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  ):



 '''simple docstring'''

 @slow
 def    __snake_case   (     self    :						Optional[Any]					)	->							List[Any]:
        _UpperCamelCase       :    str					  =					CamembertModel.from_pretrained('camembert-base'					)
        model.to(lowerCamelCase					)

        _UpperCamelCase       :    Optional[Any]					  =					torch.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]]    ,     device=lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
        with torch.no_grad():
               _UpperCamelCase       :    str					  =					model(lowerCamelCase					)['last_hidden_state']
        _UpperCamelCase       :    Any					  =					torch.Size((1, 1_0, 7_6_8)					)
        self.assertEqual(output.shape    ,     lowerCamelCase					)
        # compare the actual values for a slice.
        _UpperCamelCase       :    str					  =					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=lowerCamelCase    ,     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]    ,     lowerCamelCase    ,     atol=1e-4					)					)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			['''MPNetTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MPNetForMaskedLM''',
     '''MPNetForMultipleChoice''',
     '''MPNetForQuestionAnswering''',
     '''MPNetForSequenceClassification''',
     '''MPNetForTokenClassification''',
     '''MPNetLayer''',
     '''MPNetModel''',
     '''MPNetPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFMPNetEmbeddings''',
     '''TFMPNetForMaskedLM''',
     '''TFMPNetForMultipleChoice''',
     '''TFMPNetForQuestionAnswering''',
     '''TFMPNetForSequenceClassification''',
     '''TFMPNetForTokenClassification''',
     '''TFMPNetMainLayer''',
     '''TFMPNetModel''',
     '''TFMPNetPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase     						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



