






"""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							=  16
__lowerCamelCase							=  32



def   a    (     __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    Union[str, Any] = 1_6  )    ->							Optional[int]:
						__magic_name__:	Dict							=       AutoTokenizer.from_pretrained("""bert-base-cased"""  )
						__magic_name__:	str							=       load_dataset("""glue"""      ,   """mrpc"""  )

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

						def collate_fn(__UpperCAmelCase      :    Any  ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__magic_name__:	int							=       1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__magic_name__:	Any							=       1_6
												elif accelerator.mixed_precision != "no":
																		__magic_name__:	Tuple							=       8
												else:
																		__magic_name__:	Union[str, Any]							=       None

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

						# Instantiate dataloaders.
						__magic_name__:	str							=       DataLoader(
						    tokenized_datasets["""train"""]      ,   shuffle=__lowerCamelCase      ,   collate_fn=__lowerCamelCase      ,   batch_size=__lowerCamelCase  )
						__magic_name__:	List[Any]							=       DataLoader(
						    tokenized_datasets["""validation"""]      ,   shuffle=__lowerCamelCase      ,   collate_fn=__lowerCamelCase      ,   batch_size=__lowerCamelCase  )

						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   a    (     __UpperCAmelCase      :    Union[str, Any]      ,   __UpperCAmelCase      :    str  )    ->							List[Any]:
						# For testing only
						if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,   __lowerCamelCase  ) == "1":
												__magic_name__:	Union[str, Any]							=       2
						# New Code #
						__magic_name__:	Any							=       int(args.gradient_accumulation_steps  )
						__magic_name__:	List[Any]							=       int(args.local_sgd_steps  )
						# Initialize accelerator
						__magic_name__:	Dict							=       Accelerator(
						    cpu=args.cpu      ,   mixed_precision=args.mixed_precision      ,   gradient_accumulation_steps=__lowerCamelCase  )
						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
						__magic_name__:	List[str]							=       config["""lr"""]
						__magic_name__:	List[str]							=       int(config["""num_epochs"""]  )
						__magic_name__:	Optional[Any]							=       int(config["""seed"""]  )
						__magic_name__:	int							=       int(config["""batch_size"""]  )

						__magic_name__:	Tuple							=       evaluate.load("""glue"""      ,   """mrpc"""  )

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

						# 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).
						__magic_name__:	Tuple							=       model.to(accelerator.device  )

						# Instantiate optimizer
						__magic_name__:	int							=       AdamW(params=model.parameters()      ,   lr=__lowerCamelCase  )

						# Instantiate scheduler
						__magic_name__:	Any							=       get_linear_schedule_with_warmup(
						    optimizer=__lowerCamelCase      ,   num_warmup_steps=1_0_0      ,   num_training_steps=(len(__lowerCamelCase  ) * 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.
						__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	str							=       accelerator.prepare(
						    __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase  )

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

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

												__magic_name__:	int							=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'epoch {epoch}:'      ,   __lowerCamelCase  )



def   a    (     )    ->							List[Any]:
						__magic_name__:	Optional[Any]							=       argparse.ArgumentParser(description="""Simple example of training script."""  )
						parser.add_argument(
						    """--mixed_precision"""      ,   type=__lowerCamelCase      ,   default=__lowerCamelCase      ,   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=__lowerCamelCase      ,   default=1      ,   help="""The number of minibatches to be ran before gradients are accumulated."""      ,   )
						parser.add_argument(
						    """--local_sgd_steps"""      ,   type=__lowerCamelCase      ,   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."""  )
						__magic_name__:	Optional[Any]							=       parser.parse_args()
						__magic_name__:	str							=       {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
						training_function(__lowerCamelCase      ,   __lowerCamelCase  )


if __name__ == "__main__":
							main()









from bisect import bisect
from itertools import accumulate





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				sorted(zip(__lowerCamelCase				,     __lowerCamelCase       )				,     key=lambda __lowerCamelCase       : x[0] / x[1]				,     reverse=__lowerCamelCase       )
	a							,					a									=				[i[0] for i in r], [i[1] for i in r]
	a									=				list(accumulate(__lowerCamelCase       )       )
	a									=				bisect(__lowerCamelCase				,     __lowerCamelCase       )
	return (
	    0
	    if k == 0
	    else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
	    if k != n
	    else sum(vl[:k]       )
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

