'''simple docstring'''




import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 		__A       (__magic_name__ ,					unittest.TestCase   ):
       snake_case       :Dict				    =					LayoutLMTokenizer
       snake_case       :Union[str, Any]				    =					LayoutLMTokenizerFast
       snake_case       :Any				    =					True
       snake_case       :int				    =					True






       def 			_snake_case						(      self      ):
              super().setUp()

              __UpperCAmelCase  :				List[str]       				=		[
                  "[UNK]",
                  "[CLS]",
                  "[SEP]",
                  "want",
                  "##want",
                  "##ed",
                  "wa",
                  "un",
                  "runn",
                  "##ing",
                  ",",
                  "low",
                  "lowest",
              ]
              __UpperCAmelCase  :				Tuple       				=		os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["vocab_file"]      )
              with open(self.vocab_file				,     "w"				,     encoding="utf-8"      ) as vocab_writer:
                     vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
              return LayoutLMTokenizer.from_pretrained(self.tmpdirname				,     **UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Dict       				=		"UNwant\u00E9d,running"
              __UpperCAmelCase  :				Optional[int]       				=		"unwanted, running"
              return input_text, output_text






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		self.tokenizer_class(self.vocab_file      )

              __UpperCAmelCase  :				Tuple       				=		tokenizer.tokenize("UNwant\u00E9d,running"      )
              self.assertListEqual(UpperCamelCase_				,     ["un", "##want", "##ed", ",", "runn", "##ing"]      )
              self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_      )				,     [7, 4, 5, 10, 8, 9]      )







       def 			_snake_case						(      self      ):
              pass







'''simple docstring'''




from collections import Counter

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

_a   :	str 							=   datasets.load_iris()

_a   :	List[Any] 							=   np.array(data["data"])
_a   :	Optional[Any] 							=   np.array(data["target"])
_a   :	Dict 							=   data["target_names"]

_a							,					_a							,					_a							,					_a   :	Any 							=   train_test_split(X, y)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       return np.linalg.norm(np.array(lowerCamelCase__						) - np.array(lowerCamelCase__						)						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=5						)					->       int:





       """simple docstring"""


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






