

import copy
from typing import Dict, Optional

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


_lowerCamelCase							=logging.get_logger(__name__)

_lowerCamelCase							={
    "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 						a_			(				lowerCamelCase_	):



      """simple docstring"""







      __UpperCAmelCase     						= 'oneformer'
      __UpperCAmelCase     						= {'hidden_size': 'hidden_dim'}
      def __init__( self	:				Optional[Any]     ,snake_case	:				Optional[Dict] = None     ,snake_case	:				int = 255     ,snake_case	:				int = 150     ,snake_case	:				int = 0.1     ,snake_case	:				float = 2.0     ,snake_case	:				float = 5.0     ,snake_case	:				float = 5.0     ,snake_case	:				float = 0.5     ,snake_case	:				float = 0.07     ,snake_case	:				int = 12544     ,snake_case	:				float = 3.0     ,snake_case	:				float = 0.75     ,snake_case	:				float = 0.02     ,snake_case	:				float = 1.0     ,snake_case	:				float = 1e-05     ,snake_case	:				bool = False     ,snake_case	:				bool = True     ,snake_case	:				bool = True     ,snake_case	:				Optional[list] = [4, 8, 16, 32]     ,snake_case	:				int = 77     ,snake_case	:				int = 256     ,snake_case	:				int = 77     ,snake_case	:				int = 6     ,snake_case	:				int = 49408     ,snake_case	:				int = 2     ,snake_case	:				int = 16     ,snake_case	:				int = 256     ,snake_case	:				int = 256     ,snake_case	:				int = 256     ,snake_case	:				int = 1024     ,snake_case	:				str = "GN"     ,snake_case	:				int = 6     ,snake_case	:				int = 10     ,snake_case	:				bool = True     ,snake_case	:				int = 8     ,snake_case	:				float = 0.1     ,snake_case	:				int = 2048     ,snake_case	:				bool = False     ,snake_case	:				bool = False     ,snake_case	:				int = 2     ,snake_case	:				int = 4     ,**snake_case	:				Dict     ,):
           if backbone_config is None:
                logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'							)
                SCREAMING_SNAKE_CASE 		=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=snake_case     ,out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,)
           elif isinstance(snake_case     ,snake_case							):
                SCREAMING_SNAKE_CASE 		=backbone_config.get('model_type'							)
                SCREAMING_SNAKE_CASE 		=CONFIG_MAPPING[backbone_model_type]
                SCREAMING_SNAKE_CASE 		=config_class.from_dict(snake_case							)

           SCREAMING_SNAKE_CASE 		=backbone_config

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

           super().__init__(**snake_case							)


      def 			_lowerCAmelCase	( self	:				Tuple							):
           SCREAMING_SNAKE_CASE 		=copy.deepcopy(self.__dict__							)
           SCREAMING_SNAKE_CASE 		=self.backbone_config.to_dict()
           SCREAMING_SNAKE_CASE 		=self.__class__.model_type
           return output




import math



class 						a_			:



      """simple docstring"""







      def __init__( self	:				str     ,snake_case	:				List[Any]     ,snake_case	:				Union[str, Any]     ,snake_case	:				Optional[int]							):
           SCREAMING_SNAKE_CASE 		=x
           SCREAMING_SNAKE_CASE 		=y
           SCREAMING_SNAKE_CASE 		=z


      def __repr__( self	:				Any							):
           return f'Point({self.x}, {self.y}, {self.z})'







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2     )     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

