






"""simple docstring"""





import copy
from typing import Dict, Optional

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


A__       :			int=						logging.get_logger(__name__)

A__       :			List[str]=						{
    """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  __lowerCamelCase     (	lowerCamelCase_	):
   a   :					Optional[int]			    ="""oneformer"""
   a   :					Union[str, Any]			    ={"""hidden_size""": """hidden_dim"""}

   def __init__(       self			,					snake_case_ = None			,					snake_case_ = 255			,					snake_case_ = 150			,					snake_case_ = 0.1			,					snake_case_ = 2.0			,					snake_case_ = 5.0			,					snake_case_ = 5.0			,					snake_case_ = 0.5			,					snake_case_ = 0.07			,					snake_case_ = 1_2544			,					snake_case_ = 3.0			,					snake_case_ = 0.75			,					snake_case_ = 0.02			,					snake_case_ = 1.0			,					snake_case_ = 1E-05			,					snake_case_ = False			,					snake_case_ = True			,					snake_case_ = True			,					snake_case_ = [4, 8, 16, 32]			,					snake_case_ = 77			,					snake_case_ = 256			,					snake_case_ = 77			,					snake_case_ = 6			,					snake_case_ = 4_9408			,					snake_case_ = 2			,					snake_case_ = 16			,					snake_case_ = 256			,					snake_case_ = 256			,					snake_case_ = 256			,					snake_case_ = 1024			,					snake_case_ = "GN"			,					snake_case_ = 6			,					snake_case_ = 10			,					snake_case_ = True			,					snake_case_ = 8			,					snake_case_ = 0.1			,					snake_case_ = 2048			,					snake_case_ = False			,					snake_case_ = False			,					snake_case_ = 2			,					snake_case_ = 4			,					**snake_case_			,					)		->							Dict:
       if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'   )
           UpperCamelCase__								=     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=snake_case_			,					out_features=['stage1', 'stage2', 'stage3', 'stage4']			,					)
       elif isinstance(snake_case_			,					snake_case_   ):
           UpperCamelCase__								=     backbone_config.get('model_type'   )
           UpperCamelCase__								=     CONFIG_MAPPING[backbone_model_type]
           UpperCamelCase__								=     config_class.from_dict(snake_case_   )

       UpperCamelCase__								=     backbone_config

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

       super().__init__(**snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       UpperCamelCase__								=     copy.deepcopy(self.__dict__   )
       UpperCamelCase__								=     self.backbone_config.to_dict()
       UpperCamelCase__								=     self.__class__.model_type
       return output







def      snake_case__							(  lowerCAmelCase_ = 100     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=[[0 for _ in range(lowerCAmelCase_     )] for _ in range(m + 1     )]
     for i in range(m + 1     ):
          SCREAMING_SNAKE_CASE 		=1

     for n in range(m + 1     ):
          for k in range(1,       lowerCAmelCase_     ):
               memo[n][k] += memo[n][k - 1]
               if n > k:
                    memo[n][k] += memo[n - k - 1][k]

     return memo[m][m - 1] - 1


if __name__ == "__main__":
 print(solution(int(input("Enter a number: ").strip())))

