





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 A_      (    unittest.TestCase     ):




   @slow
   def _lowerCAmelCase   (self			:List[Any]				)->      Union[str, Any]:
         __A      =       CamembertModel.from_pretrained('''camembert-base'''				)
         model.to(_UpperCamelCase				)

         __A      =       torch.tensor(
             [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]					,     device=_UpperCamelCase					,     dtype=torch.long					,     )  # J'aime le camembert !
         with torch.no_grad():
               __A      =       model(_UpperCamelCase				)['''last_hidden_state''']
         __A      =       torch.Size((1, 10, 768)				)
         self.assertEqual(output.shape					,     _UpperCamelCase				)
         # compare the actual values for a slice.
         __A      =       torch.tensor(
             [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]					,     device=_UpperCamelCase					,     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]					,     _UpperCamelCase					,     atol=1e-4				)				)












from typing import TYPE_CHECKING

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


snake_case__      :							Optional[int]									=  {'configuration_cvt': ['CVT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CvtConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__      :							Union[str, Any]									=  [
           'CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CvtForImageClassification',
           'CvtModel',
           'CvtPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__      :							List[str]									=  [
           'TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFCvtForImageClassification',
           'TFCvtModel',
           'TFCvtPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_cvt import (
                  CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CvtForImageClassification,
                  CvtModel,
                  CvtPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_cvt import (
                  TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCvtForImageClassification,
                  TFCvtModel,
                  TFCvtPreTrainedModel,
              )


else:
       import sys

       snake_case__      :							Dict									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





