





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


UpperCAmelCase_           =	{
    "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
}

UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  Optional[int]							  =   """maskformer"""
     __A	:  List[str]							  =   {"""hidden_size""": """mask_feature_size"""}
     __A	:  Union[str, Any]							  =   ["""resnet""", """swin"""]
     __A	:  Union[str, Any]							  =   ["""detr"""]





     def __init__(     self				,							_UpperCamelCase = 256				,							_UpperCamelCase = 256				,							_UpperCamelCase = 0.1				,							_UpperCamelCase = False				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = 0.02				,							_UpperCamelCase = 1.0				,							_UpperCamelCase = 1.0				,							_UpperCamelCase = 1.0				,							_UpperCamelCase = 20.0				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           _UpperCAmelCase           =   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(_UpperCamelCase				,							_UpperCamelCase					):
           _UpperCAmelCase           =   backbone_config.pop('''model_type'''					)
           _UpperCAmelCase           =   CONFIG_MAPPING[backbone_model_type]
           _UpperCAmelCase           =   config_class.from_dict(_UpperCamelCase					)

        # 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
           _UpperCAmelCase           =   DetrConfig()
        else:
           # verify that the decoder is supported
           _UpperCAmelCase           =   (
               decoder_config.pop('''model_type'''					) if isinstance(_UpperCamelCase				,							_UpperCamelCase					) 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(_UpperCamelCase				,							_UpperCamelCase					):
              _UpperCAmelCase           =   CONFIG_MAPPING[decoder_type]
              _UpperCAmelCase           =   config_class.from_dict(_UpperCamelCase					)

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

        _UpperCAmelCase           =   self.decoder_config.encoder_attention_heads
        _UpperCAmelCase           =   self.decoder_config.num_hidden_layers
        super().__init__(**_UpperCamelCase					)





     @classmethod
     def 		UpperCamelCase(     cls				,							_UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase					):
        return cls(
            backbone_config=_UpperCamelCase				,							decoder_config=_UpperCamelCase				,							**_UpperCamelCase				,							)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   copy.deepcopy(self.__dict__					)
        _UpperCAmelCase           =   self.backbone_config.to_dict()
        _UpperCAmelCase           =   self.decoder_config.to_dict()
        _UpperCAmelCase           =   self.__class__.model_type
        return output






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=A__    )
class 			__UpperCamelCase    (							A__    ):
     __A	:  str							  =   field(default="""language-modeling"""	, metadata={"""include_in_asdict_even_if_is_default""": True}    )

     __A	:  ClassVar[Features]							  =   Features({"""text""": Value("""string"""    )}    )
     __A	:  ClassVar[Features]							  =   Features({}    )
     __A	:  str							  =   "text"





     @property
     def 		UpperCamelCase(     self					):
        return {self.text_column: "text"}