



"""simple docstring"""






import copy
from typing import Dict, Optional

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


_lowercase					:					Optional[Any] 		=		logging.get_logger(__name__)

_lowercase					:					List[Any] 		=		{
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				'oneformer'
					_a       =				{'hidden_size': 'hidden_dim'}



					def __init__(			self				:    Tuple,    lowerCamelCase				:    Optional[Dict] = None,    lowerCamelCase				:    int = 255,    lowerCamelCase				:    int = 150,    lowerCamelCase				:    int = 0.1,    lowerCamelCase				:    float = 2.0,    lowerCamelCase				:    float = 5.0,    lowerCamelCase				:    float = 5.0,    lowerCamelCase				:    float = 0.5,    lowerCamelCase				:    float = 0.07,    lowerCamelCase				:    int = 1_2544,    lowerCamelCase				:    float = 3.0,    lowerCamelCase				:    float = 0.75,    lowerCamelCase				:    float = 0.02,    lowerCamelCase				:    float = 1.0,    lowerCamelCase				:    float = 1E-05,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    Optional[list] = [4, 8, 16, 32],    lowerCamelCase				:    int = 77,    lowerCamelCase				:    int = 256,    lowerCamelCase				:    int = 77,    lowerCamelCase				:    int = 6,    lowerCamelCase				:    int = 4_9408,    lowerCamelCase				:    int = 2,    lowerCamelCase				:    int = 16,    lowerCamelCase				:    int = 256,    lowerCamelCase				:    int = 256,    lowerCamelCase				:    int = 256,    lowerCamelCase				:    int = 1024,    lowerCamelCase				:    str = "GN",    lowerCamelCase				:    int = 6,    lowerCamelCase				:    int = 10,    lowerCamelCase				:    bool = True,    lowerCamelCase				:    int = 8,    lowerCamelCase				:    float = 0.1,    lowerCamelCase				:    int = 2048,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    bool = False,    lowerCamelCase				:    int = 2,    lowerCamelCase				:    int = 4,    **lowerCamelCase				:    Dict,    )->   Tuple:
								if backbone_config is None:
											logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''	)
											lowerCamelCase__						:						int						      =CONFIG_MAPPING['''swin'''](
											    image_size=224,    in_channels=3,    patch_size=4,    embed_dim=96,    depths=[2, 2, 6, 2],    num_heads=[3, 6, 12, 24],    window_size=7,    drop_path_rate=0.3,    use_absolute_embeddings=lowerCamelCase,    out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''],    )
								elif isinstance(lowerCamelCase,    lowerCamelCase	):
											lowerCamelCase__						:						Any						      =backbone_config.get('''model_type'''	)
											lowerCamelCase__						:						int						      =CONFIG_MAPPING[backbone_model_type]
											lowerCamelCase__						:						List[Any]						      =config_class.from_dict(lowerCamelCase	)

								lowerCamelCase__						:						Optional[Any]						      =backbone_config

								lowerCamelCase__						:						Dict						      =ignore_value
								lowerCamelCase__						:						Tuple						      =num_queries
								lowerCamelCase__						:						Any						      =no_object_weight
								lowerCamelCase__						:						List[str]						      =class_weight
								lowerCamelCase__						:						Any						      =mask_weight
								lowerCamelCase__						:						Optional[Any]						      =dice_weight
								lowerCamelCase__						:						List[Any]						      =contrastive_weight
								lowerCamelCase__						:						int						      =contrastive_temperature
								lowerCamelCase__						:						List[str]						      =train_num_points
								lowerCamelCase__						:						List[Any]						      =oversample_ratio
								lowerCamelCase__						:						Tuple						      =importance_sample_ratio
								lowerCamelCase__						:						List[str]						      =init_std
								lowerCamelCase__						:						Tuple						      =init_xavier_std
								lowerCamelCase__						:						Optional[int]						      =layer_norm_eps
								lowerCamelCase__						:						Any						      =is_training
								lowerCamelCase__						:						Dict						      =use_auxiliary_loss
								lowerCamelCase__						:						List[Any]						      =output_auxiliary_logits
								lowerCamelCase__						:						Any						      =strides
								lowerCamelCase__						:						Tuple						      =task_seq_len
								lowerCamelCase__						:						List[Any]						      =text_encoder_width
								lowerCamelCase__						:						Any						      =text_encoder_context_length
								lowerCamelCase__						:						Optional[Any]						      =text_encoder_num_layers
								lowerCamelCase__						:						Dict						      =text_encoder_vocab_size
								lowerCamelCase__						:						Optional[int]						      =text_encoder_proj_layers
								lowerCamelCase__						:						str						      =text_encoder_n_ctx
								lowerCamelCase__						:						Union[str, Any]						      =conv_dim
								lowerCamelCase__						:						Union[str, Any]						      =mask_dim
								lowerCamelCase__						:						List[Any]						      =hidden_dim
								lowerCamelCase__						:						str						      =encoder_feedforward_dim
								lowerCamelCase__						:						Tuple						      =norm
								lowerCamelCase__						:						Union[str, Any]						      =encoder_layers
								lowerCamelCase__						:						Tuple						      =decoder_layers
								lowerCamelCase__						:						Optional[int]						      =use_task_norm
								lowerCamelCase__						:						Optional[Any]						      =num_attention_heads
								lowerCamelCase__						:						Tuple						      =dropout
								lowerCamelCase__						:						str						      =dim_feedforward
								lowerCamelCase__						:						Tuple						      =pre_norm
								lowerCamelCase__						:						List[Any]						      =enforce_input_proj
								lowerCamelCase__						:						Any						      =query_dec_layers
								lowerCamelCase__						:						str						      =common_stride
								lowerCamelCase__						:						List[str]						      =decoder_layers

								super().__init__(**lowerCamelCase	)




					def 						snake_case			(			self				:    Optional[Any]	)->   Dict[str, any]:
								lowerCamelCase__						:						Any						      =copy.deepcopy(self.__dict__	)
								lowerCamelCase__						:						Optional[Any]						      =self.backbone_config.to_dict()
								lowerCamelCase__						:						str						      =self.__class__.model_type
								return output









"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       int = 998001      ):
			"""simple docstring"""

			# fetches the next number
			for number in range(n - 1				,						9999				,						-1      ):
						lowerCamelCase__						:						Optional[int]						      =str(__lowerCamelCase      )

						# checks whether 'str_number' is a palindrome.
						if str_number == str_number[::-1]:
									lowerCamelCase__						:						Optional[int]						      =999

									# if 'number' is a product of two 3-digit numbers
									# then number is the answer otherwise fetch next number.
									while divisor != 99:
												if (number % divisor == 0) and (len(str(number // divisor      )      ) == 3.0):
															return number
												divisor -= 1
			raise ValueError('''That number is larger than our acceptable range.'''      )


if __name__ == "__main__":
		print(f'{solution() = }')




