code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if not isinstance(lowercase_		,							lowercase_						):
  				raise ValueError("Input series is not valid, valid series - [2, 4, 8]"						)
  		if len(lowercase_						) == 0:
  				raise ValueError("Input list must be a non empty list"						)
  		if len(lowercase_						) == 1:
  				return True
  		try:
  				_snake_case			 = series[1] / series[0]
  				for index in range(len(lowercase_						) - 1						):
  						if series[index + 1] / series[index] != common_ratio:
  								return False
  		except ZeroDivisionError:
  				return False
  		return True
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if not isinstance(lowercase_		,							lowercase_						):
  				raise ValueError("Input series is not valid, valid series - [2, 4, 8]"						)
  		if len(lowercase_						) == 0:
  				raise ValueError("Input list must be a non empty list"						)
  		_snake_case			 = 1
  		for value in series:
  				answer *= value
  		return pow(lowercase_		,							1 / len(lowercase_						)						)
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import unittest
  import warnings
  from dataclasses import dataclass
  
  from transformers.convert_slow_tokenizer import SpmConverter
  from transformers.testing_utils import get_tests_dir
  
  
  
  
  
  @dataclass
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				42
  
  
  
  
  
  class   lowerCamelCase_							( unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece.model"				)
  							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model"				)
  
  							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)
  
  							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
  									_snake_case			 = SpmConverter(__UpperCamelCase				)
  							self.assertEqual(len(__UpperCamelCase				),						0				)
  
  							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)
  
  							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
  									_snake_case			 = SpmConverter(__UpperCamelCase				)
  							self.assertEqual(len(__UpperCamelCase				),						1				)
  
  							self.assertIn(
  							    "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
  							    " which is not implemented in the fast tokenizers.",						str(w[0].message				),						)
  
  
  
  
  
  