



import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  a__												= tempfile.mkdtemp()
  a__												= 5

  # Realm tok
  a__												= [
      '''[UNK]''',
      '''[CLS]''',
      '''[SEP]''',
      '''[PAD]''',
      '''[MASK]''',
      '''test''',
      '''question''',
      '''this''',
      '''is''',
      '''the''',
      '''first''',
      '''second''',
      '''third''',
      '''fourth''',
      '''fifth''',
      '''record''',
      '''want''',
      '''##want''',
      '''##ed''',
      '''wa''',
      '''un''',
      '''runn''',
      '''##ing''',
      ''',''',
      '''low''',
      '''lowest''',
  ]
  a__												= os.path.join(self.tmpdirname		,				'''realm_tokenizer'''      )
  os.makedirs(SCREAMING_SNAKE_CASE		,				exist_ok=SCREAMING_SNAKE_CASE      )
  a__												= os.path.join(SCREAMING_SNAKE_CASE		,				VOCAB_FILES_NAMES['''vocab_file''']      )
  with open(self.vocab_file		,				'''w'''		,				encoding='''utf-8'''      ) as vocab_writer:
   vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]      )      )

  a__												= os.path.join(self.tmpdirname		,				'''realm_block_records'''      )
  os.makedirs(SCREAMING_SNAKE_CASE		,				exist_ok=SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self      ) ->			RealmTokenizer:
  return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname		,				'''realm_tokenizer'''      )      )

 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  shutil.rmtree(self.tmpdirname      )

 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__												= RealmConfig(num_block_records=self.num_block_records      )
  return config

 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__												= Dataset.from_dict(
      {
          '''id''': ['''0''', '''1'''],
          '''question''': ['''foo''', '''bar'''],
          '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
      }      )
  return dataset

 def 						_UpperCAmelCase       (	self      ) ->			str:
  a__												= np.array(
      [
          b'''This is the first record''',
          b'''This is the second record''',
          b'''This is the third record''',
          b'''This is the fourth record''',
          b'''This is the fifth record''',
          b'''This is a longer longer longer record''',
      ]		,				dtype=SCREAMING_SNAKE_CASE		,				)
  return block_records

 def 						_UpperCAmelCase       (	self      ) ->			Any:
  a__												= RealmRetriever(
      block_records=self.get_dummy_block_records()		,				tokenizer=self.get_tokenizer()		,				)
  return retriever

 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  a__												= self.get_config()
  a__												= self.get_dummy_retriever()
  a__												= retriever.tokenizer

  a__												= np.array([0, 3]		,				dtype='''long'''      )
  a__												= tokenizer(['''Test question''']      ).input_ids
  a__												= tokenizer(
      ['''the fourth''']		,				add_special_tokens=SCREAMING_SNAKE_CASE		,				return_token_type_ids=SCREAMING_SNAKE_CASE		,				return_attention_mask=SCREAMING_SNAKE_CASE		,				).input_ids
  a__												= config.reader_seq_len

  a__							,			a__							,			a__							,			a__												= retriever(
      SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				answer_ids=SCREAMING_SNAKE_CASE		,				max_length=SCREAMING_SNAKE_CASE		,				return_tensors='''np'''      )

  self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,				2      )
  self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,				2      )
  self.assertEqual(len(SCREAMING_SNAKE_CASE      )		,				2      )
  self.assertEqual(concat_inputs.input_ids.shape		,				(2, 1_0)      )
  self.assertEqual(concat_inputs.attention_mask.shape		,				(2, 1_0)      )
  self.assertEqual(concat_inputs.token_type_ids.shape		,				(2, 1_0)      )
  self.assertEqual(concat_inputs.special_tokens_mask.shape		,				(2, 1_0)      )
  self.assertEqual(
      tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]      )		,				['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]''']		,				)
  self.assertEqual(
      tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]      )		,				['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]''']		,				)

 def 						_UpperCAmelCase       (	self      ) ->			Any:
  a__												= self.get_config()
  a__												= self.get_dummy_retriever()
  a__												= retriever.tokenizer

  a__												= np.array([0, 3, 5]		,				dtype='''long'''      )
  a__												= tokenizer(['''Test question''']      ).input_ids
  a__												= tokenizer(
      ['''the fourth''', '''longer longer''']		,				add_special_tokens=SCREAMING_SNAKE_CASE		,				return_token_type_ids=SCREAMING_SNAKE_CASE		,				return_attention_mask=SCREAMING_SNAKE_CASE		,				).input_ids
  a__												= config.reader_seq_len

  a__							,			a__							,			a__							,			a__												= retriever(
      SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				answer_ids=SCREAMING_SNAKE_CASE		,				max_length=SCREAMING_SNAKE_CASE		,				return_tensors='''np'''      )

  self.assertEqual([False, True, True]		,				SCREAMING_SNAKE_CASE      )
  self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]		,				SCREAMING_SNAKE_CASE      )
  self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]		,				SCREAMING_SNAKE_CASE      )



 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__												= self.get_dummy_retriever()
  retriever.save_pretrained(os.path.join(self.tmpdirname		,				'''realm_block_records'''      )      )

  # Test local path
  a__												= retriever.from_pretrained(os.path.join(self.tmpdirname		,				'''realm_block_records'''      )      )
  self.assertEqual(retriever.block_records[0]		,				b'''This is the first record'''      )

  # Test mocked remote path
  with patch('''transformers.models.realm.retrieval_realm.hf_hub_download'''      ) as mock_hf_hub_download:
   a__												= os.path.join(
       os.path.join(self.tmpdirname		,				'''realm_block_records'''      )		,				_REALM_BLOCK_RECORDS_FILENAME      )
   a__												= RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa'''      )

  self.assertEqual(retriever.block_records[0]		,				b'''This is the first record'''      )










import math
from collections.abc import Iterator
from itertools import takewhile






def     __a					(		__UpperCAmelCase       ):

 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5							,  int(math.sqrt(__UpperCAmelCase       ) + 1       )							,  6       ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def     __a					(		):
 a__												= 2
 while True:
  if is_prime(__UpperCAmelCase       ):
   yield num
  num += 1






def     __a					(		__UpperCAmelCase = 200_0000       ):
 return sum(takewhile(lambda __UpperCAmelCase       : x < n							,  prime_generator()       )       )


if __name__ == "__main__":
     print(f'{solution() = }')





