
'''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					):






						'''simple docstring'''






						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )

												_lowerCAmelCase    		=					tf.convert_to_tensor(
												    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]       ,  dtype=tf.intaa       ,  )  # J'aime le camembert !"

												_lowerCAmelCase    		=					model(_lowercase )["""last_hidden_state"""]
												_lowerCAmelCase    		=					tf.TensorShape((1, 10, 768) )
												self.assertEqual(output.shape       ,  _lowercase )
												# compare the actual values for a slice.
												_lowerCAmelCase    		=					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 ) )


'''simple docstring'''



from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip
