


"""simple docstring"""


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			if not isinstance(_UpperCamelCase					,				_UpperCamelCase   ):
						raise ValueError("multiplicative_persistence() only accepts integral values"   )
			if num < 0:
						raise ValueError("multiplicative_persistence() does not accept negative values"   )

			__lowerCAmelCase       					=    0
			__lowerCAmelCase       					=    str(_UpperCamelCase   )

			while len(_UpperCamelCase   ) != 1:
						__lowerCAmelCase       					=    [int(_UpperCamelCase   ) for i in num_string]

						__lowerCAmelCase       					=    1
						for i in range(0					,				len(_UpperCamelCase   )   ):
									total *= numbers[i]

						__lowerCAmelCase       					=    str(_UpperCamelCase   )

						steps += 1
			return steps


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			if not isinstance(_UpperCamelCase					,				_UpperCamelCase   ):
						raise ValueError("additive_persistence() only accepts integral values"   )
			if num < 0:
						raise ValueError("additive_persistence() does not accept negative values"   )

			__lowerCAmelCase       					=    0
			__lowerCAmelCase       					=    str(_UpperCamelCase   )

			while len(_UpperCamelCase   ) != 1:
						__lowerCAmelCase       					=    [int(_UpperCamelCase   ) for i in num_string]

						__lowerCAmelCase       					=    0
						for i in range(0					,				len(_UpperCamelCase   )   ):
									total += numbers[i]

						__lowerCAmelCase       					=    str(_UpperCamelCase   )

						steps += 1
			return steps


if __name__ == "__main__":
	import doctest

	doctest.testmod()








"""simple docstring"""


import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


A  :     Dict    					=    1_6
A  :     Optional[int]    					=    3_2


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase = 16   ):

			'''simple docstring'''




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

			def tokenize_function(_UpperCamelCase   ):
						# max_length=None => use the model max length (it's actually the default)
						__lowerCAmelCase       					=    tokenizer(examples["sentence1"]					,				examples["sentence2"]					,				truncation=_UpperCamelCase					,				max_length=_UpperCamelCase   )
						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(
						    _UpperCamelCase					,				batched=_UpperCamelCase					,				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(_UpperCamelCase   ):
						# 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(
						    _UpperCamelCase					,				padding="longest"					,				max_length=_UpperCamelCase					,				pad_to_multiple_of=_UpperCamelCase					,				return_tensors="pt"					,				)

			# Instantiate dataloaders.
			__lowerCAmelCase       					=    DataLoader(
			    tokenized_datasets["train"]					,				shuffle=_UpperCamelCase					,				collate_fn=_UpperCamelCase					,				batch_size=_UpperCamelCase   )
			__lowerCAmelCase       					=    DataLoader(
			    tokenized_datasets["validation"]					,				shuffle=_UpperCamelCase					,				collate_fn=_UpperCamelCase					,				batch_size=_UpperCamelCase   )

			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

	A  :     List[Any]    					=    mocked_dataloaders  # noqa: F811


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			if os.environ.get("TESTING_MOCKED_DATALOADERS"					,				_UpperCamelCase   ) == "1":
						__lowerCAmelCase       					=    2
			# Initialize accelerator
			__lowerCAmelCase       					=    Accelerator(cpu=args.cpu					,				mixed_precision=args.mixed_precision   )
			# 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"   )

			# New Code #
			# We now can define an inner training loop function. It should take a batch size as the only parameter,
			# and build the dataloaders in there.
			# It also gets our decorator
			@find_executable_batch_size(starting_batch_size=_UpperCamelCase   )
			def inner_training_loop(_UpperCamelCase   ):
						# And now just move everything below under this function
						# We need to bring in the Accelerator object from earlier
						nonlocal accelerator
						# And reset all of its attributes that could hold onto any memory:
						accelerator.free_memory()

						# Then we can declare the model, optimizer, and everything else:
						set_seed(_UpperCamelCase   )

						# 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=_UpperCamelCase   )

						# 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=_UpperCamelCase   )
						__lowerCAmelCase					,			__lowerCAmelCase       					=    get_dataloaders(_UpperCamelCase					,				_UpperCamelCase   )

						# Instantiate scheduler
						__lowerCAmelCase       					=    get_linear_schedule_with_warmup(
						    optimizer=_UpperCamelCase					,				num_warmup_steps=100					,				num_training_steps=(len(_UpperCamelCase   ) * 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					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    accelerator.prepare(
						    _UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )

						# Now we train the model
						for epoch in range(_UpperCamelCase   ):
									model.train()
									for step, batch in enumerate(_UpperCamelCase   ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device   )
												__lowerCAmelCase       					=    model(**_UpperCamelCase   )
												__lowerCAmelCase       					=    outputs.loss
												accelerator.backward(_UpperCamelCase   )
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

									__lowerCAmelCase       					=    metric.compute()
									# Use accelerator.print to print only on the main process.
									accelerator.print(f"epoch {epoch}:"					,				_UpperCamelCase   )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
			inner_training_loop()


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    argparse.ArgumentParser(description="Simple example of training script."   )
			parser.add_argument(
			    "--mixed_precision"					,				type=_UpperCamelCase					,				default=_UpperCamelCase					,				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."					,				)
			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(_UpperCamelCase					,				_UpperCamelCase   )


if __name__ == "__main__":
	main()




