





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
#
########################################################################


lowerCAmelCase_    =    1_6
lowerCAmelCase_    =    3_2






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase = 16			)		->						Tuple:






				"""simple docstring"""


				snake_case_						:   Dict           =  AutoTokenizer.from_pretrained('''bert-base-cased'''			)
				snake_case_						:   Any           =  load_dataset('''glue'''			,    '''mrpc'''			)

				def tokenize_function(_UpperCamelCase			):
								# max_length=None => use the model max length (it's actually the default)
								snake_case_						:   Optional[int]           =  tokenizer(examples['''sentence1''']			,    examples['''sentence2''']			,    truncation=_UpperCamelCase			,    max_length=_UpperCamelCase			)
								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():
								snake_case_						:   List[str]           =  datasets.map(
								    _UpperCamelCase			,    batched=_UpperCamelCase			,    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
				snake_case_						:   Optional[Any]           =  tokenized_datasets.rename_column('''label'''			,    '''labels'''			)

				def collate_fn(_UpperCamelCase			):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								snake_case_						:   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":
												snake_case_						:   int           =  16
								elif accelerator.mixed_precision != "no":
												snake_case_						:   Tuple           =  8
								else:
												snake_case_						:   Optional[Any]           =  None

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

				# Instantiate dataloaders.
				snake_case_						:   Tuple           =  DataLoader(
				    tokenized_datasets['''train''']			,    shuffle=_UpperCamelCase			,    collate_fn=_UpperCamelCase			,    batch_size=_UpperCamelCase			)
				snake_case_						:   List[str]           =  DataLoader(
				    tokenized_datasets['''validation''']			,    shuffle=_UpperCamelCase			,    collate_fn=_UpperCamelCase			,    batch_size=_UpperCamelCase			)

				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

	lowerCAmelCase_    =    mocked_dataloaders  # noqa: F811






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,    _UpperCamelCase			) == "1":
								snake_case_						:   Tuple           =  2
				# New Code #
				snake_case_						:   Tuple           =  int(args.gradient_accumulation_steps			)
				snake_case_						:   Union[str, Any]           =  int(args.local_sgd_steps			)
				# Initialize accelerator
				snake_case_						:   List[Any]           =  Accelerator(
				    cpu=args.cpu			,    mixed_precision=args.mixed_precision			,    gradient_accumulation_steps=_UpperCamelCase			)
				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
				snake_case_						:   Any           =  config['''lr''']
				snake_case_						:   Any           =  int(config['''num_epochs''']			)
				snake_case_						:   Tuple           =  int(config['''seed''']			)
				snake_case_						:   Dict           =  int(config['''batch_size''']			)

				snake_case_						:   List[str]           =  evaluate.load('''glue'''			,    '''mrpc'''			)

				set_seed(_UpperCamelCase			)
				snake_case_      ,							snake_case_						:   int           =  get_dataloaders(_UpperCamelCase			,    _UpperCamelCase			)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				snake_case_						:   Union[str, Any]           =  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,    return_dict=_UpperCamelCase			)

				# 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).
				snake_case_						:   str           =  model.to(accelerator.device			)

				# Instantiate optimizer
				snake_case_						:   Tuple           =  AdamW(params=model.parameters()			,    lr=_UpperCamelCase			)

				# Instantiate scheduler
				snake_case_						:   str           =  get_linear_schedule_with_warmup(
				    optimizer=_UpperCamelCase			,    num_warmup_steps=100			,    num_training_steps=(len(_UpperCamelCase			) * 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.
				snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Dict           =  accelerator.prepare(
				    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)

				# Now we train the model
				for epoch in range(_UpperCamelCase			):
								model.train()
								with LocalSGD(
								    accelerator=_UpperCamelCase			,    model=_UpperCamelCase			,    local_sgd_steps=_UpperCamelCase			,    enabled=local_sgd_steps is not None			) as local_sgd:
												for step, batch in enumerate(_UpperCamelCase			):
																# 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(_UpperCamelCase			):
																				snake_case_						:   List[str]           =  model(**_UpperCamelCase			)
																				snake_case_						:   int           =  output.loss
																				accelerator.backward(_UpperCamelCase			)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

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

								snake_case_						:   Any           =  metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''			,    _UpperCamelCase			)






def 					lowerCamelCase_						(    )		->						Tuple:






				"""simple docstring"""


				snake_case_						:   List[str]           =  argparse.ArgumentParser(description='''Simple example of training script.'''			)
				parser.add_argument(
				    '''--mixed_precision'''			,    type=_UpperCamelCase			,    default=_UpperCamelCase			,    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=_UpperCamelCase			,    default=1			,    help='''The number of minibatches to be ran before gradients are accumulated.'''			,    )
				parser.add_argument(
				    '''--local_sgd_steps'''			,    type=_UpperCamelCase			,    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.'''			)
				snake_case_						:   Optional[int]           =  parser.parse_args()
				snake_case_						:   List[str]           =  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(_UpperCamelCase			,    _UpperCamelCase			)


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 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=False			)		->						int:






				"""simple docstring"""


				try:
								snake_case_						:   Optional[Any]           =  os.environ[key]
				except KeyError:
								# KEY isn't set, default to `default`.
								snake_case_						:   Tuple           =  default
				else:
								# KEY is set, convert it to True or False.
								try:
												snake_case_						:   Union[str, Any]           =  strtobool(_UpperCamelCase			)
								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


lowerCAmelCase_    =    parse_flag_from_env('''RUN_SLOW''', default=False)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skip('''Test was skipped'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				return unittest.skipUnless(_run_slow_tests			,    '''test is slow'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				return unittest.skipUnless(not torch.cuda.is_available()			,    '''test requires only a CPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.is_available()			,    '''test requires a GPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				return unittest.skipUnless(is_xpu_available()			,    '''test requires a XPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_mps_available()			,    '''test requires a `mps` backend support in `torch`'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				return unittest.skipUnless(
				    is_transformers_available() and is_datasets_available()			,    '''test requires the Hugging Face suite'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(is_bnb_available()			,    '''test requires the bitsandbytes library'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_tpu_available()			,    '''test requires TPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.device_count() == 1			,    '''test requires a GPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(torch.xpu.device_count() == 1			,    '''test requires a XPU'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(torch.cuda.device_count() > 1			,    '''test requires multiple GPUs'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(torch.xpu.device_count() > 1			,    '''test requires multiple XPUs'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				return unittest.skipUnless(is_safetensors_available()			,    '''test requires safetensors'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(is_deepspeed_available()			,    '''test requires DeepSpeed'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				return unittest.skipUnless(is_torch_version('''>='''			,    '''1.12.0'''			)			,    '''test requires torch version >= 1.12.0'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase=None			,    _UpperCamelCase=None			)		->						Tuple:






				"""simple docstring"""


				if test_case is None:
								return partial(_UpperCamelCase			,    version=_UpperCamelCase			)
				return unittest.skipUnless(is_torch_version('''>='''			,    _UpperCamelCase			)			,    f'''test requires torch version >= {version}'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return unittest.skipUnless(is_tensorboard_available()			,    '''test requires Tensorboard'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				return unittest.skipUnless(is_wandb_available()			,    '''test requires wandb'''			)(_UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				return unittest.skipUnless(is_comet_ml_available()			,    '''test requires comet_ml'''			)(_UpperCamelCase			)


lowerCAmelCase_    =    (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				return unittest.skipUnless(
				    _atleast_one_tracker_available			,    '''test requires at least one tracker to be available and for `comet_ml` to not be installed'''			,    )(_UpperCamelCase			)



class  __lowerCAmelCase	(      unittest.TestCase					):
			lowerCamelCase_					:					str	      =				True






			@classmethod
			def       lowerCamelCase  (cls   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  tempfile.mkdtemp()






			@classmethod
			def       lowerCamelCase  (cls   )					->       List[Any]:


							'''simple docstring'''



							if os.path.exists(cls.tmpdir   ):
											shutil.rmtree(cls.tmpdir   )






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


							'''simple docstring'''



							if self.clear_on_setup:
											for path in Path(self.tmpdir   ).glob('''**/*'''   ):
															if path.is_file():
																			path.unlink()
															elif path.is_dir():
																			shutil.rmtree(__magic_name__   )




class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							super().tearDown()
							# Reset the state of the AcceleratorState singleton.
							AcceleratorState._reset_state()
							PartialState._reset_state()




class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   str           =  mocks if isinstance(__magic_name__ ,							(tuple, list)   ) else [mocks]
							for m in self.mocks:
											m.start()
											self.addCleanup(m.stop   )






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  AcceleratorState()
				snake_case_						:   List[str]           =  tensor[None].clone().to(state.device			)
				snake_case_						:   Optional[Any]           =  gather(_UpperCamelCase			).cpu()
				snake_case_						:   Optional[Any]           =  tensor[0].cpu()
				for i in range(tensors.shape[0]			):
								if not torch.equal(tensors[i]			,    _UpperCamelCase			):
												return False
				return True



class  __lowerCAmelCase	:






			def __init__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  returncode
							snake_case_						:   List[Any]           =  stdout
							snake_case_						:   Tuple           =  stderr






async def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				while True:
								snake_case_						:   Tuple           =  await stream.readline()
								if line:
												callback(_UpperCamelCase			)
								else:
												break






async def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=False			,    _UpperCamelCase=False			)		->						_RunOutput:






				"""simple docstring"""


				if echo:
								print('''\nRunning: '''			,    ''' '''.join(_UpperCamelCase			)			)

				snake_case_						:   List[str]           =  await asyncio.create_subprocess_exec(
				    cmd[0]			,    *cmd[1:]			,    stdin=_UpperCamelCase			,    stdout=asyncio.subprocess.PIPE			,    stderr=asyncio.subprocess.PIPE			,    env=_UpperCamelCase			,    )

				# 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)

				snake_case_						:   List[Any]           =  []
				snake_case_						:   List[Any]           =  []

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

    # XXX: the timeout doesn't seem to make any difference here
				await asyncio.wait(
				    [
				        asyncio.create_task(_read_stream(p.stdout			,    lambda _UpperCamelCase			: tee(_UpperCamelCase			,    _UpperCamelCase			,    sys.stdout			,    label='''stdout:'''			)			)			),
				        asyncio.create_task(_read_stream(p.stderr			,    lambda _UpperCamelCase			: tee(_UpperCamelCase			,    _UpperCamelCase			,    sys.stderr			,    label='''stderr:'''			)			)			),
				    ]			,    timeout=_UpperCamelCase			,    )
				return _RunOutput(await p.wait()			,    _UpperCamelCase			,    _UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=None			,    _UpperCamelCase=None			,    _UpperCamelCase=180			,    _UpperCamelCase=False			,    _UpperCamelCase=True			)		->						_RunOutput:






				"""simple docstring"""


				snake_case_						:   List[str]           =  asyncio.get_event_loop()
				snake_case_						:   List[Any]           =  loop.run_until_complete(
				    _stream_subprocess(_UpperCamelCase			,    env=_UpperCamelCase			,    stdin=_UpperCamelCase			,    timeout=_UpperCamelCase			,    quiet=_UpperCamelCase			,    echo=_UpperCamelCase			)			)

				snake_case_						:   Optional[int]           =  ''' '''.join(_UpperCamelCase			)
				if result.returncode > 0:
								snake_case_						:   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 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=False			)		->						List[Any]:






				"""simple docstring"""


				try:
								snake_case_						:   List[str]           =  subprocess.check_output(_UpperCamelCase			,    stderr=subprocess.STDOUT			)
								if return_stdout:
												if hasattr(_UpperCamelCase			,    '''decode'''			):
																snake_case_						:   Tuple           =  output.decode('''utf-8'''			)
												return output
				except subprocess.CalledProcessError as e:
								raise SubprocessCallException(
								    f'''Command `{" ".join(_UpperCamelCase			)}` failed with the following error:\n\n{e.output.decode()}'''			) from e
