



'''simple docstring'''





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowercase_	 =						{
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowercase_	 =						logging.get_logger(__name__)






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Optional[int]    = 'maskformer'
 __lowerCamelCase					: List[Any]    = {'hidden_size': 'mask_feature_size'}
 __lowerCamelCase					: Optional[Any]    = ['resnet', 'swin']
 __lowerCamelCase					: Dict    = ['detr']



 def __init__(self				,     A = 256				,     A = 256				,     A = 0.1				,     A = False				,     A = None				,     A = None				,     A = 0.02				,     A = 1.0				,     A = 1.0				,     A = 1.0				,     A = 20.0				,     A = None				,     **A				,     )					->  Any:


      """simple docstring"""



      if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           _a											=						SwinConfig(
               image_size=384				,     in_channels=3				,     patch_size=4				,     embed_dim=128				,     depths=[2, 2, 18, 2]				,     num_heads=[4, 8, 16, 32]				,     window_size=12				,     drop_path_rate=0.3				,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,     )

      if isinstance(A				,     A ):
           _a											=						backbone_config.pop('''model_type''' )
           _a											=						CONFIG_MAPPING[backbone_model_type]
           _a											=						config_class.from_dict(A )

      # 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 MaskFormer. '''
               f'''Supported model types: {','.join(self.backbones_supported )}''' )

      if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           _a											=						DetrConfig()
      else:
           # verify that the decoder is supported
           _a											=						(
               decoder_config.pop('''model_type''' ) if isinstance(A				,     A ) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
                raise ValueError(
                    f'''Transformer Decoder {decoder_type} not supported, please use one of'''
                    f''' {','.join(self.decoders_supported )}''' )
           if isinstance(A				,     A ):
                _a											=						CONFIG_MAPPING[decoder_type]
                _a											=						config_class.from_dict(A )

      _a											=						backbone_config
      _a											=						decoder_config
      # main feature dimension for the model
      _a											=						fpn_feature_size
      _a											=						mask_feature_size
      # initializer
      _a											=						init_std
      _a											=						init_xavier_std
      # Hungarian matcher && loss
      _a											=						cross_entropy_weight
      _a											=						dice_weight
      _a											=						mask_weight
      _a											=						use_auxiliary_loss
      _a											=						no_object_weight
      _a											=						output_auxiliary_logits

      _a											=						self.decoder_config.encoder_attention_heads
      _a											=						self.decoder_config.num_hidden_layers
      super().__init__(**A )



 @classmethod
 def       a__						(cls				,     A				,     A				,     **A )					->  Optional[int]:


      """simple docstring"""



      return cls(
          backbone_config=A				,     decoder_config=A				,     **A				,     )



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


      """simple docstring"""



      _a											=						copy.deepcopy(self.__dict__ )
      _a											=						self.backbone_config.to_dict()
      _a											=						self.decoder_config.to_dict()
      _a											=						self.__class__.model_type
      return output








'''simple docstring'''





from __future__ import annotations

import math




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     if depth < 0:
          raise ValueError('''Depth cannot be less than 0''')

     if not scores:
          raise ValueError('''Scores cannot be empty''')

     if depth == height:
          return scores[node_index]

     return (
         max(
             minimax(depth + 1						,			node_index * 2						,			__A						,			__A						,			__A)						,			minimax(depth + 1						,			node_index * 2 + 1						,			__A						,			__A						,			__A)						,			)
         if is_max
         else min(
             minimax(depth + 1						,			node_index * 2						,			__A						,			__A						,			__A)						,			minimax(depth + 1						,			node_index * 2 + 1						,			__A						,			__A						,			__A)						,			)
     )




def lowerCAmelCase						():



     """simple docstring"""



     _a											=						[90, 23, 6, 33, 21, 65, 123, 34_423]
     _a											=						math.log(len(__A)						,			2)
     print(F'''Optimal value : {minimax(0						,			0						,			__A						,			__A						,			__A)}''')


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



