"""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


_a      :		str           =   {
    '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
}

_a      :		Optional[int]           =   logging.get_logger(__name__)
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Any							=		"maskformer"
     _UpperCamelCase							:			Optional[Any]							=		{"hidden_size": "mask_feature_size"}
     _UpperCamelCase							:			Any							=		["resnet", "swin"]
     _UpperCamelCase							:			Dict							=		["detr"]






     def __init__(		self				,     a__ = 256				,     a__ = 256				,     a__ = 0.1				,     a__ = False				,     a__ = None				,     a__ = None				,     a__ = 0.0_2				,     a__ = 1.0				,     a__ = 1.0				,     a__ = 1.0				,     a__ = 2_0.0				,     a__ = None				,     **a__				,     ):
       if backbone_config is None:
         # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
         _lowerCAmelCase						:       Union[str, Any]  			=     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__  ):
         _lowerCAmelCase						:       str  			=     backbone_config.pop("""model_type"""  )
         _lowerCAmelCase						:       List[str]  			=     CONFIG_MAPPING[backbone_model_type]
         _lowerCAmelCase						:       Dict  			=     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
         _lowerCAmelCase						:       int  			=     DetrConfig()
       else:
         # verify that the decoder is supported
         _lowerCAmelCase						:       Union[str, Any]  			=     (
             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__  ):
           _lowerCAmelCase						:       int  			=     CONFIG_MAPPING[decoder_type]
           _lowerCAmelCase						:       Tuple  			=     config_class.from_dict(a__  )

       _lowerCAmelCase						:       List[Any]  			=     backbone_config
       _lowerCAmelCase						:       List[str]  			=     decoder_config
       # main feature dimension for the model
       _lowerCAmelCase						:       int  			=     fpn_feature_size
       _lowerCAmelCase						:       Tuple  			=     mask_feature_size
       # initializer
       _lowerCAmelCase						:       Any  			=     init_std
       _lowerCAmelCase						:       Any  			=     init_xavier_std
       # Hungarian matcher && loss
       _lowerCAmelCase						:       Dict  			=     cross_entropy_weight
       _lowerCAmelCase						:       Union[str, Any]  			=     dice_weight
       _lowerCAmelCase						:       Tuple  			=     mask_weight
       _lowerCAmelCase						:       int  			=     use_auxiliary_loss
       _lowerCAmelCase						:       List[Any]  			=     no_object_weight
       _lowerCAmelCase						:       int  			=     output_auxiliary_logits

       _lowerCAmelCase						:       str  			=     self.decoder_config.encoder_attention_heads
       _lowerCAmelCase						:       int  			=     self.decoder_config.num_hidden_layers
       super().__init__(**a__  )






     @classmethod
     def     __A  (		cls				,     a__				,     a__				,     **a__  ):
       return cls(
           backbone_config=a__				,     decoder_config=a__				,     **a__				,     )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     copy.deepcopy(self.__dict__  )
       _lowerCAmelCase						:       str  			=     self.backbone_config.to_dict()
       _lowerCAmelCase						:       Dict  			=     self.decoder_config.to_dict()
       _lowerCAmelCase						:       List[Any]  			=     self.__class__.model_type
       return output





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a      :		List[Any]           =   logging.get_logger(__name__)

_a      :		Any           =   {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			int							=		"swinv2"

     _UpperCamelCase							:			List[str]							=		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self				,     a__=224				,     a__=4				,     a__=3				,     a__=96				,     a__=[2, 2, 6, 2]				,     a__=[3, 6, 12, 24]				,     a__=7				,     a__=4.0				,     a__=True				,     a__=0.0				,     a__=0.0				,     a__=0.1				,     a__="gelu"				,     a__=False				,     a__=0.0_2				,     a__=1e-5				,     a__=32				,     **a__				,     ):
       super().__init__(**a__  )

       _lowerCAmelCase						:       int  			=     image_size
       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       Any  			=     num_channels
       _lowerCAmelCase						:       List[Any]  			=     embed_dim
       _lowerCAmelCase						:       Optional[int]  			=     depths
       _lowerCAmelCase						:       List[Any]  			=     len(a__  )
       _lowerCAmelCase						:       Any  			=     num_heads
       _lowerCAmelCase						:       Tuple  			=     window_size
       _lowerCAmelCase						:       Tuple  			=     mlp_ratio
       _lowerCAmelCase						:       Any  			=     qkv_bias
       _lowerCAmelCase						:       Optional[int]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Tuple  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       str  			=     drop_path_rate
       _lowerCAmelCase						:       List[str]  			=     hidden_act
       _lowerCAmelCase						:       List[str]  			=     use_absolute_embeddings
       _lowerCAmelCase						:       Optional[int]  			=     layer_norm_eps
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:       Tuple  			=     int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:       Tuple  			=     (0, 0, 0, 0)




