



'''simple docstring'''




from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
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_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
						import PIL

# soft dependency
if is_pytesseract_available():
						import pytesseract

lowerCamelCase							:Tuple				    =					logging.get_logger(__name__)



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			return [
			    int(10_00 * (box[0] / width)    ),
			    int(10_00 * (box[1] / height)    ),
			    int(10_00 * (box[2] / width)    ),
			    int(10_00 * (box[3] / height)    ),
			]



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						List[Any]	=     to_pil_image(lowerCamelCase__    )
			A_,    A_	:						int	=     pil_image.size
			A_	:						Optional[Any]	=     pytesseract.image_to_data(lowerCamelCase__						,      lang=lowerCamelCase__						,      output_type="""dict"""						,      config=lowerCamelCase__    )
			A_,    A_,    A_,    A_,    A_	:						Any	=     data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

			# filter empty words and corresponding coordinates
			A_	:						Optional[int]	=     [idx for idx, word in enumerate(lowerCamelCase__    ) if not word.strip()]
			A_	:						str	=     [word for idx, word in enumerate(lowerCamelCase__    ) if idx not in irrelevant_indices]
			A_	:						int	=     [coord for idx, coord in enumerate(lowerCamelCase__    ) if idx not in irrelevant_indices]
			A_	:						List[str]	=     [coord for idx, coord in enumerate(lowerCamelCase__    ) if idx not in irrelevant_indices]
			A_	:						Optional[Any]	=     [coord for idx, coord in enumerate(lowerCamelCase__    ) if idx not in irrelevant_indices]
			A_	:						Optional[int]	=     [coord for idx, coord in enumerate(lowerCamelCase__    ) if idx not in irrelevant_indices]

			# turn coordinates into (left, top, left+width, top+height) format
			A_	:						List[str]	=     []
			for x, y, w, h in zip(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
						A_	:						Dict	=     [x, y, x + w, y + h]
						actual_boxes.append(lowerCamelCase__    )

			# finally, normalize the bounding boxes
			A_	:						List[str]	=     []
			for box in actual_boxes:
						normalized_boxes.append(normalize_box(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )    )

			assert len(lowerCamelCase__    ) == len(lowerCamelCase__    ), "Not as many words as there are bounding boxes"

			return words, normalized_boxes







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		int					=					['pixel_values']


	def __init__(self       ,      lowercase = True       ,      lowercase = None       ,      lowercase = PILImageResampling.BILINEAR       ,      lowercase = True       ,      lowercase = 1 / 255       ,      lowercase = True       ,      lowercase = None       ,      lowercase = None       ,      lowercase = True       ,      lowercase = None       ,      lowercase = ""       ,      **lowercase       ,      ):
				super().__init__(**lowercase							)
				A_	:						Optional[Any]	=     size if size is not None else {"""height""": 224, """width""": 224}
				A_	:						int	=     get_size_dict(lowercase							)

				A_	:						Optional[Any]	=     do_resize
				A_	:						Optional[Any]	=     size
				A_	:						str	=     resample
				A_	:						List[str]	=     do_rescale
				A_	:						List[Any]	=     rescale_value
				A_	:						List[str]	=     do_normalize
				A_	:						Any	=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				A_	:						Tuple	=     image_std if image_std is not None else IMAGENET_STANDARD_STD
				A_	:						Union[str, Any]	=     apply_ocr
				A_	:						Dict	=     ocr_lang
				A_	:						Any	=     tesseract_config


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase = PILImageResampling.BILINEAR       ,      lowercase = None       ,      **lowercase       ,      ):
				A_	:						int	=     get_size_dict(lowercase							)
				if "height" not in size or "width" not in size:
							raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'							)
				A_	:						Union[str, Any]	=     (size["""height"""], size["""width"""])
				return resize(lowercase       ,      size=lowercase       ,      resample=lowercase       ,      data_format=lowercase       ,      **lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase = None       ,      **lowercase       ,      ):
				return rescale(lowercase       ,      scale=lowercase       ,      data_format=lowercase       ,      **lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase = None       ,      **lowercase       ,      ):
				return normalize(lowercase       ,      mean=lowercase       ,      std=lowercase       ,      data_format=lowercase       ,      **lowercase							)


	def     _a	(self       ,      lowercase       ,      lowercase = None       ,      lowercase = None       ,      lowercase=None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = None       ,      lowercase = ChannelDimension.FIRST       ,      **lowercase       ,      ):
				A_	:						Dict	=     do_resize if do_resize is not None else self.do_resize
				A_	:						Dict	=     size if size is not None else self.size
				A_	:						Union[str, Any]	=     get_size_dict(lowercase							)
				A_	:						List[Any]	=     resample if resample is not None else self.resample
				A_	:						List[Any]	=     do_rescale if do_rescale is not None else self.do_rescale
				A_	:						Union[str, Any]	=     rescale_factor if rescale_factor is not None else self.rescale_factor
				A_	:						str	=     do_normalize if do_normalize is not None else self.do_normalize
				A_	:						Optional[int]	=     image_mean if image_mean is not None else self.image_mean
				A_	:						Any	=     image_std if image_std is not None else self.image_std
				A_	:						Optional[int]	=     apply_ocr if apply_ocr is not None else self.apply_ocr
				A_	:						Dict	=     ocr_lang if ocr_lang is not None else self.ocr_lang
				A_	:						Optional[int]	=     tesseract_config if tesseract_config is not None else self.tesseract_config

				A_	:						int	=     make_list_of_images(lowercase							)

				if not valid_images(lowercase							):
							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_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("""If do_normalize is True, image_mean and image_std must be specified."""							)

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

				# Tesseract OCR to get words + normalized bounding boxes
				if apply_ocr:
							requires_backends(self       ,      """pytesseract"""							)
							A_	:						Optional[int]	=     []
							A_	:						Dict	=     []
							for image in images:
										A_,    A_	:						Tuple	=     apply_tesseract(lowercase       ,      lowercase       ,      lowercase							)
										words_batch.append(lowercase							)
										boxes_batch.append(lowercase							)

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

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

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

				A_	:						int	=     [to_channel_dimension_format(lowercase       ,      lowercase							) for image in images]

				A_	:						str	=     BatchFeature(data={"""pixel_values""": images}       ,      tensor_type=lowercase							)

				if apply_ocr:
							A_	:						Tuple	=     words_batch
							A_	:						int	=     boxes_batch
				return data




'''simple docstring'''




from __future__ import annotations

import math



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





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

			A_	:						Dict	=     [True] * (num + 1)
			A_	:						List[Any]	=     []
			A_	:						Tuple	=     2
			A_	:						Optional[int]	=     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:
															A_	:						List[Any]	=     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())))