



"""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 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					@slow
					def 						snake_case			(			self				:    Union[str, Any]	)->   str:
								lowerCamelCase__						:						Any						      =CamembertModel.from_pretrained('''camembert-base'''	)
								model.to(lowerCamelCase	)

								lowerCamelCase__						:						Tuple						      =torch.tensor(
								    [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]],    device=lowerCamelCase,    dtype=torch.long,    )  # J'aime le camembert !
								with torch.no_grad():
											lowerCamelCase__						:						Union[str, Any]						      =model(lowerCamelCase	)['''last_hidden_state''']
								lowerCamelCase__						:						int						      =torch.Size((1, 10, 768)	)
								self.assertEqual(output.shape,    lowerCamelCase	)
								# compare the actual values for a slice.
								lowerCamelCase__						:						List[str]						      =torch.tensor(
								    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]],    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	)	)






"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,*lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          warnings.warn(
              "The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV1ImageProcessor instead."				,lowerCamelCase_				,)
          super().__init__(*lowerCamelCase_				,**lowerCamelCase_		)

