




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 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Optional[int]:





           '''simple docstring'''

           A__       =    TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''')
           A__       =    AutoTokenizer.from_pretrained('''google/mt5-small''')

           A__       =    tokenizer('''Hello there'''					, return_tensors='''tf''').input_ids
           A__       =    tokenizer('''Hi I am'''					, return_tensors='''tf''').input_ids

           A__       =    model(UpperCAmelCase__					, labels=UpperCAmelCase__).loss
           A__       =    -tf.math.reduce_mean(UpperCAmelCase__).numpy()

           A__       =    -21.228168
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)








_lowerCamelCase	:		Optional[int]							       =   65521







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       int:



       """simple docstring"""

       A__       =    1
       A__       =    0
       for plain_chr in plain_text:
              A__       =    (a + ord(lowercase_						)) % MOD_ADLER
              A__       =    (b + a) % MOD_ADLER
       return (b << 16) | a


