
'''simple docstring'''







import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu


@require_cpu
class 						__UpperCAmelCase      (					unittest.TestCase						):


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		torch.nn.Linear(10    ,   10  )
								_snake_case   						=		torch.optim.SGD(model.parameters()    ,   0.1  )
								_snake_case   						=		Accelerator()
								_snake_case   						=		accelerator.prepare(lowerCAmelCase_  )
								try:
									pickle.loads(pickle.dumps(lowerCAmelCase_  )  )
								except Exception as e:
									self.fail(F'Accelerated optimizer pickling failed with {e}'  )
								AcceleratorState._reset_state()



'''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 numpy as np
					import tensorflow as tf

					from transformers import TFCamembertModel


@require_tf
@require_sentencepiece
@require_tokenizers
class 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		TFCamembertModel.from_pretrained('jplu/tf-camembert-base'  )

								_snake_case   						=		tf.convert_to_tensor(
								    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]    ,   dtype=tf.intaa    ,   )  # J'aime le camembert !"

								_snake_case   						=		model(lowerCAmelCase_  )['last_hidden_state']
								_snake_case   						=		tf.TensorShape((1, 10, 7_68)  )
								self.assertEqual(output.shape    ,   lowerCAmelCase_  )
								# compare the actual values for a slice.
								_snake_case   						=		tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,   dtype=tf.floataa    ,   )
								# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
								# camembert.eval()
								# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,   expected_slice.numpy()    ,   atol=1E-4  )  )

