

'''simple docstring'''

from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowercase__         =      "\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

lowercase__         =      "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

lowercase__         =      "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 return float((preds == labels).mean()     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     str						 =			simple_accuracy(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			float(fa_score(y_true=UpperCAmelCase_  ,	y_pred=UpperCAmelCase_     )     )
 return {
     "accuracy": acc,
     "f1": fa,
 }




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			float(pearsonr(UpperCAmelCase_  ,	UpperCAmelCase_     )[0]     )
 UpperCAmelCase	:     int						 =			float(spearmanr(UpperCAmelCase_  ,	UpperCAmelCase_     )[0]     )
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,       _KWARGS_DESCRIPTION							)
class 	A_			(    datasets.Metric							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Tuple:
      if self.config_name not in [
          "sst2",
          "mnli",
          "mnli_mismatched",
          "mnli_matched",
          "cola",
          "stsb",
          "mrpc",
          "qqp",
          "qnli",
          "rte",
          "wnli",
          "hans",
      ]:
       raise KeyError(
           'You should supply a configuration name selected in '
           '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
           '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'	)
      return datasets.MetricInfo(
          description=_DESCRIPTION					,      citation=_CITATION					,      inputs_description=_KWARGS_DESCRIPTION					,      features=datasets.Features(
              {
                  'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'	),
                  'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'	),
              }	)					,      codebase_urls=[]					,      reference_urls=[]					,      format='numpy'					,      )




     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					Optional[int]					,      lowercase_			:					List[str]	)						->     int:
      if self.config_name == "cola":
       return {"matthews_correlation": matthews_corrcoef(lowercase_					,      lowercase_	)}
      elif self.config_name == "stsb":
       return pearson_and_spearman(lowercase_					,      lowercase_	)
      elif self.config_name in ["mrpc", "qqp"]:
       return acc_and_fa(lowercase_					,      lowercase_	)
      elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
       return {"accuracy": simple_accuracy(lowercase_					,      lowercase_	)}
      else:
       raise KeyError(
           'You should supply a configuration name selected in '
           '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
           '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'	)







'''simple docstring'''

from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowercase__         =      TypeVar("KEY")
lowercase__         =      TypeVar("VAL")




@dataclass(frozen=_snake_case	,       slots=_snake_case							)
class 	A_			(    Generic[KEY, VAL]							):






     '''simple docstring'''



     UpperCAmelCase_						:     KEY
     UpperCAmelCase_						:     VAL




class 	A_			(    _Item							):






     '''simple docstring'''



     def __init__(		self			:					Any	)						->     None:
      super().__init__(lowercase_					,      lowercase_	)




     def __bool__(		self			:					List[str]	)						->     bool:
      return False



lowercase__         =      _DeletedItem()




class 	A_			(    MutableMapping[KEY, VAL]							):






     '''simple docstring'''



     def __init__(		self			:					Optional[int]					,      lowercase_			:					int = 8					,      lowercase_			:					float = 0.75	)						->     None:
      UpperCAmelCase	:     Dict						 =			initial_block_size
      UpperCAmelCase	:     list[_Item | None]						 =			[None] * initial_block_size
      assert 0.0 < capacity_factor < 1.0
      UpperCAmelCase	:     Any						 =			capacity_factor
      UpperCAmelCase	:     Union[str, Any]						 =			0


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					KEY	)						->     int:
      return hash(lowercase_	) % len(self._buckets	)


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					int	)						->     int:
      return (ind + 1) % len(self._buckets	)


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					int					,      lowercase_			:					KEY					,      lowercase_			:					VAL	)						->     bool:
      UpperCAmelCase	:     List[Any]						 =			self._buckets[ind]
      if not stored:
       UpperCAmelCase	:     Dict						 =			_Item(lowercase_					,      lowercase_	)
       self._len += 1
       return True
      elif stored.key == key:
       UpperCAmelCase	:     Dict						 =			_Item(lowercase_					,      lowercase_	)
       return True
      else:
       return False


     def        UpperCAmelCase_	(		self			:					Any	)						->     bool:
      UpperCAmelCase	:     List[str]						 =			len(self._buckets	) * self._capacity_factor
      return len(self	) >= int(lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     bool:
      if len(self._buckets	) <= self._initial_block_size:
       return False
      UpperCAmelCase	:     Optional[int]						 =			len(self._buckets	) * self._capacity_factor / 2
      return len(self	) < limit


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					int	)						->     None:
      UpperCAmelCase	:     int						 =			self._buckets
      UpperCAmelCase	:     List[str]						 =			[None] * new_size
      UpperCAmelCase	:     Dict						 =			0
      for item in old_buckets:
       if item:
        self._add_item(item.key					,      item.val	)


     def        UpperCAmelCase_	(		self			:					Dict	)						->     None:
      self._resize(len(self._buckets	) * 2	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     None:
      self._resize(len(self._buckets	) // 2	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					KEY	)						->     Iterator[int]:
      UpperCAmelCase	:     Dict						 =			self._get_bucket_index(lowercase_	)
      for _ in range(len(self._buckets	)	):
       yield ind
       UpperCAmelCase	:     Union[str, Any]						 =			self._get_next_ind(lowercase_	)


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					KEY					,      lowercase_			:					VAL	)						->     None:
      for ind in self._iterate_buckets(lowercase_	):
       if self._try_set(lowercase_					,      lowercase_					,      lowercase_	):
        break


     def __setitem__(		self			:					Union[str, Any]					,      lowercase_			:					KEY					,      lowercase_			:					VAL	)						->     None:
      if self._is_full():
       self._size_up()

      self._add_item(lowercase_					,      lowercase_	)


     def __delitem__(		self			:					Tuple					,      lowercase_			:					KEY	)						->     None:
      for ind in self._iterate_buckets(lowercase_	):
       UpperCAmelCase	:     int						 =			self._buckets[ind]
       if item is None:
        raise KeyError(lowercase_	)
       if item is _deleted:
        continue
       if item.key == key:
        UpperCAmelCase	:     Union[str, Any]						 =			_deleted
        self._len -= 1
        break
      if self._is_sparse():
       self._size_down()


     def __getitem__(		self			:					Dict					,      lowercase_			:					KEY	)						->     VAL:
      for ind in self._iterate_buckets(lowercase_	):
       UpperCAmelCase	:     List[str]						 =			self._buckets[ind]
       if item is None:
        break
       if item is _deleted:
        continue
       if item.key == key:
        return item.val
      raise KeyError(lowercase_	)


     def __len__(		self			:					Optional[Any]	)						->     int:
      return self._len


     def __iter__(		self			:					int	)						->     Iterator[KEY]:
      yield from (item.key for item in self._buckets if item)




     def __repr__(		self			:					Optional[Any]	)						->     str:
      UpperCAmelCase	:     int						 =			' ,'.join(
          f"""{item.key}: {item.val}""" for item in self._buckets if item	)
      return f"""HashMap({val_string})"""




