




"""simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment





def 						UpperCAmelCase				(			)  ->     Tuple:
			__lowerCamelCase					:	List[Any]	       =				torch.nn.Linear(2							,				4     )
			__lowerCamelCase					:	Optional[Any]	       =				torch.optim.AdamW(model.parameters()							,				lr=1.0     )
			__lowerCamelCase					:	Dict	       =				torch.optim.lr_scheduler.OneCycleLR(A__							,				max_lr=0.01							,				steps_per_epoch=2							,				epochs=1     )
			__lowerCamelCase					:	Union[str, Any]	       =				DataLoader(TensorDataset(torch.tensor([1, 2, 3]     )     )     )
			__lowerCamelCase					:	int	       =				DataLoader(TensorDataset(torch.tensor([4, 5, 6]     )     )     )

			return model, optimizer, scheduler, train_dl, valid_dl





def 						UpperCAmelCase				(			A__:    Any     )  ->     Optional[int]:
			return (model.weight.abs().sum() + model.bias.abs().sum()).item()





def 						UpperCAmelCase				(			A__:    Optional[Any]     )  ->     str:
			__lowerCamelCase					:	List[Any]	       =				torch.nn.Linear(*tuple(model.weight.T.shape     )     ).state_dict()
			model.load_state_dict(A__     )






class __lowercase(      lowercase__				):




					'''simple docstring'''


					@require_cuda
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				Accelerator()
								assert PartialState._shared_state["_cpu"] is False
								assert PartialState._shared_state["device"].type == "cuda"
								with self.assertRaises(__a     ):
											__lowerCamelCase					:	List[str]	       =				Accelerator(cpu=__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				Accelerator()
								__lowerCamelCase					:	Union[str, Any]	       =				GradientState()
								assert state.num_steps == 1
								__lowerCamelCase					:	List[str]	       =				4
								assert state.num_steps == 4

								assert state.sync_gradients is True
								__lowerCamelCase					:	Optional[Any]	       =				False
								assert state.sync_gradients is False
								GradientState._reset_state()
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				Accelerator()
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	str	       =				create_components()

								(
								    (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      (
								    __lowerCamelCase
								)       ,      
								)					:	str	       =				accelerator.prepare(__a						,	__a						,	__a						,	__a						,	__a     )

								self.assertTrue(prepared_model in accelerator._models     )
								self.assertTrue(prepared_optimizer in accelerator._optimizers     )
								self.assertTrue(prepared_scheduler in accelerator._schedulers     )
								self.assertTrue(prepared_train_dl in accelerator._dataloaders     )
								self.assertTrue(prepared_valid_dl in accelerator._dataloaders     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				Accelerator()
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Dict	       =				create_components()
								accelerator.prepare(__a						,	__a						,	__a						,	__a						,	__a     )
								accelerator.free_memory()

								self.assertTrue(len(accelerator._models     ) == 0     )
								self.assertTrue(len(accelerator._optimizers     ) == 0     )
								self.assertTrue(len(accelerator._schedulers     ) == 0     )
								self.assertTrue(len(accelerator._dataloaders     ) == 0     )
					def     snake_case_				(    self     ):
								PartialState._reset_state()

								# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
								def noop(*__a						,	**__a     ):
											pass

								with patch('torch.cuda.set_device'						,	__a     ), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64'     ):
											__lowerCamelCase					:	Tuple	       =				Accelerator()
											self.assertEqual(str(accelerator.state.device     )						,	'cuda:64'     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				Accelerator()
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	int	       =				create_components()
								accelerator.prepare(__a						,	__a						,	__a						,	__a						,	__a     )

								__lowerCamelCase					:	List[Any]	       =				get_signature(__a     )

								with tempfile.TemporaryDirectory() as tmpdirname:
											accelerator.save_state(__a     )

											# make sure random weights don't match
											load_random_weights(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) > 1E-3     )

											# make sure loaded weights match
											accelerator.load_state(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) < 1E-3     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				Accelerator()
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	int	       =				create_components()
								accelerator.prepare(__a						,	__a						,	__a						,	__a						,	__a     )

								__lowerCamelCase					:	int	       =				get_signature(__a     )

								# saving hook
								def save_config(__a						,	__a						,	__a     ):
											__lowerCamelCase					:	Tuple	       =				{'class_name': models[0].__class__.__name__}

											with open(os.path.join(__a						,	'data.json'     )						,	'w'     ) as f:
														json.dump(__a						,	__a     )

        # loading hook
								def load_config(__a						,	__a     ):
											with open(os.path.join(__a						,	'data.json'     )						,	'r'     ) as f:
														__lowerCamelCase					:	str	       =				json.load(__a     )

											__lowerCamelCase					:	Optional[int]	       =				config['class_name']

								__lowerCamelCase					:	Dict	       =				accelerator.register_save_state_pre_hook(__a     )
								__lowerCamelCase					:	Optional[int]	       =				accelerator.register_load_state_pre_hook(__a     )

								with tempfile.TemporaryDirectory() as tmpdirname:
											accelerator.save_state(__a     )

											# make sure random weights don't match with hooks
											load_random_weights(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) > 1E-3     )

											# random class name to verify correct one is loaded
											__lowerCamelCase					:	List[str]	       =				'random'

											# make sure loaded weights match with hooks
											accelerator.load_state(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) < 1E-3     )

											# mode.class_name is loaded from config
											self.assertTrue(model.class_name == model.__class__.__name__     )

								# remove hooks
								save_hook.remove()
								load_hook.remove()

								with tempfile.TemporaryDirectory() as tmpdirname:
											accelerator.save_state(__a     )

											# make sure random weights don't match with hooks removed
											load_random_weights(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) > 1E-3     )

											# random class name to verify correct one is loaded
											__lowerCamelCase					:	Optional[int]	       =				'random'

											# make sure loaded weights match with hooks removed
											accelerator.load_state(__a     )
											self.assertTrue(abs(model_signature - get_signature(__a     )     ) < 1E-3     )

											# mode.class_name is NOT loaded from config
											self.assertTrue(model.class_name != model.__class__.__name__     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Dict	       =				Accelerator()
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				create_components()
								__lowerCamelCase					:	Union[str, Any]	       =				None

								# This should work
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				accelerator.prepare(
								    __a						,	__a						,	__a						,	__a						,	__a						,	__a     )
								self.assertTrue(dummy_obj is None     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				Accelerator()
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	List[str]	       =				create_components()
								__lowerCamelCase					:	Union[str, Any]	       =				[1, 2, 3]

								# This should work
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Dict	       =				accelerator.prepare(
								    __a						,	__a						,	__a						,	__a						,	__a						,	__a     )
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Dummy object should have `_is_accelerate_prepared` set to `True`'						,	)
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Model is missing `_is_accelerator_prepared` or is set to `False`'						,	)
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Optimizer is missing `_is_accelerator_prepared` or is set to `False`'						,	)
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Scheduler is missing `_is_accelerator_prepared` or is set to `False`'						,	)
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'						,	)
								self.assertEqual(
								    getattr(__a						,	'_is_accelerate_prepared'						,	__a     )						,	__a						,	'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'						,	)
					@slow
					@require_bnb
					def     snake_case_				(    self     ):
								from transformers import AutoModelForCausalLM

								__lowerCamelCase					:	Any	       =				AutoModelForCausalLM.from_pretrained(
								    'EleutherAI/gpt-neo-125m'						,	load_in_abit=__a						,	device_map={'': 0}						,	)
								__lowerCamelCase					:	Optional[Any]	       =				Accelerator()

								# This should work
								__lowerCamelCase					:	Any	       =				accelerator.prepare(__a     )
					@slow
					@require_bnb
					def     snake_case_				(    self     ):
								from transformers import AutoModelForCausalLM

								__lowerCamelCase					:	Tuple	       =				Accelerator()

								with init_empty_weights():
											__lowerCamelCase					:	Dict	       =				AutoModelForCausalLM.from_pretrained(
											    'EleutherAI/gpt-neo-125m'						,	)
											model.tie_weights()
											__lowerCamelCase					:	List[Any]	       =				infer_auto_device_map(__a     )
											__lowerCamelCase					:	Any	       =				'cpu'

								__lowerCamelCase					:	Any	       =				AutoModelForCausalLM.from_pretrained(
								    'EleutherAI/gpt-neo-125m'						,	device_map=__a						,	load_in_abit=__a						,	llm_inta_enable_fpaa_cpu_offload=__a     )

								# This should not work and get value error
								with self.assertRaises(__a     ):
											__lowerCamelCase					:	Optional[int]	       =				accelerator.prepare(__a     )
					@slow
					@require_bnb
					@require_multi_gpu
					def     snake_case_				(    self     ):
								from transformers import AutoModelForCausalLM

								__lowerCamelCase					:	Optional[Any]	       =				{'distributed_type': DistributedType.MULTI_GPU}

								with init_empty_weights():
											__lowerCamelCase					:	Union[str, Any]	       =				AutoModelForCausalLM.from_pretrained(
											    'EleutherAI/gpt-neo-125m'						,	)
											model.tie_weights()
											__lowerCamelCase					:	Any	       =				infer_auto_device_map(__a     )
											__lowerCamelCase					:	List[str]	       =				1

								__lowerCamelCase					:	Dict	       =				AutoModelForCausalLM.from_pretrained(
								    'EleutherAI/gpt-neo-125m'						,	load_in_abit=__a						,	device_map=__a						,	)
								__lowerCamelCase					:	Dict	       =				Accelerator()

								# This should not work and get value error
								with self.assertRaises(__a     ):
											__lowerCamelCase					:	Optional[Any]	       =				accelerator.prepare(__a     )

								PartialState._reset_state()
					@slow
					@require_bnb
					@require_multi_gpu
					def     snake_case_				(    self     ):
								from transformers import AutoModelForCausalLM

								with init_empty_weights():
											__lowerCamelCase					:	Optional[int]	       =				AutoModelForCausalLM.from_pretrained(
											    'EleutherAI/gpt-neo-125m'						,	)
											__lowerCamelCase					:	Dict	       =				infer_auto_device_map(__a     )
											__lowerCamelCase					:	Any	       =				1

								__lowerCamelCase					:	Optional[Any]	       =				AutoModelForCausalLM.from_pretrained(
								    'EleutherAI/gpt-neo-125m'						,	load_in_abit=__a						,	device_map=__a						,	)
								__lowerCamelCase					:	Optional[Any]	       =				Accelerator()

								# This should work
								__lowerCamelCase					:	List[str]	       =				accelerator.prepare(__a     )





					@require_cuda
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				torch.nn.Linear(10						,	10     )
								__lowerCamelCase					:	Any	       =				torch.optim.SGD(model.parameters()						,	lr=0.01     )
								__lowerCamelCase					:	Tuple	       =				Accelerator(cpu=__a     )
								__lowerCamelCase					:	Any	       =				accelerator.prepare(__a     )









"""simple docstring"""
import math





def 						UpperCAmelCase				(			A__:    int = 100     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				sum(i * i for i in range(1							,				n + 1     )     )
			__lowerCamelCase					:	Union[str, Any]	       =				int(math.pow(sum(range(1							,				n + 1     )     )							,				2     )     )
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
					print(F"""{solution() = }""")



