






"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_    =							{
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							['''BlenderbotTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							[
						    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BlenderbotForCausalLM''',
						    '''BlenderbotForConditionalGeneration''',
						    '''BlenderbotModel''',
						    '''BlenderbotPreTrainedModel''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							[
						    '''TFBlenderbotForConditionalGeneration''',
						    '''TFBlenderbotModel''',
						    '''TFBlenderbotPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							[
						    '''FlaxBlenderbotForConditionalGeneration''',
						    '''FlaxBlenderbotModel''',
						    '''FlaxBlenderbotPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_blenderbot import (
						    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    BlenderbotConfig,
						    BlenderbotOnnxConfig,
						)
						from .tokenization_blenderbot import BlenderbotTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blenderbot import (
												    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlenderbotForCausalLM,
												    BlenderbotForConditionalGeneration,
												    BlenderbotModel,
												    BlenderbotPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blenderbot import (
												    TFBlenderbotForConditionalGeneration,
												    TFBlenderbotModel,
												    TFBlenderbotPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_blenderbot import (
												    FlaxBlenderbotForConditionalGeneration,
												    FlaxBlenderbotModel,
												    FlaxBlenderbotPreTrainedModel,
												)

else:
						import sys

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














"""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


A_    =							logging.get_logger(__name__)






class 							__SCREAMING_SNAKE_CASE						:
							snake_case_     =							42
							snake_case_     =							None


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Optional[int]							):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													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      :						Optional[int]							):
													'''simple docstring'''





													return F'`pip install {cls.pip_package or cls.name}`'






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'optuna'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_optuna_available()


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													return run_hp_search_optuna(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						int							):
													'''simple docstring'''





													return default_hp_space_optuna(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'ray'
							snake_case_     =							'\'ray[tune]\''


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_ray_available()


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Tuple			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						List[Any]							):
													'''simple docstring'''





													return run_hp_search_ray(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						Dict							):
													'''simple docstring'''





													return default_hp_space_ray(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'sigopt'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_sigopt_available()


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return run_hp_search_sigopt(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						Any							):
													'''simple docstring'''





													return default_hp_space_sigopt(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'wandb'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_wandb_available()


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													return run_hp_search_wandb(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return default_hp_space_wandb(snake_case							)




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




def      _lowerCAmelCase		(							)		->str:
						A__						:  List[Any]		  =  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
						if len(UpperCAmelCase__      ) > 0:
												A__						:  Dict		  =  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()      )      )






