





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        _a    (    unittest.TestCase):

      """simple docstring"""


      @slow
      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     TFCamembertModel.from_pretrained("jplu/tf-camembert-base"					)

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

          UpperCamelCase__:	List[str]					=     model(__lowerCamelCase					)["last_hidden_state"]
          UpperCamelCase__:	Optional[Any]					=     tf.TensorShape((1, 10, 768)					)
          self.assertEqual(output.shape	,    __lowerCamelCase					)
          # compare the actual values for a slice.
          UpperCamelCase__:	List[Any]					=     tf.convert_to_tensor(
              [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]	,    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					)					)








import logging

from transformers import PretrainedConfig


A__:    Dict  	=	logging.getLogger(__name__)


A__:    List[Any]  	=	{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       """bertabs"""






      def __init__(		self:			Any	,    __lowerCamelCase:			Optional[int]=3_0522	,    __lowerCamelCase:			List[Any]=512	,    __lowerCamelCase:			Optional[Any]=6	,    __lowerCamelCase:			Optional[Any]=512	,    __lowerCamelCase:			List[str]=8	,    __lowerCamelCase:			List[Any]=512	,    __lowerCamelCase:			Optional[int]=0.2	,    __lowerCamelCase:			Optional[int]=6	,    __lowerCamelCase:			Optional[Any]=768	,    __lowerCamelCase:			int=8	,    __lowerCamelCase:			Union[str, Any]=2048	,    __lowerCamelCase:			Dict=0.2	,    **__lowerCamelCase:			Dict	,    ):
          '''simple docstring'''


          super().__init__(**__lowerCamelCase					)

          UpperCamelCase__:	str					=     vocab_size
          UpperCamelCase__:	Tuple					=     max_pos

          UpperCamelCase__:	Union[str, Any]					=     enc_layers
          UpperCamelCase__:	Optional[Any]					=     enc_hidden_size
          UpperCamelCase__:	Optional[Any]					=     enc_heads
          UpperCamelCase__:	Dict					=     enc_ff_size
          UpperCamelCase__:	Tuple					=     enc_dropout

          UpperCamelCase__:	Tuple					=     dec_layers
          UpperCamelCase__:	int					=     dec_hidden_size
          UpperCamelCase__:	int					=     dec_heads
          UpperCamelCase__:	str					=     dec_ff_size
          UpperCamelCase__:	Union[str, Any]					=     dec_dropout

