
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  lowercase__:



	"""simple docstring"""




	a			:str
	a			:str         =	None
	@staticmethod
	def 						_lowercase  (       )       ->     Optional[int]:
					raise NotImplementedError
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			int       )       ->     Union[str, Any]:
					raise NotImplementedError
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     Optional[int]:
					raise NotImplementedError
	def 						_lowercase  (       self  :			Tuple       )       ->     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  :			Dict       )       ->     Dict:
					return f'''`pip install {cls.pip_package or cls.name}`'''

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Any         =	'optuna'
	@staticmethod
	def 						_lowercase  (       )       ->     str:
					return is_optuna_available()
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			int       )       ->     str:
					return run_hp_search_optuna(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       )       ->     str:
					return default_hp_space_optuna(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Tuple         =	'ray'
	a			:Optional[int]         =	'\'ray[tune]\''
	@staticmethod
	def 						_lowercase  (       )       ->     Any:
					return is_ray_available()
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			Any       )       ->     str:
					return run_hp_search_ray(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     int:
					return default_hp_space_ray(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:List[str]         =	'sigopt'
	@staticmethod
	def 						_lowercase  (       )       ->     Union[str, Any]:
					return is_sigopt_available()
	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Union[str, Any]:
					return run_hp_search_sigopt(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       )       ->     Tuple:
					return default_hp_space_sigopt(SCREAMING_SNAKE_CASE_       )

class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:str         =	'wandb'
	@staticmethod
	def 						_lowercase  (       )       ->     str:
					return is_wandb_available()
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Tuple:
					return run_hp_search_wandb(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     List[Any]:
					return default_hp_space_wandb(SCREAMING_SNAKE_CASE_       )

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




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
				if len(snake_case__					) > 0:
								lowercase_       =					available_backends[0].name
								if len(snake_case__					) > 1:
												logger.info(
												    F'''{len(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 TYPE_CHECKING

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


__a  			=					{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizer']

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RemBertForCausalLM',
						    'RemBertForMaskedLM',
						    'RemBertForMultipleChoice',
						    'RemBertForQuestionAnswering',
						    'RemBertForSequenceClassification',
						    'RemBertForTokenClassification',
						    'RemBertLayer',
						    'RemBertModel',
						    'RemBertPreTrainedModel',
						    'load_tf_weights_in_rembert',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRemBertForCausalLM',
						    'TFRemBertForMaskedLM',
						    'TFRemBertForMultipleChoice',
						    'TFRemBertForQuestionAnswering',
						    'TFRemBertForSequenceClassification',
						    'TFRemBertForTokenClassification',
						    'TFRemBertLayer',
						    'TFRemBertModel',
						    'TFRemBertPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert import RemBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert_fast import RemBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rembert import (
												    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RemBertForCausalLM,
												    RemBertForMaskedLM,
												    RemBertForMultipleChoice,
												    RemBertForQuestionAnswering,
												    RemBertForSequenceClassification,
												    RemBertForTokenClassification,
												    RemBertLayer,
												    RemBertModel,
												    RemBertPreTrainedModel,
												    load_tf_weights_in_rembert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rembert import (
												    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRemBertForCausalLM,
												    TFRemBertForMaskedLM,
												    TFRemBertForMultipleChoice,
												    TFRemBertForQuestionAnswering,
												    TFRemBertForSequenceClassification,
												    TFRemBertForTokenClassification,
												    TFRemBertLayer,
												    TFRemBertModel,
												    TFRemBertPreTrainedModel,
												)


else:
						import sys

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




