




'''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()











'''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				),						)





