



from __future__ import annotations

from typing import Any



class     _UpperCAmelCase			:




     def __init__(				self ,						a__):
         A__		 =					num_of_nodes
         A__		 =					[]
         A__		 =					{}




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         self.m_edges.append([u_node, v_node, weight])




     def 		snake_case_		(				self ,						a__):

         if self.m_component[u_node] == u_node:
             return u_node
         return self.find_component(self.m_component[u_node])




     def 		snake_case_		(				self ,						a__):

         if self.m_component[u_node] != u_node:
             for k in self.m_component:
                 A__		 =					self.find_component(a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):

         if component_size[u_node] <= component_size[v_node]:
             A__		 =					v_node
             component_size[v_node] += component_size[u_node]
             self.set_component(a__)

         elif component_size[u_node] >= component_size[v_node]:
             A__		 =					self.find_component(a__)
             component_size[u_node] += component_size[v_node]
             self.set_component(a__)




     def 		snake_case_		(				self):
         A__		 =					[]
         A__		 =					0

         A__		 =					[-1] * self.m_num_of_nodes

         # A list of components (initialized to all of the nodes)
         for node in range(self.m_num_of_nodes):
             self.m_component.update({node: node})
             component_size.append(1)

         A__		 =					self.m_num_of_nodes

         while num_of_components > 1:
             for edge in self.m_edges:
                 A__ ,	A__ ,	A__		 =					edge

                 A__		 =					self.m_component[u]
                 A__		 =					self.m_component[v]

                 if u_component != v_component:

                     for component in (u_component, v_component):
                         if (
                             minimum_weight_edge[component] == -1
                             or minimum_weight_edge[component][2] > w
                         ):
                             A__		 =					[u, v, w]

             for edge in minimum_weight_edge:
                 if isinstance(a__ ,						a__):
                     A__ ,	A__ ,	A__		 =					edge

                     A__		 =					self.m_component[u]
                     A__		 =					self.m_component[v]

                     if u_component != v_component:
                         mst_weight += w
                         self.union(a__ ,						a__ ,						a__)
                         print(F"Added edge [{u} - {v}]\nAdded weight: {w}\n")
                         num_of_components -= 1

             A__		 =					[-1] * self.m_num_of_nodes
         print(F"The total weight of the minimal spanning tree is: {mst_weight}")







def 		lowerCAmelCase__    (					)->  None:
    pass


if __name__ == "__main__":
   import doctest

   doctest.testmod()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_lowercase						  =		{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		["MobileViTFeatureExtractor"]
   _lowercase						  =		["MobileViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MobileViTForImageClassification",
       "MobileViTForSemanticSegmentation",
       "MobileViTModel",
       "MobileViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFMobileViTForImageClassification",
       "TFMobileViTForSemanticSegmentation",
       "TFMobileViTModel",
       "TFMobileViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilevit import MobileViTFeatureExtractor
      from .image_processing_mobilevit import MobileViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilevit import (
          MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileViTForImageClassification,
          MobileViTForSemanticSegmentation,
          MobileViTModel,
          MobileViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mobilevit import (
          TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMobileViTForImageClassification,
          TFMobileViTForSemanticSegmentation,
          TFMobileViTModel,
          TFMobileViTPreTrainedModel,
      )


else:
   import sys

   _lowercase						  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
