



from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCAmelCase_		 =					"""\
@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}
}
"""

UpperCAmelCase_		 =					"""\
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.
"""

UpperCAmelCase_		 =					"""
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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]	,   _snake_case	:Dict				)  ->     Optional[int]:
   return float((preds == labels).mean()				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:int	,   _snake_case	:str="binary"				)  ->     int:
   _A      =       simple_accuracy(_snake_case	,   _snake_case				)
   _A      =       float(fa_score(y_true=_snake_case	,   y_pred=_snake_case	,   average=_snake_case				)				)
   return {
       "accuracy": acc,
       "f1": fa,
   }

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[Any]	,   _snake_case	:int				)  ->     Any:
   _A      =       {}
   for id_pred, label in zip(_snake_case	,   _snake_case				):
      _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(*_snake_case				)
      _A      =       fa_score(y_true=_snake_case	,   y_pred=_snake_case	,   average='''macro'''				)
      fas.append(_snake_case				)
      _A      =       int(sum(pred == label for pred, label in preds_labels				) == len(_snake_case				)				)
      ems.append(_snake_case				)
   _A      =       float(sum(_snake_case				) / len(_snake_case				)				)
   _A      =       sum(_snake_case				) / len(_snake_case				)
   _A      =       float(fa_score(y_true=_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     snake_case_      (    self					:					Tuple      )   ->	List[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     snake_case_      (    self					:					str      )   ->	Dict:
    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					:					Any			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					List[Any]      )   ->	Any:
    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":
       _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(__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",]'''      )






"""simple docstring"""

from __future__ import annotations

_UpperCamelCase		     =    []



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						bool:
 for i in range(len(lowercase__   )   ):
  if board[row][i] == 1:
   return False
 for i in range(len(lowercase__   )   ):
  if board[i][column] == 1:
   return False
 for i, j in zip(range(lowercase__					,					-1					,					-1   )					,					range(lowercase__					,					-1					,					-1   )   ):
  if board[i][j] == 1:
   return False
 for i, j in zip(range(lowercase__					,					-1					,					-1   )					,					range(lowercase__					,					len(lowercase__   )   )   ):
  if board[i][j] == 1:
   return False
 return True



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						bool:
 if row >= len(lowercase__   ):
  solution.append(lowercase__   )
  printboard(lowercase__   )
  print()
  return True
 for i in range(len(lowercase__   )   ):
  if is_safe(lowercase__					,					lowercase__					,					lowercase__   ):
   lowerCAmelCase__   :				List[Any] 			=				1
   solve(lowercase__					,					row + 1   )
   lowerCAmelCase__   :				Dict 			=				0
 return False



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						None:
 for i in range(len(lowercase__   )   ):
  for j in range(len(lowercase__   )   ):
   if board[i][j] == 1:
    print("Q"					,					end=" "   )
   else:
    print("."					,					end=" "   )
  print()


# n=int(input("The no. of queens"))
_UpperCamelCase		     =    8
_UpperCamelCase		     =    [[0 for i in range(n)] for j in range(n)]
solve(board, 0)






print("""The total no. of solutions are :""", len(solution))






