
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							  =						'''\
'''

__a							  =						'''\
    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.
'''

__a							  =						'''
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}
'''

__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        UpperCamelCase__(    tr.AbstractTransform	):

		"""simple docstring"""

		def __init__(      self				:			List[Any]							,			snake_case__				:			Optional[int] = " " ):
				"""simple docstring"""





				A				=sentence_delimiter







		def        _a					(      self				:			Optional[Any]							,			snake_case__				:			str ):
				"""simple docstring"""





				return list(UpperCAmelCase__ )







		def        _a					(      self				:			Optional[Any]							,			snake_case__				:			List[Any] ):
				"""simple docstring"""





				A				=[]
				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    UpperCamelCase_ (	a_  ,				a_		)     ->Any:
		return float((preds == labels).mean()		)

def    UpperCamelCase_ (	a_  ,				a_		)     ->Optional[Any]:
		return {
		    "f1": float(fa_score(y_true=a_  ,				y_pred=a_  ,				average="macro"		)		),
		    "accuracy": simple_accuracy(a_  ,				a_		),
		}

def    UpperCamelCase_ (	a_  ,				a_  ,				a_="exp"  ,				a_=None  ,				a_=False  ,				a_=False  ,				a_=None  ,				a_=False  ,				)     ->Optional[Any]:
		A				=[[label] for label in labels]
		A				=list(a_		)
		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(a_		) != 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(a_		)]
		A				=scb.corpus_bleu(
		    a_  ,				a_  ,				smooth_method=a_  ,				smooth_value=a_  ,				force=a_  ,				lowercase=a_  ,				use_effective_order=a_  ,				**({"tokenize": tokenize} if tokenize else {})  ,				)
		return {"bleu": output.score}

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->Tuple:


		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(a_  ,				a_		)['''wer''']

				A				=compute_measures(a_  ,				a_  ,				truth_transform=a_  ,				hypothesis_transform=a_		)['''wer''']
				return {"wer": wer, "cer": cer}
		else:

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

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






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

	"""simple docstring"""

	def        _a					(      self				:			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(UpperCAmelCase__ ), "references": datasets.Value(UpperCAmelCase__ )} )							,			codebase_urls=[]							,			reference_urls=[]							,			format="numpy"							,			)







	def        _a					(      self				:			str							,			snake_case__				:			Union[str, Any]							,			snake_case__				:			Any							,			snake_case__				:			Any=None							,			snake_case__				:			Tuple=None ):
			"""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(UpperCAmelCase__							,			UpperCAmelCase__ )}
			elif self.config_name == "minds14":
					return fa_and_simple_accuracy(UpperCAmelCase__							,			UpperCAmelCase__ )
			elif self.config_name == "covost2":
					A				=bleu_kwargs.pop("smooth_method"							,			"exp" )
					A				=bleu_kwargs.pop("smooth_value"							,			UpperCAmelCase__ )
					A				=bleu_kwargs.pop("force"							,			UpperCAmelCase__ )
					A				=bleu_kwargs.pop("lowercase"							,			UpperCAmelCase__ )
					A				=bleu_kwargs.pop("tokenize"							,			UpperCAmelCase__ )
					A				=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				=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'''






import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				_A :				int				=  tmp_path / '''text.txt'''
				_A :				int				=  textwrap.dedent(
				    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        ''')
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Tuple):



				"""simple docstring"""


				_A :				str				=  tmp_path / '''text_with_image.txt'''
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.mark.parametrize('''keep_linebreaks'''   , [True, False])
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read().splitlines(keepends=lowerCAmelCase)
				_A :				Any				=  Text(keep_linebreaks=lowerCAmelCase   , encoding='''utf-8''')
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				Dict				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content





@require_pil
def      lowercase  (  lowerCAmelCase :						Dict):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Tuple				=  f.read().splitlines()[0]
				_A :				Optional[int]				=  Text(encoding='''utf-8'''   , features=Features({'''image''': Image()}))
				_A :				Any				=  text._generate_tables([[text_file_with_image]])
				_A :				str				=  pa.concat_tables([table for _, table in generator])
				assert pa_table.schema.field('''image''').type == Image()()
				_A :				List[str]				=  pa_table.to_pydict()['''image''']
				assert generated_content == [{"path": image_file, "bytes": None}]





@pytest.mark.parametrize('''sample_by'''   , ['''line''', '''paragraph''', '''document'''])
def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read()
				if sample_by == "line":
								_A :				str				=  expected_content.splitlines()
				elif sample_by == "paragraph":
								_A :				str				=  expected_content.split('''\n\n''')
				elif sample_by == "document":
								_A :				Tuple				=  [expected_content]
				_A :				str				=  Text(sample_by=lowerCAmelCase   , encoding='''utf-8'''   , chunksize=100)
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				int				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content
