





def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int = 10**6						):

     if n <= 0:
          raise ValueError('''Please enter an integer greater than 0'''						)

     __magic_name__				=       list(range(n + 1						)						)
     for number in range(2 ,     n + 1						):
          if phi[number] == number:
               phi[number] -= 1
               for multiple in range(number * 2 ,     n + 1 ,     snake_case_						):
                    __magic_name__				=       (phi[multiple] // number) * (number - 1)

     __magic_name__				=       1
     for number in range(1 ,     n + 1						):
          if (answer / phi[answer]) < (number / phi[number]):
               __magic_name__				=       number

     return answer


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






import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list



class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





  def 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







       __magic_name__				=       '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''						)
       __magic_name__				=       dict(zip(A	,  range(len(A						)						)						)						)

       __magic_name__				=       {
           '''pad_token''': '''<pad>''',
           '''unk_token''': '''<unk>''',
           '''bos_token''': '''<s>''',
           '''eos_token''': '''</s>''',
       }
       __magic_name__				=       {
           '''feature_size''': 1,
           '''padding_value''': 0.0,
           '''sampling_rate''': 1_60_00,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }

       __magic_name__				=       tempfile.mkdtemp()
       __magic_name__				=       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES['''vocab_file''']						)
       __magic_name__				=       os.path.join(self.tmpdirname	,  A						)
       with open(self.vocab_file	,  '''w'''	,  encoding='''utf-8'''						) as fp:
            fp.write(json.dumps(A						) + '''\n'''						)

       with open(self.feature_extraction_file	,  '''w'''	,  encoding='''utf-8'''						) as fp:
            fp.write(json.dumps(A						) + '''\n'''						)





  def 			__A      (  self	,  **A						)		->   str:





       '''simple docstring'''







       __magic_name__				=       self.add_kwargs_tokens_map.copy()
       kwargs.update(A						)
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,  **A						)





  def 			__A      (  self	,  **A						)		->   List[Any]:





       '''simple docstring'''







       return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,  **A						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       shutil.rmtree(self.tmpdirname						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       self.get_tokenizer()
       __magic_name__				=       self.get_feature_extractor()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       processor.save_pretrained(self.tmpdirname						)
       __magic_name__				=       WavaVecaProcessor.from_pretrained(self.tmpdirname						)

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

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





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       WavaVecaProcessor(tokenizer=self.get_tokenizer()	,  feature_extractor=self.get_feature_extractor()						)
       processor.save_pretrained(self.tmpdirname						)

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

       __magic_name__				=       WavaVecaProcessor.from_pretrained(
           self.tmpdirname	,  bos_token='''(BOS)'''	,  eos_token='''(EOS)'''	,  do_normalize=A	,  padding_value=1.0						)

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

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





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       __magic_name__				=       floats_list((3, 10_00)						)

       __magic_name__				=       feature_extractor(A	,  return_tensors='''np'''						)
       __magic_name__				=       processor(A	,  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 			__A      (  self						)		->   Optional[Any]:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       __magic_name__				=       '''This is a test string'''

       __magic_name__				=       processor(text=A						)

       __magic_name__				=       tokenizer(A						)

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





  def 			__A      (  self						)		->   Tuple:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

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

       __magic_name__				=       processor.batch_decode(A						)
       __magic_name__				=       tokenizer.batch_decode(A						)

       self.assertListEqual(A	,  A						)







  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

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