


"""simple docstring"""
from typing import Any





class   __A  :





							'''simple docstring'''

							def __init__(					self				:   List[Any]							)       ->int:



														"""simple docstring"""



														snake_case_     							=		[]
														snake_case_     							=		0



							def __str__(					self				:   List[str]							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		"""<""" + str(self.stack							)[1:-1] + """>"""
														return printed



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Any							)       ->None:



														"""simple docstring"""



														self.stack.append(UpperCAmelCase_							)
														snake_case_     							=		self.length + 1



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Any:



														"""simple docstring"""



														self.rotate(1							)
														snake_case_     							=		self.stack[self.length - 1]
														snake_case_     							=		self.stack[:-1]
														self.rotate(self.length - 1							)
														snake_case_     							=		self.length - 1
														return dequeued



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   int							)       ->None:



														"""simple docstring"""



														for _ in range(UpperCAmelCase_							):
																					snake_case_     							=		self.stack[0]
																					snake_case_     							=		self.stack[1:]
																					self.put(UpperCAmelCase_							)
																					snake_case_     							=		self.length - 1



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.get()
														self.put(UpperCAmelCase_							)
														self.rotate(self.length - 1							)
														return front



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->int:



														"""simple docstring"""



														return self.length







"""simple docstring"""
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)
__SCREAMING_SNAKE_CASE				:     str		=     '\\n'

__SCREAMING_SNAKE_CASE				:     Union[str, 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'

__SCREAMING_SNAKE_CASE				:     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'

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

try:
				from jiwer import transforms as tr

				__SCREAMING_SNAKE_CASE				:     Dict		=     True
except ImportError:
				__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     False

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





				class   __A  (tr.AbstractTransform):





											'''simple docstring'''

											def __init__(					self				:   Optional[int]		,							UpperCAmelCase_				:   str = " "							)       ->str:



																		"""simple docstring"""



																		snake_case_     							=		sentence_delimiter



											def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   str							)       ->List[str]:



																		"""simple docstring"""



																		return list(UpperCAmelCase_							)



											def 		lowerCAmelCase							(					self				:   int		,							UpperCAmelCase_				:   List[str]							)       ->Any:



																		"""simple docstring"""



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


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







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Any:
							return float((preds == labels).mean()					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					int:
							return {
							    "f1": float(fa_score(y_true=_SCREAMING_SNAKE_CASE					,			y_pred=_SCREAMING_SNAKE_CASE					,			average="""macro"""					)					),
							    "accuracy": simple_accuracy(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					),
							}







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE="exp"					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=False					,			_SCREAMING_SNAKE_CASE=False					,			_SCREAMING_SNAKE_CASE=None					,			_SCREAMING_SNAKE_CASE=False					,			)     ->					Optional[Any]:
							# xtreme-s can only have one label
							snake_case_     							=		[[label] for label in labels]
							snake_case_     							=		list(_SCREAMING_SNAKE_CASE					)
							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\"`."""					)

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







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							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:
														snake_case_     							=		compute_measures(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)["""wer"""]

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

														def compute_score(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE="wer"					):
																					snake_case_     							=		0
																					snake_case_     							=		0
																					for prediction, reference in zip(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					):
																												if score_type == "wer":
																																			snake_case_     							=		compute_measures(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)
																												elif score_type == "cer":
																																			snake_case_     							=		compute_measures(
																																			    _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			truth_transform=_SCREAMING_SNAKE_CASE					,			hypothesis_transform=_SCREAMING_SNAKE_CASE					)
																												incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																												total += measures["substitutions"] + measures["deletions"] + measures["hits"]
																					return incorrect / total

														return {"wer": compute_score(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			"""wer"""					), "cer": compute_score(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			"""cer"""					)}





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





							'''simple docstring'''

							def 		lowerCAmelCase							(					self				:   Any							)       ->List[str]:



														"""simple docstring"""



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

														snake_case_     							=		"""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(UpperCAmelCase_							), """references""": datasets.Value(UpperCAmelCase_							)}							)		,							codebase_urls=[]		,							reference_urls=[]		,							format="""numpy"""		,							)



							def 		lowerCAmelCase							(					self				:   Optional[int]		,							UpperCAmelCase_				:   List[str]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   List[str]=None		,							UpperCAmelCase_				:   List[Any]=None							)       ->List[str]:



														"""simple docstring"""



														snake_case_     							=		bleu_kwargs if bleu_kwargs is not None else {}
														snake_case_     							=		wer_kwargs if wer_kwargs is not None else {}

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



