

from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list	,       SCREAMING_SNAKE_CASE_:							int | None = None	,       SCREAMING_SNAKE_CASE_:							int | None = None      )			->      None:




				'''simple docstring'''





				if start is None:
								A__        =			0

				if end is None:
								A__        =			len(SCREAMING_SNAKE_CASE_      ) - 1

				if start >= end:
								return

				A__        =			(start + end) // 2

				slowsort(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				slowsort(SCREAMING_SNAKE_CASE_	,       mid + 1	,       SCREAMING_SNAKE_CASE_      )

				if sequence[end] < sequence[mid]:
								A__    ,				A__        =			sequence[mid], sequence[end]

				slowsort(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       end - 1      )


if __name__ == "__main__":
	from doctest import testmod

	testmod()




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 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






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

							A__        =			tf.convert_to_tensor(
							    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,      dtype=tf.intaa      ,      )  # J'aime le camembert !"

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

