



import argparse
import os

import evaluate
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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCAmelCase	       =	16
__UpperCAmelCase	       =	32
def 		UpperCamelCase     (       snake_case__	:   Accelerator	,					snake_case__	:   int = 16     )     ->					Dict:
   UpperCamelCase   :  str						     =     AutoTokenizer.from_pretrained('bert-base-cased'     )
   UpperCamelCase   :  str						     =     load_dataset('glue'	,					'mrpc'     )

   def tokenize_function(snake_case__	:   Dict     ):
      # max_length=None => use the model max length (it's actually the default)
      UpperCamelCase   :  Optional[Any]						     =     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
   # starting with the main process first:
   with accelerator.main_process_first():
      UpperCamelCase   :  List[str]						     =     datasets.map(
          snake_case__	,					batched=snake_case__	,					remove_columns=['idx', 'sentence1', 'sentence2']	,					)

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

   def collate_fn(snake_case__	:   Tuple     ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      UpperCamelCase   :  List[str]						     =     128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         UpperCamelCase   :  str						     =     16
      elif accelerator.mixed_precision != "no":
         UpperCamelCase   :  Dict						     =     8
      else:
         UpperCamelCase   :  Union[str, Any]						     =     None

      return tokenizer.pad(
          snake_case__	,					padding='longest'	,					max_length=snake_case__	,					pad_to_multiple_of=snake_case__	,					return_tensors='pt'	,					)

   # Instantiate dataloaders.
   UpperCamelCase   :  Optional[int]						     =     DataLoader(
       tokenized_datasets['train']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )
   UpperCamelCase   :  List[Any]						     =     DataLoader(
       tokenized_datasets['validation']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    __UpperCAmelCase	       =	mocked_dataloaders  # noqa: F811
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   List[Any]     )     ->					List[str]:
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'	,					snake_case__     ) == "1":
      UpperCamelCase   :  List[str]						     =     2
   # New Code #
   UpperCamelCase   :  Optional[int]						     =     int(args.gradient_accumulation_steps     )
   UpperCamelCase   :  List[str]						     =     int(args.local_sgd_steps     )
   # Initialize accelerator
   UpperCamelCase   :  Optional[Any]						     =     Accelerator(
       cpu=args.cpu	,					mixed_precision=args.mixed_precision	,					gradient_accumulation_steps=snake_case__     )
   if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
      raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'     )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCamelCase   :  Union[str, Any]						     =     config['lr']
   UpperCamelCase   :  Optional[int]						     =     int(config['num_epochs']     )
   UpperCamelCase   :  List[Any]						     =     int(config['seed']     )
   UpperCamelCase   :  List[str]						     =     int(config['batch_size']     )

   UpperCamelCase   :  Optional[Any]						     =     evaluate.load('glue'	,					'mrpc'     )

   set_seed(snake_case__     )
   UpperCamelCase     ,					UpperCamelCase   :  int						     =     get_dataloaders(snake_case__	,					snake_case__     )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCamelCase   :  Tuple						     =     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	,					return_dict=snake_case__     )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   UpperCamelCase   :  Dict						     =     model.to(accelerator.device     )

   # Instantiate optimizer
   UpperCamelCase   :  List[str]						     =     AdamW(params=model.parameters()	,					lr=snake_case__     )

   # Instantiate scheduler
   UpperCamelCase   :  Any						     =     get_linear_schedule_with_warmup(
       optimizer=snake_case__	,					num_warmup_steps=100	,					num_training_steps=(len(snake_case__     ) * num_epochs)	,					)

   # 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.
   UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  str						     =     accelerator.prepare(
       snake_case__	,					snake_case__	,					snake_case__	,					snake_case__	,					snake_case__     )

   # Now we train the model
   for epoch in range(snake_case__     ):
      model.train()
      with LocalSGD(
          accelerator=snake_case__	,					model=snake_case__	,					local_sgd_steps=snake_case__	,					enabled=local_sgd_steps is not None     ) as local_sgd:
         for step, batch in enumerate(snake_case__     ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device     )
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(snake_case__     ):
               UpperCamelCase   :  int						     =     model(**snake_case__     )
               UpperCamelCase   :  Union[str, Any]						     =     output.loss
               accelerator.backward(snake_case__     )
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               # LocalSGD-specific line
               local_sgd.step()

      model.eval()
      for step, batch in enumerate(snake_case__     ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device     )
         with torch.no_grad():
            UpperCamelCase   :  Dict						     =     model(**snake_case__     )
         UpperCamelCase   :  Any						     =     outputs.logits.argmax(dim=-1     )
         UpperCamelCase     ,					UpperCamelCase   :  Any						     =     accelerator.gather_for_metrics((predictions, batch['labels'])     )
         metric.add_batch(
             predictions=snake_case__	,					references=snake_case__	,					)

      UpperCamelCase   :  List[Any]						     =     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""	,					snake_case__     )
def 		UpperCamelCase     (       )     ->					Dict:
   UpperCamelCase   :  Any						     =     argparse.ArgumentParser(description='Simple example of training script.'     )
   parser.add_argument(
       '--mixed_precision'	,					type=snake_case__	,					default=snake_case__	,					choices=['no', 'fp16', 'bf16', 'fp8']	,					help='Whether to use mixed precision. Choose'
       'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
       'and an Nvidia Ampere GPU.'	,					)
   # New Code #
   parser.add_argument(
       '--gradient_accumulation_steps'	,					type=snake_case__	,					default=1	,					help='The number of minibatches to be ran before gradients are accumulated.'	,					)
   parser.add_argument(
       '--local_sgd_steps'	,					type=snake_case__	,					default=8	,					help='Number of local SGD steps or None to disable local SGD'     )
   parser.add_argument('--cpu'	,					action='store_true'	,					help='If passed, will train on the CPU.'     )
   UpperCamelCase   :  str						     =     parser.parse_args()
   UpperCamelCase   :  Tuple						     =     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(snake_case__	,					snake_case__     )


if __name__ == "__main__":
    main()








import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   List[str]=False     )     ->					Optional[Any]:
   try:
      UpperCamelCase   :  List[Any]						     =     os.environ[key]
   except KeyError:
      # KEY isn't set, default to `default`.
      UpperCamelCase   :  List[Any]						     =     default
   else:
      # KEY is set, convert it to True or False.
      try:
         UpperCamelCase   :  Optional[Any]						     =     strtobool(snake_case__     )
      except ValueError:
         # More values are supported, but let's keep the message simple.
         raise ValueError(F"""If set, {key} must be yes or no."""     )
   return _value


__UpperCAmelCase	       =	parse_flag_from_env('''RUN_SLOW''', default=False)
def 		UpperCamelCase     (       snake_case__	:   int     )     ->					str:
   return unittest.skip('Test was skipped'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Optional[Any]:
   return unittest.skipUnless(_run_slow_tests	,					'test is slow'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Dict:
   return unittest.skipUnless(not torch.cuda.is_available()	,					'test requires only a CPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[Any]     )     ->					Dict:
   return unittest.skipUnless(torch.cuda.is_available()	,					'test requires a GPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					List[Any]:
   return unittest.skipUnless(is_xpu_available()	,					'test requires a XPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					List[Any]:
   return unittest.skipUnless(is_mps_available()	,					'test requires a `mps` backend support in `torch`'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   List[str]     )     ->					Tuple:
   return unittest.skipUnless(
       is_transformers_available() and is_datasets_available()	,					'test requires the Hugging Face suite'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					List[Any]:
   return unittest.skipUnless(is_bnb_available()	,					'test requires the bitsandbytes library'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					List[str]:
   return unittest.skipUnless(is_tpu_available()	,					'test requires TPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Optional[int]:
   return unittest.skipUnless(torch.cuda.device_count() == 1	,					'test requires a GPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Dict:
   return unittest.skipUnless(torch.xpu.device_count() == 1	,					'test requires a XPU'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					Any:
   return unittest.skipUnless(torch.cuda.device_count() > 1	,					'test requires multiple GPUs'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					int:
   return unittest.skipUnless(torch.xpu.device_count() > 1	,					'test requires multiple XPUs'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Any:
   return unittest.skipUnless(is_safetensors_available()	,					'test requires safetensors'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Optional[int]     )     ->					Dict:
   return unittest.skipUnless(is_deepspeed_available()	,					'test requires DeepSpeed'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					Optional[Any]:
   return unittest.skipUnless(is_torch_version('>='	,					'1.12.0'     )	,					'test requires torch version >= 1.12.0'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict=None	,					snake_case__	:   Union[str, Any]=None     )     ->					Optional[Any]:
   if test_case is None:
      return partial(snake_case__	,					version=snake_case__     )
   return unittest.skipUnless(is_torch_version('>='	,					snake_case__     )	,					F"""test requires torch version >= {version}"""     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict     )     ->					Optional[Any]:
   return unittest.skipUnless(is_tensorboard_available()	,					'test requires Tensorboard'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   str     )     ->					Tuple:
   return unittest.skipUnless(is_wandb_available()	,					'test requires wandb'     )(snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Any     )     ->					List[Any]:
   return unittest.skipUnless(is_comet_ml_available()	,					'test requires comet_ml'     )(snake_case__     )


__UpperCAmelCase	       =	(
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]     )     ->					Optional[Any]:
   return unittest.skipUnless(
       _atleast_one_tracker_available	,					'test requires at least one tracker to be available and for `comet_ml` to not be installed'	,					)(snake_case__     )



class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Optional[Any]       					=  True
       @classmethod
       def  snake_case_	(      cls	)					->		Any:
          UpperCamelCase   :  Optional[Any]						     =     tempfile.mkdtemp()
       @classmethod
       def  snake_case_	(      cls	)					->		Tuple:
          if os.path.exists(cls.tmpdir	):
             shutil.rmtree(cls.tmpdir	)






       def  snake_case_	(      self	)					->		str:
          if self.clear_on_setup:
             for path in Path(self.tmpdir	).glob('**/*'	):
                if path.is_file():
                   path.unlink()
                elif path.is_dir():
                   shutil.rmtree(SCREAMING_SNAKE_CASE_	)






class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self	)					->		Optional[int]:
          super().tearDown()
          # Reset the state of the AcceleratorState singleton.
          AcceleratorState._reset_state()
          PartialState._reset_state()






class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  Optional[Any]						     =     mocks if isinstance(SCREAMING_SNAKE_CASE_,      (tuple, list)	) else [mocks]
          for m in self.mocks:
             m.start()
             self.addCleanup(m.stop	)






def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					Optional[int]:
   UpperCamelCase   :  Tuple						     =     AcceleratorState()
   UpperCamelCase   :  Tuple						     =     tensor[None].clone().to(state.device     )
   UpperCamelCase   :  str						     =     gather(snake_case__     ).cpu()
   UpperCamelCase   :  Union[str, Any]						     =     tensor[0].cpu()
   for i in range(tensors.shape[0]     ):
      if not torch.equal(tensors[i]	,					snake_case__     ):
         return False
   return True



class     lowerCAmelCase_		:
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		int:
          UpperCamelCase   :  List[Any]						     =     returncode
          UpperCamelCase   :  Tuple						     =     stdout
          UpperCamelCase   :  Any						     =     stderr






async def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Optional[int]     )     ->					Union[str, Any]:
   while True:
      UpperCamelCase   :  List[str]						     =     await stream.readline()
      if line:
         callback(snake_case__     )
      else:
         break
async def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   int=None	,					snake_case__	:   Dict=None	,					snake_case__	:   Any=None	,					snake_case__	:   Optional[Any]=False	,					snake_case__	:   Union[str, Any]=False     )     ->					_RunOutput:
   if echo:
      print('\nRunning: '	,					' '.join(snake_case__     )     )

   UpperCamelCase   :  Dict						     =     await asyncio.create_subprocess_exec(
       cmd[0]	,					*cmd[1:]	,					stdin=snake_case__	,					stdout=asyncio.subprocess.PIPE	,					stderr=asyncio.subprocess.PIPE	,					env=snake_case__	,					)

   # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
   # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
   #
   # If it starts hanging, will need to switch to the following code. The problem is that no data
   # will be seen until it's done and if it hangs for example there will be no debug info.
   # out, err = await p.communicate()
   # return _RunOutput(p.returncode, out, err)

   UpperCamelCase   :  Tuple						     =     []
   UpperCamelCase   :  int						     =     []

   def tee(snake_case__	:   List[Any]	,					snake_case__	:   Optional[int]	,					snake_case__	:   Optional[int]	,					snake_case__	:   List[Any]=""     ):
      UpperCamelCase   :  Union[str, Any]						     =     line.decode('utf-8'     ).rstrip()
      sink.append(snake_case__     )
      if not quiet:
         print(snake_case__	,					snake_case__	,					file=snake_case__     )

    # XXX: the timeout doesn't seem to make any difference here
   await asyncio.wait(
       [
           asyncio.create_task(_read_stream(p.stdout	,					lambda snake_case__     : tee(snake_case__	,					snake_case__	,					sys.stdout	,					label='stdout:'     )     )     ),
           asyncio.create_task(_read_stream(p.stderr	,					lambda snake_case__     : tee(snake_case__	,					snake_case__	,					sys.stderr	,					label='stderr:'     )     )     ),
       ]	,					timeout=snake_case__	,					)
   return _RunOutput(await p.wait()	,					snake_case__	,					snake_case__     )
def 		UpperCamelCase     (       snake_case__	:   Dict	,					snake_case__	:   Any=None	,					snake_case__	:   Tuple=None	,					snake_case__	:   Any=180	,					snake_case__	:   Any=False	,					snake_case__	:   Optional[int]=True     )     ->					_RunOutput:
   UpperCamelCase   :  int						     =     asyncio.get_event_loop()
   UpperCamelCase   :  Tuple						     =     loop.run_until_complete(
       _stream_subprocess(snake_case__	,					env=snake_case__	,					stdin=snake_case__	,					timeout=snake_case__	,					quiet=snake_case__	,					echo=snake_case__     )     )

   UpperCamelCase   :  str						     =     ' '.join(snake_case__     )
   if result.returncode > 0:
      UpperCamelCase   :  Union[str, Any]						     =     '\n'.join(result.stderr     )
      raise RuntimeError(
          F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
          F"""The combined stderr from workers follows:\n{stderr}"""     )

   return result



class     lowerCAmelCase_		(  a__							):
       pass






def 		UpperCamelCase     (       snake_case__	:   List[str]	,					snake_case__	:   str=False     )     ->					int:
   try:
      UpperCamelCase   :  Union[str, Any]						     =     subprocess.check_output(snake_case__	,					stderr=subprocess.STDOUT     )
      if return_stdout:
         if hasattr(snake_case__	,					'decode'     ):
            UpperCamelCase   :  Optional[int]						     =     output.decode('utf-8'     )
         return output
   except subprocess.CalledProcessError as e:
      raise SubprocessCallException(
          F"""Command `{" ".join(snake_case__     )}` failed with the following error:\n\n{e.output.decode()}"""     ) from e



