
'''simple docstring'''



from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_lowercase    =		"""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  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},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

_lowercase    =		"""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

_lowercase    =		"""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""


def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:List[Any]		):
						return float((preds == labels).mean()		)


def A				(__lowerCamelCase				:str			,     __lowerCamelCase				:List[Any]			,     __lowerCamelCase				:Any="binary"		):
						_lowerCAmelCase  						=			simple_accuracy(__lowerCamelCase			,     __lowerCamelCase		)
						_lowerCAmelCase  						=			float(fa_score(y_true=__lowerCamelCase			,     y_pred=__lowerCamelCase			,     average=__lowerCamelCase		)		)
						return {
						    "accuracy": acc,
						    "f1": fa,
						}


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






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""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  _lowercase     ( self				):

												"""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  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""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":
																		_lowerCAmelCase  						=			[
																		    {
																		        """qas""": [
																		            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
																		            for ref in references
																		        ]
																		    }
																		]
																		_lowerCAmelCase  						=			{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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase    =		logging.get_logger(__name__)

_lowercase    =		{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				List[Any]									=       '''convbert'''




						def __init__( self   ,       _lowercase=30_522   ,       _lowercase=768   ,       _lowercase=12   ,       _lowercase=12   ,       _lowercase=3_072   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=2   ,       _lowercase=0.02   ,       _lowercase=1e-12   ,       _lowercase=1   ,       _lowercase=0   ,       _lowercase=2   ,       _lowercase=768   ,       _lowercase=2   ,       _lowercase=9   ,       _lowercase=1   ,       _lowercase=None   ,       **_lowercase   ,       ):

												"""simple docstring"""



												super().__init__(
												    pad_token_id=_lowercase   ,       bos_token_id=_lowercase   ,       eos_token_id=_lowercase   ,       **_lowercase   ,       )

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			type_vocab_size
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			layer_norm_eps
												_lowerCAmelCase  						=			embedding_size
												_lowerCAmelCase  						=			head_ratio
												_lowerCAmelCase  						=			conv_kernel_size
												_lowerCAmelCase  						=			num_groups
												_lowerCAmelCase  						=			classifier_dropout






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												if self.task == "multiple-choice":
																		_lowerCAmelCase  						=			{0: """batch""", 1: """choice""", 2: """sequence"""}
												else:
																		_lowerCAmelCase  						=			{0: """batch""", 1: """sequence"""}
												return OrderedDict(
												    [
												        ("""input_ids""", dynamic_axis),
												        ("""attention_mask""", dynamic_axis),
												        ("""token_type_ids""", dynamic_axis),
												    ]				)
