




from __future__ import annotations

from typing import Generic, TypeVar

__a          =			TypeVar('T')

class   __a(					Generic[T]       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE  )			->		None:
         UpperCAmelCase_      :							List[str]       =      data
         UpperCAmelCase_      :							Optional[int]       =      self
         UpperCAmelCase_      :							str       =      0


class   __a(					Generic[T]       ):






    """simple docstring"""





    def __init__(				self  )			->		None:
         # map from node name to the node object
         UpperCAmelCase_      :							dict[T, DisjointSetTreeNode[T]]       =      {}





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		None:
         # create a new set with x as its member
         UpperCAmelCase_      :							Any       =      DisjointSetTreeNode(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		DisjointSetTreeNode[T]:
         # find the set x belongs to (with path-compression)
         UpperCAmelCase_      :							List[Any]       =      self.map[data]
         if elem_ref != elem_ref.parent:
              UpperCAmelCase_      :							List[str]       =      self.find_set(elem_ref.parent.data  )
         return elem_ref.parent





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		None:
         # helper function for union operation
         if nodea.rank > nodea.rank:
              UpperCAmelCase_      :							List[str]       =      nodea
         else:
              UpperCAmelCase_      :							List[Any]       =      nodea
              if nodea.rank == nodea.rank:
                   nodea.rank += 1





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		None:
         # merge 2 disjoint sets
         self.link(self.find_set(_SCREAMING_SNAKE_CASE  )       ,self.find_set(_SCREAMING_SNAKE_CASE  )  )


class   __a(					Generic[T]       ):






    """simple docstring"""





    def __init__(				self  )			->		None:
         # connections: map from the node to the neighbouring nodes (with weights)
         UpperCAmelCase_      :							dict[T, dict[T, int]]       =      {}





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		None:
         # add a node ONLY if its not present in the graph
         if node not in self.connections:
              UpperCAmelCase_      :							Optional[int]       =      {}





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		None:
         # add an edge with the given weight
         self.add_node(_SCREAMING_SNAKE_CASE  )
         self.add_node(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      weight
         UpperCAmelCase_      :							Tuple       =      weight





    def 							a__				(				self  )			->		GraphUndirectedWeighted[T]:
         UpperCAmelCase_      :							Any       =      []
         UpperCAmelCase_      :							Union[str, Any]       =      set()
         for start in self.connections:
              for end in self.connections[start]:
                   if (start, end) not in seen:
                        seen.add((end, start)  )
                        edges.append((start, end, self.connections[start][end])  )
         edges.sort(key=lambda _SCREAMING_SNAKE_CASE  : x[2]  )

         # creating the disjoint set
         UpperCAmelCase_      :							List[str]       =      DisjointSetTree[T]()
         for node in self.connections:
              disjoint_set.make_set(_SCREAMING_SNAKE_CASE  )

         # MST generation
         UpperCAmelCase_      :							List[Any]       =      0
         UpperCAmelCase_      :							List[str]       =      0
         UpperCAmelCase_      :							Dict       =      GraphUndirectedWeighted[T]()
         while num_edges < len(self.connections  ) - 1:
              UpperCAmelCase_      :							Tuple       =      edges[index]
              index += 1
              UpperCAmelCase_      :							int       =      disjoint_set.find_set(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							int       =      disjoint_set.find_set(_SCREAMING_SNAKE_CASE  )
              if parent_u != parent_v:
                   num_edges += 1
                   graph.add_edge(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
                   disjoint_set.union(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         return graph





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a          =			{
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			['BloomTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			[
         'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BloomForCausalLM',
         'BloomModel',
         'BloomPreTrainedModel',
         'BloomForSequenceClassification',
         'BloomForTokenClassification',
         'BloomForQuestionAnswering',
     ]

if TYPE_CHECKING:
     from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_bloom_fast import BloomTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_bloom import (
              BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
              BloomForCausalLM,
              BloomForQuestionAnswering,
              BloomForSequenceClassification,
              BloomForTokenClassification,
              BloomModel,
              BloomPreTrainedModel,
          )

else:
     import sys

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