






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

import datasets


lowerCamelCase     :							int    ="\\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     :							Union[str, Any]    ="\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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Tuple:
					return float((preds == labels).mean()      )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Dict:
					UpperCamelCase__      :       List[str]	    =  simple_accuracy(UpperCAmelCase__ ,   UpperCAmelCase__      )
					UpperCamelCase__      :       List[Any]	    =  float(fa_score(y_true=UpperCAmelCase__ ,   y_pred=UpperCAmelCase__      )      )
					return {
					    "accuracy": acc,
					    "f1": fa,
					}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     List[Any]:
					UpperCamelCase__      :       List[Any]	    =  float(pearsonr(UpperCAmelCase__ ,   UpperCAmelCase__      )[0]      )
					UpperCamelCase__      :       List[Any]	    =  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  __lowercase (		self :       Tuple						):


						'''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  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       str							,       SCREAMING_SNAKE_CASE :       List[Any]						):


						'''simple docstring'''






						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\"]"						)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     list:
     UpperCamelCase__      :       Tuple	    =  False
     while is_sorted is False:  # Until all the indices are traversed keep looping
          UpperCamelCase__      :       Tuple	    =  True
          for i in range(0 ,   len(__lowerCAmelCase      ) - 1 ,   2      ):  # iterating over all even indices
               if input_list[i] > input_list[i + 1]:
                    UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCamelCase__      :       List[str]	    =  False

          for i in range(1 ,   len(__lowerCAmelCase      ) - 1 ,   2      ):  # iterating over all odd indices
               if input_list[i] > input_list[i + 1]:
                    UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCamelCase__      :       List[Any]	    =  False
     return input_list


if __name__ == "__main__":
 print('''Enter list to be sorted''')
 lowerCamelCase     :							Any    =[int(x) for x in input().split()]
 # inputing elements of the list in one line
 lowerCamelCase     :							Optional[int]    =odd_even_sort(input_list)
 print('''The sorted list is''')
 print(sorted_list)