




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


SCREAMING_SNAKE_CASE__       :      Tuple  =   logging.get_logger(__name__)




class 		UpperCamelCase__	:

    '''simple docstring'''



    lowerCamelCase_      :	str
    lowerCamelCase_      :	str	     =      None







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







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      str:
           raise NotImplementedError







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[Any]:
           raise NotImplementedError







    def     _lowercase     (    self     )     ->      Union[str, 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     _lowercase     (    cls     )     ->      List[str]:
           return F'''`pip install {cls.pip_package or cls.name}`'''




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Tuple	     =      """optuna"""







    @staticmethod
    def     _lowercase     (    )     ->      int:
           return is_optuna_available()







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      List[str]:
           return run_hp_search_optuna(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Optional[int]:
           return default_hp_space_optuna(UpperCamelCase__     )




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	List[Any]	     =      """ray"""
    lowerCamelCase_      :	Optional[Any]	     =      """'ray[tune]'"""







    @staticmethod
    def     _lowercase     (    )     ->      Any:
           return is_ray_available()







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      Union[str, Any]:
           return run_hp_search_ray(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Dict:
           return default_hp_space_ray(UpperCamelCase__     )




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	str	     =      """sigopt"""







    @staticmethod
    def     _lowercase     (    )     ->      str:
           return is_sigopt_available()







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      Optional[int]:
           return run_hp_search_sigopt(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Optional[Any]:
           return default_hp_space_sigopt(UpperCamelCase__     )




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	List[str]	     =      """wandb"""







    @staticmethod
    def     _lowercase     (    )     ->      List[Any]:
           return is_wandb_available()







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      str:
           return run_hp_search_wandb(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Dict:
           return default_hp_space_wandb(UpperCamelCase__     )




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

def      A      (      )	-> str:
       lowerCamelCase   :      Tuple			 =					[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
       if len(_SCREAMING_SNAKE_CASE    ) > 0:
              lowerCamelCase   :      List[Any]			 =					available_backends[0].name
              if len(_SCREAMING_SNAKE_CASE    ) > 1:
                     logger.info(
                         f'''{len(_SCREAMING_SNAKE_CASE    )} 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 typing import Union

import fire
import torch
from tqdm import tqdm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str = "cpu"    ,			SCREAMING_SNAKE_CASE__			:	Union[str, None] = None      )							->      None:





    '''simple docstring'''






    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location=SCREAMING_SNAKE_CASE__      )
    for k, v in tqdm(state_dict.items()      ):
        if not isinstance(SCREAMING_SNAKE_CASE__    ,			torch.Tensor      ):
            raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'      )
        A__          =   v.half()
    if save_path is None:  # overwrite src_path
        A__          =   src_path
    torch.save(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     fire.Fire(convert)

