



'''simple docstring'''





import copy

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


_UpperCamelCase											=		logging.get_logger(__name__)

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






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




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




  def __init__(					self :      Any     ,      _a :      List[Any]=None     ,      _a :      int=900     ,      _a :      Union[str, Any]=2048     ,      _a :      Tuple=6     ,      _a :      Optional[int]=2048     ,      _a :      Union[str, Any]=8     ,      _a :      str=6     ,      _a :      Tuple=1024     ,      _a :      Optional[Any]=8     ,      _a :      Optional[Any]=0.0     ,      _a :      Tuple=True     ,      _a :      Optional[int]="relu"     ,      _a :      int=256     ,      _a :      Union[str, Any]=0.1     ,      _a :      str=0.0     ,      _a :      List[str]=0.0     ,      _a :      Tuple=0.02     ,      _a :      Optional[Any]=1.0     ,      _a :      List[Any]=True     ,      _a :      Tuple=False     ,      _a :      Optional[int]="sine"     ,      _a :      Any=5     ,      _a :      Any=4     ,      _a :      Union[str, Any]=4     ,      _a :      List[str]=True     ,      _a :      Union[str, Any]=300     ,      _a :      Union[str, Any]=True     ,      _a :      Any=True     ,      _a :      List[Any]=1     ,      _a :      str=5     ,      _a :      Any=2     ,      _a :      Optional[Any]=1     ,      _a :      Optional[Any]=1     ,      _a :      Union[str, Any]=5     ,      _a :      int=2     ,      _a :      Any=0.1     ,      _a :      List[Any]=0.25     ,      **_a :      str     ,      )   ->   int:
    if backbone_config is None:
      logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'  )
      __lowerCamelCase			:			List[Any]       			=					CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4']  )
    else:
      if isinstance(_a     ,      _a  ):
        __lowerCamelCase			:			List[str]       			=					backbone_config.pop('model_type'  )
        __lowerCamelCase			:			List[Any]       			=					CONFIG_MAPPING[backbone_model_type]
        __lowerCamelCase			:			List[Any]       			=					config_class.from_dict(_a  )

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




  @property
  def 		_lowercase	(					self :      int  )   ->   int:
    return self.encoder_attention_heads




  @property
  def 		_lowercase	(					self :      int  )   ->   int:
    return self.d_model




  def 		_lowercase	(					self :      List[str]  )   ->   Any:
    __lowerCamelCase			:			Dict       			=					copy.deepcopy(self.__dict__  )
    __lowerCamelCase			:			List[Any]       			=					self.backbone_config.to_dict()
    __lowerCamelCase			:			Optional[Any]       			=					self.__class__.model_type
    return output











'''simple docstring'''





import functools

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> int:

  # Validation
  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			) or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for day in days			):
    raise ValueError('The parameter days should be a list of integers'			)

  if len(_lowerCAmelCase			) != 3 or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for cost in costs			):
    raise ValueError('The parameter costs should be a list of three integers'			)

  if len(_lowerCAmelCase			) == 0:
    return 0

  if min(_lowerCAmelCase			) <= 0:
    raise ValueError('All days elements should be greater than 0'			)

  if max(_lowerCAmelCase			) >= 366:
    raise ValueError('All days elements should be less than 366'			)

  __lowerCamelCase			:			int       			=					set(_lowerCAmelCase			)

  @functools.cache
  def dynamic_programming(_lowerCAmelCase			) -> int:
    if index > 365:
      return 0

    if index not in days_set:
      return dynamic_programming(index + 1			)

    return min(
        costs[0] + dynamic_programming(index + 1			)			,costs[1] + dynamic_programming(index + 7			)			,costs[2] + dynamic_programming(index + 30			)			,)

  return dynamic_programming(1			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






