



def 					a(   A :					str )	-> bool:
					"""simple docstring"""





					a        =     9

					while num > 0:
										if num % 10 != digit:
															return False
										num //= 100
										digit -= 1

					return True






def 					a(   )	-> int:
					"""simple docstring"""





					a        =     1_3890_2663

					while not is_square_form(num * num ):
										if num % 10 == 3:
															num -= 6  # (3 - 6) % 10 = 7
										else:
															num -= 4  # (7 - 4) % 10 = 3

					return num * 10


if __name__ == "__main__":
			print(F"""{solution() = }""")





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)
_lowercase:				Optional[Any]       =	"\\n"

_lowercase:				List[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"

_lowercase:				Dict       =	"\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"

_lowercase:				List[Any]       =	["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
_lowercase:				Tuple       =	""

try:
				from jiwer import transforms as tr

				_lowercase:				Any       =	True
except ImportError:
				_lowercase:				Optional[int]       =	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"""

										a        =     sentence_delimiter




					def   UpperCamelCase_ (self	, lowerCamelCase_	):
										"""simple docstring"""

										return list(lowerCamelCase_	)




					def   UpperCamelCase_ (self	, lowerCamelCase_	):
										"""simple docstring"""

										a        =     []
										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




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






def 					a(   A :					List[Any]				,  A :					int )	-> Union[str, Any]:
					"""simple docstring"""





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






def 					a(   A :					int				,  A :					List[str] )	-> str:
					"""simple docstring"""





					return {
					    "f1": float(fa_score(y_true=A				,  y_pred=A				,  average="macro" ) ),
					    "accuracy": simple_accuracy(A				,  A ),
					}






def 					a(   A :					Optional[Any]				,  A :					Dict				,  A :					int="exp"				,  A :					int=None				,  A :					List[Any]=False				,  A :					Tuple=False				,  A :					Tuple=None				,  A :					Tuple=False				,  )	-> Tuple:
					"""simple docstring"""





					a        =     [[label] for label in labels]
					a        =     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\"`." )

					a        =     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" )
					a        =     [[refs[i] for refs in labels] for i in range(A )]
					a        =     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 :					str				,  A :					Tuple				,  A :					Optional[Any]				,  A :					str )	-> int:
					"""simple docstring"""





					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:
										a        =     compute_measures(A				,  A )["wer"]

										a        =     compute_measures(A				,  A				,  truth_transform=A				,  hypothesis_transform=A )["wer"]
										return {"wer": wer, "cer": cer}
					else:

										def compute_score(A :					List[Any]				,  A :					Any				,  A :					Union[str, Any]="wer" ):
															a        =     0
															a        =     0
															for prediction, reference in zip(A				,  A ):
																				if score_type == "wer":
																									a        =     compute_measures(A				,  A )
																				elif score_type == "cer":
																									a        =     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}'''	)

						a        =     "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"""

						a        =     bleu_kwargs if bleu_kwargs is not None else {}
						a        =     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":
											a        =     bleu_kwargs.pop("smooth_method"	, "exp"	)
											a        =     bleu_kwargs.pop("smooth_value"	, lowerCamelCase_	)
											a        =     bleu_kwargs.pop("force"	, lowerCamelCase_	)
											a        =     bleu_kwargs.pop("lowercase"	, lowerCamelCase_	)
											a        =     bleu_kwargs.pop("tokenize"	, lowerCamelCase_	)
											a        =     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"]:
											a        =     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}'''	)
