"""simple docstring"""




import os





def 							SCREAMING_SNAKE_CASE					(      )	->				Tuple:
  _lowerCAmelCase						:	Optional[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())





"""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 							SCREAMING_SNAKE_CASE					(      )	->				Tuple:
  _lowerCAmelCase						:	Tuple        =					torch.nn.Linear(2      ,4     )
  _lowerCAmelCase						:	Union[str, Any]        =					torch.optim.AdamW(model.parameters()      ,lr=1.0     )
  _lowerCAmelCase						:	Tuple        =					torch.optim.lr_scheduler.OneCycleLR(_lowerCamelCase      ,max_lr=0.01      ,steps_per_epoch=2      ,epochs=1     )
  _lowerCAmelCase						:	Tuple        =					DataLoader(TensorDataset(torch.tensor([1, 2, 3]     )     )     )
  _lowerCAmelCase						:	List[Any]        =					DataLoader(TensorDataset(torch.tensor([4, 5, 6]     )     )     )

  return model, optimizer, scheduler, train_dl, valid_dl





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Any     )	->				int:
  return (model.weight.abs().sum() + model.bias.abs().sum()).item()





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]     )	->				Any:
  _lowerCAmelCase						:	List[str]        =					torch.nn.Linear(*tuple(model.weight.T.shape     )     ).state_dict()
  model.load_state_dict(_lowerCamelCase     )






class 						__A							(			SCREAMING_SNAKE_CASE_		):






     @require_cuda
     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					Accelerator()
       assert PartialState._shared_state["_cpu"] is False
       assert PartialState._shared_state["device"].type == "cuda"
       with self.assertRaises(a__  ):
         _lowerCAmelCase						:	Tuple        =					Accelerator(cpu=a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					Accelerator()
       _lowerCAmelCase						:	Any        =					GradientState()
       assert state.num_steps == 1
       _lowerCAmelCase						:	Optional[int]        =					4
       assert state.num_steps == 4

       assert state.sync_gradients is True
       _lowerCAmelCase						:	Dict        =					False
       assert state.sync_gradients is False
       GradientState._reset_state()






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Any        =					create_components()

       (
           (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     
       )						:	int        =					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    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	List[Any]        =					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    __A  (		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						:	Dict        =					Accelerator()
         self.assertEqual(str(accelerator.state.device  )    ,					"""cuda:64"""  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	List[str]        =					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    __A  (		self  ):
       _lowerCAmelCase						:	str        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	str        =					create_components()
       accelerator.prepare(a__    ,					a__    ,					a__    ,					a__    ,					a__  )

       _lowerCAmelCase						:	Optional[Any]        =					get_signature(a__  )

       # saving hook
       def save_config(a__    ,					a__    ,					a__  ):
         _lowerCAmelCase						:	Dict        =					{"""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						:	int        =					json.load(a__  )

         _lowerCAmelCase						:	str        =					config["""class_name"""]

       _lowerCAmelCase						:	Union[str, Any]        =					accelerator.register_save_state_pre_hook(a__  )
       _lowerCAmelCase						:	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						:	Dict        =					"""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[Any]        =					"""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    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Any        =					create_components()
       _lowerCAmelCase						:	Any        =					None

       # This should work
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	int        =					accelerator.prepare(
           a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  )
       self.assertTrue(dummy_obj is None  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					Accelerator()
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Any        =					create_components()
       _lowerCAmelCase						:	Optional[int]        =					[1, 2, 3]

       # This should work
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	str        =					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    __A  (		self  ):
       from transformers import AutoModelForCausalLM

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

       # This should work
       _lowerCAmelCase						:	List[Any]        =					accelerator.prepare(a__  )






     @slow
     @require_bnb
     def    __A  (		self  ):
       from transformers import AutoModelForCausalLM

       _lowerCAmelCase						:	Any        =					Accelerator()

       with init_empty_weights():
         _lowerCAmelCase						:	Dict        =					AutoModelForCausalLM.from_pretrained(
             """EleutherAI/gpt-neo-125m"""    ,					)
         model.tie_weights()
         _lowerCAmelCase						:	int        =					infer_auto_device_map(a__  )
         _lowerCAmelCase						:	Optional[Any]        =					"""cpu"""

       _lowerCAmelCase						:	Dict        =					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						:	List[str]        =					accelerator.prepare(a__  )






     @slow
     @require_bnb
     @require_multi_gpu
     def    __A  (		self  ):
       from transformers import AutoModelForCausalLM

       _lowerCAmelCase						:	Dict        =					{"""distributed_type""": DistributedType.MULTI_GPU}

       with init_empty_weights():
         _lowerCAmelCase						:	Optional[Any]        =					AutoModelForCausalLM.from_pretrained(
             """EleutherAI/gpt-neo-125m"""    ,					)
         model.tie_weights()
         _lowerCAmelCase						:	List[str]        =					infer_auto_device_map(a__  )
         _lowerCAmelCase						:	Union[str, Any]        =					1

       _lowerCAmelCase						:	int        =					AutoModelForCausalLM.from_pretrained(
           """EleutherAI/gpt-neo-125m"""    ,					load_in_abit=a__    ,					device_map=a__    ,					)
       _lowerCAmelCase						:	Tuple        =					Accelerator()

       # This should not work and get value error
       with self.assertRaises(a__  ):
         _lowerCAmelCase						:	Optional[int]        =					accelerator.prepare(a__  )

       PartialState._reset_state()






     @slow
     @require_bnb
     @require_multi_gpu
     def    __A  (		self  ):
       from transformers import AutoModelForCausalLM

       with init_empty_weights():
         _lowerCAmelCase						:	Dict        =					AutoModelForCausalLM.from_pretrained(
             """EleutherAI/gpt-neo-125m"""    ,					)
         _lowerCAmelCase						:	int        =					infer_auto_device_map(a__  )
         _lowerCAmelCase						:	List[Any]        =					1

       _lowerCAmelCase						:	Optional[Any]        =					AutoModelForCausalLM.from_pretrained(
           """EleutherAI/gpt-neo-125m"""    ,					load_in_abit=a__    ,					device_map=a__    ,					)
       _lowerCAmelCase						:	str        =					Accelerator()

       # This should work
       _lowerCAmelCase						:	str        =					accelerator.prepare(a__  )






     @require_cuda
     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					torch.nn.Linear(10    ,					10  )
       _lowerCAmelCase						:	Any        =					torch.optim.SGD(model.parameters()    ,					lr=0.0_1  )
       _lowerCAmelCase						:	List[str]        =					Accelerator(cpu=a__  )
       _lowerCAmelCase						:	Tuple        =					accelerator.prepare(a__  )




