






"""simple docstring"""





UpperCamelCase					=     {
    """0""": 1,
    """1""": 1,
    """2""": 2,
    """3""": 6,
    """4""": 24,
    """5""": 1_20,
    """6""": 7_20,
    """7""": 50_40,
    """8""": 4_03_20,
    """9""": 36_28_80,
}

UpperCamelCase					=     {1_45: 1_45}

UpperCamelCase					=     {
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}






def 							A(    snake_case_						):
       """simple docstring"""





       if n in CACHE_SUM_DIGIT_FACTORIALS:
              return CACHE_SUM_DIGIT_FACTORIALS[n]
       lowercase__:							Union[str, Any]         =     sum(DIGIT_FACTORIALS[let] for let in str(snake_case_						)						)
       lowercase__:							int         =     ret
       return ret






def 							A(    snake_case_		,  snake_case_ = None						):
       """simple docstring"""





       lowercase__:							Optional[Any]         =     previous or set()
       if n in CHAIN_LENGTH_CACHE:
              return CHAIN_LENGTH_CACHE[n]
       lowercase__:							Tuple         =     sum_digit_factorials(snake_case_						)
       if next_number in previous:
              lowercase__:							List[str]         =     0
              return 0
       else:
              previous.add(snake_case_						)
              lowercase__:							str         =     1 + chain_length(snake_case_		,  snake_case_						)
              lowercase__:							Union[str, Any]         =     ret
              return ret






def 							A(    snake_case_ = 60		,  snake_case_ = 1000000						):
       """simple docstring"""





       return sum(1 for i in range(1		,  snake_case_						) if chain_length(snake_case_						) == num_terms						)


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








import copy
from typing import Dict, Optional

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


_UpperCamelCase:					str     =logging.get_logger(__name__)

_UpperCamelCase:					Optional[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 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







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



       def __init__(     self					:			Optional[Any]  ,      _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					:			Any  ,      )					->   Tuple:
         if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
           _lowerCAmelCase						=		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						=		backbone_config.get('model_type'       )
           _lowerCAmelCase						=		CONFIG_MAPPING[backbone_model_type]
           _lowerCAmelCase						=		config_class.from_dict(_lowerCAmelCase       )

         _lowerCAmelCase						=		backbone_config

         _lowerCAmelCase						=		ignore_value
         _lowerCAmelCase						=		num_queries
         _lowerCAmelCase						=		no_object_weight
         _lowerCAmelCase						=		class_weight
         _lowerCAmelCase						=		mask_weight
         _lowerCAmelCase						=		dice_weight
         _lowerCAmelCase						=		contrastive_weight
         _lowerCAmelCase						=		contrastive_temperature
         _lowerCAmelCase						=		train_num_points
         _lowerCAmelCase						=		oversample_ratio
         _lowerCAmelCase						=		importance_sample_ratio
         _lowerCAmelCase						=		init_std
         _lowerCAmelCase						=		init_xavier_std
         _lowerCAmelCase						=		layer_norm_eps
         _lowerCAmelCase						=		is_training
         _lowerCAmelCase						=		use_auxiliary_loss
         _lowerCAmelCase						=		output_auxiliary_logits
         _lowerCAmelCase						=		strides
         _lowerCAmelCase						=		task_seq_len
         _lowerCAmelCase						=		text_encoder_width
         _lowerCAmelCase						=		text_encoder_context_length
         _lowerCAmelCase						=		text_encoder_num_layers
         _lowerCAmelCase						=		text_encoder_vocab_size
         _lowerCAmelCase						=		text_encoder_proj_layers
         _lowerCAmelCase						=		text_encoder_n_ctx
         _lowerCAmelCase						=		conv_dim
         _lowerCAmelCase						=		mask_dim
         _lowerCAmelCase						=		hidden_dim
         _lowerCAmelCase						=		encoder_feedforward_dim
         _lowerCAmelCase						=		norm
         _lowerCAmelCase						=		encoder_layers
         _lowerCAmelCase						=		decoder_layers
         _lowerCAmelCase						=		use_task_norm
         _lowerCAmelCase						=		num_attention_heads
         _lowerCAmelCase						=		dropout
         _lowerCAmelCase						=		dim_feedforward
         _lowerCAmelCase						=		pre_norm
         _lowerCAmelCase						=		enforce_input_proj
         _lowerCAmelCase						=		query_dec_layers
         _lowerCAmelCase						=		common_stride
         _lowerCAmelCase						=		decoder_layers

         super().__init__(**_lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Dict[str, any]:
         _lowerCAmelCase						=		copy.deepcopy(self.__dict__       )
         _lowerCAmelCase						=		self.backbone_config.to_dict()
         _lowerCAmelCase						=		self.__class__.model_type
         return output
