



from __future__ import annotations

import math

def        __a			(   __lowerCAmelCase   )     ->     list[int]:

					if num <= 0:
										SCREAMING_SNAKE_CASE							:     List[Any]						=				F'''{num}: Invalid input, please enter a positive integer.'''
										raise ValueError(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				[True] * (num + 1)
					SCREAMING_SNAKE_CASE							:     Tuple						=				[]
					SCREAMING_SNAKE_CASE							:     Tuple						=				2
					SCREAMING_SNAKE_CASE							:     List[Any]						=				int(math.sqrt(__lowerCAmelCase   )   )

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

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

										start += 1

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

					return prime


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




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase    :		List[str]		      =    logging.get_logger(__name__)


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		List[str]		=		['pixel_values']







						def __init__(  self    :				List[Any]       ,	snake_case    :				bool = True       ,	snake_case    :				Optional[Dict[str, int]] = None       ,	snake_case    :				PILImageResampling = PILImageResampling.BICUBIC       ,	snake_case    :				bool = True       ,	snake_case    :				bool = True       ,	snake_case    :				Union[int, float] = 1 / 255       ,	snake_case    :				Dict[str, int] = None       ,	snake_case    :				bool = True       ,	snake_case    :				Optional[Union[float, List[float]]] = None       ,	snake_case    :				Optional[Union[float, List[float]]] = None       ,	**snake_case    :				Optional[int]       ,	):



											'''simple docstring'''


											super().__init__(**snake_case	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				size if size is not None else {'height': 224, 'width': 224}
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				get_size_dict(snake_case	)
											SCREAMING_SNAKE_CASE							:     Any						=				crop_size if crop_size is not None else {'height': 224, 'width': 224}
											SCREAMING_SNAKE_CASE							:     int						=				get_size_dict(snake_case       ,	default_to_square=snake_case       ,	param_name='crop_size'	)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				do_resize
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				do_rescale
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				do_normalize
											SCREAMING_SNAKE_CASE							:     Dict						=				do_center_crop
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				crop_size
											SCREAMING_SNAKE_CASE							:     str						=				size
											SCREAMING_SNAKE_CASE							:     List[str]						=				resample
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				rescale_factor
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				image_std if image_std is not None else IMAGENET_DEFAULT_STD







						def       lowerCamelCase_				(  self    :				int       ,	snake_case    :				np.ndarray       ,	snake_case    :				Dict[str, int]       ,	snake_case    :				PILImageResampling = PILImageResampling.BILINEAR       ,	snake_case    :				Optional[Union[str, ChannelDimension]] = None       ,	**snake_case    :				Any       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				get_size_dict(snake_case	)

											if "shortest_edge" in size:
																SCREAMING_SNAKE_CASE							:     Tuple						=				get_resize_output_image_size(snake_case       ,	size=size['shortest_edge']       ,	default_to_square=snake_case	)
																# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
											elif "height" in size and "width" in size:
																SCREAMING_SNAKE_CASE							:     int						=				(size['height'], size['width'])
											else:
																raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''	)
											return resize(snake_case       ,	size=snake_case       ,	resample=snake_case       ,	data_format=snake_case       ,	**snake_case	)







						def       lowerCamelCase_				(  self    :				List[str]       ,	snake_case    :				np.ndarray       ,	snake_case    :				Dict[str, int]       ,	snake_case    :				Optional[Union[str, ChannelDimension]] = None       ,	**snake_case    :				int       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				get_size_dict(snake_case	)
											if "height" not in size or "width" not in size:
																raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
											return center_crop(snake_case       ,	size=(size['height'], size['width'])       ,	data_format=snake_case       ,	**snake_case	)







						def       lowerCamelCase_				(  self    :				Any       ,	snake_case    :				np.ndarray       ,	snake_case    :				float       ,	snake_case    :				Optional[Union[str, ChannelDimension]] = None       ,	**snake_case    :				Dict	):



											'''simple docstring'''


											return rescale(snake_case       ,	scale=snake_case       ,	data_format=snake_case       ,	**snake_case	)







						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				np.ndarray       ,	snake_case    :				Union[float, List[float]]       ,	snake_case    :				Union[float, List[float]]       ,	snake_case    :				Optional[Union[str, ChannelDimension]] = None       ,	**snake_case    :				List[Any]       ,	):



											'''simple docstring'''


											return normalize(snake_case       ,	mean=snake_case       ,	std=snake_case       ,	data_format=snake_case       ,	**snake_case	)







						def       lowerCamelCase_				(  self    :				str       ,	snake_case    :				ImageInput       ,	snake_case    :				Optional[bool] = None       ,	snake_case    :				Dict[str, int] = None       ,	snake_case    :				PILImageResampling = None       ,	snake_case    :				bool = None       ,	snake_case    :				int = None       ,	snake_case    :				Optional[bool] = None       ,	snake_case    :				Optional[float] = None       ,	snake_case    :				Optional[bool] = None       ,	snake_case    :				Optional[Union[float, List[float]]] = None       ,	snake_case    :				Optional[Union[float, List[float]]] = None       ,	snake_case    :				Optional[Union[str, TensorType]] = None       ,	snake_case    :				Union[str, ChannelDimension] = ChannelDimension.FIRST       ,	**snake_case    :				Optional[Any]       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Any						=				do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE							:     int						=				do_rescale if do_rescale is not None else self.do_rescale
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				do_normalize if do_normalize is not None else self.do_normalize
											SCREAMING_SNAKE_CASE							:     List[str]						=				do_center_crop if do_center_crop is not None else self.do_center_crop
											SCREAMING_SNAKE_CASE							:     List[str]						=				crop_size if crop_size is not None else self.crop_size
											SCREAMING_SNAKE_CASE							:     List[Any]						=				get_size_dict(snake_case       ,	param_name='crop_size'       ,	default_to_square=snake_case	)
											SCREAMING_SNAKE_CASE							:     Any						=				resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE							:     Any						=				rescale_factor if rescale_factor is not None else self.rescale_factor
											SCREAMING_SNAKE_CASE							:     str						=				image_mean if image_mean is not None else self.image_mean
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				image_std if image_std is not None else self.image_std

											SCREAMING_SNAKE_CASE							:     List[str]						=				size if size is not None else self.size
											SCREAMING_SNAKE_CASE							:     str						=				get_size_dict(snake_case	)

											if not is_batched(snake_case	):
																SCREAMING_SNAKE_CASE							:     str						=				[images]

											if not valid_images(snake_case	):
																raise ValueError(
																    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																    'torch.Tensor, tf.Tensor or jax.ndarray.'	)

											if do_resize and size is None:
																raise ValueError('Size must be specified if do_resize is True.'	)

											if do_center_crop and crop_size is None:
																raise ValueError('Crop size must be specified if do_center_crop is True.'	)

											if do_rescale and rescale_factor is None:
																raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

											# All transformations expect numpy arrays.
											SCREAMING_SNAKE_CASE							:     Dict						=				[to_numpy_array(snake_case	) for image in images]

											if do_resize:
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				[self.resize(image=snake_case       ,	size=snake_case       ,	resample=snake_case	) for image in images]

											if do_center_crop:
																SCREAMING_SNAKE_CASE							:     str						=				[self.center_crop(image=snake_case       ,	size=snake_case	) for image in images]

											if do_rescale:
																SCREAMING_SNAKE_CASE							:     Tuple						=				[self.rescale(image=snake_case       ,	scale=snake_case	) for image in images]

											if do_normalize:
																SCREAMING_SNAKE_CASE							:     int						=				[self.normalize(image=snake_case       ,	mean=snake_case       ,	std=snake_case	) for image in images]

											SCREAMING_SNAKE_CASE							:     List[str]						=				[to_channel_dimension_format(snake_case       ,	snake_case	) for image in images]

											SCREAMING_SNAKE_CASE							:     Any						=				{'pixel_values': images}
											return BatchFeature(data=snake_case       ,	tensor_type=snake_case	)