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 					lowercase__						(   __A):


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							tempfile.mkdtemp()
												SCREAMING_SNAKE_CASE							:    List[str]							=							5

												# Realm tok
												SCREAMING_SNAKE_CASE							:    str							=							[
												    '''[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''',
												]
												SCREAMING_SNAKE_CASE							:    int							=							os.path.join(self.tmpdirname    ,		'''realm_tokenizer'''						)
												os.makedirs(_UpperCamelCase    ,		exist_ok=_UpperCamelCase						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							os.path.join(_UpperCamelCase    ,		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]						)						)

												SCREAMING_SNAKE_CASE							:    Any							=							os.path.join(self.tmpdirname    ,		'''realm_block_records'''						)
												os.makedirs(_UpperCamelCase    ,		exist_ok=_UpperCamelCase						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,		'''realm_tokenizer'''						)						)


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												shutil.rmtree(self.tmpdirname						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							RealmConfig(num_block_records=self.num_block_records						)
												return config


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							Dataset.from_dict(
												    {
												        '''id''': ['''0''', '''1'''],
												        '''question''': ['''foo''', '''bar'''],
												        '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
												    }						)
												return dataset


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							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=_UpperCamelCase    ,		)
												return block_records


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							RealmRetriever(
												    block_records=self.get_dummy_block_records()    ,		tokenizer=self.get_tokenizer()    ,		)
												return retriever


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							self.get_config()
												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_dummy_retriever()
												SCREAMING_SNAKE_CASE							:    Tuple							=							retriever.tokenizer

												SCREAMING_SNAKE_CASE							:    List[Any]							=							np.array([0, 3]    ,		dtype='''long'''						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer(['''Test question''']						).input_ids
												SCREAMING_SNAKE_CASE							:    Tuple							=							tokenizer(
												    ['''the fourth''']    ,		add_special_tokens=_UpperCamelCase    ,		return_token_type_ids=_UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							config.reader_seq_len

												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[Any]							=							retriever(
												    _UpperCamelCase    ,		_UpperCamelCase    ,		answer_ids=_UpperCamelCase    ,		max_length=_UpperCamelCase    ,		return_tensors='''np'''						)

												self.assertEqual(len(_UpperCamelCase						)    ,		2						)
												self.assertEqual(len(_UpperCamelCase						)    ,		2						)
												self.assertEqual(len(_UpperCamelCase						)    ,		2						)
												self.assertEqual(concat_inputs.input_ids.shape    ,		(2, 10)						)
												self.assertEqual(concat_inputs.attention_mask.shape    ,		(2, 10)						)
												self.assertEqual(concat_inputs.token_type_ids.shape    ,		(2, 10)						)
												self.assertEqual(concat_inputs.special_tokens_mask.shape    ,		(2, 10)						)
												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        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							self.get_config()
												SCREAMING_SNAKE_CASE							:    List[str]							=							self.get_dummy_retriever()
												SCREAMING_SNAKE_CASE							:    List[str]							=							retriever.tokenizer

												SCREAMING_SNAKE_CASE							:    Dict							=							np.array([0, 3, 5]    ,		dtype='''long'''						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							tokenizer(['''Test question''']						).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer(
												    ['''the fourth''', '''longer longer''']    ,		add_special_tokens=_UpperCamelCase    ,		return_token_type_ids=_UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		).input_ids
												SCREAMING_SNAKE_CASE							:    List[str]							=							config.reader_seq_len

												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Dict							=							retriever(
												    _UpperCamelCase    ,		_UpperCamelCase    ,		answer_ids=_UpperCamelCase    ,		max_length=_UpperCamelCase    ,		return_tensors='''np'''						)

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



						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.get_dummy_retriever()
												retriever.save_pretrained(os.path.join(self.tmpdirname    ,		'''realm_block_records'''						)						)

												# Test local path
												SCREAMING_SNAKE_CASE							:    str							=							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:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							os.path.join(
																		    os.path.join(self.tmpdirname    ,		'''realm_block_records'''						)    ,		_REALM_BLOCK_RECORDS_FILENAME						)
																		SCREAMING_SNAKE_CASE							:    Tuple							=							RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa'''						)

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








import math


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						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(SCREAMING_SNAKE_CASE__       ) + 1       )  ,		6       ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = 10001       ):

						try:
												snake_case_   =	int(SCREAMING_SNAKE_CASE__       )
						except (TypeError, ValueError):
												raise TypeError('''Parameter nth must be int or castable to int.'''       ) from None
						if nth <= 0:
												raise ValueError('''Parameter nth must be greater than or equal to one.'''       )
						snake_case_   =	[]
						snake_case_   =	2
						while len(SCREAMING_SNAKE_CASE__       ) < nth:
												if is_prime(SCREAMING_SNAKE_CASE__       ):
																		primes.append(SCREAMING_SNAKE_CASE__       )
																		num += 1
												else:
																		num += 1
						return primes[len(SCREAMING_SNAKE_CASE__       ) - 1]


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