

"""simple docstring"""


from ...configuration_utils import PretrainedConfig

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """bert-generation"""

     def __init__(						self	,    _a=50_358	,    _a=1_024	,    _a=24	,    _a=16	,    _a=4_096	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=512	,    _a=0.02	,    _a=1E-1_2	,    _a=0	,    _a=2	,    _a=1	,    _a="absolute"	,    _a=True	,    **_a	,    )  ->      List[str]:


           """simple docstring"""


           super().__init__(pad_token_id=_a	,    bos_token_id=_a	,    eos_token_id=_a	,    **_a  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      int      		=	num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	intermediate_size
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	max_position_embeddings
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Dict      		=	layer_norm_eps
           SCREAMING_SNAKE_CASE__   :      Dict      		=	position_embedding_type
           SCREAMING_SNAKE_CASE__   :      Any      		=	use_cache







"""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      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""  )

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

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(_a  )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tf.TensorShape((1, 10, 768)  )
           self.assertEqual(output.shape	,    _a  )
           # compare the actual values for a slice.
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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  )  )




