5  ,							snake_case=7_6_8  ,							snake_case=1_2  ,							snake_case=1_2  ,							snake_case=3_0_7_2  ,							snake_case="gelu"  ,							snake_case=0.1  ,							snake_case=0.1  ,							snake_case=5_1_2  ,							snake_case=2  ,							snake_case=0.02  ,							snake_case=1E-1_2  ,							snake_case=1  ,							snake_case=0  ,							snake_case=2  ,							snake_case="absolute"  ,							snake_case=True  ,							snake_case=None  ,							**snake_case  ,							)						->  Tuple:




          '''simple docstring'''
          super().__init__(pad_token_id=UpperCamelCase__  ,							bos_token_id=UpperCamelCase__  ,							eos_token_id=UpperCamelCase__  ,							**UpperCamelCase__)

          _UpperCAmelCase       :       Any							   =vocab_size
          _UpperCAmelCase       :       List[Any]							   =hidden_size
          _UpperCAmelCase       :       List[str]							   =num_hidden_layers
          _UpperCAmelCase       :       Any							   =num_attention_heads
          _UpperCAmelCase       :       Tuple							   =hidden_act
          _UpperCAmelCase       :       int							   =intermediate_size
          _UpperCAmelCase       :       List[Any]							   =hidden_dropout_prob
          _UpperCAmelCase       :       List[str]							   =attention_probs_dropout_prob
          _UpperCAmelCase       :       Optional[int]							   =max_position_embeddings
          _UpperCAmelCase       :       Union[str, Any]							   =type_vocab_size
          _UpperCAmelCase       :       Any							   =initializer_range
          _UpperCAmelCase       :       Dict							   =layer_norm_eps
          _UpperCAmelCase       :       Optional[Any]							   =position_embedding_type
          _UpperCAmelCase       :       Union[str, Any]							   =use_cache
          _UpperCAmelCase       :       Tuple							   =classifier_dropout






class  __magic_name__							(		__SCREAMING_SNAKE_CASE    ):






       @property
       def       lowerCAmelCase		(      self)						->  Mapping[str, Mapping[int, str]]:




          '''simple docstring'''







          if self.task == "multiple-choice":
             _UpperCAmelCase       :       Any							   ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
             _UpperCAmelCase       :       Any							   ={0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])





'''simple docstring'''






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  __magic_name__							(		datasets.Metric    ):






       def       lowerCAmelCase		(      self)						->  List[str]:




          '''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       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  int:




          '''simple docstring'''
          _UpperCAmelCase       :       Optional[int]							   ={prediction['id']: prediction['prediction_text'] for prediction in predictions}
          _UpperCAmelCase       :       Optional[int]							   =[
              {
                  'paragraphs': [
                      {
                          'qas': [
                              {
                                  'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
                                  'id': ref['id'],
                              }
                              for ref in references
                          ]
                      }
                  ]
              }
          ]
          _UpperCAmelCase       :       List[Any]							   =evaluate(dataset=snake_case  ,							predictions=snake_case)
          return score

