
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)
A  :			Optional[int]     				=				'\\n'

A  :			int     				=				'\\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'

A  :			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'

A  :			Optional[int]     				=				['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
A  :			List[Any]     				=				''

try:
					from jiwer import transforms as tr

					A  :			Optional[Any]     				=				True
except ImportError:
					A  :			Optional[int]     				=				False

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







					class     __A(				tr.AbstractTransform    ):




									def __init__(  self	,  _snake_case = " "       )					->   Dict:


													'''simple docstring'''




													__a    							=					sentence_delimiter




									def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Dict:


													'''simple docstring'''




													return list(_snake_case       )




									def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   str:


													'''simple docstring'''




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



					A  :			Dict     				=				tr.Compose(
					    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
					)
elif _jiwer_available:
					A  :			Optional[int]     				=				tr.Compose(
					    [
					        tr.RemoveMultipleSpaces(),
					        tr.Strip(),
					        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
					        tr.ReduceToListOfListOfChars(),
					    ]
					)
else:
					A  :			str     				=				None


def 					__lowerCAmelCase    (    a__		,					a__						) ->						Tuple:
				return float((preds == labels).mean()						)


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


def 					__lowerCAmelCase    (    a__		,					a__		,					a__="exp"		,					a__=None		,					a__=False		,					a__=False		,					a__=None		,					a__=False		,					) ->						List[str]:
				# xtreme-s can only have one label
				__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 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						Optional[int]:
				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__		,					a__		,					a__="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     __A(				datasets.Metric    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''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(_snake_case       ), '''references''': datasets.Value(_snake_case       )}       )	,  codebase_urls=[]	,  reference_urls=[]	,  format='''numpy'''	,  )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case=None	,  _snake_case=None       )					->   List[str]:


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

def 					__lowerCAmelCase    (    a__ = 99_8001						) ->						int:
				__a    							=					0
				for i in range(999		,					99		,					-1						):  # 3 digit numbers range from 999 down to 100
								for j in range(999		,					99		,					-1						):
												__a    							=					str(i * j						)
												if product_string == product_string[::-1] and i * j < n:
																__a    							=					max(a__		,					i * j						)
				return answer


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