





import copy

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


_lowerCamelCase :    List[Any]  							= logging.get_logger(__name__)

_lowerCamelCase :    Union[str, Any]  							= {
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




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






							def __init__( self     :					Union[str, Any],			_UpperCAmelCase     :					Union[str, Any]=None,			_UpperCAmelCase     :					List[str]=9_0_0,			_UpperCAmelCase     :					List[Any]=2_0_4_8,			_UpperCAmelCase     :					Union[str, Any]=6,			_UpperCAmelCase     :					List[Any]=2_0_4_8,			_UpperCAmelCase     :					Optional[int]=8,			_UpperCAmelCase     :					str=6,			_UpperCAmelCase     :					Dict=1_0_2_4,			_UpperCAmelCase     :					List[str]=8,			_UpperCAmelCase     :					Dict=0.0,			_UpperCAmelCase     :					Dict=True,			_UpperCAmelCase     :					int="relu",			_UpperCAmelCase     :					Optional[Any]=2_5_6,			_UpperCAmelCase     :					Union[str, Any]=0.1,			_UpperCAmelCase     :					Tuple=0.0,			_UpperCAmelCase     :					List[Any]=0.0,			_UpperCAmelCase     :					Tuple=0.02,			_UpperCAmelCase     :					Optional[Any]=1.0,			_UpperCAmelCase     :					Union[str, Any]=True,			_UpperCAmelCase     :					Tuple=False,			_UpperCAmelCase     :					Dict="sine",			_UpperCAmelCase     :					Union[str, Any]=5,			_UpperCAmelCase     :					Dict=4,			_UpperCAmelCase     :					List[str]=4,			_UpperCAmelCase     :					Union[str, Any]=True,			_UpperCAmelCase     :					int=3_0_0,			_UpperCAmelCase     :					List[Any]=True,			_UpperCAmelCase     :					Union[str, Any]=True,			_UpperCAmelCase     :					Any=1,			_UpperCAmelCase     :					Dict=5,			_UpperCAmelCase     :					List[str]=2,			_UpperCAmelCase     :					str=1,			_UpperCAmelCase     :					str=1,			_UpperCAmelCase     :					Optional[int]=5,			_UpperCAmelCase     :					List[str]=2,			_UpperCAmelCase     :					Tuple=0.1,			_UpperCAmelCase     :					str=0.25,			**_UpperCAmelCase     :					Any,			)				->			int:





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

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






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





														"""simple docstring"""
														return self.encoder_attention_heads






							@property
							def  A_     ( self     :					Optional[Any]						)				->			int:





														"""simple docstring"""
														return self.d_model






							def  A_     ( self     :					str						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						copy.deepcopy(self.__dict__						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.backbone_config.to_dict()
														SCREAMING_SNAKE_CASE__       :      int     =						self.__class__.model_type
														return output













_lowerCamelCase :    dict[tuple[int, int, int], int]  							= {}




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int       )	->    int:

							'''simple docstring'''






							if late == 3 or absent == 2:
														return 0

							# if we have no days left, and have not failed any other rules,
							# we have a prize string
							if days == 0:
														return 1

							# No easy solution, so now we need to do the recursive calculation

							# First, check if the combination is already in the cache, and
							# if yes, return the stored value from there since we already
							# know the number of possible prize strings from this point on
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						(days, absent, late)
							if key in cache:
														return cache[key]

							# now we calculate the three possible ways that can unfold from
							# this point on, depending on our attendance today

							# 1) if we are late (but not absent), the "absent" counter stays as
							# it is, but the "late" counter increases by one
							SCREAMING_SNAKE_CASE__       :      Tuple     =						_calculate(days - 1			,				SCREAMING_SNAKE_CASE__			,				late + 1       )

							# 2) if we are absent, the "absent" counter increases by 1, and the
							# "late" counter resets to 0
							SCREAMING_SNAKE_CASE__       :      Dict     =						_calculate(days - 1			,				absent + 1			,				0       )

							# 3) if we are on time, this resets the "late" counter and keeps the
							# absent counter
							SCREAMING_SNAKE_CASE__       :      Any     =						_calculate(days - 1			,				SCREAMING_SNAKE_CASE__			,				0       )

							SCREAMING_SNAKE_CASE__       :      str     =						state_late + state_absent + state_ontime

							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						prizestrings
							return prizestrings




def _a (  SCREAMING_SNAKE_CASE__      :  int = 30       )	->    int:

							'''simple docstring'''






							return _calculate(SCREAMING_SNAKE_CASE__			,				absent=0			,				late=0       )


if __name__ == "__main__":
		print(solution())






