






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 __lowerCamelCase					( tr.AbstractTransform):





			"""simple docstring"""






			def __init__(				self			,       UpperCAmelCase = " "       ):
									"""simple docstring"""







									_UpperCAmelCase							   =     sentence_delimiter

			def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
									"""simple docstring"""







									return list(UpperCAmelCase       )

			def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
									"""simple docstring"""







									_UpperCAmelCase							   =     []
									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 __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    int:





						"""simple docstring"""



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


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Any:





						"""simple docstring"""



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


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase="exp"					,						__lowerCAmelCase=None					,						__lowerCAmelCase=False					,						__lowerCAmelCase=False					,						__lowerCAmelCase=None					,						__lowerCAmelCase=False					,						)->    Any:





						"""simple docstring"""



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

						_UpperCAmelCase							   =     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'      )
						_UpperCAmelCase							   =     [[refs[i] for refs in labels] for i in range(__lowerCAmelCase      )]
						_UpperCAmelCase							   =     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 __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__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:
												_UpperCAmelCase							   =     compute_measures(__lowerCAmelCase					,						__lowerCAmelCase      )['wer']

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

												def compute_score(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase="wer"      ):
																		_UpperCAmelCase							   =     0
																		_UpperCAmelCase							   =     0
																		for prediction, reference in zip(__lowerCAmelCase					,						__lowerCAmelCase      ):
																								if score_type == "wer":
																														_UpperCAmelCase							   =     compute_measures(__lowerCAmelCase					,						__lowerCAmelCase      )
																								elif score_type == "cer":
																														_UpperCAmelCase							   =     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 			UpperCamelCase       (				self       ):
							"""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(UpperCAmelCase       ), 'references': datasets.Value(UpperCAmelCase       )}       )			,       codebase_urls=[]			,       reference_urls=[]			,       format='numpy'			,       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase=None			,       UpperCAmelCase=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(UpperCAmelCase			,       UpperCAmelCase       )}
							elif self.config_name == "minds14":
													return fa_and_simple_accuracy(UpperCAmelCase			,       UpperCAmelCase       )
							elif self.config_name == "covost2":
													_UpperCAmelCase							   =     bleu_kwargs.pop('smooth_method'			,       'exp'       )
													_UpperCAmelCase							   =     bleu_kwargs.pop('smooth_value'			,       UpperCAmelCase       )
													_UpperCAmelCase							   =     bleu_kwargs.pop('force'			,       UpperCAmelCase       )
													_UpperCAmelCase							   =     bleu_kwargs.pop('lowercase'			,       UpperCAmelCase       )
													_UpperCAmelCase							   =     bleu_kwargs.pop('tokenize'			,       UpperCAmelCase       )
													_UpperCAmelCase							   =     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"]:
													_UpperCAmelCase							   =     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 __future__ import annotations


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						)->    list[float | int]:





						"""simple docstring"""



						if not all((nth_term, start_term_a, common_ratio_r)      ):
												return []
						_UpperCAmelCase							   =     []
						_UpperCAmelCase							   =     1
						_UpperCAmelCase							   =     common_ratio_r
						for _ in range(int(__lowerCAmelCase      )      ):
												if not series:
																		series.append(__lowerCAmelCase      )
												else:
																		power += 1
																		series.append(float(start_term_a * multiple      )      )
																		_UpperCAmelCase							   =     pow(float(__lowerCAmelCase      )					,						__lowerCAmelCase      )
						return series


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_a       =    float(input('''Enter the last number (n term) of the Geometric Series'''))
		_a       =    float(input('''Enter the starting term (a) of the Geometric Series'''))
		_a       =    float(
		    input('''Enter the common ratio between two terms (r) of the Geometric Series''')
		)
		print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
		print(geometric_series(nth_term, start_term_a, common_ratio_r))
