
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCamelCase      =logging.get_logger(__name__)

lowerCamelCase      ={
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''dpt'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=7_6_8						,						__SCREAMING_SNAKE_CASE=1_2						,						__SCREAMING_SNAKE_CASE=1_2						,						__SCREAMING_SNAKE_CASE=3_0_7_2						,						__SCREAMING_SNAKE_CASE="gelu"						,						__SCREAMING_SNAKE_CASE=0.0						,						__SCREAMING_SNAKE_CASE=0.0						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=1e-12						,						__SCREAMING_SNAKE_CASE=3_8_4						,						__SCREAMING_SNAKE_CASE=1_6						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=[2, 5, 8, 1_1]						,						__SCREAMING_SNAKE_CASE="project"						,						__SCREAMING_SNAKE_CASE=[4, 2, 1, 0.5]						,						__SCREAMING_SNAKE_CASE=[9_6, 1_9_2, 3_8_4, 7_6_8]						,						__SCREAMING_SNAKE_CASE=2_5_6						,						__SCREAMING_SNAKE_CASE=-1						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=0.4						,						__SCREAMING_SNAKE_CASE=2_5_5						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=[1, 1_0_2_4, 2_4, 2_4]						,						__SCREAMING_SNAKE_CASE=[0, 1]						,						__SCREAMING_SNAKE_CASE=None						,						**__SCREAMING_SNAKE_CASE						,						)  ->   Any:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				hidden_size
           UpperCamelCase__    :   Tuple					=				is_hybrid

           if self.is_hybrid:
                 if backbone_config is None:
                       logger.info('''Initializing the config with a `BiT` backbone.'''  )
                       UpperCamelCase__    :   Dict					=				{
                           '''global_padding''': '''same''',
                           '''layer_type''': '''bottleneck''',
                           '''depths''': [3, 4, 9],
                           '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
                           '''embedding_dynamic_padding''': True,
                       }
                       UpperCamelCase__    :   List[Any]					=				BitConfig(**__SCREAMING_SNAKE_CASE  )
                 elif isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                       logger.info('''Initializing the config with a `BiT` backbone.'''  )
                       UpperCamelCase__    :   Tuple					=				BitConfig(**__SCREAMING_SNAKE_CASE  )
                 elif isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                       UpperCamelCase__    :   Optional[int]					=				backbone_config
                 else:
                       raise ValueError(
                           F'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''  )

                 UpperCamelCase__    :   Optional[int]					=				backbone_featmap_shape
                 UpperCamelCase__    :   Optional[Any]					=				neck_ignore_stages

                 if readout_type != "project":
                       raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''  )
           else:
                 UpperCamelCase__    :   Union[str, Any]					=				None
                 UpperCamelCase__    :   int					=				None
                 UpperCamelCase__    :   Optional[int]					=				[]

           UpperCamelCase__    :   List[Any]					=				num_hidden_layers
           UpperCamelCase__    :   Dict					=				num_attention_heads
           UpperCamelCase__    :   int					=				intermediate_size
           UpperCamelCase__    :   Union[str, Any]					=				hidden_act
           UpperCamelCase__    :   Tuple					=				hidden_dropout_prob
           UpperCamelCase__    :   Tuple					=				attention_probs_dropout_prob
           UpperCamelCase__    :   int					=				initializer_range
           UpperCamelCase__    :   Dict					=				layer_norm_eps
           UpperCamelCase__    :   Optional[int]					=				image_size
           UpperCamelCase__    :   Any					=				patch_size
           UpperCamelCase__    :   int					=				num_channels
           UpperCamelCase__    :   Union[str, Any]					=				qkv_bias
           UpperCamelCase__    :   Optional[Any]					=				backbone_out_indices
           if readout_type not in ["ignore", "add", "project"]:
                 raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''  )
           UpperCamelCase__    :   Tuple					=				readout_type
           UpperCamelCase__    :   Dict					=				reassemble_factors
           UpperCamelCase__    :   Any					=				neck_hidden_sizes
           UpperCamelCase__    :   Optional[Any]					=				fusion_hidden_size
           UpperCamelCase__    :   Optional[Any]					=				head_in_index
           UpperCamelCase__    :   int					=				use_batch_norm_in_fusion_residual
           # auxiliary head attributes (semantic segmentation)
           UpperCamelCase__    :   Optional[int]					=				use_auxiliary_head
           UpperCamelCase__    :   List[str]					=				auxiliary_loss_weight
           UpperCamelCase__    :   str					=				semantic_loss_ignore_index
           UpperCamelCase__    :   Optional[Any]					=				semantic_classifier_dropout






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				copy.deepcopy(self.__dict__  )

           if output["backbone_config"] is not None:
                 UpperCamelCase__    :   Optional[Any]					=				self.backbone_config.to_dict()

           UpperCamelCase__    :   Optional[int]					=				self.__class__.model_type
           return output




lowerCamelCase      ={"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
lowerCamelCase      =["a", "b", "c", "d", "e"]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				start
      # add current to visited
      visited.append(UpperCamelCase__		)
      UpperCamelCase__    :   int					=				edges[current]
      for neighbor in neighbors:
            # if neighbor not in visited, visit
            if neighbor not in visited:
                  UpperCamelCase__    :   int					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # if all neighbors visited add current to sort
      sort.append(UpperCamelCase__		)
      # if all vertices haven't been visited select a new one to visit
      if len(UpperCamelCase__		) != len(UpperCamelCase__		):
            for vertice in vertices:
                  if vertice not in visited:
                        UpperCamelCase__    :   Optional[int]					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # return sort
      return sort


if __name__ == "__main__":
    lowerCamelCase      =topological_sort("a", [], [])
    print(sort)


