
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__lowerCAmelCase												=	'\\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'

__lowerCAmelCase												=	'\\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'

__lowerCAmelCase												=	'\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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       return float((preds == labels).mean()				)






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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
       _snake_case       =  {}
       for id_pred, label in zip(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
              _snake_case       =  f"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
              _snake_case       =  id_pred['''prediction''']
              if question_id in question_map:
                     question_map[question_id].append((pred, label)				)
              else:
                     _snake_case       =  [(pred, label)]
       _snake_case       =  [], []
       for question, preds_labels in question_map.items():
              _snake_case       =  zip(*_SCREAMING_SNAKE_CASE				)
              _snake_case       =  fa_score(y_true=_SCREAMING_SNAKE_CASE							, y_pred=_SCREAMING_SNAKE_CASE							, average="""macro"""				)
              fas.append(_SCREAMING_SNAKE_CASE				)
              _snake_case       =  int(sum(pred == label for pred, label in preds_labels				) == len(_SCREAMING_SNAKE_CASE				)				)
              ems.append(_SCREAMING_SNAKE_CASE				)
       _snake_case       =  float(sum(_SCREAMING_SNAKE_CASE				) / len(_SCREAMING_SNAKE_CASE				)				)
       _snake_case       =  sum(_SCREAMING_SNAKE_CASE				) / len(_SCREAMING_SNAKE_CASE				)
       _snake_case       =  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       _lowerCAmelCase   (   datasets.Metric				):







       '''simple docstring'''

       def 	lowercase							(self  )					->       str:
              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 	lowercase							(self  )					->       str:
              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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       int:
              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":
                     _snake_case       =  [
                         {
                             '''qas''': [
                                 {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]}
                                 for ref in references
                             ]
                         }
                     ]
                     _snake_case       =  {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\",]"""  )

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase												=	{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ResNetForImageClassification',
				    'ResNetModel',
				    'ResNetPreTrainedModel',
				    'ResNetBackbone',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFResNetForImageClassification',
				    'TFResNetModel',
				    'TFResNetPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'FlaxResNetForImageClassification',
				    'FlaxResNetModel',
				    'FlaxResNetPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_resnet import (
								    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ResNetBackbone,
								    ResNetForImageClassification,
								    ResNetModel,
								    ResNetPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_resnet import (
								    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFResNetForImageClassification,
								    TFResNetModel,
								    TFResNetPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure)