

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

import datasets


lowerCamelCase :      List[Any]					 =     "\\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"

lowerCamelCase :      Optional[int]					 =     "\\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"

lowerCamelCase :      Dict					 =     "\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        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     Optional[Any]				)      ->			Dict:
     """simple docstring"""




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


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Union[str, Any]				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =simple_accuracy(_UpperCamelCase							, _UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =float(fa_score(y_true=_UpperCamelCase							, y_pred=_UpperCamelCase				)				)
     return {
         "accuracy": acc,
         "f1": fa,
     }


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     str				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =float(pearsonr(_UpperCamelCase							, _UpperCamelCase				)[0]				)
     _SCREAMING_SNAKE_CASE							    =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  ):





  def 						A  (		self      :	List[str] )	->    List[str]:





       '''simple docstring'''






       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 						A  (		self      :	int							,					_a      :	Any							,					_a      :	int )	->    Tuple:





       '''simple docstring'''






       if self.config_name == "cola":
            return {"matthews_correlation": matthews_corrcoef(_a							,					_a )}
       elif self.config_name == "stsb":
            return pearson_and_spearman(_a							,					_a )
       elif self.config_name in ["mrpc", "qqp"]:
            return acc_and_fa(_a							,					_a )
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
            return {"accuracy": simple_accuracy(_a							,					_a )}
       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"]' )







import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows





def lowerCAmelCase__(__snake_case							)				->      int:  # picklable for multiprocessing







	'''simple docstring'''







	return i + 1





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def lowerCAmelCase__()				->      Any:







	'''simple docstring'''







	with parallel_backend('''spark'''							):
		assert ParallelBackendConfig.backend_name == "spark"

	lowerCamelCase__								=		[1, 2, 3]
	with pytest.raises(__snake_case							):
		with parallel_backend('''unsupported backend'''							):
			map_nested(__snake_case       ,__snake_case       ,num_proc=2							)

	with pytest.raises(__snake_case							):
		with parallel_backend('''unsupported backend'''							):
			map_nested(__snake_case       ,__snake_case       ,num_proc=-1							)





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('''num_proc'''       ,[2, -1]							)
def lowerCAmelCase__(__snake_case							)				->      Tuple:







	'''simple docstring'''







	lowerCamelCase__								=		[1, 2]
	lowerCamelCase__								=		{'''a''': 1, '''b''': 2}
	lowerCamelCase__								=		{'''a''': [1, 2], '''b''': [3, 4]}
	lowerCamelCase__								=		{'''a''': {'''1''': 1}, '''b''': 2}
	lowerCamelCase__								=		{'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
	lowerCamelCase__								=		[2, 3]
	lowerCamelCase__								=		{'''a''': 2, '''b''': 3}
	lowerCamelCase__								=		{'''a''': [2, 3], '''b''': [4, 5]}
	lowerCamelCase__								=		{'''a''': {'''1''': 2}, '''b''': 3}
	lowerCamelCase__								=		{'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}

	with parallel_backend('''spark'''							):
		assert map_nested(__snake_case       ,__snake_case       ,num_proc=__snake_case							) == expected_map_nested_sa
		assert map_nested(__snake_case       ,__snake_case       ,num_proc=__snake_case							) == expected_map_nested_sa
		assert map_nested(__snake_case       ,__snake_case       ,num_proc=__snake_case							) == expected_map_nested_sa
		assert map_nested(__snake_case       ,__snake_case       ,num_proc=__snake_case							) == expected_map_nested_sa
		assert map_nested(__snake_case       ,__snake_case       ,num_proc=__snake_case							) == expected_map_nested_sa



