"""simple docstring"""



from __future__ import annotations

from typing import Any



class   _A						:




    """simple docstring"""




    def __init__(		self				:  int						,   A_				:  int     )     ->							Optional[int]:
         __snake_case				=				num_of_nodes
         __snake_case				=				[]
         __snake_case				=				{}
    def 			lowercase       (		self				:  int						,   A_				:  int						,   A_				:  int						,   A_				:  int     )     ->							Dict:
         self.m_edges.append([u_node, v_node, weight]     )
    def 			lowercase       (		self				:  List[Any]						,   A_				:  int     )     ->							List[Any]:






         if self.m_component[u_node] == u_node:
              return u_node
         return self.find_component(self.m_component[u_node]     )
    def 			lowercase       (		self				:  Union[str, Any]						,   A_				:  int     )     ->							int:






         if self.m_component[u_node] != u_node:
              for k in self.m_component:
                   __snake_case				=				self.find_component(A_     )
    def 			lowercase       (		self				:  Optional[Any]						,   A_				:  list[int]						,   A_				:  int						,   A_				:  int     )     ->							Optional[Any]:






         if component_size[u_node] <= component_size[v_node]:
              __snake_case				=				v_node
              component_size[v_node] += component_size[u_node]
              self.set_component(A_     )

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







    def 			lowercase       (		self				:  List[str]     )     ->							Tuple:
         __snake_case				=				[]
         __snake_case				=				0

         __snake_case				=				[-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     )

         __snake_case				=				self.m_num_of_nodes

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

                   __snake_case				=				self.m_component[u]
                   __snake_case				=				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
                             ):
                                  __snake_case				=				[u, v, w]

              for edge in minimum_weight_edge:
                   if isinstance(A_						,   A_     ):
                        __snake_case				=				edge

                        __snake_case				=				self.m_component[u]
                        __snake_case				=				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

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






def  SCREAMING_SNAKE_CASE							(  ):
     pass






if __name__ == "__main__":
       import doctest

       doctest.testmod()







'''simple docstring'''
from typing import TYPE_CHECKING

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


UpperCamelCase__			=      {
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['LayoutLMv3TokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
     'LayoutLMv3ForQuestionAnswering',
     'LayoutLMv3ForSequenceClassification',
     'LayoutLMv3ForTokenClassification',
     'LayoutLMv3Model',
     'LayoutLMv3PreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFLayoutLMv3ForQuestionAnswering',
     'TFLayoutLMv3ForSequenceClassification',
     'TFLayoutLMv3ForTokenClassification',
     'TFLayoutLMv3Model',
     'TFLayoutLMv3PreTrainedModel',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['LayoutLMv3FeatureExtractor']
 UpperCamelCase__			=      ['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
 from .configuration_layoutlmva import (
     LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LayoutLMvaConfig,
     LayoutLMvaOnnxConfig,
 )
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlmva import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
      TFLayoutLMvaPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
  from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
 import sys

 UpperCamelCase__			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




