code: 
  
  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    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):
  
  
  
  
  
      @slow
      def    snake_case_       (   self):
           lowercase__					:     Any   = CamembertModel.from_pretrained('camembert-base')
           model.to(a)
  
           lowercase__					:     Any   = torch.tensor(
               [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]		,     device=a		,     dtype=torch.long		,     )  # J'aime le camembert !
           with torch.no_grad():
                lowercase__					:     int   = model(a)['last_hidden_state']
           lowercase__					:     List[Any]   = torch.Size((1, 10, 768))
           self.assertEqual(output.shape		,     a)
           # compare the actual values for a slice.
           lowercase__					:     List[str]   = torch.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]]]		,     device=a		,     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]		,     a		,     atol=1e-4))
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  snake_case_    						=				{
      '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
      '''processing_donut''': ['''DonutProcessor'''],
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DonutSwinModel''',
             '''DonutSwinPreTrainedModel''',
         ]
  
  try:
         if not is_vision_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				['''DonutFeatureExtractor''']
         snake_case_    						=				['''DonutImageProcessor''']
  
  
  if TYPE_CHECKING:
         from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
         from .processing_donut import DonutProcessor
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_donut_swin import (
                    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DonutSwinModel,
                    DonutSwinPreTrainedModel,
                )
  
         try:
                if not is_vision_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .feature_extraction_donut import DonutFeatureExtractor
                from .image_processing_donut import DonutImageProcessor
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  