




import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase_						=			{
    'facebook/mask2former-swin-small-coco-instance': (
        'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

lowerCAmelCase_						=			logging.get_logger(__name__)
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Optional[int]			=      '''mask2former'''
  _UpperCamelCase       :      Optional[int]			=      ['''swin''']
  _UpperCamelCase       :      Tuple			=      {'''hidden_size''': '''hidden_dim'''}






  def __init__(  self		:     Optional[int]				,						_A		:     Optional[Dict] = None				,						_A		:     int = 256				,						_A		:     int = 256				,						_A		:     int = 256				,						_A		:     int = 1_024				,						_A		:     str = "relu"				,						_A		:     int = 6				,						_A		:     int = 10				,						_A		:     int = 8				,						_A		:     float = 0.0				,						_A		:     int = 2_048				,						_A		:     bool = False				,						_A		:     bool = False				,						_A		:     int = 4				,						_A		:     int = 255				,						_A		:     int = 100				,						_A		:     float = 0.1				,						_A		:     float = 2.0				,						_A		:     float = 5.0				,						_A		:     float = 5.0				,						_A		:     int = 12_544				,						_A		:     float = 3.0				,						_A		:     float = 0.75				,						_A		:     float = 0.02				,						_A		:     float = 1.0				,						_A		:     bool = True				,						_A		:     List[int] = [4, 8, 16, 32]				,						_A		:     bool = None				,						**_A		:     Any				,						)		->   Union[str, Any]:






       """simple docstring"""







       if backbone_config is None:
            logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''				)
            lowercase :       Optional[Any]       =			CONFIG_MAPPING['''swin'''](
                image_size=224				,						in_channels=3				,						patch_size=4				,						embed_dim=96				,						depths=[2, 2, 18, 2]				,						num_heads=[3, 6, 12, 24]				,						window_size=7				,						drop_path_rate=0.3				,						use_absolute_embeddings=_A				,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,						)

       if isinstance(_A				,						_A				):
            lowercase :       Union[str, Any]       =			backbone_config.pop('''model_type'''				)
            lowercase :       Optional[int]       =			CONFIG_MAPPING[backbone_model_type]
            lowercase :       Tuple       =			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 Mask2Former. """
                f"""Supported model types: {','.join(self.backbones_supported				)}"""				)

       lowercase :       List[str]       =			backbone_config
       lowercase :       List[Any]       =			feature_size
       lowercase :       Optional[Any]       =			mask_feature_size
       lowercase :       List[str]       =			hidden_dim
       lowercase :       Dict       =			encoder_feedforward_dim
       lowercase :       List[str]       =			activation_function
       lowercase :       Optional[Any]       =			encoder_layers
       lowercase :       Dict       =			decoder_layers
       lowercase :       List[str]       =			num_attention_heads
       lowercase :       Dict       =			dropout
       lowercase :       Tuple       =			dim_feedforward
       lowercase :       Any       =			pre_norm
       lowercase :       str       =			enforce_input_projection
       lowercase :       Tuple       =			common_stride
       lowercase :       int       =			ignore_value
       lowercase :       str       =			num_queries
       lowercase :       Tuple       =			no_object_weight
       lowercase :       Optional[int]       =			class_weight
       lowercase :       str       =			mask_weight
       lowercase :       List[str]       =			dice_weight
       lowercase :       List[Any]       =			train_num_points
       lowercase :       List[str]       =			oversample_ratio
       lowercase :       List[Any]       =			importance_sample_ratio
       lowercase :       int       =			init_std
       lowercase :       Dict       =			init_xavier_std
       lowercase :       Optional[Any]       =			use_auxiliary_loss
       lowercase :       Union[str, Any]       =			feature_strides
       lowercase :       Optional[int]       =			output_auxiliary_logits
       lowercase :       Optional[Any]       =			decoder_layers

       super().__init__(**_A				)






  @classmethod
  def  __a      (  cls		:     Optional[int]				,						_A		:     PretrainedConfig				,						**_A		:     List[str]				)		->   str:






       """simple docstring"""







       return cls(
           backbone_config=_A				,						**_A				,						)






  def  __a      (  self		:     Tuple				)		->   Dict[str, any]:






       """simple docstring"""







       lowercase :       str       =			copy.deepcopy(self.__dict__				)
       lowercase :       Optional[Any]       =			self.backbone_config.to_dict()
       lowercase :       Dict       =			self.__class__.model_type
       return output





import os
from collections.abc import Iterator






def  snake_case(    __magic_name__ = "."   )			->							Iterator[str]:





     '''simple docstring'''


     for dir_path, dir_names, filenames in os.walk(__magic_name__   ):
          lowercase :       Tuple       =			[d for d in dir_names if d != '''scripts''' and d[0] not in '''._''']
          for filename in filenames:
               if filename == "__init__.py":
                    continue
               if os.path.splitext(__magic_name__   )[1] in (".py", ".ipynb"):
                    yield os.path.join(__magic_name__			,		__magic_name__   ).lstrip('''./'''   )






def  snake_case(    __magic_name__   )			->							Dict:





     '''simple docstring'''


     return F"""{i * '  '}*""" if i else "\n##"






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Dict       =			old_path.split(os.sep   )
     for i, new_part in enumerate(new_path.split(os.sep   )   ):
          if (i + 1 > len(__magic_name__   ) or old_parts[i] != new_part) and new_part:
               print(F"""{md_prefix(__magic_name__   )} {new_part.replace('_'			,		' '   ).title()}"""   )
     return new_path






def  snake_case(    __magic_name__ = "."   )			->							None:





     '''simple docstring'''


     lowercase :       str       =			''''''
     for filepath in sorted(good_file_paths(__magic_name__   )   ):
          lowercase      ,	lowercase :       Optional[int]       =			os.path.split(__magic_name__   )
          if filepath != old_path:
               lowercase :       str       =			print_path(__magic_name__			,		__magic_name__   )
          lowercase :       Optional[int]       =			(filepath.count(os.sep   ) + 1) if filepath else 0
          lowercase :       Optional[Any]       =			F"""{filepath}/{filename}""".replace(''' '''			,		'''%20'''   )
          lowercase :       List[str]       =			os.path.splitext(filename.replace('''_'''			,		''' '''   ).title()   )[0]
          print(F"""{md_prefix(__magic_name__   )} [{filename}]({url})"""   )


if __name__ == "__main__":
 print_directory_md('.')