





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


_snake_case    :       Optional[int]		       =    {
    '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
}

_snake_case    :       Union[str, Any]		       =    logging.get_logger(__name__)



class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					'maskformer'
  a_              =					{'hidden_size': 'mask_feature_size'}
  a_              =					['resnet', 'swin']
  a_              =					['detr']






  def __init__(							self					:						Any   ,    lowerCAmelCase_					:						Optional[int] = 2_5_6   ,    lowerCAmelCase_					:						Optional[Any] = 2_5_6   ,    lowerCAmelCase_					:						Optional[Any] = 0.1   ,    lowerCAmelCase_					:						Union[str, Any] = False   ,    lowerCAmelCase_					:						Union[str, Any] = None   ,    lowerCAmelCase_					:						Any = None   ,    lowerCAmelCase_					:						Union[str, Any] = 0.02   ,    lowerCAmelCase_					:						Optional[int] = 1.0   ,    lowerCAmelCase_					:						List[str] = 1.0   ,    lowerCAmelCase_					:						List[Any] = 1.0   ,    lowerCAmelCase_					:						Dict = 20.0   ,    lowerCAmelCase_					:						Tuple = None   ,    **lowerCAmelCase_					:						List[Any]   ,    )				->		List[Any]:



     if backbone_config is None:
        # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
        __lowerCAmelCase				      =      SwinConfig(
            image_size=3_8_4   ,    in_channels=3   ,    patch_size=4   ,    embed_dim=1_2_8   ,    depths=[2, 2, 1_8, 2]   ,    num_heads=[4, 8, 1_6, 3_2]   ,    window_size=1_2   ,    drop_path_rate=0.3   ,    out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,    )

     if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	):
        __lowerCAmelCase				      =      backbone_config.pop('model_type'	)
        __lowerCAmelCase				      =      CONFIG_MAPPING[backbone_model_type]
        __lowerCAmelCase				      =      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 MaskFormer. """
            f"""Supported model types: {",".join(self.backbones_supported	)}"""	)

     if decoder_config is None:
        # fall back to https://huggingface.co/facebook/detr-resnet-50
        __lowerCAmelCase				      =      DetrConfig()
     else:
        # verify that the decoder is supported
        __lowerCAmelCase				      =      (
            decoder_config.pop('model_type'	) if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	) 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(lowerCAmelCase_   ,    lowerCAmelCase_	):
           __lowerCAmelCase				      =      CONFIG_MAPPING[decoder_type]
           __lowerCAmelCase				      =      config_class.from_dict(lowerCAmelCase_	)

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

     __lowerCAmelCase				      =      self.decoder_config.encoder_attention_heads
     __lowerCAmelCase				      =      self.decoder_config.num_hidden_layers
     super().__init__(**lowerCAmelCase_	)






  @classmethod
  def 							lowercase						(							cls					:						Optional[Any]   ,    lowerCAmelCase_					:						Dict   ,    lowerCAmelCase_					:						Any   ,    **lowerCAmelCase_					:						Optional[Any]	)				->		Optional[Any]:
     return cls(
         backbone_config=lowerCAmelCase_   ,    decoder_config=lowerCAmelCase_   ,    **lowerCAmelCase_   ,    )






  def 							lowercase						(							self					:						List[Any]	)				->		Dict[str, any]:
     __lowerCAmelCase				      =      copy.deepcopy(self.__dict__	)
     __lowerCAmelCase				      =      self.backbone_config.to_dict()
     __lowerCAmelCase				      =      self.decoder_config.to_dict()
     __lowerCAmelCase				      =      self.__class__.model_type
     return output






'''simple docstring'''





def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if digit_amount > 0:
          return round(number - int(__A)    ,      __A)
     return number - int(__A)


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))



