




'''simple docstring'''







from __future__ import annotations






def 	_lowercase	(			lowercase_						):



		'''simple docstring'''







		if len(lowercase_						) == 0:
				raise ValueError("find_min() arg is an empty sequence"						)
		_snake_case			 = nums[0]
		for num in nums:
				_snake_case			 = min(lowercase_		,							lowercase_						)
		return min_num


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)











'''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				),						)





