





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        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	List[str]				      = 'maskformer'
   _UpperCamelCase					:	List[str]				      = {'hidden_size': 'mask_feature_size'}
   _UpperCamelCase					:	Optional[int]				      = ['resnet', 'swin']
   _UpperCamelCase					:	List[Any]				      = ['detr']


   def __init__(					self    :       List[str]    ,  a    :       int = 256    ,  a    :       int = 256    ,  a    :       float = 0.1    ,  a    :       bool = False    ,  a    :       Optional[Dict] = None    ,  a    :       Optional[Dict] = None    ,  a    :       float = 0.02    ,  a    :       float = 1.0    ,  a    :       float = 1.0    ,  a    :       float = 1.0    ,  a    :       float = 20.0    ,  a    :       Optional[bool] = None    ,  **a    :       Tuple    ,  )->     str:





     """simple docstring"""







     if backbone_config is None:
       # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
       lowercase__			    =						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 ):
       lowercase__			    =						backbone_config.pop('model_type' )
       lowercase__			    =						CONFIG_MAPPING[backbone_model_type]
       lowercase__			    =						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
       lowercase__			    =						DetrConfig()
     else:
       # verify that the decoder is supported
       lowercase__			    =						(
           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 ):
         lowercase__			    =						CONFIG_MAPPING[decoder_type]
         lowercase__			    =						config_class.from_dict(a )

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

     lowercase__			    =						self.decoder_config.encoder_attention_heads
     lowercase__			    =						self.decoder_config.num_hidden_layers
     super().__init__(**a )


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





     """simple docstring"""







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


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





     """simple docstring"""







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









from string import ascii_uppercase

lowercase_					     =	{str(ord(c) - 55): c for c in ascii_uppercase}




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     str:
  if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise TypeError('int() can\'t convert non-string with explicit base'	)
  if num < 0:
    raise ValueError('parameter must be positive int'	)
  if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise TypeError('\'str\' object cannot be interpreted as an integer'	)
  if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise TypeError('\'float\' object cannot be interpreted as an integer'	)
  if base in (0, 1):
    raise ValueError('base must be >= 2'	)
  if base > 36:
    raise ValueError('base must be <= 36'	)
  lowercase__			    =						''
  lowercase__			    =						0
  lowercase__			    =						0
  while div != 1:
    lowercase__    ,					lowercase__			    =						divmod(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)
    if base >= 11 and 9 < mod < 36:
      lowercase__			    =						ALPHABET_VALUES[str(_SCREAMING_SNAKE_CASE	)]
    else:
      lowercase__			    =						str(_SCREAMING_SNAKE_CASE	)
    new_value += actual_value
    lowercase__			    =						num // base
    lowercase__			    =						div
    if div == 0:
      return str(new_value[::-1]	)
    elif div == 1:
      new_value += str(_SCREAMING_SNAKE_CASE	)
      return str(new_value[::-1]	)

  return new_value[::-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      for base in range(2, 37):
            for num in range(1_000):
                  assert int(decimal_to_any(num, base), base) == num, (
                      num,
                      base,
                      decimal_to_any(num, base),
                      int(decimal_to_any(num, base), base),
                  )


