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       __lowerCAmelCase      (						A_   : int						,	A_   : Dict					)    ->  List[str]:
  return np.linalg.norm(np.array(A_					) - np.array(A_					)					)




def       __lowerCAmelCase      (						A_   : Any						,	A_   : List[str]						,	A_   : List[Any]						,	A_   : List[Any]						,	A_   : int=5					)    ->  Dict:
  __UpperCAmelCase						    =       zip(A_						,	A_					)
  # List of distances of all points from the point to be classified
  __UpperCAmelCase						    =       []
  for data_point in data:
    __UpperCAmelCase						    =       euclidean_distance(data_point[0]						,	A_					)
    distances.append((distance, data_point[1])					)
  # Choosing 'k' points with the least distances.
  __UpperCAmelCase						    =       [i[1] for i in sorted(A_					)[:k]]
  # Most commonly occurring class among them
  # is the class into which the point is classified
  __UpperCAmelCase						    =       Counter(A_					).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]))




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_							=      {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

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

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

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



