



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'				,							return_dict=_A		).to(_A		)
													__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained('google/mt5-small'		)

													__SCREAMING_SNAKE_CASE									= tokenizer('Hello there'				,							return_tensors='pt'		).input_ids
													__SCREAMING_SNAKE_CASE									= tokenizer('Hi I am'				,							return_tensors='pt'		).input_ids

													__SCREAMING_SNAKE_CASE									= model(input_ids.to(_A		)				,							labels=labels.to(_A		)		).loss
													__SCREAMING_SNAKE_CASE									= -(labels.shape[-1] * loss.item())

													__SCREAMING_SNAKE_CASE									= -8_4.9_1_2_7
													self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1e-4		)











def      __lowercase       (			a__ = 10_00       )    ->			int:
							__SCREAMING_SNAKE_CASE									= -1
							__SCREAMING_SNAKE_CASE									= 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
														__SCREAMING_SNAKE_CASE									= (n * n - 2 * a * n) // (2 * n - 2 * a)
														__SCREAMING_SNAKE_CASE									= n - a - b
														if c * c == (a * a + b * b):
																					__SCREAMING_SNAKE_CASE									= a * b * c
																					if candidate >= product:
																												__SCREAMING_SNAKE_CASE									= candidate
							return product


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






