






"""simple docstring"""



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A_    =							logging.get_logger(__name__)

A_    =							{
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'deta'
							snake_case_     =							{
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							}


							def __init__(			self      :						List[Any]			,    snake_case      :						Dict=None			,    snake_case      :						Union[str, Any]=900			,    snake_case      :						Tuple=2048			,    snake_case      :						int=6			,    snake_case      :						List[str]=2048			,    snake_case      :						Dict=8			,    snake_case      :						Any=6			,    snake_case      :						Optional[int]=1024			,    snake_case      :						Tuple=8			,    snake_case      :						List[str]=0.0			,    snake_case      :						Tuple=True			,    snake_case      :						Dict="relu"			,    snake_case      :						List[str]=256			,    snake_case      :						str=0.1			,    snake_case      :						int=0.0			,    snake_case      :						Union[str, Any]=0.0			,    snake_case      :						List[Any]=0.02			,    snake_case      :						Optional[Any]=1.0			,    snake_case      :						List[Any]=True			,    snake_case      :						Union[str, Any]=False			,    snake_case      :						Union[str, Any]="sine"			,    snake_case      :						int=5			,    snake_case      :						Any=4			,    snake_case      :						Optional[Any]=4			,    snake_case      :						Optional[int]=True			,    snake_case      :						Tuple=300			,    snake_case      :						Union[str, Any]=True			,    snake_case      :						Any=True			,    snake_case      :						List[str]=1			,    snake_case      :						List[str]=5			,    snake_case      :						List[Any]=2			,    snake_case      :						List[Any]=1			,    snake_case      :						Optional[int]=1			,    snake_case      :						str=5			,    snake_case      :						Optional[Any]=2			,    snake_case      :						Union[str, Any]=0.1			,    snake_case      :						List[str]=0.25			,    **snake_case      :						Tuple			,    ):
													'''simple docstring'''





													if backbone_config is None:
																			logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
																			A__						:  Union[str, Any]		  =  CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
													else:
																			if isinstance(snake_case			,    snake_case							):
																									A__						:  Dict		  =  backbone_config.pop("""model_type"""							)
																									A__						:  Dict		  =  CONFIG_MAPPING[backbone_model_type]
																									A__						:  List[str]		  =  config_class.from_dict(snake_case							)

													A__						:  List[Any]		  =  backbone_config
													A__						:  int		  =  num_queries
													A__						:  str		  =  max_position_embeddings
													A__						:  int		  =  d_model
													A__						:  Dict		  =  encoder_ffn_dim
													A__						:  Union[str, Any]		  =  encoder_layers
													A__						:  Tuple		  =  encoder_attention_heads
													A__						:  Tuple		  =  decoder_ffn_dim
													A__						:  Tuple		  =  decoder_layers
													A__						:  Optional[Any]		  =  decoder_attention_heads
													A__						:  Optional[Any]		  =  dropout
													A__						:  int		  =  attention_dropout
													A__						:  Dict		  =  activation_dropout
													A__						:  int		  =  activation_function
													A__						:  Union[str, Any]		  =  init_std
													A__						:  Union[str, Any]		  =  init_xavier_std
													A__						:  Union[str, Any]		  =  encoder_layerdrop
													A__						:  List[str]		  =  auxiliary_loss
													A__						:  Union[str, Any]		  =  position_embedding_type
													# deformable attributes
													A__						:  Optional[Any]		  =  num_feature_levels
													A__						:  Any		  =  encoder_n_points
													A__						:  List[str]		  =  decoder_n_points
													A__						:  str		  =  two_stage
													A__						:  List[Any]		  =  two_stage_num_proposals
													A__						:  Tuple		  =  with_box_refine
													A__						:  List[Any]		  =  assign_first_stage
													if two_stage is True and with_box_refine is False:
																			raise ValueError("""If two_stage is True, with_box_refine must be True."""							)
													# Hungarian matcher
													A__						:  Optional[int]		  =  class_cost
													A__						:  List[Any]		  =  bbox_cost
													A__						:  Dict		  =  giou_cost
													# Loss coefficients
													A__						:  List[Any]		  =  mask_loss_coefficient
													A__						:  Any		  =  dice_loss_coefficient
													A__						:  Optional[int]		  =  bbox_loss_coefficient
													A__						:  Optional[int]		  =  giou_loss_coefficient
													A__						:  Union[str, Any]		  =  eos_coefficient
													A__						:  Optional[int]		  =  focal_alpha
													super().__init__(is_encoder_decoder=snake_case			,    **snake_case							)


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





													return self.encoder_attention_heads


							@property
							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													return self.d_model


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





													A__						:  Dict		  =  copy.deepcopy(self.__dict__							)
													A__						:  str		  =  self.backbone_config.to_dict()
													A__						:  int		  =  self.__class__.model_type
													return output









"""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()      )      )






