

"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a											=							{
    'configuration_clap': [
        'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ClapAudioConfig',
        'ClapConfig',
        'ClapTextConfig',
    ],
    'processing_clap': ['ClapProcessor'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a											=							[
       'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ClapModel',
       'ClapPreTrainedModel',
       'ClapTextModel',
       'ClapTextModelWithProjection',
       'ClapAudioModel',
       'ClapAudioModelWithProjection',
   ]
   _a											=							['ClapFeatureExtractor']

if TYPE_CHECKING:
   from .configuration_clap import (
       CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
       ClapAudioConfig,
       ClapConfig,
       ClapTextConfig,
   )
   from .processing_clap import ClapProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_clap import ClapFeatureExtractor
      from .modeling_clap import (
          CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ClapAudioModel,
          ClapAudioModelWithProjection,
          ClapModel,
          ClapPreTrainedModel,
          ClapTextModel,
          ClapTextModelWithProjection,
      )


else:
   import sys

   _a											=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







"""simple docstring"""





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  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base"     )

    __lowercase								=						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 !"

    __lowercase								=						model(UpperCAmelCase__     )["last_hidden_state"]
    __lowercase								=						tf.TensorShape((1, 1_0, 7_6_8)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )
    # compare the actual values for a slice.
    __lowercase								=						tf.convert_to_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]]], 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     )     )




