1#![deny(non_upper_case_globals)]
20#![deny(non_camel_case_types)]
21#![deny(non_snake_case)]
22#![deny(unused_mut)]
23#![deny(dead_code)]
24#![deny(unused_imports)]
25#![deny(missing_docs)]
26#![cfg_attr(all(not(test), not(feature = "std")), no_std)]
27#![cfg_attr(docsrs, feature(doc_auto_cfg))]
28
29#[cfg(any(test, feature = "std"))]
30pub extern crate core;
31
32#[cfg(feature = "alloc")]
33extern crate alloc;
34
35extern crate bitcoin_hashes;
36
37#[cfg(feature = "unicode-normalization")]
38extern crate unicode_normalization;
39
40#[cfg(feature = "rand")]
41pub extern crate crate_rand as rand;
42#[cfg(feature = "rand_core")]
43pub extern crate rand_core;
44#[cfg(feature = "serde")]
45pub extern crate serde;
46
47#[cfg(feature = "alloc")]
48use alloc::{borrow::Cow, string::ToString, vec::Vec};
49use core::{fmt, str};
50
51#[cfg(all(feature = "rand", feature = "rand_core"))]
54use rand::{CryptoRng, RngCore};
55#[cfg(all(not(feature = "rand"), feature = "rand_core"))]
56use rand_core::{CryptoRng, RngCore};
57
58#[cfg(feature = "std")]
59use std::error;
60
61use bitcoin_hashes::{sha256, Hash};
62
63#[cfg(feature = "unicode-normalization")]
64use unicode_normalization::UnicodeNormalization;
65
66#[cfg(feature = "zeroize")]
67extern crate zeroize;
68#[cfg(feature = "zeroize")]
69use zeroize::{Zeroize, ZeroizeOnDrop};
70
71#[macro_use]
72mod internal_macros;
73mod language;
74mod pbkdf2;
75
76pub use language::Language;
77
78#[allow(unused)]
80const MIN_NB_WORDS: usize = 12;
81
82const MAX_NB_WORDS: usize = 24;
84
85const EOF: u16 = u16::max_value();
87
88#[derive(Debug, Clone, PartialEq, Eq, Copy)]
91pub struct AmbiguousLanguages([bool; language::MAX_NB_LANGUAGES]);
92
93impl AmbiguousLanguages {
94 pub fn as_bools(&self) -> &[bool; language::MAX_NB_LANGUAGES] {
97 &self.0
98 }
99
100 pub fn iter(&self) -> impl Iterator<Item = Language> + '_ {
102 Language::all().iter().enumerate().filter(move |(i, _)| self.0[*i]).map(|(_, l)| *l)
103 }
104
105 #[cfg(feature = "alloc")]
107 pub fn to_vec(&self) -> Vec<Language> {
108 self.iter().collect()
109 }
110}
111
112#[derive(Debug, Clone, PartialEq, Eq, Copy)]
114pub enum Error {
115 BadWordCount(usize),
117 UnknownWord(usize),
121 BadEntropyBitCount(usize),
123 InvalidChecksum,
125 AmbiguousLanguages(AmbiguousLanguages),
129}
130
131impl fmt::Display for Error {
132 fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
133 match *self {
134 Error::BadWordCount(c) => {
135 write!(
136 f,
137 "mnemonic has an invalid word count: {}. Word count must be 12, 15, 18, 21, \
138 or 24",
139 c
140 )
141 }
142 Error::UnknownWord(i) => write!(f, "mnemonic contains an unknown word (word {})", i,),
143 Error::BadEntropyBitCount(c) => write!(
144 f,
145 "entropy was not between 128-256 bits or not a multiple of 32 bits: {} bits",
146 c,
147 ),
148 Error::InvalidChecksum => write!(f, "the mnemonic has an invalid checksum"),
149 Error::AmbiguousLanguages(a) => {
150 write!(f, "ambiguous word list: ")?;
151 for (i, lang) in a.iter().enumerate() {
152 if i == 0 {
153 write!(f, "{}", lang)?;
154 } else {
155 write!(f, ", {}", lang)?;
156 }
157 }
158 Ok(())
159 }
160 }
161 }
162}
163
164#[cfg(feature = "std")]
165impl error::Error for Error {}
166
167#[derive(Clone, Debug, Hash, PartialEq, Eq, PartialOrd, Ord)]
175#[cfg_attr(feature = "zeroize", derive(Zeroize, ZeroizeOnDrop))]
176pub struct Mnemonic {
177 lang: Language,
179 words: [u16; MAX_NB_WORDS],
182}
183
184#[cfg(feature = "zeroize")]
185impl zeroize::DefaultIsZeroes for Language {}
186
187serde_string_impl!(Mnemonic, "a BIP-39 Mnemonic Code");
188
189impl Mnemonic {
190 #[inline]
194 #[cfg(feature = "unicode-normalization")]
195 pub fn normalize_utf8_cow<'a>(cow: &mut Cow<'a, str>) {
196 let is_nfkd = unicode_normalization::is_nfkd_quick(cow.as_ref().chars());
197 if is_nfkd != unicode_normalization::IsNormalized::Yes {
198 *cow = Cow::Owned(cow.as_ref().nfkd().to_string());
199 }
200 }
201
202 pub fn from_entropy_in(language: Language, entropy: &[u8]) -> Result<Mnemonic, Error> {
205 const MAX_ENTROPY_BITS: usize = 256;
206 const MIN_ENTROPY_BITS: usize = 128;
207 const MAX_CHECKSUM_BITS: usize = 8;
208
209 let nb_bytes = entropy.len();
210 let nb_bits = nb_bytes * 8;
211
212 if nb_bits % 32 != 0 {
213 return Err(Error::BadEntropyBitCount(nb_bits));
214 }
215 if nb_bits < MIN_ENTROPY_BITS || nb_bits > MAX_ENTROPY_BITS {
216 return Err(Error::BadEntropyBitCount(nb_bits));
217 }
218
219 let check = sha256::Hash::hash(&entropy);
220 let mut bits = [false; MAX_ENTROPY_BITS + MAX_CHECKSUM_BITS];
221 for i in 0..nb_bytes {
222 for j in 0..8 {
223 bits[i * 8 + j] = (entropy[i] & (1 << (7 - j))) > 0;
224 }
225 }
226 for i in 0..nb_bytes / 4 {
227 bits[8 * nb_bytes + i] = (check[i / 8] & (1 << (7 - (i % 8)))) > 0;
228 }
229
230 let mut words = [EOF; MAX_NB_WORDS];
231 let nb_words = nb_bytes * 3 / 4;
232 for i in 0..nb_words {
233 let mut idx = 0;
234 for j in 0..11 {
235 if bits[i * 11 + j] {
236 idx += 1 << (10 - j);
237 }
238 }
239 words[i] = idx;
240 }
241
242 Ok(Mnemonic {
243 lang: language,
244 words: words,
245 })
246 }
247
248 pub fn from_entropy(entropy: &[u8]) -> Result<Mnemonic, Error> {
251 Mnemonic::from_entropy_in(Language::English, entropy)
252 }
253
254 #[cfg(feature = "rand_core")]
267 pub fn generate_in_with<R>(
268 rng: &mut R,
269 language: Language,
270 word_count: usize,
271 ) -> Result<Mnemonic, Error>
272 where
273 R: RngCore + CryptoRng,
274 {
275 if is_invalid_word_count(word_count) {
276 return Err(Error::BadWordCount(word_count));
277 }
278
279 let entropy_bytes = (word_count / 3) * 4;
280 let mut entropy = [0u8; (MAX_NB_WORDS / 3) * 4];
281 RngCore::fill_bytes(rng, &mut entropy[0..entropy_bytes]);
282 Mnemonic::from_entropy_in(language, &entropy[0..entropy_bytes])
283 }
284
285 #[cfg(feature = "rand")]
296 pub fn generate_in(language: Language, word_count: usize) -> Result<Mnemonic, Error> {
297 Mnemonic::generate_in_with(&mut rand::thread_rng(), language, word_count)
298 }
299
300 #[cfg(feature = "rand")]
311 pub fn generate(word_count: usize) -> Result<Mnemonic, Error> {
312 Mnemonic::generate_in(Language::English, word_count)
313 }
314
315 pub fn language(&self) -> Language {
317 self.lang
318 }
319
320 pub fn words(&self) -> impl Iterator<Item = &'static str> + Clone + '_ {
335 let list = self.lang.word_list();
336 self.word_indices().map(move |i| list[i])
337 }
338
339 #[deprecated(note = "Use Mnemonic::words instead")]
341 pub fn word_iter(&self) -> impl Iterator<Item = &'static str> + Clone + '_ {
342 self.words()
343 }
344
345 pub fn word_indices(&self) -> impl Iterator<Item = usize> + Clone + '_ {
361 self.words.iter().take_while(|&&w| w != EOF).map(|w| *w as usize)
362 }
363
364 fn language_of_iter<'a, W: Iterator<Item = &'a str>>(words: W) -> Result<Language, Error> {
367 let mut words = words.peekable();
368 let langs = Language::all();
369 {
370 let first_word = words.peek().ok_or(Error::BadWordCount(0))?;
372 if first_word.len() == 0 {
373 return Err(Error::BadWordCount(0));
374 }
375
376 for language in langs.iter().filter(|l| l.unique_words()) {
379 if language.find_word(first_word).is_some() {
380 return Ok(*language);
381 }
382 }
383 }
384
385 let mut possible = [false; language::MAX_NB_LANGUAGES];
389 for (i, lang) in langs.iter().enumerate() {
390 possible[i] = !lang.unique_words();
393 }
394 for (idx, word) in words.enumerate() {
395 for (i, lang) in langs.iter().enumerate() {
397 possible[i] &= lang.find_word(word).is_some();
398 }
399
400 let mut iter = possible.iter().zip(langs.iter()).filter(|(p, _)| **p).map(|(_, l)| l);
402
403 match iter.next() {
404 None => return Err(Error::UnknownWord(idx)),
406 Some(remaining) => {
408 if iter.next().is_none() {
409 return Ok(*remaining);
411 }
412 }
413 }
414 }
415
416 return Err(Error::AmbiguousLanguages(AmbiguousLanguages(possible)));
417 }
418
419 pub fn language_of<S: AsRef<str>>(mnemonic: S) -> Result<Language, Error> {
430 Mnemonic::language_of_iter(mnemonic.as_ref().split_whitespace())
431 }
432
433 pub fn parse_in_normalized(language: Language, s: &str) -> Result<Mnemonic, Error> {
435 let nb_words = s.split_whitespace().count();
436 if is_invalid_word_count(nb_words) {
437 return Err(Error::BadWordCount(nb_words));
438 }
439
440 let mut words = [EOF; MAX_NB_WORDS];
442
443 let mut bits = [false; MAX_NB_WORDS * 11];
446
447 for (i, word) in s.split_whitespace().enumerate() {
448 let idx = language.find_word(word).ok_or(Error::UnknownWord(i))?;
449
450 words[i] = idx;
451
452 for j in 0..11 {
453 bits[i * 11 + j] = idx >> (10 - j) & 1 == 1;
454 }
455 }
456
457 let mut entropy = [0u8; MAX_NB_WORDS / 3 * 4];
460 let nb_bytes_entropy = nb_words / 3 * 4;
461 for i in 0..nb_bytes_entropy {
462 for j in 0..8 {
463 if bits[i * 8 + j] {
464 entropy[i] += 1 << (7 - j);
465 }
466 }
467 }
468 let check = sha256::Hash::hash(&entropy[0..nb_bytes_entropy]);
469 for i in 0..nb_bytes_entropy / 4 {
470 if bits[8 * nb_bytes_entropy + i] != ((check[i / 8] & (1 << (7 - (i % 8)))) > 0) {
471 return Err(Error::InvalidChecksum);
472 }
473 }
474
475 Ok(Mnemonic {
476 lang: language,
477 words: words,
478 })
479 }
480
481 pub fn parse_in_normalized_without_checksum_check(
487 language: Language,
488 s: &str,
489 ) -> Result<Mnemonic, Error> {
490 let nb_words = s.split_whitespace().count();
491 if is_invalid_word_count(nb_words) {
492 return Err(Error::BadWordCount(nb_words));
493 }
494
495 let mut words = [EOF; MAX_NB_WORDS];
497
498 for (i, word) in s.split_whitespace().enumerate() {
499 let idx = language.find_word(word).ok_or(Error::UnknownWord(i))?;
500
501 words[i] = idx;
502 }
503
504 Ok(Mnemonic {
505 lang: language,
506 words: words,
507 })
508 }
509
510 pub fn parse_normalized(s: &str) -> Result<Mnemonic, Error> {
512 let lang = Mnemonic::language_of(s)?;
513 Mnemonic::parse_in_normalized(lang, s)
514 }
515
516 #[cfg(feature = "unicode-normalization")]
518 pub fn parse_in<'a, S: Into<Cow<'a, str>>>(
519 language: Language,
520 s: S,
521 ) -> Result<Mnemonic, Error> {
522 let mut cow = s.into();
523 Mnemonic::normalize_utf8_cow(&mut cow);
524 Ok(Mnemonic::parse_in_normalized(language, cow.as_ref())?)
525 }
526
527 #[cfg(feature = "unicode-normalization")]
529 pub fn parse<'a, S: Into<Cow<'a, str>>>(s: S) -> Result<Mnemonic, Error> {
530 let mut cow = s.into();
531 Mnemonic::normalize_utf8_cow(&mut cow);
532
533 let language = if Language::all().len() == 1 {
534 Language::all()[0]
535 } else {
536 Mnemonic::language_of(cow.as_ref())?
537 };
538
539 Ok(Mnemonic::parse_in_normalized(language, cow.as_ref())?)
540 }
541
542 pub fn word_count(&self) -> usize {
544 self.word_indices().count()
545 }
546
547 pub fn to_seed_normalized(&self, normalized_passphrase: &str) -> [u8; 64] {
549 const PBKDF2_ROUNDS: usize = 2048;
550 const PBKDF2_BYTES: usize = 64;
551
552 let mut seed = [0u8; PBKDF2_BYTES];
553 pbkdf2::pbkdf2(self.words(), normalized_passphrase.as_bytes(), PBKDF2_ROUNDS, &mut seed);
554 seed
555 }
556
557 #[cfg(feature = "unicode-normalization")]
559 pub fn to_seed<'a, P: Into<Cow<'a, str>>>(&self, passphrase: P) -> [u8; 64] {
560 let normalized_passphrase = {
561 let mut cow = passphrase.into();
562 Mnemonic::normalize_utf8_cow(&mut cow);
563 cow
564 };
565 self.to_seed_normalized(normalized_passphrase.as_ref())
566 }
567
568 pub fn to_entropy_array(&self) -> ([u8; 33], usize) {
572 let language = Mnemonic::language_of_iter(self.words()).unwrap();
576
577 let mut entropy = [0; 33];
579 let mut cursor = 0;
580 let mut offset = 0;
581 let mut remainder = 0;
582
583 let nb_words = self.word_count();
584 for word in self.words() {
585 let idx = language.find_word(word).expect("invalid mnemonic");
586
587 remainder |= ((idx as u32) << (32 - 11)) >> offset;
588 offset += 11;
589
590 while offset >= 8 {
591 entropy[cursor] = (remainder >> 24) as u8;
592 cursor += 1;
593 remainder <<= 8;
594 offset -= 8;
595 }
596 }
597
598 if offset != 0 {
599 entropy[cursor] = (remainder >> 24) as u8;
600 }
601
602 let entropy_bytes = (nb_words / 3) * 4;
603 (entropy, entropy_bytes)
604 }
605
606 #[cfg(feature = "alloc")]
608 pub fn to_entropy(&self) -> Vec<u8> {
609 let (arr, len) = self.to_entropy_array();
610 arr[0..len].to_vec()
611 }
612
613 pub fn checksum(&self) -> u8 {
636 let word_count = self.word_count();
637 let last_word = self.words[word_count - 1];
638 let mask = 0xFF >> (8 - word_count / 3);
639 last_word as u8 & mask
640 }
641}
642
643impl fmt::Display for Mnemonic {
644 fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
645 for (i, word) in self.words().enumerate() {
646 if i > 0 {
647 f.write_str(" ")?;
648 }
649 f.write_str(word)?;
650 }
651 Ok(())
652 }
653}
654
655impl str::FromStr for Mnemonic {
656 type Err = Error;
657
658 fn from_str(s: &str) -> Result<Mnemonic, Error> {
659 #[cfg(feature = "unicode-normalization")]
660 {
661 Mnemonic::parse(s)
662 }
663 #[cfg(not(feature = "unicode-normalization"))]
664 {
665 Mnemonic::parse_normalized(s)
666 }
667 }
668}
669
670fn is_invalid_word_count(word_count: usize) -> bool {
671 word_count < MIN_NB_WORDS || word_count % 3 != 0 || word_count > MAX_NB_WORDS
672}
673
674#[cfg(test)]
675mod tests {
676 use super::*;
677
678 use bitcoin_hashes::hex::FromHex;
679
680 #[cfg(feature = "rand")]
681 #[test]
682 fn test_language_of() {
683 for lang in Language::all() {
684 let m = Mnemonic::generate_in(*lang, 24).unwrap();
685 assert_eq!(*lang, Mnemonic::language_of_iter(m.words()).unwrap());
686 assert_eq!(
687 *lang,
688 Mnemonic::language_of_iter(m.to_string().split_whitespace()).unwrap()
689 );
690 assert_eq!(*lang, Mnemonic::language_of(m.to_string()).unwrap());
691 assert_eq!(*lang, Mnemonic::language_of(&m.to_string()).unwrap());
692 }
693 }
694
695 #[cfg(feature = "std")]
696 #[test]
697 fn test_ambiguous_languages() {
698 let mut present = [false; language::MAX_NB_LANGUAGES];
699 let mut present_vec = Vec::new();
700 let mut alternate = true;
701 for i in 0..Language::all().len() {
702 present[i] = alternate;
703 if alternate {
704 present_vec.push(Language::all()[i]);
705 }
706 alternate = !alternate;
707 }
708 let amb = AmbiguousLanguages(present);
709 assert_eq!(amb.to_vec(), present_vec);
710 assert_eq!(amb.iter().collect::<Vec<_>>(), present_vec);
711 }
712
713 #[cfg(feature = "rand")]
714 #[test]
715 fn test_generate() {
716 let _ = Mnemonic::generate(24).unwrap();
717 let _ = Mnemonic::generate_in(Language::English, 24).unwrap();
718 let _ = Mnemonic::generate_in_with(&mut rand::thread_rng(), Language::English, 24).unwrap();
719 }
720
721 #[cfg(feature = "rand")]
722 #[test]
723 fn test_generate_word_counts() {
724 for word_count in [12, 15, 18, 21, 24].iter() {
725 let _ = Mnemonic::generate(*word_count).unwrap();
726 }
727 }
728
729 #[test]
730 fn test_vectors_english() {
731 let test_vectors = [
734 (
735 "00000000000000000000000000000000",
736 "abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon about",
737 "c55257c360c07c72029aebc1b53c05ed0362ada38ead3e3e9efa3708e53495531f09a6987599d18264c1e1c92f2cf141630c7a3c4ab7c81b2f001698e7463b04",
738 ),
739 (
740 "7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f",
741 "legal winner thank year wave sausage worth useful legal winner thank yellow",
742 "2e8905819b8723fe2c1d161860e5ee1830318dbf49a83bd451cfb8440c28bd6fa457fe1296106559a3c80937a1c1069be3a3a5bd381ee6260e8d9739fce1f607",
743 ),
744 (
745 "80808080808080808080808080808080",
746 "letter advice cage absurd amount doctor acoustic avoid letter advice cage above",
747 "d71de856f81a8acc65e6fc851a38d4d7ec216fd0796d0a6827a3ad6ed5511a30fa280f12eb2e47ed2ac03b5c462a0358d18d69fe4f985ec81778c1b370b652a8",
748 ),
749 (
750 "ffffffffffffffffffffffffffffffff",
751 "zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo wrong",
752 "ac27495480225222079d7be181583751e86f571027b0497b5b5d11218e0a8a13332572917f0f8e5a589620c6f15b11c61dee327651a14c34e18231052e48c069",
753 ),
754 (
755 "000000000000000000000000000000000000000000000000",
756 "abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon agent",
757 "035895f2f481b1b0f01fcf8c289c794660b289981a78f8106447707fdd9666ca06da5a9a565181599b79f53b844d8a71dd9f439c52a3d7b3e8a79c906ac845fa",
758 ),
759 (
760 "7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f",
761 "legal winner thank year wave sausage worth useful legal winner thank year wave sausage worth useful legal will",
762 "f2b94508732bcbacbcc020faefecfc89feafa6649a5491b8c952cede496c214a0c7b3c392d168748f2d4a612bada0753b52a1c7ac53c1e93abd5c6320b9e95dd",
763 ),
764 (
765 "808080808080808080808080808080808080808080808080",
766 "letter advice cage absurd amount doctor acoustic avoid letter advice cage absurd amount doctor acoustic avoid letter always",
767 "107d7c02a5aa6f38c58083ff74f04c607c2d2c0ecc55501dadd72d025b751bc27fe913ffb796f841c49b1d33b610cf0e91d3aa239027f5e99fe4ce9e5088cd65",
768 ),
769 (
770 "ffffffffffffffffffffffffffffffffffffffffffffffff",
771 "zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo when",
772 "0cd6e5d827bb62eb8fc1e262254223817fd068a74b5b449cc2f667c3f1f985a76379b43348d952e2265b4cd129090758b3e3c2c49103b5051aac2eaeb890a528",
773 ),
774 (
775 "0000000000000000000000000000000000000000000000000000000000000000",
776 "abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon art",
777 "bda85446c68413707090a52022edd26a1c9462295029f2e60cd7c4f2bbd3097170af7a4d73245cafa9c3cca8d561a7c3de6f5d4a10be8ed2a5e608d68f92fcc8",
778 ),
779 (
780 "7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f",
781 "legal winner thank year wave sausage worth useful legal winner thank year wave sausage worth useful legal winner thank year wave sausage worth title",
782 "bc09fca1804f7e69da93c2f2028eb238c227f2e9dda30cd63699232578480a4021b146ad717fbb7e451ce9eb835f43620bf5c514db0f8add49f5d121449d3e87",
783 ),
784 (
785 "8080808080808080808080808080808080808080808080808080808080808080",
786 "letter advice cage absurd amount doctor acoustic avoid letter advice cage absurd amount doctor acoustic avoid letter advice cage absurd amount doctor acoustic bless",
787 "c0c519bd0e91a2ed54357d9d1ebef6f5af218a153624cf4f2da911a0ed8f7a09e2ef61af0aca007096df430022f7a2b6fb91661a9589097069720d015e4e982f",
788 ),
789 (
790 "ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff",
791 "zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo zoo vote",
792 "dd48c104698c30cfe2b6142103248622fb7bb0ff692eebb00089b32d22484e1613912f0a5b694407be899ffd31ed3992c456cdf60f5d4564b8ba3f05a69890ad",
793 ),
794 (
795 "9e885d952ad362caeb4efe34a8e91bd2",
796 "ozone drill grab fiber curtain grace pudding thank cruise elder eight picnic",
797 "274ddc525802f7c828d8ef7ddbcdc5304e87ac3535913611fbbfa986d0c9e5476c91689f9c8a54fd55bd38606aa6a8595ad213d4c9c9f9aca3fb217069a41028",
798 ),
799 (
800 "6610b25967cdcca9d59875f5cb50b0ea75433311869e930b",
801 "gravity machine north sort system female filter attitude volume fold club stay feature office ecology stable narrow fog",
802 "628c3827a8823298ee685db84f55caa34b5cc195a778e52d45f59bcf75aba68e4d7590e101dc414bc1bbd5737666fbbef35d1f1903953b66624f910feef245ac",
803 ),
804 (
805 "68a79eaca2324873eacc50cb9c6eca8cc68ea5d936f98787c60c7ebc74e6ce7c",
806 "hamster diagram private dutch cause delay private meat slide toddler razor book happy fancy gospel tennis maple dilemma loan word shrug inflict delay length",
807 "64c87cde7e12ecf6704ab95bb1408bef047c22db4cc7491c4271d170a1b213d20b385bc1588d9c7b38f1b39d415665b8a9030c9ec653d75e65f847d8fc1fc440",
808 ),
809 (
810 "c0ba5a8e914111210f2bd131f3d5e08d",
811 "scheme spot photo card baby mountain device kick cradle pact join borrow",
812 "ea725895aaae8d4c1cf682c1bfd2d358d52ed9f0f0591131b559e2724bb234fca05aa9c02c57407e04ee9dc3b454aa63fbff483a8b11de949624b9f1831a9612",
813 ),
814 (
815 "6d9be1ee6ebd27a258115aad99b7317b9c8d28b6d76431c3",
816 "horn tenant knee talent sponsor spell gate clip pulse soap slush warm silver nephew swap uncle crack brave",
817 "fd579828af3da1d32544ce4db5c73d53fc8acc4ddb1e3b251a31179cdb71e853c56d2fcb11aed39898ce6c34b10b5382772db8796e52837b54468aeb312cfc3d",
818 ),
819 (
820 "9f6a2878b2520799a44ef18bc7df394e7061a224d2c33cd015b157d746869863",
821 "panda eyebrow bullet gorilla call smoke muffin taste mesh discover soft ostrich alcohol speed nation flash devote level hobby quick inner drive ghost inside",
822 "72be8e052fc4919d2adf28d5306b5474b0069df35b02303de8c1729c9538dbb6fc2d731d5f832193cd9fb6aeecbc469594a70e3dd50811b5067f3b88b28c3e8d",
823 ),
824 (
825 "23db8160a31d3e0dca3688ed941adbf3",
826 "cat swing flag economy stadium alone churn speed unique patch report train",
827 "deb5f45449e615feff5640f2e49f933ff51895de3b4381832b3139941c57b59205a42480c52175b6efcffaa58a2503887c1e8b363a707256bdd2b587b46541f5",
828 ),
829 (
830 "8197a4a47f0425faeaa69deebc05ca29c0a5b5cc76ceacc0",
831 "light rule cinnamon wrap drastic word pride squirrel upgrade then income fatal apart sustain crack supply proud access",
832 "4cbdff1ca2db800fd61cae72a57475fdc6bab03e441fd63f96dabd1f183ef5b782925f00105f318309a7e9c3ea6967c7801e46c8a58082674c860a37b93eda02",
833 ),
834 (
835 "066dca1a2bb7e8a1db2832148ce9933eea0f3ac9548d793112d9a95c9407efad",
836 "all hour make first leader extend hole alien behind guard gospel lava path output census museum junior mass reopen famous sing advance salt reform",
837 "26e975ec644423f4a4c4f4215ef09b4bd7ef924e85d1d17c4cf3f136c2863cf6df0a475045652c57eb5fb41513ca2a2d67722b77e954b4b3fc11f7590449191d",
838 ),
839 (
840 "f30f8c1da665478f49b001d94c5fc452",
841 "vessel ladder alter error federal sibling chat ability sun glass valve picture",
842 "2aaa9242daafcee6aa9d7269f17d4efe271e1b9a529178d7dc139cd18747090bf9d60295d0ce74309a78852a9caadf0af48aae1c6253839624076224374bc63f",
843 ),
844 (
845 "c10ec20dc3cd9f652c7fac2f1230f7a3c828389a14392f05",
846 "scissors invite lock maple supreme raw rapid void congress muscle digital elegant little brisk hair mango congress clump",
847 "7b4a10be9d98e6cba265566db7f136718e1398c71cb581e1b2f464cac1ceedf4f3e274dc270003c670ad8d02c4558b2f8e39edea2775c9e232c7cb798b069e88",
848 ),
849 (
850 "f585c11aec520db57dd353c69554b21a89b20fb0650966fa0a9d6f74fd989d8f",
851 "void come effort suffer camp survey warrior heavy shoot primary clutch crush open amazing screen patrol group space point ten exist slush involve unfold",
852 "01f5bced59dec48e362f2c45b5de68b9fd6c92c6634f44d6d40aab69056506f0e35524a518034ddc1192e1dacd32c1ed3eaa3c3b131c88ed8e7e54c49a5d0998",
853 )
854 ];
855
856 for vector in &test_vectors {
857 let entropy = Vec::<u8>::from_hex(&vector.0).unwrap();
858 let mnemonic_str = vector.1;
859 let seed = Vec::<u8>::from_hex(&vector.2).unwrap();
860
861 let mnemonic = Mnemonic::from_entropy(&entropy).unwrap();
862
863 assert_eq!(
864 mnemonic,
865 Mnemonic::parse_in_normalized(Language::English, mnemonic_str).unwrap(),
866 "failed vector: {}",
867 mnemonic_str
868 );
869 assert_eq!(
870 mnemonic,
871 Mnemonic::parse_normalized(mnemonic_str).unwrap(),
872 "failed vector: {}",
873 mnemonic_str
874 );
875 assert_eq!(
876 &seed[..],
877 &mnemonic.to_seed_normalized("TREZOR")[..],
878 "failed vector: {}",
879 mnemonic_str
880 );
881
882 #[cfg(feature = "unicode-normalization")]
883 {
884 assert_eq!(&mnemonic.to_string(), mnemonic_str, "failed vector: {}", mnemonic_str);
885 assert_eq!(
886 mnemonic,
887 Mnemonic::parse_in(Language::English, mnemonic_str).unwrap(),
888 "failed vector: {}",
889 mnemonic_str
890 );
891 assert_eq!(
892 mnemonic,
893 Mnemonic::parse(mnemonic_str).unwrap(),
894 "failed vector: {}",
895 mnemonic_str
896 );
897 assert_eq!(
898 &seed[..],
899 &mnemonic.to_seed("TREZOR")[..],
900 "failed vector: {}",
901 mnemonic_str
902 );
903 assert_eq!(&entropy, &mnemonic.to_entropy(), "failed vector: {}", mnemonic_str);
904 assert_eq!(
905 &entropy[..],
906 &mnemonic.to_entropy_array().0[0..entropy.len()],
907 "failed vector: {}",
908 mnemonic_str
909 );
910 }
911 }
912 }
913
914 #[test]
915 fn checksum() {
916 let vectors = [
917 "00000000000000000000000000000000",
918 "7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f",
919 "80808080808080808080808080808080",
920 "ffffffffffffffffffffffffffffffff",
921 "000000000000000000000000000000000000000000000000",
922 "7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f",
923 "808080808080808080808080808080808080808080808080",
924 "ffffffffffffffffffffffffffffffffffffffffffffffff",
925 "0000000000000000000000000000000000000000000000000000000000000000",
926 "7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f",
927 "8080808080808080808080808080808080808080808080808080808080808080",
928 "ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff",
929 "9e885d952ad362caeb4efe34a8e91bd2",
930 "6610b25967cdcca9d59875f5cb50b0ea75433311869e930b",
931 "68a79eaca2324873eacc50cb9c6eca8cc68ea5d936f98787c60c7ebc74e6ce7c",
932 "c0ba5a8e914111210f2bd131f3d5e08d",
933 "6d9be1ee6ebd27a258115aad99b7317b9c8d28b6d76431c3",
934 "9f6a2878b2520799a44ef18bc7df394e7061a224d2c33cd015b157d746869863",
935 "23db8160a31d3e0dca3688ed941adbf3",
936 "8197a4a47f0425faeaa69deebc05ca29c0a5b5cc76ceacc0",
937 "066dca1a2bb7e8a1db2832148ce9933eea0f3ac9548d793112d9a95c9407efad",
938 "f30f8c1da665478f49b001d94c5fc452",
939 "c10ec20dc3cd9f652c7fac2f1230f7a3c828389a14392f05",
940 "f585c11aec520db57dd353c69554b21a89b20fb0650966fa0a9d6f74fd989d8f",
941 "ed3b83f0d7913a19667a1cfd7298cd57",
942 "70639a4e81b151277b345476d169a3743ff3c141",
943 "ba2520298b92063a7a0ee1d453ba92513af81d4f86e1d336",
944 "9447d2cf44349cd88a58f5b4ff6f83b9a2d54c42f033e12b8e4d00cc",
945 "38711e550dc6557df8082b2a87f7860ebbe47ea5867a7068f5f0f5b85db68be8",
946 ];
947
948 for entropy_hex in &vectors {
949 let ent = Vec::from_hex(entropy_hex).unwrap();
950 let m = Mnemonic::from_entropy(&ent).unwrap();
951 let word_count = m.word_count();
952 let cs = m.checksum();
953 let digest = sha256::Hash::hash(&ent);
954 dbg!(digest);
955 assert_eq!(digest[0] >> (8 - word_count / 3), cs);
956 }
957 }
958
959 #[test]
960 fn test_invalid_engish() {
961 assert_eq!(
965 Mnemonic::parse_normalized(
966 "getter advice cage absurd amount doctor acoustic avoid letter advice cage above",
967 ),
968 Err(Error::UnknownWord(0))
969 );
970
971 assert_eq!(
972 Mnemonic::parse_normalized(
973 "letter advice cagex absurd amount doctor acoustic avoid letter advice cage above",
974 ),
975 Err(Error::UnknownWord(2))
976 );
977
978 assert_eq!(
979 Mnemonic::parse_normalized(
980 "advice cage absurd amount doctor acoustic avoid letter advice cage above",
981 ),
982 Err(Error::BadWordCount(11))
983 );
984
985 assert_eq!(
986 Mnemonic::parse_normalized(
987 "primary advice cage absurd amount doctor acoustic avoid letter advice cage above",
988 ),
989 Err(Error::InvalidChecksum)
990 );
991 }
992
993 #[test]
994 fn test_invalid_entropy() {
995 assert_eq!(Mnemonic::from_entropy(&vec![b'x'; 17]), Err(Error::BadEntropyBitCount(136)));
997
998 assert_eq!(Mnemonic::from_entropy(&vec![b'x'; 4]), Err(Error::BadEntropyBitCount(32)));
1000
1001 assert_eq!(Mnemonic::from_entropy(&vec![b'x'; 36]), Err(Error::BadEntropyBitCount(288)));
1003 }
1004
1005 #[cfg(all(feature = "japanese", feature = "std"))]
1006 #[test]
1007 fn test_vectors_japanese() {
1008 let vectors = [
1016 (
1017 "00000000000000000000000000000000",
1018 "あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あおぞら",
1019 "㍍ガバヴァぱばぐゞちぢ十人十色",
1020 "a262d6fb6122ecf45be09c50492b31f92e9beb7d9a845987a02cefda57a15f9c467a17872029a9e92299b5cbdf306e3a0ee620245cbd508959b6cb7ca637bd55",
1021 ),
1022 (
1023 "7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f",
1024 "そつう れきだい ほんやく わかす りくつ ばいか ろせん やちん そつう れきだい ほんやく わかめ",
1025 "㍍ガバヴァぱばぐゞちぢ十人十色",
1026 "aee025cbe6ca256862f889e48110a6a382365142f7d16f2b9545285b3af64e542143a577e9c144e101a6bdca18f8d97ec3366ebf5b088b1c1af9bc31346e60d9",
1027 ),
1028 (
1029 "80808080808080808080808080808080",
1030 "そとづら あまど おおう あこがれる いくぶん けいけん あたえる いよく そとづら あまど おおう あかちゃん",
1031 "㍍ガバヴァぱばぐゞちぢ十人十色",
1032 "e51736736ebdf77eda23fa17e31475fa1d9509c78f1deb6b4aacfbd760a7e2ad769c714352c95143b5c1241985bcb407df36d64e75dd5a2b78ca5d2ba82a3544",
1033 ),
1034 (
1035 "ffffffffffffffffffffffffffffffff",
1036 "われる われる われる われる われる われる われる われる われる われる われる ろんぶん",
1037 "㍍ガバヴァぱばぐゞちぢ十人十色",
1038 "4cd2ef49b479af5e1efbbd1e0bdc117f6a29b1010211df4f78e2ed40082865793e57949236c43b9fe591ec70e5bb4298b8b71dc4b267bb96ed4ed282c8f7761c",
1039 ),
1040 (
1041 "000000000000000000000000000000000000000000000000",
1042 "あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あらいぐま",
1043 "㍍ガバヴァぱばぐゞちぢ十人十色",
1044 "d99e8f1ce2d4288d30b9c815ae981edd923c01aa4ffdc5dee1ab5fe0d4a3e13966023324d119105aff266dac32e5cd11431eeca23bbd7202ff423f30d6776d69",
1045 ),
1046 (
1047 "7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f",
1048 "そつう れきだい ほんやく わかす りくつ ばいか ろせん やちん そつう れきだい ほんやく わかす りくつ ばいか ろせん やちん そつう れいぎ",
1049 "㍍ガバヴァぱばぐゞちぢ十人十色",
1050 "eaaf171efa5de4838c758a93d6c86d2677d4ccda4a064a7136344e975f91fe61340ec8a615464b461d67baaf12b62ab5e742f944c7bd4ab6c341fbafba435716",
1051 ),
1052 (
1053 "808080808080808080808080808080808080808080808080",
1054 "そとづら あまど おおう あこがれる いくぶん けいけん あたえる いよく そとづら あまど おおう あこがれる いくぶん けいけん あたえる いよく そとづら いきなり",
1055 "㍍ガバヴァぱばぐゞちぢ十人十色",
1056 "aec0f8d3167a10683374c222e6e632f2940c0826587ea0a73ac5d0493b6a632590179a6538287641a9fc9df8e6f24e01bf1be548e1f74fd7407ccd72ecebe425",
1057 ),
1058 (
1059 "ffffffffffffffffffffffffffffffffffffffffffffffff",
1060 "われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる りんご",
1061 "㍍ガバヴァぱばぐゞちぢ十人十色",
1062 "f0f738128a65b8d1854d68de50ed97ac1831fc3a978c569e415bbcb431a6a671d4377e3b56abd518daa861676c4da75a19ccb41e00c37d086941e471a4374b95",
1063 ),
1064 (
1065 "0000000000000000000000000000000000000000000000000000000000000000",
1066 "あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん あいこくしん いってい",
1067 "㍍ガバヴァぱばぐゞちぢ十人十色",
1068 "23f500eec4a563bf90cfda87b3e590b211b959985c555d17e88f46f7183590cd5793458b094a4dccc8f05807ec7bd2d19ce269e20568936a751f6f1ec7c14ddd",
1069 ),
1070 (
1071 "7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f7f",
1072 "そつう れきだい ほんやく わかす りくつ ばいか ろせん やちん そつう れきだい ほんやく わかす りくつ ばいか ろせん やちん そつう れきだい ほんやく わかす りくつ ばいか ろせん まんきつ",
1073 "㍍ガバヴァぱばぐゞちぢ十人十色",
1074 "cd354a40aa2e241e8f306b3b752781b70dfd1c69190e510bc1297a9c5738e833bcdc179e81707d57263fb7564466f73d30bf979725ff783fb3eb4baa86560b05",
1075 ),
1076 (
1077 "8080808080808080808080808080808080808080808080808080808080808080",
1078 "そとづら あまど おおう あこがれる いくぶん けいけん あたえる いよく そとづら あまど おおう あこがれる いくぶん けいけん あたえる いよく そとづら あまど おおう あこがれる いくぶん けいけん あたえる うめる",
1079 "㍍ガバヴァぱばぐゞちぢ十人十色",
1080 "6b7cd1b2cdfeeef8615077cadd6a0625f417f287652991c80206dbd82db17bf317d5c50a80bd9edd836b39daa1b6973359944c46d3fcc0129198dc7dc5cd0e68",
1081 ),
1082 (
1083 "ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff",
1084 "われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる われる らいう",
1085 "㍍ガバヴァぱばぐゞちぢ十人十色",
1086 "a44ba7054ac2f9226929d56505a51e13acdaa8a9097923ca07ea465c4c7e294c038f3f4e7e4b373726ba0057191aced6e48ac8d183f3a11569c426f0de414623",
1087 ),
1088 (
1089 "77c2b00716cec7213839159e404db50d",
1090 "せまい うちがわ あずき かろう めずらしい だんち ますく おさめる ていぼう あたる すあな えしゃく",
1091 "㍍ガバヴァぱばぐゞちぢ十人十色",
1092 "344cef9efc37d0cb36d89def03d09144dd51167923487eec42c487f7428908546fa31a3c26b7391a2b3afe7db81b9f8c5007336b58e269ea0bd10749a87e0193",
1093 ),
1094 (
1095 "b63a9c59a6e641f288ebc103017f1da9f8290b3da6bdef7b",
1096 "ぬすむ ふっかつ うどん こうりつ しつじ りょうり おたがい せもたれ あつめる いちりゅう はんしゃ ごますり そんけい たいちょう らしんばん ぶんせき やすみ ほいく",
1097 "㍍ガバヴァぱばぐゞちぢ十人十色",
1098 "b14e7d35904cb8569af0d6a016cee7066335a21c1c67891b01b83033cadb3e8a034a726e3909139ecd8b2eb9e9b05245684558f329b38480e262c1d6bc20ecc4",
1099 ),
1100 (
1101 "3e141609b97933b66a060dcddc71fad1d91677db872031e85f4c015c5e7e8982",
1102 "くのう てぬぐい そんかい すろっと ちきゅう ほあん とさか はくしゅ ひびく みえる そざい てんすう たんぴん くしょう すいようび みけん きさらぎ げざん ふくざつ あつかう はやい くろう おやゆび こすう",
1103 "㍍ガバヴァぱばぐゞちぢ十人十色",
1104 "32e78dce2aff5db25aa7a4a32b493b5d10b4089923f3320c8b287a77e512455443298351beb3f7eb2390c4662a2e566eec5217e1a37467af43b46668d515e41b",
1105 ),
1106 (
1107 "0460ef47585604c5660618db2e6a7e7f",
1108 "あみもの いきおい ふいうち にげる ざんしょ じかん ついか はたん ほあん すんぽう てちがい わかめ",
1109 "㍍ガバヴァぱばぐゞちぢ十人十色",
1110 "0acf902cd391e30f3f5cb0605d72a4c849342f62bd6a360298c7013d714d7e58ddf9c7fdf141d0949f17a2c9c37ced1d8cb2edabab97c4199b142c829850154b",
1111 ),
1112 (
1113 "72f60ebac5dd8add8d2a25a797102c3ce21bc029c200076f",
1114 "すろっと にくしみ なやむ たとえる へいこう すくう きない けってい とくべつ ねっしん いたみ せんせい おくりがな まかい とくい けあな いきおい そそぐ",
1115 "㍍ガバヴァぱばぐゞちぢ十人十色",
1116 "9869e220bec09b6f0c0011f46e1f9032b269f096344028f5006a6e69ea5b0b8afabbb6944a23e11ebd021f182dd056d96e4e3657df241ca40babda532d364f73",
1117 ),
1118 (
1119 "2c85efc7f24ee4573d2b81a6ec66cee209b2dcbd09d8eddc51e0215b0b68e416",
1120 "かほご きうい ゆたか みすえる もらう がっこう よそう ずっと ときどき したうけ にんか はっこう つみき すうじつ よけい くげん もくてき まわり せめる げざい にげる にんたい たんそく ほそく",
1121 "㍍ガバヴァぱばぐゞちぢ十人十色",
1122 "713b7e70c9fbc18c831bfd1f03302422822c3727a93a5efb9659bec6ad8d6f2c1b5c8ed8b0b77775feaf606e9d1cc0a84ac416a85514ad59f5541ff5e0382481",
1123 ),
1124 (
1125 "eaebabb2383351fd31d703840b32e9e2",
1126 "めいえん さのう めだつ すてる きぬごし ろんぱ はんこ まける たいおう さかいし ねんいり はぶらし",
1127 "㍍ガバヴァぱばぐゞちぢ十人十色",
1128 "06e1d5289a97bcc95cb4a6360719131a786aba057d8efd603a547bd254261c2a97fcd3e8a4e766d5416437e956b388336d36c7ad2dba4ee6796f0249b10ee961",
1129 ),
1130 (
1131 "7ac45cfe7722ee6c7ba84fbc2d5bd61b45cb2fe5eb65aa78",
1132 "せんぱい おしえる ぐんかん もらう きあい きぼう やおや いせえび のいず じゅしん よゆう きみつ さといも ちんもく ちわわ しんせいじ とめる はちみつ",
1133 "㍍ガバヴァぱばぐゞちぢ十人十色",
1134 "1fef28785d08cbf41d7a20a3a6891043395779ed74503a5652760ee8c24dfe60972105ee71d5168071a35ab7b5bd2f8831f75488078a90f0926c8e9171b2bc4a",
1135 ),
1136 (
1137 "4fa1a8bc3e6d80ee1316050e862c1812031493212b7ec3f3bb1b08f168cabeef",
1138 "こころ いどう きあつ そうがんきょう へいあん せつりつ ごうせい はいち いびき きこく あんい おちつく きこえる けんとう たいこ すすめる はっけん ていど はんおん いんさつ うなぎ しねま れいぼう みつかる",
1139 "㍍ガバヴァぱばぐゞちぢ十人十色",
1140 "43de99b502e152d4c198542624511db3007c8f8f126a30818e856b2d8a20400d29e7a7e3fdd21f909e23be5e3c8d9aee3a739b0b65041ff0b8637276703f65c2",
1141 ),
1142 (
1143 "18ab19a9f54a9274f03e5209a2ac8a91",
1144 "うりきれ さいせい じゆう むろん とどける ぐうたら はいれつ ひけつ いずれ うちあわせ おさめる おたく",
1145 "㍍ガバヴァぱばぐゞちぢ十人十色",
1146 "3d711f075ee44d8b535bb4561ad76d7d5350ea0b1f5d2eac054e869ff7963cdce9581097a477d697a2a9433a0c6884bea10a2193647677977c9820dd0921cbde",
1147 ),
1148 (
1149 "18a2e1d81b8ecfb2a333adcb0c17a5b9eb76cc5d05db91a4",
1150 "うりきれ うねる せっさたくま きもち めんきょ へいたく たまご ぜっく びじゅつかん さんそ むせる せいじ ねくたい しはらい せおう ねんど たんまつ がいけん",
1151 "㍍ガバヴァぱばぐゞちぢ十人十色",
1152 "753ec9e333e616e9471482b4b70a18d413241f1e335c65cd7996f32b66cf95546612c51dcf12ead6f805f9ee3d965846b894ae99b24204954be80810d292fcdd",
1153 ),
1154 (
1155 "15da872c95a13dd738fbf50e427583ad61f18fd99f628c417a61cf8343c90419",
1156 "うちゅう ふそく ひしょ がちょう うけもつ めいそう みかん そざい いばる うけとる さんま さこつ おうさま ぱんつ しひょう めした たはつ いちぶ つうじょう てさぎょう きつね みすえる いりぐち かめれおん",
1157 "㍍ガバヴァぱばぐゞちぢ十人十色",
1158 "346b7321d8c04f6f37b49fdf062a2fddc8e1bf8f1d33171b65074531ec546d1d3469974beccb1a09263440fc92e1042580a557fdce314e27ee4eabb25fa5e5fe",
1159 )
1160 ];
1161
1162 for vector in &vectors {
1163 let entropy = Vec::<u8>::from_hex(&vector.0).unwrap();
1164 let mnemonic_str = vector.1;
1165 let passphrase = vector.2;
1166 let seed = Vec::<u8>::from_hex(&vector.3).unwrap();
1167
1168 let mnemonic = Mnemonic::from_entropy_in(Language::Japanese, &entropy).unwrap();
1169
1170 assert_eq!(seed, &mnemonic.to_seed(passphrase)[..], "failed vector: {}", mnemonic_str);
1171 let rt = Mnemonic::parse_in(Language::Japanese, mnemonic.to_string())
1172 .expect(&format!("vector: {}", mnemonic_str));
1173 assert_eq!(seed, &rt.to_seed(passphrase)[..]);
1174
1175 let mnemonic = Mnemonic::parse_in(Language::Japanese, mnemonic_str)
1176 .expect(&format!("vector: {}", mnemonic_str));
1177 assert_eq!(seed, &mnemonic.to_seed(passphrase)[..], "failed vector: {}", mnemonic_str);
1178 }
1179 }
1180}