






'''simple docstring'''
from typing import List, Optional, Union

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


class  _snake_case (					lowerCAmelCase__       ):

					"""simple docstring"""





					_UpperCamelCase					=   ['''image_processor''', '''tokenizer''']
					_UpperCamelCase					=   '''Pix2StructImageProcessor'''
					_UpperCamelCase					=   ('''T5Tokenizer''', '''T5TokenizerFast''')




					def __init__(  self ,			UpperCAmelCase__ ,			UpperCAmelCase__  )   ->						Optional[Any]:
										a_						= False
										super().__init__(__lowerCAmelCase ,			__lowerCAmelCase  )




					def __call__(  self ,			UpperCAmelCase__=None ,			UpperCAmelCase__ = None ,			UpperCAmelCase__ = True ,			UpperCAmelCase__ = False ,			UpperCAmelCase__ = None ,			UpperCAmelCase__ = None ,			UpperCAmelCase__ = 2048 ,			UpperCAmelCase__ = 0 ,			UpperCAmelCase__ = None ,			UpperCAmelCase__ = None ,			UpperCAmelCase__ = False ,			UpperCAmelCase__ = False ,			UpperCAmelCase__ = False ,			UpperCAmelCase__ = False ,			UpperCAmelCase__ = False ,			UpperCAmelCase__ = True ,			UpperCAmelCase__ = None ,			**UpperCAmelCase__ ,			)   ->						Tuple:


										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 and not self.image_processor.is_vqa:
															a_						= self.tokenizer
															a_						= 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

										if not self.image_processor.is_vqa:
															# add pixel_values
															a_						= self.image_processor(
															    __lowerCAmelCase ,			return_tensors=__lowerCAmelCase ,			max_patches=__lowerCAmelCase ,			**__lowerCAmelCase  )
										else:
															# add pixel_values and bbox
															a_						= self.image_processor(
															    __lowerCAmelCase ,			return_tensors=__lowerCAmelCase ,			max_patches=__lowerCAmelCase ,			header_text=__lowerCAmelCase ,			**__lowerCAmelCase  )

										if text is not None and not self.image_processor.is_vqa:
															a_						= 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 ,			)

															if "attention_mask" in text_encoding:
																				a_						= text_encoding.pop('attention_mask'  )
															if "input_ids" in text_encoding:
																				a_						= text_encoding.pop('input_ids'  )
										else:
															a_						= None

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

										return encoding_image_processor




					def 			__SCREAMING_SNAKE_CASE			(  self ,			*UpperCAmelCase__ ,			**UpperCAmelCase__  )   ->						Union[str, Any]:
										return self.tokenizer.batch_decode(*__lowerCAmelCase ,			**__lowerCAmelCase  )




					def 			__SCREAMING_SNAKE_CASE			(  self ,			*UpperCAmelCase__ ,			**UpperCAmelCase__  )   ->						Dict:
										return self.tokenizer.decode(*__lowerCAmelCase ,			**__lowerCAmelCase  )




					@property
					def 			__SCREAMING_SNAKE_CASE			(  self  )   ->						Optional[int]:
										a_						= self.tokenizer.model_input_names
										a_						= 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 							snake_case__				(     _A:   int					)       ->      list[int]:

		'''simple docstring'''


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

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

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

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

				start += 1

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

		return prime


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




