

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			):



       '''simple docstring'''







       @slow
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''     )

             __A    =tf.convert_to_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]] ,   dtype=tf.intaa ,   )  # J'aime le camembert !"

             __A    =model(lowercase__     )['''last_hidden_state''']
             __A    =tf.TensorShape((1, 1_0, 7_6_8)     )
             self.assertEqual(output.shape ,   lowercase__     )
             # compare the actual values for a slice.
             __A    =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     )     )





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import (
       VersatileDiffusionDualGuidedPipeline,
       VersatileDiffusionImageVariationPipeline,
       VersatileDiffusionPipeline,
       VersatileDiffusionTextToImagePipeline,
   )
else:
   from .modeling_text_unet import UNetFlatConditionModel
   from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
   from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
   from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
   from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline


