

"""simple docstring"""




__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.00_09_84_20_73,
    "short-ton": 0.00_11_02_31_22,
    "pound": 2.20_46_24_42_02,
    "stone": 0.15_74_73_17_28,
    "ounce": 35.2_73_99_07_23,
    "carrat": 50_00,
    "atomic-mass-unit": 6.0_2213_6652E26,
}

__A	:       dict[str, float]        =						{
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 10_16.0_46_08,
    "short-ton": 9_07.1_84,
    "pound": 0.45_35_92,
    "stone": 6.3_50_29,
    "ounce": 0.0_28_34_95,
    "carrat": 0.00_02,
    "atomic-mass-unit": 1.6_6054_0199E-27,
}
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
										A     =   (
										    F"""Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
										    F"""Supported values are: {", ".join(lowercase__ )}"""
										)
										raise ValueError(lowercase__ )
					return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
	import doctest

	doctest.testmod()









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)
__UpperCAmelCase			:			Union[str, Any]			      =					"\\n"

__UpperCAmelCase			:			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"

__UpperCAmelCase			:			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"

__UpperCAmelCase			:			Union[str, Any]			      =					["fleurs-asr", "mls", "voxpopuli", "babel", "covost2", "fleurs-lang_id", "minds14"]
__UpperCAmelCase			:			List[str]			      =					""

try:
  from jiwer import transforms as tr

  __UpperCAmelCase			:			Optional[int]			      =					True
except ImportError:
  __UpperCAmelCase			:			str			      =					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			,       __SCREAMING_SNAKE_CASE = " "    ):




                """simple docstring"""




                UpperCamelCase     :						List[str] 						=		sentence_delimiter






         def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




                """simple docstring"""




                return list(__SCREAMING_SNAKE_CASE    )






         def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




                """simple docstring"""




                UpperCamelCase     :						Dict 						=		[]
                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




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


def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]							):






       """simple docstring"""





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


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		Dict							):






       """simple docstring"""





       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_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Union[str, Any]				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]="exp"				,      SCREAMING_SNAKE_CASE_   :		Any=None				,      SCREAMING_SNAKE_CASE_   :		Any=False				,      SCREAMING_SNAKE_CASE_   :		List[str]=False				,      SCREAMING_SNAKE_CASE_   :		Dict=None				,      SCREAMING_SNAKE_CASE_   :		Tuple=False				,      ):






       """simple docstring"""





       UpperCamelCase     :						str 						=		[[label] for label in labels]
       UpperCamelCase     :						int 						=		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"`.'''							)

       UpperCamelCase     :						Optional[int] 						=		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'''							)
       UpperCamelCase     :						Optional[Any] 						=		[[refs[i] for refs in labels] for i in range(SCREAMING_SNAKE_CASE_							)]
       UpperCamelCase     :						Optional[Any] 						=		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_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Tuple				,      SCREAMING_SNAKE_CASE_   :		Tuple				,      SCREAMING_SNAKE_CASE_   :		Optional[int]							):






       """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     :						List[Any] 						=		compute_measures(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)['''wer''']

              UpperCamelCase     :						List[str] 						=		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_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[str]="wer"							):
                     UpperCamelCase     :						int 						=		0
                     UpperCamelCase     :						Union[str, Any] 						=		0
                     for prediction, reference in zip(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							):
                            if score_type == "wer":
                                   UpperCamelCase     :						Optional[int] 						=		compute_measures(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)
                            elif score_type == "cer":
                                   UpperCamelCase     :						Union[str, Any] 						=		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  UpperCAmelCase_						(				datasets.Metric):
       '''simple docstring'''






       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}"""    )

              UpperCamelCase     :						Optional[Any] 						=		'''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"""




              UpperCamelCase     :						List[Any] 						=		bleu_kwargs if bleu_kwargs is not None else {}
              UpperCamelCase     :						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":
                     UpperCamelCase     :						List[str] 						=		bleu_kwargs.pop('''smooth_method'''			,       '''exp'''    )
                     UpperCamelCase     :						Tuple 						=		bleu_kwargs.pop('''smooth_value'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						int 						=		bleu_kwargs.pop('''force'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Union[str, Any] 						=		bleu_kwargs.pop('''lowercase'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						Optional[Any] 						=		bleu_kwargs.pop('''tokenize'''			,       __SCREAMING_SNAKE_CASE    )
                     UpperCamelCase     :						int 						=		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"]:
                     UpperCamelCase     :						List[Any] 						=		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}"""    )

