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


_UpperCamelCase								=		16
_UpperCamelCase								=		32

def  _A(		lowerCAmelCase			,  lowerCAmelCase = 16							):
							A__       :     Optional[int]								=			AutoTokenizer.from_pretrained("""bert-base-cased"""							)
							A__       :     int								=			load_dataset("""glue"""			,  """mrpc"""							)

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

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

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

							# Instantiate dataloaders.
							A__       :     List[Any]								=			DataLoader(
							    tokenized_datasets["""train"""]			,  shuffle=snake_case__			,  collate_fn=snake_case__			,  batch_size=snake_case__							)
							A__       :     Optional[int]								=			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								=		mocked_dataloaders  # noqa: F811

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):


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

							A__       :     List[str]								=			evaluate.load("""glue"""			,  """mrpc"""							)

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

							# Instantiate optimizer
							A__       :     int								=			AdamW(params=model.parameters()			,  lr=snake_case__							)

							# Instantiate scheduler
							A__       :     int								=			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.
							A__				,							A__				,							A__				,							A__				,							A__       :     str								=			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__							):
																																			A__       :     Union[str, Any]								=			model(**snake_case__							)
																																			A__       :     Tuple								=			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():
																												A__       :     Union[str, Any]								=			model(**snake_case__							)
																					A__       :     Dict								=			outputs.logits.argmax(dim=-1							)
																					A__				,							A__       :     Dict								=			accelerator.gather_for_metrics((predictions, batch["""labels"""])							)
																					metric.add_batch(
																					    predictions=snake_case__			,  references=snake_case__			,  )

														A__       :     List[str]								=			metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F'''epoch {epoch}:'''			,  snake_case__							)

def  _A(		):
							A__       :     Optional[int]								=			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."""							)
							A__       :     Tuple								=			parser.parse_args()
							A__       :     Dict								=			{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(snake_case__			,  snake_case__							)


if __name__ == "__main__":
				main()








"""simple docstring"""




def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   2
			while i * i <= n:
						SCREAMING_SNAKE_CASE__										=   0
						while n % i == 0:
									n //= i
									multiplicity += 1
						n_divisors *= multiplicity + 1
						i += 1
			if n > 1:
						n_divisors *= 2
			return n_divisors

def 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   1

			while True:
						i += 1
						t_num += i

						if count_divisors(snake_case__     ) > 5_00:
									break

			return t_num


if __name__ == "__main__":
						print(solution())


