code: 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_:      Optional[int]        =			'\\n'
  
  A_:      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'
  
  A_:      Optional[Any]        =			'\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_:      Any        =			['fleurs-asr', 'mls', 'voxpopuli', 'babel', 'covost2', 'fleurs-lang_id', 'minds14']
  A_:      Tuple        =			''
  
  try:
  				from jiwer import transforms as tr
  
  				A_:      List[str]        =			True
  except ImportError:
  				A_:      Dict        =			False
  
  if _jiwer_available and version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):
  
  
  				class _lowercase					(       tr.AbstractTransform						):
  
  
  
  
  
  											"""simple docstring"""
  
  
  
  
  											def __init__(    self     ,   UpperCAmelCase = " "  ):
  
  
  
  
  
  
  
  																		'''simple docstring'''
  
  
  
  
  
  
  																		_lowercase				=	sentence_delimiter
  
  
  
  											def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  																		'''simple docstring'''
  
  
  
  
  
  
  																		return list(UpperCAmelCase  )
  
  
  
  											def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  																		'''simple docstring'''
  
  
  
  
  
  
  																		_lowercase				=	[]
  																		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_:      Any        =			tr.Compose(
  				    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
  				)
  elif _jiwer_available:
  				A_:      List[str]        =			tr.Compose(
  				    [
  				        tr.RemoveMultipleSpaces(),
  				        tr.Strip(),
  				        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
  				        tr.ReduceToListOfListOfChars(),
  				    ]
  				)
  else:
  				A_:      Optional[Any]        =			None
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							return float((preds == labels).mean()					)
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							return {
  							    "f1": float(fa_score(y_true=_A ,y_pred=_A ,average="""macro"""					)					),
  							    "accuracy": simple_accuracy(_A ,_A					),
  							}
  
  
  def 					__lowerCAmelCase			(  _A ,_A ,_A="exp" ,_A=None ,_A=False ,_A=False ,_A=None ,_A=False ,):
  
  
  
  							"""simple docstring"""
  
  
  							_lowercase				=	[[label] for label in labels]
  							_lowercase				=	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\"`."""					)
  
  							_lowercase				=	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"""					)
  							_lowercase				=	[[refs[i] for refs in labels] for i in range(_A					)]
  							_lowercase				=	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 					__lowerCAmelCase			(  _A ,_A ,_A ,_A					):
  
  
  
  							"""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:
  														_lowercase				=	compute_measures(_A ,_A					)["""wer"""]
  
  														_lowercase				=	compute_measures(_A ,_A ,truth_transform=_A ,hypothesis_transform=_A					)["""wer"""]
  														return {"wer": wer, "cer": cer}
  							else:
  
  														def compute_score(_A ,_A ,_A="wer"					):
  																					_lowercase				=	0
  																					_lowercase				=	0
  																					for prediction, reference in zip(_A ,_A					):
  																												if score_type == "wer":
  																																			_lowercase				=	compute_measures(_A ,_A					)
  																												elif score_type == "cer":
  																																			_lowercase				=	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 _lowercase					(       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}'''  )
  
  														_lowercase				=	"""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'''
  
  
  
  
  
  
  														_lowercase				=	bleu_kwargs if bleu_kwargs is not None else {}
  														_lowercase				=	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":
  																					_lowercase				=	bleu_kwargs.pop("""smooth_method"""     ,   """exp"""  )
  																					_lowercase				=	bleu_kwargs.pop("""smooth_value"""     ,   UpperCAmelCase  )
  																					_lowercase				=	bleu_kwargs.pop("""force"""     ,   UpperCAmelCase  )
  																					_lowercase				=	bleu_kwargs.pop("""lowercase"""     ,   UpperCAmelCase  )
  																					_lowercase				=	bleu_kwargs.pop("""tokenize"""     ,   UpperCAmelCase  )
  																					_lowercase				=	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"]:
  																					_lowercase				=	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}'''  )
  
  
  
style_context: import random
  
  
  class _lowercase					:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = 0.01     ,   UpperCAmelCase = 1000     ,   UpperCAmelCase = -1     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	sample
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														_lowercase				=	target
  														if len(self.target  ) == 0:
  																					raise ValueError("""Target data can not be empty"""  )
  														if len(self.sample  ) != len(self.target  ):
  																					raise ValueError("""Sample data and Target data do not have matching lengths"""  )
  														_lowercase				=	learning_rate
  														_lowercase				=	epoch_number
  														_lowercase				=	bias
  														_lowercase				=	len(UpperCAmelCase  )
  														_lowercase				=	len(sample[0]  )  # number of columns in dataset
  														_lowercase				=	[]
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														for sample in self.sample:
  																					sample.insert(0     ,   self.bias  )
  
  														for _ in range(self.col_sample  ):
  																					self.weight.append(random.random()  )
  
  														self.weight.insert(0     ,   self.bias  )
  
  														_lowercase				=	0
  
  														while True:
  																					_lowercase				=	False
  																					for i in range(self.number_sample  ):
  																												_lowercase				=	0
  																												for j in range(self.col_sample + 1  ):
  																																			_lowercase				=	u + self.weight[j] * self.sample[i][j]
  																												_lowercase				=	self.sign(UpperCAmelCase  )
  																												if y != self.target[i]:
  																																			for j in range(self.col_sample + 1  ):
  																																										_lowercase				=	(
  																																										    self.weight[j]
  																																										    + self.learning_rate
  																																										    * (self.target[i] - y)
  																																										    * self.sample[i][j]
  																																										)
  																																			_lowercase				=	True
              # print('Epoch: \n',epoch_count)
  																					_lowercase				=	epoch_count + 1
  																					# if you want control the epoch or just by error
  																					if not has_misclassified:
  																												print(("""\nEpoch:\n""", epoch_count)  )
  																												print("""------------------------\n"""  )
  																												# if epoch_count > self.epoch_number or not error:
  																												break
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if len(self.sample  ) == 0:
  																					raise ValueError("""Sample data can not be empty"""  )
  														sample.insert(0     ,   self.bias  )
  														_lowercase				=	0
  														for i in range(self.col_sample + 1  ):
  																					_lowercase				=	u + self.weight[i] * sample[i]
  
  														_lowercase				=	self.sign(UpperCAmelCase  )
  
  														if y == -1:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P1"""  )
  														else:
  																					print(("""Sample: """, sample)  )
  																					print("""classification: P2"""  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return 1 if u >= 0 else -1
  
  
  
  
  
  
  A_:      Optional[int]        =			[
      [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
      [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
      [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
      [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
      [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
      [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
      [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
      [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
      [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
      [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
      [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
      [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
      [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
      [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
      [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
      [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
      [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
      [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
      [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
      [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
      [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
      [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
      [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
      [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
      [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
      [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
      [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
      [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
      [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
      [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
  ]
  
  A_:      Optional[int]        =			[
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  				A_:      str        =			Perceptron(
  				    sample=samples, target=target, learning_rate=0.0_1, epoch_number=1000, bias=-1
  				)
  				network.training()
  				print('Finished training perceptron')
  				print('Enter values to predict or q to exit')
  				while True:
  								A_:      list        =			[]
  								for i in range(len(samples[0])):
  												A_:      Dict        =			input('value: ').strip()
  												if user_input == "q":
  																break
  												A_:      Union[str, Any]        =			float(user_input)
  												sample.insert(i, observation)
  								network.sort(sample)
  
  
  