

import copy
from typing import Dict, List, Optional

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


__UpperCAmelCase								=			{
    """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
}

__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      str										="mask2former"
  lowerCamelCase   :      int										=["swin"]
  lowerCamelCase   :      Any										={"hidden_size": "hidden_dim"}







  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   Optional[Dict] = None	,							lowerCAmelCase			:   int = 2_56	,							lowerCAmelCase			:   int = 2_56	,							lowerCAmelCase			:   int = 2_56	,							lowerCAmelCase			:   int = 10_24	,							lowerCAmelCase			:   str = "relu"	,							lowerCAmelCase			:   int = 6	,							lowerCAmelCase			:   int = 10	,							lowerCAmelCase			:   int = 8	,							lowerCAmelCase			:   float = 0.0	,							lowerCAmelCase			:   int = 20_48	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   bool = False	,							lowerCAmelCase			:   int = 4	,							lowerCAmelCase			:   int = 2_55	,							lowerCAmelCase			:   int = 1_00	,							lowerCAmelCase			:   float = 0.1	,							lowerCAmelCase			:   float = 2.0	,							lowerCAmelCase			:   float = 5.0	,							lowerCAmelCase			:   float = 5.0	,							lowerCAmelCase			:   int = 1_25_44	,							lowerCAmelCase			:   float = 3.0	,							lowerCAmelCase			:   float = 0.75	,							lowerCAmelCase			:   float = 0.02	,							lowerCAmelCase			:   float = 1.0	,							lowerCAmelCase			:   bool = True	,							lowerCAmelCase			:   List[int] = [4, 8, 16, 32]	,							lowerCAmelCase			:   bool = None	,							**lowerCAmelCase			:   List[Any]	,							)							->     List[str]:






     """simple docstring"""







     if backbone_config is None:
        logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""   )
        __lowerCAmelCase  :      Dict    =						CONFIG_MAPPING["""swin"""](
            image_size=2_24	,							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=lowerCAmelCase	,							out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]	,							)

     if isinstance(lowerCAmelCase	,							lowerCAmelCase   ):
        __lowerCAmelCase  :      Optional[Any]    =						backbone_config.pop("""model_type"""   )
        __lowerCAmelCase  :      Optional[Any]    =						CONFIG_MAPPING[backbone_model_type]
        __lowerCAmelCase  :      Union[str, Any]    =						config_class.from_dict(lowerCAmelCase   )

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

     super().__init__(**lowerCAmelCase   )







  @classmethod
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Tuple	,							lowerCAmelCase			:   PretrainedConfig	,							**lowerCAmelCase			:   int   )							->     int:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Dict[str, any]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						copy.deepcopy(self.__dict__   )
     __lowerCAmelCase  :      int    =						self.backbone_config.to_dict()
     __lowerCAmelCase  :      Optional[int]    =						self.__class__.model_type
     return output





from __future__ import annotations





def      snake_case_				(__A					:						list[float] ,       __A					:						list[float]					)      ->     float:
   __lowerCAmelCase  :      Union[str, Any]    =						sorted(numsa + numsa					)
   __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						divmod(len(__A					) ,       2					)
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of first array: """).split()]
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')


