





'''simple docstring'''






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      :   Optional[int]								=     '''\
'''

__UpperCamelCase      :   List[str]								=     '''\
    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.
'''

__UpperCamelCase      :   Union[str, 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}
'''

__UpperCamelCase      :   Dict								=     ['''fleurs-asr''', '''mls''', '''voxpopuli''', '''babel''', '''covost2''', '''fleurs-lang_id''', '''minds14''']
__UpperCamelCase      :   int								=     ''''''

try:
	from jiwer import transforms as tr

	__UpperCamelCase      :   List[str]								=     True
except ImportError:
	__UpperCamelCase      :   List[Any]								=     False

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



	class  lowerCamelCase__				(						tr.AbstractTransform						):


		"""simple docstring"""





		def __init__(					self   ,     UpperCAmelCase__ = " "			)       ->				Dict:
						_A :				int				=  sentence_delimiter




		def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__			)       ->				Any:
						return list(UpperCAmelCase__			)




		def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__			)       ->				List[Any]:
						_A :				Tuple				=  []
						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
	__UpperCamelCase      :   int								=     tr.Compose(
	    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
	)
elif _jiwer_available:
	__UpperCamelCase      :   str								=     tr.Compose(
	    [
	        tr.RemoveMultipleSpaces(),
	        tr.Strip(),
	        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
	        tr.ReduceToListOfListOfChars(),
	    ]
	)
else:
	__UpperCamelCase      :   Optional[Any]								=     None





def      lowercase  (  lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


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





def      lowercase  (  lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						List[Any]):



				"""simple docstring"""


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





def      lowercase  (  lowerCAmelCase :						List[str]   , lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						Tuple="exp"   , lowerCAmelCase :						Any=None   , lowerCAmelCase :						List[str]=False   , lowerCAmelCase :						Dict=False   , lowerCAmelCase :						Dict=None   , lowerCAmelCase :						int=False   , ):



				"""simple docstring"""


				_A :				Any				=  [[label] for label in labels]
				_A :				str				=  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"`.''')

				_A :				Union[str, Any]				=  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''')
				_A :				Optional[int]				=  [[refs[i] for refs in labels] for i in range(lowerCAmelCase)]
				_A :				Optional[int]				=  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      lowercase  (  lowerCAmelCase :						Tuple   , lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						int   , lowerCAmelCase :						Union[str, 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:
								_A :				Any				=  compute_measures(lowerCAmelCase   , lowerCAmelCase)['''wer''']

								_A :				Dict				=  compute_measures(lowerCAmelCase   , lowerCAmelCase   , truth_transform=lowerCAmelCase   , hypothesis_transform=lowerCAmelCase)['''wer''']
								return {"wer": wer, "cer": cer}
				else:

								def compute_score(lowerCAmelCase :						Any   , lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						List[Any]="wer"):
												_A :				Dict				=  0
												_A :				str				=  0
												for prediction, reference in zip(lowerCAmelCase   , lowerCAmelCase):
																if score_type == "wer":
																				_A :				Optional[Any]				=  compute_measures(lowerCAmelCase   , lowerCAmelCase)
																elif score_type == "cer":
																				_A :				Union[str, Any]				=  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						):


	"""simple docstring"""





	def 				_lowerCamelCase				(					self			)       ->				Dict:
					if self.config_name not in _CONFIG_NAMES:
									raise KeyError(F"""You should supply a configuration name selected in {_CONFIG_NAMES}"""			)

					_A :				Dict				=  '''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 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None   ,     UpperCAmelCase__=None			)       ->				Dict:
					_A :				List[str]				=  bleu_kwargs if bleu_kwargs is not None else {}
					_A :				List[Any]				=  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":
									_A :				List[str]				=  bleu_kwargs.pop('''smooth_method'''   ,     '''exp'''			)
									_A :				str				=  bleu_kwargs.pop('''smooth_value'''   ,     UpperCAmelCase__			)
									_A :				Optional[Any]				=  bleu_kwargs.pop('''force'''   ,     UpperCAmelCase__			)
									_A :				int				=  bleu_kwargs.pop('''lowercase'''   ,     UpperCAmelCase__			)
									_A :				Tuple				=  bleu_kwargs.pop('''tokenize'''   ,     UpperCAmelCase__			)
									_A :				str				=  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"]:
									_A :				Dict				=  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}"""			)







'''simple docstring'''






from typing import TYPE_CHECKING

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


__UpperCamelCase      :   List[str]								=     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   List[Any]								=     [
	    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTJForCausalLM''',
	    '''GPTJForQuestionAnswering''',
	    '''GPTJForSequenceClassification''',
	    '''GPTJModel''',
	    '''GPTJPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Optional[int]								=     [
	    '''TFGPTJForCausalLM''',
	    '''TFGPTJForQuestionAnswering''',
	    '''TFGPTJForSequenceClassification''',
	    '''TFGPTJModel''',
	    '''TFGPTJPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Union[str, Any]								=     [
	    '''FlaxGPTJForCausalLM''',
	    '''FlaxGPTJModel''',
	    '''FlaxGPTJPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	__UpperCamelCase      :   List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
