

'''simple docstring'''
import os
from collections import deque

import torch
from torch.utils.data import Dataset







class 							A__    (       A__  ):





  def __init__(		self      :	List[str]							,					_a      :	Optional[Any]=""							,					_a      :	Any="train" )	->    int:





       '''simple docstring'''






       assert os.path.isdir(_a )

       _SCREAMING_SNAKE_CASE							    =[]
       _SCREAMING_SNAKE_CASE							    =os.listdir(_a )
       for story_filename in story_filenames_list:
            if "summary" in story_filename:
                 continue
            _SCREAMING_SNAKE_CASE							    =os.path.join(_a							,					_a )
            if not os.path.isfile(_a ):
                 continue
            self.documents.append(_a )





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





       '''simple docstring'''






       return len(self.documents )





  def __getitem__(		self      :	Optional[int]							,					_a      :	Dict )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.documents[idx]
       _SCREAMING_SNAKE_CASE							    =document_path.split('/' )[-1]
       with open(_a							,					encoding='utf-8' ) as source:
            _SCREAMING_SNAKE_CASE							    =source.read()
            _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =process_story(_a )
       return document_name, story_lines, summary_lines


def        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]				)      ->			str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =list(filter(lambda _UpperCamelCase				: len(_UpperCamelCase				) != 0							, [line.strip() for line in raw_story.split('\n'				)]				)				)

     # for some unknown reason some lines miss a period, add it
     _SCREAMING_SNAKE_CASE							    =[_add_missing_period(_UpperCamelCase				) for line in nonempty_lines]

     # gather article lines
     _SCREAMING_SNAKE_CASE							    =[]
     _SCREAMING_SNAKE_CASE							    =deque(_UpperCamelCase				)
     while True:
          try:
               _SCREAMING_SNAKE_CASE							    =lines.popleft()
               if element.startswith('@highlight'				):
                    break
               story_lines.append(_UpperCamelCase				)
          except IndexError:
               # if "@highlight" is absent from the file we pop
               # all elements until there is None, raising an exception.
               return story_lines, []

    # gather summary lines
     _SCREAMING_SNAKE_CASE							    =list(filter(lambda _UpperCamelCase				: not t.startswith('@highlight'				)							, _UpperCamelCase				)				)

     return story_lines, summary_lines


def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]				)      ->			Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
     if line.startswith('@highlight'				):
          return line
     if line[-1] in END_TOKENS:
          return line
     return line + "."


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     int							, _UpperCamelCase						:     str				)      ->			Optional[Any]:
     """simple docstring"""




     if len(_UpperCamelCase				) > block_size:
          return sequence[:block_size]
     else:
          sequence.extend([pad_token_id] * (block_size - len(_UpperCamelCase				))				)
          return sequence


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]							, _UpperCamelCase						:     str				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =torch.ones_like(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =sequence == pad_token_id
     _SCREAMING_SNAKE_CASE							    =0
     return mask


def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[Any]				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[tokenizer.encode(_UpperCamelCase				) for line in story_lines]
     _SCREAMING_SNAKE_CASE							    =[token for sentence in story_lines_token_ids for token in sentence]
     _SCREAMING_SNAKE_CASE							    =[tokenizer.encode(_UpperCamelCase				) for line in summary_lines]
     _SCREAMING_SNAKE_CASE							    =[token for sentence in summary_lines_token_ids for token in sentence]

     return story_token_ids, summary_token_ids







def        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     Tuple				)      ->			str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =[]
     for sequence in batch:
          _SCREAMING_SNAKE_CASE							    =-1
          _SCREAMING_SNAKE_CASE							    =[]
          for s in sequence:
               if s == separator_token_id:
                    sentence_num += 1
               embeddings.append(sentence_num % 2				)
          batch_embeddings.append(_UpperCamelCase				)
     return torch.tensor(_UpperCamelCase				)








import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


__UpperCAmelCase								=						logging.getLogger(__name__)




def      lowercase__      (     __snake_case    :							List[Any]=2     ,       __snake_case    :							Union[str, Any]=3     ,       __snake_case    :							Any=16     ,       __snake_case    :							int = 10     ,       __snake_case    :							int = 2  ):


					'''simple docstring'''


					def get_dataset(__snake_case    :							Optional[Any]  ):
										UpperCAmelCase_    :      Optional[Any]            =						torch.randn(batch_size * n_batches     ,       1  )
										return TensorDataset(__snake_case     ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches     ,       1  )  )

					UpperCAmelCase_    :      Any            =						get_dataset(__snake_case  )
					UpperCAmelCase_    :      str            =						get_dataset(__snake_case  )
					UpperCAmelCase_    :      int            =						DataLoader(__snake_case     ,       shuffle=__snake_case     ,       batch_size=__snake_case     ,       num_workers=4  )
					UpperCAmelCase_    :      int            =						DataLoader(__snake_case     ,       shuffle=__snake_case     ,       batch_size=__snake_case     ,       num_workers=4  )
					return (train_dataloader, valid_dataloader)




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							str     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							List[str]     ,       __snake_case    :							Any     ,       __snake_case    :							Tuple=None  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[int]            =						[]
					for epoch in range(__snake_case  ):
										# Train quickly
										model.train()
										for batch in dataloader:
															UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						batch
															UpperCAmelCase_    :      List[Any]            =						model(__snake_case  )
															UpperCAmelCase_    :      int            =						torch.nn.functional.mse_loss(__snake_case     ,       __snake_case  )
															accelerator.backward(__snake_case  )
															optimizer.step()
															optimizer.zero_grad()
										rands.append(random.random()  )  # Introduce some randomness
										if scheduler is not None:
															scheduler.step()
					return rands



class   lowerCamelCase      (nn.Module			):







		'''simple docstring'''



		def __init__(							self   )	->							Optional[Any]:
							super().__init__()
							UpperCAmelCase_    :      List[Any]            =						nn.Parameter(torch.randn(1   )   )
							UpperCAmelCase_    :      Optional[int]            =						nn.Parameter(torch.randn(1   )   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[Any]:
							return x * self.a + self.b





class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Tuple            =						DummyModel()
												UpperCAmelCase_    :      List[str]            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Optional[int]            =						ProjectConfiguration(total_limit=1				,		project_dir=_UpperCamelCase				,		automatic_checkpoint_naming=_UpperCamelCase   )
												# Train baseline
												UpperCAmelCase_    :      Dict            =						Accelerator(project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()

												# Save second state
												accelerator.save_state()
												self.assertEqual(len(os.listdir(accelerator.project_dir   )   )				,		1   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Optional[Any]            =						DummyModel()
												UpperCAmelCase_    :      str            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						dummy_dataloaders()
												# Train baseline
												UpperCAmelCase_    :      Tuple            =						Accelerator()
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												UpperCAmelCase_    :      Any            =						os.path.join(_UpperCamelCase				,		'initial'   )
												accelerator.save_state(_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[int]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Dict            =						optimizer.state_dict()
												UpperCAmelCase_    :      Union[str, Any]            =						train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Union[str, Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Any            =						optimizer.state_dict()

												# Train partially
												set_seed(4_2   )
												UpperCAmelCase_    :      int            =						DummyModel()
												UpperCAmelCase_    :      int            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						dummy_dataloaders()
												UpperCAmelCase_    :      Optional[Any]            =						Accelerator()
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												accelerator.load_state(_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      List[str]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      Dict            =						train(2				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save everything
												UpperCAmelCase_    :      Union[str, Any]            =						os.path.join(_UpperCamelCase				,		'checkpoint'   )
												accelerator.save_state(_UpperCamelCase   )

												# Load everything back in and make sure all states work
												accelerator.load_state(_UpperCamelCase   )
												test_rands += train(1				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Union[str, Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Tuple            =						DummyModel()
												UpperCAmelCase_    :      Optional[int]            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Any            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase   )

												# Train baseline
												UpperCAmelCase_    :      str            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[int]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[int]            =						optimizer.state_dict()
												UpperCAmelCase_    :      Optional[Any]            =						train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Tuple            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[int]            =						optimizer.state_dict()

												# Train partially
												set_seed(4_2   )
												UpperCAmelCase_    :      Any            =						DummyModel()
												UpperCAmelCase_    :      Any            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Tuple            =						ProjectConfiguration(iteration=1				,		automatic_checkpoint_naming=_UpperCamelCase   )
												UpperCAmelCase_    :      List[Any]            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												accelerator.load_state(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_0'   )   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      str            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      List[Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      Union[str, Any]            =						train(2				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save everything
												accelerator.save_state()

												# Load everything back in and make sure all states work
												accelerator.load_state(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_1'   )   )
												test_rands += train(1				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      List[Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Dict            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      Optional[Any]            =						torch.tensor([1, 2, 3]   )
							UpperCAmelCase_    :      Any            =						torch.tensor([2, 3, 4]   )
							UpperCAmelCase_    :      Union[str, Any]            =						DummyModel()
							UpperCAmelCase_    :      List[str]            =						torch.optim.Adam(net.parameters()   )
							UpperCAmelCase_    :      Any            =						Accelerator()
							with self.assertRaises(_UpperCamelCase   ) as ve:
												accelerator.register_for_checkpointing(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[int]            =						str(ve.exception   )
							self.assertTrue('Item at index 0' in message   )
							self.assertTrue('Item at index 1' in message   )
							self.assertFalse('Item at index 2' in message   )
							self.assertFalse('Item at index 3' in message   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      int            =						DummyModel()
												UpperCAmelCase_    :      Any            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_    :      Dict            =						torch.optim.lr_scheduler.StepLR(_UpperCamelCase				,		step_size=1				,		gamma=0.99   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						dummy_dataloaders()
												UpperCAmelCase_    :      Tuple            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase   )
												# Train baseline
												UpperCAmelCase_    :      Tuple            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()
												UpperCAmelCase_    :      Dict            =						scheduler.state_dict()
												train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												self.assertNotEqual(_UpperCamelCase				,		scheduler.state_dict()   )

												# Load everything back in and make sure all states work
												accelerator.load_state(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_0'   )   )
												self.assertEqual(_UpperCamelCase				,		scheduler.state_dict()   )



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Optional[int]            =						DummyModel()
												UpperCAmelCase_    :      Dict            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase				,		total_limit=2   )
												# Train baseline
												UpperCAmelCase_    :      Optional[int]            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_    :      str            =						accelerator.prepare(_UpperCamelCase   )
												# Save 3 states:
												for _ in range(1_1   ):
																	accelerator.save_state()
												self.assertTrue(not os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_0'   )   )   )
												self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_9'   )   )   )
												self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_10'   )   )   )



		@require_cuda
		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      List[str]            =						['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__   )]
							execute_subprocess_async(_UpperCamelCase				,		env=os.environ.copy()   )


if __name__ == "__main__":
		__UpperCAmelCase								=						'/tmp/accelerate/state_checkpointing'
		__UpperCAmelCase								=						DummyModel()
		__UpperCAmelCase								=						torch.optim.Adam(params=model.parameters(), lr=1E-3)
		__UpperCAmelCase								=						torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
		__UpperCAmelCase							,   __UpperCAmelCase								=						dummy_dataloaders()
		__UpperCAmelCase								=						ProjectConfiguration(automatic_checkpoint_naming=True)
		# Train baseline
		__UpperCAmelCase								=						Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
		if accelerator.process_index == 0:
				if os.path.exists(savedir):
						shutil.rmtree(savedir)
				os.makedirs(savedir)
		__UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase								=						accelerator.prepare(
		    model, optimizer, train_dataloader, valid_dataloader, scheduler
		)
		__UpperCAmelCase							,   __UpperCAmelCase								=						accelerator.prepare(model, optimizer)
		train(3, model, train_dataloader, optimizer, accelerator, scheduler)
		# Check that the intial optimizer is loaded on the GPU
		for group in optimizer.param_groups:
				__UpperCAmelCase								=						group['params'][0].device
				break
		assert param_device.type == accelerator.device.type
		__UpperCAmelCase								=						model.cpu()
		accelerator.wait_for_everyone()
		accelerator.save_state()
		accelerator.wait_for_everyone()

		# Check CPU state
		accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
		for group in optimizer.param_groups:
				__UpperCAmelCase								=						group['params'][0].device
				break
		assert (
		    param_device.type == torch.device('cpu').type
		), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

		# Check device state
		model.to(accelerator.device)
		accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
		for group in optimizer.param_groups:
				__UpperCAmelCase								=						group['params'][0].device
				break
		assert (
		    param_device.type == accelerator.device.type
		), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

		# Check error
		with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
				accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
		accelerator.wait_for_everyone()
		if accelerator.process_index == 0:
				shutil.rmtree(savedir)
		accelerator.wait_for_everyone()






