
"""simple docstring"""





from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
							import timm


if is_torch_available():
							from torch import Tensor
class 						SCREAMING_SNAKE_CASE_ (						__a     ,				__a ):




		"""simple docstring"""





		__lowercase							:  Optional[Any]						   =		'''pixel_values'''
		__lowercase							:  Any						   =		False
		__lowercase							:  Optional[Any]						   =		TimmBackboneConfig




		def __init__(     self							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(self							,  """timm""")
				super().__init__(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = config

				if config.backbone is None:
						raise ValueError("""backbone is not set in the config. Please set it to a timm model name.""")

				if config.backbone not in timm.list_models():
						raise ValueError(f"backbone {config.backbone} is not supported by timm.")

				if hasattr(lowerCAmelCase__							,  """out_features""") and config.out_features is not None:
						raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead.""")

				__SCREAMING_SNAKE_CASE            = getattr(lowerCAmelCase__							,  """use_pretrained_backbone"""							,  lowerCAmelCase__)
				if pretrained is None:
						raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False.""")

				# We just take the final layer by default. This matches the default for the transformers models.
				__SCREAMING_SNAKE_CASE            = config.out_indices if getattr(lowerCAmelCase__							,  """out_indices"""							,  lowerCAmelCase__) is not None else (-1,)

				__SCREAMING_SNAKE_CASE            = timm.create_model(
				    config.backbone							,  pretrained=lowerCAmelCase__							,  features_only=config.features_only							,  in_chans=config.num_channels							,  out_indices=lowerCAmelCase__							,  **lowerCAmelCase__							,  )
				# These are used to control the output of the model when called. If output_hidden_states is True, then
				# return_layers is modified to include all layers.
				__SCREAMING_SNAKE_CASE            = self._backbone.return_layers
				__SCREAMING_SNAKE_CASE            = {layer["""module"""]: str(lowerCAmelCase__) for i, layer in enumerate(self._backbone.feature_info.info)}
				super()._init_backbone(lowerCAmelCase__)




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(cls							,  ["""vision""", """timm"""])
				from ...models.timm_backbone import TimmBackboneConfig

				__SCREAMING_SNAKE_CASE            = kwargs.pop("""config"""							,  TimmBackboneConfig())

				__SCREAMING_SNAKE_CASE            = kwargs.pop("""use_timm_backbone"""							,  lowerCAmelCase__)
				if not use_timm:
						raise ValueError("""use_timm_backbone must be True for timm backbones""")

				__SCREAMING_SNAKE_CASE            = kwargs.pop("""num_channels"""							,  config.num_channels)
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""features_only"""							,  config.features_only)
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""use_pretrained_backbone"""							,  config.use_pretrained_backbone)
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""out_indices"""							,  config.out_indices)
				__SCREAMING_SNAKE_CASE            = TimmBackboneConfig(
				    backbone=lowerCAmelCase__							,  num_channels=lowerCAmelCase__							,  features_only=lowerCAmelCase__							,  use_pretrained_backbone=lowerCAmelCase__							,  out_indices=lowerCAmelCase__							,  )
				return super()._from_config(lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				pass




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = return_dict if return_dict is not None else self.config.use_return_dict
				__SCREAMING_SNAKE_CASE            = (
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				__SCREAMING_SNAKE_CASE            = output_attentions if output_attentions is not None else self.config.output_attentions

				if output_attentions:
						raise ValueError("""Cannot output attentions for timm backbones at the moment""")

				if output_hidden_states:
						# We modify the return layers to include all the stages of the backbone
						__SCREAMING_SNAKE_CASE            = self._all_layers
						__SCREAMING_SNAKE_CASE            = self._backbone(lowerCAmelCase__							,  **lowerCAmelCase__)
						__SCREAMING_SNAKE_CASE            = self._return_layers
						__SCREAMING_SNAKE_CASE            = tuple(hidden_states[i] for i in self.out_indices)
				else:
						__SCREAMING_SNAKE_CASE            = self._backbone(lowerCAmelCase__							,  **lowerCAmelCase__)
						__SCREAMING_SNAKE_CASE            = None

				__SCREAMING_SNAKE_CASE            = tuple(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = tuple(lowerCAmelCase__) if hidden_states is not None else None

				if not return_dict:
						__SCREAMING_SNAKE_CASE            = (feature_maps,)
						if output_hidden_states:
								__SCREAMING_SNAKE_CASE            = output + (hidden_states,)
						return output

				return BackboneOutput(feature_maps=lowerCAmelCase__							,  hidden_states=lowerCAmelCase__							,  attentions=lowerCAmelCase__)









'''simple docstring'''





import math






class __A			:



 '''simple docstring'''







 def __init__(self ,   A=0       )    ->     Dict:  # a graph with Node 0,1,...,N-1


      """simple docstring"""



      _a      =  n
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # adjacency matrix for weight
      _a      =  [
          [math.inf for j in range(0 ,   A       )] for i in range(0 ,   A       )
      ]  # dp[i][j] stores minimum distance from i to j



 def 					a__  (self ,   A ,   A ,   A       )    ->     Tuple:


      """simple docstring"""



      _a      =  w



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      for k in range(0 ,   self.n       ):
           for i in range(0 ,   self.n       ):
                for j in range(0 ,   self.n       ):
                     _a      =  min(self.dp[i][j] ,   self.dp[i][k] + self.dp[k][j]       )



 def 					a__  (self ,   A ,   A       )    ->     str:


      """simple docstring"""



      return self.dp[u][v]


if __name__ == "__main__":
  lowercase_		       =  Graph(5)
  graph.add_edge(0, 2, 9)
  graph.add_edge(0, 4, 10)
  graph.add_edge(1, 3, 5)
  graph.add_edge(2, 3, 7)
  graph.add_edge(3, 0, 10)
  graph.add_edge(3, 1, 2)
  graph.add_edge(3, 2, 1)
  graph.add_edge(3, 4, 6)
  graph.add_edge(4, 1, 3)
  graph.add_edge(4, 2, 4)
  graph.add_edge(4, 3, 9)
  graph.floyd_warshall()
  graph.show_min(1, 4)
  graph.show_min(0, 3)



