





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

       return model, optimizer, scheduler, train_dl, valid_dl



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



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





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   @require_cuda
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

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

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







          '''simple docstring'''

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

          assert state.sync_gradients is True
          a__						:	Optional[int] =				False
          assert state.sync_gradients is False
          GradientState._reset_state()

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Tuple =				Accelerator()
          a__ ,							a__ ,							a__ ,							a__ ,							a__						:	Tuple =				create_components()

          (
              (
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							
          )						:	List[Any] =				accelerator.prepare(lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)

          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 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	Union[str, Any] =				Accelerator()
          a__ ,							a__ ,							a__ ,							a__ ,							a__						:	List[str] =				create_components()
          accelerator.prepare(lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)
          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 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          PartialState._reset_state()

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

          with patch('torch.cuda.set_device'					,  lowercase), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64'):
                 a__						:	Tuple =				Accelerator()
                 self.assertEqual(str(accelerator.state.device)					,  'cuda:64')

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	Optional[int] =				Accelerator()
          a__ ,							a__ ,							a__ ,							a__ ,							a__						:	Union[str, Any] =				create_components()
          accelerator.prepare(lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)

          a__						:	Tuple =				get_signature(lowercase)

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

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

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

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	List[str] =				Accelerator()
          a__ ,							a__ ,							a__ ,							a__ ,							a__						:	Optional[int] =				create_components()
          accelerator.prepare(lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)

          a__						:	List[str] =				get_signature(lowercase)

          # saving hook
          def save_config(lowercase					,  lowercase					,  lowercase):
                 a__						:	int =				{'class_name': models[0].__class__.__name__}

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

        # loading hook
          def load_config(lowercase					,  lowercase):
                 with open(os.path.join(lowercase					,  'data.json')					,  'r') as f:
                        a__						:	List[Any] =				json.load(lowercase)

                 a__						:	Union[str, Any] =				config['class_name']

          a__						:	Optional[Any] =				accelerator.register_save_state_pre_hook(lowercase)
          a__						:	str =				accelerator.register_load_state_pre_hook(lowercase)

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

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

                 # random class name to verify correct one is loaded
                 a__						:	int =				'random'

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

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

                 # random class name to verify correct one is loaded
                 a__						:	Union[str, Any] =				'random'

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

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

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	str =				Accelerator()
          a__ ,							a__ ,							a__ ,							a__ ,							a__						:	str =				create_components()
          a__						:	Dict =				None

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

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

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

          # This should work
          a__ ,							a__ ,							a__ ,							a__ ,							a__ ,							a__						:	Any =				accelerator.prepare(
              lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)
          self.assertEqual(
              getattr(lowercase					,  '_is_accelerate_prepared'					,  lowercase)					,  lowercase					,  'Dummy object should have `_is_accelerate_prepared` set to `True`'					,  )
          self.assertEqual(
              getattr(lowercase					,  '_is_accelerate_prepared'					,  lowercase)					,  lowercase					,  'Model is missing `_is_accelerator_prepared` or is set to `False`'					,  )
          self.assertEqual(
              getattr(lowercase					,  '_is_accelerate_prepared'					,  lowercase)					,  lowercase					,  'Optimizer is missing `_is_accelerator_prepared` or is set to `False`'					,  )
          self.assertEqual(
              getattr(lowercase					,  '_is_accelerate_prepared'					,  lowercase)					,  lowercase					,  'Scheduler is missing `_is_accelerator_prepared` or is set to `False`'					,  )
          self.assertEqual(
              getattr(lowercase					,  '_is_accelerate_prepared'					,  lowercase)					,  lowercase					,  'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'					,  )
          self.assertEqual(
              getattr(lowercase					,  '_is_accelerate_prepared'					,  lowercase)					,  lowercase					,  'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'					,  )

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







          '''simple docstring'''

          from transformers import AutoModelForCausalLM

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

          # This should work
          a__						:	Union[str, Any] =				accelerator.prepare(lowercase)

   @slow
   @require_bnb
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          from transformers import AutoModelForCausalLM

          a__						:	Dict =				Accelerator()

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

          a__						:	str =				AutoModelForCausalLM.from_pretrained(
              'EleutherAI/gpt-neo-125m'					,  device_map=lowercase					,  load_in_abit=lowercase					,  llm_inta_enable_fpaa_cpu_offload=lowercase)

          # This should not work and get value error
          with self.assertRaises(lowercase):
                 a__						:	List[str] =				accelerator.prepare(lowercase)

   @slow
   @require_bnb
   @require_multi_gpu
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          from transformers import AutoModelForCausalLM

          a__						:	int =				{'distributed_type': DistributedType.MULTI_GPU}

          with init_empty_weights():
                 a__						:	Optional[int] =				AutoModelForCausalLM.from_pretrained(
                     'EleutherAI/gpt-neo-125m'					,  )
                 model.tie_weights()
                 a__						:	Optional[int] =				infer_auto_device_map(lowercase)
                 a__						:	Optional[Any] =				1

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

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

          PartialState._reset_state()

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







          '''simple docstring'''

          from transformers import AutoModelForCausalLM

          with init_empty_weights():
                 a__						:	int =				AutoModelForCausalLM.from_pretrained(
                     'EleutherAI/gpt-neo-125m'					,  )
                 a__						:	List[str] =				infer_auto_device_map(lowercase)
                 a__						:	List[str] =				1

          a__						:	List[str] =				AutoModelForCausalLM.from_pretrained(
              'EleutherAI/gpt-neo-125m'					,  load_in_abit=lowercase					,  device_map=lowercase					,  )
          a__						:	int =				Accelerator()

          # This should work
          a__						:	Optional[Any] =				accelerator.prepare(lowercase)

   @require_cuda
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	Optional[int] =				torch.nn.Linear(10					,  10)
          a__						:	Optional[int] =				torch.optim.SGD(model.parameters()					,  lr=0.01)
          a__						:	Optional[int] =				Accelerator(cpu=lowercase)
          a__						:	str =				accelerator.prepare(lowercase)









def A_	(		A__				)  ->		int:
       stooge(A__		,      0		,      len(A__				) - 1				)
       return arr



def A_	(		A__		,      A__		,      A__				)  ->		List[Any]:
       if i >= h:
              return

       # If first element is smaller than the last then swap them
       if arr[i] > arr[h]:
              a__ ,							a__						:	List[str] =				arr[h], arr[i]

       # If there are more than 2 elements in the array
       if h - i + 1 > 2:
              a__						:	Dict =				(int)((h - i + 1) / 3				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)

              # Recursively sort last 2/3 elements
              stooge(A__		,      i + t		,      (A__)				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)


if __name__ == "__main__":
    lowercase      :       Dict									=  input("""Enter numbers separated by a comma:\n""").strip()
    lowercase      :       Dict									=  [int(item) for item in user_input.split(""",""")]
    print(stooge_sort(unsorted))


