"""simple docstring"""


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCamelCase__				  =   "\\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"

lowerCamelCase__				  =   "\\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"

lowerCamelCase__				  =   "\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__    (					lowercase_		,lowercase_			)					->		Optional[Any]:




      """simple docstring"""

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




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_="binary"			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	Tuple						=       simple_accuracy(lowercase_		,lowercase_			)
      _UpperCamelCase      :	Union[str, Any]						=       float(fa_score(y_true=lowercase_		,y_pred=lowercase_		,average=lowercase_			)			)
      return {
          "accuracy": acc,
          "f1": fa,
      }




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Tuple:




      """simple docstring"""

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







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				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    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				str:
        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    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       Dict					,   __a   :       Dict	)		->				List[Any]:
        if self.config_name == "axb":
              return {"matthews_correlation": matthews_corrcoef(__a					,   __a	)}
        elif self.config_name == "cb":
              return acc_and_fa(__a					,   __a					,   fa_avg="macro"	)
        elif self.config_name == "record":
              _UpperCamelCase      :	Optional[Any]						=       [
                  {
                      "qas": [
                          {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                          for ref in references
                      ]
                  }
              ]
              _UpperCamelCase      :	Optional[int]						=       {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
              return evaluate_record(__a					,   __a	)[0]
        elif self.config_name == "multirc":
              return evaluate_multirc(__a					,   __a	)
        elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
              return {"accuracy": simple_accuracy(__a					,   __a	)}
        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 defaultdict
from math import ceil, sqrt




def 					lowercase__    (					lowercase_ = 1_000_000		,lowercase_ = 10			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	defaultdict						=       defaultdict(lowercase_			)

      for outer_width in range(3		,(t_limit // 4) + 2			):
            if outer_width * outer_width > t_limit:
                  _UpperCamelCase      :	Any						=       max(
                      ceil(sqrt(outer_width * outer_width - t_limit			)			)		,1			)
            else:
                  _UpperCamelCase      :	str						=       1

            hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

            for hole_width in range(lowercase_		,outer_width - 1		,2			):
                  count[outer_width * outer_width - hole_width * hole_width] += 1

      return sum(1 for n in count.values() if 1 <= n <= 10			)


if __name__ == "__main__":
  print(f"""{solution() = }""")
