
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	):




							def __init__( self    ,    _UpperCamelCase = " "					)    ->				List[Any]:
										lowerCAmelCase_      =     sentence_delimiter




							def 					__a						( self    ,    _UpperCamelCase					)    ->				Any:
										return list(_UpperCamelCase					)




							def 					__a						( self    ,    _UpperCamelCase					)    ->				str:
										lowerCAmelCase_      =     []
										for sent_idx, sentence in enumerate(_UpperCamelCase					):
													chars.extend(self.process_string(_UpperCamelCase					)					)
													if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(_UpperCamelCase					) - 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        lowerCamelCase__    (    __lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  Any    ):




			"""simple docstring"""


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






def        lowerCamelCase__    (    __lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  Tuple    ):




			"""simple docstring"""


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






def        lowerCamelCase__    (    __lowerCAmelCase  :  str   ,							__lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  List[Any]="exp"   ,							__lowerCAmelCase  :  List[str]=None   ,							__lowerCAmelCase  :  Dict=False   ,							__lowerCAmelCase  :  List[str]=False   ,							__lowerCAmelCase  :  Optional[int]=None   ,							__lowerCAmelCase  :  Any=False   ,							):




			"""simple docstring"""


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






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  List[Any]   ,							__lowerCAmelCase  :  Any   ,							__lowerCAmelCase  :  List[Any]    ):




			"""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:
						lowerCAmelCase_      =     compute_measures(__lowerCAmelCase   ,							__lowerCAmelCase    )["wer"]

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

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

						return {"wer": compute_score(__lowerCAmelCase   ,							__lowerCAmelCase   ,							"wer"    ), "cer": compute_score(__lowerCAmelCase   ,							__lowerCAmelCase   ,							"cer"    )}





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,				_KWARGS_DESCRIPTION	)
class      _lowerCAmelCase (     datasets.Metric	):




				def 					__a						( self					)    ->				List[str]:
							if self.config_name not in _CONFIG_NAMES:
										raise KeyError(f"""You should supply a configuration name selected in {_CONFIG_NAMES}"""					)

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




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase=None    ,    _UpperCamelCase=None					)    ->				Optional[Any]:
							lowerCAmelCase_      =     bleu_kwargs if bleu_kwargs is not None else {}
							lowerCAmelCase_      =     wer_kwargs if wer_kwargs is not None else {}

							if self.config_name == "fleurs-lang_id":
										return {"accuracy": simple_accuracy(_UpperCamelCase    ,    _UpperCamelCase					)}
							elif self.config_name == "minds14":
										return fa_and_simple_accuracy(_UpperCamelCase    ,    _UpperCamelCase					)
							elif self.config_name == "covost2":
										lowerCAmelCase_      =     bleu_kwargs.pop("smooth_method"    ,    "exp"					)
										lowerCAmelCase_      =     bleu_kwargs.pop("smooth_value"    ,    _UpperCamelCase					)
										lowerCAmelCase_      =     bleu_kwargs.pop("force"    ,    _UpperCamelCase					)
										lowerCAmelCase_      =     bleu_kwargs.pop("lowercase"    ,    _UpperCamelCase					)
										lowerCAmelCase_      =     bleu_kwargs.pop("tokenize"    ,    _UpperCamelCase					)
										lowerCAmelCase_      =     bleu_kwargs.pop("use_effective_order"    ,    _UpperCamelCase					)
										return bleu(
										    preds=_UpperCamelCase    ,    labels=_UpperCamelCase    ,    smooth_method=_UpperCamelCase    ,    smooth_value=_UpperCamelCase    ,    force=_UpperCamelCase    ,    lowercase=_UpperCamelCase    ,    tokenize=_UpperCamelCase    ,    use_effective_order=_UpperCamelCase    ,    )
							elif self.config_name in ["fleurs-asr", "mls", "voxpopuli", "babel"]:
										lowerCAmelCase_      =     wer_kwargs.pop("concatenate_texts"    ,    _UpperCamelCase					)
										return wer_and_cer(_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    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


_A       =					{"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "NatForImageClassification",
			    "NatModel",
			    "NatPreTrainedModel",
			    "NatBackbone",
			]

if TYPE_CHECKING:
			from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nat import (
						    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NatBackbone,
						    NatForImageClassification,
						    NatModel,
						    NatPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



