
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__       :   str   						=					"""\
"""

A__       :   Optional[int]   						=					"""\
    XTREME-S is a benchmark to evaluate universal cross-lingual speech representations in many languages.
    XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval.
"""

A__       :   Optional[Any]   						=					"""
Compute XTREME-S evaluation metric associated to each XTREME-S dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    bleu_kwargs: optional dict of keywords to be passed when computing \'bleu\'.
        Keywords include Dict can be one of \'smooth_method\', \'smooth_value\', \'force\', \'lowercase\',
        \'tokenize\', \'use_effective_order\'.
    wer_kwargs: optional dict of keywords to be passed when computing \'wer\' and \'cer\'.
        Keywords include \'concatenate_texts\'.
Returns: depending on the XTREME-S task, one or several of:
    \"accuracy\": Accuracy - for \'fleurs-lang_id\', \'minds14\'
    \"f1\": F1 score - for \'minds14\'
    \"wer\": Word error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'
    \"cer\": Character error rate - for \'mls\', \'fleurs-asr\', \'voxpopuli\', \'babel\'
    \"bleu\": BLEU score according to the `sacrebleu` metric - for \'covost2\'
Examples:

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'mls\')  # \'mls\', \'voxpopuli\', \'fleurs-asr\' or \'babel\'
    >>> references = [\"it is sunny here\", \"paper and pen are essentials\"]
    >>> predictions = [\"it\'s sunny\", \"paper pen are essential\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'wer\': 0.56, \'cer\': 0.27}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'covost2\')
    >>> references = [\"bonjour paris\", \"il est necessaire de faire du sport de temps en temp\"]
    >>> predictions = [\"bonjour paris\", \"il est important de faire du sport souvent\"]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'bleu\': 31.65}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'fleurs-lang_id\')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'accuracy\': 0.6}

    >>> xtreme_s_metric = datasets.load_metric(\'xtreme_s\', \'minds14\')
    >>> references = [0, 1, 0, 0, 1]
    >>> predictions = [0, 1, 1, 0, 0]
    >>> results = xtreme_s_metric.compute(predictions=predictions, references=references)
    >>> print({k: round(v, 2) for k, v in results.items()})
    {\'f1\': 0.58, \'accuracy\': 0.6}
"""

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

try:
							from jiwer import transforms as tr

							A__       :   Optional[Any]   						=					True
except ImportError:
							A__       :   Tuple   						=					False

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





							class 						lowercase       (  tr.AbstractTransform					):
												def __init__(		self ,	SCREAMING_SNAKE_CASE__ = " "    ):
															"""simple docstring"""







															lowerCAmelCase__ :    Optional[Any]				      =			sentence_delimiter
												def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
															"""simple docstring"""







															return list(SCREAMING_SNAKE_CASE_    )
												def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
															"""simple docstring"""







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





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



def      _a     (   __UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  Dict    ):
			return float((preds == labels).mean()    )



def      _a     (   __UpperCamelCase       :  Any	,__UpperCamelCase       :  Optional[int]    ):
			return {
			    "f1": float(fa_score(y_true=snake_case__	,y_pred=snake_case__	,average='''macro'''    )    ),
			    "accuracy": simple_accuracy(snake_case__	,snake_case__    ),
			}



def      _a     (   __UpperCamelCase       :  Dict	,__UpperCamelCase       :  Dict	,__UpperCamelCase       :  List[str]="exp"	,__UpperCamelCase       :  Optional[int]=None	,__UpperCamelCase       :  Dict=False	,__UpperCamelCase       :  List[str]=False	,__UpperCamelCase       :  Any=None	,__UpperCamelCase       :  List[Any]=False	,):
			# xtreme-s can only have one label
			lowerCAmelCase__ :    List[Any]				      =			[[label] for label in labels]
			lowerCAmelCase__ :    List[Any]				      =			list(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"`.'''    )

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



def      _a     (   __UpperCamelCase       :  Tuple	,__UpperCamelCase       :  List[str]	,__UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  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:
						lowerCAmelCase__ :    Dict				      =			compute_measures(snake_case__	,snake_case__    )['wer']

						lowerCAmelCase__ :    Union[str, Any]				      =			compute_measures(snake_case__	,snake_case__	,truth_transform=snake_case__	,hypothesis_transform=snake_case__    )['wer']
						return {"wer": wer, "cer": cer}
			else:

						def compute_score(__UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  Any="wer"    ):
									lowerCAmelCase__ :    Union[str, Any]				      =			0
									lowerCAmelCase__ :    Optional[int]				      =			0
									for prediction, reference in zip(snake_case__	,snake_case__    ):
												if score_type == "wer":
															lowerCAmelCase__ :    Optional[Any]				      =			compute_measures(snake_case__	,snake_case__    )
												elif score_type == "cer":
															lowerCAmelCase__ :    List[Any]				      =			compute_measures(
															    snake_case__	,snake_case__	,truth_transform=snake_case__	,hypothesis_transform=snake_case__    )
												incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
												total += measures["substitutions"] + measures["deletions"] + measures["hits"]
									return incorrect / total

						return {"wer": compute_score(snake_case__	,snake_case__	,'''wer'''    ), "cer": compute_score(snake_case__	,snake_case__	,'''cer'''    )}





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,		_KWARGS_DESCRIPTION					)
class 						lowercase       (  datasets.Metric					):
					def lowercase_			(		self    ):
								"""simple docstring"""







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

								lowerCAmelCase__ :    str				      =			'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(SCREAMING_SNAKE_CASE_    ), '''references''': datasets.Value(SCREAMING_SNAKE_CASE_    )}    ) ,	codebase_urls=[] ,	reference_urls=[] ,	format='''numpy''' ,	)
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			bleu_kwargs if bleu_kwargs is not None else {}
								lowerCAmelCase__ :    Dict				      =			wer_kwargs if wer_kwargs is not None else {}

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





import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			np.shape(__UpperCamelCase    )

			lowerCAmelCase__ :    NDArray[floataa]				      =			np.zeros((rows, 1)	,dtype=__UpperCamelCase    )
			for row in reversed(range(__UpperCamelCase    )    ):
						lowerCAmelCase__ :    List[str]				      =			0
						for col in range(row + 1	,__UpperCamelCase    ):
									total += coefficients[row, col] * x[col]

						lowerCAmelCase__ :    int				      =			(vector[row] - total) / coefficients[row, row]

			return x



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			np.shape(__UpperCamelCase    )
			if rows != columns:
						return np.array(()	,dtype=__UpperCamelCase    )

			# augmented matrix
			lowerCAmelCase__ :    NDArray[floataa]				      =			np.concatenate((coefficients, vector)	,axis=1    )
			lowerCAmelCase__ :    List[str]				      =			augmented_mat.astype('''float64'''    )

			# scale the matrix leaving it triangular
			for row in range(rows - 1    ):
						lowerCAmelCase__ :    str				      =			augmented_mat[row, row]
						for col in range(row + 1	,__UpperCamelCase    ):
									lowerCAmelCase__ :    Optional[int]				      =			augmented_mat[col, row] / pivot
									augmented_mat[col, :] -= factor * augmented_mat[row, :]

			lowerCAmelCase__ :    int				      =			retroactive_resolution(
			    augmented_mat[:, 0:columns]	,augmented_mat[:, columns : columns + 1]    )

			return x


if __name__ == "__main__":
							import doctest

							doctest.testmod()



