





'''simple docstring'''

import datasets

from .evaluate import evaluate


__lowerCamelCase : Optional[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"

__lowerCamelCase : Any											=			"\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 : Union[str, Any]											=			"\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       UpperCAmelCase (						datasets.Metric  ):




  def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> Union[str, Any]:
      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 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	List[Any]							,			A__      :	List[str]				)					-> str:
      lowercase         = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
      lowercase         = [
          {
              "paragraphs": [
                  {
                      "qas": [
                          {
                              "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                              "id": ref["id"],
                          }
                          for ref in references
                      ]
                  }
              ]
          }
      ]
      lowercase         = evaluate(dataset=A__							,			predictions=A__				)
      return score











'''simple docstring'''

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


__lowerCamelCase : Optional[Any]											=			logging.get_logger(__name__)

__lowerCamelCase : Optional[Any]											=			{
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Union[str, Any] 			=		'''vit_mae'''




		def __init__(self      :	Dict							,			A__      :	Tuple=7_6_8							,			A__      :	str=1_2							,			A__      :	Dict=1_2							,			A__      :	Optional[Any]=3_0_7_2							,			A__      :	Union[str, Any]="gelu"							,			A__      :	Optional[int]=0.0							,			A__      :	str=0.0							,			A__      :	Optional[Any]=0.0_2							,			A__      :	Optional[Any]=1e-12							,			A__      :	Tuple=2_2_4							,			A__      :	str=1_6							,			A__      :	Optional[int]=3							,			A__      :	List[Any]=True							,			A__      :	Dict=1_6							,			A__      :	Dict=5_1_2							,			A__      :	Dict=8							,			A__      :	Union[str, Any]=2_0_4_8							,			A__      :	str=0.7_5							,			A__      :	str=False							,			**A__      :	Any							,			)					-> Union[str, Any]:
						super().__init__(**A__				)

						lowercase         = hidden_size
						lowercase         = num_hidden_layers
						lowercase         = num_attention_heads
						lowercase         = intermediate_size
						lowercase         = hidden_act
						lowercase         = hidden_dropout_prob
						lowercase         = attention_probs_dropout_prob
						lowercase         = initializer_range
						lowercase         = layer_norm_eps
						lowercase         = image_size
						lowercase         = patch_size
						lowercase         = num_channels
						lowercase         = qkv_bias
						lowercase         = decoder_num_attention_heads
						lowercase         = decoder_hidden_size
						lowercase         = decoder_num_hidden_layers
						lowercase         = decoder_intermediate_size
						lowercase         = mask_ratio
						lowercase         = norm_pix_loss




