
"""simple docstring"""




import argparse

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


lowerCAmelCase__				=					16
lowerCAmelCase__				=					32



def 							_lowerCamelCase			(			__a,				__a = 16     ):
		SCREAMING_SNAKE_CASE_    			=			AutoTokenizer.from_pretrained('''bert-base-cased'''     )
		SCREAMING_SNAKE_CASE_    			=			load_dataset('''glue''',				'''mrpc'''     )

		def tokenize_function(__a     ):
				# max_length=None => use the model max length (it's actually the default)
				SCREAMING_SNAKE_CASE_    			=			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():
				SCREAMING_SNAKE_CASE_    			=			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
		SCREAMING_SNAKE_CASE_    			=			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.
				SCREAMING_SNAKE_CASE_    			=			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":
						SCREAMING_SNAKE_CASE_    			=			16
				elif accelerator.mixed_precision != "no":
						SCREAMING_SNAKE_CASE_    			=			8
				else:
						SCREAMING_SNAKE_CASE_    			=			None

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

		# Instantiate dataloaders.
		SCREAMING_SNAKE_CASE_    			=			DataLoader(
		    tokenized_datasets['''train'''],				shuffle=__a,				collate_fn=__a,				batch_size=__a,				drop_last=__a     )
		SCREAMING_SNAKE_CASE_    			=			DataLoader(
		    tokenized_datasets['''validation'''],				shuffle=__a,				collate_fn=__a,				batch_size=__a,				drop_last=(accelerator.mixed_precision == '''fp8'''),				)

		return train_dataloader, eval_dataloader



def 							_lowerCamelCase			(			__a,				__a     ):
		# Initialize accelerator
		SCREAMING_SNAKE_CASE_    			=			Accelerator(cpu=args.cpu,				mixed_precision=args.mixed_precision     )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		SCREAMING_SNAKE_CASE_    			=			config['''lr''']
		SCREAMING_SNAKE_CASE_    			=			int(config['''num_epochs''']     )
		SCREAMING_SNAKE_CASE_    			=			int(config['''seed''']     )
		SCREAMING_SNAKE_CASE_    			=			int(config['''batch_size''']     )

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

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

		set_seed(__a     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			get_dataloaders(__a,				__a     )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		SCREAMING_SNAKE_CASE_    			=			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).
		SCREAMING_SNAKE_CASE_    			=			model.to(accelerator.device     )
		# Instantiate optimizer
		SCREAMING_SNAKE_CASE_    			=			AdamW(params=model.parameters(),				lr=__a     )

		# Instantiate scheduler
		SCREAMING_SNAKE_CASE_    			=			get_linear_schedule_with_warmup(
		    optimizer=__a,				num_warmup_steps=100,				num_training_steps=(len(__a     ) * 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.

		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			accelerator.prepare(
		    __a,				__a,				__a,				__a,				__a     )

		# Now we train the model
		for epoch in range(__a     ):
				model.train()
				for step, batch in enumerate(__a     ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device     )
						SCREAMING_SNAKE_CASE_    			=			model(**__a     )
						SCREAMING_SNAKE_CASE_    			=			outputs.loss
						SCREAMING_SNAKE_CASE_    			=			loss / gradient_accumulation_steps
						accelerator.backward(__a     )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				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():
								SCREAMING_SNAKE_CASE_    			=			model(**__a     )
						SCREAMING_SNAKE_CASE_    			=			outputs.logits.argmax(dim=-1     )
						SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
						metric.add_batch(
						    predictions=__a,				references=__a,				)

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



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			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.''',				)
		parser.add_argument('''--cpu''',				action='''store_true''',				help='''If passed, will train on the CPU.'''     )
		SCREAMING_SNAKE_CASE_    			=			parser.parse_args()
		SCREAMING_SNAKE_CASE_    			=			{'''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

import math



def 							_lowerCamelCase			(			__a     ):

		if num <= 0:
				SCREAMING_SNAKE_CASE_    			=			F'{num}: Invalid input, please enter a positive integer.'
				raise ValueError(__a     )

		SCREAMING_SNAKE_CASE_    			=			[True] * (num + 1)
		SCREAMING_SNAKE_CASE_    			=			[]
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			int(math.sqrt(__a     )     )

		while start <= end:
				# If start is a prime
				if sieve[start] is True:
						prime.append(__a     )

						# Set multiples of start be False
						for i in range(start * start,				num + 1,				__a     ):
								if sieve[i] is True:
										SCREAMING_SNAKE_CASE_    			=			False

				start += 1

		for j in range(end + 1,				num + 1     ):
				if sieve[j] is True:
						prime.append(__a     )

		return prime


if __name__ == "__main__":
						print(prime_sieve(int(input('Enter a positive integer: ').strip())))