
import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_UpperCamelCase			      =		logging.get_logger(__name__)

_UpperCamelCase			      =		{
    '''google/vit-hybrid-base-bit-384''': '''https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json''',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}







class   __lowercase				(UpperCamelCase__			):
 _UpperCamelCase        =  """vit-hybrid"""
 def __init__(							self				,							A_=None				,							A_=768				,							A_=12				,							A_=12				,							A_=3072				,							A_="gelu"				,							A_=0.0				,							A_=0.0				,							A_=0.02				,							A_=1e-12				,							A_=224				,							A_=1				,							A_=3				,							A_=[1, 1024, 24, 24]				,							A_=True				,							**A_				,							) ->Optional[Any]:



       '''simple docstring'''





       super().__init__(**__lowerCamelCase )

       if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.''' )
             __lowerCAmelCase :						Optional[int] = {
                 "global_padding": "same",
                 "layer_type": "bottleneck",
                 "depths": [3, 4, 9],
                 "out_features": ["stage3"],
                 "embedding_dynamic_padding": True,
             }

       if isinstance(__lowerCamelCase				,							__lowerCamelCase ):
             if "model_type" in backbone_config:
                   __lowerCAmelCase :						Any = CONFIG_MAPPING[backbone_config["model_type"]]
             else:
                   logger.info(
                       '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.''' )
                   __lowerCAmelCase :						str = BitConfig
             __lowerCAmelCase :						Tuple = backbone_config_class(**__lowerCamelCase )

       __lowerCAmelCase :						Any = backbone_featmap_shape
       __lowerCAmelCase :						Dict = backbone_config
       __lowerCAmelCase :						int = hidden_size
       __lowerCAmelCase :						List[Any] = num_hidden_layers
       __lowerCAmelCase :						Optional[Any] = num_attention_heads
       __lowerCAmelCase :						str = intermediate_size
       __lowerCAmelCase :						Any = hidden_act
       __lowerCAmelCase :						Union[str, Any] = hidden_dropout_prob
       __lowerCAmelCase :						Optional[int] = attention_probs_dropout_prob
       __lowerCAmelCase :						List[str] = initializer_range
       __lowerCAmelCase :						List[Any] = layer_norm_eps
       __lowerCAmelCase :						Union[str, Any] = image_size
       __lowerCAmelCase :						Optional[int] = patch_size
       __lowerCAmelCase :						Union[str, Any] = num_channels
       __lowerCAmelCase :						int = qkv_bias





 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





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





import heapq







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Union[str, Any] = [(0, start)]  # cost from start node,end node
      __lowerCAmelCase :						Any = set()
      while heap:
            ((__lowerCAmelCase),							(__lowerCAmelCase)) :						str = heapq.heappop(lowercase__						)
            if u in visited:
                  continue
            visited.add(lowercase__						)
            if u == end:
                  return cost
            for v, c in graph[u]:
                  if v in visited:
                        continue
                  __lowerCAmelCase :						str = cost + c
                  heapq.heappush(lowercase__       ,     (next_item, v)						)
      return -1


_UpperCamelCase			      =		{
    "A": [["B", 2], ["C", 5]],
    "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
    "C": [["A", 5], ["F", 3]],
    "D": [["B", 3]],
    "E": [["B", 4], ["F", 3]],
    "F": [["C", 3], ["E", 3]],
}
_UpperCamelCase			      =		{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["F", 3]],
    "F": [],
}
_UpperCamelCase			      =		{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}

_UpperCamelCase			      =		dijkstra(G, "E", "C")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

_UpperCamelCase			      =		dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 3 --> F == 3

_UpperCamelCase			      =		dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
 import doctest

 doctest.testmod()


