






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 					_snake_case						(			unittest.TestCase ):






	@slow
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :List[Any]         =   TFCamembertModel.from_pretrained("jplu/tf-camembert-base"       )

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

					snake_case__      :Optional[int]         =   model(UpperCamelCase       )["last_hidden_state"]
					snake_case__      :Optional[Any]         =   tf.TensorShape((1, 10, 768)       )
					self.assertEqual(output.shape					,UpperCamelCase       )
					# compare the actual values for a slice.
					snake_case__      :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       )       )







def 					lowercase_ (    __snake_case     :							int = 10_00      )     -> int:





				'''simple docstring'''


				snake_case__      :int         =   3
				snake_case__      :int         =   0
				while a < n:
								if a % 3 == 0 or a % 5 == 0:
												result += a
								elif a % 15 == 0:
												result -= a
								a += 1
				return result


if __name__ == "__main__":
	print(F'''{solution() = }''')