




from typing import TYPE_CHECKING

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


lowerCAmelCase_					    = {
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """XLMForMultipleChoice""",
         """XLMForQuestionAnswering""",
         """XLMForQuestionAnsweringSimple""",
         """XLMForSequenceClassification""",
         """XLMForTokenClassification""",
         """XLMModel""",
         """XLMPreTrainedModel""",
         """XLMWithLMHeadModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase_					    = [
         """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFXLMForMultipleChoice""",
         """TFXLMForQuestionAnsweringSimple""",
         """TFXLMForSequenceClassification""",
         """TFXLMForTokenClassification""",
         """TFXLMMainLayer""",
         """TFXLMModel""",
         """TFXLMPreTrainedModel""",
         """TFXLMWithLMHeadModel""",
     ]


if TYPE_CHECKING:
     from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
     from .tokenization_xlm import XLMTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm import (
              XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMForMultipleChoice,
              XLMForQuestionAnswering,
              XLMForQuestionAnsweringSimple,
              XLMForSequenceClassification,
              XLMForTokenClassification,
              XLMModel,
              XLMPreTrainedModel,
              XLMWithLMHeadModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlm import (
              TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLMForMultipleChoice,
              TFXLMForQuestionAnsweringSimple,
              TFXLMForSequenceClassification,
              TFXLMForTokenClassification,
              TFXLMMainLayer,
              TFXLMModel,
              TFXLMPreTrainedModel,
              TFXLMWithLMHeadModel,
          )

else:
     import sys

     lowerCAmelCase_					    = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import numpy as np
     import tensorflow as tf

     from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class  _lowerCAmelCase     (  unittest.TestCase	):

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''		)

        lowerCAmelCase__				:  List[str]		       =     tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]      ,				dtype=tf.intaa      ,				)  # J'aime le camembert !"

        lowerCAmelCase__				:  str		       =     model(__UpperCAmelCase		)['''last_hidden_state''']
        lowerCAmelCase__				:  List[Any]		       =     tf.TensorShape((1, 10, 768)		)
        self.assertEqual(output.shape      ,				__UpperCAmelCase		)
        # compare the actual values for a slice.
        lowerCAmelCase__				:  int		       =     tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]      ,				dtype=tf.floataa      ,				)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,				expected_slice.numpy()      ,				atol=1e-4		)		)

