

'''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 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  @slow
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  )
    UpperCAmelCase    				=						AutoTokenizer.from_pretrained('google/mt5-small'  )

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

    UpperCAmelCase    				=						model(a_ ,			labels=a_  ).loss
    UpperCAmelCase    				=						-tf.math.reduce_mean(a_  ).numpy()

    UpperCAmelCase    				=						-21.22_8168
    self.assertTrue(abs(mtf_score - EXPECTED_SCORE  ) < 2E-4  )





'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int = 1000    ):
  UpperCAmelCase    				=						-1
  UpperCAmelCase    				=						0
  for a in range(1							, n // 3    ):
    # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
    UpperCAmelCase    				=						(n * n - 2 * a * n) // (2 * n - 2 * a)
    UpperCAmelCase    				=						n - a - b
    if c * c == (a * a + b * b):
      UpperCAmelCase    				=						a * b * c
      if candidate >= product:
        UpperCAmelCase    				=						candidate
  return product


if __name__ == "__main__":
  print(F'''{solution() = }''')


