
'''simple docstring'''
class 			__A					:
 def __init__( self      :     Any					, lowerCamelCase      :     int							):



   """simple docstring"""





   __A		:  List[str]       				=		data
   __A		:  int
   __A		:  Node







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Node			)					->							None:





  """simple docstring"""

  __A		:  Tuple       				=		0
  __A		:  Optional[Any]       				=		x







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Node      ,  __SCREAMING_SNAKE_CASE    :	Node			)					->							None:





  """simple docstring"""

  __A		:  str       				=		find_set(__lowerCAmelCase			), find_set(__lowerCAmelCase			)
  if x == y:
    return

  elif x.rank > y.rank:
    __A		:  Union[str, Any]       				=		x
  else:
    __A		:  List[Any]       				=		y
    if x.rank == y.rank:
      y.rank += 1







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Node			)					->							Node:





  """simple docstring"""

  if x != x.parent:
    __A		:  Union[str, Any]       				=		find_set(x.parent			)
  return x.parent







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Node			)					->							set:





  """simple docstring"""

  __A		:  int       				=		({0, 1, 2}, {3, 4, 5})
  for s in sets:
    if node.data in s:
      return s
  __A		:  List[Any]       				=		F"{node.data} is not in {sets}"
  raise ValueError(__lowerCAmelCase			)







def 							A_  (    )					->							None:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		[Node(__lowerCAmelCase			) for i in range(6			)]
  for v in vertex:
    make_set(__lowerCAmelCase			)

  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(__lowerCAmelCase			).isdisjoint(find_python_set(__lowerCAmelCase			)			):
        assert find_set(__lowerCAmelCase			) != find_set(__lowerCAmelCase			)
      else:
        assert find_set(__lowerCAmelCase			) == find_set(__lowerCAmelCase			)


if __name__ == "__main__":
     test_disjoint_set()









"""simple docstring"""
from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE						 =     {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5Tokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """T5EncoderModel""",
        """T5ForConditionalGeneration""",
        """T5Model""",
        """T5PreTrainedModel""",
        """load_tf_weights_in_t5""",
        """T5ForQuestionAnswering""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFT5EncoderModel""",
        """TFT5ForConditionalGeneration""",
        """TFT5Model""",
        """TFT5PreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """FlaxT5EncoderModel""",
        """FlaxT5ForConditionalGeneration""",
        """FlaxT5Model""",
        """FlaxT5PreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta import TaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta_fast import TaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_ta import (
            T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TaEncoderModel,
            TaForConditionalGeneration,
            TaForQuestionAnswering,
            TaModel,
            TaPreTrainedModel,
            load_tf_weights_in_ta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_ta import (
            TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFTaEncoderModel,
            TFTaForConditionalGeneration,
            TFTaModel,
            TFTaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_ta import (
            FlaxTaEncoderModel,
            FlaxTaForConditionalGeneration,
            FlaxTaModel,
            FlaxTaPreTrainedModel,
        )


else:
    import sys

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



