


'''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      :List[str]    	=			1_6
lowerCAmelCase      :Any    	=			3_2





def lowerCamelCase			(			lowerCAmelCase				:	Optional[Any]		,		lowerCAmelCase				:	Any = 16		):





	"""simple docstring"""







	__magic_name__       :    Optional[int]     	=     AutoTokenizer.from_pretrained('bert-base-cased'		)
	__magic_name__       :    str     	=     load_dataset('glue'		,		'mrpc'		)

	def tokenize_function(lowerCAmelCase				:	List[str]		):
		# max_length=None => use the model max length (it's actually the default)
		__magic_name__       :    Optional[Any]     	=     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():
		__magic_name__       :    int     	=     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
	__magic_name__       :    Optional[int]     	=     tokenized_datasets.rename_column('label'		,		'labels'		)

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

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

	# Instantiate dataloaders.
	__magic_name__       :    Tuple     	=     DataLoader(
	    tokenized_datasets['train']		,		shuffle=_a		,		collate_fn=_a		,		batch_size=_a		)
	__magic_name__       :    Dict     	=     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 lowerCamelCase			(			lowerCAmelCase				:	Optional[Any]		,		lowerCAmelCase				:	List[str]		):





	"""simple docstring"""







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

	__magic_name__       :    Optional[int]     	=     evaluate.load('glue'		,		'mrpc'		)

	set_seed(_a		)
	__magic_name__       :    Optional[int]     	=     get_dataloaders(_a		,		_a		)
	# 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=_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).
	__magic_name__       :    Tuple     	=     model.to(accelerator.device		)

	# Instantiate optimizer
	__magic_name__       :    List[str]     	=     AdamW(params=model.parameters()		,		lr=_a		)

	# Instantiate scheduler
	__magic_name__       :    Union[str, Any]     	=     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.
	__magic_name__       :    Union[str, Any]     	=     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		):
					__magic_name__       :    int     	=     model(**_a		)
					__magic_name__       :    List[str]     	=     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():
				__magic_name__       :    int     	=     model(**_a		)
			__magic_name__       :    Optional[int]     	=     outputs.logits.argmax(dim=-1		)
			__magic_name__       :    List[Any]     	=     accelerator.gather_for_metrics((predictions, batch['labels'])		)
			metric.add_batch(
			    predictions=_a		,		references=_a		,		)

		__magic_name__       :    Union[str, Any]     	=     metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'		,		_a		)





def lowerCamelCase			(			):





	"""simple docstring"""







	__magic_name__       :    List[str]     	=     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.'		)
	__magic_name__       :    Dict     	=     parser.parse_args()
	__magic_name__       :    Optional[Any]     	=     {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
	training_function(_a		,		_a		)


if __name__ == "__main__":
							main()




"""simple docstring"""

from __future__ import annotations




def __lowercase			(							_a		,			_a		,			_a		,			):
    if (stress, tangential_force, area).count(0    ) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''    )
    elif stress < 0:
        raise ValueError('''Stress cannot be negative'''    )
    elif tangential_force < 0:
        raise ValueError('''Tangential Force cannot be negative'''    )
    elif area < 0:
        raise ValueError('''Area cannot be negative'''    )
    elif stress == 0:
        return (
            "stress",
            tangential_force / area,
        )
    elif tangential_force == 0:
        return (
            "tangential_force",
            stress * area,
        )
    else:
        return (
            "area",
            tangential_force / stress,
        )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





