'''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 _lowerCAmelCase			(	unittest.TestCase					):







      @slow
      def      lowerCamelCase__      (  self     ):
        lowercase				:							int									=   CamembertModel.from_pretrained('''camembert-base'''     )
        model.to(lowercase     )

        lowercase				:							Optional[Any]									=   torch.tensor(
            [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,    device=lowercase  ,    dtype=torch.long  ,    )  # J'aime le camembert !
        with torch.no_grad():
          lowercase				:							Dict									=   model(lowercase     )['''last_hidden_state''']
        lowercase				:							Union[str, Any]									=   torch.Size((1, 10, 768)     )
        self.assertEqual(output.shape  ,    lowercase     )
        # compare the actual values for a slice.
        lowercase				:							Union[str, Any]									=   torch.tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]  ,    device=lowercase  ,    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]  ,    lowercase  ,    atol=1e-4     )     )
'''simple docstring'''
from typing import TYPE_CHECKING

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


_A		:      Optional[Any]						   ={
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:      Union[str, Any]						   =[
     '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MobileViTV2ForImageClassification''',
     '''MobileViTV2ForSemanticSegmentation''',
     '''MobileViTV2Model''',
     '''MobileViTV2PreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 _A		:      Tuple						   =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)