


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 					lowerCAmelCase_ (       _a):




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






									"""simple docstring"""






									a__ :Dict						  =    tempfile.mkdtemp()
									a__ :str						  =    5

									# Realm tok
									a__ :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",
									]
									a__ :Dict						  =    os.path.join(self.tmpdirname   ,     "realm_tokenizer"						)
									os.makedirs(__A   ,     exist_ok=__A						)
									a__ :Dict						  =    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]						)						)

									a__ :Dict						  =    os.path.join(self.tmpdirname   ,     "realm_block_records"						)
									os.makedirs(__A   ,     exist_ok=__A						)




						def  _snake_case					(	self						:   Any						)      ->RealmTokenizer:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Any						)      ->Optional[Any]:






									"""simple docstring"""






									shutil.rmtree(self.tmpdirname						)




						def  _snake_case					(	self						:   int						)      ->Tuple:






									"""simple docstring"""






									a__ :Optional[int]						  =    RealmConfig(num_block_records=self.num_block_records						)
									return config




						def  _snake_case					(	self						:   Dict						)      ->Optional[Any]:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   int						)      ->Dict:






									"""simple docstring"""






									a__ :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  _snake_case					(	self						:   Any						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    RealmRetriever(
									    block_records=self.get_dummy_block_records()   ,     tokenizer=self.get_tokenizer()   ,     )
									return retriever




						def  _snake_case					(	self						:   Any						)      ->Tuple:






									"""simple docstring"""






									a__ :List[Any]						  =    self.get_config()
									a__ :Optional[int]						  =    self.get_dummy_retriever()
									a__ :Optional[Any]						  =    retriever.tokenizer

									a__ :Dict						  =    np.array([0, 3]   ,     dtype="long"						)
									a__ :Optional[Any]						  =    tokenizer(["Test question"]						).input_ids
									a__ :List[str]						  =    tokenizer(
									    ["the fourth"]   ,     add_special_tokens=__A   ,     return_token_type_ids=__A   ,     return_attention_mask=__A   ,     ).input_ids
									a__ :Any						  =    config.reader_seq_len

									a__  ,  a__  ,  a__  ,  a__ :str						  =    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  _snake_case					(	self						:   Dict						)      ->Any:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.get_config()
									a__ :int						  =    self.get_dummy_retriever()
									a__ :Tuple						  =    retriever.tokenizer

									a__ :int						  =    np.array([0, 3, 5]   ,     dtype="long"						)
									a__ :str						  =    tokenizer(["Test question"]						).input_ids
									a__ :List[Any]						  =    tokenizer(
									    ["the fourth", "longer longer"]   ,     add_special_tokens=__A   ,     return_token_type_ids=__A   ,     return_attention_mask=__A   ,     ).input_ids
									a__ :List[str]						  =    config.reader_seq_len

									a__  ,  a__  ,  a__  ,  a__ :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  _snake_case					(	self						:   List[str]						)      ->List[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    self.get_dummy_retriever()
									retriever.save_pretrained(os.path.join(self.tmpdirname   ,     "realm_block_records"						)						)

									# Test local path
									a__ :Optional[int]						  =    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__ :str						  =    os.path.join(
												    os.path.join(self.tmpdirname   ,     "realm_block_records"						)   ,     _REALM_BLOCK_RECORDS_FILENAME						)
												a__ :Optional[int]						  =    RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa"						)

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






def 				lowerCamelCase__	(      a	:   int = 1_000_000				)    ->	int:





			"""simple docstring"""
			a__ :int						  =    [i - 1 for i in range(limit + 1				)]

			for i in range(2	,  limit + 1				):
						if phi[i] == i - 1:
									for j in range(2 * i	,  limit + 1	,  a				):
												phi[j] -= phi[j] // i

			return sum(phi[2 : limit + 1]				)


if __name__ == "__main__":
		print(solution())


