
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
#
########################################################################


__a  			=					1_6
__a  			=					3_2




def 				a						(							snake_case__:					Accelerator						,   snake_case__:					int = 16					):


				'''simple docstring'''
				lowercase_       =					AutoTokenizer.from_pretrained('''bert-base-cased'''					)
				lowercase_       =					load_dataset('''glue'''						,   '''mrpc'''					)

				def tokenize_function(snake_case__:					int					):
								# max_length=None => use the model max length (it's actually the default)
								lowercase_       =					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():
								lowercase_       =					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
				lowercase_       =					tokenized_datasets.rename_column('''label'''						,   '''labels'''					)

				def collate_fn(snake_case__:					Dict					):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowercase_       =					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":
												lowercase_       =					16
								elif accelerator.mixed_precision != "no":
												lowercase_       =					8
								else:
												lowercase_       =					None

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

				# Instantiate dataloaders.
				lowercase_       =					DataLoader(
				    tokenized_datasets['''train''']						,   shuffle=snake_case__						,   collate_fn=snake_case__						,   batch_size=snake_case__					)
				lowercase_       =					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

						__a  			=					mocked_dataloaders  # noqa: F811




def 				a						(							snake_case__:					int						,   snake_case__:					Tuple					):


				'''simple docstring'''
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,   snake_case__					) == "1":
								lowercase_       =					2
				# New Code #
				lowercase_       =					int(args.gradient_accumulation_steps					)
				lowercase_       =					int(args.local_sgd_steps					)
				# Initialize accelerator
				lowercase_       =					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
				lowercase_       =					config['''lr''']
				lowercase_       =					int(config['''num_epochs''']					)
				lowercase_       =					int(config['''seed''']					)
				lowercase_       =					int(config['''batch_size''']					)

				lowercase_       =					evaluate.load('''glue'''						,   '''mrpc'''					)

				set_seed(snake_case__					)
				lowercase_  ,	lowercase_       =					get_dataloaders(snake_case__						,   snake_case__					)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowercase_       =					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).
				lowercase_       =					model.to(accelerator.device					)

				# Instantiate optimizer
				lowercase_       =					AdamW(params=model.parameters()						,   lr=snake_case__					)

				# Instantiate scheduler
				lowercase_       =					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.
				lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_       =					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__					):
																				lowercase_       =					model(**snake_case__					)
																				lowercase_       =					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():
																lowercase_       =					model(**snake_case__					)
												lowercase_       =					outputs.logits.argmax(dim=-1					)
												lowercase_  ,	lowercase_       =					accelerator.gather_for_metrics((predictions, batch['''labels'''])					)
												metric.add_batch(
												    predictions=snake_case__						,   references=snake_case__						,   )

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




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					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.'''					)
				lowercase_       =					parser.parse_args()
				lowercase_       =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(snake_case__						,   snake_case__					)


if __name__ == "__main__":
						main()






import itertools
import math




def 				a						(							snake_case__:					int					):


				'''simple docstring'''

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5						,   int(math.sqrt(snake_case__					) + 1					)						,   6					):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					2
				while True:
								if is_prime(snake_case__					):
												yield num
								num += 1




def 				a						(							snake_case__:					int = 10_001					):


				'''simple docstring'''
				return next(itertools.islice(prime_generator()						,   nth - 1						,   snake_case__					)					)


if __name__ == "__main__":
						print(f"{solution() = }")




