





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									=				{
    "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									=				logging.get_logger(__name__)



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			"maskformer"
 lowercase_		=			{"hidden_size": "mask_feature_size"}
 lowercase_		=			["resnet", "swin"]
 lowercase_		=			["detr"]

 def __init__(self				:   Optional[int]       , 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:



   '''simple docstring'''



   if backbone_config is None:
     # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
     lowerCamelCase__:      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(UpperCAmelCase_       , UpperCAmelCase_):
     lowerCamelCase__:      List[str]			  =backbone_config.pop("model_type")
     lowerCamelCase__:      int			  =CONFIG_MAPPING[backbone_model_type]
     lowerCamelCase__:      List[Any]			  =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
     lowerCamelCase__:      Tuple			  =DetrConfig()
   else:
     # verify that the decoder is supported
     lowerCamelCase__:      Any			  =(
         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_):
       lowerCamelCase__:      Union[str, Any]			  =CONFIG_MAPPING[decoder_type]
       lowerCamelCase__:      List[Any]			  =config_class.from_dict(UpperCAmelCase_)

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

   lowerCamelCase__:      Any			  =self.decoder_config.encoder_attention_heads
   lowerCamelCase__:      List[Any]			  =self.decoder_config.num_hidden_layers
   super().__init__(**UpperCAmelCase_)

 @classmethod
 def 							SCREAMING_SNAKE_CASE_					(cls				:   List[str]       , UpperCAmelCase_				:   PretrainedConfig       , UpperCAmelCase_				:   PretrainedConfig       , **UpperCAmelCase_				:   List[str])       ->int:



   '''simple docstring'''



   return cls(
       backbone_config=UpperCAmelCase_       , decoder_config=UpperCAmelCase_       , **UpperCAmelCase_       , )







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Dict[str, any]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =copy.deepcopy(self.__dict__)
   lowerCamelCase__:      Any			  =self.backbone_config.to_dict()
   lowerCamelCase__:      Any			  =self.decoder_config.to_dict()
   lowerCamelCase__:      List[str]			  =self.__class__.model_type
   return output








import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__A									=				"."


if __name__ == "__main__":
     __A									=				os.path.join(REPO_PATH, "utils/documentation_tests.txt")
     __A									=				[]
     __A									=				[]
     with open(doctest_file_path) as fp:
          for line in fp:
               __A									=				line.strip()
               __A									=				os.path.join(REPO_PATH, line)
               if not (os.path.isfile(path) or os.path.isdir(path)):
                    non_existent_paths.append(line)
               all_paths.append(path)
     if len(non_existent_paths) > 0:
          __A									=				"\n".join(non_existent_paths)
          raise ValueError(f'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
     if all_paths != sorted(all_paths):
          raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")

