


"""simple docstring"""
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 (							__snake_case				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				tempfile.mkdtemp()
			__snake_case	:						Dict						=				5

			# Realm tok
			__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''',
			]
			__snake_case	:						int						=				os.path.join(self.tmpdirname				,       '''realm_tokenizer'''					)
			os.makedirs(a_				,       exist_ok=a_					)
			__snake_case	:						int						=				os.path.join(a_				,       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]					)					)

			__snake_case	:						Optional[Any]						=				os.path.join(self.tmpdirname				,       '''realm_block_records'''					)
			os.makedirs(a_				,       exist_ok=a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			shutil.rmtree(self.tmpdirname					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				RealmConfig(num_block_records=self.num_block_records					)
			return config



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				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=a_				,       )
			return block_records



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				RealmRetriever(
			    block_records=self.get_dummy_block_records()				,       tokenizer=self.get_tokenizer()				,       )
			return retriever



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				self.get_config()
			__snake_case	:						str						=				self.get_dummy_retriever()
			__snake_case	:						List[str]						=				retriever.tokenizer

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

			__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	:						Optional[Any]						=				retriever(
			    a_				,       a_				,       answer_ids=a_				,       max_length=a_				,       return_tensors='''np'''					)

			self.assertEqual(len(a_					)				,       2					)
			self.assertEqual(len(a_					)				,       2					)
			self.assertEqual(len(a_					)				,       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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				self.get_config()
			__snake_case	:						List[str]						=				self.get_dummy_retriever()
			__snake_case	:						Tuple						=				retriever.tokenizer

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

			__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	:						str						=				retriever(
			    a_				,       a_				,       answer_ids=a_				,       max_length=a_				,       return_tensors='''np'''					)

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				self.get_dummy_retriever()
			retriever.save_pretrained(os.path.join(self.tmpdirname				,       '''realm_block_records'''					)					)

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

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









"""simple docstring"""
import math






def 		lowercase							(     _snake_case						:      int     )     ->bool:
	"""simple docstring"""





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






def 		lowercase							(     _snake_case						:      float = 0.1     )     ->int:
	"""simple docstring"""





	__snake_case	:						Tuple						=				3
	__snake_case	:						Any						=				3

	while primes / (2 * j - 1) >= ratio:
		for i in range(j * j + j + 1     ,   (j + 2) * (j + 2)     ,   j + 1     ):
			primes += is_prime(_snake_case     )
		j += 2
	return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()





