





'''simple docstring'''

from collections import Counter

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

lowercase__		:     str			=							datasets.load_iris()

lowercase__		:     str			=							np.array(data['data'])
lowercase__		:     int			=							np.array(data['target'])
lowercase__		:     Union[str, Any]			=							data['target_names']

lowercase__			,  lowercase__			,  lowercase__			,  lowercase__		:     List[str]			=							train_test_split(X, y)







def    a__							(							lowercase   :	str,   lowercase   :	Union[str, Any] )						->						Optional[Any]:





     """simple docstring"""

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







def    a__							(							lowercase   :	int,   lowercase   :	Union[str, Any],   lowercase   :	List[Any],   lowercase   :	Tuple,   lowercase   :	str=5 )						->						List[Any]:





     """simple docstring"""

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

import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


lowercase__		:     int			=							logging.get_logger(__name__)

lowercase__		:     Any			=							'T5Config'







def    a__							(							lowercase   :	jnp.array,   lowercase   :	int,   lowercase   :	int )						->						jnp.ndarray:





     """simple docstring"""

     _UpperCamelCase							       =			jnp.zeros_like(lowercase )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 0].set(lowercase )

     _UpperCamelCase							       =			jnp.where(shifted_input_ids == -100,   lowercase,   lowercase )
     return shifted_input_ids
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'mt5'
       _snake_case      :	Union[str, Any]									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Tuple									=       'mt5'
       _snake_case      :	int									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[int]									=       'mt5'
       _snake_case      :	Optional[Any]									=       MTaConfig



