



"""simple docstring"""

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

import datasets


A_						:    str				=      '\\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'

A_						:    Optional[Any]				=      '\\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'

A_						:    List[str]				=      '\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  __snake_case							(	__A      :						Union[str, Any]							,		__A      :						List[str]     )     ->       Any:






				'''simple docstring'''
				return float((preds == labels).mean()     )
def  __snake_case							(	__A      :						Union[str, Any]							,		__A      :						Tuple     )     ->       str:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Tuple							=     simple_accuracy(__A							,		__A     )
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     float(fa_score(y_true=__A							,		y_pred=__A     )     )
				return {
				    "accuracy": acc,
				    "f1": fa,
				}
def  __snake_case							(	__A      :						List[Any]							,		__A      :						str     )     ->       str:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[str]							=     float(pearsonr(__A							,		__A     )[0]     )
				SCREAMING_SNAKE_CASE						:							Tuple							=     float(spearmanr(__A							,		__A     )[0]     )
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}







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


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Optional[Any]:





					"""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 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]    )						->	Union[str, Any]:





					"""simple docstring"""


					if self.config_name == "cola":
									return {"matthews_correlation": matthews_corrcoef(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )}
					elif self.config_name == "stsb":
									return pearson_and_spearman(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					elif self.config_name in ["mrpc", "qqp"]:
									return acc_and_fa(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
					elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
									return {"accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )}
					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 __future__ import annotations
def  __snake_case							(	__A      :						list[int]     )     ->       list[int]:






				'''simple docstring'''
				if len(__A     ) == 0:
								return array

				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     min(__A     ), max(__A     )

				# Compute the variables
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     _max - _min + 1
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     [0] * holes_range, [0] * holes_range

				# Make the sorting.
				for i in array:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     i - _min
								SCREAMING_SNAKE_CASE						:							Dict							=     i
								holes_repeat[index] += 1

				# Makes the array back by replacing the numbers.
				SCREAMING_SNAKE_CASE						:							int							=     0
				for i in range(__A     ):
								while holes_repeat[i] > 0:
												SCREAMING_SNAKE_CASE						:							int							=     holes[i]
												index += 1
												holes_repeat[i] -= 1

    # Returns the sorted array.
				return array


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							A_						:    Optional[Any]				=      input('Enter numbers separated by comma:\n')
							A_						:    Union[str, Any]				=      [int(x) for x in user_input.split(',')]
							print(pigeon_sort(unsorted))





