'''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


lowercase__  :      List[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
}

lowercase__  :      Optional[int]        =					logging.get_logger(__name__)







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



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






  def __init__(      self			:       List[str]  ,							UpperCAmelCase__			:       int = 256  ,							UpperCAmelCase__			:       int = 256  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       bool = False  ,							UpperCAmelCase__			:       Optional[Dict] = None  ,							UpperCAmelCase__			:       Optional[Dict] = None  ,							UpperCAmelCase__			:       float = 0.02  ,							UpperCAmelCase__			:       float = 1.0  ,							UpperCAmelCase__			:       float = 1.0  ,							UpperCAmelCase__			:       float = 1.0  ,							UpperCAmelCase__			:       float = 20.0  ,							UpperCAmelCase__			:       Optional[bool] = None  ,							**UpperCAmelCase__			:       Optional[Any]  ,							)				->str:
        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  lowerCAmelCase__ (      cls			:       Tuple  ,							UpperCAmelCase__			:       PretrainedConfig  ,							UpperCAmelCase__			:       PretrainedConfig  ,							**UpperCAmelCase__			:       List[Any]  )				->Dict:
        return cls(
            backbone_config=UpperCAmelCase__  ,							decoder_config=UpperCAmelCase__  ,							**UpperCAmelCase__  ,							)






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






'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__  :      Optional[Any]        =					{
    "configuration_encodec": [
        "ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EncodecConfig",
    ],
    "feature_extraction_encodec": ["EncodecFeatureExtractor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      List[str]        =					[
          "ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST",
          "EncodecModel",
          "EncodecPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_encodec import (
          ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EncodecConfig,
      )
      from .feature_extraction_encodec import EncodecFeatureExtractor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_encodec import (
                ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
                EncodecModel,
                EncodecPreTrainedModel,
            )

else:
      import sys

      lowercase__  :      str        =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





