



from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


snake_case__					:     List[Any]    		=   """\
@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}
}
"""

snake_case__					:     Optional[int]    		=   """\
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.
"""

snake_case__					:     Optional[int]    		=   """
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    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	return float((preds == labels).mean()   )



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



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


						'''simple docstring'''





						def     _snake_case	(	self	:							Tuple	):






							'''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     _snake_case	(	self	:							Tuple	):






							'''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     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							Optional[Any]							,      lowerCamelCase	:							Dict	):






							'''simple docstring'''



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








import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
	import ctypes
	import msvcrt  # noqa

	class   _A   ( ctypes.Structure	):


							'''simple docstring'''





							_snake_case			:     Optional[Any]      							=      [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]



def    snake_case_				(    ):
	if os.name == "nt":
		__lowercase		       =			CursorInfo()
		__lowercase		       =			ctypes.windll.kernelaa.GetStdHandle(-1_1   )
		ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE  ,	ctypes.byref(_SCREAMING_SNAKE_CASE   )   )
		__lowercase		       =			False
		ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE  ,	ctypes.byref(_SCREAMING_SNAKE_CASE   )   )
	elif os.name == "posix":
		sys.stdout.write("\033[?25l"   )
		sys.stdout.flush()



def    snake_case_				(    ):
	if os.name == "nt":
		__lowercase		       =			CursorInfo()
		__lowercase		       =			ctypes.windll.kernelaa.GetStdHandle(-1_1   )
		ctypes.windll.kernelaa.GetConsoleCursorInfo(_SCREAMING_SNAKE_CASE  ,	ctypes.byref(_SCREAMING_SNAKE_CASE   )   )
		__lowercase		       =			True
		ctypes.windll.kernelaa.SetConsoleCursorInfo(_SCREAMING_SNAKE_CASE  ,	ctypes.byref(_SCREAMING_SNAKE_CASE   )   )
	elif os.name == "posix":
		sys.stdout.write("\033[?25h"   )
		sys.stdout.flush()



@contextmanager
def    snake_case_				(    ):
	try:
		hide_cursor()
		yield
	finally:
		show_cursor()



