



from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


lowercase_													=      logging.get_logger(__name__)






class     __a			:
	SCREAMING_SNAKE_CASE  	=							42
	SCREAMING_SNAKE_CASE  	=							None



	@staticmethod
	def 		UpperCamelCase				(    )->				Dict:
						raise NotImplementedError



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     str					,						snake_case_				:     int					,						snake_case_				:     str					,						**snake_case_				:     Union[str, Any])->				int:
						raise NotImplementedError



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     int)->				List[str]:
						raise NotImplementedError



	def 		UpperCamelCase				(    self				:     Optional[int])->				Any:
						if not self.is_available():
											raise RuntimeError(
											    F"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.""")



	@classmethod
	def 		UpperCamelCase				(    cls				:     str)->				str:
						return F"""`pip install {cls.pip_package or cls.name}`"""






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"optuna"



	@staticmethod
	def 		UpperCamelCase				(    )->				Any:
						return is_optuna_available()



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     Tuple					,						snake_case_				:     int					,						snake_case_				:     str					,						**snake_case_				:     str)->				str:
						return run_hp_search_optuna(snake_case_					,						snake_case_					,						snake_case_					,						**snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[int]					,						snake_case_				:     Tuple)->				List[Any]:
						return default_hp_space_optuna(snake_case_)






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"ray"
	SCREAMING_SNAKE_CASE  	=							"'ray[tune]'"



	@staticmethod
	def 		UpperCamelCase				(    )->				Optional[Any]:
						return is_ray_available()



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     List[str]					,						snake_case_				:     int					,						snake_case_				:     str					,						**snake_case_				:     Optional[Any])->				Tuple:
						return run_hp_search_ray(snake_case_					,						snake_case_					,						snake_case_					,						**snake_case_)



	def 		UpperCamelCase				(    self				:     List[Any]					,						snake_case_				:     Any)->				Optional[int]:
						return default_hp_space_ray(snake_case_)






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"sigopt"



	@staticmethod
	def 		UpperCamelCase				(    )->				int:
						return is_sigopt_available()



	def 		UpperCamelCase				(    self				:     Any					,						snake_case_				:     Optional[int]					,						snake_case_				:     int					,						snake_case_				:     str					,						**snake_case_				:     List[str])->				Tuple:
						return run_hp_search_sigopt(snake_case_					,						snake_case_					,						snake_case_					,						**snake_case_)



	def 		UpperCamelCase				(    self				:     Any					,						snake_case_				:     Optional[int])->				Union[str, Any]:
						return default_hp_space_sigopt(snake_case_)






class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							"wandb"



	@staticmethod
	def 		UpperCamelCase				(    )->				Optional[int]:
						return is_wandb_available()



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     Any					,						snake_case_				:     int					,						snake_case_				:     str					,						**snake_case_				:     List[Any])->				Optional[Any]:
						return run_hp_search_wandb(snake_case_					,						snake_case_					,						snake_case_					,						**snake_case_)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Any)->				int:
						return default_hp_space_wandb(snake_case_)


lowercase_													=      {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}






def        __lowerCAmelCase		(	)	->    str:
					__lowerCAmelCase						 =[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
					if len(__lowerCamelCase						) > 0:
										__lowerCAmelCase						 =available_backends[0].name
										if len(__lowerCamelCase						) > 1:
															logger.info(
															    f"""{len(__lowerCamelCase						)} hyperparameter search backends available. Using {name} as the default."""						)
										return name
					raise RuntimeError(
					    """No hyperparameter search backend available.\n"""
					    + """\n""".join(
					        f""" - To install {backend.name} run {backend.pip_install()}"""
					        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()						)						)









from collections import Counter

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

lowercase_													=      datasets.load_iris()

lowercase_													=      np.array(data['''data'''])
lowercase_													=      np.array(data['''target'''])
lowercase_													=      data['''target_names''']

lowercase_							,							lowercase_							,							lowercase_							,							lowercase_													=      train_test_split(X, y)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]						)	->    Optional[int]:
					return np.linalg.norm(np.array(__lowerCamelCase						) - np.array(__lowerCamelCase						)						)






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




