





class 	UpperCamelCase_							:


							def __init__(				self     :int				,					__A     :list		) -> None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   set_counts
									SCREAMING_SNAKE_CASE__								=   max(__A		)
									SCREAMING_SNAKE_CASE__								=   len(__A		)
									SCREAMING_SNAKE_CASE__								=   [1] * num_sets
									SCREAMING_SNAKE_CASE__								=   list(range(__A		)		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :int				,					__A     :int		) -> bool:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.get_parent(__A		)
									SCREAMING_SNAKE_CASE__								=   self.get_parent(__A		)

									if src_parent == dst_parent:
											return False

									if self.ranks[dst_parent] >= self.ranks[src_parent]:
											self.set_counts[dst_parent] += self.set_counts[src_parent]
											SCREAMING_SNAKE_CASE__								=   0
											SCREAMING_SNAKE_CASE__								=   dst_parent
											if self.ranks[dst_parent] == self.ranks[src_parent]:
													self.ranks[dst_parent] += 1
											SCREAMING_SNAKE_CASE__								=   self.set_counts[dst_parent]
									else:
											self.set_counts[src_parent] += self.set_counts[dst_parent]
											SCREAMING_SNAKE_CASE__								=   0
											SCREAMING_SNAKE_CASE__								=   src_parent
											SCREAMING_SNAKE_CASE__								=   self.set_counts[src_parent]

									SCREAMING_SNAKE_CASE__								=   max(self.max_set				,					__A		)
									return True


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :int		) -> int:



									"""simple docstring"""



									if self.parents[disj_set] == disj_set:
											return disj_set
									SCREAMING_SNAKE_CASE__								=   self.get_parent(self.parents[disj_set]		)
									return self.parents[disj_set]





from typing import TYPE_CHECKING

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


_lowerCamelCase      :						Any							=  {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						int							=  ["""XLNetTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						int							=  ["""XLNetTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Union[str, Any]							=  [
      """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XLNetForMultipleChoice""",
      """XLNetForQuestionAnswering""",
      """XLNetForQuestionAnsweringSimple""",
      """XLNetForSequenceClassification""",
      """XLNetForTokenClassification""",
      """XLNetLMHeadModel""",
      """XLNetModel""",
      """XLNetPreTrainedModel""",
      """load_tf_weights_in_xlnet""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Dict							=  [
      """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXLNetForMultipleChoice""",
      """TFXLNetForQuestionAnsweringSimple""",
      """TFXLNetForSequenceClassification""",
      """TFXLNetForTokenClassification""",
      """TFXLNetLMHeadModel""",
      """TFXLNetMainLayer""",
      """TFXLNetModel""",
      """TFXLNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase      :						int							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


