


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


A      = "\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

A      = "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

A      = "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"





def __UpperCAmelCase       (		__A ,						__A			)						->					List[str]:



       '''simple docstring'''





       return float((preds == labels).mean()			)





def __UpperCAmelCase       (		__A ,						__A ,						__A="binary"			)						->					Union[str, Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     simple_accuracy(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__										=     float(fa_score(y_true=__SCREAMING_SNAKE_CASE ,						y_pred=__SCREAMING_SNAKE_CASE ,						average=__SCREAMING_SNAKE_CASE			)			)
       return {
           "accuracy": acc,
           "f1": fa,
       }





def __UpperCAmelCase       (		__A ,						__A			)						->					List[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     {}
       for id_pred, label in zip(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE			):
              UpperCAmelCase__										=     F"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
              UpperCAmelCase__										=     id_pred["prediction"]
              if question_id in question_map:
                     question_map[question_id].append((pred, label)			)
              else:
                     UpperCAmelCase__										=     [(pred, label)]
       UpperCAmelCase__						,			UpperCAmelCase__										=     [], []
       for question, preds_labels in question_map.items():
              UpperCAmelCase__						,			UpperCAmelCase__										=     zip(*__SCREAMING_SNAKE_CASE			)
              UpperCAmelCase__										=     fa_score(y_true=__SCREAMING_SNAKE_CASE ,						y_pred=__SCREAMING_SNAKE_CASE ,						average="macro"			)
              fas.append(__SCREAMING_SNAKE_CASE			)
              UpperCAmelCase__										=     int(sum(pred == label for pred, label in preds_labels			) == len(__SCREAMING_SNAKE_CASE			)			)
              ems.append(__SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__										=     float(sum(__SCREAMING_SNAKE_CASE			) / len(__SCREAMING_SNAKE_CASE			)			)
       UpperCAmelCase__										=     sum(__SCREAMING_SNAKE_CASE			) / len(__SCREAMING_SNAKE_CASE			)
       UpperCAmelCase__										=     float(fa_score(y_true=__SCREAMING_SNAKE_CASE ,						y_pred=[id_pred["prediction"] for id_pred in ids_preds]			)			)
       return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






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






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         if self.config_name not in [
             "boolq",
             "cb",
             "copa",
             "multirc",
             "record",
             "rte",
             "wic",
             "wsc",
             "wsc.fixed",
             "axb",
             "axg",
         ]:
                raise KeyError(
                    "You should supply a configuration name selected in "
                    "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
         return datasets.MetricInfo(
             description=_DESCRIPTION		,						citation=_CITATION		,						inputs_description=_KWARGS_DESCRIPTION		,						features=datasets.Features(self._get_feature_types() )		,						codebase_urls=[]		,						reference_urls=[]		,						format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None		,						)






  def 			_UpperCAmelCase  (					self      :					str ):




         """simple docstring"""







         if self.config_name == "record":
                return {
                    "predictions": {
                        "idx": {
                            "passage": datasets.Value("int64" ),
                            "query": datasets.Value("int64" ),
                        },
                        "prediction_text": datasets.Value("string" ),
                    },
                    "references": {
                        "idx": {
                            "passage": datasets.Value("int64" ),
                            "query": datasets.Value("int64" ),
                        },
                        "answers": datasets.Sequence(datasets.Value("string" ) ),
                    },
                }
         elif self.config_name == "multirc":
                return {
                    "predictions": {
                        "idx": {
                            "answer": datasets.Value("int64" ),
                            "paragraph": datasets.Value("int64" ),
                            "question": datasets.Value("int64" ),
                        },
                        "prediction": datasets.Value("int64" ),
                    },
                    "references": datasets.Value("int64" ),
                }
         else:
                return {
                    "predictions": datasets.Value("int64" ),
                    "references": datasets.Value("int64" ),
                }






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					Tuple		,						_lowercase      :					Union[str, Any] ):




         """simple docstring"""







         if self.config_name == "axb":
                return {"matthews_correlation": matthews_corrcoef(_lowercase		,						_lowercase )}
         elif self.config_name == "cb":
                return acc_and_fa(_lowercase		,						_lowercase		,						fa_avg="macro" )
         elif self.config_name == "record":
                UpperCAmelCase__										=     [
                    {
                        "qas": [
                            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                            for ref in references
                        ]
                    }
                ]
                UpperCAmelCase__										=     {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
                return evaluate_record(_lowercase		,						_lowercase )[0]
         elif self.config_name == "multirc":
                return evaluate_multirc(_lowercase		,						_lowercase )
         elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
                return {"accuracy": simple_accuracy(_lowercase		,						_lowercase )}
         else:
                raise KeyError(
                    "You should supply a configuration name selected in "
                    "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )









import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


A      = logging.get_logger(__name__)


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					Union[str, Any]		,						*_lowercase      :					Any		,						**_lowercase      :					Optional[int] ):




         """simple docstring"""







         warnings.warn(
             "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use DPTImageProcessor instead."		,						_lowercase		,						)
         super().__init__(*_lowercase		,						**_lowercase )





