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


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		{
    """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
}

SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """maskformer"""
  _SCREAMING_SNAKE_CASE     					=      {"""hidden_size""": """mask_feature_size"""}
  _SCREAMING_SNAKE_CASE     					=      ["""resnet""", """swin"""]
  _SCREAMING_SNAKE_CASE     					=      ["""detr"""]

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int = 2_56  ,				__SCREAMING_SNAKE_CASE			:   int = 2_56  ,				__SCREAMING_SNAKE_CASE			:   float = 0.1  ,				__SCREAMING_SNAKE_CASE			:   bool = False  ,				__SCREAMING_SNAKE_CASE			:   Optional[Dict] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Dict] = None  ,				__SCREAMING_SNAKE_CASE			:   float = 0.02  ,				__SCREAMING_SNAKE_CASE			:   float = 1.0  ,				__SCREAMING_SNAKE_CASE			:   float = 1.0  ,				__SCREAMING_SNAKE_CASE			:   float = 1.0  ,				__SCREAMING_SNAKE_CASE			:   float = 20.0  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				**__SCREAMING_SNAKE_CASE			:   int  ,				):
   if backbone_config is None:
    # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
    __a							=	SwinConfig(
        image_size=3_84  ,				in_channels=3  ,				patch_size=4  ,				embed_dim=1_28  ,				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(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
    __a							=	backbone_config.pop("model_type"							)
    __a							=	CONFIG_MAPPING[backbone_model_type]
    __a							=	config_class.from_dict(__SCREAMING_SNAKE_CASE							)

   # 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(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) 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(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
     __a							=	CONFIG_MAPPING[decoder_type]
     __a							=	config_class.from_dict(__SCREAMING_SNAKE_CASE							)

   __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__(**__SCREAMING_SNAKE_CASE							)

  @classmethod
  def 		_UpperCAmelCase (		cls			:   str  ,				__SCREAMING_SNAKE_CASE			:   PretrainedConfig  ,				__SCREAMING_SNAKE_CASE			:   PretrainedConfig  ,				**__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   return cls(
       backbone_config=__SCREAMING_SNAKE_CASE  ,				decoder_config=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE  ,				)

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   __a							=	copy.deepcopy(self.__dict__							)
   __a							=	self.backbone_config.to_dict()
   __a							=	self.decoder_config.to_dict()
   __a							=	self.__class__.model_type
   return output



from typing import Any



def 	__A		(      _A       ):



 """simple docstring"""


 if not input_list:
  return []
 __a							=	[input_list.count(_A       ) for value in input_list]
 __a							=	max(_A       )  # Gets the maximum count in the input list.
 # Gets values of modes
 return sorted({input_list[i] for i, value in enumerate(_A       ) if value == y}       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


