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__     :   Tuple           =							logging.get_logger(__name__)
class   _lowerCAmelCase		:
							_UpperCAmelCase						    =     4_2
							_UpperCAmelCase						    =     None







							@staticmethod
							def 				snake_case  (				):
										raise NotImplementedError







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    int	,					__snake_case     :    str	,					**__snake_case     :    str		):
										raise NotImplementedError







							def 				snake_case  (				self     :    Union[str, Any]	,					__snake_case     :    List[str]		):
										raise NotImplementedError







							def 				snake_case  (				self     :    Optional[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 				snake_case  (				cls     :    Optional[Any]		):
										return F"`pip install {cls.pip_package or cls.name}`"


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'optuna'







							@staticmethod
							def 				snake_case  (				):
										return is_optuna_available()







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    Any	,					__snake_case     :    int	,					__snake_case     :    str	,					**__snake_case     :    Dict		):
										return run_hp_search_optuna(__snake_case	,					__snake_case	,					__snake_case	,					**__snake_case		)







							def 				snake_case  (				self     :    List[str]	,					__snake_case     :    List[str]		):
										return default_hp_space_optuna(__snake_case		)


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'ray'
							_UpperCAmelCase						    =     '\'ray[tune]\''







							@staticmethod
							def 				snake_case  (				):
										return is_ray_available()







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    str	,					__snake_case     :    int	,					__snake_case     :    str	,					**__snake_case     :    Optional[int]		):
										return run_hp_search_ray(__snake_case	,					__snake_case	,					__snake_case	,					**__snake_case		)







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    Any		):
										return default_hp_space_ray(__snake_case		)


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'sigopt'







							@staticmethod
							def 				snake_case  (				):
										return is_sigopt_available()







							def 				snake_case  (				self     :    Any	,					__snake_case     :    str	,					__snake_case     :    int	,					__snake_case     :    str	,					**__snake_case     :    Optional[int]		):
										return run_hp_search_sigopt(__snake_case	,					__snake_case	,					__snake_case	,					**__snake_case		)







							def 				snake_case  (				self     :    Union[str, Any]	,					__snake_case     :    List[Any]		):
										return default_hp_space_sigopt(__snake_case		)


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'wandb'







							@staticmethod
							def 				snake_case  (				):
										return is_wandb_available()







							def 				snake_case  (				self     :    str	,					__snake_case     :    Any	,					__snake_case     :    int	,					__snake_case     :    str	,					**__snake_case     :    List[Any]		):
										return run_hp_search_wandb(__snake_case	,					__snake_case	,					__snake_case	,					**__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    List[Any]		):
										return default_hp_space_wandb(__snake_case		)


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





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Union[str, Any]						  =     [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()))




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__           =							logging.get_logger(__name__)

A__           =							{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'roberta-prelayernorm'







							def __init__(				self     :    str	,					__snake_case     :    List[str]=50265	,					__snake_case     :    Union[str, Any]=768	,					__snake_case     :    Tuple=12	,					__snake_case     :    int=12	,					__snake_case     :    Any=3072	,					__snake_case     :    Optional[int]="gelu"	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    int=0.1	,					__snake_case     :    Union[str, Any]=512	,					__snake_case     :    Dict=2	,					__snake_case     :    int=0.0_2	,					__snake_case     :    Any=1e-1_2	,					__snake_case     :    Optional[int]=1	,					__snake_case     :    Dict=0	,					__snake_case     :    Optional[int]=2	,					__snake_case     :    Any="absolute"	,					__snake_case     :    Union[str, Any]=True	,					__snake_case     :    List[str]=None	,					**__snake_case     :    Optional[int]	,					):
										super().__init__(pad_token_id=__snake_case	,					bos_token_id=__snake_case	,					eos_token_id=__snake_case	,					**__snake_case		)

										lowerCamelCase						:Optional[int]						  =     vocab_size
										lowerCamelCase						:Dict						  =     hidden_size
										lowerCamelCase						:Tuple						  =     num_hidden_layers
										lowerCamelCase						:Optional[int]						  =     num_attention_heads
										lowerCamelCase						:Any						  =     hidden_act
										lowerCamelCase						:List[Any]						  =     intermediate_size
										lowerCamelCase						:Union[str, Any]						  =     hidden_dropout_prob
										lowerCamelCase						:str						  =     attention_probs_dropout_prob
										lowerCamelCase						:Tuple						  =     max_position_embeddings
										lowerCamelCase						:int						  =     type_vocab_size
										lowerCamelCase						:Optional[Any]						  =     initializer_range
										lowerCamelCase						:Union[str, Any]						  =     layer_norm_eps
										lowerCamelCase						:Dict						  =     position_embedding_type
										lowerCamelCase						:List[Any]						  =     use_cache
										lowerCamelCase						:Optional[int]						  =     classifier_dropout


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							@property
							def 				snake_case  (				self     :    Any		):
										if self.task == "multiple-choice":
													lowerCamelCase						:Optional[Any]						  =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
													lowerCamelCase						:List[str]						  =     {0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]		)



