




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)
lowercase_			:       int						       =				'\\n'

lowercase_			:       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'

lowercase_			:       Tuple						       =				'\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'

lowercase_			:       Tuple						       =				['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
lowercase_			:       List[str]						       =				''

try:
				from jiwer import transforms as tr

				lowercase_			:       Optional[int]						       =				True
except ImportError:
				lowercase_			:       Tuple						       =				False

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






				class 					__lowerCAmelCase				( tr.AbstractTransform				):







							def __init__( self					:    Dict					,					snake_case__					:    str = " "   ):




													"""simple docstring"""


													_UpperCAmelCase									=  sentence_delimiter







							def UpperCamelCase    ( self					:    Optional[Any]					,					snake_case__					:    str   ):




													"""simple docstring"""


													return list(snake_case__   )







							def UpperCamelCase    ( self					:    List[Any]					,					snake_case__					:    List[str]   ):




													"""simple docstring"""


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




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


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_		):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_		):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_="exp"			,					snake_case_=None			,					snake_case_=False			,					snake_case_=False			,					snake_case_=None			,					snake_case_=False			,					):

						'''simple docstring'''


						_UpperCAmelCase									=  [[label] for label in labels]
						_UpperCAmelCase									=  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\"`."		)

						_UpperCAmelCase									=  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"		)
						_UpperCAmelCase									=  [[refs[i] for refs in labels] for i in range(snake_case_		)]
						_UpperCAmelCase									=  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 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_			,					snake_case_		):

						'''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(snake_case_			,					snake_case_		)["wer"]

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

												def compute_score(snake_case_			,					snake_case_			,					snake_case_="wer"		):
																		_UpperCAmelCase									=  0
																		_UpperCAmelCase									=  0
																		for prediction, reference in zip(snake_case_			,					snake_case_		):
																								if score_type == "wer":
																														_UpperCAmelCase									=  compute_measures(snake_case_			,					snake_case_		)
																								elif score_type == "cer":
																														_UpperCAmelCase									=  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 					__lowerCAmelCase				( datasets.Metric				):







			def UpperCamelCase    ( self					:    Optional[int]   ):




									"""simple docstring"""


									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(snake_case__   ), "references": datasets.Value(snake_case__   )}   )					,					codebase_urls=[]					,					reference_urls=[]					,					format="numpy"					,					)







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    Dict					,					snake_case__					:    Any					,					snake_case__					:    Tuple=None					,					snake_case__					:    Dict=None   ):




									"""simple docstring"""


									_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(snake_case__					,					snake_case__   )}
									elif self.config_name == "minds14":
															return fa_and_simple_accuracy(snake_case__					,					snake_case__   )
									elif self.config_name == "covost2":
															_UpperCAmelCase									=  bleu_kwargs.pop("smooth_method"					,					"exp"   )
															_UpperCAmelCase									=  bleu_kwargs.pop("smooth_value"					,					snake_case__   )
															_UpperCAmelCase									=  bleu_kwargs.pop("force"					,					snake_case__   )
															_UpperCAmelCase									=  bleu_kwargs.pop("lowercase"					,					snake_case__   )
															_UpperCAmelCase									=  bleu_kwargs.pop("tokenize"					,					snake_case__   )
															_UpperCAmelCase									=  bleu_kwargs.pop("use_effective_order"					,					snake_case__   )
															return bleu(
															    preds=snake_case__					,					labels=snake_case__					,					smooth_method=snake_case__					,					smooth_value=snake_case__					,					force=snake_case__					,					lowercase=snake_case__					,					tokenize=snake_case__					,					use_effective_order=snake_case__					,					)
									elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
															_UpperCAmelCase									=  wer_kwargs.pop("concatenate_texts"					,					snake_case__   )
															return wer_and_cer(snake_case__					,					snake_case__					,					snake_case__					,					self.config_name   )
									else:
															raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""   )










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase_			:       List[str]						       =				{
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			:       Any						       =				[
				    'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'RwkvForCausalLM',
				    'RwkvModel',
				    'RwkvPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rwkv import (
								    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RwkvForCausalLM,
								    RwkvModel,
								    RwkvPreTrainedModel,
								)
else:
				import sys

				lowercase_			:       Union[str, Any]						       =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




