code: '''simple docstring'''
  
  class  UpperCAmelCase :
  
   def __init__( self :	Union[str, Any],					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						data
      UpperCamelCase__ 						=						42
      UpperCamelCase__ 						=						42
  
  
  
  
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Node   )		->					None:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						0
     UpperCamelCase__ 						=						x
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Node    ,		_UpperCamelCase      :				Node   )		->					None:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__						,		UpperCamelCase__ 						=						find_set(_UpperCamelCase   ), find_set(_UpperCamelCase   )
     if x == y:
        return
  
     elif x.rank > y.rank:
        UpperCamelCase__ 						=						x
     else:
        UpperCamelCase__ 						=						y
        if x.rank == y.rank:
           y.rank += 1
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Node   )		->					Node:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     if x != x.parent:
        UpperCamelCase__ 						=						find_set(x.parent   )
     return x.parent
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Node   )		->					set:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						({0, 1, 2}, {3, 4, 5})
     for s in sets:
        if node.data in s:
           return s
     UpperCamelCase__ 						=						F'{node.data} is not in {sets}'
     raise ValueError(_UpperCamelCase   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		)		->					None:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						[Node(_UpperCamelCase   ) for i in range(6   )]
     for v in vertex:
        make_set(_UpperCamelCase   )
  
     union_set(vertex[0]    ,		vertex[1]   )
     union_set(vertex[1]    ,		vertex[2]   )
     union_set(vertex[3]    ,		vertex[4]   )
     union_set(vertex[3]    ,		vertex[5]   )
  
     for nodea in vertex:
        for nodea in vertex:
           if find_python_set(_UpperCamelCase   ).isdisjoint(find_python_set(_UpperCamelCase   )   ):
              assert find_set(_UpperCamelCase   ) != find_set(_UpperCamelCase   )
           else:
              assert find_set(_UpperCamelCase   ) == find_set(_UpperCamelCase   )
  
  
  if __name__ == "__main__":
         test_disjoint_set()
style_context: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowercase:		int							=     {
      "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
  }
  
  try:
         if not is_sentencepiece_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		str							=     ["LlamaTokenizer"]
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		List[Any]							=     ["LlamaTokenizerFast"]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		Union[str, Any]							=     [
             "OpenLlamaForCausalLM",
             "OpenLlamaModel",
             "OpenLlamaPreTrainedModel",
             "OpenLlamaForSequenceClassification",
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
         try:
                if not is_sentencepiece_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from transformers import LlamaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from transformers import LlamaTokenizerFast
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_open_llama import (
                    OpenLlamaForCausalLM,
                    OpenLlamaForSequenceClassification,
                    OpenLlamaModel,
                    OpenLlamaPreTrainedModel,
                )
  
  
  else:
         import sys
  
         __lowercase:		Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)