





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  (a__ :Accelerator ,      a__ :int = 16							):






							"""simple docstring"""





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

							def tokenize_function(a__ :Any							):
														# 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__ :int							):
														# 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

					UpperCamelCase__           =			mocked_dataloaders  # noqa: F811





def      _UpperCamelCase  (a__ :Union[str, Any] ,      a__ :Any							):






							"""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__  ,  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__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  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__  ,  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      _UpperCamelCase  ():






							"""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()







def 						lowerCamelCase__      ( __A    :int	,__A    :int		):


       """simple docstring"""




       if a < 0 or b < 0:
              raise ValueError("""the value of both inputs must be positive"""		)

       __snake_case       	=							str(bin(__A		)		)[2:]  # remove the leading "0b"
       __snake_case       	=							str(bin(__A		)		)[2:]  # remove the leading "0b"

       __snake_case       	=							max(len(__A		)	,len(__A		)		)

       return "0b" + "".join(
           str(int(char_a == """1""" and char_b == """1"""		)		)
           for char_a, char_b in zip(a_binary.zfill(__A		)	,b_binary.zfill(__A		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

