"""simple docstring"""
import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_UpperCAmelCase     =			{
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_UpperCAmelCase     =			logging.get_logger(__name__)
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				List[str]     						=					'maskformer'
  UpperCamelCase	:				List[str]     						=					{'hidden_size': 'mask_feature_size'}
  UpperCamelCase	:				Union[str, Any]     						=					['resnet', 'swin']
  UpperCamelCase	:				Optional[int]     						=					['detr']



  def __init__(						self :					List[str]       ,		lowerCAmelCase :					int = 256       ,		lowerCAmelCase :					int = 256       ,		lowerCAmelCase :					float = 0.1       ,		lowerCAmelCase :					bool = False       ,		lowerCAmelCase :					Optional[Dict] = None       ,		lowerCAmelCase :					Optional[Dict] = None       ,		lowerCAmelCase :					float = 0.0_2       ,		lowerCAmelCase :					float = 1.0       ,		lowerCAmelCase :					float = 1.0       ,		lowerCAmelCase :					float = 1.0       ,		lowerCAmelCase :					float = 2_0.0       ,		lowerCAmelCase :					Optional[bool] = None       ,		**lowerCAmelCase :					Optional[Any]       ,		)     ->					str:


     '''simple docstring'''




     if backbone_config is None:
        # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
        SCREAMING_SNAKE_CASE_: str     			=SwinConfig(
            image_size=384       ,		in_channels=3       ,		patch_size=4       ,		embed_dim=128       ,		depths=[2, 2, 18, 2]       ,		num_heads=[4, 8, 16, 32]       ,		window_size=12       ,		drop_path_rate=0.3       ,		out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]       ,		)

     if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__		):
        SCREAMING_SNAKE_CASE_: List[str]     			=backbone_config.pop("""model_type"""		)
        SCREAMING_SNAKE_CASE_: int     			=CONFIG_MAPPING[backbone_model_type]
        SCREAMING_SNAKE_CASE_: str     			=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 MaskFormer. '''
            f'''Supported model types: {",".join(self.backbones_supported		)}'''		)

     if decoder_config is None:
        # fall back to https://huggingface.co/facebook/detr-resnet-50
        SCREAMING_SNAKE_CASE_: Tuple     			=DetrConfig()
     else:
        # verify that the decoder is supported
        SCREAMING_SNAKE_CASE_: Optional[int]     			=(
            decoder_config.pop("""model_type"""		) if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__		) else decoder_config.model_type
        )
        if decoder_type not in self.decoders_supported:
           raise ValueError(
               f'''Transformer Decoder {decoder_type} not supported, please use one of'''
               f''' {",".join(self.decoders_supported		)}'''		)
        if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__		):
           SCREAMING_SNAKE_CASE_: Optional[Any]     			=CONFIG_MAPPING[decoder_type]
           SCREAMING_SNAKE_CASE_: Dict     			=config_class.from_dict(UpperCAmelCase__		)

     SCREAMING_SNAKE_CASE_: List[Any]     			=backbone_config
     SCREAMING_SNAKE_CASE_: Optional[int]     			=decoder_config
     # main feature dimension for the model
     SCREAMING_SNAKE_CASE_: Any     			=fpn_feature_size
     SCREAMING_SNAKE_CASE_: Dict     			=mask_feature_size
     # initializer
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=init_std
     SCREAMING_SNAKE_CASE_: Optional[int]     			=init_xavier_std
     # Hungarian matcher && loss
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=cross_entropy_weight
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=dice_weight
     SCREAMING_SNAKE_CASE_: Tuple     			=mask_weight
     SCREAMING_SNAKE_CASE_: List[Any]     			=use_auxiliary_loss
     SCREAMING_SNAKE_CASE_: Optional[int]     			=no_object_weight
     SCREAMING_SNAKE_CASE_: List[Any]     			=output_auxiliary_logits

     SCREAMING_SNAKE_CASE_: Tuple     			=self.decoder_config.encoder_attention_heads
     SCREAMING_SNAKE_CASE_: Dict     			=self.decoder_config.num_hidden_layers
     super().__init__(**UpperCAmelCase__		)



  @classmethod
  def 	lowerCamelCase__       (						cls :					Tuple       ,		lowerCAmelCase :					PretrainedConfig       ,		lowerCAmelCase :					PretrainedConfig       ,		**lowerCAmelCase :					List[Any]		)     ->					Dict:


     '''simple docstring'''




     return cls(
         backbone_config=UpperCAmelCase__       ,		decoder_config=UpperCAmelCase__       ,		**UpperCAmelCase__       ,		)





  def 	lowerCamelCase__       (						self :					Dict		)     ->					Dict[str, any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=copy.deepcopy(self.__dict__		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.backbone_config.to_dict()
     SCREAMING_SNAKE_CASE_: Dict     			=self.decoder_config.to_dict()
     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.__class__.model_type
     return output








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   if upper_limit < 0:
      raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )

   SCREAMING_SNAKE_CASE_: Tuple     			=[0] * (upper_limit + 1)

   # Base case: C(0) = C(1) = 1
   SCREAMING_SNAKE_CASE_: Any     			=1
   if upper_limit > 0:
      SCREAMING_SNAKE_CASE_: List[str]     			=1

   # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
   for i in range(2							,			upper_limit + 1 ):
      for j in range(lowercase ):
         catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

   return catalan_list


if __name__ == "__main__":
   print("""\n********* Catalan Numbers Using Dynamic Programming ************\n""")
   print("""\n*** Enter -1 at any time to quit ***""")
   print("""\nEnter the upper limit (≥ 0) for the Catalan number sequence: """, end="""""")
   try:
      while True:
         _UpperCAmelCase     =			int(input().strip())
         if N < 0:
            print("""\n********* Goodbye!! ************""")
            break
         else:
            print(f"""The Catalan numbers from 0 through {N} are:""")
            print(catalan_numbers(N))
            print("""Try another upper limit for the sequence: """, end="""""")
   except (NameError, ValueError):
      print("""\n********* Invalid input, goodbye! ************\n""")

   import doctest

   doctest.testmod()



