




from __future__ import annotations

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

a_										=       TypeVar("T", bound=bool)

class 	_lowerCamelCase  (     Generic[T]				):

      """simple docstring"""

      def __init__(       self   :	int	,    snake_case   :	T			):
          __UpperCamelCase											=  value
          __UpperCamelCase											=  None
          __UpperCamelCase											=  None







      @property
      def  snake_case		(       self   :	Dict			):
          return self._value







      @staticmethod
      def  snake_case		(       snake_case   :	RandomizedHeapNode[T] | None	,    snake_case   :	RandomizedHeapNode[T] | None			):
          if not roota:
              return roota

          if not roota:
              return roota

          if roota.value > roota.value:
              __UpperCamelCase						, __UpperCamelCase											=  roota, roota

          if random.choice([True, False]			):
              __UpperCamelCase						, __UpperCamelCase											=  roota.right, roota.left

          __UpperCamelCase											=  RandomizedHeapNode.merge(roota.left	,    snake_case			)

          return roota







class 	_lowerCamelCase  (     Generic[T]				):

      """simple docstring"""

      def __init__(       self   :	Dict	,    snake_case   :	Iterable[T] | None = ()			):
          __UpperCamelCase											=  None

          if data:
              for item in data:
                  self.insert(snake_case			)







      def  snake_case		(       self   :	Any	,    snake_case   :	T			):
          __UpperCamelCase											=  RandomizedHeapNode.merge(self._root	,    RandomizedHeapNode(snake_case			)			)







      def  snake_case		(       self   :	Union[str, Any]			):
          __UpperCamelCase											=  self.top()

          if self._root is None:
              return None

          __UpperCamelCase											=  RandomizedHeapNode.merge(self._root.left	,    self._root.right			)

          return result







      def  snake_case		(       self   :	Tuple			):
          if not self._root:
              raise IndexError('''Can\'t get top element for the empty heap.'''			)
          return self._root.value







      def  snake_case		(       self   :	Optional[int]			):
          __UpperCamelCase											=  None







      def  snake_case		(       self   :	Tuple			):
          __UpperCamelCase											=  []
          while self:
              result.append(self.pop()			)

          return result







      def __bool__(       self   :	str			):
          return self._root is not None


if __name__ == "__main__":
 import doctest

 doctest.testmod()












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,
)


a_										=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       [
     "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MarianForCausalLM",
     "MarianModel",
     "MarianMTModel",
     "MarianPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
 from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_marian import MarianTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_marian import (
      MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarianForCausalLM,
      MarianModel,
      MarianMTModel,
      MarianPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
 import sys

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






