


"""simple docstring"""







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

import datasets


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

a__				:							str						=     '''\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
'''

a__				:							int						=     '''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "pearson": Pearson Correlation
    "spearmanr": Spearman Correlation
    "matthews_correlation": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
    {\'pearson\': 1.0, \'spearmanr\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				float(pearsonr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]   )
					__SCREAMING_SNAKE_CASE						   =				float(spearmanr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]   )
					return {
					    "pearson": pearson_corr,
					    "spearmanr": spearman_corr,
					}




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

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	Union[str, Any]:
						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			:				List[Any]			,			UpperCAmelCase__			:				List[str]			,			UpperCAmelCase__			:				Dict   )       ->	Optional[Any]:
						if self.config_name == "cola":
											return {"matthews_correlation": matthews_corrcoef(UpperCAmelCase__			,			UpperCAmelCase__   )}
						elif self.config_name == "stsb":
											return pearson_and_spearman(UpperCAmelCase__			,			UpperCAmelCase__   )
						elif self.config_name in ["mrpc", "qqp"]:
											return acc_and_fa(UpperCAmelCase__			,			UpperCAmelCase__   )
						elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
											return {"accuracy": simple_accuracy(UpperCAmelCase__			,			UpperCAmelCase__   )}
						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 PIL import Image




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				image.size
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				image.load()
					for i in range(lowerCAmelCase_   ):
										for j in range(lowerCAmelCase_   ):
															__SCREAMING_SNAKE_CASE						   =				pixels[j, i]
															mean += pixel
					mean //= width * height

					for j in range(lowerCAmelCase_   ):
										for i in range(lowerCAmelCase_   ):
															__SCREAMING_SNAKE_CASE						   =				255 if pixels[i, j] > mean else 0
					return image


if __name__ == "__main__":
		a__				:							List[str]						=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
		image.save('''output_image_path''')
