



import copy
from typing import Dict, Optional

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


a__      =		logging.get_logger(__name__)

a__      =		{
    """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 						snake_case  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   snake_case_   :	List[str]       =						"""oneformer"""
   snake_case_   :	Optional[int]       =						{"""hidden_size""": """hidden_dim"""}

   def __init__(   self							:     str			,						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							:     int			,						)			->			Any:

    """simple docstring"""







    if backbone_config is None:
     logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone.""")
     _snake_case      :					Optional[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):
     _snake_case      :					Any					=   backbone_config.get("""model_type""")
     _snake_case      :					Optional[Any]					=   CONFIG_MAPPING[backbone_model_type]
     _snake_case      :					List[str]					=   config_class.from_dict(lowerCAmelCase)

    _snake_case      :					Union[str, Any]					=   backbone_config

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

    super().__init__(**lowerCAmelCase)


   def        UpperCamelCase_					(   self							:     Optional[Any])			->			Dict[str, any]:

    """simple docstring"""







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





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""recursive_lucas_number accepts only integer arguments."""			)
 if n_th_number == 0:
  return 2
 if n_th_number == 1:
  return 1

 return recursive_lucas_number(n_th_number - 1			) + recursive_lucas_number(
     n_th_number - 2			)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""dynamic_lucas_number accepts only integer arguments."""			)
 _snake_case		,      _snake_case      :					Any					=   2, 1
 for _ in range(SCREAMING_SNAKE_CASE__			):
  _snake_case		,      _snake_case      :					Union[str, Any]					=   b, a + b
 return a


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  a__      =		int(input("""Enter the number of terms in lucas series:\n""").strip())
  print("""Using recursive function to calculate lucas series:""")
  print(""" """.join(str(recursive_lucas_number(i)) for i in range(n)))
  print("""\nUsing dynamic function to calculate lucas series:""")
  print(""" """.join(str(dynamic_lucas_number(i)) for i in range(n)))
