






"""simple docstring"""

from __future__ import annotations






def _lowerCAmelCase						(	__lowerCamelCase:list[int]			,    __lowerCamelCase:int ):

						'''simple docstring'''






						__magic_name__   ,     __magic_name__      =  0, len(__lowerCamelCase ) - 1
						while start_ind <= end_ind:
												if array[start_ind] == search_item:
																		return start_ind
												elif array[end_ind] == search_item:
																		return end_ind
												else:
																		start_ind += 1
																		end_ind -= 1
    # returns -1 if search_item is not found in array
						return -1


if __name__ == "__main__":
			print(double_linear_search(list(range(100)), 40))











"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel

@require_torch
@require_sentencepiece
@require_tokenizers
class     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.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]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,     device=__lowerCamelCase    ,     dtype=torch.float    ,     )
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]    ,     __lowerCamelCase    ,     atol=1e-4						)						)



