


'''simple docstring'''



def       _UpperCamelCase (						UpperCamelCase__   ):
			if not all(char in """01""" for char in bin_string   ):
						raise ValueError("""Non-binary value was passed to the function"""   )
			if not bin_string:
						raise ValueError("""Empty string was passed to the function"""   )
			UpperCAmelCase__					:   str       				=       """"""
			while len(UpperCamelCase__   ) % 3 != 0:
						UpperCAmelCase__					:   Optional[Any]       				=       """0""" + bin_string
			UpperCAmelCase__					:   Optional[int]       				=       [
			    bin_string[index : index + 3]
			    for index in range(len(UpperCamelCase__   )   )
			    if index % 3 == 0
			]
			for bin_group in bin_string_in_3_list:
						UpperCAmelCase__					:   Optional[Any]       				=       0
						for index, val in enumerate(UpperCamelCase__   ):
									oct_val += int(2 ** (2 - index) * int(UpperCamelCase__   )   )
						oct_string += str(UpperCamelCase__   )
			return oct_string


if __name__ == "__main__":
		from doctest import testmod

		testmod()



'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
		import tensorflow as tf

		from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM


@require_tf
@require_sentencepiece
@require_tokenizers
class 						_snake_case   (       unittest.TestCase		):




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""")
								UpperCAmelCase__					:   str       				=       AutoTokenizer.from_pretrained("""google/mt5-small""")

								UpperCAmelCase__					:   Optional[Any]       				=       tokenizer("""Hello there"""							,							return_tensors="""tf""").input_ids
								UpperCAmelCase__					:   Tuple       				=       tokenizer("""Hi I am"""							,							return_tensors="""tf""").input_ids

								UpperCAmelCase__					:   List[Any]       				=       model(_lowerCamelCase							,							labels=_lowerCamelCase).loss
								UpperCAmelCase__					:   Tuple       				=       -tf.math.reduce_mean(_lowerCamelCase).numpy()

								UpperCAmelCase__					:   Optional[int]       				=       -21.228168
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)