

'''simple docstring'''


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_				: Optional[int]    	=							16
lowerCamelCase_				: Optional[int]    	=							32







def 	__magic_name__( _A						,      _A = 16					):
						'''simple docstring'''





						UpperCamelCase__         = AutoTokenizer.from_pretrained("""bert-base-cased"""					)
						UpperCamelCase__         = load_dataset("""glue"""						,      """mrpc"""					)

						def tokenize_function(_A					):
												# max_length=None => use the model max length (it's actually the default)
												UpperCamelCase__         = tokenizer(examples["""sentence1"""]						,      examples["""sentence2"""]						,      truncation=_A						,      max_length=_A					)
												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__         = datasets.map(
												    _A						,      batched=_A						,      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__         = tokenized_datasets.rename_column("""label"""						,      """labels"""					)

						def collate_fn(_A					):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												UpperCamelCase__         = 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__         = 16
												elif accelerator.mixed_precision != "no":
																		UpperCamelCase__         = 8
												else:
																		UpperCamelCase__         = None

												return tokenizer.pad(
												    _A						,      padding="""longest"""						,      max_length=_A						,      pad_to_multiple_of=_A						,      return_tensors="""pt"""						,      )

						# Instantiate dataloaders.
						UpperCamelCase__         = DataLoader(
						    tokenized_datasets["""train"""]						,      shuffle=_A						,      collate_fn=_A						,      batch_size=_A					)
						UpperCamelCase__         = DataLoader(
						    tokenized_datasets["""validation"""]						,      shuffle=_A						,      collate_fn=_A						,      batch_size=_A					)

						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_				: Optional[int]    	=							mocked_dataloaders  # noqa: F811







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,      _A					) == "1":
												UpperCamelCase__         = 2
						# New Code #
						UpperCamelCase__         = int(args.gradient_accumulation_steps					)
						UpperCamelCase__         = int(args.local_sgd_steps					)
						# Initialize accelerator
						UpperCamelCase__         = Accelerator(
						    cpu=args.cpu						,      mixed_precision=args.mixed_precision						,      gradient_accumulation_steps=_A					)
						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__         = config['''lr''']
						UpperCamelCase__         = int(config["""num_epochs"""]					)
						UpperCamelCase__         = int(config["""seed"""]					)
						UpperCamelCase__         = int(config["""batch_size"""]					)

						UpperCamelCase__         = evaluate.load("""glue"""						,      """mrpc"""					)

						set_seed(_A					)
						UpperCamelCase__         = get_dataloaders(_A						,      _A					)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCamelCase__         = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""						,      return_dict=_A					)

						# 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__         = model.to(accelerator.device					)

						# Instantiate optimizer
						UpperCamelCase__         = AdamW(params=model.parameters()						,      lr=_A					)

						# Instantiate scheduler
						UpperCamelCase__         = get_linear_schedule_with_warmup(
						    optimizer=_A						,      num_warmup_steps=100						,      num_training_steps=(len(_A					) * 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__         = accelerator.prepare(
						    _A						,      _A						,      _A						,      _A						,      _A					)

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

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

												UpperCamelCase__         = metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:"						,      _A					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = argparse.ArgumentParser(description="""Simple example of training script."""					)
						parser.add_argument(
						    """--mixed_precision"""						,      type=_A						,      default=_A						,      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=_A						,      default=1						,      help="""The number of minibatches to be ran before gradients are accumulated."""						,      )
						parser.add_argument(
						    """--local_sgd_steps"""						,      type=_A						,      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__         = parser.parse_args()
						UpperCamelCase__         = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(_A						,      _A					)


if __name__ == "__main__":
					main()








'''simple docstring'''


import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowerCamelCase_				: Any    	=							{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

lowerCamelCase_				: Optional[int]    	=							logging.WARNING







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = os.getenv("""DATASETS_VERBOSITY"""						,      _A					)
						if env_level_str:
												if env_level_str in log_levels:
																		return log_levels[env_level_str]
												else:
																		logging.getLogger().warning(
																		    f"Unknown option DATASETS_VERBOSITY={env_level_str}, "
																		    f"has to be one of: { ', '.join(log_levels.keys()					) }"					)
						return _default_log_level







def 	__magic_name__( ):
						'''simple docstring'''





						return __name__.split("""."""					)[0]







def 	__magic_name__( ):
						'''simple docstring'''





						return logging.getLogger(_get_library_name()					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = _get_library_root_logger()
						library_root_logger.setLevel(_get_default_logging_level()					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = _get_library_root_logger()
						library_root_logger.setLevel(logging.NOTSET					)







def 	__magic_name__( _A = None					):
						'''simple docstring'''





						if name is None:
												UpperCamelCase__         = _get_library_name()
						return logging.getLogger(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return _get_library_root_logger().getEffectiveLevel()







def 	__magic_name__( _A					):
						'''simple docstring'''





						_get_library_root_logger().setLevel(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						return set_verbosity(_A					)







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = False







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __init__(   self							:						int ,      *lowercase							:						Any ,      **lowercase							:						int			)							->    List[Any]:  # pylint: disable=unused-argument

									'''simple docstring'''







									UpperCamelCase__         = args[0] if args else None






			def __iter__(   self							:						str			)							->    List[str]:

									'''simple docstring'''







									return iter(self._iterator			)






			def __getattr__(   self							:						Optional[int] ,      lowercase							:						Tuple			)							->    int:

									'''simple docstring'''







									def empty_fn(*lowercase							:						List[Any] ,      **lowercase							:						Dict			):  # pylint: disable=unused-argument
															return

									return empty_fn






			def __enter__(   self							:						Dict			)							->    Tuple:

									'''simple docstring'''







									return self







			def __exit__(   self							:						Tuple ,      lowercase							:						Optional[Any] ,      lowercase							:						Any ,      lowercase							:						int			)							->    Any:

									'''simple docstring'''







									return
lowerCamelCase_				: str    	=							True
class    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			def __call__(   self							:						List[Any] ,      *lowercase							:						str ,      lowercase							:						Optional[int]=False ,      **lowercase							:						Optional[int]			)							->    Optional[int]:

									'''simple docstring'''







									if _tqdm_active and not disable:
															return tqdm_lib.tqdm(*lowercase ,      **lowercase			)
									else:
															return EmptyTqdm(*lowercase ,      **lowercase			)






			def 				A      (   self							:						Optional[Any] ,      *lowercase							:						Tuple ,      **lowercase							:						Any			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = None
									if _tqdm_active:
															return tqdm_lib.tqdm.set_lock(*lowercase ,      **lowercase			)







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

									'''simple docstring'''







									if _tqdm_active:
															return tqdm_lib.tqdm.get_lock()
lowerCamelCase_				: int    	=							_tqdm_cls()







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						return bool(_tqdm_active					)







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						UpperCamelCase__         = True







def 	__magic_name__( ):
						'''simple docstring'''





						global _tqdm_active
						UpperCamelCase__         = False
