

"""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


__UpperCamelCase  :      Optional[Any]							      =		logging.get_logger(__name__)







class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       42
					lowercase__				 =       None







					@staticmethod
					def        __lowerCAmelCase (       ):
						raise NotImplementedError







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				List[Any]   ,lowercase_			:				int   ,lowercase_			:				str   ,**lowercase_			:				List[str]		):
						raise NotImplementedError







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				int		):
						raise NotImplementedError







					def        __lowerCAmelCase (       self			:				List[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        __lowerCAmelCase (       cls			:				str		):
						return F'`pip install {cls.pip_package or cls.name}`'







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "optuna"







					@staticmethod
					def        __lowerCAmelCase (       ):
						return is_optuna_available()







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				List[str]   ,lowercase_			:				int   ,lowercase_			:				str   ,**lowercase_			:				Any		):
						return run_hp_search_optuna(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				List[Any]		):
						return default_hp_space_optuna(lowercase_		)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "ray"
					lowercase__				 =       "'ray[tune]'"







					@staticmethod
					def        __lowerCAmelCase (       ):
						return is_ray_available()







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				int   ,lowercase_			:				int   ,lowercase_			:				str   ,**lowercase_			:				str		):
						return run_hp_search_ray(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				Tuple		):
						return default_hp_space_ray(lowercase_		)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "sigopt"







					@staticmethod
					def        __lowerCAmelCase (       ):
						return is_sigopt_available()







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				List[str]   ,lowercase_			:				int   ,lowercase_			:				str   ,**lowercase_			:				List[Any]		):
						return run_hp_search_sigopt(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Union[str, Any]		):
						return default_hp_space_sigopt(lowercase_		)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "wandb"







					@staticmethod
					def        __lowerCAmelCase (       ):
						return is_wandb_available()







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Optional[int]   ,lowercase_			:				int   ,lowercase_			:				str   ,**lowercase_			:				List[Any]		):
						return run_hp_search_wandb(lowercase_   ,lowercase_   ,lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Optional[int]		):
						return default_hp_space_wandb(lowercase_		)

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

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Tuple  				=	[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
	if len(A_ ) > 0:
		lowerCAmelCase__	:						Optional[int]  				=	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() ) )







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase  :      Optional[Any]							      =		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''RagModel''',
							    '''RagPreTrainedModel''',
							    '''RagSequenceForGeneration''',
							    '''RagTokenForGeneration''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Union[str, Any]							      =		[
							    '''TFRagModel''',
							    '''TFRagPreTrainedModel''',
							    '''TFRagSequenceForGeneration''',
							    '''TFRagTokenForGeneration''',
							]


if TYPE_CHECKING:
							from .configuration_rag import RagConfig
							from .retrieval_rag import RagRetriever
							from .tokenization_rag import RagTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_rag import (
														    TFRagModel,
														    TFRagPreTrainedModel,
														    TFRagSequenceForGeneration,
														    TFRagTokenForGeneration,
														)

else:
							import sys

							__UpperCamelCase  :      Dict							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




