





"""simple docstring"""





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

__A											=						datasets.load_iris()

__A											=						np.array(data["""data"""])
__A											=						np.array(data["""target"""])
__A											=						data["""target_names"""]

__A							,				__A							,				__A							,				__A											=						train_test_split(X, y)
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Dict:





  """simple docstring"""


  return np.linalg.norm(np.array(_SCREAMING_SNAKE_CASE     ) - np.array(_SCREAMING_SNAKE_CASE     )     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=5     )   ->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__     :str											=				zip(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  # List of distances of all points from the point to be classified
  lowerCAmelCase__     :Optional[Any]											=				[]
  for data_point in data:
    lowerCAmelCase__     :Tuple											=				euclidean_distance(data_point[0]	,  _SCREAMING_SNAKE_CASE     )
    distances.append((distance, data_point[1])     )
  # Choosing 'k' points with the least distances.
  lowerCAmelCase__     :str											=				[i[1] for i in sorted(_SCREAMING_SNAKE_CASE     )[:k]]
  # Most commonly occurring class among them
  # is the class into which the point is classified
  lowerCAmelCase__     :Optional[int]											=				Counter(_SCREAMING_SNAKE_CASE     ).most_common(1     )[0][0]
  return classes[result]


if __name__ == "__main__":
  print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__A											=						{}


try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						["""MLukeTokenizer"""]

if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mluke import MLukeTokenizer


else:
  import sys

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