'''simple docstring'''

import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A_     :						str    							=16
A_     :						List[str]    							=32



def snake_case_      (					__snake_case  :							str)			->					Optional[int]:
   return int(x / 2**20)


class     __UpperCAmelCase       :





  def __enter__(	self ):
     gc.collect()
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
     lowerCAmelCase_			=      torch.cuda.memory_allocated()
     return self







  def __exit__(	self    ,     *_lowerCamelCase ):
     gc.collect()
     torch.cuda.empty_cache()
     lowerCAmelCase_			=      torch.cuda.memory_allocated()
     lowerCAmelCase_			=      torch.cuda.max_memory_allocated()
     lowerCAmelCase_			=      bamb(self.end - self.begin )
     lowerCAmelCase_			=      bamb(self.peak - self.begin )
     # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def snake_case_      (					__snake_case  :							Accelerator	,   __snake_case  :							int = 16	,   __snake_case  :							str = "bert-base-cased"	,   __snake_case  :							int = 320	,   __snake_case  :							int = 160	,   )			->					Optional[int]:
   lowerCAmelCase_			=      AutoTokenizer.from_pretrained(__snake_case)
   lowerCAmelCase_			=      load_dataset(
       '''glue'''	,   '''mrpc'''	,   split={'''train''': F'''train[:{n_train}]''', '''validation''': F'''validation[:{n_val}]'''})

   def tokenize_function(__snake_case  :							int):
      # max_length=None => use the model max length (it's actually the default)
      lowerCAmelCase_			=      tokenizer(examples['''sentence1''']	,   examples['''sentence2''']	,   truncation=__snake_case	,   max_length=__snake_case)
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   lowerCAmelCase_			=      datasets.map(
       __snake_case	,   batched=__snake_case	,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,   load_from_cache_file=__snake_case)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   lowerCAmelCase_			=      tokenized_datasets.rename_column('''label'''	,   '''labels''')

   def collate_fn(__snake_case  :							List[str]):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(__snake_case	,   padding='''max_length'''	,   max_length=128	,   return_tensors='''pt''')
      return tokenizer.pad(__snake_case	,   padding='''longest'''	,   return_tensors='''pt''')

   # Instantiate dataloaders.
   lowerCAmelCase_			=      DataLoader(
       tokenized_datasets['''train''']	,   shuffle=__snake_case	,   collate_fn=__snake_case	,   batch_size=__snake_case)
   lowerCAmelCase_			=      DataLoader(
       tokenized_datasets['''validation''']	,   shuffle=__snake_case	,   collate_fn=__snake_case	,   batch_size=__snake_case)

   return train_dataloader, eval_dataloader



def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int])			->					List[Any]:
   # Initialize accelerator
   lowerCAmelCase_			=      Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCAmelCase_			=      config['''lr''']
   lowerCAmelCase_			=      int(config['''num_epochs'''])
   lowerCAmelCase_			=      int(config['''seed'''])
   lowerCAmelCase_			=      int(config['''batch_size'''])
   lowerCAmelCase_			=      args.model_name_or_path

   set_seed(__snake_case)
   lowerCAmelCase_	,lowerCAmelCase_			=      get_dataloaders(__snake_case	,   __snake_case	,   __snake_case	,   args.n_train	,   args.n_val)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCAmelCase_			=      AutoModelForSequenceClassification.from_pretrained(__snake_case	,   return_dict=__snake_case)

   # Instantiate optimizer
   lowerCAmelCase_			=      (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   lowerCAmelCase_			=      optimizer_cls(params=model.parameters()	,   lr=__snake_case)

   if accelerator.state.deepspeed_plugin is not None:
      lowerCAmelCase_			=      accelerator.state.deepspeed_plugin.deepspeed_config[
          '''gradient_accumulation_steps'''
      ]
   else:
      lowerCAmelCase_			=      1
   lowerCAmelCase_			=      (len(__snake_case) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      lowerCAmelCase_			=      get_linear_schedule_with_warmup(
          optimizer=__snake_case	,   num_warmup_steps=0	,   num_training_steps=__snake_case	,   )
   else:
      lowerCAmelCase_			=      DummyScheduler(__snake_case	,   total_num_steps=__snake_case	,   warmup_num_steps=0)

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      accelerator.prepare(
       __snake_case	,   __snake_case	,   __snake_case	,   __snake_case	,   __snake_case)

   # We need to keep track of how many total steps we have iterated over
   lowerCAmelCase_			=      0
   # We also need to keep track of the stating epoch so files are named properly
   lowerCAmelCase_			=      0

   # Now we train the model
   lowerCAmelCase_			=      {}
   for epoch in range(__snake_case	,   __snake_case):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(__snake_case):
            lowerCAmelCase_			=      model(**__snake_case)
            lowerCAmelCase_			=      outputs.loss
            lowerCAmelCase_			=      loss / gradient_accumulation_steps
            accelerator.backward(__snake_case)
            if step % gradient_accumulation_steps == 0:
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
      accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin)))
      accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used))
      accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked))
      accelerator.print(
          '''Total Peak Memory consumed during the train (max): {}'''.format(
              tracemalloc.peaked + bamb(tracemalloc.begin)))
      lowerCAmelCase_			=      tracemalloc.peaked + bamb(tracemalloc.begin)
      if args.peak_memory_upper_bound is not None:
         assert (
             train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
         ), "Peak memory usage exceeded the upper bound"

   accelerator.wait_for_everyone()
   if accelerator.is_main_process:
      with open(os.path.join(args.output_dir	,   '''peak_memory_utilization.json''')	,   '''w''') as f:
         json.dump(__snake_case	,   __snake_case)



def snake_case_      (					)			->					Optional[Any]:
   lowerCAmelCase_			=      argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''')
   parser.add_argument(
       '''--model_name_or_path'''	,   type=__snake_case	,   default='''bert-base-cased'''	,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''	,   required=__snake_case	,   )
   parser.add_argument(
       '''--output_dir'''	,   type=__snake_case	,   default='''.'''	,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,   )
   parser.add_argument(
       '''--peak_memory_upper_bound'''	,   type=__snake_case	,   default=__snake_case	,   help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''	,   )
   parser.add_argument(
       '''--n_train'''	,   type=__snake_case	,   default=320	,   help='''Number of training examples to use.'''	,   )
   parser.add_argument(
       '''--n_val'''	,   type=__snake_case	,   default=160	,   help='''Number of validation examples to use.'''	,   )
   parser.add_argument(
       '''--num_epochs'''	,   type=__snake_case	,   default=1	,   help='''Number of train epochs.'''	,   )
   lowerCAmelCase_			=      parser.parse_args()
   lowerCAmelCase_			=      {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
   training_function(__snake_case	,   __snake_case)


if __name__ == "__main__":
 main()






'''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 snake_case_      (					)			->					str:
   lowerCAmelCase_			=      torch.nn.Linear(2	,   4)
   lowerCAmelCase_			=      torch.optim.AdamW(model.parameters()	,   lr=1.0)
   lowerCAmelCase_			=      torch.optim.lr_scheduler.OneCycleLR(__snake_case	,   max_lr=0.0_1	,   steps_per_epoch=2	,   epochs=1)
   lowerCAmelCase_			=      DataLoader(TensorDataset(torch.tensor([1, 2, 3])))
   lowerCAmelCase_			=      DataLoader(TensorDataset(torch.tensor([4, 5, 6])))

   return model, optimizer, scheduler, train_dl, valid_dl



def snake_case_      (					__snake_case  :							Any)			->					List[Any]:
   return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def snake_case_      (					__snake_case  :							Optional[int])			->					Any:
   lowerCAmelCase_			=      torch.nn.Linear(*tuple(model.weight.T.shape)).state_dict()
   model.load_state_dict(__snake_case)






class     __UpperCAmelCase       ( __a		):





  @require_cuda
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     assert PartialState._shared_state["_cpu"] is False
     assert PartialState._shared_state["device"].type == "cuda"
     with self.assertRaises(_lowerCamelCase ):
        lowerCAmelCase_			=      Accelerator(cpu=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_			=      GradientState()
     assert state.num_steps == 1
     lowerCAmelCase_			=      4
     assert state.num_steps == 4

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





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()

     (
         (
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,
     )			=      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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):
     PartialState._reset_state()

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

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





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     accelerator.prepare(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )

     lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     accelerator.prepare(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )

     lowerCAmelCase_			=      get_signature(_lowerCamelCase )

     # saving hook
     def save_config(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
        lowerCAmelCase_			=      {'''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    ,     _lowerCamelCase ):
        with open(os.path.join(_lowerCamelCase    ,     '''data.json''' )    ,     '''r''' ) as f:
           lowerCAmelCase_			=      json.load(_lowerCamelCase )

        lowerCAmelCase_			=      config['''class_name''']

     lowerCAmelCase_			=      accelerator.register_save_state_pre_hook(_lowerCamelCase )
     lowerCAmelCase_			=      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
        lowerCAmelCase_			=      '''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
        lowerCAmelCase_			=      '''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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     lowerCAmelCase_			=      None

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





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     lowerCAmelCase_			=      [1, 2, 3]

     # This should work
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):
     from transformers import AutoModelForCausalLM

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

     # This should work
     lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )





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

     lowerCAmelCase_			=      Accelerator()

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

     lowerCAmelCase_			=      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 ):
        lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )





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

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

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

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

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

     PartialState._reset_state()





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

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

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

     # This should work
     lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )







  @require_cuda
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      torch.nn.Linear(10    ,     10 )
     lowerCAmelCase_			=      torch.optim.SGD(model.parameters()    ,     lr=0.01 )
     lowerCAmelCase_			=      Accelerator(cpu=_lowerCamelCase )
     lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )





