






'''simple docstring'''



from __future__ import annotations

from random import random
class 							__SCREAMING_SNAKE_CASE       :




      def __init__(  self ,   lowerCamelCase = None      )							->Tuple:



         '''simple docstring'''
         __a				     =    value
         __a				     =    random()
         __a				     =    None
         __a				     =    None




      def __repr__(  self      )							->str:



         '''simple docstring'''
         from pprint import pformat

         if self.left is None and self.right is None:
            return F"""'{self.value}: {self.prior:.5}'"""
         else:
            return pformat(
                {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)} ,   indent=1      )






      def __str__(  self      )							->str:



         '''simple docstring'''
         __a				     =    str(self.value      ) + ' '
         __a				     =    str(self.left or ''      )
         __a				     =    str(self.right or ''      )
         return value + left + right







def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Node | None,					SCREAMING_SNAKE_CASE__:	int    )  ->			tuple[Node | None, Node | None]:

   """simple docstring"""
   if root is None:  # None tree is split into 2 Nones
      return None, None
   elif root.value is None:
      return None, None
   else:
      if value < root.value:
         __a     ,			__a				     =    split(root.left,					SCREAMING_SNAKE_CASE__    )
         return left, root
      else:
         __a     ,			__a				     =    split(root.right,					SCREAMING_SNAKE_CASE__    )
         return root, right



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Node | None,					SCREAMING_SNAKE_CASE__:	Node | None    )  ->			Node | None:

   """simple docstring"""
   if (not left) or (not right):  # If one node is None, return the other
      return left or right
   elif left.prior < right.prior:
      __a				     =    merge(left.right,					SCREAMING_SNAKE_CASE__    )
      return left
   else:
      __a				     =    merge(SCREAMING_SNAKE_CASE__,					right.left    )
      return right



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Node | None,					SCREAMING_SNAKE_CASE__:	int    )  ->			Node | None:

   """simple docstring"""
   __a				     =    Node(SCREAMING_SNAKE_CASE__    )
   __a     ,			__a				     =    split(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
   return merge(merge(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ),					SCREAMING_SNAKE_CASE__    )



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Node | None,					SCREAMING_SNAKE_CASE__:	int    )  ->			Node | None:

   """simple docstring"""
   __a     ,			__a				     =    split(SCREAMING_SNAKE_CASE__,					value - 1    )
   __a     ,			__a				     =    split(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
   return merge(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Node | None    )  ->			None:

   """simple docstring"""
   if not root:  # None
      return
   else:
      inorder(root.left    )
      print(root.value,					end=','    )
      inorder(root.right    )



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Node | None,					SCREAMING_SNAKE_CASE__:	str    )  ->			Node | None:

   """simple docstring"""
   for arg in args.split():
      if arg[0] == "+":
         __a				     =    insert(SCREAMING_SNAKE_CASE__,					int(arg[1:]    )    )

      elif arg[0] == "-":
         __a				     =    erase(SCREAMING_SNAKE_CASE__,					int(arg[1:]    )    )

      else:
         print('Unknown command'    )

   return root



def    __UpperCAmelCase	(				)  ->			None:

   """simple docstring"""
   __a				     =    None
   print(
       'enter numbers to create a tree, + value to add value into treap, '
       '- value to erase all nodes with value. \'q\' to quit. '    )

   __a				     =    input()
   while args != "q":
      __a				     =    interact_treap(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
      print(SCREAMING_SNAKE_CASE__    )
      __a				     =    input()

   print('good by!'    )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()







'''simple docstring'''



import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class 							__SCREAMING_SNAKE_CASE       (							nn.Module     ):




      def __init__(  self      )							->Dict:



         '''simple docstring'''
         super().__init__()
         __a				     =    nn.Linear(3 ,   4      )
         __a				     =    nn.BatchNormad(4      )
         __a				     =    nn.Linear(4 ,   5      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->str:



         '''simple docstring'''
         return self.lineara(self.batchnorm(self.lineara(lowerCamelCase      )      )      )







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   *lowerCamelCase ,   **lowerCamelCase      )							->str:



         '''simple docstring'''
         return (args[0] + 1,) + args[1:], kwargs







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->int:



         '''simple docstring'''
         return output + 1







class 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         __a				     =    ModelForTest()
         __a				     =    ModelHook()

         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         self.assertEqual(test_model._hf_hook ,   lowerCamelCase      )
         self.assertTrue(hasattr(lowerCamelCase ,   '_old_forward'      )      )

         # Check adding the hook did not change the name or the signature
         self.assertEqual(test_model.forward.__name__ ,   'forward'      )
         self.assertListEqual(list(inspect.signature(test_model.forward      ).parameters      ) ,   ['x']      )

         remove_hook_from_module(lowerCamelCase      )
         self.assertFalse(hasattr(lowerCamelCase ,   '_hf_hook'      )      )
         self.assertFalse(hasattr(lowerCamelCase ,   '_old_forward'      )      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    ModelForTest()
         __a				     =    ModelHook()

         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase ,   append=lowerCamelCase      )

         self.assertEqual(isinstance(test_model._hf_hook ,   lowerCamelCase      ) ,   lowerCamelCase      )
         self.assertEqual(len(test_model._hf_hook.hooks      ) ,   2      )
         self.assertTrue(hasattr(lowerCamelCase ,   '_old_forward'      )      )

         # Check adding the hook did not change the name or the signature
         self.assertEqual(test_model.forward.__name__ ,   'forward'      )
         self.assertListEqual(list(inspect.signature(test_model.forward      ).parameters      ) ,   ['x']      )

         remove_hook_from_module(lowerCamelCase      )
         self.assertFalse(hasattr(lowerCamelCase ,   '_hf_hook'      )      )
         self.assertFalse(hasattr(lowerCamelCase ,   '_old_forward'      )      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    ModelForTest()
         __a				     =    torch.randn(2 ,   3      )
         __a				     =    test_model(x + 1      )
         __a				     =    test_model(x + 2      )

         __a				     =    PreForwardHook()
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    test_model(lowerCamelCase      )
         self.assertTrue(torch.allclose(lowerCamelCase ,   lowerCamelCase ,   atol=1e-5      )      )

         # Attaching a hook to a model when it already has one replaces, does not chain
         __a				     =    PreForwardHook()
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    test_model(lowerCamelCase      )
         self.assertTrue(torch.allclose(lowerCamelCase ,   lowerCamelCase ,   atol=1e-5      )      )

         # You need to use the sequential hook to chain two or more hooks
         __a				     =    SequentialHook(PreForwardHook() ,   PreForwardHook()      )
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    test_model(lowerCamelCase      )
         assert torch.allclose(lowerCamelCase ,   lowerCamelCase ,   atol=1e-5      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         __a				     =    ModelForTest()
         __a				     =    torch.randn(2 ,   3      )
         __a				     =    test_model(lowerCamelCase      )

         __a				     =    PostForwardHook()
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    test_model(lowerCamelCase      )
         self.assertTrue(torch.allclose(lowerCamelCase ,   output + 1 ,   atol=1e-5      )      )

         # Attaching a hook to a model when it already has one replaces, does not chain
         __a				     =    PostForwardHook()
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    test_model(lowerCamelCase      )
         self.assertTrue(torch.allclose(lowerCamelCase ,   output + 1 ,   atol=1e-5      )      )

         # You need to use the sequential hook to chain two or more hooks
         __a				     =    SequentialHook(PostForwardHook() ,   PostForwardHook()      )
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )

         __a				     =    test_model(lowerCamelCase      )
         assert torch.allclose(lowerCamelCase ,   output + 2 ,   atol=1e-5      )




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    ModelForTest()
         __a				     =    torch.randn(2 ,   3      )
         __a				     =    test_model(lowerCamelCase      )

         __a				     =    PostForwardHook()
         add_hook_to_module(lowerCamelCase ,   lowerCamelCase      )
         __a				     =    test_model(lowerCamelCase      )
         self.assertTrue(torch.allclose(lowerCamelCase ,   output + 1      )      )
         self.assertTrue(outputa.requires_grad      )

         __a				     =    True
         __a				     =    test_model(lowerCamelCase      )
         self.assertFalse(outputa.requires_grad      )




      @require_multi_gpu
      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    ModelForTest()
         # Everything is on CPU
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )

         # This will move each submodule on different devices
         add_hook_to_module(model.lineara ,   AlignDevicesHook(execution_device=0      )      )
         add_hook_to_module(model.batchnorm ,   AlignDevicesHook(execution_device=0      )      )
         add_hook_to_module(model.lineara ,   AlignDevicesHook(execution_device=1      )      )

         self.assertEqual(model.lineara.weight.device ,   torch.device(0      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device(0      )      )
         self.assertEqual(model.batchnorm.running_mean.device ,   torch.device(0      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device(1      )      )

         # We can still make a forward pass. The input does not need to be on any particular device
         __a				     =    torch.randn(2 ,   3      )
         __a				     =    model(lowerCamelCase      )
         self.assertEqual(output.device ,   torch.device(1      )      )

         # We can add a general hook to put back output on same device as input.
         add_hook_to_module(lowerCamelCase ,   AlignDevicesHook(io_same_device=lowerCamelCase      )      )
         __a				     =    torch.randn(2 ,   3      ).to(0      )
         __a				     =    model(lowerCamelCase      )
         self.assertEqual(output.device ,   torch.device(0      )      )




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    ModelForTest()

         # Everything is on CPU
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )

         # This will move each submodule on different devices
         __a				     =    {'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}

         add_hook_to_module(model.lineara ,   AlignDevicesHook(**lowerCamelCase      )      )
         add_hook_to_module(model.batchnorm ,   AlignDevicesHook(**lowerCamelCase      )      )
         add_hook_to_module(model.lineara ,   AlignDevicesHook(**lowerCamelCase      )      )

         # Parameters have been offloaded, so on the meta device
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         # Buffers are not included in the offload by default, so are on the execution device
         __a				     =    torch.device(hook_kwargs['execution_device']      )
         self.assertEqual(model.batchnorm.running_mean.device ,   lowerCamelCase      )

         __a				     =    torch.randn(2 ,   3      )
         __a				     =    model(lowerCamelCase      )
         self.assertEqual(output.device ,   lowerCamelCase      )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_module(model.lineara      )
         remove_hook_from_module(model.batchnorm      )
         remove_hook_from_module(model.lineara      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )

         # Now test with buffers included in the offload
         __a				     =    {
             'execution_device': 0 if torch.cuda.is_available() else 'cpu',
             'offload': True,
             'offload_buffers': True,
         }

         add_hook_to_module(model.lineara ,   AlignDevicesHook(**lowerCamelCase      )      )
         add_hook_to_module(model.batchnorm ,   AlignDevicesHook(**lowerCamelCase      )      )
         add_hook_to_module(model.lineara ,   AlignDevicesHook(**lowerCamelCase      )      )

         # Parameters have been offloaded, so on the meta device, buffers included
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.batchnorm.running_mean.device ,   torch.device('meta'      )      )

         __a				     =    torch.randn(2 ,   3      )
         __a				     =    model(lowerCamelCase      )
         self.assertEqual(output.device ,   lowerCamelCase      )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_module(model.lineara      )
         remove_hook_from_module(model.batchnorm      )
         remove_hook_from_module(model.lineara      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    ModelForTest()

         # Everything is on CPU
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )

         # This will move each submodule on different devices
         __a				     =    0 if torch.cuda.is_available() else 'cpu'
         attach_align_device_hook(lowerCamelCase ,   execution_device=lowerCamelCase ,   offload=lowerCamelCase      )

         # Parameters have been offloaded, so on the meta device
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         # Buffers are not included in the offload by default, so are on the execution device
         __a				     =    torch.device(lowerCamelCase      )
         self.assertEqual(model.batchnorm.running_mean.device ,   lowerCamelCase      )

         __a				     =    torch.randn(2 ,   3      )
         __a				     =    model(lowerCamelCase      )
         self.assertEqual(output.device ,   lowerCamelCase      )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_submodules(lowerCamelCase      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )

         # Now test with buffers included in the offload
         attach_align_device_hook(lowerCamelCase ,   execution_device=lowerCamelCase ,   offload=lowerCamelCase ,   offload_buffers=lowerCamelCase      )

         # Parameters have been offloaded, so on the meta device, buffers included
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.batchnorm.running_mean.device ,   torch.device('meta'      )      )

         __a				     =    torch.randn(2 ,   3      )
         __a				     =    model(lowerCamelCase      )
         self.assertEqual(output.device ,   lowerCamelCase      )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_submodules(lowerCamelCase      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )






      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         __a				     =    ModelForTest()

         # Everything is on CPU
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )

         # This will move each submodule on different devices
         __a				     =    0 if torch.cuda.is_available() else 'cpu'
         attach_align_device_hook(
             lowerCamelCase ,   execution_device=lowerCamelCase ,   offload=lowerCamelCase ,   weights_map=model.state_dict()      )

         # Parameters have been offloaded, so on the meta device
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         # Buffers are not included in the offload by default, so are on the execution device
         __a				     =    torch.device(lowerCamelCase      )
         self.assertEqual(model.batchnorm.running_mean.device ,   lowerCamelCase      )

         __a				     =    torch.randn(2 ,   3      )
         __a				     =    model(lowerCamelCase      )
         self.assertEqual(output.device ,   lowerCamelCase      )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_submodules(lowerCamelCase      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )

         # Now test with buffers included in the offload
         attach_align_device_hook(
             lowerCamelCase ,   execution_device=lowerCamelCase ,   offload=lowerCamelCase ,   weights_map=model.state_dict() ,   offload_buffers=lowerCamelCase ,   )

         # Parameters have been offloaded, so on the meta device, buffers included
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('meta'      )      )
         self.assertEqual(model.batchnorm.running_mean.device ,   torch.device('meta'      )      )

         __a				     =    torch.randn(2 ,   3      )
         __a				     =    model(lowerCamelCase      )
         self.assertEqual(output.device ,   lowerCamelCase      )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_submodules(lowerCamelCase      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.batchnorm.weight.device ,   torch.device('cpu'      )      )
         self.assertEqual(model.lineara.weight.device ,   torch.device('cpu'      )      )