

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))




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__)

