





import copy
from typing import Dict, List, Optional

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


__A             =       {
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

__A             =       logging.get_logger(__name__)



class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''
 lowercase_	=    "mask2former"
 lowercase_	=    ["swin"]
 lowercase_	=    {"hidden_size": "hidden_dim"}

 def __init__(self       :     str ,				UpperCAmelCase_       :     Optional[Dict] = None ,				UpperCAmelCase_       :     int = 256 ,				UpperCAmelCase_       :     int = 256 ,				UpperCAmelCase_       :     int = 256 ,				UpperCAmelCase_       :     int = 1_024 ,				UpperCAmelCase_       :     str = "relu" ,				UpperCAmelCase_       :     int = 6 ,				UpperCAmelCase_       :     int = 10 ,				UpperCAmelCase_       :     int = 8 ,				UpperCAmelCase_       :     float = 0.0 ,				UpperCAmelCase_       :     int = 2_048 ,				UpperCAmelCase_       :     bool = False ,				UpperCAmelCase_       :     bool = False ,				UpperCAmelCase_       :     int = 4 ,				UpperCAmelCase_       :     int = 255 ,				UpperCAmelCase_       :     int = 100 ,				UpperCAmelCase_       :     float = 0.1 ,				UpperCAmelCase_       :     float = 2.0 ,				UpperCAmelCase_       :     float = 5.0 ,				UpperCAmelCase_       :     float = 5.0 ,				UpperCAmelCase_       :     int = 12_544 ,				UpperCAmelCase_       :     float = 3.0 ,				UpperCAmelCase_       :     float = 0.75 ,				UpperCAmelCase_       :     float = 0.02 ,				UpperCAmelCase_       :     float = 1.0 ,				UpperCAmelCase_       :     bool = True ,				UpperCAmelCase_       :     List[int] = [4, 8, 16, 32] ,				UpperCAmelCase_       :     bool = None ,				**UpperCAmelCase_       :     Union[str, Any] ,				) ->List[Any]:



   '''simple docstring'''



   if backbone_config is None:
     logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.")
     lowerCamelCase__:   List[str]    						=CONFIG_MAPPING["swin"](
         image_size=224 ,				in_channels=3 ,				patch_size=4 ,				embed_dim=96 ,				depths=[2, 2, 18, 2] ,				num_heads=[3, 6, 12, 24] ,				window_size=7 ,				drop_path_rate=0.3 ,				use_absolute_embeddings=UpperCAmelCase_ ,				out_features=["stage1", "stage2", "stage3", "stage4"] ,				)

   if isinstance(UpperCAmelCase_ ,				UpperCAmelCase_):
     lowerCamelCase__:   Any    						=backbone_config.pop("model_type")
     lowerCamelCase__:   Optional[int]    						=CONFIG_MAPPING[backbone_model_type]
     lowerCamelCase__:   Tuple    						=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 Mask2Former. """
         F"""Supported model types: {",".join(self.backbones_supported)}""")

   lowerCamelCase__:   Union[str, Any]    						=backbone_config
   lowerCamelCase__:   Dict    						=feature_size
   lowerCamelCase__:   str    						=mask_feature_size
   lowerCamelCase__:   Optional[Any]    						=hidden_dim
   lowerCamelCase__:   Union[str, Any]    						=encoder_feedforward_dim
   lowerCamelCase__:   Tuple    						=activation_function
   lowerCamelCase__:   Dict    						=encoder_layers
   lowerCamelCase__:   str    						=decoder_layers
   lowerCamelCase__:   List[str]    						=num_attention_heads
   lowerCamelCase__:   List[str]    						=dropout
   lowerCamelCase__:   Dict    						=dim_feedforward
   lowerCamelCase__:   int    						=pre_norm
   lowerCamelCase__:   Tuple    						=enforce_input_projection
   lowerCamelCase__:   int    						=common_stride
   lowerCamelCase__:   Optional[int]    						=ignore_value
   lowerCamelCase__:   Any    						=num_queries
   lowerCamelCase__:   Any    						=no_object_weight
   lowerCamelCase__:   Any    						=class_weight
   lowerCamelCase__:   List[Any]    						=mask_weight
   lowerCamelCase__:   Union[str, Any]    						=dice_weight
   lowerCamelCase__:   Any    						=train_num_points
   lowerCamelCase__:   Dict    						=oversample_ratio
   lowerCamelCase__:   Dict    						=importance_sample_ratio
   lowerCamelCase__:   Tuple    						=init_std
   lowerCamelCase__:   int    						=init_xavier_std
   lowerCamelCase__:   Optional[int]    						=use_auxiliary_loss
   lowerCamelCase__:   int    						=feature_strides
   lowerCamelCase__:   Dict    						=output_auxiliary_logits
   lowerCamelCase__:   Tuple    						=decoder_layers

   super().__init__(**UpperCAmelCase_)

 @classmethod
 def 							SCREAMING_SNAKE_CASE_      (cls       :     Union[str, Any] ,				UpperCAmelCase_       :     PretrainedConfig ,				**UpperCAmelCase_       :     Union[str, Any]) ->List[Any]:



   '''simple docstring'''



   return cls(
       backbone_config=UpperCAmelCase_ ,				**UpperCAmelCase_ ,				)







 def 							SCREAMING_SNAKE_CASE_      (self       :     Union[str, Any]) ->Dict[str, any]:



   '''simple docstring'''



   lowerCamelCase__:   int    						=copy.deepcopy(self.__dict__)
   lowerCamelCase__:   Optional[Any]    						=self.backbone_config.to_dict()
   lowerCamelCase__:   Dict    						=self.__class__.model_type
   return output








from bisect import bisect
from itertools import accumulate





def   lowerCAmelCase_				( __a			,					__a			,					__a			,					__a	)  ->	List[Any]:







  """simple docstring"""





  lowerCamelCase__:   str    						=sorted(zip(__a			,					__a	)			,					key=lambda __a	: x[0] / x[1]			,					reverse=__a	)
  lowerCamelCase__						,      lowerCamelCase__:   str    						=[i[0] for i in r], [i[1] for i in r]
  lowerCamelCase__:   Tuple    						=list(accumulate(__a	)	)
  lowerCamelCase__:   Any    						=bisect(__a			,					__a	)
  return (
      0
      if k == 0
      else sum(vl[:k]	) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
      if k != n
      else sum(vl[:k]	)
  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

