




'''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 		UpperCamelCase__      (		)    ->   List[Any]:
      snake_case__       :   List[Any]	      =    torch.nn.Linear(2				,      4		)
      snake_case__       :   List[Any]	      =    torch.optim.AdamW(model.parameters()				,      lr=1.0		)
      snake_case__       :   Any	      =    torch.optim.lr_scheduler.OneCycleLR(__SCREAMING_SNAKE_CASE				,      max_lr=0.01				,      steps_per_epoch=2				,      epochs=1		)
      snake_case__       :   Optional[Any]	      =    DataLoader(TensorDataset(torch.tensor([1, 2, 3]		)		)		)
      snake_case__       :   List[Any]	      =    DataLoader(TensorDataset(torch.tensor([4, 5, 6]		)		)		)

      return model, optimizer, scheduler, train_dl, valid_dl






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Dict:
      return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Optional[int]:
      snake_case__       :   str	      =    torch.nn.Linear(*tuple(model.weight.T.shape		)		).state_dict()
      model.load_state_dict(__SCREAMING_SNAKE_CASE		)






class        lowercase_      (						lowerCAmelCase_							):





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





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

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





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   List[str]	      =    Accelerator()
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   Any	      =    create_components()

           (
               (
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							(
               snake_case__
           )      ,							
           )       :   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     :					Optional[Any]			):
           snake_case__       :   List[Any]	      =    Accelerator()
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   List[Any]	      =    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     :					Optional[int]			):
           PartialState._reset_state()

           # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
           def noop(*__lowerCamelCase     :					Union[str, Any]     ,							**__lowerCamelCase     :					Tuple			):
                 pass

           with patch('torch.cuda.set_device'     ,							__lowerCamelCase			), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64'			):
                 snake_case__       :   List[Any]	      =    Accelerator()
                 self.assertEqual(str(accelerator.state.device			)     ,							'cuda:64'			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           snake_case__       :   List[Any]	      =    Accelerator()
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   Optional[Any]	      =    create_components()
           accelerator.prepare(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase			)

           snake_case__       :   Optional[Any]	      =    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     :					List[Any]			):
           snake_case__       :   str	      =    Accelerator()
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   int	      =    create_components()
           accelerator.prepare(__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase			)

           snake_case__       :   Any	      =    get_signature(__lowerCamelCase			)

           # saving hook
           def save_config(__lowerCamelCase     :					Any     ,							__lowerCamelCase     :					Optional[Any]     ,							__lowerCamelCase     :					Any			):
                 snake_case__       :   Tuple	      =    {'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     :					Tuple     ,							__lowerCamelCase     :					Optional[int]			):
                 with open(os.path.join(__lowerCamelCase     ,							'data.json'			)     ,							'r'			) as f:
                       snake_case__       :   Dict	      =    json.load(__lowerCamelCase			)

                 snake_case__       :   Any	      =    config['class_name']

           snake_case__       :   str	      =    accelerator.register_save_state_pre_hook(__lowerCamelCase			)
           snake_case__       :   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
                 snake_case__       :   int	      =    '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
                 snake_case__       :   List[Any]	      =    '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			):
           snake_case__       :   List[str]	      =    Accelerator()
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   Tuple	      =    create_components()
           snake_case__       :   Optional[Any]	      =    None

           # This should work
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   Optional[int]	      =    accelerator.prepare(
               __lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase     ,							__lowerCamelCase			)
           self.assertTrue(dummy_obj is None			)





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   str	      =    Accelerator()
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   List[str]	      =    create_components()
           snake_case__       :   Union[str, Any]	      =    [1, 2, 3]

           # This should work
           snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__      ,							snake_case__       :   List[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     :					Tuple			):
           from transformers import AutoModelForCausalLM

           snake_case__       :   Tuple	      =    AutoModelForCausalLM.from_pretrained(
               'EleutherAI/gpt-neo-125m'     ,							load_in_abit=__lowerCamelCase     ,							device_map={'': 0}     ,							)
           snake_case__       :   Any	      =    Accelerator()

           # This should work
           snake_case__       :   Optional[int]	      =    accelerator.prepare(__lowerCamelCase			)





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

           snake_case__       :   int	      =    Accelerator()

           with init_empty_weights():
                 snake_case__       :   Union[str, Any]	      =    AutoModelForCausalLM.from_pretrained(
                     'EleutherAI/gpt-neo-125m'     ,							)
                 model.tie_weights()
                 snake_case__       :   Tuple	      =    infer_auto_device_map(__lowerCamelCase			)
                 snake_case__       :   Tuple	      =    'cpu'

           snake_case__       :   str	      =    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			):
                 snake_case__       :   Tuple	      =    accelerator.prepare(__lowerCamelCase			)





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

           snake_case__       :   Dict	      =    {'distributed_type': DistributedType.MULTI_GPU}

           with init_empty_weights():
                 snake_case__       :   List[str]	      =    AutoModelForCausalLM.from_pretrained(
                     'EleutherAI/gpt-neo-125m'     ,							)
                 model.tie_weights()
                 snake_case__       :   Optional[int]	      =    infer_auto_device_map(__lowerCamelCase			)
                 snake_case__       :   str	      =    1

           snake_case__       :   Tuple	      =    AutoModelForCausalLM.from_pretrained(
               'EleutherAI/gpt-neo-125m'     ,							load_in_abit=__lowerCamelCase     ,							device_map=__lowerCamelCase     ,							)
           snake_case__       :   List[str]	      =    Accelerator()

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

           PartialState._reset_state()





     @slow
     @require_bnb
     @require_multi_gpu
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           from transformers import AutoModelForCausalLM

           with init_empty_weights():
                 snake_case__       :   Tuple	      =    AutoModelForCausalLM.from_pretrained(
                     'EleutherAI/gpt-neo-125m'     ,							)
                 snake_case__       :   Any	      =    infer_auto_device_map(__lowerCamelCase			)
                 snake_case__       :   Dict	      =    1

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

           # This should work
           snake_case__       :   int	      =    accelerator.prepare(__lowerCamelCase			)





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






'''simple docstring'''






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__      ,							snake_case__       :   Optional[int]	      =    [], []
      while len(__SCREAMING_SNAKE_CASE		) > 1:
            snake_case__      ,							snake_case__       :   Tuple	      =    min(__SCREAMING_SNAKE_CASE		), max(__SCREAMING_SNAKE_CASE		)
            start.append(__SCREAMING_SNAKE_CASE		)
            end.append(__SCREAMING_SNAKE_CASE		)
            collection.remove(__SCREAMING_SNAKE_CASE		)
            collection.remove(__SCREAMING_SNAKE_CASE		)
      end.reverse()
      return start + collection + end


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