


"""simple docstring"""


import sys




def   snake_case		(     UpperCamelCase__       :						Any    )   ->      Optional[int]:
      lowerCamelCase					:					List[str]        =	len(UpperCamelCase__    )
      lowerCamelCase					:					str        =	[[0 for x in range(UpperCamelCase__    )] for x in range(UpperCamelCase__    )]
      lowerCamelCase					:					Optional[Any]        =	[[0 for x in range(UpperCamelCase__    )] for x in range(UpperCamelCase__    )]

      for chain_length in range(2			,				UpperCamelCase__    ):
            for a in range(1			,				n - chain_length + 1    ):
                  lowerCamelCase					:					Optional[int]        =	a + chain_length - 1

                  lowerCamelCase					:					Dict        =	sys.maxsize
                  for c in range(UpperCamelCase__			,				UpperCamelCase__    ):
                        lowerCamelCase					:					Dict        =	(
                            matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
                        )
                        if cost < matrix[a][b]:
                              lowerCamelCase					:					Any        =	cost
                              lowerCamelCase					:					int        =	c
      return matrix, sol




def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						Optional[int]			,				UpperCamelCase__       :						List[Any]    )   ->      Optional[Any]:
      if i == j:
            print("""A""" + str(UpperCamelCase__    )			,				end=""" """    )
      else:
            print("""("""			,				end=""" """    )
            print_optiomal_solution(UpperCamelCase__			,				UpperCamelCase__			,				optimal_solution[i][j]    )
            print_optiomal_solution(UpperCamelCase__			,				optimal_solution[i][j] + 1			,				UpperCamelCase__    )
            print(""")"""			,				end=""" """    )




def   snake_case		(     )   ->      int:
      lowerCamelCase					:					List[Any]        =	[30, 35, 15, 5, 10, 20, 25]
      lowerCamelCase					:					Dict        =	len(UpperCamelCase__    )
      # Size of matrix created from above array will be
      # 30*35 35*15 15*5 5*10 10*20 20*25
      lowerCamelCase					:					Any        =	matrix_chain_order(UpperCamelCase__    )

      print("""No. of Operation required: """ + str(matrix[1][n - 1]    )    )
      print_optiomal_solution(UpperCamelCase__			,				1			,				n - 1    )


if __name__ == "__main__":
   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 			A__	(						nn.Module):


     """simple docstring"""

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







     def 		a__						(	self:						List[str]     ,		__a:						List[Any]	)->    Optional[Any]:
           return self.lineara(self.batchnorm(self.lineara(__a	)	)	)


class 			A__	(						__lowercase):


     """simple docstring"""

     def 		a__						(	self:						Tuple     ,		__a:						int     ,		*__a:						Any     ,		**__a:						Tuple	)->    Tuple:
           return (args[0] + 1,) + args[1:], kwargs


class 			A__	(						__lowercase):


     """simple docstring"""

     def 		a__						(	self:						Optional[int]     ,		__a:						List[str]     ,		__a:						List[Any]	)->    List[str]:
           return output + 1


class 			A__	(						unittest.TestCase):


     """simple docstring"""

     def 		a__						(	self:						int	)->    str:
           lowerCamelCase					:					List[str]        =	ModelForTest()
           lowerCamelCase					:					Dict        =	ModelHook()

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

     def 		a__						(	self:						int	)->    str:
           lowerCamelCase					:					List[str]        =	ModelForTest()
           lowerCamelCase					:					Union[str, Any]        =	ModelHook()

           add_hook_to_module(__a     ,		__a	)
           add_hook_to_module(__a     ,		__a     ,		append=__a	)

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

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

           lowerCamelCase					:					List[Any]        =	PreForwardHook()
           add_hook_to_module(__a     ,		__a	)
           lowerCamelCase					:					Optional[int]        =	test_model(__a	)
           self.assertTrue(torch.allclose(__a     ,		__a     ,		atol=1e-5	)	)

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

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

           lowerCamelCase					:					Optional[Any]        =	test_model(__a	)
           assert torch.allclose(__a     ,		__a     ,		atol=1e-5	)

     def 		a__						(	self:						Any	)->    Optional[int]:
           lowerCamelCase					:					str        =	ModelForTest()
           lowerCamelCase					:					List[str]        =	torch.randn(2     ,		3	)
           lowerCamelCase					:					int        =	test_model(__a	)

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

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

           # You need to use the sequential hook to chain two or more hooks
           lowerCamelCase					:					Union[str, Any]        =	SequentialHook(PostForwardHook()     ,		PostForwardHook()	)
           add_hook_to_module(__a     ,		__a	)

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

     def 		a__						(	self:						int	)->    Dict:
           lowerCamelCase					:					List[Any]        =	ModelForTest()
           lowerCamelCase					:					Optional[int]        =	torch.randn(2     ,		3	)
           lowerCamelCase					:					List[str]        =	test_model(__a	)

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

           lowerCamelCase					:					Optional[int]        =	True
           lowerCamelCase					:					Optional[int]        =	test_model(__a	)
           self.assertFalse(outputa.requires_grad	)

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

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

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

           # 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
           lowerCamelCase					:					List[Any]        =	torch.device(hook_kwargs["""execution_device"""]	)
           self.assertEqual(model.batchnorm.running_mean.device     ,		__a	)

           lowerCamelCase					:					Optional[Any]        =	torch.randn(2     ,		3	)
           lowerCamelCase					:					Optional[Any]        =	model(__a	)
           self.assertEqual(output.device     ,		__a	)

           # 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
           lowerCamelCase					:					Any        =	{
               """execution_device""": 0 if torch.cuda.is_available() else """cpu""",
               """offload""": True,
               """offload_buffers""": True,
           }

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

           # 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"""	)	)

           lowerCamelCase					:					int        =	torch.randn(2     ,		3	)
           lowerCamelCase					:					Optional[int]        =	model(__a	)
           self.assertEqual(output.device     ,		__a	)

           # 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 		a__						(	self:						Any	)->    List[str]:
           lowerCamelCase					:					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
           lowerCamelCase					:					int        =	0 if torch.cuda.is_available() else """cpu"""
           attach_align_device_hook(__a     ,		execution_device=__a     ,		offload=__a	)

           # 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
           lowerCamelCase					:					List[Any]        =	torch.device(__a	)
           self.assertEqual(model.batchnorm.running_mean.device     ,		__a	)

           lowerCamelCase					:					Dict        =	torch.randn(2     ,		3	)
           lowerCamelCase					:					Optional[Any]        =	model(__a	)
           self.assertEqual(output.device     ,		__a	)

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

           # 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"""	)	)

           lowerCamelCase					:					Optional[int]        =	torch.randn(2     ,		3	)
           lowerCamelCase					:					int        =	model(__a	)
           self.assertEqual(output.device     ,		__a	)

           # Removing hooks loads back the weights in the model.
           remove_hook_from_submodules(__a	)
           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 		a__						(	self:						Optional[Any]	)->    List[Any]:
           lowerCamelCase					:					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
           lowerCamelCase					:					Any        =	0 if torch.cuda.is_available() else """cpu"""
           attach_align_device_hook(
               __a     ,		execution_device=__a     ,		offload=__a     ,		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
           lowerCamelCase					:					List[Any]        =	torch.device(__a	)
           self.assertEqual(model.batchnorm.running_mean.device     ,		__a	)

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

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

           # 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"""	)	)

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

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




