






"""simple docstring"""

from __future__ import annotations

lowercase           =							'''Alexander Joslin'''






def _lowerCAmelCase						(	__lowerCamelCase:int ):

						'''simple docstring'''






						if number <= 0:
												__magic_name__      =  f'''n must be greater than 0. Got n = {number}'''
												raise ValueError(__lowerCamelCase )

						__magic_name__      =  [number + 1] * (number + 1)

						# starting position
						__magic_name__      =  0
						for i in range(1			,    __lowerCamelCase ):
												__magic_name__      =  min(table[i + 1]			,    table[i] + 1 )
												# check if out of bounds
												if i * 2 <= number:
																		__magic_name__      =  min(table[i * 2]			,    table[i] + 1 )
												# check if out of bounds
												if i * 3 <= number:
																		__magic_name__      =  min(table[i * 3]			,    table[i] + 1 )
						return table[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()











"""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():
			import torch

			from transformers import CamembertModel

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







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     device=__lowerCamelCase    ,     dtype=torch.float    ,     )
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]    ,     __lowerCamelCase    ,     atol=1e-4						)						)



