






'''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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

UpperCamelCase     :   Tuple	      =		16
UpperCamelCase     :   List[Any]	      =		32
def     A__      (					__lowerCAmelCase		:      Accelerator ,     __lowerCAmelCase		:      int = 16    ):
						lowerCamelCase__								=	AutoTokenizer.from_pretrained("""bert-base-cased"""    )
						lowerCamelCase__								=	load_dataset("""glue""" ,     """mrpc"""    )

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

						def collate_fn(__lowerCAmelCase		:      Union[str, Any]    ):
												# 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(
												    __lowerCAmelCase ,     padding="""longest""" ,     max_length=__lowerCAmelCase ,     pad_to_multiple_of=__lowerCAmelCase ,     return_tensors="""pt""" ,     )

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

							UpperCamelCase     :   Optional[Any]	      =		mocked_dataloaders  # noqa: F811
def     A__      (					__lowerCAmelCase		:      int ,     __lowerCAmelCase		:      Union[str, Any]    ):
						# For testing only
						if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,     __lowerCAmelCase    ) == "1":
												lowerCamelCase__								=	2
						# Initialize Accelerator

						# New Code #
						# We pass in "all" to `log_with` to grab all available trackers in the environment
						# Note: If using a custom `Tracker` class, should be passed in here such as:
						# >>> log_with = ["all", MyCustomTrackerClassInstance()]
						if args.with_tracking:
												lowerCamelCase__								=	Accelerator(
												    cpu=args.cpu ,     mixed_precision=args.mixed_precision ,     log_with="""all""" ,     project_dir=args.project_dir    )
						else:
												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"""]    )
						set_seed(__lowerCAmelCase    )

						lowerCamelCase__							,      lowerCamelCase__								=	get_dataloaders(__lowerCAmelCase ,     __lowerCAmelCase    )
						lowerCamelCase__								=	evaluate.load("""glue""" ,     """mrpc"""    )

						# If the batch size is too big we use gradient accumulation
						lowerCamelCase__								=	1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												lowerCamelCase__								=	batch_size // MAX_GPU_BATCH_SIZE
												lowerCamelCase__								=	MAX_GPU_BATCH_SIZE

						# 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=__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).
						lowerCamelCase__								=	model.to(accelerator.device    )

						# Instantiate optimizer
						lowerCamelCase__								=	AdamW(params=model.parameters() ,     lr=__lowerCAmelCase    )

						# Instantiate scheduler
						lowerCamelCase__								=	get_linear_schedule_with_warmup(
						    optimizer=__lowerCAmelCase ,     num_warmup_steps=100 ,     num_training_steps=(len(__lowerCAmelCase    ) * num_epochs) // gradient_accumulation_steps ,     )

						# 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(
						    __lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase    )

						# New Code #
						# We need to initialize the trackers we use. Overall configurations can also be stored
						if args.with_tracking:
												lowerCamelCase__								=	os.path.split(__lowerCAmelCase    )[-1].split("""."""    )[0]
												accelerator.init_trackers(__lowerCAmelCase ,     __lowerCAmelCase    )

						# Now we train the model
						for epoch in range(__lowerCAmelCase    ):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		lowerCamelCase__								=	0
												for step, batch in enumerate(__lowerCAmelCase    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		lowerCamelCase__								=	model(**__lowerCAmelCase    )
																		lowerCamelCase__								=	outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		lowerCamelCase__								=	loss / gradient_accumulation_steps
																		accelerator.backward(__lowerCAmelCase    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												lowerCamelCase__								=	metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:''' ,     __lowerCAmelCase    )

												# New Code #
												# To actually log, we call `Accelerator.log`
												# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        """accuracy""": eval_metric["""accuracy"""],
																		        """f1""": eval_metric["""f1"""],
																		        """train_loss""": total_loss.item() / len(__lowerCAmelCase    ),
																		        """epoch""": epoch,
																		    } ,     step=__lowerCAmelCase ,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
						if args.with_tracking:
												accelerator.end_training()
def     A__      (					):
						lowerCamelCase__								=	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.""" ,     )
						parser.add_argument("""--cpu""" ,     action="""store_true""" ,     help="""If passed, will train on the CPU."""    )
						parser.add_argument(
						    """--with_tracking""" ,     action="""store_true""" ,     help="""Whether to load in all available experiment trackers from the environment and use them for logging.""" ,     )
						parser.add_argument(
						    """--project_dir""" ,     type=__lowerCAmelCase ,     default="""logs""" ,     help="""Location on where to store experiment tracking logs` and relevent project information""" ,     )
						lowerCamelCase__								=	parser.parse_args()
						lowerCamelCase__								=	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(__lowerCAmelCase ,     __lowerCAmelCase    )


if __name__ == "__main__":
							main()








'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	bool:
     return sum(i for i in range(1					,				number // 2 + 1     ) if number % i == 0     ) == number


if __name__ == "__main__":
  print('''Program to check whether a number is a Perfect number or not...''')
  lowercase__       : int         =       int(input('''Enter number: ''').strip())
  print(f"""{number} is {"" if perfect(number) else "not "}a Perfect Number.""")