





import datasets

from .evaluate import evaluate


__lowercase  :   int						      ="""\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
"""

__lowercase  :   Union[str, Any]						      ="""
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
"""

__lowercase  :   Tuple						      ="""
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the SQuAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_metric = datasets.load_metric(\"squad\")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0}
"""

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



      def 	lowerCAmelCase__    (	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 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						Any       )						->    Dict:
         '''simple docstring'''

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











from ....configuration_utils import PretrainedConfig
from ....utils import logging


__lowercase  :   Optional[int]						      =logging.get_logger(__name__)

__lowercase  :   Union[str, Any]						      ={
    """Visual-Attention-Network/van-base""": (
        """https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
    ),
}

class      A       (	__lowercase						):
      _snake_case							     ='''van'''



      def __init__(	self:						str		,  _lowerCAmelCase:						List[Any]=224		,  _lowerCAmelCase:						Any=3		,  _lowerCAmelCase:						Tuple=[7, 3, 3, 3]		,  _lowerCAmelCase:						Union[str, Any]=[4, 2, 2, 2]		,  _lowerCAmelCase:						List[str]=[64, 128, 320, 512]		,  _lowerCAmelCase:						Tuple=[3, 3, 12, 3]		,  _lowerCAmelCase:						Union[str, Any]=[8, 8, 4, 4]		,  _lowerCAmelCase:						Any="gelu"		,  _lowerCAmelCase:						Optional[int]=0.02		,  _lowerCAmelCase:						List[str]=1e-6		,  _lowerCAmelCase:						Any=1e-2		,  _lowerCAmelCase:						Any=0.0		,  _lowerCAmelCase:						Optional[Any]=0.0		,  **_lowerCAmelCase:						List[str]		,  )						->    Dict:
         '''simple docstring'''

         super().__init__(**_lowerCAmelCase       )
         UpperCAmelCase_											=image_size
         UpperCAmelCase_											=num_channels
         UpperCAmelCase_											=patch_sizes
         UpperCAmelCase_											=strides
         UpperCAmelCase_											=hidden_sizes
         UpperCAmelCase_											=depths
         UpperCAmelCase_											=mlp_ratios
         UpperCAmelCase_											=hidden_act
         UpperCAmelCase_											=initializer_range
         UpperCAmelCase_											=layer_norm_eps
         UpperCAmelCase_											=layer_scale_init_value
         UpperCAmelCase_											=drop_path_rate
         UpperCAmelCase_											=dropout_rate




