

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCamelCase					     =						logging.get_logger(__name__)


if is_vision_available():
 import PIL







class     __lowercase (__snake_case					):
 _UpperCamelCase         =    ["""pixel_values"""]
 def __init__(				self ,				A_ = True ,				A_ = None ,				A_ = PILImageResampling.BICUBIC ,				A_ = True ,				A_ = None ,				A_ = True ,				A_ = 1 / 255 ,				A_ = True ,				A_ = None ,				A_ = None ,				A_ = True ,				**A_ ,				)   ->List[str]:



       '''simple docstring'''





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

       __lowerCAmelCase   :		Union[str, Any]   =   do_resize
       __lowerCAmelCase   :		Dict   =   size
       __lowerCAmelCase   :		int   =   resample
       __lowerCAmelCase   :		List[str]   =   do_center_crop
       __lowerCAmelCase   :		List[Any]   =   crop_size
       __lowerCAmelCase   :		Dict   =   do_rescale
       __lowerCAmelCase   :		Tuple   =   rescale_factor
       __lowerCAmelCase   :		Optional[int]   =   do_normalize
       __lowerCAmelCase   :		List[Any]   =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       __lowerCAmelCase   :		Optional[int]   =   image_std if image_std is not None else OPENAI_CLIP_STD
       __lowerCAmelCase   :		Optional[int]   =   do_convert_rgb
 def       UpperCamelCase__    (				self ,				A_ ,				A_ ,				A_ = PILImageResampling.BICUBIC ,				A_ = None ,				**A_ ,				)   ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[Any]   =   get_size_dict(snake_case_ ,				default_to_square=snake_case_   )
       if "shortest_edge" not in size:
             raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""   )
       __lowerCAmelCase   :		str   =   get_resize_output_image_size(snake_case_ ,				size=size['''shortest_edge'''] ,				default_to_square=snake_case_   )
       return resize(snake_case_ ,				size=snake_case_ ,				resample=snake_case_ ,				data_format=snake_case_ ,				**snake_case_   )
 def       UpperCamelCase__    (				self ,				A_ ,				A_ ,				A_ = None ,				**A_ ,				)   ->Any:



       '''simple docstring'''





       __lowerCAmelCase   :		Optional[Any]   =   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       UpperCamelCase__    (				self ,				A_ ,				A_ ,				A_ = None ,				**A_ ,				)   ->List[str]:



       '''simple docstring'''





       return rescale(snake_case_ ,				scale=snake_case_ ,				data_format=snake_case_ ,				**snake_case_   )
 def       UpperCamelCase__    (				self ,				A_ ,				A_ ,				A_ ,				A_ = None ,				**A_ ,				)   ->Dict:



       '''simple docstring'''





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





 def       UpperCamelCase__    (				self ,				A_ ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = None ,				A_ = ChannelDimension.FIRST ,				**A_ ,				)   ->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase   :		List[str]   =   do_resize if do_resize is not None else self.do_resize
       __lowerCAmelCase   :		Tuple   =   size if size is not None else self.size
       __lowerCAmelCase   :		List[str]   =   get_size_dict(snake_case_ ,				param_name='''size''' ,				default_to_square=snake_case_   )
       __lowerCAmelCase   :		List[str]   =   resample if resample is not None else self.resample
       __lowerCAmelCase   :		List[str]   =   do_center_crop if do_center_crop is not None else self.do_center_crop
       __lowerCAmelCase   :		Any   =   crop_size if crop_size is not None else self.crop_size
       __lowerCAmelCase   :		str   =   get_size_dict(snake_case_ ,				param_name='''crop_size''' ,				default_to_square=snake_case_   )
       __lowerCAmelCase   :		Tuple   =   do_rescale if do_rescale is not None else self.do_rescale
       __lowerCAmelCase   :		List[str]   =   rescale_factor if rescale_factor is not None else self.rescale_factor
       __lowerCAmelCase   :		int   =   do_normalize if do_normalize is not None else self.do_normalize
       __lowerCAmelCase   :		List[Any]   =   image_mean if image_mean is not None else self.image_mean
       __lowerCAmelCase   :		List[str]   =   image_std if image_std is not None else self.image_std
       __lowerCAmelCase   :		Tuple   =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       __lowerCAmelCase   :		List[Any]   =   make_list_of_images(snake_case_   )

       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.'''   )

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''   )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
             __lowerCAmelCase   :		str   =   [convert_to_rgb(snake_case_   ) for image in images]

       # All transformations expect numpy arrays.
       __lowerCAmelCase   :		List[str]   =   [to_numpy_array(snake_case_   ) for image in images]

       if do_resize:
             __lowerCAmelCase   :		List[Any]   =   [self.resize(image=snake_case_ ,				size=snake_case_ ,				resample=snake_case_   ) for image in images]

       if do_center_crop:
             __lowerCAmelCase   :		Optional[Any]   =   [self.center_crop(image=snake_case_ ,				size=snake_case_   ) for image in images]

       if do_rescale:
             __lowerCAmelCase   :		str   =   [self.rescale(image=snake_case_ ,				scale=snake_case_   ) for image in images]

       if do_normalize:
             __lowerCAmelCase   :		Any   =   [self.normalize(image=snake_case_ ,				mean=snake_case_ ,				std=snake_case_   ) for image in images]

       __lowerCAmelCase   :		Tuple   =   [to_channel_dimension_format(snake_case_ ,				snake_case_   ) for image in images]

       __lowerCAmelCase   :		Any   =   {'''pixel_values''': images}
       return BatchFeature(data=snake_case_ ,				tensor_type=snake_case_   )









from __future__ import annotations

import math





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




    """simple docstring"""




    if num <= 0:
        _A     =							F"{num}: Invalid input, please enter a positive integer."
        raise ValueError(_SCREAMING_SNAKE_CASE				)

    _A     =							[True] * (num + 1)
    _A     =							[]
    _A     =							2
    _A     =							int(math.sqrt(_SCREAMING_SNAKE_CASE				)				)

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

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

        start += 1

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

    return prime


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





