from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


SCREAMING_SNAKE_CASE :	Optional[Any]							     =		'\\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'

SCREAMING_SNAKE_CASE :	Tuple							     =		'\\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'

SCREAMING_SNAKE_CASE :	str							     =		'\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   __A		(			_A							,				_A					):



 """simple docstring"""


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



def   __A		(			_A							,				_A							,				_A="binary"					):



 """simple docstring"""


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



def   __A		(			_A							,				_A					):



 """simple docstring"""


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

  def 		_UpperCAmelCase   (		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 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   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 :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Optional[int]       ):
   if self.config_name == "axb":
    return {"matthews_correlation": matthews_corrcoef(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE       )}
   elif self.config_name == "cb":
    return acc_and_fa(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE     ,							fa_avg="macro"       )
   elif self.config_name == "record":
    __a       =  [
        {
            "qas": [
                {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                for ref in references
            ]
        }
    ]
    __a       =  {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
    return evaluate_record(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE       )[0]
   elif self.config_name == "multirc":
    return evaluate_multirc(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE       )
   elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
    return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE     ,							_SCREAMING_SNAKE_CASE       )}
   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 ):
    # _fields is a specific attr expected by ctypes
    _SCREAMING_SNAKE_CASE    				=			[("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]



def   __A		(			):



 """simple docstring"""


 if os.name == "nt":
  __a       =  CursorInfo()
  __a       =  ctypes.windll.kernelaa.GetStdHandle(-11					)
  ctypes.windll.kernelaa.GetConsoleCursorInfo(_A							,				ctypes.byref(_A					)					)
  __a       =  False
  ctypes.windll.kernelaa.SetConsoleCursorInfo(_A							,				ctypes.byref(_A					)					)
 elif os.name == "posix":
  sys.stdout.write("\033[?25l"					)
  sys.stdout.flush()



def   __A		(			):



 """simple docstring"""


 if os.name == "nt":
  __a       =  CursorInfo()
  __a       =  ctypes.windll.kernelaa.GetStdHandle(-11					)
  ctypes.windll.kernelaa.GetConsoleCursorInfo(_A							,				ctypes.byref(_A					)					)
  __a       =  True
  ctypes.windll.kernelaa.SetConsoleCursorInfo(_A							,				ctypes.byref(_A					)					)
 elif os.name == "posix":
  sys.stdout.write("\033[?25h"					)
  sys.stdout.flush()






@contextmanager
def   __A		(			):



 """simple docstring"""


 try:
  hide_cursor()
  yield
 finally:
  show_cursor()


