



"""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
#
########################################################################


lowercase__    :  List[str]							    =	16
lowercase__    :  Any							    =	32




def __lowercase			(							_a		,			_a = 16    ):
    snake_case_ :     Optional[int]				=	AutoTokenizer.from_pretrained('''bert-base-cased'''    )
    snake_case_ :     str				=	load_dataset('''glue'''		,			'''mrpc'''    )

    def tokenize_function(_a    ):
        # max_length=None => use the model max length (it's actually the default)
        snake_case_ :     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():
        snake_case_ :     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
    snake_case_ :     Optional[int]				=	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.
        snake_case_ :     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":
            snake_case_ :     Dict				=	16
        elif accelerator.mixed_precision != "no":
            snake_case_ :     int				=	8
        else:
            snake_case_ :     Dict				=	None

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

    # Instantiate dataloaders.
    snake_case_ :     Tuple				=	DataLoader(
        tokenized_datasets['''train''']		,			shuffle=_a		,			collate_fn=_a		,			batch_size=_a    )
    snake_case_ :     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

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




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

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

    set_seed(_a    )
    snake_case_,	snake_case_ :     Optional[int]				=	get_dataloaders(_a		,			_a    )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case_ :     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).
    snake_case_ :     Tuple				=	model.to(accelerator.device    )

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

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

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




def __lowercase			(							):
    snake_case_ :     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.'''    )
    snake_case_ :     Dict				=	parser.parse_args()
    snake_case_ :     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()





