'''simple docstring'''






from __future__ import annotations

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

_UpperCAmelCase						:    Tuple     					=					TypeVar('''T''', bound=bool)







class lowercase_					(		Generic[T] ):







   """simple docstring"""

   def __init__(			self			: List[str],   UpperCamelCase__			: T    )     -> None:
        _A								=     value
        _A								=     None
        _A								=     None
   @property
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> T:
        return self._value

   @staticmethod
   def   __UpperCAmelCase      (			UpperCamelCase__			: RandomizedHeapNode[T] | None,   UpperCamelCase__			: RandomizedHeapNode[T] | None    )     -> RandomizedHeapNode[T] | None:
        if not roota:
             return roota

        if not roota:
             return roota

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

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

        _A								=     RandomizedHeapNode.merge(roota.left,   UpperCamelCase__    )

        return roota







class lowercase_					(		Generic[T] ):







   """simple docstring"""

   def __init__(			self			: Optional[int],   UpperCamelCase__			: Iterable[T] | None = ()    )     -> None:
        _A								=     None

        if data:
             for item in data:
                  self.insert(UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Union[str, Any],   UpperCamelCase__			: T    )     -> None:
        _A								=     RandomizedHeapNode.merge(self._root,   RandomizedHeapNode(UpperCamelCase__    )    )
   def   __UpperCAmelCase      (			self			: Any    )     -> T | None:
        _A								=     self.top()

        if self._root is None:
             return None

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

        return result
   def   __UpperCAmelCase      (			self			: str    )     -> T:
        if not self._root:
             raise IndexError('Can\'t get top element for the empty heap.'    )
        return self._root.value
   def   __UpperCAmelCase      (			self			: Optional[int]    )     -> None:
        _A								=     None
   def   __UpperCAmelCase      (			self			: Tuple    )     -> list[Any]:
        _A								=     []
        while self:
             result.append(self.pop()    )

        return result

   def __bool__(			self			: Optional[int]    )     -> bool:
        return self._root is not None


if __name__ == "__main__":
    import doctest

    doctest.testmod()




'''simple docstring'''






import torch

from accelerate import Accelerator, DistributedType







class lowercase_					:







   """simple docstring"""

   def __enter__(			self			: Optional[int]    )     -> Union[str, Any]:
        if self.enabled:
             _A								=     self.model.no_sync()
             self.model_sync_obj.__enter__()

        return self
   def __exit__(			self			: Dict,   UpperCamelCase__			: Any,   UpperCamelCase__			: int,   UpperCamelCase__			: Any    )     -> str:
        if self.enabled:
             # Average all models on exit
             self._sync_and_avg_model_params()
             self.model_sync_obj.__exit__(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__    )
   def __init__(			self			: Optional[int],   UpperCamelCase__			: Accelerator,   UpperCamelCase__			: torch.nn.Module,   UpperCamelCase__			: int,   UpperCamelCase__			: bool = True    )     -> str:
        if accelerator.distributed_type not in [
            DistributedType.NO,
            DistributedType.MULTI_CPU,
            DistributedType.MULTI_GPU,
        ]:
             raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'    )
        _A								=     enabled and accelerator.distributed_type != DistributedType.NO
        _A								=     0
        if self.enabled:
             _A								=     accelerator
             _A								=     model
             _A								=     local_sgd_steps
   def   __UpperCAmelCase      (			self			: Any    )     -> int:
        self.num_steps += 1
        if not self.enabled:
             return

        if self.num_steps % self.local_sgd_steps == 0:
             self._sync_and_avg_model_params()

   def   __UpperCAmelCase      (			self			: Dict    )     -> int:
        self.accelerator.wait_for_everyone()
        with self.accelerator.autocast():
             for param in self.model.parameters():
                  _A								=     self.accelerator.reduce(param.data,   reduction='mean'    )



