






def   lowerCamelCase      (		a_			)			->    bool:
							lowerCAmelCase_       		=  9

							while num > 0:
														if num % 10 != digit:
																					return False
														num //= 100
														digit -= 1

							return True
def   lowerCamelCase      (		)			->    int:
							lowerCAmelCase_       		=  138_902_663

							while not is_square_form(num * num			):
														if num % 10 == 3:
																					num -= 6  # (3 - 6) % 10 = 7
														else:
																					num -= 4  # (7 - 4) % 10 = 3

							return num * 10


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











import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


lowerCamelCase_		  =						get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_torch
@require_torchaudio
@require_sentencepiece
class 							a_				(		unittest.TestCase	):


							'''simple docstring'''






							def   _lowercase (	self     )      ->      Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_       		=  tempfile.mkdtemp()

														lowerCAmelCase_       		=  ['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
														lowerCAmelCase_       		=  dict(zip(lowercase_    ,    range(len(lowercase_     )     )     )     )
														lowerCAmelCase_       		=  Path(self.tmpdirname     )
														save_json(lowercase_    ,    save_dir / VOCAB_FILES_NAMES['vocab_file']     )
														if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																					copyfile(lowercase_    ,    save_dir / VOCAB_FILES_NAMES['spm_file']     )

														lowerCAmelCase_       		=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
														tokenizer.save_pretrained(self.tmpdirname     )

														lowerCAmelCase_       		=  {
														    'feature_size': 2_4,
														    'num_mel_bins': 2_4,
														    'padding_value': 0.0,
														    'sampling_rate': 1_6_0_0_0,
														    'return_attention_mask': False,
														    'do_normalize': True,
														}
														save_json(lowercase_    ,    save_dir / FEATURE_EXTRACTOR_NAME     )

							def   _lowercase (	self    ,    **lowercase_     )      ->      List[Any]:
														'''simple docstring'''

														return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,    **lowercase_     )

							def   _lowercase (	self    ,    **lowercase_     )      ->      Optional[Any]:
														'''simple docstring'''

														return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,    **lowercase_     )

							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														shutil.rmtree(self.tmpdirname     )

							def   _lowercase (	self     )      ->      List[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_tokenizer()
														lowerCAmelCase_       		=  self.get_feature_extractor()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														processor.save_pretrained(self.tmpdirname     )
														lowerCAmelCase_       		=  SpeechaTextProcessor.from_pretrained(self.tmpdirname     )

														self.assertEqual(processor.tokenizer.get_vocab()    ,    tokenizer.get_vocab()     )
														self.assertIsInstance(processor.tokenizer    ,    lowercase_     )

														self.assertEqual(processor.feature_extractor.to_json_string()    ,    feature_extractor.to_json_string()     )
														self.assertIsInstance(processor.feature_extractor    ,    lowercase_     )

							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  SpeechaTextProcessor(
														    tokenizer=self.get_tokenizer()    ,    feature_extractor=self.get_feature_extractor()     )
														processor.save_pretrained(self.tmpdirname     )

														lowerCAmelCase_       		=  self.get_tokenizer(bos_token='(BOS)'    ,    eos_token='(EOS)'     )
														lowerCAmelCase_       		=  self.get_feature_extractor(do_normalize=lowercase_    ,    padding_value=1.0     )

														lowerCAmelCase_       		=  SpeechaTextProcessor.from_pretrained(
														    self.tmpdirname    ,    bos_token='(BOS)'    ,    eos_token='(EOS)'    ,    do_normalize=lowercase_    ,    padding_value=1.0     )

														self.assertEqual(processor.tokenizer.get_vocab()    ,    tokenizer_add_kwargs.get_vocab()     )
														self.assertIsInstance(processor.tokenizer    ,    lowercase_     )

														self.assertEqual(processor.feature_extractor.to_json_string()    ,    feature_extractor_add_kwargs.to_json_string()     )
														self.assertIsInstance(processor.feature_extractor    ,    lowercase_     )

							def   _lowercase (	self     )      ->      Dict:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  floats_list((3, 1_0_0_0)     )

														lowerCAmelCase_       		=  feature_extractor(lowercase_    ,    return_tensors='np'     )
														lowerCAmelCase_       		=  processor(lowercase_    ,    return_tensors='np'     )

														for key in input_feat_extract.keys():
																					self.assertAlmostEqual(input_feat_extract[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2     )

							def   _lowercase (	self     )      ->      Union[str, Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  'This is a test string'

														lowerCAmelCase_       		=  processor(text=lowercase_     )

														lowerCAmelCase_       		=  tokenizer(lowercase_     )

														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]    ,    encoded_processor[key]     )

							def   _lowercase (	self     )      ->      Any:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														lowerCAmelCase_       		=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														lowerCAmelCase_       		=  processor.batch_decode(lowercase_     )
														lowerCAmelCase_       		=  tokenizer.batch_decode(lowercase_     )

														self.assertListEqual(lowercase_    ,    lowercase_     )






							def   _lowercase (	self     )      ->      Optional[Any]:
														'''simple docstring'''

														lowerCAmelCase_       		=  self.get_feature_extractor()
														lowerCAmelCase_       		=  self.get_tokenizer()

														lowerCAmelCase_       		=  SpeechaTextProcessor(tokenizer=lowercase_    ,    feature_extractor=lowercase_     )

														self.assertListEqual(
														    processor.model_input_names    ,    feature_extractor.model_input_names    ,    msg='`processor` and `feature_extractor` model input names do not match'    ,    )



