"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 				_A		( lowerCAmelCase			):
      snake_case__    :     List[Any]													=			['image_processor', 'tokenizer']
      snake_case__    :     str													=			'BlipImageProcessor'
      snake_case__    :     str													=			('BertTokenizer', 'BertTokenizerFast')

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			False
            super().__init__(__lowerCAmelCase	, __lowerCAmelCase		)
            lowercase														=			self.image_processor

      def __call__(		self	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = True	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = 0	, __lowerCAmelCase = None	, __lowerCAmelCase = None	, __lowerCAmelCase = False	, __lowerCAmelCase = False	, __lowerCAmelCase = False	, __lowerCAmelCase = False	, __lowerCAmelCase = False	, __lowerCAmelCase = True	, __lowerCAmelCase = None	, **__lowerCAmelCase	, ):







            """simple docstring"""



            if images is None and text is None:
                  raise ValueError("""You have to specify either images or text."""		)

            # Get only text
            if images is None:
                  lowercase														=			self.tokenizer
                  lowercase														=			self.tokenizer(
                      text=__lowerCAmelCase	, add_special_tokens=__lowerCAmelCase	, padding=__lowerCAmelCase	, truncation=__lowerCAmelCase	, max_length=__lowerCAmelCase	, stride=__lowerCAmelCase	, pad_to_multiple_of=__lowerCAmelCase	, return_attention_mask=__lowerCAmelCase	, return_overflowing_tokens=__lowerCAmelCase	, return_special_tokens_mask=__lowerCAmelCase	, return_offsets_mapping=__lowerCAmelCase	, return_token_type_ids=__lowerCAmelCase	, return_length=__lowerCAmelCase	, verbose=__lowerCAmelCase	, return_tensors=__lowerCAmelCase	, **__lowerCAmelCase	, )
                  return text_encoding

            # add pixel_values
            lowercase														=			self.image_processor(__lowerCAmelCase	, return_tensors=__lowerCAmelCase		)

            if text is not None:
                  lowercase														=			self.tokenizer(
                      text=__lowerCAmelCase	, add_special_tokens=__lowerCAmelCase	, padding=__lowerCAmelCase	, truncation=__lowerCAmelCase	, max_length=__lowerCAmelCase	, stride=__lowerCAmelCase	, pad_to_multiple_of=__lowerCAmelCase	, return_attention_mask=__lowerCAmelCase	, return_overflowing_tokens=__lowerCAmelCase	, return_special_tokens_mask=__lowerCAmelCase	, return_offsets_mapping=__lowerCAmelCase	, return_token_type_ids=__lowerCAmelCase	, return_length=__lowerCAmelCase	, verbose=__lowerCAmelCase	, return_tensors=__lowerCAmelCase	, **__lowerCAmelCase	, )
            else:
                  lowercase														=			None

            if text_encoding is not None:
                  encoding_image_processor.update(__lowerCAmelCase		)

            return encoding_image_processor

      def        A__ (		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            return self.tokenizer.batch_decode(*__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            return self.tokenizer.decode(*__lowerCAmelCase	, **__lowerCAmelCase		)







      @property
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.tokenizer.model_input_names
            lowercase														=			self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)




"""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())))



