


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					:  Optional[int]			=						16
UpperCAmelCase					:  Optional[int]			=						32






def      __lowerCamelCase		(				lowerCamelCase__						:		Accelerator   ,				lowerCamelCase__						:		int = 16	):





			'''simple docstring'''




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

			def tokenize_function(lowerCamelCase__						:		Tuple	):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase  						=   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():
						lowerCamelCase  						=   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
			lowerCamelCase  						=   tokenized_datasets.rename_column("""label"""   ,				"""labels"""	)

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

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

			# Instantiate dataloaders.
			lowerCamelCase  						=   DataLoader(
			    tokenized_datasets["""train"""]   ,				shuffle=__snake_case   ,				collate_fn=__snake_case   ,				batch_size=__snake_case	)
			lowerCamelCase  						=   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					:  Any			=						mocked_dataloaders  # noqa: F811






def      __lowerCamelCase		(				lowerCamelCase__						:		List[str]   ,				lowerCamelCase__						:		Optional[int]	):





			'''simple docstring'''




			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""   ,				__snake_case	) == "1":
						lowerCamelCase  						=   2
			# New Code #
			lowerCamelCase  						=   int(args.gradient_accumulation_steps	)
			lowerCamelCase  						=   int(args.local_sgd_steps	)
			# Initialize accelerator
			lowerCamelCase  						=   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
			lowerCamelCase  						=   config['lr']
			lowerCamelCase  						=   int(config["""num_epochs"""]	)
			lowerCamelCase  						=   int(config["""seed"""]	)
			lowerCamelCase  						=   int(config["""batch_size"""]	)

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

			set_seed(__snake_case	)
			lowerCamelCase  						=   get_dataloaders(__snake_case   ,				__snake_case	)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCamelCase  						=   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).
			lowerCamelCase  						=   model.to(accelerator.device	)

			# Instantiate optimizer
			lowerCamelCase  						=   AdamW(params=model.parameters()   ,				lr=__snake_case	)

			# Instantiate scheduler
			lowerCamelCase  						=   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.
			lowerCamelCase  						=   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	):
															lowerCamelCase  						=   model(**__snake_case	)
															lowerCamelCase  						=   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():
												lowerCamelCase  						=   model(**__snake_case	)
									lowerCamelCase  						=   outputs.logits.argmax(dim=-1	)
									lowerCamelCase  						=   accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
									metric.add_batch(
									    predictions=__snake_case   ,				references=__snake_case   ,				)

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






def      __lowerCamelCase		(				):





			'''simple docstring'''




			lowerCamelCase  						=   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."""	)
			lowerCamelCase  						=   parser.parse_args()
			lowerCamelCase  						=   {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(__snake_case   ,				__snake_case	)


if __name__ == "__main__":
		main()









def      lowercase__      (     __snake_case    :							int     ,       __snake_case    :							int  ):


					'''simple docstring'''


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

					UpperCAmelCase_    :      Tuple            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"
					UpperCAmelCase_    :      Union[str, Any]            =						str(bin(__snake_case  )  )[2:]  # remove the leading "0b"

					UpperCAmelCase_    :      List[Any]            =						max(len(__snake_case  )     ,       len(__snake_case  )  )

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()






