




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)







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  _lowerCAmelCase     (  unittest.TestCase	):

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''		)

        lowerCAmelCase__				:  List[str]		       =     tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]      ,				dtype=tf.intaa      ,				)  # J'aime le camembert !"

        lowerCAmelCase__				:  str		       =     model(__UpperCAmelCase		)['''last_hidden_state''']
        lowerCAmelCase__				:  List[Any]		       =     tf.TensorShape((1, 10, 768)		)
        self.assertEqual(output.shape      ,				__UpperCAmelCase		)
        # compare the actual values for a slice.
        lowerCAmelCase__				:  int		       =     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		)		)

