






from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class   UpperCamelCase			:







		"""simple docstring"""



		def __init__(	self		:							Any    ,_SCREAMING_SNAKE_CASE		:							int		)     -> None:


						'''simple docstring'''
						A									=   vocab
						A									=   normalizers.BertNormalizer(
						    clean_text=_SCREAMING_SNAKE_CASE    ,handle_chinese_chars=_SCREAMING_SNAKE_CASE    ,strip_accents=_SCREAMING_SNAKE_CASE    ,lowercase=_SCREAMING_SNAKE_CASE    ,)
						try:
										import rjieba
						except ImportError:
										raise ImportError(
										    'You need to install rjieba to use RoFormerTokenizer. '
										    'See https://pypi.org/project/rjieba/ for installation.'		)
						A									=   rjieba
		def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							int    ,_SCREAMING_SNAKE_CASE		:							NormalizedString		)     -> List[NormalizedString]:


						'''simple docstring'''
						A									=   []

						# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
						for token, start, end in self.jieba.tokenize(str(_SCREAMING_SNAKE_CASE		)    ,hmm=_SCREAMING_SNAKE_CASE		):
										if token in self.vocab:
														splits.append(normalized_string[start:end]		)
										else:
														A									=   self.normalizers.normalize_str(_SCREAMING_SNAKE_CASE		).split()
														for token in token_list:
																		if token:
																						A									=   start + len(_SCREAMING_SNAKE_CASE		)
																						splits.append(normalized_string[start:end]		)
																						A									=   end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

						return splits







		def    A(	self		:							List[str]    ,_SCREAMING_SNAKE_CASE		:							PreTokenizedString		)     -> Union[str, Any]:


						'''simple docstring'''
						pretok.split(self.jieba_split		)













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_     =			'\\n'

lowerCAmelCase_     =			'\\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_     =			'\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_     =			['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
lowerCAmelCase_     =			''

try:
					from jiwer import transforms as tr

					lowerCAmelCase_     =			True
except ImportError:
					lowerCAmelCase_     =			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		:							Any    ,_SCREAMING_SNAKE_CASE		:							str = " "		)     -> Dict:


											'''simple docstring'''
											A									=   sentence_delimiter
							def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							str		)     -> int:


											'''simple docstring'''
											return list(_SCREAMING_SNAKE_CASE		)







							def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							List[str]		)     -> Tuple:


											'''simple docstring'''
											A									=   []
											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


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







def   snake_case		(				UpperCAmelCase						:							List[str],    UpperCAmelCase						:							Tuple ):
				return float((preds == labels).mean() )







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							Tuple ):
				return {
				    "f1": float(fa_score(y_true=UpperCAmelCase,    y_pred=UpperCAmelCase,    average='macro' ) ),
				    "accuracy": simple_accuracy(UpperCAmelCase,    UpperCAmelCase ),
				}







def   snake_case		(				UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							List[str],    UpperCAmelCase						:							List[Any]="exp",    UpperCAmelCase						:							List[Any]=None,    UpperCAmelCase						:							int=False,    UpperCAmelCase						:							Dict=False,    UpperCAmelCase						:							List[Any]=None,    UpperCAmelCase						:							str=False,    ):
				# xtreme-s can only have one label
				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   snake_case		(				UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							Optional[Any],    UpperCAmelCase						:							Optional[int] ):
				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						:							Optional[int],    UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							List[Any]="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		:							Any		)     -> str:


						'''simple docstring'''
						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(_SCREAMING_SNAKE_CASE		), 'references': datasets.Value(_SCREAMING_SNAKE_CASE		)}		)    ,codebase_urls=[]    ,reference_urls=[]    ,format='numpy'    ,)







		def    A(	self		:							Optional[Any]    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							Dict=None    ,_SCREAMING_SNAKE_CASE		:							List[Any]=None		)     -> Tuple:


						'''simple docstring'''
						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(_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":
										A									=   bleu_kwargs.pop('smooth_method'    ,'exp'		)
										A									=   bleu_kwargs.pop('smooth_value'    ,_SCREAMING_SNAKE_CASE		)
										A									=   bleu_kwargs.pop('force'    ,_SCREAMING_SNAKE_CASE		)
										A									=   bleu_kwargs.pop('lowercase'    ,_SCREAMING_SNAKE_CASE		)
										A									=   bleu_kwargs.pop('tokenize'    ,_SCREAMING_SNAKE_CASE		)
										A									=   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"]:
										A									=   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}'		)





