



'''simple docstring'''






from typing import TYPE_CHECKING

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


A_							: Optional[int]						 =	{
    """configuration_roc_bert""": ["""ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoCBertConfig"""],
    """tokenization_roc_bert""": ["""RoCBertTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: Optional[int]						 =	[
       """ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RoCBertForCausalLM""",
       """RoCBertForMaskedLM""",
       """RoCBertForMultipleChoice""",
       """RoCBertForPreTraining""",
       """RoCBertForQuestionAnswering""",
       """RoCBertForSequenceClassification""",
       """RoCBertForTokenClassification""",
       """RoCBertLayer""",
       """RoCBertModel""",
       """RoCBertPreTrainedModel""",
       """load_tf_weights_in_roc_bert""",
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   A_							: List[str]						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''






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 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 @slow
 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""							)

        _UpperCAmelCase  :						Optional[int]			     = tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,dtype=tf.intaa  ,)  # J'aime le camembert !"

        _UpperCAmelCase  :						Dict			     = model(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Dict			     = tf.TensorShape((1, 10, 768)							)
        self.assertEqual(output.shape  ,a_							)
        # compare the actual values for a slice.
        _UpperCAmelCase  :						Tuple			     = 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							)							)



