

'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor





class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def __init__(       self     :		str		,					UpperCAmelCase__     :		Optional[int]		,					UpperCAmelCase__     :		Tuple=7		,					UpperCAmelCase__     :		Union[str, Any]=3		,					UpperCAmelCase__     :		int=18		,					UpperCAmelCase__     :		List[str]=30		,					UpperCAmelCase__     :		List[str]=400		,					UpperCAmelCase__     :		Any=True		,					UpperCAmelCase__     :		List[str]=None		,					UpperCAmelCase__     :		List[Any]=True		,					UpperCAmelCase__     :		int=None		,					UpperCAmelCase__     :		List[str]=True		,					UpperCAmelCase__     :		List[str]=[0.5, 0.5, 0.5]		,					UpperCAmelCase__     :		Optional[Any]=[0.5, 0.5, 0.5]		,					):
										'''simple docstring'''
										lowercase :							Dict    		=size if size is not None else {'''shortest_edge''': 18}
										lowercase :							Tuple    		=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
										lowercase :							Any    		=parent
										lowercase :							List[str]    		=batch_size
										lowercase :							Tuple    		=num_channels
										lowercase :							Any    		=image_size
										lowercase :							str    		=min_resolution
										lowercase :							Optional[Any]    		=max_resolution
										lowercase :							Dict    		=do_resize
										lowercase :							List[Any]    		=size
										lowercase :							Optional[int]    		=do_center_crop
										lowercase :							Tuple    		=crop_size
										lowercase :							Tuple    		=do_normalize
										lowercase :							Tuple    		=image_mean
										lowercase :							List[Any]    		=image_std







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "do_center_crop": self.do_center_crop,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}





@require_torch
@require_vision
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    LevitImageProcessor if is_vision_available() else None







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=LevitImageProcessingTester(self   )







				@property
				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''image_mean'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''image_std'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_normalize'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_resize'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_center_crop'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''size'''   )   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Any    		=self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size		,					{'''shortest_edge''': 18}   )
										self.assertEqual(image_processor.crop_size		,					{'''height''': 18, '''width''': 18}   )

										lowercase :							Union[str, Any]    		=self.image_processing_class.from_dict(self.image_processor_dict		,					size=42		,					crop_size=84   )
										self.assertEqual(image_processor.size		,					{'''shortest_edge''': 42}   )
										self.assertEqual(image_processor.crop_size		,					{'''height''': 84, '''width''': 84}   )







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							Tuple    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowercase :							Optional[Any]    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					Image.Image   )

										# Test not batched input
										lowercase :							List[str]    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)

										# Test batched
										lowercase :							Optional[Any]    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							List[Any]    		=self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowercase :							Any    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__		,					numpify=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					np.ndarray   )

										# Test not batched input
										lowercase :							Any    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)

										# Test batched
										lowercase :							List[Any]    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							Optional[Any]    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowercase :							Union[str, Any]    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=UpperCAmelCase__		,					torchify=UpperCAmelCase__   )
										for image in image_inputs:
																self.assertIsInstance(UpperCAmelCase__		,					torch.Tensor   )

										# Test not batched input
										lowercase :							Union[str, Any]    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)

										# Test batched
										lowercase :							Dict    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,					(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['''height'''],
										        self.image_processor_tester.crop_size['''width'''],
										    )		,					)







"""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())))