






'''simple docstring'''


import copy
from typing import Dict, Optional

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


SCREAMING_SNAKE_CASE_				      =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				      =		{
    "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					(							a__   ):







   """simple docstring"""







   _A						=   "oneformer"
   _A						=   {"hidden_size": "hidden_dim"}



   def __init__( self   ,   _A = None   ,   _A = 255   ,   _A = 150   ,   _A = 0.1   ,   _A = 2.0   ,   _A = 5.0   ,   _A = 5.0   ,   _A = 0.5   ,   _A = 0.07   ,   _A = 12544   ,   _A = 3.0   ,   _A = 0.75   ,   _A = 0.02   ,   _A = 1.0   ,   _A = 1E-0_5   ,   _A = False   ,   _A = True   ,   _A = True   ,   _A = [4, 8, 16, 32]   ,   _A = 77   ,   _A = 256   ,   _A = 77   ,   _A = 6   ,   _A = 49408   ,   _A = 2   ,   _A = 16   ,   _A = 256   ,   _A = 256   ,   _A = 256   ,   _A = 1024   ,   _A = "GN"   ,   _A = 6   ,   _A = 10   ,   _A = True   ,   _A = 8   ,   _A = 0.1   ,   _A = 2048   ,   _A = False   ,   _A = False   ,   _A = 2   ,   _A = 4   ,   **_A   ,   )    ->   Optional[int]:
    if backbone_config is None:
     logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'			)
     __a    :	Any									=						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=SCREAMING_SNAKE_CASE_   ,   out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,   )
    elif isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_			):
     __a    :	str									=						backbone_config.get('model_type'			)
     __a    :	str									=						CONFIG_MAPPING[backbone_model_type]
     __a    :	List[str]									=						config_class.from_dict(SCREAMING_SNAKE_CASE_			)

    __a    :	Dict									=						backbone_config

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

    super().__init__(**SCREAMING_SNAKE_CASE_			)



   def    __magic_name__    ( self			)    ->   Dict[str, any]:
    __a    :	Tuple									=						copy.deepcopy(self.__dict__			)
    __a    :	Dict									=						self.backbone_config.to_dict()
    __a    :	List[str]									=						self.__class__.model_type
    return output












'''simple docstring'''


def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__ = 30		):
 __a    :	Tuple									=						exponent + 2
 __a    :	str									=						(1 + 5**0.5) / 2
 __a    :	Any									=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

 return int(SCREAMING_SNAKE_CASE__		)


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




