






"""simple docstring"""
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


lowerCamelCase_							      = logging.get_logger(__name__)







class      UpperCamelCase_			:
	__magic_name__       =							42
	__magic_name__       =							None





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     Optional[int]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Union[str, Any]		)		->     Optional[Any]:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					Union[str, Any]		)		->     str:
				raise NotImplementedError





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Dict:
				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 	_SCREAMING_SNAKE_CASE ( cls   :					Tuple		)		->     List[str]:
				return f"""`pip install {cls.pip_package or cls.name}`"""







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''optuna'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     List[str]:
				return is_optuna_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     int:
				return run_hp_search_optuna(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Dict		)		->     Optional[Any]:
				return default_hp_space_optuna(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''ray'''
	__magic_name__       =							'''\'ray[tune]\''''





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





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Tuple		)		->     List[str]:
				return run_hp_search_ray(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Tuple		)		->     Optional[int]:
				return default_hp_space_ray(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''sigopt'''





	@staticmethod
	def 	_SCREAMING_SNAKE_CASE ( )		->     List[Any]:
				return is_sigopt_available()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					str		)		->     str:
				return run_hp_search_sigopt(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					Any		)		->     Any:
				return default_hp_space_sigopt(lowerCAmelCase_		)







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''wandb'''





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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      **lowerCAmelCase_   :					Optional[Any]		)		->     Optional[Any]:
				return run_hp_search_wandb(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]		)		->     Any:
				return default_hp_space_wandb(lowerCAmelCase_		)





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

def 							snake_case (  ):
			UpperCAmelCase_	:					List[str]     	=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
			if len(A__		) > 0:
						UpperCAmelCase_	:					Optional[Any]     	=   available_backends[0].name
						if len(A__		) > 1:
									logger.info(
									    F"""{len(A__		)} 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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a							     =					{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a							     =					[
         'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
         'EncodecModel',
         'EncodecPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_encodec import (
         ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
         EncodecConfig,
     )
     from .feature_extraction_encodec import EncodecFeatureExtractor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_encodec import (
              ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
              EncodecModel,
              EncodecPreTrainedModel,
          )

else:
     import sys

     __a							     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)