


from typing import List

from packaging import version
from sklearn.metrics import fa_score

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
				import importlib_metadata
else:
				import importlib.metadata as importlib_metadata


# TODO(Patrick/Anton)
lowerCAmelCase__       :	Optional[int]     				='\\n'

lowerCAmelCase__       :	Optional[Any]     				='\\n    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.\n    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.\n'

lowerCAmelCase__       :	Union[str, Any]     				='\nCompute XTREME-S evaluation metric associated to each XTREME-S 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.\n    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.\n        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',\n        \'tokenize\', \'use_effective_order\'.\n    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.\n        Keywords include \'concatenate_texts\'.\nReturns: depending on the XTREME-S task, one or several of:\n    "accuracy": Accuracy - for \'fleurs-lang_id\', \'minds14\'\n    "f1": F1 score - for \'minds14\'\n    "wer": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "cer": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'\n    "bleu": BLEU score according to the `sacrebleu` metric - for \'covost2\'\nExamples:\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'\n    >>> references = ["it is sunny here", "paper and pen are essentials"]\n    >>> predictions = ["it\'s sunny", "paper pen are essential"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'wer\': 0.56, \'cer\': 0.27}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')\n    >>> references = ["bonjour paris", "il est necessaire de faire du sport de temps en temp"]\n    >>> predictions = ["bonjour paris", "il est important de faire du sport souvent"]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'bleu\': 31.65}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'accuracy\': 0.6}\n\n    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')\n    >>> references = [0, 1, 0, 0, 1]\n    >>> predictions = [0, 1, 1, 0, 0]\n    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)\n    >>> print({k: round(v, 2) for k, v in results.items()})\n    {\'f1\': 0.58, \'accuracy\': 0.6}\n'

lowerCAmelCase__       :	Dict     				=['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
lowerCAmelCase__       :	List[str]     				=''

try:
				from jiwer import transforms as tr

				lowerCAmelCase__       :	Optional[Any]     				=True
except ImportError:
				lowerCAmelCase__       :	Any     				=False

if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):







				class      __lowercase  (tr.AbstractTransform   ):





											"""simple docstring"""







											def __init__(	self       ,		lowerCAmelCase__ = " "    ):





														"""simple docstring"""




														SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= sentence_delimiter






											def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





														"""simple docstring"""




														return list(lowerCAmelCase_    )






											def  UpperCamelCase__			(	self       ,		lowerCAmelCase__    ):





														"""simple docstring"""




														SCREAMING_SNAKE_CASE_						:			Tuple       						= []
														for sent_idx, sentence in enumerate(lowerCAmelCase_    ):
																	chars.extend(self.process_string(lowerCAmelCase_    )    )
																	if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(lowerCAmelCase_    ) - 1:
																				chars.append(self.sentence_delimiter    )
														return chars







				lowerCAmelCase__       :	List[Any]     				=tr.Compose(
				    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
				)
elif _jiwer_available:
				lowerCAmelCase__       :	Dict     				=tr.Compose(
				    [
				        tr.RemoveMultipleSpaces(),
				        tr.Strip(),
				        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
				        tr.ReduceToListOfListOfChars(),
				    ]
				)
else:
				lowerCAmelCase__       :	Dict     				=None






def 		a__       (							A__,	A__							):
			return float((preds == labels).mean()							)






def 		a__       (							A__,	A__							):
			return {
			    "f1": float(fa_score(y_true=A__,	y_pred=A__,	average='macro'							)							),
			    "accuracy": simple_accuracy(A__,	A__							),
			}






def 		a__       (							A__,	A__,	A__="exp",	A__=None,	A__=False,	A__=False,	A__=None,	A__=False,	):
			SCREAMING_SNAKE_CASE_						:			str       						= [[label] for label in labels]
			SCREAMING_SNAKE_CASE_						:			Dict       						= list(A__							)
			try:
						import sacrebleu as scb
			except ImportError:
						raise ValueError(
						    'sacrebleu has to be installed in order to apply the bleu metric for covost2.'
						    'You can install it via `pip install sacrebleu`.'							)

			if version.parse(scb.__version__							) < version.parse('1.4.12'							):
						raise ImportWarning(
						    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
						    'You can install it with `pip install "sacrebleu>=1.4.12"`.'							)

			SCREAMING_SNAKE_CASE_						:			List[Any]       						= len(labels[0]							)
			if any(len(A__							) != references_per_prediction for refs in labels							):
						raise ValueError('Sacrebleu requires the same number of references for each prediction'							)
			SCREAMING_SNAKE_CASE_						:			str       						= [[refs[i] for refs in labels] for i in range(A__							)]
			SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= scb.corpus_bleu(
			    A__,	A__,	smooth_method=A__,	smooth_value=A__,	force=A__,	lowercase=A__,	use_effective_order=A__,	**({'tokenize': tokenize} if tokenize else {}),	)
			return {"bleu": output.score}






def 		a__       (							A__,	A__,	A__,	A__							):


			try:
						from jiwer import compute_measures
			except ImportError:
						raise ValueError(
						    F'''jiwer has to be installed in order to apply the wer metric for {config_name}.'''
						    'You can install it via `pip install jiwer`.'							)

			if concatenate_texts:
						SCREAMING_SNAKE_CASE_						:			List[str]       						= compute_measures(A__,	A__							)['wer']

						SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= compute_measures(A__,	A__,	truth_transform=A__,	hypothesis_transform=A__							)['wer']
						return {"wer": wer, "cer": cer}
			else:

						def compute_score(A__,	A__,	A__="wer"							):
									SCREAMING_SNAKE_CASE_						:			List[str]       						= 0
									SCREAMING_SNAKE_CASE_						:			List[str]       						= 0
									for prediction, reference in zip(A__,	A__							):
												if score_type == "wer":
															SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= compute_measures(A__,	A__							)
												elif score_type == "cer":
															SCREAMING_SNAKE_CASE_						:			Tuple       						= compute_measures(
															    A__,	A__,	truth_transform=A__,	hypothesis_transform=A__							)
												incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
												total += measures["substitutions"] + measures["deletions"] + measures["hits"]
									return incorrect / total

						return {"wer": compute_score(A__,	A__,	'wer'							), "cer": compute_score(A__,	A__,	'cer'							)}







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





							"""simple docstring"""







							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										if self.config_name not in _CONFIG_NAMES:
													raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''    )

										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 'int64' if self.config_name in ['fleurs-lang_id', 'minds14'] else 'string'

										return datasets.MetricInfo(
										    description=_DESCRIPTION       ,		citation=_CITATION       ,		inputs_description=_KWARGS_DESCRIPTION       ,		features=datasets.Features(
										        {'predictions': datasets.Value(lowerCAmelCase_    ), 'references': datasets.Value(lowerCAmelCase_    )}    )       ,		codebase_urls=[]       ,		reference_urls=[]       ,		format='numpy'       ,		)






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__=None       ,		lowerCAmelCase__=None    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= bleu_kwargs if bleu_kwargs is not None else {}
										SCREAMING_SNAKE_CASE_						:			Any       						= wer_kwargs if wer_kwargs is not None else {}

										if self.config_name == "fleurs-lang_id":
													return {"accuracy": simple_accuracy(lowerCAmelCase_       ,		lowerCAmelCase_    )}
										elif self.config_name == "minds14":
													return fa_and_simple_accuracy(lowerCAmelCase_       ,		lowerCAmelCase_    )
										elif self.config_name == "covost2":
													SCREAMING_SNAKE_CASE_						:			Tuple       						= bleu_kwargs.pop('smooth_method'       ,		'exp'    )
													SCREAMING_SNAKE_CASE_						:			int       						= bleu_kwargs.pop('smooth_value'       ,		lowerCAmelCase_    )
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= bleu_kwargs.pop('force'       ,		lowerCAmelCase_    )
													SCREAMING_SNAKE_CASE_						:			int       						= bleu_kwargs.pop('lowercase'       ,		lowerCAmelCase_    )
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= bleu_kwargs.pop('tokenize'       ,		lowerCAmelCase_    )
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= bleu_kwargs.pop('use_effective_order'       ,		lowerCAmelCase_    )
													return bleu(
													    preds=lowerCAmelCase_       ,		labels=lowerCAmelCase_       ,		smooth_method=lowerCAmelCase_       ,		smooth_value=lowerCAmelCase_       ,		force=lowerCAmelCase_       ,		lowercase=lowerCAmelCase_       ,		tokenize=lowerCAmelCase_       ,		use_effective_order=lowerCAmelCase_       ,		)
										elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
													SCREAMING_SNAKE_CASE_						:			Tuple       						= wer_kwargs.pop('concatenate_texts'       ,		lowerCAmelCase_    )
													return wer_and_cer(lowerCAmelCase_       ,		lowerCAmelCase_       ,		lowerCAmelCase_       ,		self.config_name    )
										else:
													raise KeyError(F'''You should supply a configuration name selected in {_CONFIG_NAMES}'''    )











'''simple docstring'''


def 		snake_case_    (UpperCamelCase       :  List[str] ,						UpperCamelCase       :  List[Any] ,						UpperCamelCase       :  Union[str, Any]      ):





							'''simple docstring'''


							_a	     =				index
							_a	     =				2 * index + 1
							_a	     =				2 * index + 2
							if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
														_a	     =				left_index

							if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
														_a	     =				right_index

							if largest != index:
														_a     ,							_a	     =				unsorted[index], unsorted[largest]
														heapify(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
def 		snake_case_    (UpperCamelCase       :  Union[str, Any]      ):





							'''simple docstring'''


							_a	     =				len(UpperCamelCase      )
							for i in range(n // 2 - 1 ,						-1 ,						-1      ):
														heapify(UpperCamelCase ,						UpperCamelCase ,						UpperCamelCase      )
							for i in range(n - 1 ,						0 ,						-1      ):
														_a     ,							_a	     =				unsorted[i], unsorted[0]
														heapify(UpperCamelCase ,						0 ,						UpperCamelCase      )
							return unsorted


if __name__ == "__main__":
				_snake_case      :      int   							=		input('Enter numbers separated by a comma:\n').strip()
				_snake_case      :      Optional[Any]   							=		[int(item) for item in user_input.split(',')]
				print(heap_sort(unsorted))

