




'''simple docstring'''





import datasets

from .evaluate import evaluate


_SCREAMING_SNAKE_CASE							:       List[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"

_SCREAMING_SNAKE_CASE							:       Tuple						=			"\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"

_SCREAMING_SNAKE_CASE							:       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 			_snake_case		(     datasets.Metric							):




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''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    ,							a__    ,							a__							)    ->    List[str]:

										'''simple docstring'''




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










'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       Any						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       int						=			{
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Union[str, Any]							=   "mgp-str"




			def __init__( self    ,							a__=[32, 128]    ,							a__=4    ,							a__=3    ,							a__=27    ,							a__=38    ,							a__=50_257    ,							a__=30_522    ,							a__=768    ,							a__=12    ,							a__=12    ,							a__=4.0    ,							a__=True    ,							a__=False    ,							a__=1e-5    ,							a__=0.0    ,							a__=0.0    ,							a__=0.0    ,							a__=False    ,							a__=0.0_2    ,							**a__    ,							)    ->    Union[str, Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										snake_case_      =				image_size
										snake_case_      =				patch_size
										snake_case_      =				num_channels
										snake_case_      =				max_token_length
										snake_case_      =				num_character_labels
										snake_case_      =				num_bpe_labels
										snake_case_      =				num_wordpiece_labels
										snake_case_      =				hidden_size
										snake_case_      =				num_hidden_layers
										snake_case_      =				num_attention_heads
										snake_case_      =				mlp_ratio
										snake_case_      =				distilled
										snake_case_      =				layer_norm_eps
										snake_case_      =				drop_rate
										snake_case_      =				qkv_bias
										snake_case_      =				attn_drop_rate
										snake_case_      =				drop_path_rate
										snake_case_      =				output_aa_attentions
										snake_case_      =				initializer_range




