





from collections import Counter

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

SCREAMING_SNAKE_CASE__       :			str			      =						datasets.load_iris()

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

SCREAMING_SNAKE_CASE__      ,     SCREAMING_SNAKE_CASE__      ,     SCREAMING_SNAKE_CASE__      ,     SCREAMING_SNAKE_CASE__       :			List[Any]			      =						train_test_split(X, y)






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   return np.linalg.norm(np.array(lowerCamelCase   ) - np.array(lowerCamelCase   )   )






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








import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE__       :			Dict			      =						"""
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""







class   __lowerCAmelCase			(					unittest.TestCase		,_UpperCamelCase  ):





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Optional[int]      		=  load_tool("text-question-answering"      )
      self.tool.setup()
      a__	:						Dict      		=  load_tool("text-question-answering"					,			remote=snake_case      )





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[Any]      		=  self.tool(snake_case					,			"What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						List[Any]      		=  self.remote_tool(snake_case					,			"What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Any      		=  self.tool(text=snake_case					,			question="What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						List[str]      		=  self.remote_tool(text=snake_case					,			question="What did Hugging Face do in April 2021?"      )
      self.assertEqual(snake_case					,			"launched the BigScience Research Workshop"      )

