




'''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 				_lowercase	( self    )							->			Tuple:
          _snake_case							=      CamembertModel.from_pretrained("camembert-base"    )
          model.to(a__    )

          _snake_case							=      torch.tensor(
              [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]					,device=a__					,dtype=torch.long					,)  # J'aime le camembert !
          with torch.no_grad():
               _snake_case							=      model(a__    )["last_hidden_state"]
          _snake_case							=      torch.Size((1, 10, 768)    )
          self.assertEqual(output.shape					,a__    )
          # compare the actual values for a slice.
          _snake_case							=      torch.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]]]					,device=a__					,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]					,a__					,atol=1e-4    )    )






"""simple docstring"""


import warnings

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


a_											=			logging.get_logger(__name__)
class snake_case					(     _UpperCamelCase):







		def __init__(    self					:				str			,      *a__					:				Union[str, Any]			,      **a__					:				Optional[Any]						)				->  None:





						'''simple docstring'''







						warnings.warn(
						    "The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use MobileNetV2ImageProcessor instead."			,      a__			,      )
						super().__init__(*a__			,      **a__						)