





'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase			:      Union[str, Any]			=					TypeVar("""T""", bound=bool)







class 				_a   (Generic[T]  ):





     '''simple docstring'''



     def __init__(							self       ,__a       )			->							None:
          snake_case :			T					   = value
          snake_case :			SkewNode[T] | None					   = None
          snake_case :			SkewNode[T] | None					   = None







     @property
     def  snake_case_			(							self       )			->							T:
          return self._value







     @staticmethod
     def  snake_case_			(							__a       ,__a       )			->							SkewNode[T] | None:
          if not roota:
               return roota

          if not roota:
               return roota

          if roota.value > roota.value:
               snake_case :			Tuple					   = roota, roota

          snake_case :			Tuple					   = roota
          snake_case :			Union[str, Any]					   = roota.right
          snake_case :			Any					   = roota.left
          snake_case :			Union[str, Any]					   = SkewNode.merge(__a       ,__a       )

          return result







class 				_a   (Generic[T]  ):





     '''simple docstring'''



     def __init__(							self       ,__a = ()       )			->							None:
          snake_case :			SkewNode[T] | None					   = None
          if data:
               for item in data:
                    self.insert(__a       )







     def __bool__(							self       )			->							bool:
          return self._root is not None







     def __iter__(							self       )			->							Iterator[T]:
          snake_case :			list[Any]					   = []
          while self:
               result.append(self.pop()       )

          # Pushing items back to the heap not to clear it.
          for item in result:
               self.insert(__a       )

          return iter(__a       )







     def  snake_case_			(							self       ,__a       )			->							None:
          snake_case :			List[Any]					   = SkewNode.merge(self._root       ,SkewNode(__a       )       )







     def  snake_case_			(							self       )			->							T | None:
          snake_case :			Tuple					   = self.top()
          snake_case :			int					   = (
              SkewNode.merge(self._root.left       ,self._root.right       ) if self._root else None
          )

          return result







     def  snake_case_			(							self       )			->							T:
          if not self._root:
               raise IndexError("""Can't get top element for the empty heap."""       )
          return self._root.value







     def  snake_case_			(							self       )			->							None:
          snake_case :			Any					   = None


if __name__ == "__main__":
      import doctest

      doctest.testmod()









'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase			:      Tuple			=					{"""configuration_bert_generation""": ["""BertGenerationConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:      Tuple			=					["""BertGenerationTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase			:      List[Any]			=					[
          """BertGenerationDecoder""",
          """BertGenerationEncoder""",
          """BertGenerationPreTrainedModel""",
          """load_tf_weights_in_bert_generation""",
      ]


if TYPE_CHECKING:
      from .configuration_bert_generation import BertGenerationConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_generation import BertGenerationTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert_generation import (
                BertGenerationDecoder,
                BertGenerationEncoder,
                BertGenerationPreTrainedModel,
                load_tf_weights_in_bert_generation,
            )

else:
      import sys

      lowercase			:      Optional[Any]			=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



