




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      UpperCAmelCase				(				self     )		->    str:
         snake_case				:    str           =	TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""     )

         snake_case				:    Dict           =	tf.convert_to_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]]    ,				dtype=tf.intaa    ,				)  # J'aime le camembert !"

         snake_case				:    Tuple           =	model(A     )["""last_hidden_state"""]
         snake_case				:    Optional[int]           =	tf.TensorShape((1, 1_0, 7_6_8)     )
         self.assertEqual(output.shape    ,				A     )
         # compare the actual values for a slice.
         snake_case				:    Any           =	tf.convert_to_tensor(
             [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]    ,				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     )     )











from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





