




"""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 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"deta"
 lowercase__ 					=					{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
 }


 def __init__(				self:		Any,      a_:		Tuple=None,      a_:		List[str]=900,      a_:		List[str]=2_048,      a_:		List[Any]=6,      a_:		Optional[Any]=2_048,      a_:		int=8,      a_:		int=6,      a_:		List[Any]=1_024,      a_:		Union[str, Any]=8,      a_:		Any=0.0,      a_:		List[str]=True,      a_:		str="relu",      a_:		int=256,      a_:		List[Any]=0.1,      a_:		Dict=0.0,      a_:		List[Any]=0.0,      a_:		Tuple=0.02,      a_:		List[Any]=1.0,      a_:		Optional[Any]=True,      a_:		int=False,      a_:		Any="sine",      a_:		List[str]=5,      a_:		Dict=4,      a_:		Optional[int]=4,      a_:		Union[str, Any]=True,      a_:		List[str]=300,      a_:		int=True,      a_:		List[str]=True,      a_:		Optional[Any]=1,      a_:		Any=5,      a_:		str=2,      a_:		Tuple=1,      a_:		Union[str, Any]=1,      a_:		List[str]=5,      a_:		Union[str, Any]=2,      a_:		str=0.1,      a_:		str=0.25,      **a_:		Dict,      ):




   '''simple docstring'''



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

   _snake_case							:		Union[str, Any]								=  backbone_config
   _snake_case							:		int								=  num_queries
   _snake_case							:		Dict								=  max_position_embeddings
   _snake_case							:		Optional[int]								=  d_model
   _snake_case							:		int								=  encoder_ffn_dim
   _snake_case							:		Dict								=  encoder_layers
   _snake_case							:		Any								=  encoder_attention_heads
   _snake_case							:		List[str]								=  decoder_ffn_dim
   _snake_case							:		Optional[Any]								=  decoder_layers
   _snake_case							:		Optional[int]								=  decoder_attention_heads
   _snake_case							:		Dict								=  dropout
   _snake_case							:		Dict								=  attention_dropout
   _snake_case							:		int								=  activation_dropout
   _snake_case							:		int								=  activation_function
   _snake_case							:		List[str]								=  init_std
   _snake_case							:		Dict								=  init_xavier_std
   _snake_case							:		str								=  encoder_layerdrop
   _snake_case							:		int								=  auxiliary_loss
   _snake_case							:		Dict								=  position_embedding_type
   # deformable attributes
   _snake_case							:		Dict								=  num_feature_levels
   _snake_case							:		Optional[int]								=  encoder_n_points
   _snake_case							:		Union[str, Any]								=  decoder_n_points
   _snake_case							:		Optional[Any]								=  two_stage
   _snake_case							:		Dict								=  two_stage_num_proposals
   _snake_case							:		Optional[int]								=  with_box_refine
   _snake_case							:		int								=  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
   _snake_case							:		int								=  class_cost
   _snake_case							:		Optional[Any]								=  bbox_cost
   _snake_case							:		int								=  giou_cost
   # Loss coefficients
   _snake_case							:		str								=  mask_loss_coefficient
   _snake_case							:		Optional[Any]								=  dice_loss_coefficient
   _snake_case							:		Optional[int]								=  bbox_loss_coefficient
   _snake_case							:		Optional[Any]								=  giou_loss_coefficient
   _snake_case							:		Dict								=  eos_coefficient
   _snake_case							:		Any								=  focal_alpha
   super().__init__(is_encoder_decoder=a_,      **a_		)


 @property
 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   return self.encoder_attention_heads


 @property
 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   return self.d_model


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  copy.deepcopy(self.__dict__		)
   _snake_case							:		List[str]								=  self.backbone_config.to_dict()
   _snake_case							:		Tuple								=  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 					lowercase:







 '''simple docstring'''





 lowercase__ 					=					42
 lowercase__ 					=					None


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Tuple,      a_:		int,      a_:		int,      a_:		str,      **a_:		Dict		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[str]		):




   '''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:		Tuple		):




   '''simple docstring'''



   return f"`pip install {cls.pip_package or cls.name}`"







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"optuna"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_optuna_available()


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[Any],      a_:		int,      a_:		str,      **a_:		List[str]		):




   '''simple docstring'''



   return run_hp_search_optuna(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_optuna(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





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


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_ray_available()


 def  UpperCamelCase_   (				self:		int,      a_:		Optional[Any],      a_:		int,      a_:		str,      **a_:		List[Any]		):




   '''simple docstring'''



   return run_hp_search_ray(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Tuple		):




   '''simple docstring'''



   return default_hp_space_ray(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"sigopt"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_sigopt_available()


 def  UpperCamelCase_   (				self:		Dict,      a_:		str,      a_:		int,      a_:		str,      **a_:		int		):




   '''simple docstring'''



   return run_hp_search_sigopt(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		List[str]		):




   '''simple docstring'''



   return default_hp_space_sigopt(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"wandb"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_wandb_available()


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		str,      a_:		int,      a_:		str,      **a_:		Union[str, Any]		):




   '''simple docstring'''



   return run_hp_search_wandb(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_wandb(a_		)







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


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Optional[int]								=  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
  if len(snake_case__    ) > 0:
    _snake_case							:		Any								=  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()    )    )


