

"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_a							=							{
    'configuration_mobilenet_v2': [
        'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV2Config',
        'MobileNetV2OnnxConfig',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a							=							['MobileNetV2FeatureExtractor']
   _a							=							['MobileNetV2ImageProcessor']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a							=							[
       'MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MobileNetV2ForImageClassification',
       'MobileNetV2ForSemanticSegmentation',
       'MobileNetV2Model',
       'MobileNetV2PreTrainedModel',
       'load_tf_weights_in_mobilenet_v2',
   ]


if TYPE_CHECKING:
   from .configuration_mobilenet_va import (
       MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileNetVaConfig,
       MobileNetVaOnnxConfig,
   )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
      from .image_processing_mobilenet_va import MobileNetVaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilenet_va import (
          MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileNetVaForImageClassification,
          MobileNetVaForSemanticSegmentation,
          MobileNetVaModel,
          MobileNetVaPreTrainedModel,
          load_tf_weights_in_mobilenet_va,
      )


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     )     )




