




import copy
from typing import Dict, Optional

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


SCREAMING_SNAKE_CASE_    :       Any       =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    :       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 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     __UpperCAmelCase						:			str							=     """oneformer"""
     __UpperCAmelCase						:			Optional[Any]							=     {"""hidden_size""": """hidden_dim"""}

     def __init__(   self			:							Tuple    ,					A			:							Optional[Dict] = None    ,					A			:							int = 2_55    ,					A			:							int = 1_50    ,					A			:							int = 0.1    ,					A			:							float = 2.0    ,					A			:							float = 5.0    ,					A			:							float = 5.0    ,					A			:							float = 0.5    ,					A			:							float = 0.0_7    ,					A			:							int = 1_25_44    ,					A			:							float = 3.0    ,					A			:							float = 0.7_5    ,					A			:							float = 0.0_2    ,					A			:							float = 1.0    ,					A			:							float = 1E-05    ,					A			:							bool = False    ,					A			:							bool = True    ,					A			:							bool = True    ,					A			:							Optional[list] = [4, 8, 16, 32]    ,					A			:							int = 77    ,					A			:							int = 2_56    ,					A			:							int = 77    ,					A			:							int = 6    ,					A			:							int = 4_94_08    ,					A			:							int = 2    ,					A			:							int = 16    ,					A			:							int = 2_56    ,					A			:							int = 2_56    ,					A			:							int = 2_56    ,					A			:							int = 10_24    ,					A			:							str = "GN"    ,					A			:							int = 6    ,					A			:							int = 10    ,					A			:							bool = True    ,					A			:							int = 8    ,					A			:							float = 0.1    ,					A			:							int = 20_48    ,					A			:							bool = False    ,					A			:							bool = False    ,					A			:							int = 2    ,					A			:							int = 4    ,					**A			:							Optional[Any]    ,					)      ->      int:






         """simple docstring"""
         if backbone_config is None:
             logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""     )
             a__  			=							CONFIG_MAPPING["""swin"""](
                 image_size=2_24    ,					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=A    ,					out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    ,					)
         elif isinstance(A    ,					A     ):
             a__  			=							backbone_config.get("""model_type"""     )
             a__  			=							CONFIG_MAPPING[backbone_model_type]
             a__  			=							config_class.from_dict(A     )

         a__  			=							backbone_config

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

         super().__init__(**A     )






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






         """simple docstring"""
         a__  			=							copy.deepcopy(self.__dict__     )
         a__  			=							self.backbone_config.to_dict()
         a__  			=							self.__class__.model_type
         return output












def 							__lowerCAmelCase			(UpperCAmelCase_		):
    return n * (2 * n - 1)
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    a__  			=							(1 + 24 * n) ** 0.5
    return ((1 + root) / 6) % 1 == 0
def 							__lowerCAmelCase			(UpperCAmelCase_ = 144		):
    a__  			=							start
    a__  			=							hexagonal_num(UpperCAmelCase_		)
    while not is_pentagonal(UpperCAmelCase_		):
        n += 1
        a__  			=							hexagonal_num(UpperCAmelCase_		)
    return num


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






