


import copy
from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE__     					=			{
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Optional[int]       			=  """mask2former"""
		lowerCamelCase_	:			List[str]       			=  ["""swin"""]
		lowerCamelCase_	:			str       			=  {"""hidden_size""": """hidden_dim"""}






		def __init__(						self				:     str   ,  UpperCAmelCase				:     Optional[Dict] = None   ,  UpperCAmelCase				:     int = 2_56   ,  UpperCAmelCase				:     int = 2_56   ,  UpperCAmelCase				:     int = 2_56   ,  UpperCAmelCase				:     int = 10_24   ,  UpperCAmelCase				:     str = "relu"   ,  UpperCAmelCase				:     int = 6   ,  UpperCAmelCase				:     int = 10   ,  UpperCAmelCase				:     int = 8   ,  UpperCAmelCase				:     float = 0.0   ,  UpperCAmelCase				:     int = 20_48   ,  UpperCAmelCase				:     bool = False   ,  UpperCAmelCase				:     bool = False   ,  UpperCAmelCase				:     int = 4   ,  UpperCAmelCase				:     int = 2_55   ,  UpperCAmelCase				:     int = 1_00   ,  UpperCAmelCase				:     float = 0.1   ,  UpperCAmelCase				:     float = 2.0   ,  UpperCAmelCase				:     float = 5.0   ,  UpperCAmelCase				:     float = 5.0   ,  UpperCAmelCase				:     int = 1_25_44   ,  UpperCAmelCase				:     float = 3.0   ,  UpperCAmelCase				:     float = 0.75   ,  UpperCAmelCase				:     float = 0.02   ,  UpperCAmelCase				:     float = 1.0   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     List[int] = [4, 8, 16, 32]   ,  UpperCAmelCase				:     bool = None   ,  **UpperCAmelCase				:     Dict   ,  ):
				if backbone_config is None:
						logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						CONFIG_MAPPING["swin"](
						    image_size=2_24   ,  in_channels=3   ,  patch_size=4   ,  embed_dim=96   ,  depths=[2, 2, 18, 2]   ,  num_heads=[3, 6, 12, 24]   ,  window_size=7   ,  drop_path_rate=0.3   ,  use_absolute_embeddings=UpperCAmelCase   ,  out_features=["stage1", "stage2", "stage3", "stage4"]   ,  )

				if isinstance(UpperCAmelCase   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:str				     =						backbone_config.pop("model_type")
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						CONFIG_MAPPING[backbone_model_type]
						SCREAMING_SNAKE_CASE_				:int				     =						config_class.from_dict(UpperCAmelCase)

				# verify that the backbone is supported
				if backbone_config.model_type not in self.backbones_supported:
						logger.warning_once(
						    F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
						    F"Supported model types: {','.join(self.backbones_supported)}")

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						backbone_config
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						feature_size
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						mask_feature_size
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						hidden_dim
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						encoder_feedforward_dim
				SCREAMING_SNAKE_CASE_				:str				     =						activation_function
				SCREAMING_SNAKE_CASE_				:Dict				     =						encoder_layers
				SCREAMING_SNAKE_CASE_				:Any				     =						decoder_layers
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						num_attention_heads
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						dropout
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						dim_feedforward
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						pre_norm
				SCREAMING_SNAKE_CASE_				:Any				     =						enforce_input_projection
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						common_stride
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						ignore_value
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						num_queries
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						no_object_weight
				SCREAMING_SNAKE_CASE_				:Tuple				     =						class_weight
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						mask_weight
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						dice_weight
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						train_num_points
				SCREAMING_SNAKE_CASE_				:Any				     =						oversample_ratio
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						importance_sample_ratio
				SCREAMING_SNAKE_CASE_				:Tuple				     =						init_std
				SCREAMING_SNAKE_CASE_				:Dict				     =						init_xavier_std
				SCREAMING_SNAKE_CASE_				:int				     =						use_auxiliary_loss
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						feature_strides
				SCREAMING_SNAKE_CASE_				:str				     =						output_auxiliary_logits
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						decoder_layers

				super().__init__(**UpperCAmelCase)






		@classmethod
		def _snake_case		(						cls				:     Any   ,  UpperCAmelCase				:     PretrainedConfig   ,  **UpperCAmelCase				:     Dict):
				return cls(
				    backbone_config=UpperCAmelCase   ,  **UpperCAmelCase   ,  )







		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:str				     =						copy.deepcopy(self.__dict__)
				SCREAMING_SNAKE_CASE_				:Any				     =						self.backbone_config.to_dict()
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.__class__.model_type
				return output










def     lowercase	(    a = 50 ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[1] * (length + 1)

		for row_length in range(3     , length + 1 ):
				for block_length in range(3     , row_length + 1 ):
						for block_start in range(row_length - block_length ):
								ways_number[row_length] += ways_number[
								    row_length - block_start - block_length - 1
								]

						ways_number[row_length] += 1

		return ways_number[length]


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






