





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

 return model, optimizer, scheduler, train_dl, valid_dl



def 	A_       (  _lowerCAmelCase  )		->				Tuple:
 return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def 	A_       (  _lowerCAmelCase  )		->				Optional[int]:
 UpperCamelCase			:      List[Any] 			=     torch.nn.Linear(*tuple(model.weight.T.shape  )  ).state_dict()
 model.load_state_dict(_lowerCAmelCase  )



class       A__     ( __snake_case    ):





      @require_cuda
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     Accelerator()
       UpperCamelCase			:      Dict 			=     GradientState()
       assert state.num_steps == 1
       UpperCamelCase			:      Tuple 			=     4
       assert state.num_steps == 4

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     Accelerator()
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Tuple 			=     create_components()

       (
           (
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					(
           UpperCamelCase
       )			,					
       )			:      Dict 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     Accelerator()
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Optional[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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       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"     ):
        UpperCamelCase			:      Dict 			=     Accelerator()
        self.assertEqual(str(accelerator.state.device     ) ,							"cuda:64"     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     Accelerator()
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      int 			=     create_components()
       accelerator.prepare(A_ ,							A_ ,							A_ ,							A_ ,							A_     )

       UpperCamelCase			:      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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     Accelerator()
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Optional[int] 			=     create_components()
       accelerator.prepare(A_ ,							A_ ,							A_ ,							A_ ,							A_     )

       UpperCamelCase			:      Any 			=     get_signature(A_     )

       # saving hook
       def save_config(A_ ,							A_ ,							A_     ):
        UpperCamelCase			:      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:
         UpperCamelCase			:      int 			=     json.load(A_     )

        UpperCamelCase			:      List[str] 			=     config["class_name"]

       UpperCamelCase			:      List[Any] 			=     accelerator.register_save_state_pre_hook(A_     )
       UpperCamelCase			:      List[Any] 			=     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
        UpperCamelCase			:      Optional[Any] 			=     "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
        UpperCamelCase			:      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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     Accelerator()
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Optional[Any] 			=     create_components()
       UpperCamelCase			:      Dict 			=     None

       # This should work
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Optional[Any] 			=     accelerator.prepare(
           A_ ,							A_ ,							A_ ,							A_ ,							A_ ,							A_     )
       self.assertTrue(dummy_obj is None     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     Accelerator()
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Any 			=     create_components()
       UpperCamelCase			:      int 			=     [1, 2, 3]

       # This should work
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       from transformers import AutoModelForCausalLM

       UpperCamelCase			:      Any 			=     AutoModelForCausalLM.from_pretrained(
           "EleutherAI/gpt-neo-125m" ,							load_in_abit=A_ ,							device_map={"": 0} ,							)
       UpperCamelCase			:      Any 			=     Accelerator()

       # This should work
       UpperCamelCase			:      str 			=     accelerator.prepare(A_     )





      @slow
      @require_bnb
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       from transformers import AutoModelForCausalLM

       UpperCamelCase			:      Optional[Any] 			=     Accelerator()

       with init_empty_weights():
        UpperCamelCase			:      List[Any] 			=     AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m" ,							)
        model.tie_weights()
        UpperCamelCase			:      Tuple 			=     infer_auto_device_map(A_     )
        UpperCamelCase			:      Tuple 			=     "cpu"

       UpperCamelCase			:      Tuple 			=     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_     ):
        UpperCamelCase			:      Any 			=     accelerator.prepare(A_     )





      @slow
      @require_bnb
      @require_multi_gpu
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       from transformers import AutoModelForCausalLM

       UpperCamelCase			:      str 			=     {"distributed_type": DistributedType.MULTI_GPU}

       with init_empty_weights():
        UpperCamelCase			:      Union[str, Any] 			=     AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m" ,							)
        model.tie_weights()
        UpperCamelCase			:      Union[str, Any] 			=     infer_auto_device_map(A_     )
        UpperCamelCase			:      int 			=     1

       UpperCamelCase			:      Any 			=     AutoModelForCausalLM.from_pretrained(
           "EleutherAI/gpt-neo-125m" ,							load_in_abit=A_ ,							device_map=A_ ,							)
       UpperCamelCase			:      List[Any] 			=     Accelerator()

       # This should not work and get value error
       with self.assertRaises(A_     ):
        UpperCamelCase			:      int 			=     accelerator.prepare(A_     )

       PartialState._reset_state()





      @slow
      @require_bnb
      @require_multi_gpu
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       from transformers import AutoModelForCausalLM

       with init_empty_weights():
        UpperCamelCase			:      Optional[int] 			=     AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m" ,							)
        UpperCamelCase			:      Optional[int] 			=     infer_auto_device_map(A_     )
        UpperCamelCase			:      Any 			=     1

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

       # This should work
       UpperCamelCase			:      Optional[Any] 			=     accelerator.prepare(A_     )





      @require_cuda
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     torch.nn.Linear(10 ,							10     )
       UpperCamelCase			:      Tuple 			=     torch.optim.SGD(model.parameters() ,							lr=0.01     )
       UpperCamelCase			:      Tuple 			=     Accelerator(cpu=A_     )
       UpperCamelCase			:      str 			=     accelerator.prepare(A_     )







from math import loga



def 	A_       (  _lowerCAmelCase  )		->				int:
 if a < 0:
  raise ValueError("Input value must be a positive integer"  )
 elif isinstance(_lowerCAmelCase							,     _lowerCAmelCase  ):
  raise TypeError("Input value must be a 'int' type"  )
 return 0 if (a == 0) else int(loga(a & -a  )  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
