


'''simple docstring'''







def _UpperCamelCase       (     __A    )							->		list[int]:


      '''simple docstring'''



      if num <= 0:
            raise ValueError("Input must be a positive integer"    )

      UpperCamelCase__										=				[True] * (num + 1)

      UpperCamelCase__										=				2
      while p * p <= num:
            if primes[p]:
                  for i in range(p * p      ,			num + 1      ,			__A    ):
                        UpperCamelCase__										=				False
            p += 1

      return [prime for prime in range(2      ,			num + 1    ) if primes[prime]]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a__  :					Union[str, Any]        =       int(input('Enter a positive integer: ').strip())
  print(prime_sieve_eratosthenes(user_num))









"""simple docstring"""



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class _lowerCAmelCase				(      snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       str							  =  DebertaTokenizer
   __UpperCAmelCase :       Optional[int]							  =  True
   __UpperCAmelCase :       int							  =  DebertaTokenizerFast







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       snake_case    :      Union[str, Any]      					=    [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "[UNK]",
       ]
       snake_case    :      str      					=    dict(zip(UpperCamelCase__						,	range(len(UpperCamelCase__						)						)						)						)
       snake_case    :      int      					=    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       snake_case    :      Optional[Any]      					=    {"unk_token": "[UNK]"}

       snake_case    :      Union[str, Any]      					=    os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["vocab_file"]						)
       snake_case    :      Dict      					=    os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["merges_file"]						)
       with open(self.vocab_file						,	"w"						,	encoding="utf-8"						) as fp:
           fp.write(json.dumps(UpperCamelCase__						) + "\n"						)
       with open(self.merges_file						,	"w"						,	encoding="utf-8"						) as fp:
           fp.write("\n".join(UpperCamelCase__						)						)







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       kwargs.update(self.special_tokens_map						)
       return self.tokenizer_class.from_pretrained(self.tmpdirname						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      Tuple      					=    "lower newer"
       snake_case    :      Any      					=    "lower newer"
       return input_text, output_text







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    self.get_tokenizer()
       snake_case    :      str      					=    "lower newer"
       snake_case    :      Dict      					=    ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
       snake_case    :      Optional[Any]      					=    tokenizer.tokenize(UpperCamelCase__						)
       self.assertListEqual(UpperCamelCase__						,	UpperCamelCase__						)

       snake_case    :      List[Any]      					=    tokens + [tokenizer.unk_token]
       snake_case    :      Dict      					=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__						)						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.get_tokenizer()
       snake_case    :      Dict      					=    tokenizer("Hello"						,	"World"						)
       snake_case    :      str      					=    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd["token_type_ids"]						,	UpperCamelCase__						)







   @slow
   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.tokenizer_class.from_pretrained("microsoft/deberta-base"						)

       snake_case    :      Dict      					=    tokenizer.encode("sequence builders"						,	add_special_tokens=UpperCamelCase__						)
       snake_case    :      Tuple      					=    tokenizer.encode("multi-sequence build"						,	add_special_tokens=UpperCamelCase__						)

       snake_case    :      List[str]      					=    tokenizer.encode(
           "sequence builders"						,	add_special_tokens=UpperCamelCase__						,	add_prefix_space=UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    tokenizer.encode(
           "sequence builders"						,	"multi-sequence build"						,	add_special_tokens=UpperCamelCase__						,	add_prefix_space=UpperCamelCase__						)

       snake_case    :      int      					=    tokenizer.build_inputs_with_special_tokens(UpperCamelCase__						)
       snake_case    :      int      					=    tokenizer.build_inputs_with_special_tokens(UpperCamelCase__						,	UpperCamelCase__						)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode







   @slow
   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    [self.tokenizer_class]
       if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class						)

       for tokenizer_class in tokenizer_classes:
           snake_case    :      str      					=    tokenizer_class.from_pretrained("microsoft/deberta-base"						)

           snake_case    :      Union[str, Any]      					=    [
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           snake_case    :      Optional[int]      					=    tokenizer(UpperCamelCase__						,	padding=UpperCamelCase__						)
           snake_case    :      Optional[int]      					=    [tokenizer.decode(UpperCamelCase__						,	skip_special_tokens=UpperCamelCase__						) for seq in encoding["input_ids"]]

           # fmt: off
           snake_case    :      Dict      					=    {
               "input_ids": [
                   [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
               ],
               "token_type_ids": [
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
               ],
               "attention_mask": [
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           snake_case    :      int      					=    [
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           self.assertDictEqual(encoding.data						,	UpperCamelCase__						)

           for expected, decoded in zip(UpperCamelCase__						,	UpperCamelCase__						):
               self.assertEqual(UpperCamelCase__						,	UpperCamelCase__						)





