
from __future__ import annotations

from typing import Any







class   lowerCamelCase	:





					def __init__(self   :					Optional[int]       ,					_A   :					int     )		->		None:
									snake_case				  =      num_of_nodes
									snake_case				  =      []
									snake_case				  =      {}





					def 	UpperCAmelCase(self   :					int       ,					_A   :					int       ,					_A   :					int       ,					_A   :					int     )		->		None:
									self.m_edges.append([u_node, v_node, weight]     )





					def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					int     )		->		int:

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





					def 	UpperCAmelCase(self   :					str       ,					_A   :					int     )		->		None:

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





					def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					list[int]       ,					_A   :					int       ,					_A   :					int     )		->		None:

									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 	UpperCAmelCase(self   :					int     )		->		None:
									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  ,							snake_case  ,							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  ,							snake_case  ,							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 		lowercase_    (     )							->							None:
				"""simple docstring"""




if __name__ == "__main__":
			import doctest

			doctest.testmod()



from typing import TYPE_CHECKING

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


_A						      =				{
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "Wav2Vec2ForAudioFrameClassification",
       "Wav2Vec2ForCTC",
       "Wav2Vec2ForMaskedLM",
       "Wav2Vec2ForPreTraining",
       "Wav2Vec2ForSequenceClassification",
       "Wav2Vec2ForXVector",
       "Wav2Vec2Model",
       "Wav2Vec2PreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFWav2Vec2ForCTC",
       "TFWav2Vec2Model",
       "TFWav2Vec2PreTrainedModel",
       "TFWav2Vec2ForSequenceClassification",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A						      =				[
       "FlaxWav2Vec2ForCTC",
       "FlaxWav2Vec2ForPreTraining",
       "FlaxWav2Vec2Model",
       "FlaxWav2Vec2PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
   from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
   from .processing_wavaveca import WavaVecaProcessor
   from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca import (
          WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaForAudioFrameClassification,
          WavaVecaForCTC,
          WavaVecaForMaskedLM,
          WavaVecaForPreTraining,
          WavaVecaForSequenceClassification,
          WavaVecaForXVector,
          WavaVecaModel,
          WavaVecaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWavaVecaForCTC,
          TFWavaVecaForSequenceClassification,
          TFWavaVecaModel,
          TFWavaVecaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          FlaxWavaVecaForCTC,
          FlaxWavaVecaForPreTraining,
          FlaxWavaVecaModel,
          FlaxWavaVecaPreTrainedModel,
      )


else:
   import sys

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

