


"""simple docstring"""


import numpy as np


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[str]				  = np.array(a__   )
						lowerCAmelCase      :Dict				  = np.array(a__   )

						lowerCAmelCase      :Optional[Any]				  = abs(predict - actual   )
						lowerCAmelCase      :Dict				  = difference.mean()

						return score


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = np.array(a__   )
						lowerCAmelCase      :Optional[Any]				  = np.array(a__   )

						lowerCAmelCase      :List[str]				  = predict - actual
						lowerCAmelCase      :Optional[int]				  = np.square(a__   )

						lowerCAmelCase      :List[Any]				  = square_diff.mean()
						return score


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = np.array(a__   )
						lowerCAmelCase      :List[str]				  = np.array(a__   )

						lowerCAmelCase      :List[Any]				  = predict - actual
						lowerCAmelCase      :Tuple				  = np.square(a__   )
						lowerCAmelCase      :List[str]				  = square_diff.mean()
						lowerCAmelCase      :Union[str, Any]				  = np.sqrt(a__   )
						return score


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = np.array(a__   )
						lowerCAmelCase      :Optional[int]				  = np.array(a__   )

						lowerCAmelCase      :List[str]				  = np.log(predict + 1   )
						lowerCAmelCase      :List[Any]				  = np.log(actual + 1   )

						lowerCAmelCase      :List[str]				  = log_predict - log_actual
						lowerCAmelCase      :str				  = np.square(a__   )
						lowerCAmelCase      :Union[str, Any]				  = square_diff.mean()

						lowerCAmelCase      :int				  = np.sqrt(a__   )

						return score


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = np.array(a__   )
						lowerCAmelCase      :Any				  = np.array(a__   )

						lowerCAmelCase      :Any				  = predict - actual
						lowerCAmelCase      :Optional[Any]				  = np.sum(a__   ) / len(a__   )
						lowerCAmelCase      :Optional[int]				  = np.sum(a__   ) / len(a__   )
						# print(numerator, denumerator)
						lowerCAmelCase      :Optional[Any]				  = float(a__   ) / denumerator * 1_00

						return score







def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						return np.mean(np.array(a__   ) == np.array(a__   )   )







"""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 	__UpperCamelCase    :
					lowercase_						:							str




class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :List[str]				  = get_tests_dir('fixtures/test_sentencepiece.model'      )
											lowerCAmelCase      :Tuple				  = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'      )

											lowerCAmelCase      :List[str]				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :Dict				  = SpmConverter(UpperCAmelCase      )
											self.assertEqual(len(UpperCAmelCase      )    ,   0      )

											lowerCAmelCase      :int				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :List[str]				  = 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      )    ,   )