



from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowercase_:  int     						=					'\\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'

lowercase_:  int     						=					'\\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'

lowercase_:  Optional[Any]     						=					'\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 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 return float((preds == labels).mean())
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_="binary"):




 """simple docstring"""




 snake_case__				:							int      							= simple_accuracy(UpperCamelCase__						,   UpperCamelCase__)
 snake_case__				:							int      							= float(fa_score(y_true=UpperCamelCase__						,   y_pred=UpperCamelCase__						,   average=UpperCamelCase__))
 return {
     "accuracy": acc,
     "f1": fa,
 }
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 snake_case__				:							Optional[int]      							= {}
 for id_pred, label in zip(UpperCamelCase__						,   UpperCamelCase__):
  snake_case__				:							Optional[Any]      							= F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
  snake_case__				:							List[str]      							= id_pred["""prediction"""]
  if question_id in question_map:
   question_map[question_id].append((pred, label))
  else:
   snake_case__				:							Optional[int]      							= [(pred, label)]
 snake_case__							,		snake_case__				:							Dict      							= [], []
 for question, preds_labels in question_map.items():
  snake_case__							,		snake_case__				:							Union[str, Any]      							= zip(*UpperCamelCase__)
  snake_case__				:							Dict      							= fa_score(y_true=UpperCamelCase__						,   y_pred=UpperCamelCase__						,   average="""macro""")
  fas.append(UpperCamelCase__)
  snake_case__				:							List[str]      							= int(sum(pred == label for pred, label in preds_labels) == len(UpperCamelCase__))
  ems.append(UpperCamelCase__)
 snake_case__				:							str      							= float(sum(UpperCamelCase__) / len(UpperCamelCase__))
 snake_case__				:							Optional[Any]      							= sum(UpperCamelCase__) / len(UpperCamelCase__)
 snake_case__				:							Optional[int]      							= float(fa_score(y_true=UpperCamelCase__						,   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						):

     """simple docstring"""






     def 		lowercase   ( self :	Optional[Any] ):
      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 :	int ):
      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 :	Optional[int]   ,			__a :	str   ,			__a :	str ):
      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":
       snake_case__				:							Dict      							= [
           {
               """qas""": [
                   {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                   for ref in references
               ]
           }
       ]
       snake_case__				:							Optional[int]      							= {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\",]""" )










def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: int )	->			list:





				"""simple docstring"""


				__lowerCamelCase									=       word.split()

				def justify(UpperCamelCase__							: list					,	UpperCamelCase__							: int					,	UpperCamelCase__							: int ) -> str:
								__lowerCamelCase									=       max_width - width
								__lowerCamelCase									=       len(UpperCamelCase__ )
								if len(UpperCamelCase__ ) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__lowerCamelCase									=       words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__lowerCamelCase									=       spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowerCamelCase									=       (
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(UpperCamelCase__ ):
																num_spaces_between_words_list[i] += 1
												__lowerCamelCase									=       []
												for i in range(UpperCamelCase__ ):
																# add the word
																aligned_words_list.append(line[i] )
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ' ' )
												# just add the last word to the sentence
												aligned_words_list.append(line[-1] )
												# join the aligned words list to form a justified line
												return "".join(UpperCamelCase__ )

				__lowerCamelCase									=       []
				__lowerCamelCase									=       []
				__lowerCamelCase									=       0
				for word in words:
								if width + len(UpperCamelCase__ ) + len(UpperCamelCase__ ) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(UpperCamelCase__ )
												width += len(UpperCamelCase__ )
								else:
												# justify the line and add it to result
												answer.append(justify(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ ) )
												# reset new line and new width
												__lowerCamelCase			,		__lowerCamelCase									=       [word], len(UpperCamelCase__ )
				__lowerCamelCase									=       max_width - width - len(UpperCamelCase__ )
				answer.append(' '.join(UpperCamelCase__ ) + (remaining_spaces + 1) * ' ' )
				return answer


if __name__ == "__main__":
					from doctest import testmod

					testmod()


