

"""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 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					@slow
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						Optional[int]  				=	TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''		)

						lowerCAmelCase__	:						Tuple  				=	tf.convert_to_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]]   ,dtype=tf.intaa   ,)  # J'aime le camembert !"

						lowerCAmelCase__	:						Optional[Any]  				=	model(lowercase_		)['''last_hidden_state''']
						lowerCAmelCase__	:						Any  				=	tf.TensorShape((1, 1_0, 7_6_8)		)
						self.assertEqual(output.shape   ,lowercase_		)
						# compare the actual values for a slice.
						lowerCAmelCase__	:						Optional[int]  				=	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"""





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	return numa ^ numa < 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()




