



"""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 			__snake_case			(      _lowercase):




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					tempfile.mkdtemp()
         _lowerCamelCase		:							List[Any]            =					5

         # Realm tok
         _lowerCamelCase		:							Union[str, Any]            =					[
             '''[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''',
         ]
         _lowerCamelCase		:							Tuple            =					os.path.join(self.tmpdirname		,   '''realm_tokenizer'''						)
         os.makedirs(__lowerCAmelCase		,   exist_ok=__lowerCAmelCase						)
         _lowerCamelCase		:							Optional[int]            =					os.path.join(__lowerCAmelCase		,   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]						)						)

         _lowerCamelCase		:							List[Any]            =					os.path.join(self.tmpdirname		,   '''realm_block_records'''						)
         os.makedirs(__lowerCAmelCase		,   exist_ok=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Dict						):






         """simple docstring"""






         shutil.rmtree(self.tmpdirname						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Dict            =					RealmConfig(num_block_records=self.num_block_records						)
         return config




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]						):






         """simple docstring"""






         _lowerCamelCase		:							Any            =					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=__lowerCAmelCase		,   )
         return block_records




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]						):






         """simple docstring"""






         _lowerCamelCase		:							Dict            =					self.get_config()
         _lowerCamelCase		:							Dict            =					self.get_dummy_retriever()
         _lowerCamelCase		:							List[Any]            =					retriever.tokenizer

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

         _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							Dict            =					retriever(
             __lowerCAmelCase		,   __lowerCAmelCase		,   answer_ids=__lowerCAmelCase		,   max_length=__lowerCAmelCase		,   return_tensors='''np'''						)

         self.assertEqual(len(__lowerCAmelCase						)		,   2						)
         self.assertEqual(len(__lowerCAmelCase						)		,   2						)
         self.assertEqual(len(__lowerCAmelCase						)		,   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 		SCREAMING_SNAKE_CASE							(					self	:		List[str]						):






         """simple docstring"""






         _lowerCamelCase		:							int            =					self.get_config()
         _lowerCamelCase		:							Dict            =					self.get_dummy_retriever()
         _lowerCamelCase		:							List[Any]            =					retriever.tokenizer

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

         _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							Optional[Any]            =					retriever(
             __lowerCAmelCase		,   __lowerCAmelCase		,   answer_ids=__lowerCAmelCase		,   max_length=__lowerCAmelCase		,   return_tensors='''np'''						)

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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase		:							int            =					self.get_dummy_retriever()
         retriever.save_pretrained(os.path.join(self.tmpdirname		,   '''realm_block_records'''						)						)

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

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




'''simple docstring'''
from __future__ import annotations






def      UpperCamelCase_				(      A__						):
	a_      				=						[True] * limit
	a_      				=						False
	a_      				=						False
	a_      				=						True

	for i in range(3  ,      int(limit**0.5 + 1						)  ,      2						):
		a_      				=						i * 2
		while index < limit:
			a_      				=						False
			a_      				=						index + i

	a_      				=						[2]

	for i in range(3  ,      A__  ,      2						):
		if is_prime[i]:
			primes.append(A__						)

	return primes






def      UpperCamelCase_				(      A__ = 1_00_00_00						):
	a_      				=						prime_sieve(A__						)
	a_      				=						0
	a_      				=						0

	for i in range(len(A__						)						):
		for j in range(i + length  ,      len(A__						)						):
			a_      				=						sum(primes[i:j]						)
			if sol >= ceiling:
				break

			if sol in primes:
				a_      				=						j - i
				a_      				=						sol

	return largest


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