





'''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)
_lowerCamelCase					:						Tuple					     =							'\\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					:						List[str]					     =							'\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					:						List[str]					     =							['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
_lowerCamelCase					:						Optional[Any]					     =							''

try:
				from jiwer import transforms as tr

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

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



				class 				__UpperCAmelCase	(							tr.AbstractTransform    ):







						'''simple docstring'''







						def __init__(self    :   Union[str, Any]   ,   _lowerCAmelCase    :   str = " "   ):
							A					=   sentence_delimiter







						def 							A  (self    :   Any   ,   _lowerCAmelCase    :   str   ):
							return list(_lowerCAmelCase   )







						def 							A  (self    :   Any   ,   _lowerCAmelCase    :   List[str]   ):
							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






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




def  __a			(  UpperCAmelCase							,      UpperCAmelCase     )   ->List[Any]:



	"""simple docstring"""







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




def  __a			(  UpperCAmelCase							,      UpperCAmelCase     )   ->Dict:



	"""simple docstring"""







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




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase="exp"							,      UpperCAmelCase=None							,      UpperCAmelCase=False							,      UpperCAmelCase=False							,      UpperCAmelCase=None							,      UpperCAmelCase=False							,      )   ->Tuple:



	"""simple docstring"""







	A					=   [[label] for label in labels]
	A					=   list(UpperCAmelCase     )
	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(UpperCAmelCase     ) != 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(UpperCAmelCase     )]
	A					=   scb.corpus_bleu(
	    UpperCAmelCase							,      UpperCAmelCase							,      smooth_method=UpperCAmelCase							,      smooth_value=UpperCAmelCase							,      force=UpperCAmelCase							,      lowercase=UpperCAmelCase							,      use_effective_order=UpperCAmelCase							,      **({"""tokenize""": tokenize} if tokenize else {})							,      )
	return {"bleu": output.score}




def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->List[str]:



	"""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(UpperCAmelCase							,      UpperCAmelCase     )["""wer"""]

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

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

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



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







		'''simple docstring'''







		def 							A  (self    :   Dict   ):
			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 							A  (self    :   Union[str, Any]   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Optional[Any]   ,   _lowerCAmelCase    :   Optional[int]=None   ,   _lowerCAmelCase    :   Any=None   ):
			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}"""   )








'''simple docstring'''


from math import pi




def  __a			(  UpperCAmelCase     )   ->float:



	"""simple docstring"""







	return degree / (180 / pi)


if __name__ == "__main__":
				from doctest import testmod

				testmod()

