



'''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  __lowerCAmelCase ():
		_UpperCAmelCase		:       Optional[Any]								=      torch.nn.Linear(2      , 4   )
		_UpperCAmelCase		:       List[str]								=      torch.optim.AdamW(model.parameters()      , lr=1.0   )
		_UpperCAmelCase		:       Optional[Any]								=      torch.optim.lr_scheduler.OneCycleLR(__lowerCAmelCase      , max_lr=0.0_1      , steps_per_epoch=2      , epochs=1   )
		_UpperCAmelCase		:       Dict								=      DataLoader(TensorDataset(torch.tensor([1, 2, 3]   )   )   )
		_UpperCAmelCase		:       str								=      DataLoader(TensorDataset(torch.tensor([4, 5, 6]   )   )   )

		return model, optimizer, scheduler, train_dl, valid_dl






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       Optional[int]								=      torch.nn.Linear(*tuple(model.weight.T.shape   )   ).state_dict()
		model.load_state_dict(__lowerCAmelCase   )






class 	lowerCAmelCase__			(			UpperCAmelCase__	):




			@require_cuda
			def 							lowerCAmelCase__    (		self  :     str )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      Accelerator()
					assert PartialState._shared_state["_cpu"] is False
					assert PartialState._shared_state["device"].type == "cuda"
					with self.assertRaises(lowerCamelCase__ ):
							_UpperCAmelCase		:       int								=      Accelerator(cpu=lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      Accelerator()
					_UpperCAmelCase		:       List[Any]								=      GradientState()
					assert state.num_steps == 1
					_UpperCAmelCase		:       Optional[Any]								=      4
					assert state.num_steps == 4

					assert state.sync_gradients is True
					_UpperCAmelCase		:       List[str]								=      False
					assert state.sync_gradients is False
					GradientState._reset_state()




			def 							lowerCAmelCase__    (		self  :     str )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      Accelerator()
					_UpperCAmelCase		:       int								=      create_components()

					(
					    _UpperCAmelCase    
					)		:       Dict								=      accelerator.prepare(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )

					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 							lowerCAmelCase__    (		self  :     Dict )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      Accelerator()
					_UpperCAmelCase		:       int								=      create_components()
					accelerator.prepare(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )
					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 							lowerCAmelCase__    (		self  :     int )   ->Optional[Any]:



					'''simple docstring'''







					PartialState._reset_state()

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

					with patch("torch.cuda.set_device"	,  lowerCamelCase__ ), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64" ):
							_UpperCAmelCase		:       Optional[int]								=      Accelerator()
							self.assertEqual(str(accelerator.state.device )	,  "cuda:64" )




			def 							lowerCAmelCase__    (		self  :     Tuple )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      Accelerator()
					_UpperCAmelCase		:       Dict								=      create_components()
					accelerator.prepare(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )

					_UpperCAmelCase		:       Dict								=      get_signature(lowerCamelCase__ )

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

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

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




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



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      Accelerator()
					_UpperCAmelCase		:       Optional[Any]								=      create_components()
					accelerator.prepare(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )

					_UpperCAmelCase		:       Dict								=      get_signature(lowerCamelCase__ )

					# saving hook
					def save_config(lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     Dict ):
							_UpperCAmelCase		:       Union[str, Any]								=      {"class_name": models[0].__class__.__name__}

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

        # loading hook
					def load_config(lowerCamelCase__  :     int	,  lowerCamelCase__  :     int ):
							with open(os.path.join(lowerCamelCase__	,  "data.json" )	,  "r" ) as f:
									_UpperCAmelCase		:       List[str]								=      json.load(lowerCamelCase__ )

							_UpperCAmelCase		:       List[Any]								=      config["class_name"]

					_UpperCAmelCase		:       List[Any]								=      accelerator.register_save_state_pre_hook(lowerCamelCase__ )
					_UpperCAmelCase		:       List[Any]								=      accelerator.register_load_state_pre_hook(lowerCamelCase__ )

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

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

							# random class name to verify correct one is loaded
							_UpperCAmelCase		:       List[Any]								=      "random"

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

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

							# random class name to verify correct one is loaded
							_UpperCAmelCase		:       int								=      "random"

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

							# mode.class_name is NOT loaded from config
							self.assertTrue(model.class_name != model.__class__.__name__ )




			def 							lowerCAmelCase__    (		self  :     str )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      Accelerator()
					_UpperCAmelCase		:       Optional[int]								=      create_components()
					_UpperCAmelCase		:       List[str]								=      None

					# This should work
					_UpperCAmelCase		:       List[str]								=      accelerator.prepare(
					    lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )
					self.assertTrue(dummy_obj is None )




			def 							lowerCAmelCase__    (		self  :     str )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      Accelerator()
					_UpperCAmelCase		:       Optional[Any]								=      create_components()
					_UpperCAmelCase		:       List[Any]								=      [1, 2, 3]

					# This should work
					_UpperCAmelCase		:       Union[str, Any]								=      accelerator.prepare(
					    lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )
					self.assertEqual(
					    getattr(lowerCamelCase__	,  "_is_accelerate_prepared"	,  lowerCamelCase__ )	,  lowerCamelCase__	,  "Dummy object should have `_is_accelerate_prepared` set to `True`"	,  )
					self.assertEqual(
					    getattr(lowerCamelCase__	,  "_is_accelerate_prepared"	,  lowerCamelCase__ )	,  lowerCamelCase__	,  "Model is missing `_is_accelerator_prepared` or is set to `False`"	,  )
					self.assertEqual(
					    getattr(lowerCamelCase__	,  "_is_accelerate_prepared"	,  lowerCamelCase__ )	,  lowerCamelCase__	,  "Optimizer is missing `_is_accelerator_prepared` or is set to `False`"	,  )
					self.assertEqual(
					    getattr(lowerCamelCase__	,  "_is_accelerate_prepared"	,  lowerCamelCase__ )	,  lowerCamelCase__	,  "Scheduler is missing `_is_accelerator_prepared` or is set to `False`"	,  )
					self.assertEqual(
					    getattr(lowerCamelCase__	,  "_is_accelerate_prepared"	,  lowerCamelCase__ )	,  lowerCamelCase__	,  "Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"	,  )
					self.assertEqual(
					    getattr(lowerCamelCase__	,  "_is_accelerate_prepared"	,  lowerCamelCase__ )	,  lowerCamelCase__	,  "Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"	,  )




			@slow
			@require_bnb
			def 							lowerCAmelCase__    (		self  :     Any )   ->Union[str, Any]:



					'''simple docstring'''







					from transformers import AutoModelForCausalLM

					_UpperCAmelCase		:       Tuple								=      AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"	,  load_in_abit=lowerCamelCase__	,  device_map={"": 0}	,  )
					_UpperCAmelCase		:       List[str]								=      Accelerator()

					# This should work
					_UpperCAmelCase		:       Optional[int]								=      accelerator.prepare(lowerCamelCase__ )




			@slow
			@require_bnb
			def 							lowerCAmelCase__    (		self  :     int )   ->Tuple:



					'''simple docstring'''







					from transformers import AutoModelForCausalLM

					_UpperCAmelCase		:       Optional[Any]								=      Accelerator()

					with init_empty_weights():
							_UpperCAmelCase		:       Optional[Any]								=      AutoModelForCausalLM.from_pretrained(
							    "EleutherAI/gpt-neo-125m"	,  )
							model.tie_weights()
							_UpperCAmelCase		:       str								=      infer_auto_device_map(lowerCamelCase__ )
							_UpperCAmelCase		:       Any								=      "cpu"

					_UpperCAmelCase		:       Tuple								=      AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"	,  device_map=lowerCamelCase__	,  load_in_abit=lowerCamelCase__	,  llm_inta_enable_fpaa_cpu_offload=lowerCamelCase__ )

					# This should not work and get value error
					with self.assertRaises(lowerCamelCase__ ):
							_UpperCAmelCase		:       Dict								=      accelerator.prepare(lowerCamelCase__ )




			@slow
			@require_bnb
			@require_multi_gpu
			def 							lowerCAmelCase__    (		self  :     List[str] )   ->Optional[Any]:



					'''simple docstring'''







					from transformers import AutoModelForCausalLM

					_UpperCAmelCase		:       Tuple								=      {"distributed_type": DistributedType.MULTI_GPU}

					with init_empty_weights():
							_UpperCAmelCase		:       List[Any]								=      AutoModelForCausalLM.from_pretrained(
							    "EleutherAI/gpt-neo-125m"	,  )
							model.tie_weights()
							_UpperCAmelCase		:       int								=      infer_auto_device_map(lowerCamelCase__ )
							_UpperCAmelCase		:       List[Any]								=      1

					_UpperCAmelCase		:       Any								=      AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"	,  load_in_abit=lowerCamelCase__	,  device_map=lowerCamelCase__	,  )
					_UpperCAmelCase		:       Union[str, Any]								=      Accelerator()

					# This should not work and get value error
					with self.assertRaises(lowerCamelCase__ ):
							_UpperCAmelCase		:       Union[str, Any]								=      accelerator.prepare(lowerCamelCase__ )

					PartialState._reset_state()




			@slow
			@require_bnb
			@require_multi_gpu
			def 							lowerCAmelCase__    (		self  :     int )   ->Optional[int]:



					'''simple docstring'''







					from transformers import AutoModelForCausalLM

					with init_empty_weights():
							_UpperCAmelCase		:       Union[str, Any]								=      AutoModelForCausalLM.from_pretrained(
							    "EleutherAI/gpt-neo-125m"	,  )
							_UpperCAmelCase		:       Tuple								=      infer_auto_device_map(lowerCamelCase__ )
							_UpperCAmelCase		:       Dict								=      1

					_UpperCAmelCase		:       Optional[int]								=      AutoModelForCausalLM.from_pretrained(
					    "EleutherAI/gpt-neo-125m"	,  load_in_abit=lowerCamelCase__	,  device_map=lowerCamelCase__	,  )
					_UpperCAmelCase		:       Optional[int]								=      Accelerator()

					# This should work
					_UpperCAmelCase		:       int								=      accelerator.prepare(lowerCamelCase__ )






			@require_cuda
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      torch.nn.Linear(10	,  10 )
					_UpperCAmelCase		:       List[str]								=      torch.optim.SGD(model.parameters()	,  lr=0.0_1 )
					_UpperCAmelCase		:       int								=      Accelerator(cpu=lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      accelerator.prepare(lowerCamelCase__ )







'''simple docstring'''


import os






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       List[Any]								=      os.path.join(os.path.dirname(__lowerCAmelCase   )      , "num.txt"   )
		with open(__lowerCAmelCase   ) as file_hand:
				return str(sum(int(__lowerCAmelCase   ) for line in file_hand   )   )[:10]


if __name__ == "__main__":
						print(solution())



