
'''simple docstring'''







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						if length <= 0 or not isinstance(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
												raise ValueError('''Length must be a positive integer.'''							)
						return [n * (2 * n - 1) for n in range(__SCREAMING_SNAKE_CASE							)]


if __name__ == "__main__":
							print(hexagonal_numbers(length=5))
							print(hexagonal_numbers(length=1_0))





'''simple docstring'''







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  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



