




"""simple docstring"""

from __future__ import annotations

from dataclasses import dataclass
@dataclass
class 	lowerCamelCase__			:
			a__       :					float
			a__       :					TreeNode | None	    =   None
			a__       :					TreeNode | None	    =   None







def      UpperCamelCase__   (						lowercase__     :   Optional[int]      ):

		# Validation
		def is_valid_tree(lowercase__     :   Dict      ) -> bool:
				if node is None:
						return True

				if not isinstance(UpperCamelCase__				,   UpperCamelCase__      ):
						return False

				try:
						float(node.data      )
				except (TypeError, ValueError):
						return False

				return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

		if not is_valid_tree(UpperCamelCase__      ):
				raise ValueError(
				    "Each node should be type of TreeNode and data should be float."      )

		def is_binary_search_tree_recursive_check(
		    lowercase__     :   Dict				,   lowercase__     :   Tuple				,   lowercase__     :   Optional[Any]      ) -> bool:

				if node is None:
						return True

				return (
				    left_bound < node.data < right_bound
				    and is_binary_search_tree_recursive_check(node.left				,   UpperCamelCase__				,   node.data      )
				    and is_binary_search_tree_recursive_check(
				        node.right				,   node.data				,   UpperCamelCase__      )
				)

		return is_binary_search_tree_recursive_check(UpperCamelCase__				,   -float("inf"      )				,   float("inf"      )      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()









"""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        UpperCamelCase			(	nn.Module      ):


 def __init__(     self		:						Union[str, Any]							)					->       int:
   super().__init__()
   _a  :       Optional[Any]             =	nn.Linear(3							,						4							)
   _a  :       Tuple             =	nn.BatchNormad(4							)
   _a  :       Dict             =	nn.Linear(4							,						5							)







 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						List[str]							)					->       int:
   return self.lineara(self.batchnorm(self.lineara(UpperCAmelCase__							)							)							)







class        UpperCamelCase			(	snake_case_      ):


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Any							,						*UpperCAmelCase__		:						List[str]							,						**UpperCAmelCase__		:						Optional[int]							)					->       Optional[int]:
   return (args[0] + 1,) + args[1:], kwargs







class        UpperCamelCase			(	snake_case_      ):


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						Optional[Any]							)					->       List[str]:
   return output + 1







class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						Dict							)					->       str:
   _a  :       List[Any]             =	ModelForTest()
   _a  :       str             =	ModelHook()

   add_hook_to_module(UpperCAmelCase__							,						UpperCAmelCase__							)
   self.assertEqual(test_model._hf_hook							,						UpperCAmelCase__							)
   self.assertTrue(hasattr(UpperCAmelCase__							,						"""_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(UpperCAmelCase__							)
   self.assertFalse(hasattr(UpperCAmelCase__							,						"""_hf_hook"""							)							)
   self.assertFalse(hasattr(UpperCAmelCase__							,						"""_old_forward"""							)							)


 def _lowercase	(     self		:						Optional[int]							)					->       Optional[int]:
   _a  :       Dict             =	ModelForTest()
   _a  :       Dict             =	ModelHook()

   add_hook_to_module(UpperCAmelCase__							,						UpperCAmelCase__							)
   add_hook_to_module(UpperCAmelCase__							,						UpperCAmelCase__							,						append=UpperCAmelCase__							)

   self.assertEqual(isinstance(test_model._hf_hook							,						UpperCAmelCase__							)							,						UpperCAmelCase__							)
   self.assertEqual(len(test_model._hf_hook.hooks							)							,						2							)
   self.assertTrue(hasattr(UpperCAmelCase__							,						"""_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(UpperCAmelCase__							)
   self.assertFalse(hasattr(UpperCAmelCase__							,						"""_hf_hook"""							)							)
   self.assertFalse(hasattr(UpperCAmelCase__							,						"""_old_forward"""							)							)


 def _lowercase	(     self		:						Dict							)					->       int:
   _a  :       str             =	ModelForTest()
   _a  :       List[Any]             =	torch.randn(2							,						3							)
   _a  :       Optional[Any]             =	test_model(x + 1							)
   _a  :       str             =	test_model(x + 2							)

   _a  :       Union[str, Any]             =	PreForwardHook()
   add_hook_to_module(UpperCAmelCase__							,						UpperCAmelCase__							)
   _a  :       Tuple             =	test_model(UpperCAmelCase__							)
   self.assertTrue(torch.allclose(UpperCAmelCase__							,						UpperCAmelCase__							,						atol=1E-5							)							)

   # Attaching a hook to a model when it already has one replaces, does not chain
   _a  :       int             =	PreForwardHook()
   add_hook_to_module(UpperCAmelCase__							,						UpperCAmelCase__							)
   _a  :       str             =	test_model(UpperCAmelCase__							)
   self.assertTrue(torch.allclose(UpperCAmelCase__							,						UpperCAmelCase__							,						atol=1E-5							)							)

   # You need to use the sequential hook to chain two or more hooks
   _a  :       int             =	SequentialHook(PreForwardHook()							,						PreForwardHook()							)
   add_hook_to_module(UpperCAmelCase__							,						UpperCAmelCase__							)

   _a  :       Tuple             =	test_model(UpperCAmelCase__							)
   assert torch.allclose(UpperCAmelCase__							,						UpperCAmelCase__							,						atol=1E-5							)


 def _lowercase	(     self		:						Tuple							)					->       int:
   _a  :       Tuple             =	ModelForTest()
   _a  :       Union[str, Any]             =	torch.randn(2							,						3							)
   _a  :       Optional[int]             =	test_model(UpperCAmelCase__							)

   _a  :       int             =	PostForwardHook()
   add_hook_to_module(UpperCAmelCase__							,						UpperCAmelCase__							)
   _a  :       List[str]             =	test_model(UpperCAmelCase__							)
   self.assertTrue(torch.allclose(UpperCAmelCase__							,						output + 1							,						atol=1E-5							)							)

   # Attaching a hook to a model when it already has one replaces, does not chain
   _a  :       List[Any]             =	PostForwardHook()
   add_hook_to_module(UpperCAmelCase__							,						UpperCAmelCase__							)
   _a  :       Dict             =	test_model(UpperCAmelCase__							)
   self.assertTrue(torch.allclose(UpperCAmelCase__							,						output + 1							,						atol=1E-5							)							)

   # You need to use the sequential hook to chain two or more hooks
   _a  :       Any             =	SequentialHook(PostForwardHook()							,						PostForwardHook()							)
   add_hook_to_module(UpperCAmelCase__							,						UpperCAmelCase__							)

   _a  :       Optional[int]             =	test_model(UpperCAmelCase__							)
   assert torch.allclose(UpperCAmelCase__							,						output + 2							,						atol=1E-5							)


 def _lowercase	(     self		:						Dict							)					->       Optional[Any]:
   _a  :       Any             =	ModelForTest()
   _a  :       List[Any]             =	torch.randn(2							,						3							)
   _a  :       Dict             =	test_model(UpperCAmelCase__							)

   _a  :       Any             =	PostForwardHook()
   add_hook_to_module(UpperCAmelCase__							,						UpperCAmelCase__							)
   _a  :       List[str]             =	test_model(UpperCAmelCase__							)
   self.assertTrue(torch.allclose(UpperCAmelCase__							,						output + 1							)							)
   self.assertTrue(outputa.requires_grad							)

   _a  :       Any             =	True
   _a  :       Union[str, Any]             =	test_model(UpperCAmelCase__							)
   self.assertFalse(outputa.requires_grad							)


 @require_multi_gpu
 def _lowercase	(     self		:						Optional[Any]							)					->       str:
   _a  :       List[Any]             =	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  :       Optional[int]             =	torch.randn(2							,						3							)
   _a  :       Any             =	model(UpperCAmelCase__							)
   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(UpperCAmelCase__							,						AlignDevicesHook(io_same_device=UpperCAmelCase__							)							)
   _a  :       str             =	torch.randn(2							,						3							).to(0							)
   _a  :       Union[str, Any]             =	model(UpperCAmelCase__							)
   self.assertEqual(output.device							,						torch.device(0							)							)


 def _lowercase	(     self		:						str							)					->       Union[str, Any]:
   _a  :       int             =	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  :       List[Any]             =	{"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}

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

   # 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  :       Dict             =	torch.device(hook_kwargs["""execution_device"""]							)
   self.assertEqual(model.batchnorm.running_mean.device							,						UpperCAmelCase__							)

   _a  :       int             =	torch.randn(2							,						3							)
   _a  :       str             =	model(UpperCAmelCase__							)
   self.assertEqual(output.device							,						UpperCAmelCase__							)

   # 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  :       List[str]             =	{
       """execution_device""": 0 if torch.cuda.is_available() else """cpu""",
       """offload""": True,
       """offload_buffers""": True,
   }

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

   # 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  :       Tuple             =	torch.randn(2							,						3							)
   _a  :       Union[str, Any]             =	model(UpperCAmelCase__							)
   self.assertEqual(output.device							,						UpperCAmelCase__							)

   # 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 _lowercase	(     self		:						Tuple							)					->       List[str]:
   _a  :       str             =	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  :       Union[str, Any]             =	0 if torch.cuda.is_available() else """cpu"""
   attach_align_device_hook(UpperCAmelCase__							,						execution_device=UpperCAmelCase__							,						offload=UpperCAmelCase__							)

   # 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  :       Dict             =	torch.device(UpperCAmelCase__							)
   self.assertEqual(model.batchnorm.running_mean.device							,						UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	torch.randn(2							,						3							)
   _a  :       List[Any]             =	model(UpperCAmelCase__							)
   self.assertEqual(output.device							,						UpperCAmelCase__							)

   # Removing hooks loads back the weights in the model.
   remove_hook_from_submodules(UpperCAmelCase__							)
   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(UpperCAmelCase__							,						execution_device=UpperCAmelCase__							,						offload=UpperCAmelCase__							,						offload_buffers=UpperCAmelCase__							)

   # 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  :       List[str]             =	torch.randn(2							,						3							)
   _a  :       Union[str, Any]             =	model(UpperCAmelCase__							)
   self.assertEqual(output.device							,						UpperCAmelCase__							)

   # Removing hooks loads back the weights in the model.
   remove_hook_from_submodules(UpperCAmelCase__							)
   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 _lowercase	(     self		:						Dict							)					->       str:
   _a  :       Optional[Any]             =	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  :       str             =	0 if torch.cuda.is_available() else """cpu"""
   attach_align_device_hook(
       UpperCAmelCase__							,						execution_device=UpperCAmelCase__							,						offload=UpperCAmelCase__							,						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  :       Union[str, Any]             =	torch.device(UpperCAmelCase__							)
   self.assertEqual(model.batchnorm.running_mean.device							,						UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	torch.randn(2							,						3							)
   _a  :       int             =	model(UpperCAmelCase__							)
   self.assertEqual(output.device							,						UpperCAmelCase__							)

   # Removing hooks loads back the weights in the model.
   remove_hook_from_submodules(UpperCAmelCase__							)
   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(
       UpperCAmelCase__							,						execution_device=UpperCAmelCase__							,						offload=UpperCAmelCase__							,						weights_map=model.state_dict()							,						offload_buffers=UpperCAmelCase__							,						)

   # 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  :       Any             =	torch.randn(2							,						3							)
   _a  :       int             =	model(UpperCAmelCase__							)
   self.assertEqual(output.device							,						UpperCAmelCase__							)

   # Removing hooks loads back the weights in the model.
   remove_hook_from_submodules(UpperCAmelCase__							)
   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"""							)							)

