


"""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 _A						(						):







    """simple docstring"""
    a 		=torch.nn.Linear(2       ,    4		)
    a 		=torch.optim.AdamW(model.parameters()       ,    lr=1.0		)
    a 		=torch.optim.lr_scheduler.OneCycleLR(_UpperCAmelCase       ,    max_lr=0.01       ,    steps_per_epoch=2       ,    epochs=1		)
    a 		=DataLoader(TensorDataset(torch.tensor([1, 2, 3]		)		)		)
    a 		=DataLoader(TensorDataset(torch.tensor([4, 5, 6]		)		)		)

    return model, optimizer, scheduler, train_dl, valid_dl
def _A						(						lowercase		):







    """simple docstring"""
    return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def _A						(						lowercase		):







    """simple docstring"""
    a 		=torch.nn.Linear(*tuple(model.weight.T.shape		)		).state_dict()
    model.load_state_dict(_UpperCAmelCase		)






class       __A				(       __UpperCamelCase			):






 """simple docstring"""




 @require_cuda
 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     a 		=Accelerator()
     assert PartialState._shared_state["_cpu"] is False
     assert PartialState._shared_state["device"].type == "cuda"
     with self.assertRaises(a_						):
         a 		=Accelerator(cpu=a_						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     a 		=Accelerator()
     a 		=GradientState()
     assert state.num_steps == 1
     a 		=4
     assert state.num_steps == 4

     assert state.sync_gradients is True
     a 		=False
     assert state.sync_gradients is False
     GradientState._reset_state()


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     a 		=Accelerator()
     a 		=create_components()

     (
         a					
     ) 		=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       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     a 		=Accelerator()
     a 		=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       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     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'''						):
         a 		=Accelerator()
         self.assertEqual(str(accelerator.state.device						) ,    '''cuda:64'''						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     a 		=Accelerator()
     a 		=create_components()
     accelerator.prepare(a_ ,    a_ ,    a_ ,    a_ ,    a_						)

     a 		=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       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     a 		=Accelerator()
     a 		=create_components()
     accelerator.prepare(a_ ,    a_ ,    a_ ,    a_ ,    a_						)

     a 		=get_signature(a_						)

     # saving hook
     def save_config(__A ,    __A ,    __A						):
         a 		={'''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:
             a 		=json.load(a_						)

         a 		=config['''class_name''']

     a 		=accelerator.register_save_state_pre_hook(a_						)
     a 		=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
         a 		='''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
         a 		='''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       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     a 		=Accelerator()
     a 		=create_components()
     a 		=None

     # This should work
     a 		=accelerator.prepare(
         a_ ,    a_ ,    a_ ,    a_ ,    a_ ,    a_						)
     self.assertTrue(dummy_obj is None						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     a 		=Accelerator()
     a 		=create_components()
     a 		=[1, 2, 3]

     # This should work
     a 		=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       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     from transformers import AutoModelForCausalLM

     a 		=AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m''' ,    load_in_abit=a_ ,    device_map={'''''': 0} ,    )
     a 		=Accelerator()

     # This should work
     a 		=accelerator.prepare(a_						)


 @slow
 @require_bnb
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     from transformers import AutoModelForCausalLM

     a 		=Accelerator()

     with init_empty_weights():
         a 		=AutoModelForCausalLM.from_pretrained(
             '''EleutherAI/gpt-neo-125m''' ,    )
         model.tie_weights()
         a 		=infer_auto_device_map(a_						)
         a 		='''cpu'''

     a 		=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_						):
         a 		=accelerator.prepare(a_						)


 @slow
 @require_bnb
 @require_multi_gpu
 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     from transformers import AutoModelForCausalLM

     a 		={'''distributed_type''': DistributedType.MULTI_GPU}

     with init_empty_weights():
         a 		=AutoModelForCausalLM.from_pretrained(
             '''EleutherAI/gpt-neo-125m''' ,    )
         model.tie_weights()
         a 		=infer_auto_device_map(a_						)
         a 		=1

     a 		=AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m''' ,    load_in_abit=a_ ,    device_map=a_ ,    )
     a 		=Accelerator()

     # This should not work and get value error
     with self.assertRaises(a_						):
         a 		=accelerator.prepare(a_						)

     PartialState._reset_state()


 @slow
 @require_bnb
 @require_multi_gpu
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     from transformers import AutoModelForCausalLM

     with init_empty_weights():
         a 		=AutoModelForCausalLM.from_pretrained(
             '''EleutherAI/gpt-neo-125m''' ,    )
         a 		=infer_auto_device_map(a_						)
         a 		=1

     a 		=AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m''' ,    load_in_abit=a_ ,    device_map=a_ ,    )
     a 		=Accelerator()

     # This should work
     a 		=accelerator.prepare(a_						)





 @require_cuda
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[Any]:
     a 		=torch.nn.Linear(10 ,    10						)
     a 		=torch.optim.SGD(model.parameters() ,    lr=0.01						)
     a 		=Accelerator(cpu=a_						)
     a 		=accelerator.prepare(a_						)




from __future__ import annotations

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: Optional[Any]       ,       a_							: Optional[int]=None ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	data
					__UpperCAmelCase    :     Any													=	None




			def __repr__(   self							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	[]
					__UpperCAmelCase    :     Any													=	self
					while temp:
							string_rep.append(F'{temp.data}' )
							__UpperCAmelCase    :     Dict													=	temp.next
					return "->".join(a_ )

def        a						(      _UpperCAmelCase							:					list						):







		'''simple docstring'''





		if not elements_list:
				raise Exception('''The Elements List is empty'''						)

		__UpperCAmelCase    :     int													=	Node(elements_list[0]						)
		for i in range(1 ,   len(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     Any													=	Node(elements_list[i]						)
				__UpperCAmelCase    :     Optional[int]													=	current.next
		return head

def        a						(      _UpperCAmelCase							:					Node						):







		'''simple docstring'''





		if head_node is not None and isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
				print_reverse(head_node.next						)
				print(head_node.data						)

def        a						(      ):







		'''simple docstring'''





		from doctest import testmod

		testmod()

		__UpperCAmelCase    :     Tuple													=	make_linked_list([14, 52, 14, 12, 43]						)
		print('''Linked List:'''						)
		print(_UpperCAmelCase						)
		print('''Elements in Reverse:'''						)
		print_reverse(_UpperCAmelCase						)


if __name__ == "__main__":
				main()





