


"""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)
__a      			= "\\n"

__a      			= "\\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      			= "\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      			= ["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
__a      			= ""

try:
						from jiwer import transforms as tr

						__a      			= True
except ImportError:
						__a      			= False

if _jiwer_available and version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):
						class 				lowerCamelCase							(      tr.AbstractTransform			):


													"""simple docstring"""






													def __init__(    self			:  List[Any]		,			lowerCamelCase_			:  str = " "					):
																			UpperCAmelCase__												=     sentence_delimiter






													def        lowerCAmelCase  (    self			:  Any		,			lowerCamelCase_			:  str					):
																			return list(lowerCamelCase_					)






													def        lowerCAmelCase  (    self			:  Dict		,			lowerCamelCase_			:  List[str]					):
																			UpperCAmelCase__												=     []
																			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




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






def        a__			(							_UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

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






def        a__			(							_UpperCamelCase						,      _UpperCamelCase			):





						"""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						,      ):





						"""simple docstring"""

						UpperCAmelCase__												=     [[label] for label in labels]
						UpperCAmelCase__												=     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"`.'''			)

						UpperCAmelCase__												=     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'''			)
						UpperCAmelCase__												=     [[refs[i] for refs in labels] for i in range(_UpperCamelCase			)]
						UpperCAmelCase__												=     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			):





						"""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:
												UpperCAmelCase__												=     compute_measures(_UpperCamelCase						,      _UpperCamelCase			)['''wer''']

												UpperCAmelCase__												=     compute_measures(_UpperCamelCase						,      _UpperCamelCase						,      truth_transform=_UpperCamelCase						,      hypothesis_transform=_UpperCamelCase			)['''wer''']
												return {"wer": wer, "cer": cer}
						else:

												def compute_score(_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase="wer"			):
																		UpperCAmelCase__												=     0
																		UpperCAmelCase__												=     0
																		for prediction, reference in zip(_UpperCamelCase						,      _UpperCamelCase			):
																								if score_type == "wer":
																														UpperCAmelCase__												=     compute_measures(_UpperCamelCase						,      _UpperCamelCase			)
																								elif score_type == "cer":
																														UpperCAmelCase__												=     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 				lowerCamelCase							(      datasets.Metric			):


							"""simple docstring"""






							def        lowerCAmelCase  (    self			:  Union[str, Any]					):
													if self.config_name not in _CONFIG_NAMES:
																			raise KeyError(f"""You should supply a configuration name selected in {_CONFIG_NAMES}"""					)

													UpperCAmelCase__												=     '''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        lowerCAmelCase  (    self			:  Tuple		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  int		,			lowerCamelCase_			:  Dict=None		,			lowerCamelCase_			:  Optional[int]=None					):
													UpperCAmelCase__												=     bleu_kwargs if bleu_kwargs is not None else {}
													UpperCAmelCase__												=     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":
																			UpperCAmelCase__												=     bleu_kwargs.pop('''smooth_method'''		,			'''exp'''					)
																			UpperCAmelCase__												=     bleu_kwargs.pop('''smooth_value'''		,			lowerCamelCase_					)
																			UpperCAmelCase__												=     bleu_kwargs.pop('''force'''		,			lowerCamelCase_					)
																			UpperCAmelCase__												=     bleu_kwargs.pop('''lowercase'''		,			lowerCamelCase_					)
																			UpperCAmelCase__												=     bleu_kwargs.pop('''tokenize'''		,			lowerCamelCase_					)
																			UpperCAmelCase__												=     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"]:
																			UpperCAmelCase__												=     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 __future__ import annotations






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     sorted(_UpperCamelCase			)
						UpperCAmelCase__												=     len(_UpperCamelCase			)
						UpperCAmelCase__												=     length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)






def        a__			(							):





						"""simple docstring"""

						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()