
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


UpperCamelCase_						:     Union[str, Any]	       =							logging.get_logger(__name__)




class   __lowercase						:
	_A       =   42
	_A       =   None






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






	def       _a(self				: Tuple      ,	snake_case				: Dict      ,	snake_case				: int      ,	snake_case				: str      ,	**snake_case				: List[str]    ) ->			Optional[Any]:
							raise NotImplementedError






	def       _a(self				: Optional[int]      ,	snake_case				: Tuple    ) ->			Optional[int]:
							raise NotImplementedError






	def       _a(self				: int    ) ->			str:
							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       _a(cls				: List[str]    ) ->			Optional[int]:
							return F"""`pip install {cls.pip_package or cls.name}`"""




class   __lowercase						(						__snake_case       ):
	_A       =   "optuna"






	@staticmethod
	def       _a() ->			Optional[Any]:
							return is_optuna_available()






	def       _a(self				: int      ,	snake_case				: Tuple      ,	snake_case				: int      ,	snake_case				: str      ,	**snake_case				: Optional[int]    ) ->			Any:
							return run_hp_search_optuna(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    )






	def       _a(self				: Dict      ,	snake_case				: Dict    ) ->			Optional[Any]:
							return default_hp_space_optuna(snake_case    )




class   __lowercase						(						__snake_case       ):
	_A       =   "ray"
	_A       =   "'ray[tune]'"






	@staticmethod
	def       _a() ->			Optional[int]:
							return is_ray_available()






	def       _a(self				: List[str]      ,	snake_case				: Dict      ,	snake_case				: int      ,	snake_case				: str      ,	**snake_case				: Tuple    ) ->			Optional[int]:
							return run_hp_search_ray(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    )






	def       _a(self				: List[str]      ,	snake_case				: Any    ) ->			List[Any]:
							return default_hp_space_ray(snake_case    )




class   __lowercase						(						__snake_case       ):
	_A       =   "sigopt"






	@staticmethod
	def       _a() ->			Optional[Any]:
							return is_sigopt_available()






	def       _a(self				: List[str]      ,	snake_case				: Dict      ,	snake_case				: int      ,	snake_case				: str      ,	**snake_case				: Optional[Any]    ) ->			Any:
							return run_hp_search_sigopt(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    )






	def       _a(self				: Dict      ,	snake_case				: Tuple    ) ->			str:
							return default_hp_space_sigopt(snake_case    )




class   __lowercase						(						__snake_case       ):
	_A       =   "wandb"






	@staticmethod
	def       _a() ->			int:
							return is_wandb_available()






	def       _a(self				: Dict      ,	snake_case				: Any      ,	snake_case				: int      ,	snake_case				: str      ,	**snake_case				: str    ) ->			Union[str, Any]:
							return run_hp_search_wandb(snake_case      ,	snake_case      ,	snake_case      ,	**snake_case    )






	def       _a(self				: Optional[int]      ,	snake_case				: Optional[int]    ) ->			List[Any]:
							return default_hp_space_wandb(snake_case    )


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



def 		UpperCamelCase     (       )   ->			str:






						'''simple docstring'''

						_lowercase       :	int           =   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
						if len(_UpperCAmelCase  ) > 0:
												_lowercase       :	Optional[Any]           =   available_backends[0].name
												if len(_UpperCAmelCase  ) > 1:
																		logger.info(
																		    f"""{len(_UpperCAmelCase  )} 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()  )  )






import math




class   __lowercase						:






	def       _a(self				: Dict      ,	snake_case				: list[list[float]]      ,	snake_case				: list[int]    ) ->			int:
							_lowercase       :	List[Any]           =   0.0
							_lowercase       :	Any           =   0.0
							for i in range(len(snake_case    )    ):
													da += math.pow((sample[i] - weights[0][i])      ,	2    )
													da += math.pow((sample[i] - weights[1][i])      ,	2    )
													return 0 if da > da else 1
							return 0






	def       _a(self				: Dict      ,	snake_case				: list[list[int | float]]      ,	snake_case				: list[int]      ,	snake_case				: int      ,	snake_case				: float    ) ->			list[list[int | float]]:
							for i in range(len(snake_case    )    ):
													weights[j][i] += alpha * (sample[i] - weights[j][i])
							return weights



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

						# weight initialization ( n, C )
						_lowercase       :	Tuple           =   [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

						# training
						_lowercase       :	List[Any]           =   SelfOrganizingMap()
						_lowercase       :	Any           =   3
						_lowercase       :	Optional[Any]           =   0.5

						for _ in range(_UpperCAmelCase  ):
												for j in range(len(_UpperCAmelCase  )  ):
																		# training sample
																		_lowercase       :	Optional[int]           =   training_samples[j]

																		# Compute the winning vector
																		_lowercase       :	Tuple           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

																		# Update the winning vector
																		_lowercase       :	str           =   self_organizing_map.update(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

    # classify test sample
						_lowercase       :	Any           =   [0, 0, 0, 1]
						_lowercase       :	Dict           =   self_organizing_map.get_winner(_UpperCAmelCase ,			_UpperCAmelCase  )

						# results
						print(f"""Clusters that the test sample belongs to : {winner}"""  )
						print(f"""Weights that have been trained : {weights}"""  )


# running the main() function
if __name__ == "__main__":
							main()




