






"""simple docstring"""
from __future__ import annotations

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				Dict      =      0
			UpperCAmelCase_  :				Optional[int]      =      len(__snake_case		) - 1

			while i < j:
						if nums[i] + nums[j] == target:
									return [i, j]
						elif nums[i] + nums[j] < target:
									UpperCAmelCase_  :				str      =      i + 1
						else:
									UpperCAmelCase_  :				Optional[int]      =      j - 1

			return []


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(f'{two_pointer([2, 7, 11, 15], 9) = }')












"""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   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      TFCamembertModel.from_pretrained("jplu/tf-camembert-base"		)

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

				UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_  :				str      =      tf.TensorShape((1, 10, 768)		)
				self.assertEqual(output.shape			,			lowerCAmelCase_		)
				# compare the actual values for a slice.
				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,			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		)		)



