



'''simple docstring'''






def       lowerCamelCase__       (						_A ,			_A				):
  _enforce_args(__lowerCAmelCase ,			__lowerCAmelCase				)
  if n == 0:
    return 0
  a						:					Any        =     float('-inf'				)
  for i in range(1 ,			n + 1				):
    a						:					str        =     max(
        __lowerCAmelCase ,			prices[i - 1] + naive_cut_rod_recursive(n - i ,			__lowerCAmelCase				)				)

  return max_revue
def       lowerCamelCase__       (						_A ,			_A				):
  _enforce_args(__lowerCAmelCase ,			__lowerCAmelCase				)
  a						:					Tuple        =     [float('-inf'				) for _ in range(n + 1				)]
  return _top_down_cut_rod_recursive(__lowerCAmelCase ,			__lowerCAmelCase ,			__lowerCAmelCase				)
def       lowerCamelCase__       (						_A ,			_A ,			_A				):
  if max_rev[n] >= 0:
    return max_rev[n]
  elif n == 0:
    return 0
  else:
    a						:					Optional[Any]        =     float('-inf'				)
    for i in range(1 ,			n + 1				):
      a						:					Tuple        =     max(
          __lowerCAmelCase ,			prices[i - 1] + _top_down_cut_rod_recursive(n - i ,			__lowerCAmelCase ,			__lowerCAmelCase				) ,			)

    a						:					str        =     max_revenue

  return max_rev[n]
def       lowerCamelCase__       (						_A ,			_A				):
  _enforce_args(__lowerCAmelCase ,			__lowerCAmelCase				)

  # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
  # length 0.
  a						:					Optional[int]        =     [float('-inf'				) for _ in range(n + 1				)]
  a						:					List[str]        =     0

  for i in range(1 ,			n + 1				):
    a						:					Union[str, Any]        =     max_rev[i]
    for j in range(1 ,			i + 1				):
      a						:					Tuple        =     max(__lowerCAmelCase ,			prices[j - 1] + max_rev[i - j]				)

    a						:					Tuple        =     max_revenue_i

  return max_rev[n]
def       lowerCamelCase__       (						_A ,			_A				):
  if n < 0:
    a						:					Optional[Any]        =     f"""n must be greater than or equal to 0. Got n = {n}"""
    raise ValueError(__lowerCAmelCase				)

  if n > len(__lowerCAmelCase				):
    a						:					str        =     (
        "Each integral piece of rod must have a corresponding price. "
        f"""Got n = {n} but length of prices = {len(__lowerCAmelCase				)}"""
    )
    raise ValueError(__lowerCAmelCase				)
def       lowerCamelCase__       (						):
  a						:					Any        =     [6, 10, 12, 15, 20, 23]
  a						:					Optional[Any]        =     len(__lowerCAmelCase				)

  # the best revenue comes from cutting the rod into 6 pieces, each
  # of length 1 resulting in a revenue of 6 * 6 = 36.
  a						:					Optional[Any]        =     36

  a						:					str        =     top_down_cut_rod(__lowerCAmelCase ,			__lowerCAmelCase				)
  a						:					str        =     bottom_up_cut_rod(__lowerCAmelCase ,			__lowerCAmelCase				)
  a						:					str        =     naive_cut_rod_recursive(__lowerCAmelCase ,			__lowerCAmelCase				)

  assert expected_max_revenue == max_rev_top_down
  assert max_rev_top_down == max_rev_bottom_up
  assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
     main()



'''simple docstring'''


import copy

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


lowerCamelCase__					=   logging.get_logger(__name__)

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






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Any				      =   "deta"
			lowerCAmelCase       :		Union[str, Any]				      =   {
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}




			def __init__(						self     :   Optional[Any]		,     lowerCamelCase__     :   str=None		,     lowerCamelCase__     :   str=9_00		,     lowerCamelCase__     :   Any=20_48		,     lowerCamelCase__     :   Optional[int]=6		,     lowerCamelCase__     :   str=20_48		,     lowerCamelCase__     :   Dict=8		,     lowerCamelCase__     :   Any=6		,     lowerCamelCase__     :   Union[str, Any]=10_24		,     lowerCamelCase__     :   Optional[int]=8		,     lowerCamelCase__     :   List[Any]=0.0		,     lowerCamelCase__     :   Tuple=True		,     lowerCamelCase__     :   int="relu"		,     lowerCamelCase__     :   str=2_56		,     lowerCamelCase__     :   Dict=0.1		,     lowerCamelCase__     :   Dict=0.0		,     lowerCamelCase__     :   Dict=0.0		,     lowerCamelCase__     :   Optional[int]=0.0_2		,     lowerCamelCase__     :   List[Any]=1.0		,     lowerCamelCase__     :   Optional[int]=True		,     lowerCamelCase__     :   Any=False		,     lowerCamelCase__     :   Any="sine"		,     lowerCamelCase__     :   str=5		,     lowerCamelCase__     :   Optional[int]=4		,     lowerCamelCase__     :   int=4		,     lowerCamelCase__     :   Optional[Any]=True		,     lowerCamelCase__     :   Dict=3_00		,     lowerCamelCase__     :   int=True		,     lowerCamelCase__     :   Dict=True		,     lowerCamelCase__     :   Union[str, Any]=1		,     lowerCamelCase__     :   Any=5		,     lowerCamelCase__     :   List[Any]=2		,     lowerCamelCase__     :   Union[str, Any]=1		,     lowerCamelCase__     :   str=1		,     lowerCamelCase__     :   Union[str, Any]=5		,     lowerCamelCase__     :   str=2		,     lowerCamelCase__     :   Optional[Any]=0.1		,     lowerCamelCase__     :   Union[str, Any]=0.2_5		,     **lowerCamelCase__     :   Optional[Any]		,     )      ->List[str]:



					'''simple docstring'''







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

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




			@property
			def 							lowerCAmelCase__			(						self     :   int       )      ->int:



					'''simple docstring'''







					return self.encoder_attention_heads




			@property
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->int:



					'''simple docstring'''







					return self.d_model






			def 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					copy.deepcopy(self.__dict__       )
					_UpperCAmelCase						:				Tuple	    =					self.backbone_config.to_dict()
					_UpperCAmelCase						:				List[Any]	    =					self.__class__.model_type
					return output



