"""simple docstring"""






from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
       import torch


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





class 				_A		( lowerCAmelCase			):
      snake_case__    :     str													=			['pixel_values']

      def __init__(		self	, __lowerCAmelCase = True	, __lowerCAmelCase = None	, __lowerCAmelCase = PILImageResampling.BILINEAR	, __lowerCAmelCase = True	, __lowerCAmelCase = None	, __lowerCAmelCase = True	, __lowerCAmelCase = 1 / 255	, __lowerCAmelCase = True	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase		)
            lowercase														=			size if size is not None else {"""shortest_edge""": 256}
            lowercase														=			get_size_dict(__lowerCAmelCase	, default_to_square=__lowerCAmelCase		)
            lowercase														=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
            lowercase														=			get_size_dict(__lowerCAmelCase	, param_name="""crop_size"""		)
            lowercase														=			do_resize
            lowercase														=			size
            lowercase														=			resample
            lowercase														=			do_center_crop
            lowercase														=			crop_size
            lowercase														=			do_rescale
            lowercase														=			rescale_factor
            lowercase														=			do_normalize
            lowercase														=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            lowercase														=			image_std if image_std is not None else IMAGENET_STANDARD_STD

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = PILImageResampling.BICUBIC	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			get_size_dict(__lowerCAmelCase	, default_to_square=__lowerCAmelCase		)
            if "shortest_edge" not in size:
                  raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'		)
            lowercase														=			get_resize_output_image_size(__lowerCAmelCase	, size=size["""shortest_edge"""]	, default_to_square=__lowerCAmelCase		)
            return resize(__lowerCAmelCase	, size=__lowerCAmelCase	, resample=__lowerCAmelCase	, data_format=__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = None	, **__lowerCAmelCase		):







            """simple docstring"""



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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



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

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = ChannelDimension.FIRST	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			do_resize if do_resize is not None else self.do_resize
            lowercase														=			size if size is not None else self.size
            lowercase														=			get_size_dict(__lowerCAmelCase	, default_to_square=__lowerCAmelCase		)
            lowercase														=			resample if resample is not None else self.resample
            lowercase														=			do_center_crop if do_center_crop is not None else self.do_center_crop
            lowercase														=			crop_size if crop_size is not None else self.crop_size
            lowercase														=			get_size_dict(__lowerCAmelCase	, param_name="""crop_size"""		)
            lowercase														=			do_rescale if do_rescale is not None else self.do_rescale
            lowercase														=			rescale_factor if rescale_factor is not None else self.rescale_factor
            lowercase														=			do_normalize if do_normalize is not None else self.do_normalize
            lowercase														=			image_mean if image_mean is not None else self.image_mean
            lowercase														=			image_std if image_std is not None else self.image_std

            lowercase														=			make_list_of_images(__lowerCAmelCase		)

            if not valid_images(__lowerCAmelCase		):
                  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."""		)

            # All transformations expect numpy arrays.
            lowercase														=			[to_numpy_array(__lowerCAmelCase		) for image in images]

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

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

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

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

            lowercase														=			[to_channel_dimension_format(__lowerCAmelCase	, __lowerCAmelCase		) for image in images]

            lowercase														=			{"""pixel_values""": images}
            return BatchFeature(data=__lowerCAmelCase	, tensor_type=__lowerCAmelCase		)







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            lowercase														=			outputs.logits

            # Resize logits and compute semantic segmentation maps
            if target_sizes is not None:
                  if len(__lowerCAmelCase		) != len(__lowerCAmelCase		):
                        raise ValueError(
                            """Make sure that you pass in as many target sizes as the batch dimension of the logits"""		)

                  if is_torch_tensor(__lowerCAmelCase		):
                        lowercase														=			target_sizes.numpy()

                  lowercase														=			[]

                  for idx in range(len(__lowerCAmelCase		)		):
                        lowercase														=			torch.nn.functional.interpolate(
                            logits[idx].unsqueeze(dim=0		)	, size=target_sizes[idx]	, mode="""bilinear"""	, align_corners=__lowerCAmelCase		)
                        lowercase														=			resized_logits[0].argmax(dim=0		)
                        semantic_segmentation.append(__lowerCAmelCase		)
            else:
                  lowercase														=			logits.argmax(dim=1		)
                  lowercase														=			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

            return semantic_segmentation




"""simple docstring"""






from __future__ import annotations

import math


def     UpperCAmelCase__ (       lowerCAmelCase__  :int	)				->					list[int]:
      '''simple docstring'''


      if num <= 0:
            lowercase														=			f'{num}: Invalid input, please enter a positive integer.'
            raise ValueError(lowerCAmelCase__	)

      lowercase														=			[True] * (num + 1)
      lowercase														=			[]
      lowercase														=			2
      lowercase														=			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:
                              lowercase														=			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())))



